https://wiki-staging.ellak.gr/api.php?action=feedcontributions&feedformat=atom&user=Pkst-1 2026-10-07T10:29:53Z Συνεισφορές χρήστη MediaWiki 1.41.5 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2315 2025-02-27T09:59:35Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == '''Expanding HassIO smart home capabilities via low-code automation development''' ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == '''A Tool for Visualizing the Arguments, Sentiments and User Interactions of Online Discussions''' ==<br /> <br /> ==== Brief Explanation ====<br /> In recent years, the analysis and visualization of dialogue have gained prominence in fields such as computational linguistics, social sciences, and human-computer interaction. The ability to model, analyze, and visualize real-life discussions provides valuable insights into the flow of conversations, the exchange of arguments, and the sentiments conveyed. Such visualizations can improve the understanding of complex discussions, foster decision-making, and even help develop better AI systems for facilitating or mediating discussions. We are particularly interested in online text-only discussions (e.g. on platforms like Reddit).<br /> <br /> Various tools and platforms have been developed in order to facilitate structured discussions and multi-party decision making. Kialo is an online, structured debate platform, where the use of argumentation is the central component. It allows the construction of argument maps, in the form of trees. It promotes thoughtful discussion, understanding of different viewpoints and collaborative decision-making, through visualizations of argument maps. <br /> <br /> Debategraph is another online structured debate platform, using more complex graphs, called &amp;amp;quot;mind-maps&amp;amp;quot;, where arguments are interconnected in a web-like structure. It allows an even wider choice of visualizations of relationships between ideas.<br /> <br /> DebateVis is a tool that can help non-expert users explore and analyze debate transcripts. Given a transcript, the tool produces: (a) an Interactions Graph that summarizes how often each candidate spoke overall, mentioned other candidates and discussed each topic, (b) an Annotated Transcript with automatically extracted topic labels and speaker interactions, (c) a Timeline visualization providing an overview of the debate. <br /> <br /> Finally, VisArgue is a framework proposing a range of visualizations of dialogues, including: Lexical Episode Plots (a timeline representation of the topics discussed), (b) Conversational Topic Visualizations, representing the shifting of focus of individual user on topics, (c) various statistics measuring user participation, respect, justification and accommodation, (d) Lexical Units, which are timeline representations of features such as the amount of argumentation and emotions.<br /> <br /> Although tools such as the above offer important functionality, there are still issues: in most cases, either the source code is not available, or integration with new projects is not seamless, or it is difficult to parameterize the output. Furthermore, the tools above focus mostly on debate, whereas we are also interested in other types of online discussions (e.g. deliberation to improve legislation bills, non-adversarial discussions for intra-company decision making).<br /> <br /> Therefore, this project’s goal is the design and implementation of an open source tool for visualizing and analyzing real-life, online, text-only discussions, exploring subjects like: topics discussed, arguments exchanged and emotions conveyed. The project will also explore how these visualizations can be leveraged for improving public understanding of contentious issues, academic discourse, and online discussion platforms.<br /> <br /> '''Project Objectives / Contributions:'''<br /> <br /> - Select, from the literature, prominent dialogue visualization approaches / ideas (e.g. styles of graph-based, or timeline-based, visualizations used) to represent various aspects of real-life online discussions and collect available libraries (not necessarily discussion-specific) that can be used to implement them (e.g. Gephi, NetworkX).<br /> <br /> - Explore the open-source toolkits being developed in the Archimedes project “LLM3: LLMs as mediators and moderators” to measure dialogue quality aspects (e.g., sentiment, politeness, topics, user participation) and select those that can provide useful meta-data for visualizing on-line discussions.<br /> <br /> - Develop a tool capable of ingesting data from real-life online discussions generating relevant meta-data (possibly by calling other toolkits) and producing the desired visualizations of the discussions.<br /> <br /> - Potentially, evaluate the effectiveness of the tool and its visualizations in making complex online discussions understandable to diverse audiences, such as researchers, mediators, or general users.<br /> <br /> '''Project Impact:'''<br /> <br /> - A novel, easy-to-use, open source, visualization tool (with accompanying paper) for online, text-only discussions, that can help the analysis of discussions in different settings and domains (e.g. political discourse, academic debates, or customer feedback).<br /> <br /> - Expand the general understanding of how visualization techniques can make debates more accessible and informative (possibly also leading to a publication).<br /> <br />   - Contribution to the Archimedes project “LLM3: LLMs as mediators and moderators” which aims to develop and evaluate LLM-based mediation agents that will actively participate in online discussions, with or without additional human mediation.<br /> <br /> '''Key Types of Dialogue Visualizations:'''<br /> <br /> The project will develop and explore several types of dialogue visualizations. Some are briefly described below. The contributor will be free to propose and implement new ones. <br /> <br />     1) Timelines: they represent the chronological flow of a conversation, highlighting key moments such as topic shifts, argument introductions and emotional peaks. Possible Features: topic evolution over time, points of conflict or agreement, visual markers for significant events (e.g. emotional outbursts or resolution points).<br /> <br />     2) Argumentation Graphs: they visualize the logical structure of arguments, including claims, counterclaims and evidence. Possible Features: nodes representing arguments or claims, edges denoting relationships (e.g., support, contradiction).<br /> <br />     3) User Interaction Graphs: they map the relationships and interaction patterns between participants in the debate. Possible Features: nodes representing participants, weighted edges showing the frequency, tone, or sentiment of interactions, clusters indicating subgroups or coalitions in the dialogue.<br /> <br />     4) Sentiment Heatmaps: they analyze and visualize the emotional dynamics of a conversation. Possible Features: color-coded intensity for positive, negative, or neutral sentiments, overlay with timeline or topic visualization for richer insights.<br /> <br />     5) Topic Trees or Topic Flow Diagrams: they represent how topics are introduced, branched out, and revisited during the discussion. Possible Features: hierarchical or radial layouts for topic relationships, highlights of overlapping or transitioning topics.<br /> <br />     6) Hybrid Visualizations: by combining multiple visualization techniques.<br /> <br /> Importance of Dialogue Visualizations:<br /> <br /> Dialogue visualizations, such as those presented above, can support:<br /> <br /> - Topic Analysis: by identifying the main topics discussed and their transitions over time, and by highlighting overlapping topics and their importance to the dialogue.<br /> <br /> - Argumentation Analysis: by understanding the logical flow of arguments, counterarguments, and evidence, and by identifying circular reasoning, weak arguments, or areas of agreement.<br /> <br /> - Sentiment Analysis: by visualizing the emotional tone of the conversation and its impact on the debate, and by examining whether certain sentiments correlate with specific topics or arguments.<br /> <br /> - Participant Dynamics: by mapping the influence and activity of each participant, and by analyzing interaction patterns (e.g., dominance, interruptions, alliances).<br /> <br /> '''Methodology:'''<br /> <br /> - Data Collection and Preparation:<br /> <br /> The tool should be able to ingest data from various online debate platforms (e.g. Reddit, Kialo), from political debate transcripts, from academic discourse, as well as from debates among LLM-agents. Since the format of raw data may vary, we propose the use of the Convokit tool in order to homogenise and preprocess the data.<br /> <br /> - Development of Visualization Prototypes:<br /> <br /> Tools/Technologies: Python (matplotlib, seaborn, Plotly), D3.js for web-based visualizations, or tools like Gephi for network analysis. Use natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers) for topic modeling, sentiment analysis, and argument mining, along with tools being developed at the LLM3 project of Archimedes.<br /> <br /> - User Feedback and Iterative Improvement:<br /> <br /> Test the outputs (visualizations) with researchers, mediators, or other stakeholders. Refine designs based on usability feedback and task-specific performance.<br /> <br /> '''Evaluation'''<br /> <br /> If time allows, the contributor will contribute in evaluating the effectiveness of their dialogue visualizations, in the context of Archimedes’ LLM3 project, by using them for both real-life and LLM-generated dialogues. Their output will be measured on clarity, usability and informativeness.<br /> <br /> '''Desired Profile:'''<br /> <br /> We are looking for a contributor with the following characteristics:<br /> <br /> - Good programming skills in Python (experience in network analysis and / or NLP is a plus).<br /> <br /> - Experience (and interest for) coding visual representations of concepts with libraries such as: matplotlib, seaborn, Plotly, Gephi, D3.js. <br /> <br /> - Interest in the subject of human interaction through dialogue (more specifically, on themes such as: argumentation, topic identification, sentiment analysis).<br /> <br /> - A taste for concise, elegant and efficient solutions / visualizations.<br /> <br /> The contributor will be mentored/supported by members of the LLM3 project, the broader NLP group of Archimedes (&lt;nowiki&gt;https://archimedesai.gr/en/&lt;/nowiki&gt;), as well as the NLP Group (&lt;nowiki&gt;http://nlp.cs.aueb.gr/&lt;/nowiki&gt;) of the Department of Informatics, Athens University of Economics and Business. <br /> <br /> '''Conclusion:'''<br /> <br /> This GSOC project aims to develop an open-source tool that will make complex, online discussions more understandable, insightful, and actionable. By capturing the topics, arguments, sentiments, and participant dynamics, it will offer a comprehensive approach to online dialogue visualization that can benefit multiple fields, from education to public policy.<br /> <br /> ==== Related repositories ====<br /> https://sites.google.com/view/llm3/home<br /> <br /> ==== Expected Results ====<br /> A tool able to process real-life, text-only dialogues and produce selected visualizations capturing their essential points.<br /> <br /> ==== Mentors ====<br /> Dionysios Kontarinis (denniskont@gmail.com), Ion Androutsopoulos, Ioannis Pavlopoulos<br /> <br /> == '''PersonalAIs: Generative AI Agent for Personalized Music Recommendations''' ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == '''OpenRF 3D''' ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == '''Exploring and Abstracting Triplestore Alternatives''' ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == '''Flexible GovDoc Scanner''' ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == '''Extending the capabilities of OpenTRIM''' ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == '''Cleaning of HPLT Greek v2 Dataset for GlossApi LLM''' ==<br /> <br /> ==== Brief Explanation ====<br /> Cleanup of the Greek datasets at https://hplt-project.org/datasets/v2.0. The cleanup will be done with the help of the glossAPI team. <br /> <br /> For methodology see https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&amp;#x20;. <br /> <br /> ==== Expected Outcome ====<br /> The goal is to isolate from the html Greek text with normal grammar and complete sentences (not fragmented).<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/glossapi<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> <br /> == '''Add SAML and OpenID Connect support to Consul Democracy''' ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. Some of these institutions have authentication solutions based on either SAML or OpenID Connect; however, there's no built-in support for these authentication solutions in Consul Democracy, so each institution has to build their own.<br /> <br /> In Ruby, the OmniAuth library provides a standard way to manage multi-provider authentication. Consul Democracy currently uses several Ruby gems, all based on OmniAuth, to provide authentication via Facebook, Google, Twitter/X and WordPress. There's been an attempt at providing SAML support using the omniauth-saml Ruby gem, but its development hasn't been finished due to the lack of a SAML platform to test against.<br /> <br /> Consul Democracy also supports multitenancy, meaning the same application can be used to manage several institutions (with different domains or subdomains). For authentication using Facebook, Google, Twitter/X or WordPress, Consul Democracy provides the option to use the same configuration for each institution, to use different configurations for different institutions, or a mix of both (one default configuration which can be overwritten per institution).<br /> <br /> The aim of this proposal is to provide generic SAML and OpenID Connect authentication solutions in Consul Democracy so a variety of institutions can easily integrate their existing authentication platform.<br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using a SAML service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using an OpenID Connect service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions must allow different configurations for different institutions in a multitenant environment <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions should be flexible enough so institutions don't have to change the source code in order to configure their service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; The source code of the SAML and OpenID Connect solutions should be similar to the source code of the existing Facebook, Google, Twitter/X and WordPress solutions <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the documentation with instructions on how to configure SAML and OpenID Connect <br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ====Knowledge Prerequisites====<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; SAML and OpenID Connect authentication configuration * (Optional) Ruby on Rails and OmniAuth authentication<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> <br /> == '''Docker for Consul Democracy citizen participation platform''' ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. That means Consul Democracy developers don't have access to production machines, and so Consul Democracy must be as simple to install and maintain as possible so anyone can do it no matter how familiar they are with the technologies used by Consul Democracy.<br /> <br /> Currently, Consul Democracy is installed on production by running an ansible-based installer which installs all the project dependencies on a Debian GNU/Linux or Ubuntu Linux server. Deployment of new developments is then done using Capistrano.<br /> <br /> The source code of Consul Democracy contains a Dockerfile and a docker-compose.yml file that are exclusively meant for the development environment, in order to make it easier for developers who are familiar with Docker to contribute to the project. However, there's currently no way to deploy to a production environment using Docker, which is inconvenient for institutions who don't use Debian or Ubuntu on their servers, or for institutions who have adopted Docker as their preferred way to setup their servers. The main goal of this proposal is to solve this issue. Since 2024, Ruby on Rails applications are configured to use Kamal by default as a solution to deploy to production using a Docker container. To our knowledge, this would be the most simple solution to our problem.<br /> <br /> There's a third kind of Docker integration, which uses a devcontainer to allow developers to use tools like GitHub Codespaces to run the application in a development environment, which is also configured by default in new Rails applications since 2024, and we'd like to enable this option in Consul Democracy.<br /> <br /> With this developments, we could enable many more municipalities to utilise digital citizen participation - and thus offer their citizens greater involvement in the development of their cities. <br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to install and deploy Consul Democracy applications using Docker in the most simple way (probably with Kamal)<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Add a devcontainer for integration with GitHub Codespaces <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make sure the current development setup with Docker keeps working after the previous additions *<br /> <br /> The configuration files for all three environments mentioned above should have as little duplicate code as possible so they're easy to maintain <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the technical documentation for both development and production environments<br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ==== Knowledge Prerequisites ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Experience deploying to production environments using Docker <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; (Optional) Experience using Docker in Ruby on Rails applications<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> == '''Εxtending the apothesis factory pattern for seamless 2D and 3D lattice integration''' ==<br /> <br /> ==== '''Overview''' ====<br /> Apothesis is a generalized software for designing, simulating, and analyzing deposition processes using the kinetic Monte Carlo method. It consists of two main components: a lattice (e.g., simple cubic, HPC, etc.) and the processes (adsorption, desorption, diffusion, and surface reactions) that occur within it. Currently, Apothesis includes a factory-based mechanism for lattice creation, but this implementation is limited in scope, primarily focusing on basic lattice structures. To enhance its flexibility and scalability, this proposal aims to extend the factory pattern to support a broader range of 2D and 3D surfaces in a seamless and modular way. This extension will involve introducing specialized lattice factories tailored for different geometries, such as hexagonal, face-centered cubic, and custom surface representations, ensuring compatibility with kinetic Monte Carlo processes. Additionally, a dynamic factory registry system will be implemented, allowing new lattice types to be registered and instantiated at runtime without modifying the core system. This approach will not only improve adaptability but also facilitate user-defined lattice structures while preserving maintainability and efficiency. By refining the factory mechanism, Apothesis will provide a more robust framework for deposition process simulations, enabling researchers and engineers to explore a wider range of surface dynamics with greater ease.<br /> <br /> ==== '''Related work''' ====<br /> Apothesis already implements a factory pattern for lattice creation, but it needs to be expanded to support a broader range of 2D and 3D surfaces in a more seamless and flexible way. The current implementation primarily focuses on basic lattice structures, and extending it would involve introducing specialized factories for different geometries, such as hexagonal, face-centered cubic, and custom surface representations. Enhancing the factory pattern should include a more modular approach, allowing new lattice types to be dynamically registered and instantiated without modifying the core system. Additionally, ensuring that these new lattice structures fully integrate with kinetic Monte Carlo processes—such as adsorption, desorption, diffusion, and surface reactions—will be crucial for maintaining simulation accuracy and consistency. By refining the factory mechanism and introducing a more extensible registry system, Apothesis can achieve greater adaptability, making it easier for users to define and integrate new lattice types while preserving maintainability and scalability.<br /> <br /> ==== '''Details of your coding project''' ====<br /> A potential practical approach to extending the factory pattern in Apothesis for 2D and 3D surfaces involves creating a modular and extensible lattice generation system. This can be achieved by defining an abstract LatticeFactory that enforces a standard way of creating lattice structures while delegating specific implementations to derived factories. Specialized factories such as SimpleCubicLatticeFactory, HexagonalLatticeFactory, FCCLatticeFactory, and GrapheneLatticeFactory can be implemented to handle different lattice geometries while ensuring compatibility with kinetic Monte Carlo processes like adsorption, desorption, diffusion, and surface reactions. Each factory produces a lattice object implementing a common ILattice interface, encapsulating geometry, boundary conditions, and process compatibility. A dynamic factory registry mechanism allows runtime selection and registration of new lattice types, enabling users to introduce custom lattices without modifying the core code. This approach ensures scalability, flexibility, and maintainability by decoupling lattice creation from simulation logic while seamlessly supporting both 2D and 3D structures.<br /> <br /> ==== '''Size''' ====<br /> Large (350 hours)<br /> <br /> ==== '''Skills''' ====<br /> Required: C++, desing patters, experience with physicochemical based software<br /> <br /> ==== '''Expected impact''' ====<br /> The expected impact of extending the factory pattern in Apothesis includes enhanced flexibility, scalability, and efficiency in lattice creation for deposition process simulations. By introducing a more modular and extensible approach, researchers and engineers will be able to seamlessly integrate new 2D and 3D surface structures without modifying core code, reducing development time and increasing adaptability. The improved factory mechanism will ensure better compatibility with kinetic Monte Carlo processes, enabling more accurate and diverse simulations of adsorption, desorption, diffusion, and surface reactions. Additionally, the dynamic factory registry will foster customization and extensibility, allowing users to define and register their own lattice structures, thereby broadening the range of possible simulations. Overall, this enhancement will make Apothesis a more powerful and user-friendly tool, supporting advanced research, industrial applications, and innovation in surface science and material deposition technologies.<br /> <br /> ==== '''Mentors''' ====<br /> • Nikolaos (Nikos) Cheimarios &lt;n.cheimarios at gmail.com&gt; is a researcher with contributions in scientific software development. He has previous experience as mentor in 2020, 2022, 2023 and 2024. He is one of the authors of Apothesis, Chameleon software and several web-based scientific numerical applications.<br /> <br /> • Christianna Gatsiou &lt;christianna.gatsiou at gmail.com&gt;.<br /> <br /> ==== '''Tests''' ====<br /> Students, the following test will be helpful. • Easy: Compile and run Apothesis for the CO heterogeneous catalysis case. • Medium: Perform runs with SimpleCubic and HPC lattices. • Hard: Identify the part of code that creates the lattices. Briefly describe how you would implement the 2D graphene lattice. For tips and references contact the Mentors!<br /> <br /> ==== '''References''' ====<br /> [1] N. Cheimarios, D. To, G. Kokkoris, G. Memos and A.G. Boudouvis “Monte Carlo &amp; Kinetic Monte Carlo models for deposition processes: A review of recent works”, Frontiers in Physics, 9, 165 (2021).<br /> <br /> [2] N. Cheimarios, “Insights into the effect of growth on the Ziff-Gulari-Barshad model and the film properties”, Modelling and Simulation in Materials Science and Engineering, 31, 065007, (2023).<br /> <br /> [3] A.P.F Jansen, &quot;An Introduction to Kinetic Monte Carlo Simulations of Surface Reactions&quot;, Springer Berlin, Heidelberg, 2012. &lt;nowiki&gt;https://doi.org/10.1007/978-3-642-29488-4&lt;/nowiki&gt;<br /> <br /> [4] M. Andersen, C. Panosetti, K. Reuter, &quot;A Practical Guide to Surface Kinetic Monte Carlo Simulations&quot;, Frontiers in Chemistry, 7, 202 (2019).<br /> <br /> <br /> <br /> == '''Identifying transition points in the ZGB model using convolutional neural networks (CNNs)''' ==<br /> Scientific computing for physical/chemical sciences and engineering edited this page 3 weeks ago ·<br /> <br /> ==== '''Overview''' ====<br /> In 1986, Ziff, Gulari, and Barshad introduced the ZGB model to computationally study the heterogeneous catalytic process of CO oxidation to CO₂ with O₂ on a metal surface (e.g., Pt). Using Monte Carlo simulations on a simple square lattice, with the partial pressure of CO, yCO, as the only parameter, they demonstrated that at low yCO values, the catalytic surface becomes poisoned (fully covered) by oxygen atoms, preventing the surface reaction and the conversion to CO₂. In this case, the system remains out of equilibrium. As yCO increases, at approximately y1 ≈ 0.389, the system transitions to an equilibrium state where CO begins to convert into CO₂. Further increasing yCO leads to a first-order, discontinuous phase transition at y2 ≈ 0.525, where CO conversion to CO₂ ceases again due to surface poisoning by CO atoms. In 1990, Jensen and Fogedby extended this model by incorporating diffusion phenomena, showing that the transition points shift depending on the diffusion rate, pd , but do not disappear. <br /> <br /> ==== '''Related work''' ====<br /> Apothesis is a generalized open-source software for simulating heterogeneous catalysis and deposition processes via kMC. It is based on performing certain processes (adsorption, desorption, diffusion and surface reaction(s)) on lattices. Currently, Apothesis supports simple cubic, FCC, HPC and diamond lattices. It has been used to study both heterogeneous catalysis of CO and related type growth models [2].<br /> <br /> ==== '''Details of your coding project''' ====<br /> This works aims to predict the transition points using machine learning methods, specifically convolutional neural networks (CNNs), based on surfaces derived from kinetic Monte Carlo simulations from Apothesis at equilibrium states. For that, an outer shell to Apothesis must be build that will read the data from Apothesis and use it for training a CNN and then for the prediction of the transition points.<br /> <br /> ==== '''Size''' ====<br /> Large (350 hours)<br /> <br /> ==== '''Skills''' ====<br /> Required: Python, Tensorflow, experience with physicochemical based software<br /> <br /> ==== '''Expected impact''' ====<br /> The project will build an outer shell for Apothesis to be used in ML/AI applications.<br /> <br /> ==== '''Mentors''' ====<br /> <br /> * Nikolaos (Nikos) Cheimarios &lt;n.cheimarios at gmail.com&gt; is a researcher with contributions in scientific software development. He has previous experience as mentor in GSoC 2020, 2022, 2023 and 2024. He is one of the authors of Apothesis, Chameleon software and several web-based scientific numerical applications.<br /> * Konstantinos (Kostas) Eftaxias is a data scientist with more than 15 years of experience in research and industry applications. His main interests are computer vision, time series modelling/prediction and reinforcement learning.<br /> <br /> ==== '''Tests''' ====<br /> Students, the following test will be helpful. • Easy: Compile and run Apothesis for the CO heterogeneous catalysis case. • Medium: Take three surfaces (SurfaceSpecies_*.dat) generated by Apothesis and read it in Python. • Hard: Call Apothesis from Python and read the latest surface generated. For tips and references contact the Mentors!<br /> <br /> ==== '''References''' ====<br /> [1] R. M. Ziff, E. Gulari, and Y. Barshad, Kinetic Phase Transitions in an Irreversible Surface-Reaction Model, Phys. Rev. Lett. 56, 2553 (1986).<br /> <br /> [2] I. Jensen and H. C. Fogedby, Kinetic Phase Transitions in a Surface-Reaction Model with Diffusion: Computer Simulations and Mean-Field Theory, Phys. Rev. A 42, 1969 (1990).<br /> <br /> [3] N. Cheimarios, Surface diffusion effects on the system and the film properties of a Ziff–Gulari–Barshad type growth model, Mat. Today Comm. 39, 109189 (2024).<br /> <br /> [4] N. Cheimarios, Mean field approximation of a surface-reaction growth model with dissociation, Phys. Lett. A 524, 129828 (2024).<br /> <br /> [5] Y. Bahri, J. Kadmon, J. Pennington, S. S. Schoenholz, J. Sohl-Dickstein, S. Ganguli, Statistical Mechanics of Deep Learning, Annu. Rev. Condens.Matter Phys. 11, 501 (2020).<br /> <br /> [6] D.W. Tola, M. Bekele, Machine Learning of Nonequilibrium Phase Transition in an Ising Model on Square Lattice, Condens. Matter 8, 83 (2023).<br /> <br /> == '''MyUni''' ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> <br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> <br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> <br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> <br /> 6. Create a BackEnd system to manage all the data described above.<br /> <br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> <br /> 8. Rewrite in Typescript for maintainability<br /> <br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> <br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, <br /> <br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == '''GlossAPI''' ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> == '''DIY IoT Physics Experiments for education''' ==<br /> <br /> ==== Brief Explanation ====<br /> Remote physics experiments for students in all educational levels are the second best to hands-on experiments.  Especially for students who temporarily cannot attend school or in cases like Covid-19 and the lockdowns. In many practical cases, they are the only alternative, as they are available 24/7, they can involve dangerous materials or conditions, they can be accessed from anywhere and any device, they require less maintenance, have lower cost, can be easily modified, or arranged to perform another experiment, and are less probable to be damaged.  They are in line with the modern way of performing experiments, as it is desirable to have as little direct contact with the experiments as possible and use them online.  Examples include online telescopes and electronic microscopes.  This is possible due to automation; data acquisition and manipulation of the experimental data is done using a computer or a single board computer.  In this way students need not take pain stacking notes, especially for experiments that take a lot of time to collect data, sometimes days or months.  Students can concentrate on data processing, the analysis of the results, and arrive at scientifically valid conclusions.  Our laboratory has set up many remote experiments and has more than 10 years’ experience in designing, setting, and servicing remote experiments.  Our remote experiments are based on Arduino and readily available sensors and actuators. The previous year it was designed and implemented a way to make the sensors, and the actuators form an IoT local network so that it will be easier to easily utilize them in different experiments and to build new experiments.  The IoT sensors and actuators are DIY and based on open software.   The previous year GSoC stipend receiver, programed the ESP8266 to receive data from the sensor and transmit the data through MQTT to ThingsBoard. Similarly, for an actuator the ESP8266 to receive MQTT data from ThingsBoard. The stipend receiver prepared five DIY IoT sensors and five actuators. ThingsBoard provided users with visual representation of the data and the control of the experimental setup through dashboards.  There are produced five dashboards for five corresponding experiments.  The present successful applicant will have to produce a digital twin of the experiments.  This will involve open software for producing 3D models of five experiments, allowing them to manipulate the digital twins, view the evolution of the experiment, provide data presentation tools, and extract model parameters.<br /> <br /> ==== Related repositories ====<br /> <br /> ==== https://github.com/totheworld2004/DIY-Physics-IoT ====<br /> <br /> ==== Exprected Outcome: ====<br /> Five digital twins of corresponding five experiments, their documentation and instructions of how to use them<br /> <br /> ==== Knowledge Prerequisites ====<br /> Any one for case a)-e) or similar --- a) 3D Web-Based Physics Simulations: Three.js, Babylon.js, p5.js, Godot b) Interactive Dashboards: Plotly Dash, Panel, Bokeh c) Custom Data Visualizations: D3.js, Matplotlib, Jupyter Notebooks d) Game-Based Physics Experiments: Godot, Babylon.js e) Embedded 3D Simulations: Three.js, Babylon.js<br /> <br /> ==== Mentors: ====<br /> Hariton Polatoglou and Panagiotis Koustoumpardis<br /> <br /> =='''eCodeOrama, an educational interactive flow visualization tool for mit scratch programs'''==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2314 2025-02-27T09:58:06Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == '''Expanding HassIO smart home capabilities via low-code automation development''' ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == '''A Tool for Visualizing the Arguments, Sentiments and User Interactions of Online Discussions''' ==<br /> <br /> ==== Brief Explanation ====<br /> In recent years, the analysis and visualization of dialogue have gained prominence in fields such as computational linguistics, social sciences, and human-computer interaction. The ability to model, analyze, and visualize real-life discussions provides valuable insights into the flow of conversations, the exchange of arguments, and the sentiments conveyed. Such visualizations can improve the understanding of complex discussions, foster decision-making, and even help develop better AI systems for facilitating or mediating discussions. We are particularly interested in online text-only discussions (e.g. on platforms like Reddit).<br /> <br /> Various tools and platforms have been developed in order to facilitate structured discussions and multi-party decision making. Kialo is an online, structured debate platform, where the use of argumentation is the central component. It allows the construction of argument maps, in the form of trees. It promotes thoughtful discussion, understanding of different viewpoints and collaborative decision-making, through visualizations of argument maps. <br /> <br /> Debategraph is another online structured debate platform, using more complex graphs, called &amp;amp;quot;mind-maps&amp;amp;quot;, where arguments are interconnected in a web-like structure. It allows an even wider choice of visualizations of relationships between ideas.<br /> <br /> DebateVis is a tool that can help non-expert users explore and analyze debate transcripts. Given a transcript, the tool produces: (a) an Interactions Graph that summarizes how often each candidate spoke overall, mentioned other candidates and discussed each topic, (b) an Annotated Transcript with automatically extracted topic labels and speaker interactions, (c) a Timeline visualization providing an overview of the debate. <br /> <br /> Finally, VisArgue is a framework proposing a range of visualizations of dialogues, including: Lexical Episode Plots (a timeline representation of the topics discussed), (b) Conversational Topic Visualizations, representing the shifting of focus of individual user on topics, (c) various statistics measuring user participation, respect, justification and accommodation, (d) Lexical Units, which are timeline representations of features such as the amount of argumentation and emotions.<br /> <br /> Although tools such as the above offer important functionality, there are still issues: in most cases, either the source code is not available, or integration with new projects is not seamless, or it is difficult to parameterize the output. Furthermore, the tools above focus mostly on debate, whereas we are also interested in other types of online discussions (e.g. deliberation to improve legislation bills, non-adversarial discussions for intra-company decision making).<br /> <br /> Therefore, this project’s goal is the design and implementation of an open source tool for visualizing and analyzing real-life, online, text-only discussions, exploring subjects like: topics discussed, arguments exchanged and emotions conveyed. The project will also explore how these visualizations can be leveraged for improving public understanding of contentious issues, academic discourse, and online discussion platforms.<br /> <br /> '''Project Objectives / Contributions:'''<br /> <br /> - Select, from the literature, prominent dialogue visualization approaches / ideas (e.g. styles of graph-based, or timeline-based, visualizations used) to represent various aspects of real-life online discussions and collect available libraries (not necessarily discussion-specific) that can be used to implement them (e.g. Gephi, NetworkX).<br /> <br /> - Explore the open-source toolkits being developed in the Archimedes project “LLM3: LLMs as mediators and moderators” to measure dialogue quality aspects (e.g., sentiment, politeness, topics, user participation) and select those that can provide useful meta-data for visualizing on-line discussions.<br /> <br /> - Develop a tool capable of ingesting data from real-life online discussions generating relevant meta-data (possibly by calling other toolkits) and producing the desired visualizations of the discussions.<br /> <br /> - Potentially, evaluate the effectiveness of the tool and its visualizations in making complex online discussions understandable to diverse audiences, such as researchers, mediators, or general users.<br /> <br /> '''Project Impact:'''<br /> <br /> - A novel, easy-to-use, open source, visualization tool (with accompanying paper) for online, text-only discussions, that can help the analysis of discussions in different settings and domains (e.g. political discourse, academic debates, or customer feedback).<br /> <br /> - Expand the general understanding of how visualization techniques can make debates more accessible and informative (possibly also leading to a publication).<br /> <br />   - Contribution to the Archimedes project “LLM3: LLMs as mediators and moderators” which aims to develop and evaluate LLM-based mediation agents that will actively participate in online discussions, with or without additional human mediation.<br /> <br /> '''Key Types of Dialogue Visualizations:'''<br /> <br /> The project will develop and explore several types of dialogue visualizations. Some are briefly described below. The contributor will be free to propose and implement new ones. <br /> <br />     1) Timelines: they represent the chronological flow of a conversation, highlighting key moments such as topic shifts, argument introductions and emotional peaks. Possible Features: topic evolution over time, points of conflict or agreement, visual markers for significant events (e.g. emotional outbursts or resolution points).<br /> <br />     2) Argumentation Graphs: they visualize the logical structure of arguments, including claims, counterclaims and evidence. Possible Features: nodes representing arguments or claims, edges denoting relationships (e.g., support, contradiction).<br /> <br />     3) User Interaction Graphs: they map the relationships and interaction patterns between participants in the debate. Possible Features: nodes representing participants, weighted edges showing the frequency, tone, or sentiment of interactions, clusters indicating subgroups or coalitions in the dialogue.<br /> <br />     4) Sentiment Heatmaps: they analyze and visualize the emotional dynamics of a conversation. Possible Features: color-coded intensity for positive, negative, or neutral sentiments, overlay with timeline or topic visualization for richer insights.<br /> <br />     5) Topic Trees or Topic Flow Diagrams: they represent how topics are introduced, branched out, and revisited during the discussion. Possible Features: hierarchical or radial layouts for topic relationships, highlights of overlapping or transitioning topics.<br /> <br />     6) Hybrid Visualizations: by combining multiple visualization techniques.<br /> <br /> Importance of Dialogue Visualizations:<br /> <br /> Dialogue visualizations, such as those presented above, can support:<br /> <br /> - Topic Analysis: by identifying the main topics discussed and their transitions over time, and by highlighting overlapping topics and their importance to the dialogue.<br /> <br /> - Argumentation Analysis: by understanding the logical flow of arguments, counterarguments, and evidence, and by identifying circular reasoning, weak arguments, or areas of agreement.<br /> <br /> - Sentiment Analysis: by visualizing the emotional tone of the conversation and its impact on the debate, and by examining whether certain sentiments correlate with specific topics or arguments.<br /> <br /> - Participant Dynamics: by mapping the influence and activity of each participant, and by analyzing interaction patterns (e.g., dominance, interruptions, alliances).<br /> <br /> '''Methodology:'''<br /> <br /> - Data Collection and Preparation:<br /> <br /> The tool should be able to ingest data from various online debate platforms (e.g. Reddit, Kialo), from political debate transcripts, from academic discourse, as well as from debates among LLM-agents. Since the format of raw data may vary, we propose the use of the Convokit tool in order to homogenise and preprocess the data.<br /> <br /> - Development of Visualization Prototypes:<br /> <br /> Tools/Technologies: Python (matplotlib, seaborn, Plotly), D3.js for web-based visualizations, or tools like Gephi for network analysis. Use natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers) for topic modeling, sentiment analysis, and argument mining, along with tools being developed at the LLM3 project of Archimedes.<br /> <br /> - User Feedback and Iterative Improvement:<br /> <br /> Test the outputs (visualizations) with researchers, mediators, or other stakeholders. Refine designs based on usability feedback and task-specific performance.<br /> <br /> '''Evaluation'''<br /> <br /> If time allows, the contributor will contribute in evaluating the effectiveness of their dialogue visualizations, in the context of Archimedes’ LLM3 project, by using them for both real-life and LLM-generated dialogues. Their output will be measured on clarity, usability and informativeness.<br /> <br /> '''Desired Profile:'''<br /> <br /> We are looking for a contributor with the following characteristics:<br /> <br /> - Good programming skills in Python (experience in network analysis and / or NLP is a plus).<br /> <br /> - Experience (and interest for) coding visual representations of concepts with libraries such as: matplotlib, seaborn, Plotly, Gephi, D3.js. <br /> <br /> - Interest in the subject of human interaction through dialogue (more specifically, on themes such as: argumentation, topic identification, sentiment analysis).<br /> <br /> - A taste for concise, elegant and efficient solutions / visualizations.<br /> <br /> The contributor will be mentored/supported by members of the LLM3 project, the broader NLP group of Archimedes (&lt;nowiki&gt;https://archimedesai.gr/en/&lt;/nowiki&gt;), as well as the NLP Group (&lt;nowiki&gt;http://nlp.cs.aueb.gr/&lt;/nowiki&gt;) of the Department of Informatics, Athens University of Economics and Business. <br /> <br /> '''Conclusion:'''<br /> <br /> This GSOC project aims to develop an open-source tool that will make complex, online discussions more understandable, insightful, and actionable. By capturing the topics, arguments, sentiments, and participant dynamics, it will offer a comprehensive approach to online dialogue visualization that can benefit multiple fields, from education to public policy.<br /> <br /> ==== Related repositories ====<br /> https://sites.google.com/view/llm3/home<br /> <br /> ==== Expected Results ====<br /> A tool able to process real-life, text-only dialogues and produce selected visualizations capturing their essential points.<br /> <br /> ==== Mentors ====<br /> Dionysios Kontarinis (denniskont@gmail.com), Ion Androutsopoulos, Ioannis Pavlopoulos<br /> <br /> == '''PersonalAIs: Generative AI Agent for Personalized Music Recommendations''' ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == '''OpenRF 3D''' ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == '''Exploring and Abstracting Triplestore Alternatives''' ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == '''Flexible GovDoc Scanner''' ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == '''Extending the capabilities of OpenTRIM''' ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == '''Cleaning of HPLT Greek v2 Dataset for GlossApi LLM''' ==<br /> <br /> ==== Brief Explanation ====<br /> Cleanup of the Greek datasets at https://hplt-project.org/datasets/v2.0. The cleanup will be done with the help of the glossAPI team. <br /> <br /> For methodology see https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&amp;#x20;. <br /> <br /> ==== Expected Outcome ====<br /> The goal is to isolate from the html Greek text with normal grammar and complete sentences (not fragmented).<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/glossapi<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> <br /> == '''Add SAML and OpenID Connect support to Consul Democracy''' ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. Some of these institutions have authentication solutions based on either SAML or OpenID Connect; however, there's no built-in support for these authentication solutions in Consul Democracy, so each institution has to build their own.<br /> <br /> In Ruby, the OmniAuth library provides a standard way to manage multi-provider authentication. Consul Democracy currently uses several Ruby gems, all based on OmniAuth, to provide authentication via Facebook, Google, Twitter/X and WordPress. There's been an attempt at providing SAML support using the omniauth-saml Ruby gem, but its development hasn't been finished due to the lack of a SAML platform to test against.<br /> <br /> Consul Democracy also supports multitenancy, meaning the same application can be used to manage several institutions (with different domains or subdomains). For authentication using Facebook, Google, Twitter/X or WordPress, Consul Democracy provides the option to use the same configuration for each institution, to use different configurations for different institutions, or a mix of both (one default configuration which can be overwritten per institution).<br /> <br /> The aim of this proposal is to provide generic SAML and OpenID Connect authentication solutions in Consul Democracy so a variety of institutions can easily integrate their existing authentication platform.<br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using a SAML service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using an OpenID Connect service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions must allow different configurations for different institutions in a multitenant environment <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions should be flexible enough so institutions don't have to change the source code in order to configure their service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; The source code of the SAML and OpenID Connect solutions should be similar to the source code of the existing Facebook, Google, Twitter/X and WordPress solutions <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the documentation with instructions on how to configure SAML and OpenID Connect <br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ====Knowledge Prerequisites====<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; SAML and OpenID Connect authentication configuration * (Optional) Ruby on Rails and OmniAuth authentication<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> <br /> == '''Docker for Consul Democracy citizen participation platform''' ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. That means Consul Democracy developers don't have access to production machines, and so Consul Democracy must be as simple to install and maintain as possible so anyone can do it no matter how familiar they are with the technologies used by Consul Democracy.<br /> <br /> Currently, Consul Democracy is installed on production by running an ansible-based installer which installs all the project dependencies on a Debian GNU/Linux or Ubuntu Linux server. Deployment of new developments is then done using Capistrano.<br /> <br /> The source code of Consul Democracy contains a Dockerfile and a docker-compose.yml file that are exclusively meant for the development environment, in order to make it easier for developers who are familiar with Docker to contribute to the project. However, there's currently no way to deploy to a production environment using Docker, which is inconvenient for institutions who don't use Debian or Ubuntu on their servers, or for institutions who have adopted Docker as their preferred way to setup their servers. The main goal of this proposal is to solve this issue. Since 2024, Ruby on Rails applications are configured to use Kamal by default as a solution to deploy to production using a Docker container. To our knowledge, this would be the most simple solution to our problem.<br /> <br /> There's a third kind of Docker integration, which uses a devcontainer to allow developers to use tools like GitHub Codespaces to run the application in a development environment, which is also configured by default in new Rails applications since 2024, and we'd like to enable this option in Consul Democracy.<br /> <br /> With this developments, we could enable many more municipalities to utilise digital citizen participation - and thus offer their citizens greater involvement in the development of their cities. <br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to install and deploy Consul Democracy applications using Docker in the most simple way (probably with Kamal)<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Add a devcontainer for integration with GitHub Codespaces <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make sure the current development setup with Docker keeps working after the previous additions *<br /> <br /> The configuration files for all three environments mentioned above should have as little duplicate code as possible so they're easy to maintain <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the technical documentation for both development and production environments<br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ==== Knowledge Prerequisites ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Experience deploying to production environments using Docker <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; (Optional) Experience using Docker in Ruby on Rails applications<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> == '''Εxtending the apothesis factory pattern for seamless 2D and 3D lattice integration''' ==<br /> <br /> ==== '''Overview''' ====<br /> Apothesis is a generalized software for designing, simulating, and analyzing deposition processes using the kinetic Monte Carlo method. It consists of two main components: a lattice (e.g., simple cubic, HPC, etc.) and the processes (adsorption, desorption, diffusion, and surface reactions) that occur within it. Currently, Apothesis includes a factory-based mechanism for lattice creation, but this implementation is limited in scope, primarily focusing on basic lattice structures. To enhance its flexibility and scalability, this proposal aims to extend the factory pattern to support a broader range of 2D and 3D surfaces in a seamless and modular way. This extension will involve introducing specialized lattice factories tailored for different geometries, such as hexagonal, face-centered cubic, and custom surface representations, ensuring compatibility with kinetic Monte Carlo processes. Additionally, a dynamic factory registry system will be implemented, allowing new lattice types to be registered and instantiated at runtime without modifying the core system. This approach will not only improve adaptability but also facilitate user-defined lattice structures while preserving maintainability and efficiency. By refining the factory mechanism, Apothesis will provide a more robust framework for deposition process simulations, enabling researchers and engineers to explore a wider range of surface dynamics with greater ease.<br /> <br /> ==== '''Related work''' ====<br /> Apothesis already implements a factory pattern for lattice creation, but it needs to be expanded to support a broader range of 2D and 3D surfaces in a more seamless and flexible way. The current implementation primarily focuses on basic lattice structures, and extending it would involve introducing specialized factories for different geometries, such as hexagonal, face-centered cubic, and custom surface representations. Enhancing the factory pattern should include a more modular approach, allowing new lattice types to be dynamically registered and instantiated without modifying the core system. Additionally, ensuring that these new lattice structures fully integrate with kinetic Monte Carlo processes—such as adsorption, desorption, diffusion, and surface reactions—will be crucial for maintaining simulation accuracy and consistency. By refining the factory mechanism and introducing a more extensible registry system, Apothesis can achieve greater adaptability, making it easier for users to define and integrate new lattice types while preserving maintainability and scalability.<br /> <br /> ==== '''Details of your coding project''' ====<br /> A potential practical approach to extending the factory pattern in Apothesis for 2D and 3D surfaces involves creating a modular and extensible lattice generation system. This can be achieved by defining an abstract LatticeFactory that enforces a standard way of creating lattice structures while delegating specific implementations to derived factories. Specialized factories such as SimpleCubicLatticeFactory, HexagonalLatticeFactory, FCCLatticeFactory, and GrapheneLatticeFactory can be implemented to handle different lattice geometries while ensuring compatibility with kinetic Monte Carlo processes like adsorption, desorption, diffusion, and surface reactions. Each factory produces a lattice object implementing a common ILattice interface, encapsulating geometry, boundary conditions, and process compatibility. A dynamic factory registry mechanism allows runtime selection and registration of new lattice types, enabling users to introduce custom lattices without modifying the core code. This approach ensures scalability, flexibility, and maintainability by decoupling lattice creation from simulation logic while seamlessly supporting both 2D and 3D structures.<br /> <br /> ==== '''Size''' ====<br /> Large (350 hours)<br /> <br /> ==== '''Skills''' ====<br /> Required: C++, desing patters, experience with physicochemical based software<br /> <br /> ==== '''Expected impact''' ====<br /> The expected impact of extending the factory pattern in Apothesis includes enhanced flexibility, scalability, and efficiency in lattice creation for deposition process simulations. By introducing a more modular and extensible approach, researchers and engineers will be able to seamlessly integrate new 2D and 3D surface structures without modifying core code, reducing development time and increasing adaptability. The improved factory mechanism will ensure better compatibility with kinetic Monte Carlo processes, enabling more accurate and diverse simulations of adsorption, desorption, diffusion, and surface reactions. Additionally, the dynamic factory registry will foster customization and extensibility, allowing users to define and register their own lattice structures, thereby broadening the range of possible simulations. Overall, this enhancement will make Apothesis a more powerful and user-friendly tool, supporting advanced research, industrial applications, and innovation in surface science and material deposition technologies.<br /> <br /> ==== '''Mentors''' ====<br /> • Nikolaos (Nikos) Cheimarios &lt;n.cheimarios at gmail.com&gt; is a researcher with contributions in scientific software development. He has previous experience as mentor in 2020, 2022, 2023 and 2024. He is one of the authors of Apothesis, Chameleon software and several web-based scientific numerical applications.<br /> <br /> • Christianna Gatsiou &lt;christianna.gatsiou at gmail.com&gt;.<br /> <br /> ==== '''Tests''' ====<br /> Students, the following test will be helpful. • Easy: Compile and run Apothesis for the CO heterogeneous catalysis case. • Medium: Perform runs with SimpleCubic and HPC lattices. • Hard: Identify the part of code that creates the lattices. Briefly describe how you would implement the 2D graphene lattice. For tips and references contact the Mentors!<br /> <br /> ==== '''References''' ====<br /> [1] N. Cheimarios, D. To, G. Kokkoris, G. Memos and A.G. Boudouvis “Monte Carlo &amp; Kinetic Monte Carlo models for deposition processes: A review of recent works”, Frontiers in Physics, 9, 165 (2021).<br /> <br /> [2] N. Cheimarios, “Insights into the effect of growth on the Ziff-Gulari-Barshad model and the film properties”, Modelling and Simulation in Materials Science and Engineering, 31, 065007, (2023).<br /> <br /> [3] A.P.F Jansen, &quot;An Introduction to Kinetic Monte Carlo Simulations of Surface Reactions&quot;, Springer Berlin, Heidelberg, 2012. &lt;nowiki&gt;https://doi.org/10.1007/978-3-642-29488-4&lt;/nowiki&gt;<br /> <br /> [4] M. Andersen, C. Panosetti, K. Reuter, &quot;A Practical Guide to Surface Kinetic Monte Carlo Simulations&quot;, Frontiers in Chemistry, 7, 202 (2019).<br /> <br /> <br /> <br /> == '''Identifying transition points in the ZGB model using convolutional neural networks (CNNs)''' ==<br /> Scientific computing for physical/chemical sciences and engineering edited this page 3 weeks ago ·<br /> <br /> ==== '''Overview''' ====<br /> In 1986, Ziff, Gulari, and Barshad introduced the ZGB model to computationally study the heterogeneous catalytic process of CO oxidation to CO₂ with O₂ on a metal surface (e.g., Pt). Using Monte Carlo simulations on a simple square lattice, with the partial pressure of CO, yCO, as the only parameter, they demonstrated that at low yCO values, the catalytic surface becomes poisoned (fully covered) by oxygen atoms, preventing the surface reaction and the conversion to CO₂. In this case, the system remains out of equilibrium. As yCO increases, at approximately y1 ≈ 0.389, the system transitions to an equilibrium state where CO begins to convert into CO₂. Further increasing yCO leads to a first-order, discontinuous phase transition at y2 ≈ 0.525, where CO conversion to CO₂ ceases again due to surface poisoning by CO atoms. In 1990, Jensen and Fogedby extended this model by incorporating diffusion phenomena, showing that the transition points shift depending on the diffusion rate, pd , but do not disappear. The results for both the original ZGB model (pd=0p_d = 0pd=0) and the modified model with diffusion are presented in Figure 1, while the transition points y1 and y2 for different pd values are summarized in Table 1.<br /> <br /> ==== '''Related work''' ====<br /> Apothesis is a generalized open-source software for simulating heterogeneous catalysis and deposition processes via kMC. It is based on performing certain processes (adsorption, desorption, diffusion and surface reaction(s)) on lattices. Currently, Apothesis supports simple cubic, FCC, HPC and diamond lattices. It has been used to study both heterogeneous catalysis of CO and related type growth models [2].<br /> <br /> ==== '''Details of your coding project''' ====<br /> This works aims to predict the transition points using machine learning methods, specifically convolutional neural networks (CNNs), based on surfaces derived from kinetic Monte Carlo simulations from Apothesis at equilibrium states. For that, an outer shell to Apothesis must be build that will read the data from Apothesis and use it for training a CNN and then for the prediction of the transition points.<br /> <br /> ==== '''Size''' ====<br /> Large (350 hours)<br /> <br /> ==== '''Skills''' ====<br /> Required: Python, Tensorflow, experience with physicochemical based software<br /> <br /> ==== '''Expected impact''' ====<br /> The project will build an outer shell for Apothesis to be used in ML/AI applications.<br /> <br /> ==== '''Mentors''' ====<br /> <br /> * Nikolaos (Nikos) Cheimarios &lt;n.cheimarios at gmail.com&gt; is a researcher with contributions in scientific software development. He has previous experience as mentor in GSoC 2020, 2022, 2023 and 2024. He is one of the authors of Apothesis, Chameleon software and several web-based scientific numerical applications.<br /> * Konstantinos (Kostas) Eftaxias is a data scientist with more than 15 years of experience in research and industry applications. His main interests are computer vision, time series modelling/prediction and reinforcement learning.<br /> <br /> ==== '''Tests''' ====<br /> Students, the following test will be helpful. • Easy: Compile and run Apothesis for the CO heterogeneous catalysis case. • Medium: Take three surfaces (SurfaceSpecies_*.dat) generated by Apothesis and read it in Python. • Hard: Call Apothesis from Python and read the latest surface generated. For tips and references contact the Mentors!<br /> <br /> ==== '''References''' ====<br /> [1] R. M. Ziff, E. Gulari, and Y. Barshad, Kinetic Phase Transitions in an Irreversible Surface-Reaction Model, Phys. Rev. Lett. 56, 2553 (1986).<br /> <br /> [2] I. Jensen and H. C. Fogedby, Kinetic Phase Transitions in a Surface-Reaction Model with Diffusion: Computer Simulations and Mean-Field Theory, Phys. Rev. A 42, 1969 (1990).<br /> <br /> [3] N. Cheimarios, Surface diffusion effects on the system and the film properties of a Ziff–Gulari–Barshad type growth model, Mat. Today Comm. 39, 109189 (2024).<br /> <br /> [4] N. Cheimarios, Mean field approximation of a surface-reaction growth model with dissociation, Phys. Lett. A 524, 129828 (2024).<br /> <br /> [5] Y. Bahri, J. Kadmon, J. Pennington, S. S. Schoenholz, J. Sohl-Dickstein, S. Ganguli, Statistical Mechanics of Deep Learning, Annu. Rev. Condens.Matter Phys. 11, 501 (2020).<br /> <br /> [6] D.W. Tola, M. Bekele, Machine Learning of Nonequilibrium Phase Transition in an Ising Model on Square Lattice, Condens. Matter 8, 83 (2023).<br /> <br /> == '''MyUni''' ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> <br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> <br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> <br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> <br /> 6. Create a BackEnd system to manage all the data described above.<br /> <br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> <br /> 8. Rewrite in Typescript for maintainability<br /> <br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> <br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, <br /> <br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == '''GlossAPI''' ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> == '''DIY IoT Physics Experiments for education''' ==<br /> <br /> ==== Brief Explanation ====<br /> Remote physics experiments for students in all educational levels are the second best to hands-on experiments.  Especially for students who temporarily cannot attend school or in cases like Covid-19 and the lockdowns. In many practical cases, they are the only alternative, as they are available 24/7, they can involve dangerous materials or conditions, they can be accessed from anywhere and any device, they require less maintenance, have lower cost, can be easily modified, or arranged to perform another experiment, and are less probable to be damaged.  They are in line with the modern way of performing experiments, as it is desirable to have as little direct contact with the experiments as possible and use them online.  Examples include online telescopes and electronic microscopes.  This is possible due to automation; data acquisition and manipulation of the experimental data is done using a computer or a single board computer.  In this way students need not take pain stacking notes, especially for experiments that take a lot of time to collect data, sometimes days or months.  Students can concentrate on data processing, the analysis of the results, and arrive at scientifically valid conclusions.  Our laboratory has set up many remote experiments and has more than 10 years’ experience in designing, setting, and servicing remote experiments.  Our remote experiments are based on Arduino and readily available sensors and actuators. The previous year it was designed and implemented a way to make the sensors, and the actuators form an IoT local network so that it will be easier to easily utilize them in different experiments and to build new experiments.  The IoT sensors and actuators are DIY and based on open software.   The previous year GSoC stipend receiver, programed the ESP8266 to receive data from the sensor and transmit the data through MQTT to ThingsBoard. Similarly, for an actuator the ESP8266 to receive MQTT data from ThingsBoard. The stipend receiver prepared five DIY IoT sensors and five actuators. ThingsBoard provided users with visual representation of the data and the control of the experimental setup through dashboards.  There are produced five dashboards for five corresponding experiments.  The present successful applicant will have to produce a digital twin of the experiments.  This will involve open software for producing 3D models of five experiments, allowing them to manipulate the digital twins, view the evolution of the experiment, provide data presentation tools, and extract model parameters.<br /> <br /> ==== Related repositories ====<br /> <br /> ==== https://github.com/totheworld2004/DIY-Physics-IoT ====<br /> <br /> ==== Exprected Outcome: ====<br /> Five digital twins of corresponding five experiments, their documentation and instructions of how to use them<br /> <br /> ==== Knowledge Prerequisites ====<br /> Any one for case a)-e) or similar --- a) 3D Web-Based Physics Simulations: Three.js, Babylon.js, p5.js, Godot b) Interactive Dashboards: Plotly Dash, Panel, Bokeh c) Custom Data Visualizations: D3.js, Matplotlib, Jupyter Notebooks d) Game-Based Physics Experiments: Godot, Babylon.js e) Embedded 3D Simulations: Three.js, Babylon.js<br /> <br /> ==== Mentors: ====<br /> Hariton Polatoglou and Panagiotis Koustoumpardis<br /> <br /> =='''eCodeOrama, an educational interactive flow visualization tool for mit scratch programs'''==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2313 2025-02-27T09:47:54Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == '''Expanding HassIO smart home capabilities via low-code automation development''' ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == '''A Tool for Visualizing the Arguments, Sentiments and User Interactions of Online Discussions''' ==<br /> <br /> ==== Brief Explanation ====<br /> In recent years, the analysis and visualization of dialogue have gained prominence in fields such as computational linguistics, social sciences, and human-computer interaction. The ability to model, analyze, and visualize real-life discussions provides valuable insights into the flow of conversations, the exchange of arguments, and the sentiments conveyed. Such visualizations can improve the understanding of complex discussions, foster decision-making, and even help develop better AI systems for facilitating or mediating discussions. We are particularly interested in online text-only discussions (e.g. on platforms like Reddit).<br /> <br /> Various tools and platforms have been developed in order to facilitate structured discussions and multi-party decision making. Kialo is an online, structured debate platform, where the use of argumentation is the central component. It allows the construction of argument maps, in the form of trees. It promotes thoughtful discussion, understanding of different viewpoints and collaborative decision-making, through visualizations of argument maps. <br /> <br /> Debategraph is another online structured debate platform, using more complex graphs, called &amp;amp;quot;mind-maps&amp;amp;quot;, where arguments are interconnected in a web-like structure. It allows an even wider choice of visualizations of relationships between ideas.<br /> <br /> DebateVis is a tool that can help non-expert users explore and analyze debate transcripts. Given a transcript, the tool produces: (a) an Interactions Graph that summarizes how often each candidate spoke overall, mentioned other candidates and discussed each topic, (b) an Annotated Transcript with automatically extracted topic labels and speaker interactions, (c) a Timeline visualization providing an overview of the debate. <br /> <br /> Finally, VisArgue is a framework proposing a range of visualizations of dialogues, including: Lexical Episode Plots (a timeline representation of the topics discussed), (b) Conversational Topic Visualizations, representing the shifting of focus of individual user on topics, (c) various statistics measuring user participation, respect, justification and accommodation, (d) Lexical Units, which are timeline representations of features such as the amount of argumentation and emotions.<br /> <br /> Although tools such as the above offer important functionality, there are still issues: in most cases, either the source code is not available, or integration with new projects is not seamless, or it is difficult to parameterize the output. Furthermore, the tools above focus mostly on debate, whereas we are also interested in other types of online discussions (e.g. deliberation to improve legislation bills, non-adversarial discussions for intra-company decision making).<br /> <br /> Therefore, this project’s goal is the design and implementation of an open source tool for visualizing and analyzing real-life, online, text-only discussions, exploring subjects like: topics discussed, arguments exchanged and emotions conveyed. The project will also explore how these visualizations can be leveraged for improving public understanding of contentious issues, academic discourse, and online discussion platforms.<br /> <br /> '''Project Objectives / Contributions:'''<br /> <br /> - Select, from the literature, prominent dialogue visualization approaches / ideas (e.g. styles of graph-based, or timeline-based, visualizations used) to represent various aspects of real-life online discussions and collect available libraries (not necessarily discussion-specific) that can be used to implement them (e.g. Gephi, NetworkX).<br /> <br /> - Explore the open-source toolkits being developed in the Archimedes project “LLM3: LLMs as mediators and moderators” to measure dialogue quality aspects (e.g., sentiment, politeness, topics, user participation) and select those that can provide useful meta-data for visualizing on-line discussions.<br /> <br /> - Develop a tool capable of ingesting data from real-life online discussions generating relevant meta-data (possibly by calling other toolkits) and producing the desired visualizations of the discussions.<br /> <br /> - Potentially, evaluate the effectiveness of the tool and its visualizations in making complex online discussions understandable to diverse audiences, such as researchers, mediators, or general users.<br /> <br /> '''Project Impact:'''<br /> <br /> - A novel, easy-to-use, open source, visualization tool (with accompanying paper) for online, text-only discussions, that can help the analysis of discussions in different settings and domains (e.g. political discourse, academic debates, or customer feedback).<br /> <br /> - Expand the general understanding of how visualization techniques can make debates more accessible and informative (possibly also leading to a publication).<br /> <br />   - Contribution to the Archimedes project “LLM3: LLMs as mediators and moderators” which aims to develop and evaluate LLM-based mediation agents that will actively participate in online discussions, with or without additional human mediation.<br /> <br /> '''Key Types of Dialogue Visualizations:'''<br /> <br /> The project will develop and explore several types of dialogue visualizations. Some are briefly described below. The contributor will be free to propose and implement new ones. <br /> <br />     1) Timelines: they represent the chronological flow of a conversation, highlighting key moments such as topic shifts, argument introductions and emotional peaks. Possible Features: topic evolution over time, points of conflict or agreement, visual markers for significant events (e.g. emotional outbursts or resolution points).<br /> <br />     2) Argumentation Graphs: they visualize the logical structure of arguments, including claims, counterclaims and evidence. Possible Features: nodes representing arguments or claims, edges denoting relationships (e.g., support, contradiction).<br /> <br />     3) User Interaction Graphs: they map the relationships and interaction patterns between participants in the debate. Possible Features: nodes representing participants, weighted edges showing the frequency, tone, or sentiment of interactions, clusters indicating subgroups or coalitions in the dialogue.<br /> <br />     4) Sentiment Heatmaps: they analyze and visualize the emotional dynamics of a conversation. Possible Features: color-coded intensity for positive, negative, or neutral sentiments, overlay with timeline or topic visualization for richer insights.<br /> <br />     5) Topic Trees or Topic Flow Diagrams: they represent how topics are introduced, branched out, and revisited during the discussion. Possible Features: hierarchical or radial layouts for topic relationships, highlights of overlapping or transitioning topics.<br /> <br />     6) Hybrid Visualizations: by combining multiple visualization techniques.<br /> <br /> Importance of Dialogue Visualizations:<br /> <br /> Dialogue visualizations, such as those presented above, can support:<br /> <br /> - Topic Analysis: by identifying the main topics discussed and their transitions over time, and by highlighting overlapping topics and their importance to the dialogue.<br /> <br /> - Argumentation Analysis: by understanding the logical flow of arguments, counterarguments, and evidence, and by identifying circular reasoning, weak arguments, or areas of agreement.<br /> <br /> - Sentiment Analysis: by visualizing the emotional tone of the conversation and its impact on the debate, and by examining whether certain sentiments correlate with specific topics or arguments.<br /> <br /> - Participant Dynamics: by mapping the influence and activity of each participant, and by analyzing interaction patterns (e.g., dominance, interruptions, alliances).<br /> <br /> '''Methodology:'''<br /> <br /> - Data Collection and Preparation:<br /> <br /> The tool should be able to ingest data from various online debate platforms (e.g. Reddit, Kialo), from political debate transcripts, from academic discourse, as well as from debates among LLM-agents. Since the format of raw data may vary, we propose the use of the Convokit tool in order to homogenise and preprocess the data.<br /> <br /> - Development of Visualization Prototypes:<br /> <br /> Tools/Technologies: Python (matplotlib, seaborn, Plotly), D3.js for web-based visualizations, or tools like Gephi for network analysis. Use natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers) for topic modeling, sentiment analysis, and argument mining, along with tools being developed at the LLM3 project of Archimedes.<br /> <br /> - User Feedback and Iterative Improvement:<br /> <br /> Test the outputs (visualizations) with researchers, mediators, or other stakeholders. Refine designs based on usability feedback and task-specific performance.<br /> <br /> '''Evaluation'''<br /> <br /> If time allows, the contributor will contribute in evaluating the effectiveness of their dialogue visualizations, in the context of Archimedes’ LLM3 project, by using them for both real-life and LLM-generated dialogues. Their output will be measured on clarity, usability and informativeness.<br /> <br /> '''Desired Profile:'''<br /> <br /> We are looking for a contributor with the following characteristics:<br /> <br /> - Good programming skills in Python (experience in network analysis and / or NLP is a plus).<br /> <br /> - Experience (and interest for) coding visual representations of concepts with libraries such as: matplotlib, seaborn, Plotly, Gephi, D3.js. <br /> <br /> - Interest in the subject of human interaction through dialogue (more specifically, on themes such as: argumentation, topic identification, sentiment analysis).<br /> <br /> - A taste for concise, elegant and efficient solutions / visualizations.<br /> <br /> The contributor will be mentored/supported by members of the LLM3 project, the broader NLP group of Archimedes (&lt;nowiki&gt;https://archimedesai.gr/en/&lt;/nowiki&gt;), as well as the NLP Group (&lt;nowiki&gt;http://nlp.cs.aueb.gr/&lt;/nowiki&gt;) of the Department of Informatics, Athens University of Economics and Business. <br /> <br /> '''Conclusion:'''<br /> <br /> This GSOC project aims to develop an open-source tool that will make complex, online discussions more understandable, insightful, and actionable. By capturing the topics, arguments, sentiments, and participant dynamics, it will offer a comprehensive approach to online dialogue visualization that can benefit multiple fields, from education to public policy.<br /> <br /> ==== Related repositories ====<br /> https://sites.google.com/view/llm3/home<br /> <br /> ==== Expected Results ====<br /> A tool able to process real-life, text-only dialogues and produce selected visualizations capturing their essential points.<br /> <br /> ==== Mentors ====<br /> Dionysios Kontarinis (denniskont@gmail.com), Ion Androutsopoulos, Ioannis Pavlopoulos<br /> <br /> == '''PersonalAIs: Generative AI Agent for Personalized Music Recommendations''' ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == '''OpenRF 3D''' ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == '''Exploring and Abstracting Triplestore Alternatives''' ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == '''Flexible GovDoc Scanner''' ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == '''Extending the capabilities of OpenTRIM''' ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == '''Cleaning of HPLT Greek v2 Dataset for GlossApi LLM''' ==<br /> <br /> ==== Brief Explanation ====<br /> Cleanup of the Greek datasets at https://hplt-project.org/datasets/v2.0. The cleanup will be done with the help of the glossAPI team. <br /> <br /> For methodology see https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&amp;#x20;. <br /> <br /> ==== Expected Outcome ====<br /> The goal is to isolate from the html Greek text with normal grammar and complete sentences (not fragmented).<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/glossapi<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> <br /> == '''Add SAML and OpenID Connect support to Consul Democracy''' ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. Some of these institutions have authentication solutions based on either SAML or OpenID Connect; however, there's no built-in support for these authentication solutions in Consul Democracy, so each institution has to build their own.<br /> <br /> In Ruby, the OmniAuth library provides a standard way to manage multi-provider authentication. Consul Democracy currently uses several Ruby gems, all based on OmniAuth, to provide authentication via Facebook, Google, Twitter/X and WordPress. There's been an attempt at providing SAML support using the omniauth-saml Ruby gem, but its development hasn't been finished due to the lack of a SAML platform to test against.<br /> <br /> Consul Democracy also supports multitenancy, meaning the same application can be used to manage several institutions (with different domains or subdomains). For authentication using Facebook, Google, Twitter/X or WordPress, Consul Democracy provides the option to use the same configuration for each institution, to use different configurations for different institutions, or a mix of both (one default configuration which can be overwritten per institution).<br /> <br /> The aim of this proposal is to provide generic SAML and OpenID Connect authentication solutions in Consul Democracy so a variety of institutions can easily integrate their existing authentication platform.<br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using a SAML service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using an OpenID Connect service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions must allow different configurations for different institutions in a multitenant environment <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions should be flexible enough so institutions don't have to change the source code in order to configure their service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; The source code of the SAML and OpenID Connect solutions should be similar to the source code of the existing Facebook, Google, Twitter/X and WordPress solutions <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the documentation with instructions on how to configure SAML and OpenID Connect <br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ====Knowledge Prerequisites====<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; SAML and OpenID Connect authentication configuration * (Optional) Ruby on Rails and OmniAuth authentication<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> <br /> == '''Docker for Consul Democracy citizen participation platform''' ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. That means Consul Democracy developers don't have access to production machines, and so Consul Democracy must be as simple to install and maintain as possible so anyone can do it no matter how familiar they are with the technologies used by Consul Democracy.<br /> <br /> Currently, Consul Democracy is installed on production by running an ansible-based installer which installs all the project dependencies on a Debian GNU/Linux or Ubuntu Linux server. Deployment of new developments is then done using Capistrano.<br /> <br /> The source code of Consul Democracy contains a Dockerfile and a docker-compose.yml file that are exclusively meant for the development environment, in order to make it easier for developers who are familiar with Docker to contribute to the project. However, there's currently no way to deploy to a production environment using Docker, which is inconvenient for institutions who don't use Debian or Ubuntu on their servers, or for institutions who have adopted Docker as their preferred way to setup their servers. The main goal of this proposal is to solve this issue. Since 2024, Ruby on Rails applications are configured to use Kamal by default as a solution to deploy to production using a Docker container. To our knowledge, this would be the most simple solution to our problem.<br /> <br /> There's a third kind of Docker integration, which uses a devcontainer to allow developers to use tools like GitHub Codespaces to run the application in a development environment, which is also configured by default in new Rails applications since 2024, and we'd like to enable this option in Consul Democracy.<br /> <br /> With this developments, we could enable many more municipalities to utilise digital citizen participation - and thus offer their citizens greater involvement in the development of their cities. <br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to install and deploy Consul Democracy applications using Docker in the most simple way (probably with Kamal)<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Add a devcontainer for integration with GitHub Codespaces <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make sure the current development setup with Docker keeps working after the previous additions *<br /> <br /> The configuration files for all three environments mentioned above should have as little duplicate code as possible so they're easy to maintain <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the technical documentation for both development and production environments<br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ==== Knowledge Prerequisites ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Experience deploying to production environments using Docker <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; (Optional) Experience using Docker in Ruby on Rails applications<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> == '''Εxtending the apothesis factory pattern for seamless 2D and 3D lattice integration''' ==<br /> <br /> ==== '''Overview''' ====<br /> Apothesis is a generalized software for designing, simulating, and analyzing deposition processes using the kinetic Monte Carlo method. It consists of two main components: a lattice (e.g., simple cubic, HPC, etc.) and the processes (adsorption, desorption, diffusion, and surface reactions) that occur within it. Currently, Apothesis includes a factory-based mechanism for lattice creation, but this implementation is limited in scope, primarily focusing on basic lattice structures. To enhance its flexibility and scalability, this proposal aims to extend the factory pattern to support a broader range of 2D and 3D surfaces in a seamless and modular way. This extension will involve introducing specialized lattice factories tailored for different geometries, such as hexagonal, face-centered cubic, and custom surface representations, ensuring compatibility with kinetic Monte Carlo processes. Additionally, a dynamic factory registry system will be implemented, allowing new lattice types to be registered and instantiated at runtime without modifying the core system. This approach will not only improve adaptability but also facilitate user-defined lattice structures while preserving maintainability and efficiency. By refining the factory mechanism, Apothesis will provide a more robust framework for deposition process simulations, enabling researchers and engineers to explore a wider range of surface dynamics with greater ease.<br /> <br /> ==== '''Related work''' ====<br /> Apothesis already implements a factory pattern for lattice creation, but it needs to be expanded to support a broader range of 2D and 3D surfaces in a more seamless and flexible way. The current implementation primarily focuses on basic lattice structures, and extending it would involve introducing specialized factories for different geometries, such as hexagonal, face-centered cubic, and custom surface representations. Enhancing the factory pattern should include a more modular approach, allowing new lattice types to be dynamically registered and instantiated without modifying the core system. Additionally, ensuring that these new lattice structures fully integrate with kinetic Monte Carlo processes—such as adsorption, desorption, diffusion, and surface reactions—will be crucial for maintaining simulation accuracy and consistency. By refining the factory mechanism and introducing a more extensible registry system, Apothesis can achieve greater adaptability, making it easier for users to define and integrate new lattice types while preserving maintainability and scalability.<br /> <br /> ==== '''Details of your coding project''' ====<br /> A potential practical approach to extending the factory pattern in Apothesis for 2D and 3D surfaces involves creating a modular and extensible lattice generation system. This can be achieved by defining an abstract LatticeFactory that enforces a standard way of creating lattice structures while delegating specific implementations to derived factories. Specialized factories such as SimpleCubicLatticeFactory, HexagonalLatticeFactory, FCCLatticeFactory, and GrapheneLatticeFactory can be implemented to handle different lattice geometries while ensuring compatibility with kinetic Monte Carlo processes like adsorption, desorption, diffusion, and surface reactions. Each factory produces a lattice object implementing a common ILattice interface, encapsulating geometry, boundary conditions, and process compatibility. A dynamic factory registry mechanism allows runtime selection and registration of new lattice types, enabling users to introduce custom lattices without modifying the core code. This approach ensures scalability, flexibility, and maintainability by decoupling lattice creation from simulation logic while seamlessly supporting both 2D and 3D structures.<br /> <br /> ==== '''Size''' ====<br /> Large (350 hours)<br /> <br /> ==== '''Skills''' ====<br /> Required: C++, desing patters, experience with physicochemical based software<br /> <br /> ==== '''Expected impact''' ====<br /> The expected impact of extending the factory pattern in Apothesis includes enhanced flexibility, scalability, and efficiency in lattice creation for deposition process simulations. By introducing a more modular and extensible approach, researchers and engineers will be able to seamlessly integrate new 2D and 3D surface structures without modifying core code, reducing development time and increasing adaptability. The improved factory mechanism will ensure better compatibility with kinetic Monte Carlo processes, enabling more accurate and diverse simulations of adsorption, desorption, diffusion, and surface reactions. Additionally, the dynamic factory registry will foster customization and extensibility, allowing users to define and register their own lattice structures, thereby broadening the range of possible simulations. Overall, this enhancement will make Apothesis a more powerful and user-friendly tool, supporting advanced research, industrial applications, and innovation in surface science and material deposition technologies.<br /> <br /> ==== '''Mentors''' ====<br /> • Nikolaos (Nikos) Cheimarios &lt;n.cheimarios at gmail.com&gt; is a researcher with contributions in scientific software development. He has previous experience as mentor in 2020, 2022, 2023 and 2024. He is one of the authors of Apothesis, Chameleon software and several web-based scientific numerical applications.<br /> <br /> • Christianna Gatsiou &lt;christianna.gatsiou at gmail.com&gt;.<br /> <br /> ==== '''Tests''' ====<br /> Students, the following test will be helpful. • Easy: Compile and run Apothesis for the CO heterogeneous catalysis case. • Medium: Perform runs with SimpleCubic and HPC lattices. • Hard: Identify the part of code that creates the lattices. Briefly describe how you would implement the 2D graphene lattice. For tips and references contact the Mentors!<br /> <br /> ==== '''References''' ====<br /> [1] N. Cheimarios, D. To, G. Kokkoris, G. Memos and A.G. Boudouvis “Monte Carlo &amp; Kinetic Monte Carlo models for deposition processes: A review of recent works”, Frontiers in Physics, 9, 165 (2021).<br /> <br /> [2] N. Cheimarios, “Insights into the effect of growth on the Ziff-Gulari-Barshad model and the film properties”, Modelling and Simulation in Materials Science and Engineering, 31, 065007, (2023).<br /> <br /> [3] A.P.F Jansen, &quot;An Introduction to Kinetic Monte Carlo Simulations of Surface Reactions&quot;, Springer Berlin, Heidelberg, 2012. &lt;nowiki&gt;https://doi.org/10.1007/978-3-642-29488-4&lt;/nowiki&gt;<br /> <br /> [4] M. Andersen, C. Panosetti, K. Reuter, &quot;A Practical Guide to Surface Kinetic Monte Carlo Simulations&quot;, Frontiers in Chemistry, 7, 202 (2019).<br /> <br /> == '''MyUni''' ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> <br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> <br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> <br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> <br /> 6. Create a BackEnd system to manage all the data described above.<br /> <br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> <br /> 8. Rewrite in Typescript for maintainability<br /> <br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> <br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, <br /> <br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == '''GlossAPI''' ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> == '''DIY IoT Physics Experiments for education''' ==<br /> <br /> ==== Brief Explanation ====<br /> Remote physics experiments for students in all educational levels are the second best to hands-on experiments.  Especially for students who temporarily cannot attend school or in cases like Covid-19 and the lockdowns. In many practical cases, they are the only alternative, as they are available 24/7, they can involve dangerous materials or conditions, they can be accessed from anywhere and any device, they require less maintenance, have lower cost, can be easily modified, or arranged to perform another experiment, and are less probable to be damaged.  They are in line with the modern way of performing experiments, as it is desirable to have as little direct contact with the experiments as possible and use them online.  Examples include online telescopes and electronic microscopes.  This is possible due to automation; data acquisition and manipulation of the experimental data is done using a computer or a single board computer.  In this way students need not take pain stacking notes, especially for experiments that take a lot of time to collect data, sometimes days or months.  Students can concentrate on data processing, the analysis of the results, and arrive at scientifically valid conclusions.  Our laboratory has set up many remote experiments and has more than 10 years’ experience in designing, setting, and servicing remote experiments.  Our remote experiments are based on Arduino and readily available sensors and actuators. The previous year it was designed and implemented a way to make the sensors, and the actuators form an IoT local network so that it will be easier to easily utilize them in different experiments and to build new experiments.  The IoT sensors and actuators are DIY and based on open software.   The previous year GSoC stipend receiver, programed the ESP8266 to receive data from the sensor and transmit the data through MQTT to ThingsBoard. Similarly, for an actuator the ESP8266 to receive MQTT data from ThingsBoard. The stipend receiver prepared five DIY IoT sensors and five actuators. ThingsBoard provided users with visual representation of the data and the control of the experimental setup through dashboards.  There are produced five dashboards for five corresponding experiments.  The present successful applicant will have to produce a digital twin of the experiments.  This will involve open software for producing 3D models of five experiments, allowing them to manipulate the digital twins, view the evolution of the experiment, provide data presentation tools, and extract model parameters.<br /> <br /> ==== Related repositories ====<br /> <br /> ==== https://github.com/totheworld2004/DIY-Physics-IoT ====<br /> <br /> ==== Exprected Outcome: ====<br /> Five digital twins of corresponding five experiments, their documentation and instructions of how to use them<br /> <br /> ==== Knowledge Prerequisites ====<br /> Any one for case a)-e) or similar --- a) 3D Web-Based Physics Simulations: Three.js, Babylon.js, p5.js, Godot b) Interactive Dashboards: Plotly Dash, Panel, Bokeh c) Custom Data Visualizations: D3.js, Matplotlib, Jupyter Notebooks d) Game-Based Physics Experiments: Godot, Babylon.js e) Embedded 3D Simulations: Three.js, Babylon.js<br /> <br /> ==== Mentors: ====<br /> Hariton Polatoglou and Panagiotis Koustoumpardis<br /> <br /> =='''eCodeOrama, an educational interactive flow visualization tool for mit scratch programs'''==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2310 2025-02-22T06:09:39Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == '''Expanding HassIO smart home capabilities via low-code automation development''' ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == '''A Tool for Visualizing the Arguments, Sentiments and User Interactions of Online Discussions''' ==<br /> <br /> ==== Brief Explanation ====<br /> In recent years, the analysis and visualization of dialogue have gained prominence in fields such as computational linguistics, social sciences, and human-computer interaction. The ability to model, analyze, and visualize real-life discussions provides valuable insights into the flow of conversations, the exchange of arguments, and the sentiments conveyed. Such visualizations can improve the understanding of complex discussions, foster decision-making, and even help develop better AI systems for facilitating or mediating discussions. We are particularly interested in online text-only discussions (e.g. on platforms like Reddit).<br /> <br /> Various tools and platforms have been developed in order to facilitate structured discussions and multi-party decision making. Kialo is an online, structured debate platform, where the use of argumentation is the central component. It allows the construction of argument maps, in the form of trees. It promotes thoughtful discussion, understanding of different viewpoints and collaborative decision-making, through visualizations of argument maps. <br /> <br /> Debategraph is another online structured debate platform, using more complex graphs, called &amp;amp;quot;mind-maps&amp;amp;quot;, where arguments are interconnected in a web-like structure. It allows an even wider choice of visualizations of relationships between ideas.<br /> <br /> DebateVis is a tool that can help non-expert users explore and analyze debate transcripts. Given a transcript, the tool produces: (a) an Interactions Graph that summarizes how often each candidate spoke overall, mentioned other candidates and discussed each topic, (b) an Annotated Transcript with automatically extracted topic labels and speaker interactions, (c) a Timeline visualization providing an overview of the debate. <br /> <br /> Finally, VisArgue is a framework proposing a range of visualizations of dialogues, including: Lexical Episode Plots (a timeline representation of the topics discussed), (b) Conversational Topic Visualizations, representing the shifting of focus of individual user on topics, (c) various statistics measuring user participation, respect, justification and accommodation, (d) Lexical Units, which are timeline representations of features such as the amount of argumentation and emotions.<br /> <br /> Although tools such as the above offer important functionality, there are still issues: in most cases, either the source code is not available, or integration with new projects is not seamless, or it is difficult to parameterize the output. Furthermore, the tools above focus mostly on debate, whereas we are also interested in other types of online discussions (e.g. deliberation to improve legislation bills, non-adversarial discussions for intra-company decision making).<br /> <br /> Therefore, this project’s goal is the design and implementation of an open source tool for visualizing and analyzing real-life, online, text-only discussions, exploring subjects like: topics discussed, arguments exchanged and emotions conveyed. The project will also explore how these visualizations can be leveraged for improving public understanding of contentious issues, academic discourse, and online discussion platforms.<br /> <br /> '''Project Objectives / Contributions:'''<br /> <br /> - Select, from the literature, prominent dialogue visualization approaches / ideas (e.g. styles of graph-based, or timeline-based, visualizations used) to represent various aspects of real-life online discussions and collect available libraries (not necessarily discussion-specific) that can be used to implement them (e.g. Gephi, NetworkX).<br /> <br /> - Explore the open-source toolkits being developed in the Archimedes project “LLM3: LLMs as mediators and moderators” to measure dialogue quality aspects (e.g., sentiment, politeness, topics, user participation) and select those that can provide useful meta-data for visualizing on-line discussions.<br /> <br /> - Develop a tool capable of ingesting data from real-life online discussions generating relevant meta-data (possibly by calling other toolkits) and producing the desired visualizations of the discussions.<br /> <br /> - Potentially, evaluate the effectiveness of the tool and its visualizations in making complex online discussions understandable to diverse audiences, such as researchers, mediators, or general users.<br /> <br /> '''Project Impact:'''<br /> <br /> - A novel, easy-to-use, open source, visualization tool (with accompanying paper) for online, text-only discussions, that can help the analysis of discussions in different settings and domains (e.g. political discourse, academic debates, or customer feedback).<br /> <br /> - Expand the general understanding of how visualization techniques can make debates more accessible and informative (possibly also leading to a publication).<br /> <br />   - Contribution to the Archimedes project “LLM3: LLMs as mediators and moderators” which aims to develop and evaluate LLM-based mediation agents that will actively participate in online discussions, with or without additional human mediation.<br /> <br /> '''Key Types of Dialogue Visualizations:'''<br /> <br /> The project will develop and explore several types of dialogue visualizations. Some are briefly described below. The contributor will be free to propose and implement new ones. <br /> <br />     1) Timelines: they represent the chronological flow of a conversation, highlighting key moments such as topic shifts, argument introductions and emotional peaks. Possible Features: topic evolution over time, points of conflict or agreement, visual markers for significant events (e.g. emotional outbursts or resolution points).<br /> <br />     2) Argumentation Graphs: they visualize the logical structure of arguments, including claims, counterclaims and evidence. Possible Features: nodes representing arguments or claims, edges denoting relationships (e.g., support, contradiction).<br /> <br />     3) User Interaction Graphs: they map the relationships and interaction patterns between participants in the debate. Possible Features: nodes representing participants, weighted edges showing the frequency, tone, or sentiment of interactions, clusters indicating subgroups or coalitions in the dialogue.<br /> <br />     4) Sentiment Heatmaps: they analyze and visualize the emotional dynamics of a conversation. Possible Features: color-coded intensity for positive, negative, or neutral sentiments, overlay with timeline or topic visualization for richer insights.<br /> <br />     5) Topic Trees or Topic Flow Diagrams: they represent how topics are introduced, branched out, and revisited during the discussion. Possible Features: hierarchical or radial layouts for topic relationships, highlights of overlapping or transitioning topics.<br /> <br />     6) Hybrid Visualizations: by combining multiple visualization techniques.<br /> <br /> Importance of Dialogue Visualizations:<br /> <br /> Dialogue visualizations, such as those presented above, can support:<br /> <br /> - Topic Analysis: by identifying the main topics discussed and their transitions over time, and by highlighting overlapping topics and their importance to the dialogue.<br /> <br /> - Argumentation Analysis: by understanding the logical flow of arguments, counterarguments, and evidence, and by identifying circular reasoning, weak arguments, or areas of agreement.<br /> <br /> - Sentiment Analysis: by visualizing the emotional tone of the conversation and its impact on the debate, and by examining whether certain sentiments correlate with specific topics or arguments.<br /> <br /> - Participant Dynamics: by mapping the influence and activity of each participant, and by analyzing interaction patterns (e.g., dominance, interruptions, alliances).<br /> <br /> '''Methodology:'''<br /> <br /> - Data Collection and Preparation:<br /> <br /> The tool should be able to ingest data from various online debate platforms (e.g. Reddit, Kialo), from political debate transcripts, from academic discourse, as well as from debates among LLM-agents. Since the format of raw data may vary, we propose the use of the Convokit tool in order to homogenise and preprocess the data.<br /> <br /> - Development of Visualization Prototypes:<br /> <br /> Tools/Technologies: Python (matplotlib, seaborn, Plotly), D3.js for web-based visualizations, or tools like Gephi for network analysis. Use natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers) for topic modeling, sentiment analysis, and argument mining, along with tools being developed at the LLM3 project of Archimedes.<br /> <br /> - User Feedback and Iterative Improvement:<br /> <br /> Test the outputs (visualizations) with researchers, mediators, or other stakeholders. Refine designs based on usability feedback and task-specific performance.<br /> <br /> '''Evaluation'''<br /> <br /> If time allows, the contributor will contribute in evaluating the effectiveness of their dialogue visualizations, in the context of Archimedes’ LLM3 project, by using them for both real-life and LLM-generated dialogues. Their output will be measured on clarity, usability and informativeness.<br /> <br /> '''Desired Profile:'''<br /> <br /> We are looking for a contributor with the following characteristics:<br /> <br /> - Good programming skills in Python (experience in network analysis and / or NLP is a plus).<br /> <br /> - Experience (and interest for) coding visual representations of concepts with libraries such as: matplotlib, seaborn, Plotly, Gephi, D3.js. <br /> <br /> - Interest in the subject of human interaction through dialogue (more specifically, on themes such as: argumentation, topic identification, sentiment analysis).<br /> <br /> - A taste for concise, elegant and efficient solutions / visualizations.<br /> <br /> The contributor will be mentored/supported by members of the LLM3 project, the broader NLP group of Archimedes (&lt;nowiki&gt;https://archimedesai.gr/en/&lt;/nowiki&gt;), as well as the NLP Group (&lt;nowiki&gt;http://nlp.cs.aueb.gr/&lt;/nowiki&gt;) of the Department of Informatics, Athens University of Economics and Business. <br /> <br /> '''Conclusion:'''<br /> <br /> This GSOC project aims to develop an open-source tool that will make complex, online discussions more understandable, insightful, and actionable. By capturing the topics, arguments, sentiments, and participant dynamics, it will offer a comprehensive approach to online dialogue visualization that can benefit multiple fields, from education to public policy.<br /> <br /> ==== Related repositories ====<br /> https://sites.google.com/view/llm3/home<br /> <br /> ==== Expected Results ====<br /> A tool able to process real-life, text-only dialogues and produce selected visualizations capturing their essential points.<br /> <br /> ==== Mentors ====<br /> Dionysios Kontarinis (denniskont@gmail.com), Ion Androutsopoulos, Ioannis Pavlopoulos<br /> <br /> == '''PersonalAIs: Generative AI Agent for Personalized Music Recommendations''' ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == '''OpenRF 3D''' ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == '''Exploring and Abstracting Triplestore Alternatives''' ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == '''Flexible GovDoc Scanner''' ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == '''Extending the capabilities of OpenTRIM''' ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == '''Cleaning of HPLT Greek v2 Dataset for GlossApi LLM''' ==<br /> <br /> ==== Brief Explanation ====<br /> Cleanup of the Greek datasets at https://hplt-project.org/datasets/v2.0. The cleanup will be done with the help of the glossAPI team. <br /> <br /> For methodology see https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&amp;#x20;. <br /> <br /> ==== Expected Outcome ====<br /> The goal is to isolate from the html Greek text with normal grammar and complete sentences (not fragmented).<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/glossapi<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> <br /> == '''Add SAML and OpenID Connect support to Consul Democracy''' ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. Some of these institutions have authentication solutions based on either SAML or OpenID Connect; however, there's no built-in support for these authentication solutions in Consul Democracy, so each institution has to build their own.<br /> <br /> In Ruby, the OmniAuth library provides a standard way to manage multi-provider authentication. Consul Democracy currently uses several Ruby gems, all based on OmniAuth, to provide authentication via Facebook, Google, Twitter/X and WordPress. There's been an attempt at providing SAML support using the omniauth-saml Ruby gem, but its development hasn't been finished due to the lack of a SAML platform to test against.<br /> <br /> Consul Democracy also supports multitenancy, meaning the same application can be used to manage several institutions (with different domains or subdomains). For authentication using Facebook, Google, Twitter/X or WordPress, Consul Democracy provides the option to use the same configuration for each institution, to use different configurations for different institutions, or a mix of both (one default configuration which can be overwritten per institution).<br /> <br /> The aim of this proposal is to provide generic SAML and OpenID Connect authentication solutions in Consul Democracy so a variety of institutions can easily integrate their existing authentication platform.<br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using a SAML service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using an OpenID Connect service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions must allow different configurations for different institutions in a multitenant environment <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions should be flexible enough so institutions don't have to change the source code in order to configure their service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; The source code of the SAML and OpenID Connect solutions should be similar to the source code of the existing Facebook, Google, Twitter/X and WordPress solutions <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the documentation with instructions on how to configure SAML and OpenID Connect <br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ====Knowledge Prerequisites====<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; SAML and OpenID Connect authentication configuration * (Optional) Ruby on Rails and OmniAuth authentication<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> <br /> == '''Docker for Consul Democracy citizen participation platform''' ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. That means Consul Democracy developers don't have access to production machines, and so Consul Democracy must be as simple to install and maintain as possible so anyone can do it no matter how familiar they are with the technologies used by Consul Democracy.<br /> <br /> Currently, Consul Democracy is installed on production by running an ansible-based installer which installs all the project dependencies on a Debian GNU/Linux or Ubuntu Linux server. Deployment of new developments is then done using Capistrano.<br /> <br /> The source code of Consul Democracy contains a Dockerfile and a docker-compose.yml file that are exclusively meant for the development environment, in order to make it easier for developers who are familiar with Docker to contribute to the project. However, there's currently no way to deploy to a production environment using Docker, which is inconvenient for institutions who don't use Debian or Ubuntu on their servers, or for institutions who have adopted Docker as their preferred way to setup their servers. The main goal of this proposal is to solve this issue. Since 2024, Ruby on Rails applications are configured to use Kamal by default as a solution to deploy to production using a Docker container. To our knowledge, this would be the most simple solution to our problem.<br /> <br /> There's a third kind of Docker integration, which uses a devcontainer to allow developers to use tools like GitHub Codespaces to run the application in a development environment, which is also configured by default in new Rails applications since 2024, and we'd like to enable this option in Consul Democracy.<br /> <br /> With this developments, we could enable many more municipalities to utilise digital citizen participation - and thus offer their citizens greater involvement in the development of their cities. <br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to install and deploy Consul Democracy applications using Docker in the most simple way (probably with Kamal)<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Add a devcontainer for integration with GitHub Codespaces <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make sure the current development setup with Docker keeps working after the previous additions *<br /> <br /> The configuration files for all three environments mentioned above should have as little duplicate code as possible so they're easy to maintain <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the technical documentation for both development and production environments<br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ==== Knowledge Prerequisites ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Experience deploying to production environments using Docker <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; (Optional) Experience using Docker in Ruby on Rails applications<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> <br /> == '''MyUni''' ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> <br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> <br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> <br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> <br /> 6. Create a BackEnd system to manage all the data described above.<br /> <br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> <br /> 8. Rewrite in Typescript for maintainability<br /> <br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> <br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, <br /> <br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == '''GlossAPI''' ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> == '''DIY IoT Physics Experiments for education''' ==<br /> <br /> ==== Brief Explanation ====<br /> Remote physics experiments for students in all educational levels are the second best to hands-on experiments.  Especially for students who temporarily cannot attend school or in cases like Covid-19 and the lockdowns. In many practical cases, they are the only alternative, as they are available 24/7, they can involve dangerous materials or conditions, they can be accessed from anywhere and any device, they require less maintenance, have lower cost, can be easily modified, or arranged to perform another experiment, and are less probable to be damaged.  They are in line with the modern way of performing experiments, as it is desirable to have as little direct contact with the experiments as possible and use them online.  Examples include online telescopes and electronic microscopes.  This is possible due to automation; data acquisition and manipulation of the experimental data is done using a computer or a single board computer.  In this way students need not take pain stacking notes, especially for experiments that take a lot of time to collect data, sometimes days or months.  Students can concentrate on data processing, the analysis of the results, and arrive at scientifically valid conclusions.  Our laboratory has set up many remote experiments and has more than 10 years’ experience in designing, setting, and servicing remote experiments.  Our remote experiments are based on Arduino and readily available sensors and actuators. The previous year it was designed and implemented a way to make the sensors, and the actuators form an IoT local network so that it will be easier to easily utilize them in different experiments and to build new experiments.  The IoT sensors and actuators are DIY and based on open software.   The previous year GSoC stipend receiver, programed the ESP8266 to receive data from the sensor and transmit the data through MQTT to ThingsBoard. Similarly, for an actuator the ESP8266 to receive MQTT data from ThingsBoard. The stipend receiver prepared five DIY IoT sensors and five actuators. ThingsBoard provided users with visual representation of the data and the control of the experimental setup through dashboards.  There are produced five dashboards for five corresponding experiments.  The present successful applicant will have to produce a digital twin of the experiments.  This will involve open software for producing 3D models of five experiments, allowing them to manipulate the digital twins, view the evolution of the experiment, provide data presentation tools, and extract model parameters.<br /> <br /> ==== Related repositories ====<br /> <br /> ==== https://github.com/totheworld2004/DIY-Physics-IoT ====<br /> <br /> ==== Exprected Outcome: ====<br /> Five digital twins of corresponding five experiments, their documentation and instructions of how to use them<br /> <br /> ==== Knowledge Prerequisites ====<br /> Any one for case a)-e) or similar --- a) 3D Web-Based Physics Simulations: Three.js, Babylon.js, p5.js, Godot b) Interactive Dashboards: Plotly Dash, Panel, Bokeh c) Custom Data Visualizations: D3.js, Matplotlib, Jupyter Notebooks d) Game-Based Physics Experiments: Godot, Babylon.js e) Embedded 3D Simulations: Three.js, Babylon.js<br /> <br /> ==== Mentors: ====<br /> Hariton Polatoglou and Panagiotis Koustoumpardis<br /> <br /> =='''eCodeOrama, an educational interactive flow visualization tool for mit scratch programs'''==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2309 2025-02-22T06:07:34Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Expanding HassIO smart home capabilities via low-code automation development ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == A Tool for Visualizing the Arguments, Sentiments and User Interactions of Online Discussions ==<br /> <br /> ==== Brief Explanation ====<br /> In recent years, the analysis and visualization of dialogue have gained prominence in fields such as computational linguistics, social sciences, and human-computer interaction. The ability to model, analyze, and visualize real-life discussions provides valuable insights into the flow of conversations, the exchange of arguments, and the sentiments conveyed. Such visualizations can improve the understanding of complex discussions, foster decision-making, and even help develop better AI systems for facilitating or mediating discussions. We are particularly interested in online text-only discussions (e.g. on platforms like Reddit).<br /> <br /> Various tools and platforms have been developed in order to facilitate structured discussions and multi-party decision making. Kialo is an online, structured debate platform, where the use of argumentation is the central component. It allows the construction of argument maps, in the form of trees. It promotes thoughtful discussion, understanding of different viewpoints and collaborative decision-making, through visualizations of argument maps. <br /> <br /> Debategraph is another online structured debate platform, using more complex graphs, called &amp;amp;quot;mind-maps&amp;amp;quot;, where arguments are interconnected in a web-like structure. It allows an even wider choice of visualizations of relationships between ideas.<br /> <br /> DebateVis is a tool that can help non-expert users explore and analyze debate transcripts. Given a transcript, the tool produces: (a) an Interactions Graph that summarizes how often each candidate spoke overall, mentioned other candidates and discussed each topic, (b) an Annotated Transcript with automatically extracted topic labels and speaker interactions, (c) a Timeline visualization providing an overview of the debate. <br /> <br /> Finally, VisArgue is a framework proposing a range of visualizations of dialogues, including: Lexical Episode Plots (a timeline representation of the topics discussed), (b) Conversational Topic Visualizations, representing the shifting of focus of individual user on topics, (c) various statistics measuring user participation, respect, justification and accommodation, (d) Lexical Units, which are timeline representations of features such as the amount of argumentation and emotions.<br /> <br /> Although tools such as the above offer important functionality, there are still issues: in most cases, either the source code is not available, or integration with new projects is not seamless, or it is difficult to parameterize the output. Furthermore, the tools above focus mostly on debate, whereas we are also interested in other types of online discussions (e.g. deliberation to improve legislation bills, non-adversarial discussions for intra-company decision making).<br /> <br /> Therefore, this project’s goal is the design and implementation of an open source tool for visualizing and analyzing real-life, online, text-only discussions, exploring subjects like: topics discussed, arguments exchanged and emotions conveyed. The project will also explore how these visualizations can be leveraged for improving public understanding of contentious issues, academic discourse, and online discussion platforms.<br /> <br /> '''Project Objectives / Contributions:'''<br /> <br /> - Select, from the literature, prominent dialogue visualization approaches / ideas (e.g. styles of graph-based, or timeline-based, visualizations used) to represent various aspects of real-life online discussions and collect available libraries (not necessarily discussion-specific) that can be used to implement them (e.g. Gephi, NetworkX).<br /> <br /> - Explore the open-source toolkits being developed in the Archimedes project “LLM3: LLMs as mediators and moderators” to measure dialogue quality aspects (e.g., sentiment, politeness, topics, user participation) and select those that can provide useful meta-data for visualizing on-line discussions.<br /> <br /> - Develop a tool capable of ingesting data from real-life online discussions generating relevant meta-data (possibly by calling other toolkits) and producing the desired visualizations of the discussions.<br /> <br /> - Potentially, evaluate the effectiveness of the tool and its visualizations in making complex online discussions understandable to diverse audiences, such as researchers, mediators, or general users.<br /> <br /> '''Project Impact:'''<br /> <br /> - A novel, easy-to-use, open source, visualization tool (with accompanying paper) for online, text-only discussions, that can help the analysis of discussions in different settings and domains (e.g. political discourse, academic debates, or customer feedback).<br /> <br /> - Expand the general understanding of how visualization techniques can make debates more accessible and informative (possibly also leading to a publication).<br /> <br />   - Contribution to the Archimedes project “LLM3: LLMs as mediators and moderators” which aims to develop and evaluate LLM-based mediation agents that will actively participate in online discussions, with or without additional human mediation.<br /> <br /> '''Key Types of Dialogue Visualizations:'''<br /> <br /> The project will develop and explore several types of dialogue visualizations. Some are briefly described below. The contributor will be free to propose and implement new ones. <br /> <br />     1) Timelines: they represent the chronological flow of a conversation, highlighting key moments such as topic shifts, argument introductions and emotional peaks. Possible Features: topic evolution over time, points of conflict or agreement, visual markers for significant events (e.g. emotional outbursts or resolution points).<br /> <br />     2) Argumentation Graphs: they visualize the logical structure of arguments, including claims, counterclaims and evidence. Possible Features: nodes representing arguments or claims, edges denoting relationships (e.g., support, contradiction).<br /> <br />     3) User Interaction Graphs: they map the relationships and interaction patterns between participants in the debate. Possible Features: nodes representing participants, weighted edges showing the frequency, tone, or sentiment of interactions, clusters indicating subgroups or coalitions in the dialogue.<br /> <br />     4) Sentiment Heatmaps: they analyze and visualize the emotional dynamics of a conversation. Possible Features: color-coded intensity for positive, negative, or neutral sentiments, overlay with timeline or topic visualization for richer insights.<br /> <br />     5) Topic Trees or Topic Flow Diagrams: they represent how topics are introduced, branched out, and revisited during the discussion. Possible Features: hierarchical or radial layouts for topic relationships, highlights of overlapping or transitioning topics.<br /> <br />     6) Hybrid Visualizations: by combining multiple visualization techniques.<br /> <br /> Importance of Dialogue Visualizations:<br /> <br /> Dialogue visualizations, such as those presented above, can support:<br /> <br /> - Topic Analysis: by identifying the main topics discussed and their transitions over time, and by highlighting overlapping topics and their importance to the dialogue.<br /> <br /> - Argumentation Analysis: by understanding the logical flow of arguments, counterarguments, and evidence, and by identifying circular reasoning, weak arguments, or areas of agreement.<br /> <br /> - Sentiment Analysis: by visualizing the emotional tone of the conversation and its impact on the debate, and by examining whether certain sentiments correlate with specific topics or arguments.<br /> <br /> - Participant Dynamics: by mapping the influence and activity of each participant, and by analyzing interaction patterns (e.g., dominance, interruptions, alliances).<br /> <br /> '''Methodology:'''<br /> <br /> - Data Collection and Preparation:<br /> <br /> The tool should be able to ingest data from various online debate platforms (e.g. Reddit, Kialo), from political debate transcripts, from academic discourse, as well as from debates among LLM-agents. Since the format of raw data may vary, we propose the use of the Convokit tool in order to homogenise and preprocess the data.<br /> <br /> - Development of Visualization Prototypes:<br /> <br /> Tools/Technologies: Python (matplotlib, seaborn, Plotly), D3.js for web-based visualizations, or tools like Gephi for network analysis. Use natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers) for topic modeling, sentiment analysis, and argument mining, along with tools being developed at the LLM3 project of Archimedes.<br /> <br /> - User Feedback and Iterative Improvement:<br /> <br /> Test the outputs (visualizations) with researchers, mediators, or other stakeholders. Refine designs based on usability feedback and task-specific performance.<br /> <br /> '''Evaluation'''<br /> <br /> If time allows, the contributor will contribute in evaluating the effectiveness of their dialogue visualizations, in the context of Archimedes’ LLM3 project, by using them for both real-life and LLM-generated dialogues. Their output will be measured on clarity, usability and informativeness.<br /> <br /> '''Desired Profile:'''<br /> <br /> We are looking for a contributor with the following characteristics:<br /> <br /> - Good programming skills in Python (experience in network analysis and / or NLP is a plus).<br /> <br /> - Experience (and interest for) coding visual representations of concepts with libraries such as: matplotlib, seaborn, Plotly, Gephi, D3.js. <br /> <br /> - Interest in the subject of human interaction through dialogue (more specifically, on themes such as: argumentation, topic identification, sentiment analysis).<br /> <br /> - A taste for concise, elegant and efficient solutions / visualizations.<br /> <br /> The contributor will be mentored/supported by members of the LLM3 project, the broader NLP group of Archimedes (&lt;nowiki&gt;https://archimedesai.gr/en/&lt;/nowiki&gt;), as well as the NLP Group (&lt;nowiki&gt;http://nlp.cs.aueb.gr/&lt;/nowiki&gt;) of the Department of Informatics, Athens University of Economics and Business. <br /> <br /> '''Conclusion:'''<br /> <br /> This GSOC project aims to develop an open-source tool that will make complex, online discussions more understandable, insightful, and actionable. By capturing the topics, arguments, sentiments, and participant dynamics, it will offer a comprehensive approach to online dialogue visualization that can benefit multiple fields, from education to public policy.<br /> <br /> ==== Related repositories ====<br /> https://sites.google.com/view/llm3/home<br /> <br /> ==== Expected Results ====<br /> A tool able to process real-life, text-only dialogues and produce selected visualizations capturing their essential points.<br /> <br /> ==== Mentors ====<br /> Dionysios Kontarinis (denniskont@gmail.com), Ion Androutsopoulos, Ioannis Pavlopoulos<br /> <br /> == PersonalAIs: Generative AI Agent for Personalized Music Recommendations ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == OpenRF 3D ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == Flexible GovDoc Scanner ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == Extending the capabilities of OpenTRIM ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == Cleaning of HPLT Greek v2 Dataset for GlossApi LLM ==<br /> <br /> ==== Brief Explanation ====<br /> Cleanup of the Greek datasets at https://hplt-project.org/datasets/v2.0. The cleanup will be done with the help of the glossAPI team. <br /> <br /> For methodology see https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&amp;#x20;. <br /> <br /> ==== Expected Outcome ====<br /> The goal is to isolate from the html Greek text with normal grammar and complete sentences (not fragmented).<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/glossapi<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> <br /> == Add SAML and OpenID Connect support to Consul Democracy ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. Some of these institutions have authentication solutions based on either SAML or OpenID Connect; however, there's no built-in support for these authentication solutions in Consul Democracy, so each institution has to build their own.<br /> <br /> In Ruby, the OmniAuth library provides a standard way to manage multi-provider authentication. Consul Democracy currently uses several Ruby gems, all based on OmniAuth, to provide authentication via Facebook, Google, Twitter/X and WordPress. There's been an attempt at providing SAML support using the omniauth-saml Ruby gem, but its development hasn't been finished due to the lack of a SAML platform to test against.<br /> <br /> Consul Democracy also supports multitenancy, meaning the same application can be used to manage several institutions (with different domains or subdomains). For authentication using Facebook, Google, Twitter/X or WordPress, Consul Democracy provides the option to use the same configuration for each institution, to use different configurations for different institutions, or a mix of both (one default configuration which can be overwritten per institution).<br /> <br /> The aim of this proposal is to provide generic SAML and OpenID Connect authentication solutions in Consul Democracy so a variety of institutions can easily integrate their existing authentication platform.<br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using a SAML service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to authenticate in Consul Democracy using an OpenID Connect service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions must allow different configurations for different institutions in a multitenant environment <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Both SAML and OpenID Connect solutions should be flexible enough so institutions don't have to change the source code in order to configure their service <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; The source code of the SAML and OpenID Connect solutions should be similar to the source code of the existing Facebook, Google, Twitter/X and WordPress solutions <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the documentation with instructions on how to configure SAML and OpenID Connect <br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ====Knowledge Prerequisites====<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; SAML and OpenID Connect authentication configuration * (Optional) Ruby on Rails and OmniAuth authentication<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> <br /> == Docker for Consul Democracy citizen participation platform ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. That means Consul Democracy developers don't have access to production machines, and so Consul Democracy must be as simple to install and maintain as possible so anyone can do it no matter how familiar they are with the technologies used by Consul Democracy.<br /> <br /> Currently, Consul Democracy is installed on production by running an ansible-based installer which installs all the project dependencies on a Debian GNU/Linux or Ubuntu Linux server. Deployment of new developments is then done using Capistrano.<br /> <br /> The source code of Consul Democracy contains a Dockerfile and a docker-compose.yml file that are exclusively meant for the development environment, in order to make it easier for developers who are familiar with Docker to contribute to the project. However, there's currently no way to deploy to a production environment using Docker, which is inconvenient for institutions who don't use Debian or Ubuntu on their servers, or for institutions who have adopted Docker as their preferred way to setup their servers. The main goal of this proposal is to solve this issue. Since 2024, Ruby on Rails applications are configured to use Kamal by default as a solution to deploy to production using a Docker container. To our knowledge, this would be the most simple solution to our problem.<br /> <br /> There's a third kind of Docker integration, which uses a devcontainer to allow developers to use tools like GitHub Codespaces to run the application in a development environment, which is also configured by default in new Rails applications since 2024, and we'd like to enable this option in Consul Democracy.<br /> <br /> With this developments, we could enable many more municipalities to utilise digital citizen participation - and thus offer their citizens greater involvement in the development of their cities. <br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to install and deploy Consul Democracy applications using Docker in the most simple way (probably with Kamal)<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Add a devcontainer for integration with GitHub Codespaces <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make sure the current development setup with Docker keeps working after the previous additions *<br /> <br /> The configuration files for all three environments mentioned above should have as little duplicate code as possible so they're easy to maintain <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Update the technical documentation for both development and production environments<br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ==== Knowledge Prerequisites ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Experience deploying to production environments using Docker <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; (Optional) Experience using Docker in Ruby on Rails applications<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> <br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> <br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> <br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> <br /> 6. Create a BackEnd system to manage all the data described above.<br /> <br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> <br /> 8. Rewrite in Typescript for maintainability<br /> <br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> <br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, <br /> <br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == GlossAPI ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> == DIY IoT Physics Experiments for education ==<br /> <br /> ==== Brief Explanation ====<br /> Remote physics experiments for students in all educational levels are the second best to hands-on experiments.  Especially for students who temporarily cannot attend school or in cases like Covid-19 and the lockdowns. In many practical cases, they are the only alternative, as they are available 24/7, they can involve dangerous materials or conditions, they can be accessed from anywhere and any device, they require less maintenance, have lower cost, can be easily modified, or arranged to perform another experiment, and are less probable to be damaged.  They are in line with the modern way of performing experiments, as it is desirable to have as little direct contact with the experiments as possible and use them online.  Examples include online telescopes and electronic microscopes.  This is possible due to automation; data acquisition and manipulation of the experimental data is done using a computer or a single board computer.  In this way students need not take pain stacking notes, especially for experiments that take a lot of time to collect data, sometimes days or months.  Students can concentrate on data processing, the analysis of the results, and arrive at scientifically valid conclusions.  Our laboratory has set up many remote experiments and has more than 10 years’ experience in designing, setting, and servicing remote experiments.  Our remote experiments are based on Arduino and readily available sensors and actuators. The previous year it was designed and implemented a way to make the sensors, and the actuators form an IoT local network so that it will be easier to easily utilize them in different experiments and to build new experiments.  The IoT sensors and actuators are DIY and based on open software.   The previous year GSoC stipend receiver, programed the ESP8266 to receive data from the sensor and transmit the data through MQTT to ThingsBoard. Similarly, for an actuator the ESP8266 to receive MQTT data from ThingsBoard. The stipend receiver prepared five DIY IoT sensors and five actuators. ThingsBoard provided users with visual representation of the data and the control of the experimental setup through dashboards.  There are produced five dashboards for five corresponding experiments.  The present successful applicant will have to produce a digital twin of the experiments.  This will involve open software for producing 3D models of five experiments, allowing them to manipulate the digital twins, view the evolution of the experiment, provide data presentation tools, and extract model parameters.<br /> <br /> ==== Related repositories ====<br /> <br /> ==== https://github.com/totheworld2004/DIY-Physics-IoT ====<br /> <br /> ==== Exprected Outcome: ====<br /> Five digital twins of corresponding five experiments, their documentation and instructions of how to use them<br /> <br /> ==== Knowledge Prerequisites ====<br /> Any one for case a)-e) or similar --- a) 3D Web-Based Physics Simulations: Three.js, Babylon.js, p5.js, Godot b) Interactive Dashboards: Plotly Dash, Panel, Bokeh c) Custom Data Visualizations: D3.js, Matplotlib, Jupyter Notebooks d) Game-Based Physics Experiments: Godot, Babylon.js e) Embedded 3D Simulations: Three.js, Babylon.js<br /> <br /> ==== Mentors: ====<br /> Hariton Polatoglou and Panagiotis Koustoumpardis<br /> <br /> ==eCodeOrama, an educational interactive flow visualization tool for mit scratch programs==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2308 2025-02-22T06:00:05Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Expanding HassIO smart home capabilities via low-code automation development ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == A Tool for Visualizing the Arguments, Sentiments and User Interactions of Online Discussions ==<br /> <br /> ==== Brief Explanation ====<br /> In recent years, the analysis and visualization of dialogue have gained prominence in fields such as computational linguistics, social sciences, and human-computer interaction. The ability to model, analyze, and visualize real-life discussions provides valuable insights into the flow of conversations, the exchange of arguments, and the sentiments conveyed. Such visualizations can improve the understanding of complex discussions, foster decision-making, and even help develop better AI systems for facilitating or mediating discussions. We are particularly interested in online text-only discussions (e.g. on platforms like Reddit).<br /> <br /> Various tools and platforms have been developed in order to facilitate structured discussions and multi-party decision making. Kialo is an online, structured debate platform, where the use of argumentation is the central component. It allows the construction of argument maps, in the form of trees. It promotes thoughtful discussion, understanding of different viewpoints and collaborative decision-making, through visualizations of argument maps. <br /> <br /> Debategraph is another online structured debate platform, using more complex graphs, called &amp;amp;quot;mind-maps&amp;amp;quot;, where arguments are interconnected in a web-like structure. It allows an even wider choice of visualizations of relationships between ideas.<br /> <br /> DebateVis is a tool that can help non-expert users explore and analyze debate transcripts. Given a transcript, the tool produces: (a) an Interactions Graph that summarizes how often each candidate spoke overall, mentioned other candidates and discussed each topic, (b) an Annotated Transcript with automatically extracted topic labels and speaker interactions, (c) a Timeline visualization providing an overview of the debate. <br /> <br /> Finally, VisArgue is a framework proposing a range of visualizations of dialogues, including: Lexical Episode Plots (a timeline representation of the topics discussed), (b) Conversational Topic Visualizations, representing the shifting of focus of individual user on topics, (c) various statistics measuring user participation, respect, justification and accommodation, (d) Lexical Units, which are timeline representations of features such as the amount of argumentation and emotions.<br /> <br /> Although tools such as the above offer important functionality, there are still issues: in most cases, either the source code is not available, or integration with new projects is not seamless, or it is difficult to parameterize the output. Furthermore, the tools above focus mostly on debate, whereas we are also interested in other types of online discussions (e.g. deliberation to improve legislation bills, non-adversarial discussions for intra-company decision making).<br /> <br /> Therefore, this project’s goal is the design and implementation of an open source tool for visualizing and analyzing real-life, online, text-only discussions, exploring subjects like: topics discussed, arguments exchanged and emotions conveyed. The project will also explore how these visualizations can be leveraged for improving public understanding of contentious issues, academic discourse, and online discussion platforms.<br /> <br /> '''Project Objectives / Contributions:'''<br /> <br /> - Select, from the literature, prominent dialogue visualization approaches / ideas (e.g. styles of graph-based, or timeline-based, visualizations used) to represent various aspects of real-life online discussions and collect available libraries (not necessarily discussion-specific) that can be used to implement them (e.g. Gephi, NetworkX).<br /> <br /> - Explore the open-source toolkits being developed in the Archimedes project “LLM3: LLMs as mediators and moderators” to measure dialogue quality aspects (e.g., sentiment, politeness, topics, user participation) and select those that can provide useful meta-data for visualizing on-line discussions.<br /> <br /> - Develop a tool capable of ingesting data from real-life online discussions generating relevant meta-data (possibly by calling other toolkits) and producing the desired visualizations of the discussions.<br /> <br /> - Potentially, evaluate the effectiveness of the tool and its visualizations in making complex online discussions understandable to diverse audiences, such as researchers, mediators, or general users.<br /> <br /> '''Project Impact:'''<br /> <br /> - A novel, easy-to-use, open source, visualization tool (with accompanying paper) for online, text-only discussions, that can help the analysis of discussions in different settings and domains (e.g. political discourse, academic debates, or customer feedback).<br /> <br /> - Expand the general understanding of how visualization techniques can make debates more accessible and informative (possibly also leading to a publication).<br /> <br />   - Contribution to the Archimedes project “LLM3: LLMs as mediators and moderators” which aims to develop and evaluate LLM-based mediation agents that will actively participate in online discussions, with or without additional human mediation.<br /> <br /> '''Key Types of Dialogue Visualizations:'''<br /> <br /> The project will develop and explore several types of dialogue visualizations. Some are briefly described below. The contributor will be free to propose and implement new ones. <br /> <br />     1) Timelines: they represent the chronological flow of a conversation, highlighting key moments such as topic shifts, argument introductions and emotional peaks. Possible Features: topic evolution over time, points of conflict or agreement, visual markers for significant events (e.g. emotional outbursts or resolution points).<br /> <br />     2) Argumentation Graphs: they visualize the logical structure of arguments, including claims, counterclaims and evidence. Possible Features: nodes representing arguments or claims, edges denoting relationships (e.g., support, contradiction).<br /> <br />     3) User Interaction Graphs: they map the relationships and interaction patterns between participants in the debate. Possible Features: nodes representing participants, weighted edges showing the frequency, tone, or sentiment of interactions, clusters indicating subgroups or coalitions in the dialogue.<br /> <br />     4) Sentiment Heatmaps: they analyze and visualize the emotional dynamics of a conversation. Possible Features: color-coded intensity for positive, negative, or neutral sentiments, overlay with timeline or topic visualization for richer insights.<br /> <br />     5) Topic Trees or Topic Flow Diagrams: they represent how topics are introduced, branched out, and revisited during the discussion. Possible Features: hierarchical or radial layouts for topic relationships, highlights of overlapping or transitioning topics.<br /> <br />     6) Hybrid Visualizations: by combining multiple visualization techniques.<br /> <br /> Importance of Dialogue Visualizations:<br /> <br /> Dialogue visualizations, such as those presented above, can support:<br /> <br /> - Topic Analysis: by identifying the main topics discussed and their transitions over time, and by highlighting overlapping topics and their importance to the dialogue.<br /> <br /> - Argumentation Analysis: by understanding the logical flow of arguments, counterarguments, and evidence, and by identifying circular reasoning, weak arguments, or areas of agreement.<br /> <br /> - Sentiment Analysis: by visualizing the emotional tone of the conversation and its impact on the debate, and by examining whether certain sentiments correlate with specific topics or arguments.<br /> <br /> - Participant Dynamics: by mapping the influence and activity of each participant, and by analyzing interaction patterns (e.g., dominance, interruptions, alliances).<br /> <br /> '''Methodology:'''<br /> <br /> - Data Collection and Preparation:<br /> <br /> The tool should be able to ingest data from various online debate platforms (e.g. Reddit, Kialo), from political debate transcripts, from academic discourse, as well as from debates among LLM-agents. Since the format of raw data may vary, we propose the use of the Convokit tool in order to homogenise and preprocess the data.<br /> <br /> - Development of Visualization Prototypes:<br /> <br /> Tools/Technologies: Python (matplotlib, seaborn, Plotly), D3.js for web-based visualizations, or tools like Gephi for network analysis. Use natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers) for topic modeling, sentiment analysis, and argument mining, along with tools being developed at the LLM3 project of Archimedes.<br /> <br /> - User Feedback and Iterative Improvement:<br /> <br /> Test the outputs (visualizations) with researchers, mediators, or other stakeholders. Refine designs based on usability feedback and task-specific performance.<br /> <br /> '''Evaluation'''<br /> <br /> If time allows, the contributor will contribute in evaluating the effectiveness of their dialogue visualizations, in the context of Archimedes’ LLM3 project, by using them for both real-life and LLM-generated dialogues. Their output will be measured on clarity, usability and informativeness.<br /> <br /> '''Desired Profile:'''<br /> <br /> We are looking for a contributor with the following characteristics:<br /> <br /> - Good programming skills in Python (experience in network analysis and / or NLP is a plus).<br /> <br /> - Experience (and interest for) coding visual representations of concepts with libraries such as: matplotlib, seaborn, Plotly, Gephi, D3.js. <br /> <br /> - Interest in the subject of human interaction through dialogue (more specifically, on themes such as: argumentation, topic identification, sentiment analysis).<br /> <br /> - A taste for concise, elegant and efficient solutions / visualizations.<br /> <br /> The contributor will be mentored/supported by members of the LLM3 project, the broader NLP group of Archimedes (&lt;nowiki&gt;https://archimedesai.gr/en/&lt;/nowiki&gt;), as well as the NLP Group (&lt;nowiki&gt;http://nlp.cs.aueb.gr/&lt;/nowiki&gt;) of the Department of Informatics, Athens University of Economics and Business. <br /> <br /> '''Conclusion:'''<br /> <br /> This GSOC project aims to develop an open-source tool that will make complex, online discussions more understandable, insightful, and actionable. By capturing the topics, arguments, sentiments, and participant dynamics, it will offer a comprehensive approach to online dialogue visualization that can benefit multiple fields, from education to public policy.<br /> <br /> ==== Related repositories ====<br /> https://sites.google.com/view/llm3/home<br /> <br /> ==== Expected Results ====<br /> A tool able to process real-life, text-only dialogues and produce selected visualizations capturing their essential points.<br /> <br /> ==== Mentors ====<br /> Dionysios Kontarinis (denniskont@gmail.com), Ion Androutsopoulos, Ioannis Pavlopoulos<br /> <br /> == PersonalAIs: Generative AI Agent for Personalized Music Recommendations ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == OpenRF 3D ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == Flexible GovDoc Scanner ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == Extending the capabilities of OpenTRIM ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == Cleaning of HPLT Greek v2 Dataset for GlossApi LLM ==<br /> <br /> ==== Brief Explanation ====<br /> Cleanup of the Greek datasets at https://hplt-project.org/datasets/v2.0. The cleanup will be done with the help of the glossAPI team. <br /> <br /> For methodology see https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&amp;#x20;. <br /> <br /> ==== Expected Outcome ====<br /> The goal is to isolate from the html Greek text with normal grammar and complete sentences (not fragmented).<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/glossapi<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> <br /> == Add SAML and OpenID Connect support to Consul Democracy ==<br /> <br /> ==== Brief Explanation ====<br /> Consul Democracy is a web-based citizen participation tool written in Ruby, using the Ruby on Rails framework. Consul Democracy is licensed under the AGPL and its installations are decentralized, meaning there are more than 200 institutions across the world running their own independent server and using a custom version of Consul Democracy. Some of these institutions have authentication solutions based on either SAML or OpenID Connect; however, there's no built-in support for these authentication solutions in Consul Democracy, so each institution has to build their own.<br /> <br /> In Ruby, the OmniAuth library provides a standard way to manage multi-provider authentication. Consul Democracy currently uses several Ruby gems, all based on OmniAuth, to provide authentication via Facebook, Google, Twitter/X and WordPress. There's been an attempt at providing SAML support using the omniauth-saml Ruby gem, but its development hasn't been finished due to the lack of a SAML platform to test against.<br /> <br /> Consul Democracy also supports multitenancy, meaning the same application can be used to manage several institutions (with different domains or subdomains). For authentication using Facebook, Google, Twitter/X or WordPress, Consul Democracy provides the option to use the same configuration for each institution, to use different configurations for different institutions, or a mix of both (one default configuration which can be overwritten per institution).<br /> <br /> The aim of this proposal is to provide generic SAML and OpenID Connect authentication solutions in Consul Democracy so a variety of institutions can easily integrate their existing authentication platform.<br /> <br /> ==== Expected Results. ====<br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make it possible to install and deploy Consul Democracy applications using Docker in the most simple way (probably with Kamal) <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Add a devcontainer for integration with GitHub Codespaces <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; Make sure the current development setup with Docker keeps working after the previous additions *<br /> <br /> The configuration files for all three environments mentioned above should have as little duplicate code as possible so they're easy to maintain * Update the technical documentation for both development and production environments<br /> <br /> ==== Duration of the Project ====<br /> Medium Size 175 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/consuldemocracy/consuldemocracy<br /> <br /> ====Knowledge Prerequisites====<br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt; SAML and OpenID Connect authentication configuration * (Optional) Ruby on Rails and OmniAuth authentication<br /> <br /> ====Mentors====<br /> Javier Martín - &lt;nowiki&gt;https://github.com/javierm&lt;/nowiki&gt;, Sebastià Roig - &lt;nowiki&gt;https://github.com/taitus&lt;/nowiki&gt;<br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> <br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> <br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> <br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> <br /> 6. Create a BackEnd system to manage all the data described above.<br /> <br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> <br /> 8. Rewrite in Typescript for maintainability<br /> <br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> <br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, <br /> <br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == GlossAPI ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> == DIY IoT Physics Experiments for education ==<br /> <br /> ==== Brief Explanation ====<br /> Remote physics experiments for students in all educational levels are the second best to hands-on experiments.  Especially for students who temporarily cannot attend school or in cases like Covid-19 and the lockdowns. In many practical cases, they are the only alternative, as they are available 24/7, they can involve dangerous materials or conditions, they can be accessed from anywhere and any device, they require less maintenance, have lower cost, can be easily modified, or arranged to perform another experiment, and are less probable to be damaged.  They are in line with the modern way of performing experiments, as it is desirable to have as little direct contact with the experiments as possible and use them online.  Examples include online telescopes and electronic microscopes.  This is possible due to automation; data acquisition and manipulation of the experimental data is done using a computer or a single board computer.  In this way students need not take pain stacking notes, especially for experiments that take a lot of time to collect data, sometimes days or months.  Students can concentrate on data processing, the analysis of the results, and arrive at scientifically valid conclusions.  Our laboratory has set up many remote experiments and has more than 10 years’ experience in designing, setting, and servicing remote experiments.  Our remote experiments are based on Arduino and readily available sensors and actuators. The previous year it was designed and implemented a way to make the sensors, and the actuators form an IoT local network so that it will be easier to easily utilize them in different experiments and to build new experiments.  The IoT sensors and actuators are DIY and based on open software.   The previous year GSoC stipend receiver, programed the ESP8266 to receive data from the sensor and transmit the data through MQTT to ThingsBoard. Similarly, for an actuator the ESP8266 to receive MQTT data from ThingsBoard. The stipend receiver prepared five DIY IoT sensors and five actuators. ThingsBoard provided users with visual representation of the data and the control of the experimental setup through dashboards.  There are produced five dashboards for five corresponding experiments.  The present successful applicant will have to produce a digital twin of the experiments.  This will involve open software for producing 3D models of five experiments, allowing them to manipulate the digital twins, view the evolution of the experiment, provide data presentation tools, and extract model parameters.<br /> <br /> ==== Related repositories ====<br /> <br /> ==== https://github.com/totheworld2004/DIY-Physics-IoT ====<br /> <br /> ==== Exprected Outcome: ====<br /> Five digital twins of corresponding five experiments, their documentation and instructions of how to use them<br /> <br /> ==== Knowledge Prerequisites ====<br /> Any one for case a)-e) or similar --- a) 3D Web-Based Physics Simulations: Three.js, Babylon.js, p5.js, Godot b) Interactive Dashboards: Plotly Dash, Panel, Bokeh c) Custom Data Visualizations: D3.js, Matplotlib, Jupyter Notebooks d) Game-Based Physics Experiments: Godot, Babylon.js e) Embedded 3D Simulations: Three.js, Babylon.js<br /> <br /> ==== Mentors: ====<br /> Hariton Polatoglou and Panagiotis Koustoumpardis<br /> <br /> ==eCodeOrama, an educational interactive flow visualization tool for mit scratch programs==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2307 2025-02-14T06:43:01Z

<p>Pkst-1: /* Related repositories */</p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Expanding HassIO smart home capabilities via low-code automation development ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == A Tool for Visualizing the Arguments, Sentiments and User Interactions of Online Discussions ==<br /> <br /> ==== Brief Explanation ====<br /> In recent years, the analysis and visualization of dialogue have gained prominence in fields such as computational linguistics, social sciences, and human-computer interaction. The ability to model, analyze, and visualize real-life discussions provides valuable insights into the flow of conversations, the exchange of arguments, and the sentiments conveyed. Such visualizations can improve the understanding of complex discussions, foster decision-making, and even help develop better AI systems for facilitating or mediating discussions. We are particularly interested in online text-only discussions (e.g. on platforms like Reddit).<br /> <br /> Various tools and platforms have been developed in order to facilitate structured discussions and multi-party decision making. Kialo is an online, structured debate platform, where the use of argumentation is the central component. It allows the construction of argument maps, in the form of trees. It promotes thoughtful discussion, understanding of different viewpoints and collaborative decision-making, through visualizations of argument maps. <br /> <br /> Debategraph is another online structured debate platform, using more complex graphs, called &amp;amp;quot;mind-maps&amp;amp;quot;, where arguments are interconnected in a web-like structure. It allows an even wider choice of visualizations of relationships between ideas.<br /> <br /> DebateVis is a tool that can help non-expert users explore and analyze debate transcripts. Given a transcript, the tool produces: (a) an Interactions Graph that summarizes how often each candidate spoke overall, mentioned other candidates and discussed each topic, (b) an Annotated Transcript with automatically extracted topic labels and speaker interactions, (c) a Timeline visualization providing an overview of the debate. <br /> <br /> Finally, VisArgue is a framework proposing a range of visualizations of dialogues, including: Lexical Episode Plots (a timeline representation of the topics discussed), (b) Conversational Topic Visualizations, representing the shifting of focus of individual user on topics, (c) various statistics measuring user participation, respect, justification and accommodation, (d) Lexical Units, which are timeline representations of features such as the amount of argumentation and emotions.<br /> <br /> Although tools such as the above offer important functionality, there are still issues: in most cases, either the source code is not available, or integration with new projects is not seamless, or it is difficult to parameterize the output. Furthermore, the tools above focus mostly on debate, whereas we are also interested in other types of online discussions (e.g. deliberation to improve legislation bills, non-adversarial discussions for intra-company decision making).<br /> <br /> Therefore, this project’s goal is the design and implementation of an open source tool for visualizing and analyzing real-life, online, text-only discussions, exploring subjects like: topics discussed, arguments exchanged and emotions conveyed. The project will also explore how these visualizations can be leveraged for improving public understanding of contentious issues, academic discourse, and online discussion platforms.<br /> <br /> '''Project Objectives / Contributions:'''<br /> <br /> - Select, from the literature, prominent dialogue visualization approaches / ideas (e.g. styles of graph-based, or timeline-based, visualizations used) to represent various aspects of real-life online discussions and collect available libraries (not necessarily discussion-specific) that can be used to implement them (e.g. Gephi, NetworkX).<br /> <br /> - Explore the open-source toolkits being developed in the Archimedes project “LLM3: LLMs as mediators and moderators” to measure dialogue quality aspects (e.g., sentiment, politeness, topics, user participation) and select those that can provide useful meta-data for visualizing on-line discussions.<br /> <br /> - Develop a tool capable of ingesting data from real-life online discussions generating relevant meta-data (possibly by calling other toolkits) and producing the desired visualizations of the discussions.<br /> <br /> - Potentially, evaluate the effectiveness of the tool and its visualizations in making complex online discussions understandable to diverse audiences, such as researchers, mediators, or general users.<br /> <br /> '''Project Impact:'''<br /> <br /> - A novel, easy-to-use, open source, visualization tool (with accompanying paper) for online, text-only discussions, that can help the analysis of discussions in different settings and domains (e.g. political discourse, academic debates, or customer feedback).<br /> <br /> - Expand the general understanding of how visualization techniques can make debates more accessible and informative (possibly also leading to a publication).<br /> <br />   - Contribution to the Archimedes project “LLM3: LLMs as mediators and moderators” which aims to develop and evaluate LLM-based mediation agents that will actively participate in online discussions, with or without additional human mediation.<br /> <br /> '''Key Types of Dialogue Visualizations:'''<br /> <br /> The project will develop and explore several types of dialogue visualizations. Some are briefly described below. The contributor will be free to propose and implement new ones. <br /> <br />     1) Timelines: they represent the chronological flow of a conversation, highlighting key moments such as topic shifts, argument introductions and emotional peaks. Possible Features: topic evolution over time, points of conflict or agreement, visual markers for significant events (e.g. emotional outbursts or resolution points).<br /> <br />     2) Argumentation Graphs: they visualize the logical structure of arguments, including claims, counterclaims and evidence. Possible Features: nodes representing arguments or claims, edges denoting relationships (e.g., support, contradiction).<br /> <br />     3) User Interaction Graphs: they map the relationships and interaction patterns between participants in the debate. Possible Features: nodes representing participants, weighted edges showing the frequency, tone, or sentiment of interactions, clusters indicating subgroups or coalitions in the dialogue.<br /> <br />     4) Sentiment Heatmaps: they analyze and visualize the emotional dynamics of a conversation. Possible Features: color-coded intensity for positive, negative, or neutral sentiments, overlay with timeline or topic visualization for richer insights.<br /> <br />     5) Topic Trees or Topic Flow Diagrams: they represent how topics are introduced, branched out, and revisited during the discussion. Possible Features: hierarchical or radial layouts for topic relationships, highlights of overlapping or transitioning topics.<br /> <br />     6) Hybrid Visualizations: by combining multiple visualization techniques.<br /> <br /> Importance of Dialogue Visualizations:<br /> <br /> Dialogue visualizations, such as those presented above, can support:<br /> <br /> - Topic Analysis: by identifying the main topics discussed and their transitions over time, and by highlighting overlapping topics and their importance to the dialogue.<br /> <br /> - Argumentation Analysis: by understanding the logical flow of arguments, counterarguments, and evidence, and by identifying circular reasoning, weak arguments, or areas of agreement.<br /> <br /> - Sentiment Analysis: by visualizing the emotional tone of the conversation and its impact on the debate, and by examining whether certain sentiments correlate with specific topics or arguments.<br /> <br /> - Participant Dynamics: by mapping the influence and activity of each participant, and by analyzing interaction patterns (e.g., dominance, interruptions, alliances).<br /> <br /> '''Methodology:'''<br /> <br /> - Data Collection and Preparation:<br /> <br /> The tool should be able to ingest data from various online debate platforms (e.g. Reddit, Kialo), from political debate transcripts, from academic discourse, as well as from debates among LLM-agents. Since the format of raw data may vary, we propose the use of the Convokit tool in order to homogenise and preprocess the data.<br /> <br /> - Development of Visualization Prototypes:<br /> <br /> Tools/Technologies: Python (matplotlib, seaborn, Plotly), D3.js for web-based visualizations, or tools like Gephi for network analysis. Use natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers) for topic modeling, sentiment analysis, and argument mining, along with tools being developed at the LLM3 project of Archimedes.<br /> <br /> - User Feedback and Iterative Improvement:<br /> <br /> Test the outputs (visualizations) with researchers, mediators, or other stakeholders. Refine designs based on usability feedback and task-specific performance.<br /> <br /> '''Evaluation'''<br /> <br /> If time allows, the contributor will contribute in evaluating the effectiveness of their dialogue visualizations, in the context of Archimedes’ LLM3 project, by using them for both real-life and LLM-generated dialogues. Their output will be measured on clarity, usability and informativeness.<br /> <br /> '''Desired Profile:'''<br /> <br /> We are looking for a contributor with the following characteristics:<br /> <br /> - Good programming skills in Python (experience in network analysis and / or NLP is a plus).<br /> <br /> - Experience (and interest for) coding visual representations of concepts with libraries such as: matplotlib, seaborn, Plotly, Gephi, D3.js. <br /> <br /> - Interest in the subject of human interaction through dialogue (more specifically, on themes such as: argumentation, topic identification, sentiment analysis).<br /> <br /> - A taste for concise, elegant and efficient solutions / visualizations.<br /> <br /> The contributor will be mentored/supported by members of the LLM3 project, the broader NLP group of Archimedes (&lt;nowiki&gt;https://archimedesai.gr/en/&lt;/nowiki&gt;), as well as the NLP Group (&lt;nowiki&gt;http://nlp.cs.aueb.gr/&lt;/nowiki&gt;) of the Department of Informatics, Athens University of Economics and Business. <br /> <br /> '''Conclusion:'''<br /> <br /> This GSOC project aims to develop an open-source tool that will make complex, online discussions more understandable, insightful, and actionable. By capturing the topics, arguments, sentiments, and participant dynamics, it will offer a comprehensive approach to online dialogue visualization that can benefit multiple fields, from education to public policy.<br /> <br /> ==== Related repositories ====<br /> https://sites.google.com/view/llm3/home<br /> <br /> ==== Expected Results ====<br /> A tool able to process real-life, text-only dialogues and produce selected visualizations capturing their essential points.<br /> <br /> ==== Mentors ====<br /> Dionysios Kontarinis (denniskont@gmail.com), Ion Androutsopoulos, Ioannis Pavlopoulos<br /> <br /> == PersonalAIs: Generative AI Agent for Personalized Music Recommendations ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == OpenRF 3D ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == Flexible GovDoc Scanner ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == Extending the capabilities of OpenTRIM ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == Cleaning of HPLT Greek v2 Dataset for GlossApi LLM ==<br /> <br /> ==== Brief Explanation ====<br /> Cleanup of the Greek datasets at https://hplt-project.org/datasets/v2.0. The cleanup will be done with the help of the glossAPI team. <br /> <br /> For methodology see https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&amp;#x20;. <br /> <br /> ==== Expected Outcome ====<br /> The goal is to isolate from the html Greek text with normal grammar and complete sentences (not fragmented).<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/glossapi<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> <br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> <br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> <br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> <br /> 6. Create a BackEnd system to manage all the data described above.<br /> <br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> <br /> 8. Rewrite in Typescript for maintainability<br /> <br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> <br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, <br /> <br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == GlossAPI ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> == DIY IoT Physics Experiments for education ==<br /> <br /> ==== Brief Explanation ====<br /> Remote physics experiments for students in all educational levels are the second best to hands-on experiments.  Especially for students who temporarily cannot attend school or in cases like Covid-19 and the lockdowns. In many practical cases, they are the only alternative, as they are available 24/7, they can involve dangerous materials or conditions, they can be accessed from anywhere and any device, they require less maintenance, have lower cost, can be easily modified, or arranged to perform another experiment, and are less probable to be damaged.  They are in line with the modern way of performing experiments, as it is desirable to have as little direct contact with the experiments as possible and use them online.  Examples include online telescopes and electronic microscopes.  This is possible due to automation; data acquisition and manipulation of the experimental data is done using a computer or a single board computer.  In this way students need not take pain stacking notes, especially for experiments that take a lot of time to collect data, sometimes days or months.  Students can concentrate on data processing, the analysis of the results, and arrive at scientifically valid conclusions.  Our laboratory has set up many remote experiments and has more than 10 years’ experience in designing, setting, and servicing remote experiments.  Our remote experiments are based on Arduino and readily available sensors and actuators. The previous year it was designed and implemented a way to make the sensors, and the actuators form an IoT local network so that it will be easier to easily utilize them in different experiments and to build new experiments.  The IoT sensors and actuators are DIY and based on open software.   The previous year GSoC stipend receiver, programed the ESP8266 to receive data from the sensor and transmit the data through MQTT to ThingsBoard. Similarly, for an actuator the ESP8266 to receive MQTT data from ThingsBoard. The stipend receiver prepared five DIY IoT sensors and five actuators. ThingsBoard provided users with visual representation of the data and the control of the experimental setup through dashboards.  There are produced five dashboards for five corresponding experiments.  The present successful applicant will have to produce a digital twin of the experiments.  This will involve open software for producing 3D models of five experiments, allowing them to manipulate the digital twins, view the evolution of the experiment, provide data presentation tools, and extract model parameters.<br /> <br /> ==== Related repositories ====<br /> <br /> ==== https://github.com/totheworld2004/DIY-Physics-IoT ====<br /> <br /> ==== Exprected Outcome: ====<br /> Five digital twins of corresponding five experiments, their documentation and instructions of how to use them<br /> <br /> ==== Knowledge Prerequisites ====<br /> Any one for case a)-e) or similar --- a) 3D Web-Based Physics Simulations: Three.js, Babylon.js, p5.js, Godot b) Interactive Dashboards: Plotly Dash, Panel, Bokeh c) Custom Data Visualizations: D3.js, Matplotlib, Jupyter Notebooks d) Game-Based Physics Experiments: Godot, Babylon.js e) Embedded 3D Simulations: Three.js, Babylon.js<br /> <br /> ==== Mentors: ====<br /> Hariton Polatoglou and Panagiotis Koustoumpardis<br /> <br /> ==eCodeOrama, an educational interactive flow visualization tool for mit scratch programs==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2305 2025-02-12T08:21:22Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Expanding HassIO smart home capabilities via low-code automation development ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == A Tool for Visualizing the Arguments, Sentiments and User Interactions of Online Discussions ==<br /> <br /> ==== Brief Explanation ====<br /> In recent years, the analysis and visualization of dialogue have gained prominence in fields such as computational linguistics, social sciences, and human-computer interaction. The ability to model, analyze, and visualize real-life discussions provides valuable insights into the flow of conversations, the exchange of arguments, and the sentiments conveyed. Such visualizations can improve the understanding of complex discussions, foster decision-making, and even help develop better AI systems for facilitating or mediating discussions. We are particularly interested in online text-only discussions (e.g. on platforms like Reddit).<br /> <br /> Various tools and platforms have been developed in order to facilitate structured discussions and multi-party decision making. Kialo is an online, structured debate platform, where the use of argumentation is the central component. It allows the construction of argument maps, in the form of trees. It promotes thoughtful discussion, understanding of different viewpoints and collaborative decision-making, through visualizations of argument maps. <br /> <br /> Debategraph is another online structured debate platform, using more complex graphs, called &amp;amp;quot;mind-maps&amp;amp;quot;, where arguments are interconnected in a web-like structure. It allows an even wider choice of visualizations of relationships between ideas.<br /> <br /> DebateVis is a tool that can help non-expert users explore and analyze debate transcripts. Given a transcript, the tool produces: (a) an Interactions Graph that summarizes how often each candidate spoke overall, mentioned other candidates and discussed each topic, (b) an Annotated Transcript with automatically extracted topic labels and speaker interactions, (c) a Timeline visualization providing an overview of the debate. <br /> <br /> Finally, VisArgue is a framework proposing a range of visualizations of dialogues, including: Lexical Episode Plots (a timeline representation of the topics discussed), (b) Conversational Topic Visualizations, representing the shifting of focus of individual user on topics, (c) various statistics measuring user participation, respect, justification and accommodation, (d) Lexical Units, which are timeline representations of features such as the amount of argumentation and emotions.<br /> <br /> Although tools such as the above offer important functionality, there are still issues: in most cases, either the source code is not available, or integration with new projects is not seamless, or it is difficult to parameterize the output. Furthermore, the tools above focus mostly on debate, whereas we are also interested in other types of online discussions (e.g. deliberation to improve legislation bills, non-adversarial discussions for intra-company decision making).<br /> <br /> Therefore, this project’s goal is the design and implementation of an open source tool for visualizing and analyzing real-life, online, text-only discussions, exploring subjects like: topics discussed, arguments exchanged and emotions conveyed. The project will also explore how these visualizations can be leveraged for improving public understanding of contentious issues, academic discourse, and online discussion platforms.<br /> <br /> '''Project Objectives / Contributions:'''<br /> <br /> - Select, from the literature, prominent dialogue visualization approaches / ideas (e.g. styles of graph-based, or timeline-based, visualizations used) to represent various aspects of real-life online discussions and collect available libraries (not necessarily discussion-specific) that can be used to implement them (e.g. Gephi, NetworkX).<br /> <br /> - Explore the open-source toolkits being developed in the Archimedes project “LLM3: LLMs as mediators and moderators” to measure dialogue quality aspects (e.g., sentiment, politeness, topics, user participation) and select those that can provide useful meta-data for visualizing on-line discussions.<br /> <br /> - Develop a tool capable of ingesting data from real-life online discussions generating relevant meta-data (possibly by calling other toolkits) and producing the desired visualizations of the discussions.<br /> <br /> - Potentially, evaluate the effectiveness of the tool and its visualizations in making complex online discussions understandable to diverse audiences, such as researchers, mediators, or general users.<br /> <br /> '''Project Impact:'''<br /> <br /> - A novel, easy-to-use, open source, visualization tool (with accompanying paper) for online, text-only discussions, that can help the analysis of discussions in different settings and domains (e.g. political discourse, academic debates, or customer feedback).<br /> <br /> - Expand the general understanding of how visualization techniques can make debates more accessible and informative (possibly also leading to a publication).<br /> <br />   - Contribution to the Archimedes project “LLM3: LLMs as mediators and moderators” which aims to develop and evaluate LLM-based mediation agents that will actively participate in online discussions, with or without additional human mediation.<br /> <br /> '''Key Types of Dialogue Visualizations:'''<br /> <br /> The project will develop and explore several types of dialogue visualizations. Some are briefly described below. The contributor will be free to propose and implement new ones. <br /> <br />     1) Timelines: they represent the chronological flow of a conversation, highlighting key moments such as topic shifts, argument introductions and emotional peaks. Possible Features: topic evolution over time, points of conflict or agreement, visual markers for significant events (e.g. emotional outbursts or resolution points).<br /> <br />     2) Argumentation Graphs: they visualize the logical structure of arguments, including claims, counterclaims and evidence. Possible Features: nodes representing arguments or claims, edges denoting relationships (e.g., support, contradiction).<br /> <br />     3) User Interaction Graphs: they map the relationships and interaction patterns between participants in the debate. Possible Features: nodes representing participants, weighted edges showing the frequency, tone, or sentiment of interactions, clusters indicating subgroups or coalitions in the dialogue.<br /> <br />     4) Sentiment Heatmaps: they analyze and visualize the emotional dynamics of a conversation. Possible Features: color-coded intensity for positive, negative, or neutral sentiments, overlay with timeline or topic visualization for richer insights.<br /> <br />     5) Topic Trees or Topic Flow Diagrams: they represent how topics are introduced, branched out, and revisited during the discussion. Possible Features: hierarchical or radial layouts for topic relationships, highlights of overlapping or transitioning topics.<br /> <br />     6) Hybrid Visualizations: by combining multiple visualization techniques.<br /> <br /> Importance of Dialogue Visualizations:<br /> <br /> Dialogue visualizations, such as those presented above, can support:<br /> <br /> - Topic Analysis: by identifying the main topics discussed and their transitions over time, and by highlighting overlapping topics and their importance to the dialogue.<br /> <br /> - Argumentation Analysis: by understanding the logical flow of arguments, counterarguments, and evidence, and by identifying circular reasoning, weak arguments, or areas of agreement.<br /> <br /> - Sentiment Analysis: by visualizing the emotional tone of the conversation and its impact on the debate, and by examining whether certain sentiments correlate with specific topics or arguments.<br /> <br /> - Participant Dynamics: by mapping the influence and activity of each participant, and by analyzing interaction patterns (e.g., dominance, interruptions, alliances).<br /> <br /> '''Methodology:'''<br /> <br /> - Data Collection and Preparation:<br /> <br /> The tool should be able to ingest data from various online debate platforms (e.g. Reddit, Kialo), from political debate transcripts, from academic discourse, as well as from debates among LLM-agents. Since the format of raw data may vary, we propose the use of the Convokit tool in order to homogenise and preprocess the data.<br /> <br /> - Development of Visualization Prototypes:<br /> <br /> Tools/Technologies: Python (matplotlib, seaborn, Plotly), D3.js for web-based visualizations, or tools like Gephi for network analysis. Use natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers) for topic modeling, sentiment analysis, and argument mining, along with tools being developed at the LLM3 project of Archimedes.<br /> <br /> - User Feedback and Iterative Improvement:<br /> <br /> Test the outputs (visualizations) with researchers, mediators, or other stakeholders. Refine designs based on usability feedback and task-specific performance.<br /> <br /> '''Evaluation'''<br /> <br /> If time allows, the contributor will contribute in evaluating the effectiveness of their dialogue visualizations, in the context of Archimedes’ LLM3 project, by using them for both real-life and LLM-generated dialogues. Their output will be measured on clarity, usability and informativeness.<br /> <br /> '''Desired Profile:'''<br /> <br /> We are looking for a contributor with the following characteristics:<br /> <br /> - Good programming skills in Python (experience in network analysis and / or NLP is a plus).<br /> <br /> - Experience (and interest for) coding visual representations of concepts with libraries such as: matplotlib, seaborn, Plotly, Gephi, D3.js. <br /> <br /> - Interest in the subject of human interaction through dialogue (more specifically, on themes such as: argumentation, topic identification, sentiment analysis).<br /> <br /> - A taste for concise, elegant and efficient solutions / visualizations.<br /> <br /> The contributor will be mentored/supported by members of the LLM3 project, the broader NLP group of Archimedes (&lt;nowiki&gt;https://archimedesai.gr/en/&lt;/nowiki&gt;), as well as the NLP Group (&lt;nowiki&gt;http://nlp.cs.aueb.gr/&lt;/nowiki&gt;) of the Department of Informatics, Athens University of Economics and Business. <br /> <br /> '''Conclusion:'''<br /> <br /> This GSOC project aims to develop an open-source tool that will make complex, online discussions more understandable, insightful, and actionable. By capturing the topics, arguments, sentiments, and participant dynamics, it will offer a comprehensive approach to online dialogue visualization that can benefit multiple fields, from education to public policy.<br /> <br /> ==== Related repositories ====<br /> https://sites.google.com/view/llm3/home<br /> <br /> ==== Expected Results ====<br /> A tool able to process real-life, text-only dialogues and produce selected visualizations capturing their essential points.<br /> <br /> ==== Mentors ====<br /> Dionysios Kontarinis (denniskont@gmail.com), Ion Androutsopoulos, Ioannis Pavlopoulos<br /> <br /> == PersonalAIs: Generative AI Agent for Personalized Music Recommendations ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == OpenRF 3D ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == Flexible GovDoc Scanner ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == Extending the capabilities of OpenTRIM ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == Cleaning of HPLT Greek v2 Dataset for GlossApi LLM ==<br /> <br /> ==== Brief Explanation ====<br /> Καθαρισμός των Ελληνικών dataset στο &lt;nowiki&gt;https://hplt-project.org/datasets/v2.0&lt;/nowiki&gt; . Ο καθαρισμός θα γίνει με τη βοήθεια της ομάδα του glossAPI. Για μεθοδολογία βλ. &lt;nowiki&gt;https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&lt;/nowiki&gt; . <br /> <br /> ==== Expected Outcome ====<br /> Ο στόχος είναι να απομονωθεί απο την html ελληνικό κείμενο με κανονική γραμματική και ολόκληρες προτάσεις που δεν ειναι αποσπασματικό.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> 6. Create a BackEnd system to manage all the data described above.<br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> 8. Rewrite in Typescript for maintainability<br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == GlossAPI ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> <br /> <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> == DIY IoT Physics Experiments for education ==<br /> <br /> ==== Brief Explanation ====<br /> Remote physics experiments for students in all educational levels are the second best to hands-on experiments.  Especially for students who temporarily cannot attend school or in cases like Covid-19 and the lockdowns. In many practical cases, they are the only alternative, as they are available 24/7, they can involve dangerous materials or conditions, they can be accessed from anywhere and any device, they require less maintenance, have lower cost, can be easily modified, or arranged to perform another experiment, and are less probable to be damaged.  They are in line with the modern way of performing experiments, as it is desirable to have as little direct contact with the experiments as possible and use them online.  Examples include online telescopes and electronic microscopes.  This is possible due to automation; data acquisition and manipulation of the experimental data is done using a computer or a single board computer.  In this way students need not take pain stacking notes, especially for experiments that take a lot of time to collect data, sometimes days or months.  Students can concentrate on data processing, the analysis of the results, and arrive at scientifically valid conclusions.  Our laboratory has set up many remote experiments and has more than 10 years’ experience in designing, setting, and servicing remote experiments.  Our remote experiments are based on Arduino and readily available sensors and actuators. The previous year it was designed and implemented a way to make the sensors, and the actuators form an IoT local network so that it will be easier to easily utilize them in different experiments and to build new experiments.  The IoT sensors and actuators are DIY and based on open software.   The previous year GSoC stipend receiver, programed the ESP8266 to receive data from the sensor and transmit the data through MQTT to ThingsBoard. Similarly, for an actuator the ESP8266 to receive MQTT data from ThingsBoard. The stipend receiver prepared five DIY IoT sensors and five actuators. ThingsBoard provided users with visual representation of the data and the control of the experimental setup through dashboards.  There are produced five dashboards for five corresponding experiments.  The present successful applicant will have to produce a digital twin of the experiments.  This will involve open software for producing 3D models of five experiments, allowing them to manipulate the digital twins, view the evolution of the experiment, provide data presentation tools, and extract model parameters.<br /> <br /> ==== Related repositories ====<br /> <br /> ==== https://github.com/totheworld2004/DIY-Physics-IoT ====<br /> <br /> ==== Exprected Outcome: ====<br /> Five digital twins of corresponding five experiments, their documentation and instructions of how to use them<br /> <br /> ==== Knowledge Prerequisites ====<br /> Any one for case a)-e) or similar --- a) 3D Web-Based Physics Simulations: Three.js, Babylon.js, p5.js, Godot b) Interactive Dashboards: Plotly Dash, Panel, Bokeh c) Custom Data Visualizations: D3.js, Matplotlib, Jupyter Notebooks d) Game-Based Physics Experiments: Godot, Babylon.js e) Embedded 3D Simulations: Three.js, Babylon.js<br /> <br /> ==== Mentors: ====<br /> Hariton Polatoglou and Panagiotis Koustoumpardis<br /> <br /> ==eCodeOrama, an educational interactive flow visualization tool for mit scratch programs==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2304 2025-02-12T08:14:29Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Expanding HassIO smart home capabilities via low-code automation development ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == A Tool for Visualizing the Arguments, Sentiments and User Interactions of Online Discussions ==<br /> <br /> ==== Brief Explanation ====<br /> In recent years, the analysis and visualization of dialogue have gained prominence in fields such as computational linguistics, social sciences, and human-computer interaction. The ability to model, analyze, and visualize real-life discussions provides valuable insights into the flow of conversations, the exchange of arguments, and the sentiments conveyed. Such visualizations can improve the understanding of complex discussions, foster decision-making, and even help develop better AI systems for facilitating or mediating discussions. We are particularly interested in online text-only discussions (e.g. on platforms like Reddit).<br /> <br /> Various tools and platforms have been developed in order to facilitate structured discussions and multi-party decision making. Kialo is an online, structured debate platform, where the use of argumentation is the central component. It allows the construction of argument maps, in the form of trees. It promotes thoughtful discussion, understanding of different viewpoints and collaborative decision-making, through visualizations of argument maps. <br /> <br /> Debategraph is another online structured debate platform, using more complex graphs, called &amp;amp;quot;mind-maps&amp;amp;quot;, where arguments are interconnected in a web-like structure. It allows an even wider choice of visualizations of relationships between ideas.<br /> <br /> DebateVis is a tool that can help non-expert users explore and analyze debate transcripts. Given a transcript, the tool produces: (a) an Interactions Graph that summarizes how often each candidate spoke overall, mentioned other candidates and discussed each topic, (b) an Annotated Transcript with automatically extracted topic labels and speaker interactions, (c) a Timeline visualization providing an overview of the debate. <br /> <br /> Finally, VisArgue is a framework proposing a range of visualizations of dialogues, including: Lexical Episode Plots (a timeline representation of the topics discussed), (b) Conversational Topic Visualizations, representing the shifting of focus of individual user on topics, (c) various statistics measuring user participation, respect, justification and accommodation, (d) Lexical Units, which are timeline representations of features such as the amount of argumentation and emotions.<br /> <br /> Although tools such as the above offer important functionality, there are still issues: in most cases, either the source code is not available, or integration with new projects is not seamless, or it is difficult to parameterize the output. Furthermore, the tools above focus mostly on debate, whereas we are also interested in other types of online discussions (e.g. deliberation to improve legislation bills, non-adversarial discussions for intra-company decision making).<br /> <br /> Therefore, this project’s goal is the design and implementation of an open source tool for visualizing and analyzing real-life, online, text-only discussions, exploring subjects like: topics discussed, arguments exchanged and emotions conveyed. The project will also explore how these visualizations can be leveraged for improving public understanding of contentious issues, academic discourse, and online discussion platforms.<br /> <br /> '''Project Objectives / Contributions:'''<br /> <br /> - Select, from the literature, prominent dialogue visualization approaches / ideas (e.g. styles of graph-based, or timeline-based, visualizations used) to represent various aspects of real-life online discussions and collect available libraries (not necessarily discussion-specific) that can be used to implement them (e.g. Gephi, NetworkX).<br /> <br /> - Explore the open-source toolkits being developed in the Archimedes project “LLM3: LLMs as mediators and moderators” to measure dialogue quality aspects (e.g., sentiment, politeness, topics, user participation) and select those that can provide useful meta-data for visualizing on-line discussions.<br /> <br /> - Develop a tool capable of ingesting data from real-life online discussions generating relevant meta-data (possibly by calling other toolkits) and producing the desired visualizations of the discussions.<br /> <br /> - Potentially, evaluate the effectiveness of the tool and its visualizations in making complex online discussions understandable to diverse audiences, such as researchers, mediators, or general users.<br /> <br /> '''Project Impact:'''<br /> <br /> - A novel, easy-to-use, open source, visualization tool (with accompanying paper) for online, text-only discussions, that can help the analysis of discussions in different settings and domains (e.g. political discourse, academic debates, or customer feedback).<br /> <br /> - Expand the general understanding of how visualization techniques can make debates more accessible and informative (possibly also leading to a publication).<br /> <br />   - Contribution to the Archimedes project “LLM3: LLMs as mediators and moderators” which aims to develop and evaluate LLM-based mediation agents that will actively participate in online discussions, with or without additional human mediation.<br /> <br /> '''Key Types of Dialogue Visualizations:'''<br /> <br /> The project will develop and explore several types of dialogue visualizations. Some are briefly described below. The contributor will be free to propose and implement new ones. <br /> <br />     1) Timelines: they represent the chronological flow of a conversation, highlighting key moments such as topic shifts, argument introductions and emotional peaks. Possible Features: topic evolution over time, points of conflict or agreement, visual markers for significant events (e.g. emotional outbursts or resolution points).<br /> <br />     2) Argumentation Graphs: they visualize the logical structure of arguments, including claims, counterclaims and evidence. Possible Features: nodes representing arguments or claims, edges denoting relationships (e.g., support, contradiction).<br /> <br />     3) User Interaction Graphs: they map the relationships and interaction patterns between participants in the debate. Possible Features: nodes representing participants, weighted edges showing the frequency, tone, or sentiment of interactions, clusters indicating subgroups or coalitions in the dialogue.<br /> <br />     4) Sentiment Heatmaps: they analyze and visualize the emotional dynamics of a conversation. Possible Features: color-coded intensity for positive, negative, or neutral sentiments, overlay with timeline or topic visualization for richer insights.<br /> <br />     5) Topic Trees or Topic Flow Diagrams: they represent how topics are introduced, branched out, and revisited during the discussion. Possible Features: hierarchical or radial layouts for topic relationships, highlights of overlapping or transitioning topics.<br /> <br />     6) Hybrid Visualizations: by combining multiple visualization techniques.<br /> <br /> Importance of Dialogue Visualizations:<br /> <br /> Dialogue visualizations, such as those presented above, can support:<br /> <br /> - Topic Analysis: by identifying the main topics discussed and their transitions over time, and by highlighting overlapping topics and their importance to the dialogue.<br /> <br /> - Argumentation Analysis: by understanding the logical flow of arguments, counterarguments, and evidence, and by identifying circular reasoning, weak arguments, or areas of agreement.<br /> <br /> - Sentiment Analysis: by visualizing the emotional tone of the conversation and its impact on the debate, and by examining whether certain sentiments correlate with specific topics or arguments.<br /> <br /> - Participant Dynamics: by mapping the influence and activity of each participant, and by analyzing interaction patterns (e.g., dominance, interruptions, alliances).<br /> <br /> '''Methodology:'''<br /> <br /> - Data Collection and Preparation:<br /> <br /> The tool should be able to ingest data from various online debate platforms (e.g. Reddit, Kialo), from political debate transcripts, from academic discourse, as well as from debates among LLM-agents. Since the format of raw data may vary, we propose the use of the Convokit tool in order to homogenise and preprocess the data.<br /> <br /> - Development of Visualization Prototypes:<br /> <br /> Tools/Technologies: Python (matplotlib, seaborn, Plotly), D3.js for web-based visualizations, or tools like Gephi for network analysis. Use natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers) for topic modeling, sentiment analysis, and argument mining, along with tools being developed at the LLM3 project of Archimedes.<br /> <br /> - User Feedback and Iterative Improvement:<br /> <br /> Test the outputs (visualizations) with researchers, mediators, or other stakeholders. Refine designs based on usability feedback and task-specific performance.<br /> <br /> '''Evaluation'''<br /> <br /> If time allows, the contributor will contribute in evaluating the effectiveness of their dialogue visualizations, in the context of Archimedes’ LLM3 project, by using them for both real-life and LLM-generated dialogues. Their output will be measured on clarity, usability and informativeness.<br /> <br /> '''Desired Profile:'''<br /> <br /> We are looking for a contributor with the following characteristics:<br /> <br /> - Good programming skills in Python (experience in network analysis and / or NLP is a plus).<br /> <br /> - Experience (and interest for) coding visual representations of concepts with libraries such as: matplotlib, seaborn, Plotly, Gephi, D3.js. <br /> <br /> - Interest in the subject of human interaction through dialogue (more specifically, on themes such as: argumentation, topic identification, sentiment analysis).<br /> <br /> - A taste for concise, elegant and efficient solutions / visualizations.<br /> <br /> The contributor will be mentored/supported by members of the LLM3 project, the broader NLP group of Archimedes (&lt;nowiki&gt;https://archimedesai.gr/en/&lt;/nowiki&gt;), as well as the NLP Group (&lt;nowiki&gt;http://nlp.cs.aueb.gr/&lt;/nowiki&gt;) of the Department of Informatics, Athens University of Economics and Business. <br /> <br /> '''Conclusion:'''<br /> <br /> This GSOC project aims to develop an open-source tool that will make complex, online discussions more understandable, insightful, and actionable. By capturing the topics, arguments, sentiments, and participant dynamics, it will offer a comprehensive approach to online dialogue visualization that can benefit multiple fields, from education to public policy.<br /> <br /> ==== Related repositories ====<br /> https://sites.google.com/view/llm3/home<br /> <br /> ==== Expected Results ====<br /> A tool able to process real-life, text-only dialogues and produce selected visualizations capturing their essential points.<br /> <br /> ==== Mentors ====<br /> Dionysios Kontarinis (denniskont@gmail.com), Ion Androutsopoulos, Ioannis Pavlopoulos<br /> <br /> == PersonalAIs: Generative AI Agent for Personalized Music Recommendations ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == OpenRF 3D ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == Flexible GovDoc Scanner ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == Extending the capabilities of OpenTRIM ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == Cleaning of HPLT Greek v2 Dataset for GlossApi LLM ==<br /> <br /> ==== Brief Explanation ====<br /> Καθαρισμός των Ελληνικών dataset στο &lt;nowiki&gt;https://hplt-project.org/datasets/v2.0&lt;/nowiki&gt; . Ο καθαρισμός θα γίνει με τη βοήθεια της ομάδα του glossAPI. Για μεθοδολογία βλ. &lt;nowiki&gt;https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&lt;/nowiki&gt; . <br /> <br /> ==== Expected Outcome ====<br /> Ο στόχος είναι να απομονωθεί απο την html ελληνικό κείμενο με κανονική γραμματική και ολόκληρες προτάσεις που δεν ειναι αποσπασματικό.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> 6. Create a BackEnd system to manage all the data described above.<br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> 8. Rewrite in Typescript for maintainability<br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == GlossAPI ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> <br /> <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> <br /> ==eCodeOrama, an educational interactive flow visualization tool for mit scratch programs==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2303 2025-02-12T08:08:05Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Expanding HassIO smart home capabilities via low-code automation development ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == PersonalAIs: Generative AI Agent for Personalized Music Recommendations ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == OpenRF 3D ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> == Flexible GovDoc Scanner ==<br /> <br /> ==== Brief Explanation ====<br /> The goal of this project is to develop the Flex GovDoc Scanner, an application that leverages the Node.js stack, AI tools, and cloud services to transform public incorporation documents from Greece's business portal (ΓΕΜΗ, &lt;nowiki&gt;https://publicity.businessportal.gr/&lt;/nowiki&gt;) into structured, searchable data. This project aims to facilitate access to essential company information, such as legal representatives, board members, and incorporation history, by offering advanced discovery capabilities through a REST service.<br /> <br /> '''Project Overview:'''<br /> <br /> - Crawl and Index Public Documents: <br /> <br />   Develop a robust crawling mechanism to gather all relevant PDF documents from the ΓΕΜΗ portal while ensuring compliance with legal standards.<br /> <br /> - Extract and Structure Metadata: <br /> <br />   Utilize AI and OCR technologies to extract key metadata from these documents and store them in a structured format.<br /> <br /> - REST Service for Metadata Search: <br /> <br />   Create an efficient REST API to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/govdoc-scanner<br /> <br /> ==== Expected Outcome ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Implement a nodejs application to Crawl and Index Public Documents, utilize an opensource DB optimized for documents - Enhance the application with AI and OCR capabilities to extract metadata from scanned documents - Implement a REST API using nodejs to provide users with search functionalities on the extracted metadata, enabling easy access and analysis.<br /> <br /> ==== Knowledge Prerequisites ====<br /> nodejs, docker, git , AI concepts and tools, NLP, OCR, RESTful API design and implementation, Knowledge of databases (SQL or NoSQL)<br /> <br /> ==== Mentors: ====<br /> iskitsas@gmail.com, vasilisnx@gmail.com <br /> <br /> == Extending the capabilities of OpenTRIM ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == Cleaning of HPLT Greek v2 Dataset for GlossApi LLM ==<br /> <br /> ==== Brief Explanation ====<br /> Καθαρισμός των Ελληνικών dataset στο &lt;nowiki&gt;https://hplt-project.org/datasets/v2.0&lt;/nowiki&gt; . Ο καθαρισμός θα γίνει με τη βοήθεια της ομάδα του glossAPI. Για μεθοδολογία βλ. &lt;nowiki&gt;https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&lt;/nowiki&gt; . <br /> <br /> ==== Expected Outcome ====<br /> Ο στόχος είναι να απομονωθεί απο την html ελληνικό κείμενο με κανονική γραμματική και ολόκληρες προτάσεις που δεν ειναι αποσπασματικό.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> 6. Create a BackEnd system to manage all the data described above.<br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> 8. Rewrite in Typescript for maintainability<br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == GlossAPI ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> <br /> <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> <br /> ==eCodeOrama, an educational interactive flow visualization tool for mit scratch programs==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2302 2025-02-12T08:02:39Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Expanding HassIO smart home capabilities via low-code automation development ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == PersonalAIs: Generative AI Agent for Personalized Music Recommendations ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to develop an AI-powered agent that interacts with users in natural language to determine their emotional state and musical preferences in a conversational manner. The agent will then generate and refine music playlists accordingly. The system will integrate with the Spotify API to provide personalized recommendations based on user preferences, liked songs, and listening history. Users can opt-out of personal data usage for a more exploratory approach. The agent will also enable real-time conversational modifications to playlists, allowing users to tweak mood, energy, and genre preferences.<br /> <br /> ==== Core Features &amp; Technologies: ====<br /> - Natural Language Processing (NLP): Used to determine user mood and preferences based on conversation.<br /> <br /> - Generative AI: Small LLM models hosted locally or accessed via an API key for dialogue generation.<br /> <br /> - Spotify API Integration: Authentication, playlist management, retrieval of user metadata (liked songs, playlists, etc.).<br /> <br /> - Frontend UI: A web-based chatbot interface similar to ChatGPT.<br /> <br /> - Backend Processing: Handles AI model interactions, API requests, and user session management.<br /> <br /> - Real-time Modifications: Users can refine recommendations by requesting changes in mood, genre, energy, etc.<br /> <br /> Sources &amp;amp;amp;amp; References:<br /> <br /> - Spotify API Documentation: &lt;nowiki&gt;https://developer.spotify.com/documentation/web-api/&lt;/nowiki&gt;<br /> <br /> Mood-Based Playlist Research:<br /> <br /> - Generating personalized music playlists based on mood and listening data<br /> <br /> - Moodify: Emotion recognition in songs for personalized recommendations<br /> <br /> Example Datasets:<br /> <br /> - Moodify Dataset (Spotify-based mood labels)<br /> <br /> - Awesome Music Emotion Recognition (MER) Dataset Collection<br /> <br /> ==== Expected Results ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- A full-stack AI agent (local hosted and/or API Key) that generates personalized music playlists based on user input.  - Integration with Spotify API for user authentication, playlist creation, and retrieval of user metadata.  - Real-time, conversational playlist modifications through a chatbot-style UI.  - Advanced mood detection using NLP or audio analysis. Integration with external music recommendation sources.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== '''Knowledge Prerequisites''' ====<br /> &lt;nowiki&gt;''&lt;/nowiki&gt;- Basics of Machine Learning and NLP.  - Experience with pre-trained generative models (e.g., GPT, BERT) and recommendation systems.  - Familiarity with APIs, particularly Spotify API.  - Frontend/backend development experience for a chatbot-style UI.<br /> <br /> ==== Mentors ====<br /> Giannis Prokopiou, Thanos Aidinis<br /> <br /> == OpenRF 3D ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> <br /> <br /> == Extending the capabilities of OpenTRIM ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == Cleaning of HPLT Greek v2 Dataset for GlossApi LLM ==<br /> <br /> ==== Brief Explanation ====<br /> Καθαρισμός των Ελληνικών dataset στο &lt;nowiki&gt;https://hplt-project.org/datasets/v2.0&lt;/nowiki&gt; . Ο καθαρισμός θα γίνει με τη βοήθεια της ομάδα του glossAPI. Για μεθοδολογία βλ. &lt;nowiki&gt;https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&lt;/nowiki&gt; . <br /> <br /> ==== Expected Outcome ====<br /> Ο στόχος είναι να απομονωθεί απο την html ελληνικό κείμενο με κανονική γραμματική και ολόκληρες προτάσεις που δεν ειναι αποσπασματικό.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> 6. Create a BackEnd system to manage all the data described above.<br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> 8. Rewrite in Typescript for maintainability<br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == GlossAPI ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> <br /> <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> <br /> ==eCodeOrama, an educational interactive flow visualization tool for mit scratch programs==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2301 2025-02-12T07:52:17Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Expanding HassIO smart home capabilities via low-code automation development ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> <br /> == OpenRF 3D ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> <br /> <br /> Mentors<br /> <br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> <br /> <br /> == Extending the capabilities of OpenTRIM ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> == Cleaning of HPLT Greek v2 Dataset for GlossApi LLM ==<br /> <br /> ==== Brief Explanation ====<br /> Καθαρισμός των Ελληνικών dataset στο &lt;nowiki&gt;https://hplt-project.org/datasets/v2.0&lt;/nowiki&gt; . Ο καθαρισμός θα γίνει με τη βοήθεια της ομάδα του glossAPI. Για μεθοδολογία βλ. &lt;nowiki&gt;https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1&lt;/nowiki&gt; . <br /> <br /> ==== Expected Outcome ====<br /> Ο στόχος είναι να απομονωθεί απο την html ελληνικό κείμενο με κανονική γραμματική και ολόκληρες προτάσεις που δεν ειναι αποσπασματικό.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, βιβλιοθήκες NLP<br /> <br /> ==== Mentors:====<br /> Foivos Karounos, Nikolaos Vidras<br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> 6. Create a BackEnd system to manage all the data described above.<br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> 8. Rewrite in Typescript for maintainability<br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == GlossAPI ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> <br /> <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> <br /> ==eCodeOrama, an educational interactive flow visualization tool for mit scratch programs==<br /> <br /> ====Brief Explanation====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ====Expected Results====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ====Duration of the Project====<br /> 350 hours<br /> <br /> ====Related repositories====<br /> https://github.com/sarantos40/eCodeOrama<br /> <br /> ====Knowledge Prerequisites====<br /> python, mit scratch, gui development<br /> <br /> ====Mentors:====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2025_proposed_ideas&diff=2300 2025-02-12T07:40:02Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Expanding HassIO smart home capabilities via low-code automation development ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto, is a Domain Specific Language (DSL) that enables users to program complex automation scenarios and pipelines, for connected IoT devices in smart environments, that go beyond simple tasks. It was initially developed by the ISSEL research team (AUTH) as textual DSL and later evolved into a web-based low-code development environment. SmAuto lacks extra features like utilization of external REST data sources, time delays, semantic annotation, and accessing of in-house entities, etc., thus it should be expanded in this direction. Furthermore, HomeAssistant would benefit from the integration of a low-code approach for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> <br /> ==== Expected Results ====<br /> • In the context of this project, we desire to expand the SmAuto DSL with the following features: a) support the REST protocol, so as for the automations to be able to access information from external data sources, b) incorporate auxiliary concepts like Delay, Switches, or Compute nodes, c) SmAuto integration in HA. The integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA addon, where users will be able to design and deploy automations graphically, using the SmAuto low-code environment.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> [Required]: Python, Software engineering, IoT concepts, Unix/Linux, <br /> [Desired]: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> <br /> ==== Mentors ====<br /> <br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> <br /> == OpenRF 3D ==<br /> <br /> ==== Brief Explanation ====<br /> This project aims to bridge NVIDIA Sionna’s 6G simulation framework with Cesium’s 3D geospatial engine, enabling real-time, terrain-aware wireless network analysis. The student will develop a bidirectional WebSocket pipeline to dynamically stream Cesium’s elevation and 3D building data into Sionna, where channel models are enhanced to account for terrain-induced pathloss and urban blockages. Simultaneously, Sionna’s ray-traced outputs (e.g., signal strength, beamforming patterns) will be visualized in Cesium as interactive heatmaps and antenna coverage overlays. Key deliverables include a Python/JavaScript interface using Protocol Buffers for efficient data serialization, integration of 3GPP TR38.901 models with real-world terrain, and Jupyter notebooks demonstrating urban/rural 5G optimization.<br /> <br /> ==== Expected Results ====<br /> The project will deliver a fully functional YouTube data engine, interactive dashboards, automated report generation, and an LLM-powered query system, enabling intuitive data exploration and analysis for researchers and content creators. Complete documentation and an open-source release will empower community contributions.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/NVlabs/sionna, <br /> https://github.com/CesiumGS/cesium<br /> <br /> ==== Knowledge Prerequisites ====<br /> A developer with strong Python &amp;amp;amp; JavaScript skills, and an understanding of real-time networking (WebSockets, Protobuf). Experience in wireless communications &amp;amp;amp; 3D geospatial visualization, and prior exposure to Sionna, CesiumJS would be highly beneficial.<br /> <br /> <br /> ==== Mentors ====<br /> Ilias Chrysovergis (https://www.linkedin.com/in/ilias-chrysovergis/), Iason Malkotsis (https://malkotsis.com/)<br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ==== Objective ====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ==== Background ====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Methodology ====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ==== Expected Outcome ====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ==== Conclusion ====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> Related repositories<br /> <br /> New project, no existing repo available.<br /> <br /> ==== Information links ====<br /> - [[wikipedia:Triplestore|Triplestore]]<br /> - [[wikipedia:Semantic_triple|Triples]]<br /> - [[wikipedia:SPARQL|Query language]]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors:Alexios Zavras, TBD====<br /> <br /> <br /> <br /> == Extending the capabilities of OpenTRIM ==<br /> <br /> ==== Brief Explanation ====<br /> OpenTRIM is a new open-source code for simulating the passage of energetic ions through materials and calculating the associated modifications and damage that they cause to the these materials. It is based on the kinetic Monte-Carlo method and employs the Binary Collision Approximation to describe the interaction between ions and target atoms. OpenTRIM comprises of a set of C++ libraries, a command line program for executing simulations in batch mode and a Qt-based graphical user interface that can be used to configure &amp;amp;amp;amp; run a simulation and evaluate the results. Currently, there are various parts of OpenTRIM where work is needed for improving and extending the capabilities of the code. <br /> <br /> ==== Project Description ====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ==== Expected Outcome ====<br /> 1. Extend the base C++ simulation code to include new capabilities for user-defined “tallies”, i.e., scoring tables where data from the simulation are extracted as a function of ion energy, position, direction or other possible optional variables.  2. Create a tool for 2D or 3D visualization of the simulated ion trajectories. 3. Write a number of example simulations, complete with the required input files and evaluation of the output results, which will become a part of the code documentation.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> ==== Related repositories ====<br /> https://github.com/ir2-lab/OpenTRIM<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, Qt (optional), OpenGL (optional)<br /> <br /> ==== Mentors:====<br /> George Apostolopoulos (&lt;nowiki&gt;https://github.com/gapost&lt;/nowiki&gt;), Michail Axiotis (&lt;nowiki&gt;https://github.com/psaxioti&lt;/nowiki&gt;), Eleni Mitsi (&lt;nowiki&gt;https://github.com/elmitsi&lt;/nowiki&gt;)<br /> <br /> <br /> <br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ==== Expected Results. ====<br /> 1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.<br /> 2. The application should be fully customisable in terms of interface, content and functionality.<br /> 3. The content can be personalized and the home page can display tiles selected by each institution and display content that will be created as a page (wordpress type) <br /> 4. Adding a students portal where all the student's data, personal and any other information will be collected, in order to be used as a reference point.<br /> 5. Add an admin panel from where the appearance and content of the institution's application will be defined.<br /> 6. Create a BackEnd system to manage all the data described above.<br /> 7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.<br /> 8. Rewrite in Typescript for maintainability<br /> 9. Creation of a custom CMS (consisting of FrontEnd &amp; BackEnd) for data changing frequently<br /> 10. Create a system where the application is shared across domains by creating an instance.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • React.js<br /> • Express.js (for BackEnd)<br /> • MySQL (for BackEnd)<br /> • JavaScript<br /> • TypeScript<br /> • Next.js (optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Anastasios Tsalmas tsalmanastasios@gmail.com, Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> == GlossAPI ==<br /> <br /> ====Brief Explanation====<br /> <br /> GlossAPI is an open source project seeking to develop a standard open access corpus of the Greek language, and benchmark it against existing and to-be-developed language models, with the objective of providing an upstream service to the Greek tech community.<br /> The project is named after a portmandeau of the Greek word for &quot;language&quot; and &quot;API&quot; which creates a visual resemblance to the word Glossary in Greek.<br /> This is to express our objective to provide an index of the Greek language via flexible programing interfaces.<br /> <br /> Greek is a language that is under-represented in existing LLMs, while it has a complex history, grammar and writing system. Our trials with existing models have shown lack of syntactic and semantic knowledge of advanced Greek and its nuances, and we have put forth a number of analyses showing that this poses a risk for digital divides, language extinction, and subpar experience for users of public services.<br /> <br /> To our knowledge other LLM projects that tackle the problem of the Greek language are either proprietary, closed code, narrow scope, or otherwise unfit for our purpose which is to provide publicly available, fully open source language models with respect to all code/weights/procedures/data.<br /> We reach out and bring together people that have the expertise, the passion, the collections, or the hardware, to take part in this endeavor, that will help the Greek stratup/tech scene catch up with the rapid developments in downstream applications that are now common place for developers of English language generative models. <br /> <br /> <br /> ====Expected Results====<br /> <br /> The project will result to an Open Source Corpus, representative of the Greek language and its different varieties. At first emphasis will be given to the formal varieties used in government, education and the law. Additionally, we want to represent, in a subsequent training stage, a number of basic knowledge domains to an &quot;undergraduate degree&quot; level. The datasets will be versioned and benchmarked against different models and tokenizers. We also need to develop a sufficient set of evaluation tasks (such as Factual QA - Greek). Finally a couple of foundation models of different architectures will be fitted onto the dataset and the evaluation suite, and published to the community under an open source licence. With these moves we expect to pollinate the Greek tech ecosystem with reliable, inexpensive, and extensible models and datasets, that will help the Greek Open Source AI scence thrive. All data and models will be accompanied by thorough documentation and guides, to ensure replicability and reusability of the results.<br /> <br /> <br /> ====Duration of the Project====<br /> <br /> 350 hrs<br /> <br /> ====Related Repositories====<br /> <br /> https://github.com/eellak/glossAPI/<br /> https://github.com/eellak/glossAPI/wiki<br /> <br /> <br /> ====Knowledge Prerequisites====<br /> <br /> Corpus Annotation for Language Models<br /> Quantitative Corpus Linguistics or Natural Language Processing<br /> Python with transformers library, sci-kit learn, numpy, pandas and streamlit, langchain or similar<br /> Mathematical statistics or similar discipline<br /> Django knowledge is good to have<br /> <br /> ====Mentors====<br /> <br /> F.Karounos, A. Melidis, Greek Free Open Source Software/Hardware Alliance<br /> <br /> <br /> <br /> <br /> <br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2024_proposed_ideas&diff=2250 2024-02-05T07:21:26Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == EDGAR-CRAWLER: Democratizing accessibility to Financial NLP documents ==<br /> <br /> ==== Brief Explanation ====<br /> Natural Language Processing (NLP) faces big challenges in the field of business and finance since financial text data is often locked behind paywalls, making it hard to get the information we need. This problem highlights why it is so important to automate data collection from free sources, like EDGAR, the U.S. Securities and Exchange Commission's public database. EDGAR contains documents about publicly traded stocks from companies in the U.S., like Microsoft, Google, or Amazon. Our existing software, EDGAR-CRAWLER, tackles this problem by automatically downloading and “cleaning” financial documents, mainly through the use of regular expressions, and then making such data available in an easy-to-use JSON format for NLP pipelines and applications. EDGAR-CRAWLER, with over 190 stars on Github, is the go-to toolkit for making financial data accessible to everyone. However, currently, it is only limited to one type of company filing, the annual reports (10-K filings). By adding support for more types of documents, EDGAR-CRAWLER plans to make financial information even more accessible, playing a vital role in the progress of financial NLP and open data.<br /> <br /> ===== Expected Results. =====<br /> • Extend EDGAR-CRAWLER to fetch more types of US company filings like quarterly reports (10-Q) and current reports (8-K), using string-searching algorithms like regular expressions. • Write documentation for these new features. • Develop unit tests for these new features.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Proficiency in Python and Software Development • Familiarity with regular expressions and Natural Language Processing (NLP) • Experience in web scraping and data processing (beautifulsoup, pandas). • Interest in Machine Learning (ML) <br /> <br /> ==== Mentors: ====<br /> <br /> Lefteris Loukas, Ion Androutsopoulos<br /> <br /> == Greeklish-to-Greek: Development of an open-source and state-of-the-art toolkit ==<br /> <br /> ==== Brief Explanation ====<br /> Greeklish” is a way of writing Greek with the commonly used Latin alphabet instead of the Greek alphabet (think of “kalimera” instead of “καλημέρα”), and has been widely used by Greek speakers for decades due to early technological limitations in supporting Greek characters. Its ease of use for informal digital communication, such as on forums or in comments, allows for quick language switching without changing any keyboard input, and hides spelling errors. However, Greeklish complicates the development of Natural Language Processing (NLP) tools for the Greek language because Machine Learning (ML) models are trained on standard Greek, not Greeklish. Most existing Greeklish-to-Greek toolkits are limited, rule-based, and closed-source. Our team has researched and developed state-of-the-art Greeklish-to-Greek methods utilizing Transformer NLP models. The purpose of the project is to develop an open-source, user-friendly Python toolkit, based on our existing research so that the community can benefit from it.<br /> The GSOC contributor will work in close collaboration between helvia.ai (https://helvia.ai/) and the AUEB NLP Group (http://nlp.cs.aueb.gr/software.html), leveraging both industrial and academic expertise to tackle these NLP challenges.<br /> <br /> ===== Expected Results. =====<br /> • Development of a user-friendly Python wrapper based on our best Greeklish-to-Greek NLP model  • Documentation and unit testing of the library • Development of new methods for Greeklish-to-Greek conversion, utilizing more recent and promising Large Language Models (LLMs), such as Meta’s LLaMA2 or Mistral models<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> While the proposed Greeklish toolkit can be developed independently, it could also be integrated as an extra functionality to the mentors’ affiliated current state-of-the-art Greek NLP toolkit: &lt;nowiki&gt;https://github.com/nlpaueb/gr-nlp-toolkit&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Advanced Python Programming and Software Engineering • Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)<br /> <br /> ==== Mentors: ====<br /> John Pavlopoulos (AUEB), Ion Androutsopoulos (AUEB), Stavros Vassos (helvia.ai), Lefteris Loukas (helvia.ai &amp;amp;amp;amp; AUEB)<br /> <br /> <br /> == Label Buddy 3.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021 and extended during GSoC 2022. It aims to make the annotation process easy, and simple with the help of AI Assistance tools and at the same time offers a well-defined manager-annotator-reviewer system. The purpose of this project is to investigate and integrate Multi-Modal Annotation Support in data other than only sound with the use of Large Language Models (LLMs) and Active Learning for Model Improvement on the already existing models.<br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/gsoc2022-Label-buddy<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Pantelis Vikatos, Giannis Prokopiou<br /> <br /> <br /> == Enhancements for Flexbench: OpenAPI Integration and Anonymization using Machine Learning (ML) ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a fully customizable NodeJS application, generating simulated HTTP traffic. It can be used as a standalone script, desktop-app* and server* to simulate traffic with specific characteristics, such as read/write ratio, duration, number of requests to generate, in/out traffic throttling and more.<br /> <br /> It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for stress and load testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt;During the GSoC-2022 period, along with the standalone script version, desktop and server apps were developed, and major fixes were applied.<br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> [https://github.com/dspinellis/alexandria3k https://github.com/flexivian/flexbench/tree/develop ,][https://github.com/dspinellis/alexandria3k https://flexivian.github.io/flexbench/]<br /> ==== Knowledge Prerequisites ====<br /> <br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI <br /> <br /> ==== Mentors: ====<br /> <br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> == New open data sources for Alexandria3k ==<br /> <br /> ==== Brief Explanation ====<br /> The alexandria3k package supplies a library and a command-line tool providing efficient relational query access to diverse publication open data sets. The largest one is the entire Crossref data set (157 GB compressed, 1 TB uncompressed). This contains publication metadata from about 134 million publications from all major international publishers with full citation data for 60 million of them. Alternatively, works can be selected from the PubMed data set which comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books, with rich domain-specific metadata, such as MeSH indexing, funding, genetic, and chemical details. In addition, the Crossref and PubMed data sets can be linked with the ORCID summary data set (25 GB compressed, 435 GB uncompressed), containing about 78 million author records, the United States Patent Office issued patents (11 GB compressed, 115 GB uncompressed), containing about 5.4 million records, as well as data sets of funder bodies, journal names, open access journals, and research organizations.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/dspinellis/alexandria3k<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, SQL, Unix command-line <br /> <br /> ==== Mentors: ====<br /> <br /> Diomidis Spinellis<br /> <br /> == MD-Attractor ==<br /> <br /> ==== Brief Explanation ====<br /> MD-Attractor is an open-source project designed to serve as a unified aggregator for music-related data sourced from various music platforms such as Spotify, Deezer, Apple Music, and more.<br /> <br /> The primary objective of this project is to provide a centralized hub for collecting and analyzing data from diverse media sources, offering users a comprehensive view of diverse music data sources.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, APIs, Visualization <br /> <br /> ==== Mentors: ====<br /> <br /> Panagiotis Sfendourakis, Elektra Bilali Simou<br /> <br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ===== Background =====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ===== Project Description =====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://en.wikipedia.org/wiki/Triplestore Triplestore]<br /> - [https://en.wikipedia.org/wiki/Semantic_triple Triples]<br /> - [https://en.wikipedia.org/wiki/SPARQL Query language]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras, TBD<br /> <br /> <br /> == Font Validator: A System for Quality Control of Digital Typefaces Containing Greek Characters ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to develop a comprehensive set of tests for quality control on digital typefaces, with a particular focus on Greek characters. The project aims to ensure the consistency of typeface design, especially in terms of height, shape, composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters.<br /> <br /> ===== Background =====<br /> Digital typefaces play a crucial role in the readability and aesthetics of digital content. However, inconsistencies in typeface design, especially between different scripts like Latin and Greek, can lead to visual discomfort and confusion. Quality control in digital typefaces is therefore essential to ensure a consistent and pleasant reading experience.<br /> <br /> ===== Project Description =====<br /> This project will involve the development of a series of tests using Font Bakery or a similar tool. These tests will compare the height and shape of similarly shaped Latin and Greek letters, such as Latin A and Greek Alpha.<br /> <br /> Further tests will be performed on composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters. The goal is to identify any inconsistencies and provide recommendations for improvements.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Study the design principles of Latin and Greek typefaces. Identify the key parameters for comparison, such as height, shape, composites, ascenders, descenders, spacing, and kerning.<br /> <br /> ''Test Development:'' Develop a series of tests using Font Bakery or a similar tool. These tests should be able to compare the identified parameters between Latin and Greek characters.<br /> <br /> ''Testing:'' Perform the developed tests on a variety of digital typefaces.<br /> <br /> ''Analysis:'' Analyze the test results to identify any inconsistencies in the design of Latin and Greek characters.<br /> <br /> ''Recommendations:'' Based on the analysis, provide recommendations for improving the consistency of typeface design.<br /> <br /> ''Documentation:'' Document the findings, the developed tests, and the recommendations.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a comprehensive set of tests for quality control on digital typefaces, especially for Greek characters. These tests will help typeface designers and developers ensure the consistency of their designs, thereby improving the readability and aesthetics of digital content.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the quality of digital typefaces but also contribute to the body of knowledge in the field of typeface design. It will provide valuable insights into the design principles of Latin and Greek characters and help ensure their consistency in digital typefaces.<br /> <br /> ==== Duration of the Project ====<br /> Can be either short (175 hours) or long (350 hours), depending on the agreed-upon scope.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://font-bakery.readthedocs.io/en/latest/ Font Bakery documentation]<br /> - [https://github.com/fonttools/fontbakery Font Bakery source repo]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, font technologies, understanding of Greek characters<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Emilios Theofanous, Alexios Zavras<br /> <br /> <br /> == Development and Enhancement of the Cloud-Based FOSSBot Platform ==<br /> <br /> ==== Brief Explanation ====<br /> The evolution of the DIY robot kit for educators, initiated in GSoC 2019 with Christos Chronis, has significantly progressed over the years, culminating in a 3D printable, modular, and low-cost educational robot. Over the last five years, contributors from GSOC and the open source community have enriched this project with a programming stack compatible with Google Blockly, native Python through Monaco, and a Docker-based deployment system. At the same time, the initial DIY robot kit from GSOC 2019 transformed into the FOSSBot, and more educators understand and use this open-source solution to teach STEM.<br /> <br /> For GSoC 2023, the focus was on shifting the programming stack to the cloud, addressing issues like connectivity, updates, and the rising costs of Single Board Computers. Another notable addition was the web-based simulator, which allowed educators to test and use the robot virtually, reducing cost barriers and hardware requirements. In the following months, the platform will be available to all through the infrastructure of GFOSS, and at the same time, organized workshops will help make more people aware of the robot. Finally, in the last two years, two scientific publications were released, and the project started to attract the interest of the academic community.<br /> <br /> GSoC 2024 Objectives<br /> <br /> In 2024, our goal is to further enhance this cloud-based platform by:<br /> <br /> Performance Optimization: Improving both front-end and back-end performance for a seamless user experience.<br /> Development of Cooperative Modes: Enabling multiple robots to interact and collaborate in shared tasks or scenarios, fostering teamwork and advanced programming skills.<br /> Educator's Content Creation and Upload System: Developing a system where educators can create, share, and upload educational material, facilitating a dynamic and evolving educational environment.<br /> Custom Scene Creation for Godot Simulator: Integrating an interface for creating new, customized scenes in the existing Godot-based simulator, enabling tailored educational experiences.<br /> Platform Support for Physical Hardware: Introducing programming support for physical devices like the FOSSBot, Arduino, and MicroPython-supported microcontrollers, broadening the scope of practical applications and hands-on learning.<br /> Continuous Integration and Deployment (CI/CD) Enhancements: Streamlining updates and maintenance through advanced GitHub automation and cloud deployment strategies.<br /> Extensive Documentation: Ensuring comprehensive documentation to facilitate ease of use and adaptability for educators and developers.<br /> <br /> ===== Expected Results. =====<br /> A robust, cloud-based platform offering a wide range of educational and programming possibilities.<br /> Enhanced user experience with improved performance and new features.<br /> Greater accessibility and cost-effectiveness for educators worldwide.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> <br /> ==== Related repositories ==== <br /> https://github.com/eellak/gsoc2019-diyrobot<br /> https://github.com/eellak/fossbot<br /> https://github.com/chronis10/fossbot-app<br /> https://github.com/eellak/fossbot-platform<br /> https://github.com/eellak/fossbot-source<br /> https://github.com/eellak/fossbot-web-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js<br /> Python<br /> Flask API / FastAPI<br /> SQLAlchemy<br /> Godot (not mandatory)<br /> JavaScript<br /> Docker<br /> Git<br /> <br /> ==== Related publications ==== <br /> Chronis C., &amp; Varlamis I. (2022). FOSSBot: An Open Source and Open Design Educational Robot. Electronics, 11(16), 2606. https://www.mdpi.com/2079-9292/11/16/2606<br /> Kazazis G., Chronis C., Diou C. &amp; Varlamis I. Development and evaluation of Reinforcement Learning models for the FOSSBot Open-Source educational robot, Pan-Hellenic Conference on Progress in Computing and Informatics, ACM 2023 (Under Publication) <br /> <br /> <br /> ==== Mentors: ====<br /> Christos Chronis, Iraklow Varlamis<br /> <br /> <br /> <br /> == Enabling Apothesis to support atomic layer deposition and etching processes ==<br /> <br /> ==== Brief Explanation ====<br /> Αpothesis is an open source software for designing, simulating and analyzing deposition processes. It is based on Kinetic Monte Carlo method and its two main components are the lattice (simple cubic, HPC etc) where particular processes (adsorption, desorption, surface rection and diffusion) are performed. Up until now Apothesis has been used in various applications (see for example https://iopscience.iop.org/article/10.1088/1361-651X/ace276/meta). However, there is a lack of generalized software for atomic layer deposition/etching (ALD/ALE) processes. ALD/ALE are based on pulses performing over sequentially over a certain period of time (see https://www.frontiersin.org/articles/10.3389/fphy.2021.631918/full for more details). That said, the purpose of this project is to enable Apothesis to handle ALD/ALE cases. For that, there various parts of Apothesis that need to be changed with the most basic being: <br /> 1. Enabling Apothesis to read the lattice from a file. Thus creating a generalized reader for hard copy lattices. <br /> 2. Creating the sequentially calls to simulate ALD/ALE processes. <br /> 3. Design the output of the ALD/ALE process. <br /> <br /> <br /> ===== Expected Results. =====<br /> The results from a simple case of ALD in a simple cubic lattice. <br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, desing patters (factory pattern) and basic physics in deposition processes <br /> <br /> ==== Mentors: ====<br /> Cheimarios Nikolaos, Vissarion Fysikopoulos <br /> <br /> <br /> <br /> == eCodeOrama, an educational interactive flow visualization tool for mit scratch programs ==<br /> <br /> ==== Brief Explanation ====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ==== Expected Results ====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> git@github.com:sarantos40/eCodeOrama.git<br /> <br /> ==== Knowledge Prerequisites ====<br /> python, mit scratch, gui development<br /> <br /> ==== Mentors: ====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> <br /> == Enhancement of SmAuto DSL and integration into HomeAssistant ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto is a Domain Specific Language (DSL) that enables users to program complex automation scenarios, for connected IoT devices in smart environments, that go beyond simple tasks. SmAuto lacks extra features like invocation of REST/MQTT services, time delays etc., thus it should be expanded towards this direction. Furthermore, HomeAssistant would benefit from the integration of a DSL like SMAuto for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> ==== Expected Results ====<br /> In the context of this project, we desire to expand the SmAuto DSL with the following features: a) adding virtual entities, like REST calls and MQTT RPCs, so as for the automations to be able to access information from external services, b) incorporate auxiliary concepts like Delay, Conditions/Switches, or Compute nodes and c) generalize the language’s Condition concept. Furthermore, the integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA plugin, where each user can declare SmAuto automations and deploy them locally in HA.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python, Software engineering, IoT concepts, Unix/Linux. Desired: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> <br /> ==== Mentors: ====<br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == Creating a factory pattern for handling lattices in Apothesis. ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is a generalized software for designing, simulating and analyzing deposition processes. It is based on the kinetic Monte Carlo method. That said, it consists of two main components; a lattice (e.g. simple cubic, HPC etc) and the processes (adsorption, desorption, diffusion and surface reactions) performed in this lattice. Currently, the lattices are hard coded inside Apothesis making it difficult to add new ones easily. This proposal is focuses on creating a factory pattern which be used as a guide for adding lattices in Apothesis. Then this will be used to incorporate 2D lattices (like graphene) inside Apothesis.<br /> <br /> ==== Expected Results ====<br /> The factory pattern source code and a simple example for incorporating a graphene lattice. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, design (factory pattern)<br /> <br /> <br /> ==== Mentors: ====<br /> Nikolaos Cheimarios, Christina-Anna Gatsiou <br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ===== Expected Results. =====<br /> 1. We want to add Sanity CMS(https://www.sanity.io/) for data changing frequently. 2. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter. 3. Rewrite in Typescript for maintainability<br /> <br /> Whoever is interested in talking with the initial core contributors can also find us on https://my.uom.gr/about and https://opensource.uom.gr to join our discord and follow us on social media.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js, Java(Spring Boot),Typescript, Next.js(Optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> [[Κατηγορία:GSOC2024]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2024_proposed_ideas&diff=2249 2024-02-05T07:15:53Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == EDGAR-CRAWLER: Democratizing accessibility to Financial NLP documents ==<br /> <br /> ==== Brief Explanation ====<br /> Natural Language Processing (NLP) faces big challenges in the field of business and finance since financial text data is often locked behind paywalls, making it hard to get the information we need. This problem highlights why it is so important to automate data collection from free sources, like EDGAR, the U.S. Securities and Exchange Commission's public database. EDGAR contains documents about publicly traded stocks from companies in the U.S., like Microsoft, Google, or Amazon. Our existing software, EDGAR-CRAWLER, tackles this problem by automatically downloading and “cleaning” financial documents, mainly through the use of regular expressions, and then making such data available in an easy-to-use JSON format for NLP pipelines and applications. EDGAR-CRAWLER, with over 190 stars on Github, is the go-to toolkit for making financial data accessible to everyone. However, currently, it is only limited to one type of company filing, the annual reports (10-K filings). By adding support for more types of documents, EDGAR-CRAWLER plans to make financial information even more accessible, playing a vital role in the progress of financial NLP and open data.<br /> <br /> ===== Expected Results. =====<br /> • Extend EDGAR-CRAWLER to fetch more types of US company filings like quarterly reports (10-Q) and current reports (8-K), using string-searching algorithms like regular expressions. • Write documentation for these new features. • Develop unit tests for these new features.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Proficiency in Python and Software Development • Familiarity with regular expressions and Natural Language Processing (NLP) • Experience in web scraping and data processing (beautifulsoup, pandas). • Interest in Machine Learning (ML) <br /> <br /> ==== Mentors: ====<br /> <br /> Lefteris Loukas, Ion Androutsopoulos<br /> <br /> == Greeklish-to-Greek: Development of an open-source and state-of-the-art toolkit ==<br /> <br /> ==== Brief Explanation ====<br /> Greeklish” is a way of writing Greek with the commonly used Latin alphabet instead of the Greek alphabet (think of “kalimera” instead of “καλημέρα”), and has been widely used by Greek speakers for decades due to early technological limitations in supporting Greek characters. Its ease of use for informal digital communication, such as on forums or in comments, allows for quick language switching without changing any keyboard input, and hides spelling errors. However, Greeklish complicates the development of Natural Language Processing (NLP) tools for the Greek language because Machine Learning (ML) models are trained on standard Greek, not Greeklish. Most existing Greeklish-to-Greek toolkits are limited, rule-based, and closed-source. Our team has researched and developed state-of-the-art Greeklish-to-Greek methods utilizing Transformer NLP models. The purpose of the project is to develop an open-source, user-friendly Python toolkit, based on our existing research so that the community can benefit from it.<br /> The GSOC contributor will work in close collaboration between helvia.ai (https://helvia.ai/) and the AUEB NLP Group (http://nlp.cs.aueb.gr/software.html), leveraging both industrial and academic expertise to tackle these NLP challenges.<br /> <br /> ===== Expected Results. =====<br /> • Development of a user-friendly Python wrapper based on our best Greeklish-to-Greek NLP model  • Documentation and unit testing of the library • Development of new methods for Greeklish-to-Greek conversion, utilizing more recent and promising Large Language Models (LLMs), such as Meta’s LLaMA2 or Mistral models<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> While the proposed Greeklish toolkit can be developed independently, it could also be integrated as an extra functionality to the mentors’ affiliated current state-of-the-art Greek NLP toolkit: &lt;nowiki&gt;https://github.com/nlpaueb/gr-nlp-toolkit&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Advanced Python Programming and Software Engineering • Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)<br /> <br /> ==== Mentors: ====<br /> John Pavlopoulos (AUEB), Ion Androutsopoulos (AUEB), Stavros Vassos (helvia.ai), Lefteris Loukas (helvia.ai &amp;amp;amp;amp; AUEB)<br /> <br /> <br /> == Label Buddy 3.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021 and extended during GSoC 2022. It aims to make the annotation process easy, and simple with the help of AI Assistance tools and at the same time offers a well-defined manager-annotator-reviewer system. The purpose of this project is to investigate and integrate Multi-Modal Annotation Support in data other than only sound with the use of Large Language Models (LLMs) and Active Learning for Model Improvement on the already existing models.<br /> <br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/gsoc2022-Label-buddy<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI<br /> <br /> <br /> ==== Mentors: ====<br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> <br /> == Enhancements for Flexbench: OpenAPI Integration and Anonymization using Machine Learning (ML) ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a fully customizable NodeJS application, generating simulated HTTP traffic. It can be used as a standalone script, desktop-app* and server* to simulate traffic with specific characteristics, such as read/write ratio, duration, number of requests to generate, in/out traffic throttling and more.<br /> <br /> It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for stress and load testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt;During the GSoC-2022 period, along with the standalone script version, desktop and server apps were developed, and major fixes were applied.<br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> [https://github.com/dspinellis/alexandria3k https://github.com/flexivian/flexbench/tree/develop ,][https://github.com/dspinellis/alexandria3k https://flexivian.github.io/flexbench/]<br /> ==== Knowledge Prerequisites ====<br /> <br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI <br /> <br /> ==== Mentors: ====<br /> <br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> == New open data sources for Alexandria3k ==<br /> <br /> ==== Brief Explanation ====<br /> The alexandria3k package supplies a library and a command-line tool providing efficient relational query access to diverse publication open data sets. The largest one is the entire Crossref data set (157 GB compressed, 1 TB uncompressed). This contains publication metadata from about 134 million publications from all major international publishers with full citation data for 60 million of them. Alternatively, works can be selected from the PubMed data set which comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books, with rich domain-specific metadata, such as MeSH indexing, funding, genetic, and chemical details. In addition, the Crossref and PubMed data sets can be linked with the ORCID summary data set (25 GB compressed, 435 GB uncompressed), containing about 78 million author records, the United States Patent Office issued patents (11 GB compressed, 115 GB uncompressed), containing about 5.4 million records, as well as data sets of funder bodies, journal names, open access journals, and research organizations.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/dspinellis/alexandria3k<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, SQL, Unix command-line <br /> <br /> ==== Mentors: ====<br /> <br /> Diomidis Spinellis<br /> <br /> == MD-Attractor ==<br /> <br /> ==== Brief Explanation ====<br /> MD-Attractor is an open-source project designed to serve as a unified aggregator for music-related data sourced from various music platforms such as Spotify, Deezer, Apple Music, and more.<br /> <br /> The primary objective of this project is to provide a centralized hub for collecting and analyzing data from diverse media sources, offering users a comprehensive view of diverse music data sources.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, APIs, Visualization <br /> <br /> ==== Mentors: ====<br /> <br /> Panagiotis Sfendourakis, Elektra Bilali Simou<br /> <br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ===== Background =====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ===== Project Description =====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://en.wikipedia.org/wiki/Triplestore Triplestore]<br /> - [https://en.wikipedia.org/wiki/Semantic_triple Triples]<br /> - [https://en.wikipedia.org/wiki/SPARQL Query language]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras, TBD<br /> <br /> <br /> == Font Validator: A System for Quality Control of Digital Typefaces Containing Greek Characters ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to develop a comprehensive set of tests for quality control on digital typefaces, with a particular focus on Greek characters. The project aims to ensure the consistency of typeface design, especially in terms of height, shape, composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters.<br /> <br /> ===== Background =====<br /> Digital typefaces play a crucial role in the readability and aesthetics of digital content. However, inconsistencies in typeface design, especially between different scripts like Latin and Greek, can lead to visual discomfort and confusion. Quality control in digital typefaces is therefore essential to ensure a consistent and pleasant reading experience.<br /> <br /> ===== Project Description =====<br /> This project will involve the development of a series of tests using Font Bakery or a similar tool. These tests will compare the height and shape of similarly shaped Latin and Greek letters, such as Latin A and Greek Alpha.<br /> <br /> Further tests will be performed on composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters. The goal is to identify any inconsistencies and provide recommendations for improvements.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Study the design principles of Latin and Greek typefaces. Identify the key parameters for comparison, such as height, shape, composites, ascenders, descenders, spacing, and kerning.<br /> <br /> ''Test Development:'' Develop a series of tests using Font Bakery or a similar tool. These tests should be able to compare the identified parameters between Latin and Greek characters.<br /> <br /> ''Testing:'' Perform the developed tests on a variety of digital typefaces.<br /> <br /> ''Analysis:'' Analyze the test results to identify any inconsistencies in the design of Latin and Greek characters.<br /> <br /> ''Recommendations:'' Based on the analysis, provide recommendations for improving the consistency of typeface design.<br /> <br /> ''Documentation:'' Document the findings, the developed tests, and the recommendations.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a comprehensive set of tests for quality control on digital typefaces, especially for Greek characters. These tests will help typeface designers and developers ensure the consistency of their designs, thereby improving the readability and aesthetics of digital content.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the quality of digital typefaces but also contribute to the body of knowledge in the field of typeface design. It will provide valuable insights into the design principles of Latin and Greek characters and help ensure their consistency in digital typefaces.<br /> <br /> ==== Duration of the Project ====<br /> Can be either short (175 hours) or long (350 hours), depending on the agreed-upon scope.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://font-bakery.readthedocs.io/en/latest/ Font Bakery documentation]<br /> - [https://github.com/fonttools/fontbakery Font Bakery source repo]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, font technologies, understanding of Greek characters<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Emilios Theofanous, Alexios Zavras<br /> <br /> <br /> == Development and Enhancement of the Cloud-Based FOSSBot Platform ==<br /> <br /> ==== Brief Explanation ====<br /> The evolution of the DIY robot kit for educators, initiated in GSoC 2019 with Christos Chronis, has significantly progressed over the years, culminating in a 3D printable, modular, and low-cost educational robot. Over the last five years, contributors from GSOC and the open source community have enriched this project with a programming stack compatible with Google Blockly, native Python through Monaco, and a Docker-based deployment system. At the same time, the initial DIY robot kit from GSOC 2019 transformed into the FOSSBot, and more educators understand and use this open-source solution to teach STEM.<br /> <br /> For GSoC 2023, the focus was on shifting the programming stack to the cloud, addressing issues like connectivity, updates, and the rising costs of Single Board Computers. Another notable addition was the web-based simulator, which allowed educators to test and use the robot virtually, reducing cost barriers and hardware requirements. In the following months, the platform will be available to all through the infrastructure of GFOSS, and at the same time, organized workshops will help make more people aware of the robot. Finally, in the last two years, two scientific publications were released, and the project started to attract the interest of the academic community.<br /> <br /> GSoC 2024 Objectives<br /> <br /> In 2024, our goal is to further enhance this cloud-based platform by:<br /> <br /> Performance Optimization: Improving both front-end and back-end performance for a seamless user experience.<br /> Development of Cooperative Modes: Enabling multiple robots to interact and collaborate in shared tasks or scenarios, fostering teamwork and advanced programming skills.<br /> Educator's Content Creation and Upload System: Developing a system where educators can create, share, and upload educational material, facilitating a dynamic and evolving educational environment.<br /> Custom Scene Creation for Godot Simulator: Integrating an interface for creating new, customized scenes in the existing Godot-based simulator, enabling tailored educational experiences.<br /> Platform Support for Physical Hardware: Introducing programming support for physical devices like the FOSSBot, Arduino, and MicroPython-supported microcontrollers, broadening the scope of practical applications and hands-on learning.<br /> Continuous Integration and Deployment (CI/CD) Enhancements: Streamlining updates and maintenance through advanced GitHub automation and cloud deployment strategies.<br /> Extensive Documentation: Ensuring comprehensive documentation to facilitate ease of use and adaptability for educators and developers.<br /> <br /> ===== Expected Results. =====<br /> A robust, cloud-based platform offering a wide range of educational and programming possibilities.<br /> Enhanced user experience with improved performance and new features.<br /> Greater accessibility and cost-effectiveness for educators worldwide.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> <br /> ==== Related repositories ==== <br /> https://github.com/eellak/gsoc2019-diyrobot<br /> https://github.com/eellak/fossbot<br /> https://github.com/chronis10/fossbot-app<br /> https://github.com/eellak/fossbot-platform<br /> https://github.com/eellak/fossbot-source<br /> https://github.com/eellak/fossbot-web-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js<br /> Python<br /> Flask API / FastAPI<br /> SQLAlchemy<br /> Godot (not mandatory)<br /> JavaScript<br /> Docker<br /> Git<br /> <br /> ==== Related publications ==== <br /> Chronis C., &amp; Varlamis I. (2022). FOSSBot: An Open Source and Open Design Educational Robot. Electronics, 11(16), 2606. https://www.mdpi.com/2079-9292/11/16/2606<br /> Kazazis G., Chronis C., Diou C. &amp; Varlamis I. Development and evaluation of Reinforcement Learning models for the FOSSBot Open-Source educational robot, Pan-Hellenic Conference on Progress in Computing and Informatics, ACM 2023 (Under Publication) <br /> <br /> <br /> ==== Mentors: ====<br /> Christos Chronis, Iraklow Varlamis<br /> <br /> <br /> <br /> == Enabling Apothesis to support atomic layer deposition and etching processes ==<br /> <br /> ==== Brief Explanation ====<br /> Αpothesis is an open source software for designing, simulating and analyzing deposition processes. It is based on Kinetic Monte Carlo method and its two main components are the lattice (simple cubic, HPC etc) where particular processes (adsorption, desorption, surface rection and diffusion) are performed. Up until now Apothesis has been used in various applications (see for example https://iopscience.iop.org/article/10.1088/1361-651X/ace276/meta). However, there is a lack of generalized software for atomic layer deposition/etching (ALD/ALE) processes. ALD/ALE are based on pulses performing over sequentially over a certain period of time (see https://www.frontiersin.org/articles/10.3389/fphy.2021.631918/full for more details). That said, the purpose of this project is to enable Apothesis to handle ALD/ALE cases. For that, there various parts of Apothesis that need to be changed with the most basic being: <br /> 1. Enabling Apothesis to read the lattice from a file. Thus creating a generalized reader for hard copy lattices. <br /> 2. Creating the sequentially calls to simulate ALD/ALE processes. <br /> 3. Design the output of the ALD/ALE process. <br /> <br /> <br /> ===== Expected Results. =====<br /> The results from a simple case of ALD in a simple cubic lattice. <br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, desing patters (factory pattern) and basic physics in deposition processes <br /> <br /> ==== Mentors: ====<br /> Cheimarios Nikolaos, Vissarion Fysikopoulos <br /> <br /> <br /> <br /> == eCodeOrama, an educational interactive flow visualization tool for mit scratch programs ==<br /> <br /> ==== Brief Explanation ====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ==== Expected Results ====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> git@github.com:sarantos40/eCodeOrama.git<br /> <br /> ==== Knowledge Prerequisites ====<br /> python, mit scratch, gui development<br /> <br /> ==== Mentors: ====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> <br /> == Enhancement of SmAuto DSL and integration into HomeAssistant ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto is a Domain Specific Language (DSL) that enables users to program complex automation scenarios, for connected IoT devices in smart environments, that go beyond simple tasks. SmAuto lacks extra features like invocation of REST/MQTT services, time delays etc., thus it should be expanded towards this direction. Furthermore, HomeAssistant would benefit from the integration of a DSL like SMAuto for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> ==== Expected Results ====<br /> In the context of this project, we desire to expand the SmAuto DSL with the following features: a) adding virtual entities, like REST calls and MQTT RPCs, so as for the automations to be able to access information from external services, b) incorporate auxiliary concepts like Delay, Conditions/Switches, or Compute nodes and c) generalize the language’s Condition concept. Furthermore, the integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA plugin, where each user can declare SmAuto automations and deploy them locally in HA.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python, Software engineering, IoT concepts, Unix/Linux. Desired: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> <br /> ==== Mentors: ====<br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == Creating a factory pattern for handling lattices in Apothesis. ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is a generalized software for designing, simulating and analyzing deposition processes. It is based on the kinetic Monte Carlo method. That said, it consists of two main components; a lattice (e.g. simple cubic, HPC etc) and the processes (adsorption, desorption, diffusion and surface reactions) performed in this lattice. Currently, the lattices are hard coded inside Apothesis making it difficult to add new ones easily. This proposal is focuses on creating a factory pattern which be used as a guide for adding lattices in Apothesis. Then this will be used to incorporate 2D lattices (like graphene) inside Apothesis.<br /> <br /> ==== Expected Results ====<br /> The factory pattern source code and a simple example for incorporating a graphene lattice. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, design (factory pattern)<br /> <br /> <br /> ==== Mentors: ====<br /> Nikolaos Cheimarios, Christina-Anna Gatsiou <br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ===== Expected Results. =====<br /> 1. We want to add Sanity CMS(https://www.sanity.io/) for data changing frequently. 2. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter. 3. Rewrite in Typescript for maintainability<br /> <br /> Whoever is interested in talking with the initial core contributors can also find us on https://my.uom.gr/about and https://opensource.uom.gr to join our discord and follow us on social media.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js, Java(Spring Boot),Typescript, Next.js(Optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> [[Κατηγορία:GSOC2024]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2024_proposed_ideas&diff=2248 2024-02-05T07:14:49Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> <br /> == Enhancements for Flexbench: OpenAPI Integration and Anonymization using Machine Learning (ML) ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a fully customizable NodeJS application, generating simulated HTTP traffic. It can be used as a standalone script, desktop-app* and server* to simulate traffic with specific characteristics, such as read/write ratio, duration, number of requests to generate, in/out traffic throttling and more.<br /> <br /> It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for stress and load testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. <br /> <br /> &lt;nowiki&gt;*&lt;/nowiki&gt;During the GSoC-2022 period, along with the standalone script version, desktop and server apps were developed, and major fixes were applied.<br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> [https://github.com/dspinellis/alexandria3k https://github.com/flexivian/flexbench/tree/develop ,][https://github.com/dspinellis/alexandria3k https://flexivian.github.io/flexbench/]<br /> ==== Knowledge Prerequisites ====<br /> <br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI <br /> <br /> ==== Mentors: ====<br /> <br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> == New open data sources for Alexandria3k ==<br /> <br /> ==== Brief Explanation ====<br /> The alexandria3k package supplies a library and a command-line tool providing efficient relational query access to diverse publication open data sets. The largest one is the entire Crossref data set (157 GB compressed, 1 TB uncompressed). This contains publication metadata from about 134 million publications from all major international publishers with full citation data for 60 million of them. Alternatively, works can be selected from the PubMed data set which comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books, with rich domain-specific metadata, such as MeSH indexing, funding, genetic, and chemical details. In addition, the Crossref and PubMed data sets can be linked with the ORCID summary data set (25 GB compressed, 435 GB uncompressed), containing about 78 million author records, the United States Patent Office issued patents (11 GB compressed, 115 GB uncompressed), containing about 5.4 million records, as well as data sets of funder bodies, journal names, open access journals, and research organizations.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/dspinellis/alexandria3k<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, SQL, Unix command-line <br /> <br /> ==== Mentors: ====<br /> <br /> Diomidis Spinellis<br /> <br /> == MD-Attractor ==<br /> <br /> ==== Brief Explanation ====<br /> MD-Attractor is an open-source project designed to serve as a unified aggregator for music-related data sourced from various music platforms such as Spotify, Deezer, Apple Music, and more.<br /> <br /> The primary objective of this project is to provide a centralized hub for collecting and analyzing data from diverse media sources, offering users a comprehensive view of diverse music data sources.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, APIs, Visualization <br /> <br /> ==== Mentors: ====<br /> <br /> Panagiotis Sfendourakis, Elektra Bilali Simou<br /> <br /> <br /> == EDGAR-CRAWLER: Democratizing accessibility to Financial NLP documents ==<br /> <br /> ==== Brief Explanation ====<br /> Natural Language Processing (NLP) faces big challenges in the field of business and finance since financial text data is often locked behind paywalls, making it hard to get the information we need. This problem highlights why it is so important to automate data collection from free sources, like EDGAR, the U.S. Securities and Exchange Commission's public database. EDGAR contains documents about publicly traded stocks from companies in the U.S., like Microsoft, Google, or Amazon. Our existing software, EDGAR-CRAWLER, tackles this problem by automatically downloading and “cleaning” financial documents, mainly through the use of regular expressions, and then making such data available in an easy-to-use JSON format for NLP pipelines and applications. EDGAR-CRAWLER, with over 190 stars on Github, is the go-to toolkit for making financial data accessible to everyone. However, currently, it is only limited to one type of company filing, the annual reports (10-K filings). By adding support for more types of documents, EDGAR-CRAWLER plans to make financial information even more accessible, playing a vital role in the progress of financial NLP and open data.<br /> <br /> ===== Expected Results. =====<br /> • Extend EDGAR-CRAWLER to fetch more types of US company filings like quarterly reports (10-Q) and current reports (8-K), using string-searching algorithms like regular expressions. • Write documentation for these new features. • Develop unit tests for these new features.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Proficiency in Python and Software Development • Familiarity with regular expressions and Natural Language Processing (NLP) • Experience in web scraping and data processing (beautifulsoup, pandas). • Interest in Machine Learning (ML) <br /> <br /> ==== Mentors: ====<br /> <br /> Lefteris Loukas, Ion Androutsopoulos<br /> <br /> == Greeklish-to-Greek: Development of an open-source and state-of-the-art toolkit ==<br /> <br /> ==== Brief Explanation ====<br /> Greeklish” is a way of writing Greek with the commonly used Latin alphabet instead of the Greek alphabet (think of “kalimera” instead of “καλημέρα”), and has been widely used by Greek speakers for decades due to early technological limitations in supporting Greek characters. Its ease of use for informal digital communication, such as on forums or in comments, allows for quick language switching without changing any keyboard input, and hides spelling errors. However, Greeklish complicates the development of Natural Language Processing (NLP) tools for the Greek language because Machine Learning (ML) models are trained on standard Greek, not Greeklish. Most existing Greeklish-to-Greek toolkits are limited, rule-based, and closed-source. Our team has researched and developed state-of-the-art Greeklish-to-Greek methods utilizing Transformer NLP models. The purpose of the project is to develop an open-source, user-friendly Python toolkit, based on our existing research so that the community can benefit from it.<br /> The GSOC contributor will work in close collaboration between helvia.ai (https://helvia.ai/) and the AUEB NLP Group (http://nlp.cs.aueb.gr/software.html), leveraging both industrial and academic expertise to tackle these NLP challenges.<br /> <br /> ===== Expected Results. =====<br /> • Development of a user-friendly Python wrapper based on our best Greeklish-to-Greek NLP model  • Documentation and unit testing of the library • Development of new methods for Greeklish-to-Greek conversion, utilizing more recent and promising Large Language Models (LLMs), such as Meta’s LLaMA2 or Mistral models<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> While the proposed Greeklish toolkit can be developed independently, it could also be integrated as an extra functionality to the mentors’ affiliated current state-of-the-art Greek NLP toolkit: &lt;nowiki&gt;https://github.com/nlpaueb/gr-nlp-toolkit&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Advanced Python Programming and Software Engineering • Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)<br /> <br /> ==== Mentors: ====<br /> John Pavlopoulos (AUEB), Ion Androutsopoulos (AUEB), Stavros Vassos (helvia.ai), Lefteris Loukas (helvia.ai &amp;amp;amp;amp; AUEB)<br /> <br /> <br /> <br /> <br /> == Label Buddy 3.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021 and extended during GSoC 2022. It aims to make the annotation process easy, and simple with the help of AI Assistance tools and at the same time offers a well-defined manager-annotator-reviewer system. The purpose of this project is to investigate and integrate Multi-Modal Annotation Support in data other than only sound with the use of Large Language Models (LLMs) and Active Learning for Model Improvement on the already existing models.<br /> <br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/gsoc2022-Label-buddy<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI<br /> <br /> <br /> ==== Mentors: ====<br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> <br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ===== Background =====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ===== Project Description =====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://en.wikipedia.org/wiki/Triplestore Triplestore]<br /> - [https://en.wikipedia.org/wiki/Semantic_triple Triples]<br /> - [https://en.wikipedia.org/wiki/SPARQL Query language]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras, TBD<br /> <br /> <br /> == Font Validator: A System for Quality Control of Digital Typefaces Containing Greek Characters ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to develop a comprehensive set of tests for quality control on digital typefaces, with a particular focus on Greek characters. The project aims to ensure the consistency of typeface design, especially in terms of height, shape, composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters.<br /> <br /> ===== Background =====<br /> Digital typefaces play a crucial role in the readability and aesthetics of digital content. However, inconsistencies in typeface design, especially between different scripts like Latin and Greek, can lead to visual discomfort and confusion. Quality control in digital typefaces is therefore essential to ensure a consistent and pleasant reading experience.<br /> <br /> ===== Project Description =====<br /> This project will involve the development of a series of tests using Font Bakery or a similar tool. These tests will compare the height and shape of similarly shaped Latin and Greek letters, such as Latin A and Greek Alpha.<br /> <br /> Further tests will be performed on composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters. The goal is to identify any inconsistencies and provide recommendations for improvements.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Study the design principles of Latin and Greek typefaces. Identify the key parameters for comparison, such as height, shape, composites, ascenders, descenders, spacing, and kerning.<br /> <br /> ''Test Development:'' Develop a series of tests using Font Bakery or a similar tool. These tests should be able to compare the identified parameters between Latin and Greek characters.<br /> <br /> ''Testing:'' Perform the developed tests on a variety of digital typefaces.<br /> <br /> ''Analysis:'' Analyze the test results to identify any inconsistencies in the design of Latin and Greek characters.<br /> <br /> ''Recommendations:'' Based on the analysis, provide recommendations for improving the consistency of typeface design.<br /> <br /> ''Documentation:'' Document the findings, the developed tests, and the recommendations.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a comprehensive set of tests for quality control on digital typefaces, especially for Greek characters. These tests will help typeface designers and developers ensure the consistency of their designs, thereby improving the readability and aesthetics of digital content.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the quality of digital typefaces but also contribute to the body of knowledge in the field of typeface design. It will provide valuable insights into the design principles of Latin and Greek characters and help ensure their consistency in digital typefaces.<br /> <br /> ==== Duration of the Project ====<br /> Can be either short (175 hours) or long (350 hours), depending on the agreed-upon scope.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://font-bakery.readthedocs.io/en/latest/ Font Bakery documentation]<br /> - [https://github.com/fonttools/fontbakery Font Bakery source repo]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, font technologies, understanding of Greek characters<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Emilios Theofanous, Alexios Zavras<br /> <br /> <br /> == Development and Enhancement of the Cloud-Based FOSSBot Platform ==<br /> <br /> ==== Brief Explanation ====<br /> The evolution of the DIY robot kit for educators, initiated in GSoC 2019 with Christos Chronis, has significantly progressed over the years, culminating in a 3D printable, modular, and low-cost educational robot. Over the last five years, contributors from GSOC and the open source community have enriched this project with a programming stack compatible with Google Blockly, native Python through Monaco, and a Docker-based deployment system. At the same time, the initial DIY robot kit from GSOC 2019 transformed into the FOSSBot, and more educators understand and use this open-source solution to teach STEM.<br /> <br /> For GSoC 2023, the focus was on shifting the programming stack to the cloud, addressing issues like connectivity, updates, and the rising costs of Single Board Computers. Another notable addition was the web-based simulator, which allowed educators to test and use the robot virtually, reducing cost barriers and hardware requirements. In the following months, the platform will be available to all through the infrastructure of GFOSS, and at the same time, organized workshops will help make more people aware of the robot. Finally, in the last two years, two scientific publications were released, and the project started to attract the interest of the academic community.<br /> <br /> GSoC 2024 Objectives<br /> <br /> In 2024, our goal is to further enhance this cloud-based platform by:<br /> <br /> Performance Optimization: Improving both front-end and back-end performance for a seamless user experience.<br /> Development of Cooperative Modes: Enabling multiple robots to interact and collaborate in shared tasks or scenarios, fostering teamwork and advanced programming skills.<br /> Educator's Content Creation and Upload System: Developing a system where educators can create, share, and upload educational material, facilitating a dynamic and evolving educational environment.<br /> Custom Scene Creation for Godot Simulator: Integrating an interface for creating new, customized scenes in the existing Godot-based simulator, enabling tailored educational experiences.<br /> Platform Support for Physical Hardware: Introducing programming support for physical devices like the FOSSBot, Arduino, and MicroPython-supported microcontrollers, broadening the scope of practical applications and hands-on learning.<br /> Continuous Integration and Deployment (CI/CD) Enhancements: Streamlining updates and maintenance through advanced GitHub automation and cloud deployment strategies.<br /> Extensive Documentation: Ensuring comprehensive documentation to facilitate ease of use and adaptability for educators and developers.<br /> <br /> ===== Expected Results. =====<br /> A robust, cloud-based platform offering a wide range of educational and programming possibilities.<br /> Enhanced user experience with improved performance and new features.<br /> Greater accessibility and cost-effectiveness for educators worldwide.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> <br /> ==== Related repositories ==== <br /> https://github.com/eellak/gsoc2019-diyrobot<br /> https://github.com/eellak/fossbot<br /> https://github.com/chronis10/fossbot-app<br /> https://github.com/eellak/fossbot-platform<br /> https://github.com/eellak/fossbot-source<br /> https://github.com/eellak/fossbot-web-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js<br /> Python<br /> Flask API / FastAPI<br /> SQLAlchemy<br /> Godot (not mandatory)<br /> JavaScript<br /> Docker<br /> Git<br /> <br /> ==== Related publications ==== <br /> Chronis C., &amp; Varlamis I. (2022). FOSSBot: An Open Source and Open Design Educational Robot. Electronics, 11(16), 2606. https://www.mdpi.com/2079-9292/11/16/2606<br /> Kazazis G., Chronis C., Diou C. &amp; Varlamis I. Development and evaluation of Reinforcement Learning models for the FOSSBot Open-Source educational robot, Pan-Hellenic Conference on Progress in Computing and Informatics, ACM 2023 (Under Publication) <br /> <br /> <br /> ==== Mentors: ====<br /> Christos Chronis, Iraklow Varlamis<br /> <br /> <br /> <br /> == Enabling Apothesis to support atomic layer deposition and etching processes ==<br /> <br /> ==== Brief Explanation ====<br /> Αpothesis is an open source software for designing, simulating and analyzing deposition processes. It is based on Kinetic Monte Carlo method and its two main components are the lattice (simple cubic, HPC etc) where particular processes (adsorption, desorption, surface rection and diffusion) are performed. Up until now Apothesis has been used in various applications (see for example https://iopscience.iop.org/article/10.1088/1361-651X/ace276/meta). However, there is a lack of generalized software for atomic layer deposition/etching (ALD/ALE) processes. ALD/ALE are based on pulses performing over sequentially over a certain period of time (see https://www.frontiersin.org/articles/10.3389/fphy.2021.631918/full for more details). That said, the purpose of this project is to enable Apothesis to handle ALD/ALE cases. For that, there various parts of Apothesis that need to be changed with the most basic being: <br /> 1. Enabling Apothesis to read the lattice from a file. Thus creating a generalized reader for hard copy lattices. <br /> 2. Creating the sequentially calls to simulate ALD/ALE processes. <br /> 3. Design the output of the ALD/ALE process. <br /> <br /> <br /> ===== Expected Results. =====<br /> The results from a simple case of ALD in a simple cubic lattice. <br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, desing patters (factory pattern) and basic physics in deposition processes <br /> <br /> ==== Mentors: ====<br /> Cheimarios Nikolaos, Vissarion Fysikopoulos <br /> <br /> <br /> <br /> == eCodeOrama, an educational interactive flow visualization tool for mit scratch programs ==<br /> <br /> ==== Brief Explanation ====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ==== Expected Results ====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> git@github.com:sarantos40/eCodeOrama.git<br /> <br /> ==== Knowledge Prerequisites ====<br /> python, mit scratch, gui development<br /> <br /> ==== Mentors: ====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> <br /> == Enhancement of SmAuto DSL and integration into HomeAssistant ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto is a Domain Specific Language (DSL) that enables users to program complex automation scenarios, for connected IoT devices in smart environments, that go beyond simple tasks. SmAuto lacks extra features like invocation of REST/MQTT services, time delays etc., thus it should be expanded towards this direction. Furthermore, HomeAssistant would benefit from the integration of a DSL like SMAuto for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> ==== Expected Results ====<br /> In the context of this project, we desire to expand the SmAuto DSL with the following features: a) adding virtual entities, like REST calls and MQTT RPCs, so as for the automations to be able to access information from external services, b) incorporate auxiliary concepts like Delay, Conditions/Switches, or Compute nodes and c) generalize the language’s Condition concept. Furthermore, the integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA plugin, where each user can declare SmAuto automations and deploy them locally in HA.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python, Software engineering, IoT concepts, Unix/Linux. Desired: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> <br /> ==== Mentors: ====<br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == Creating a factory pattern for handling lattices in Apothesis. ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is a generalized software for designing, simulating and analyzing deposition processes. It is based on the kinetic Monte Carlo method. That said, it consists of two main components; a lattice (e.g. simple cubic, HPC etc) and the processes (adsorption, desorption, diffusion and surface reactions) performed in this lattice. Currently, the lattices are hard coded inside Apothesis making it difficult to add new ones easily. This proposal is focuses on creating a factory pattern which be used as a guide for adding lattices in Apothesis. Then this will be used to incorporate 2D lattices (like graphene) inside Apothesis.<br /> <br /> ==== Expected Results ====<br /> The factory pattern source code and a simple example for incorporating a graphene lattice. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, design (factory pattern)<br /> <br /> <br /> ==== Mentors: ====<br /> Nikolaos Cheimarios, Christina-Anna Gatsiou <br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ===== Expected Results. =====<br /> 1. We want to add Sanity CMS(https://www.sanity.io/) for data changing frequently. 2. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter. 3. Rewrite in Typescript for maintainability<br /> <br /> Whoever is interested in talking with the initial core contributors can also find us on https://my.uom.gr/about and https://opensource.uom.gr to join our discord and follow us on social media.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js, Java(Spring Boot),Typescript, Next.js(Optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> [[Κατηγορία:GSOC2024]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2024_proposed_ideas&diff=2247 2024-02-05T07:09:26Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> <br /> == New open data sources for Alexandria3k ==<br /> <br /> ==== Brief Explanation ====<br /> The alexandria3k package supplies a library and a command-line tool providing efficient relational query access to diverse publication open data sets. The largest one is the entire Crossref data set (157 GB compressed, 1 TB uncompressed). This contains publication metadata from about 134 million publications from all major international publishers with full citation data for 60 million of them. Alternatively, works can be selected from the PubMed data set which comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books, with rich domain-specific metadata, such as MeSH indexing, funding, genetic, and chemical details. In addition, the Crossref and PubMed data sets can be linked with the ORCID summary data set (25 GB compressed, 435 GB uncompressed), containing about 78 million author records, the United States Patent Office issued patents (11 GB compressed, 115 GB uncompressed), containing about 5.4 million records, as well as data sets of funder bodies, journal names, open access journals, and research organizations.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/dspinellis/alexandria3k<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, SQL, Unix command-line <br /> <br /> ==== Mentors: ====<br /> <br /> Diomidis Spinellis<br /> <br /> == MD-Attractor ==<br /> <br /> ==== Brief Explanation ====<br /> MD-Attractor is an open-source project designed to serve as a unified aggregator for music-related data sourced from various music platforms such as Spotify, Deezer, Apple Music, and more.<br /> <br /> The primary objective of this project is to provide a centralized hub for collecting and analyzing data from diverse media sources, offering users a comprehensive view of diverse music data sources.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, APIs, Visualization <br /> <br /> ==== Mentors: ====<br /> <br /> Panagiotis Sfendourakis, Elektra Bilali Simou<br /> <br /> <br /> == EDGAR-CRAWLER: Democratizing accessibility to Financial NLP documents ==<br /> <br /> ==== Brief Explanation ====<br /> Natural Language Processing (NLP) faces big challenges in the field of business and finance since financial text data is often locked behind paywalls, making it hard to get the information we need. This problem highlights why it is so important to automate data collection from free sources, like EDGAR, the U.S. Securities and Exchange Commission's public database. EDGAR contains documents about publicly traded stocks from companies in the U.S., like Microsoft, Google, or Amazon. Our existing software, EDGAR-CRAWLER, tackles this problem by automatically downloading and “cleaning” financial documents, mainly through the use of regular expressions, and then making such data available in an easy-to-use JSON format for NLP pipelines and applications. EDGAR-CRAWLER, with over 190 stars on Github, is the go-to toolkit for making financial data accessible to everyone. However, currently, it is only limited to one type of company filing, the annual reports (10-K filings). By adding support for more types of documents, EDGAR-CRAWLER plans to make financial information even more accessible, playing a vital role in the progress of financial NLP and open data.<br /> <br /> ===== Expected Results. =====<br /> • Extend EDGAR-CRAWLER to fetch more types of US company filings like quarterly reports (10-Q) and current reports (8-K), using string-searching algorithms like regular expressions. • Write documentation for these new features. • Develop unit tests for these new features.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Proficiency in Python and Software Development • Familiarity with regular expressions and Natural Language Processing (NLP) • Experience in web scraping and data processing (beautifulsoup, pandas). • Interest in Machine Learning (ML) <br /> <br /> ==== Mentors: ====<br /> <br /> Lefteris Loukas, Ion Androutsopoulos<br /> <br /> == Greeklish-to-Greek: Development of an open-source and state-of-the-art toolkit ==<br /> <br /> ==== Brief Explanation ====<br /> Greeklish” is a way of writing Greek with the commonly used Latin alphabet instead of the Greek alphabet (think of “kalimera” instead of “καλημέρα”), and has been widely used by Greek speakers for decades due to early technological limitations in supporting Greek characters. Its ease of use for informal digital communication, such as on forums or in comments, allows for quick language switching without changing any keyboard input, and hides spelling errors. However, Greeklish complicates the development of Natural Language Processing (NLP) tools for the Greek language because Machine Learning (ML) models are trained on standard Greek, not Greeklish. Most existing Greeklish-to-Greek toolkits are limited, rule-based, and closed-source. Our team has researched and developed state-of-the-art Greeklish-to-Greek methods utilizing Transformer NLP models. The purpose of the project is to develop an open-source, user-friendly Python toolkit, based on our existing research so that the community can benefit from it.<br /> The GSOC contributor will work in close collaboration between helvia.ai (https://helvia.ai/) and the AUEB NLP Group (http://nlp.cs.aueb.gr/software.html), leveraging both industrial and academic expertise to tackle these NLP challenges.<br /> <br /> ===== Expected Results. =====<br /> • Development of a user-friendly Python wrapper based on our best Greeklish-to-Greek NLP model  • Documentation and unit testing of the library • Development of new methods for Greeklish-to-Greek conversion, utilizing more recent and promising Large Language Models (LLMs), such as Meta’s LLaMA2 or Mistral models<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> While the proposed Greeklish toolkit can be developed independently, it could also be integrated as an extra functionality to the mentors’ affiliated current state-of-the-art Greek NLP toolkit: &lt;nowiki&gt;https://github.com/nlpaueb/gr-nlp-toolkit&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Advanced Python Programming and Software Engineering • Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)<br /> <br /> ==== Mentors: ====<br /> John Pavlopoulos (AUEB), Ion Androutsopoulos (AUEB), Stavros Vassos (helvia.ai), Lefteris Loukas (helvia.ai &amp;amp;amp;amp; AUEB)<br /> <br /> <br /> <br /> <br /> == Label Buddy 3.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021 and extended during GSoC 2022. It aims to make the annotation process easy, and simple with the help of AI Assistance tools and at the same time offers a well-defined manager-annotator-reviewer system. The purpose of this project is to investigate and integrate Multi-Modal Annotation Support in data other than only sound with the use of Large Language Models (LLMs) and Active Learning for Model Improvement on the already existing models.<br /> <br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/gsoc2022-Label-buddy<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI<br /> <br /> <br /> ==== Mentors: ====<br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> <br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ===== Background =====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ===== Project Description =====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://en.wikipedia.org/wiki/Triplestore Triplestore]<br /> - [https://en.wikipedia.org/wiki/Semantic_triple Triples]<br /> - [https://en.wikipedia.org/wiki/SPARQL Query language]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras, TBD<br /> <br /> <br /> == Font Validator: A System for Quality Control of Digital Typefaces Containing Greek Characters ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to develop a comprehensive set of tests for quality control on digital typefaces, with a particular focus on Greek characters. The project aims to ensure the consistency of typeface design, especially in terms of height, shape, composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters.<br /> <br /> ===== Background =====<br /> Digital typefaces play a crucial role in the readability and aesthetics of digital content. However, inconsistencies in typeface design, especially between different scripts like Latin and Greek, can lead to visual discomfort and confusion. Quality control in digital typefaces is therefore essential to ensure a consistent and pleasant reading experience.<br /> <br /> ===== Project Description =====<br /> This project will involve the development of a series of tests using Font Bakery or a similar tool. These tests will compare the height and shape of similarly shaped Latin and Greek letters, such as Latin A and Greek Alpha.<br /> <br /> Further tests will be performed on composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters. The goal is to identify any inconsistencies and provide recommendations for improvements.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Study the design principles of Latin and Greek typefaces. Identify the key parameters for comparison, such as height, shape, composites, ascenders, descenders, spacing, and kerning.<br /> <br /> ''Test Development:'' Develop a series of tests using Font Bakery or a similar tool. These tests should be able to compare the identified parameters between Latin and Greek characters.<br /> <br /> ''Testing:'' Perform the developed tests on a variety of digital typefaces.<br /> <br /> ''Analysis:'' Analyze the test results to identify any inconsistencies in the design of Latin and Greek characters.<br /> <br /> ''Recommendations:'' Based on the analysis, provide recommendations for improving the consistency of typeface design.<br /> <br /> ''Documentation:'' Document the findings, the developed tests, and the recommendations.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a comprehensive set of tests for quality control on digital typefaces, especially for Greek characters. These tests will help typeface designers and developers ensure the consistency of their designs, thereby improving the readability and aesthetics of digital content.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the quality of digital typefaces but also contribute to the body of knowledge in the field of typeface design. It will provide valuable insights into the design principles of Latin and Greek characters and help ensure their consistency in digital typefaces.<br /> <br /> ==== Duration of the Project ====<br /> Can be either short (175 hours) or long (350 hours), depending on the agreed-upon scope.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://font-bakery.readthedocs.io/en/latest/ Font Bakery documentation]<br /> - [https://github.com/fonttools/fontbakery Font Bakery source repo]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, font technologies, understanding of Greek characters<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Emilios Theofanous, Alexios Zavras<br /> <br /> <br /> == Development and Enhancement of the Cloud-Based FOSSBot Platform ==<br /> <br /> ==== Brief Explanation ====<br /> The evolution of the DIY robot kit for educators, initiated in GSoC 2019 with Christos Chronis, has significantly progressed over the years, culminating in a 3D printable, modular, and low-cost educational robot. Over the last five years, contributors from GSOC and the open source community have enriched this project with a programming stack compatible with Google Blockly, native Python through Monaco, and a Docker-based deployment system. At the same time, the initial DIY robot kit from GSOC 2019 transformed into the FOSSBot, and more educators understand and use this open-source solution to teach STEM.<br /> <br /> For GSoC 2023, the focus was on shifting the programming stack to the cloud, addressing issues like connectivity, updates, and the rising costs of Single Board Computers. Another notable addition was the web-based simulator, which allowed educators to test and use the robot virtually, reducing cost barriers and hardware requirements. In the following months, the platform will be available to all through the infrastructure of GFOSS, and at the same time, organized workshops will help make more people aware of the robot. Finally, in the last two years, two scientific publications were released, and the project started to attract the interest of the academic community.<br /> <br /> GSoC 2024 Objectives<br /> <br /> In 2024, our goal is to further enhance this cloud-based platform by:<br /> <br /> Performance Optimization: Improving both front-end and back-end performance for a seamless user experience.<br /> Development of Cooperative Modes: Enabling multiple robots to interact and collaborate in shared tasks or scenarios, fostering teamwork and advanced programming skills.<br /> Educator's Content Creation and Upload System: Developing a system where educators can create, share, and upload educational material, facilitating a dynamic and evolving educational environment.<br /> Custom Scene Creation for Godot Simulator: Integrating an interface for creating new, customized scenes in the existing Godot-based simulator, enabling tailored educational experiences.<br /> Platform Support for Physical Hardware: Introducing programming support for physical devices like the FOSSBot, Arduino, and MicroPython-supported microcontrollers, broadening the scope of practical applications and hands-on learning.<br /> Continuous Integration and Deployment (CI/CD) Enhancements: Streamlining updates and maintenance through advanced GitHub automation and cloud deployment strategies.<br /> Extensive Documentation: Ensuring comprehensive documentation to facilitate ease of use and adaptability for educators and developers.<br /> <br /> ===== Expected Results. =====<br /> A robust, cloud-based platform offering a wide range of educational and programming possibilities.<br /> Enhanced user experience with improved performance and new features.<br /> Greater accessibility and cost-effectiveness for educators worldwide.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> <br /> ==== Related repositories ==== <br /> https://github.com/eellak/gsoc2019-diyrobot<br /> https://github.com/eellak/fossbot<br /> https://github.com/chronis10/fossbot-app<br /> https://github.com/eellak/fossbot-platform<br /> https://github.com/eellak/fossbot-source<br /> https://github.com/eellak/fossbot-web-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js<br /> Python<br /> Flask API / FastAPI<br /> SQLAlchemy<br /> Godot (not mandatory)<br /> JavaScript<br /> Docker<br /> Git<br /> <br /> ==== Related publications ==== <br /> Chronis C., &amp; Varlamis I. (2022). FOSSBot: An Open Source and Open Design Educational Robot. Electronics, 11(16), 2606. https://www.mdpi.com/2079-9292/11/16/2606<br /> Kazazis G., Chronis C., Diou C. &amp; Varlamis I. Development and evaluation of Reinforcement Learning models for the FOSSBot Open-Source educational robot, Pan-Hellenic Conference on Progress in Computing and Informatics, ACM 2023 (Under Publication) <br /> <br /> <br /> ==== Mentors: ====<br /> Christos Chronis, Iraklow Varlamis<br /> <br /> <br /> <br /> == Enabling Apothesis to support atomic layer deposition and etching processes ==<br /> <br /> ==== Brief Explanation ====<br /> Αpothesis is an open source software for designing, simulating and analyzing deposition processes. It is based on Kinetic Monte Carlo method and its two main components are the lattice (simple cubic, HPC etc) where particular processes (adsorption, desorption, surface rection and diffusion) are performed. Up until now Apothesis has been used in various applications (see for example https://iopscience.iop.org/article/10.1088/1361-651X/ace276/meta). However, there is a lack of generalized software for atomic layer deposition/etching (ALD/ALE) processes. ALD/ALE are based on pulses performing over sequentially over a certain period of time (see https://www.frontiersin.org/articles/10.3389/fphy.2021.631918/full for more details). That said, the purpose of this project is to enable Apothesis to handle ALD/ALE cases. For that, there various parts of Apothesis that need to be changed with the most basic being: <br /> 1. Enabling Apothesis to read the lattice from a file. Thus creating a generalized reader for hard copy lattices. <br /> 2. Creating the sequentially calls to simulate ALD/ALE processes. <br /> 3. Design the output of the ALD/ALE process. <br /> <br /> <br /> ===== Expected Results. =====<br /> The results from a simple case of ALD in a simple cubic lattice. <br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, desing patters (factory pattern) and basic physics in deposition processes <br /> <br /> ==== Mentors: ====<br /> Cheimarios Nikolaos, Vissarion Fysikopoulos <br /> <br /> <br /> <br /> == eCodeOrama, an educational interactive flow visualization tool for mit scratch programs ==<br /> <br /> ==== Brief Explanation ====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ==== Expected Results ====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> git@github.com:sarantos40/eCodeOrama.git<br /> <br /> ==== Knowledge Prerequisites ====<br /> python, mit scratch, gui development<br /> <br /> ==== Mentors: ====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> <br /> == Enhancement of SmAuto DSL and integration into HomeAssistant ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto is a Domain Specific Language (DSL) that enables users to program complex automation scenarios, for connected IoT devices in smart environments, that go beyond simple tasks. SmAuto lacks extra features like invocation of REST/MQTT services, time delays etc., thus it should be expanded towards this direction. Furthermore, HomeAssistant would benefit from the integration of a DSL like SMAuto for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> ==== Expected Results ====<br /> In the context of this project, we desire to expand the SmAuto DSL with the following features: a) adding virtual entities, like REST calls and MQTT RPCs, so as for the automations to be able to access information from external services, b) incorporate auxiliary concepts like Delay, Conditions/Switches, or Compute nodes and c) generalize the language’s Condition concept. Furthermore, the integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA plugin, where each user can declare SmAuto automations and deploy them locally in HA.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python, Software engineering, IoT concepts, Unix/Linux. Desired: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> <br /> ==== Mentors: ====<br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == Creating a factory pattern for handling lattices in Apothesis. ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is a generalized software for designing, simulating and analyzing deposition processes. It is based on the kinetic Monte Carlo method. That said, it consists of two main components; a lattice (e.g. simple cubic, HPC etc) and the processes (adsorption, desorption, diffusion and surface reactions) performed in this lattice. Currently, the lattices are hard coded inside Apothesis making it difficult to add new ones easily. This proposal is focuses on creating a factory pattern which be used as a guide for adding lattices in Apothesis. Then this will be used to incorporate 2D lattices (like graphene) inside Apothesis.<br /> <br /> ==== Expected Results ====<br /> The factory pattern source code and a simple example for incorporating a graphene lattice. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, design (factory pattern)<br /> <br /> <br /> ==== Mentors: ====<br /> Nikolaos Cheimarios, Christina-Anna Gatsiou <br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ===== Expected Results. =====<br /> 1. We want to add Sanity CMS(https://www.sanity.io/) for data changing frequently. 2. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter. 3. Rewrite in Typescript for maintainability<br /> <br /> Whoever is interested in talking with the initial core contributors can also find us on https://my.uom.gr/about and https://opensource.uom.gr to join our discord and follow us on social media.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js, Java(Spring Boot),Typescript, Next.js(Optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> [[Κατηγορία:GSOC2024]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2024_proposed_ideas&diff=2246 2024-02-05T07:08:31Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> <br /> == New open data sources for Alexandria3k ==<br /> <br /> ==== Brief Explanation ====<br /> The alexandria3k package supplies a library and a command-line tool providing efficient relational query access to diverse publication open data sets. The largest one is the entire Crossref data set (157 GB compressed, 1 TB uncompressed). This contains publication metadata from about 134 million publications from all major international publishers with full citation data for 60 million of them. Alternatively, works can be selected from the PubMed data set which comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books, with rich domain-specific metadata, such as MeSH indexing, funding, genetic, and chemical details. In addition, the Crossref and PubMed data sets can be linked with the ORCID summary data set (25 GB compressed, 435 GB uncompressed), containing about 78 million author records, the United States Patent Office issued patents (11 GB compressed, 115 GB uncompressed), containing about 5.4 million records, as well as data sets of funder bodies, journal names, open access journals, and research organizations.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/dspinellis/alexandria3k<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, SQL, Unix command-line <br /> <br /> ==== Mentors: ====<br /> <br /> Panagiotis Sfendourakis, Elektra Bilali Simou<br /> <br /> == MD-Attractor ==<br /> <br /> ==== Brief Explanation ====<br /> MD-Attractor is an open-source project designed to serve as a unified aggregator for music-related data sourced from various music platforms such as Spotify, Deezer, Apple Music, and more.<br /> <br /> The primary objective of this project is to provide a centralized hub for collecting and analyzing data from diverse media sources, offering users a comprehensive view of diverse music data sources.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, APIs, Visualization <br /> <br /> ==== Mentors: ====<br /> <br /> Panagiotis Sfendourakis, Elektra Bilali Simou<br /> <br /> <br /> == EDGAR-CRAWLER: Democratizing accessibility to Financial NLP documents ==<br /> <br /> ==== Brief Explanation ====<br /> Natural Language Processing (NLP) faces big challenges in the field of business and finance since financial text data is often locked behind paywalls, making it hard to get the information we need. This problem highlights why it is so important to automate data collection from free sources, like EDGAR, the U.S. Securities and Exchange Commission's public database. EDGAR contains documents about publicly traded stocks from companies in the U.S., like Microsoft, Google, or Amazon. Our existing software, EDGAR-CRAWLER, tackles this problem by automatically downloading and “cleaning” financial documents, mainly through the use of regular expressions, and then making such data available in an easy-to-use JSON format for NLP pipelines and applications. EDGAR-CRAWLER, with over 190 stars on Github, is the go-to toolkit for making financial data accessible to everyone. However, currently, it is only limited to one type of company filing, the annual reports (10-K filings). By adding support for more types of documents, EDGAR-CRAWLER plans to make financial information even more accessible, playing a vital role in the progress of financial NLP and open data.<br /> <br /> ===== Expected Results. =====<br /> • Extend EDGAR-CRAWLER to fetch more types of US company filings like quarterly reports (10-Q) and current reports (8-K), using string-searching algorithms like regular expressions. • Write documentation for these new features. • Develop unit tests for these new features.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Proficiency in Python and Software Development • Familiarity with regular expressions and Natural Language Processing (NLP) • Experience in web scraping and data processing (beautifulsoup, pandas). • Interest in Machine Learning (ML) <br /> <br /> ==== Mentors: ====<br /> <br /> Lefteris Loukas, Ion Androutsopoulos<br /> <br /> == Greeklish-to-Greek: Development of an open-source and state-of-the-art toolkit ==<br /> <br /> ==== Brief Explanation ====<br /> Greeklish” is a way of writing Greek with the commonly used Latin alphabet instead of the Greek alphabet (think of “kalimera” instead of “καλημέρα”), and has been widely used by Greek speakers for decades due to early technological limitations in supporting Greek characters. Its ease of use for informal digital communication, such as on forums or in comments, allows for quick language switching without changing any keyboard input, and hides spelling errors. However, Greeklish complicates the development of Natural Language Processing (NLP) tools for the Greek language because Machine Learning (ML) models are trained on standard Greek, not Greeklish. Most existing Greeklish-to-Greek toolkits are limited, rule-based, and closed-source. Our team has researched and developed state-of-the-art Greeklish-to-Greek methods utilizing Transformer NLP models. The purpose of the project is to develop an open-source, user-friendly Python toolkit, based on our existing research so that the community can benefit from it.<br /> The GSOC contributor will work in close collaboration between helvia.ai (https://helvia.ai/) and the AUEB NLP Group (http://nlp.cs.aueb.gr/software.html), leveraging both industrial and academic expertise to tackle these NLP challenges.<br /> <br /> ===== Expected Results. =====<br /> • Development of a user-friendly Python wrapper based on our best Greeklish-to-Greek NLP model  • Documentation and unit testing of the library • Development of new methods for Greeklish-to-Greek conversion, utilizing more recent and promising Large Language Models (LLMs), such as Meta’s LLaMA2 or Mistral models<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> While the proposed Greeklish toolkit can be developed independently, it could also be integrated as an extra functionality to the mentors’ affiliated current state-of-the-art Greek NLP toolkit: &lt;nowiki&gt;https://github.com/nlpaueb/gr-nlp-toolkit&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Advanced Python Programming and Software Engineering • Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)<br /> <br /> ==== Mentors: ====<br /> John Pavlopoulos (AUEB), Ion Androutsopoulos (AUEB), Stavros Vassos (helvia.ai), Lefteris Loukas (helvia.ai &amp;amp;amp;amp; AUEB)<br /> <br /> <br /> <br /> <br /> == Label Buddy 3.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021 and extended during GSoC 2022. It aims to make the annotation process easy, and simple with the help of AI Assistance tools and at the same time offers a well-defined manager-annotator-reviewer system. The purpose of this project is to investigate and integrate Multi-Modal Annotation Support in data other than only sound with the use of Large Language Models (LLMs) and Active Learning for Model Improvement on the already existing models.<br /> <br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/gsoc2022-Label-buddy<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI<br /> <br /> <br /> ==== Mentors: ====<br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> <br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ===== Background =====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ===== Project Description =====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://en.wikipedia.org/wiki/Triplestore Triplestore]<br /> - [https://en.wikipedia.org/wiki/Semantic_triple Triples]<br /> - [https://en.wikipedia.org/wiki/SPARQL Query language]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras, TBD<br /> <br /> <br /> == Font Validator: A System for Quality Control of Digital Typefaces Containing Greek Characters ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to develop a comprehensive set of tests for quality control on digital typefaces, with a particular focus on Greek characters. The project aims to ensure the consistency of typeface design, especially in terms of height, shape, composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters.<br /> <br /> ===== Background =====<br /> Digital typefaces play a crucial role in the readability and aesthetics of digital content. However, inconsistencies in typeface design, especially between different scripts like Latin and Greek, can lead to visual discomfort and confusion. Quality control in digital typefaces is therefore essential to ensure a consistent and pleasant reading experience.<br /> <br /> ===== Project Description =====<br /> This project will involve the development of a series of tests using Font Bakery or a similar tool. These tests will compare the height and shape of similarly shaped Latin and Greek letters, such as Latin A and Greek Alpha.<br /> <br /> Further tests will be performed on composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters. The goal is to identify any inconsistencies and provide recommendations for improvements.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Study the design principles of Latin and Greek typefaces. Identify the key parameters for comparison, such as height, shape, composites, ascenders, descenders, spacing, and kerning.<br /> <br /> ''Test Development:'' Develop a series of tests using Font Bakery or a similar tool. These tests should be able to compare the identified parameters between Latin and Greek characters.<br /> <br /> ''Testing:'' Perform the developed tests on a variety of digital typefaces.<br /> <br /> ''Analysis:'' Analyze the test results to identify any inconsistencies in the design of Latin and Greek characters.<br /> <br /> ''Recommendations:'' Based on the analysis, provide recommendations for improving the consistency of typeface design.<br /> <br /> ''Documentation:'' Document the findings, the developed tests, and the recommendations.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a comprehensive set of tests for quality control on digital typefaces, especially for Greek characters. These tests will help typeface designers and developers ensure the consistency of their designs, thereby improving the readability and aesthetics of digital content.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the quality of digital typefaces but also contribute to the body of knowledge in the field of typeface design. It will provide valuable insights into the design principles of Latin and Greek characters and help ensure their consistency in digital typefaces.<br /> <br /> ==== Duration of the Project ====<br /> Can be either short (175 hours) or long (350 hours), depending on the agreed-upon scope.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://font-bakery.readthedocs.io/en/latest/ Font Bakery documentation]<br /> - [https://github.com/fonttools/fontbakery Font Bakery source repo]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, font technologies, understanding of Greek characters<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Emilios Theofanous, Alexios Zavras<br /> <br /> <br /> == Development and Enhancement of the Cloud-Based FOSSBot Platform ==<br /> <br /> ==== Brief Explanation ====<br /> The evolution of the DIY robot kit for educators, initiated in GSoC 2019 with Christos Chronis, has significantly progressed over the years, culminating in a 3D printable, modular, and low-cost educational robot. Over the last five years, contributors from GSOC and the open source community have enriched this project with a programming stack compatible with Google Blockly, native Python through Monaco, and a Docker-based deployment system. At the same time, the initial DIY robot kit from GSOC 2019 transformed into the FOSSBot, and more educators understand and use this open-source solution to teach STEM.<br /> <br /> For GSoC 2023, the focus was on shifting the programming stack to the cloud, addressing issues like connectivity, updates, and the rising costs of Single Board Computers. Another notable addition was the web-based simulator, which allowed educators to test and use the robot virtually, reducing cost barriers and hardware requirements. In the following months, the platform will be available to all through the infrastructure of GFOSS, and at the same time, organized workshops will help make more people aware of the robot. Finally, in the last two years, two scientific publications were released, and the project started to attract the interest of the academic community.<br /> <br /> GSoC 2024 Objectives<br /> <br /> In 2024, our goal is to further enhance this cloud-based platform by:<br /> <br /> Performance Optimization: Improving both front-end and back-end performance for a seamless user experience.<br /> Development of Cooperative Modes: Enabling multiple robots to interact and collaborate in shared tasks or scenarios, fostering teamwork and advanced programming skills.<br /> Educator's Content Creation and Upload System: Developing a system where educators can create, share, and upload educational material, facilitating a dynamic and evolving educational environment.<br /> Custom Scene Creation for Godot Simulator: Integrating an interface for creating new, customized scenes in the existing Godot-based simulator, enabling tailored educational experiences.<br /> Platform Support for Physical Hardware: Introducing programming support for physical devices like the FOSSBot, Arduino, and MicroPython-supported microcontrollers, broadening the scope of practical applications and hands-on learning.<br /> Continuous Integration and Deployment (CI/CD) Enhancements: Streamlining updates and maintenance through advanced GitHub automation and cloud deployment strategies.<br /> Extensive Documentation: Ensuring comprehensive documentation to facilitate ease of use and adaptability for educators and developers.<br /> <br /> ===== Expected Results. =====<br /> A robust, cloud-based platform offering a wide range of educational and programming possibilities.<br /> Enhanced user experience with improved performance and new features.<br /> Greater accessibility and cost-effectiveness for educators worldwide.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> <br /> ==== Related repositories ==== <br /> https://github.com/eellak/gsoc2019-diyrobot<br /> https://github.com/eellak/fossbot<br /> https://github.com/chronis10/fossbot-app<br /> https://github.com/eellak/fossbot-platform<br /> https://github.com/eellak/fossbot-source<br /> https://github.com/eellak/fossbot-web-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js<br /> Python<br /> Flask API / FastAPI<br /> SQLAlchemy<br /> Godot (not mandatory)<br /> JavaScript<br /> Docker<br /> Git<br /> <br /> ==== Related publications ==== <br /> Chronis C., &amp; Varlamis I. (2022). FOSSBot: An Open Source and Open Design Educational Robot. Electronics, 11(16), 2606. https://www.mdpi.com/2079-9292/11/16/2606<br /> Kazazis G., Chronis C., Diou C. &amp; Varlamis I. Development and evaluation of Reinforcement Learning models for the FOSSBot Open-Source educational robot, Pan-Hellenic Conference on Progress in Computing and Informatics, ACM 2023 (Under Publication) <br /> <br /> <br /> ==== Mentors: ====<br /> Christos Chronis, Iraklow Varlamis<br /> <br /> <br /> <br /> == Enabling Apothesis to support atomic layer deposition and etching processes ==<br /> <br /> ==== Brief Explanation ====<br /> Αpothesis is an open source software for designing, simulating and analyzing deposition processes. It is based on Kinetic Monte Carlo method and its two main components are the lattice (simple cubic, HPC etc) where particular processes (adsorption, desorption, surface rection and diffusion) are performed. Up until now Apothesis has been used in various applications (see for example https://iopscience.iop.org/article/10.1088/1361-651X/ace276/meta). However, there is a lack of generalized software for atomic layer deposition/etching (ALD/ALE) processes. ALD/ALE are based on pulses performing over sequentially over a certain period of time (see https://www.frontiersin.org/articles/10.3389/fphy.2021.631918/full for more details). That said, the purpose of this project is to enable Apothesis to handle ALD/ALE cases. For that, there various parts of Apothesis that need to be changed with the most basic being: <br /> 1. Enabling Apothesis to read the lattice from a file. Thus creating a generalized reader for hard copy lattices. <br /> 2. Creating the sequentially calls to simulate ALD/ALE processes. <br /> 3. Design the output of the ALD/ALE process. <br /> <br /> <br /> ===== Expected Results. =====<br /> The results from a simple case of ALD in a simple cubic lattice. <br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, desing patters (factory pattern) and basic physics in deposition processes <br /> <br /> ==== Mentors: ====<br /> Cheimarios Nikolaos, Vissarion Fysikopoulos <br /> <br /> <br /> <br /> == eCodeOrama, an educational interactive flow visualization tool for mit scratch programs ==<br /> <br /> ==== Brief Explanation ====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ==== Expected Results ====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> git@github.com:sarantos40/eCodeOrama.git<br /> <br /> ==== Knowledge Prerequisites ====<br /> python, mit scratch, gui development<br /> <br /> ==== Mentors: ====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> <br /> == Enhancement of SmAuto DSL and integration into HomeAssistant ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto is a Domain Specific Language (DSL) that enables users to program complex automation scenarios, for connected IoT devices in smart environments, that go beyond simple tasks. SmAuto lacks extra features like invocation of REST/MQTT services, time delays etc., thus it should be expanded towards this direction. Furthermore, HomeAssistant would benefit from the integration of a DSL like SMAuto for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> ==== Expected Results ====<br /> In the context of this project, we desire to expand the SmAuto DSL with the following features: a) adding virtual entities, like REST calls and MQTT RPCs, so as for the automations to be able to access information from external services, b) incorporate auxiliary concepts like Delay, Conditions/Switches, or Compute nodes and c) generalize the language’s Condition concept. Furthermore, the integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA plugin, where each user can declare SmAuto automations and deploy them locally in HA.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python, Software engineering, IoT concepts, Unix/Linux. Desired: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> <br /> ==== Mentors: ====<br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == Creating a factory pattern for handling lattices in Apothesis. ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is a generalized software for designing, simulating and analyzing deposition processes. It is based on the kinetic Monte Carlo method. That said, it consists of two main components; a lattice (e.g. simple cubic, HPC etc) and the processes (adsorption, desorption, diffusion and surface reactions) performed in this lattice. Currently, the lattices are hard coded inside Apothesis making it difficult to add new ones easily. This proposal is focuses on creating a factory pattern which be used as a guide for adding lattices in Apothesis. Then this will be used to incorporate 2D lattices (like graphene) inside Apothesis.<br /> <br /> ==== Expected Results ====<br /> The factory pattern source code and a simple example for incorporating a graphene lattice. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, design (factory pattern)<br /> <br /> <br /> ==== Mentors: ====<br /> Nikolaos Cheimarios, Christina-Anna Gatsiou <br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ===== Expected Results. =====<br /> 1. We want to add Sanity CMS(https://www.sanity.io/) for data changing frequently. 2. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter. 3. Rewrite in Typescript for maintainability<br /> <br /> Whoever is interested in talking with the initial core contributors can also find us on https://my.uom.gr/about and https://opensource.uom.gr to join our discord and follow us on social media.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js, Java(Spring Boot),Typescript, Next.js(Optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> [[Κατηγορία:GSOC2024]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2024_proposed_ideas&diff=2245 2024-02-05T07:05:00Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == MD-Attractor ==<br /> <br /> ==== Brief Explanation ====<br /> MD-Attractor is an open-source project designed to serve as a unified aggregator for music-related data sourced from various music platforms such as Spotify, Deezer, Apple Music, and more.<br /> <br /> The primary objective of this project is to provide a centralized hub for collecting and analyzing data from diverse media sources, offering users a comprehensive view of diverse music data sources.<br /> <br /> ===== Expected Results. =====<br /> 1. An extension of data sources and usage parameters. 2. Metadata artists and song analysis option. 3  Creation of artist's ego-networks and similarity component.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the proposal<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> Python, APIs, Visualization <br /> <br /> ==== Mentors: ====<br /> <br /> Panagiotis Sfendourakis, Elektra Bilali Simou<br /> <br /> <br /> == EDGAR-CRAWLER: Democratizing accessibility to Financial NLP documents ==<br /> <br /> ==== Brief Explanation ====<br /> Natural Language Processing (NLP) faces big challenges in the field of business and finance since financial text data is often locked behind paywalls, making it hard to get the information we need. This problem highlights why it is so important to automate data collection from free sources, like EDGAR, the U.S. Securities and Exchange Commission's public database. EDGAR contains documents about publicly traded stocks from companies in the U.S., like Microsoft, Google, or Amazon. Our existing software, EDGAR-CRAWLER, tackles this problem by automatically downloading and “cleaning” financial documents, mainly through the use of regular expressions, and then making such data available in an easy-to-use JSON format for NLP pipelines and applications. EDGAR-CRAWLER, with over 190 stars on Github, is the go-to toolkit for making financial data accessible to everyone. However, currently, it is only limited to one type of company filing, the annual reports (10-K filings). By adding support for more types of documents, EDGAR-CRAWLER plans to make financial information even more accessible, playing a vital role in the progress of financial NLP and open data.<br /> <br /> ===== Expected Results. =====<br /> • Extend EDGAR-CRAWLER to fetch more types of US company filings like quarterly reports (10-Q) and current reports (8-K), using string-searching algorithms like regular expressions. • Write documentation for these new features. • Develop unit tests for these new features.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Proficiency in Python and Software Development • Familiarity with regular expressions and Natural Language Processing (NLP) • Experience in web scraping and data processing (beautifulsoup, pandas). • Interest in Machine Learning (ML) <br /> <br /> ==== Mentors: ====<br /> <br /> Lefteris Loukas, Ion Androutsopoulos<br /> <br /> == Greeklish-to-Greek: Development of an open-source and state-of-the-art toolkit ==<br /> <br /> ==== Brief Explanation ====<br /> Greeklish” is a way of writing Greek with the commonly used Latin alphabet instead of the Greek alphabet (think of “kalimera” instead of “καλημέρα”), and has been widely used by Greek speakers for decades due to early technological limitations in supporting Greek characters. Its ease of use for informal digital communication, such as on forums or in comments, allows for quick language switching without changing any keyboard input, and hides spelling errors. However, Greeklish complicates the development of Natural Language Processing (NLP) tools for the Greek language because Machine Learning (ML) models are trained on standard Greek, not Greeklish. Most existing Greeklish-to-Greek toolkits are limited, rule-based, and closed-source. Our team has researched and developed state-of-the-art Greeklish-to-Greek methods utilizing Transformer NLP models. The purpose of the project is to develop an open-source, user-friendly Python toolkit, based on our existing research so that the community can benefit from it.<br /> The GSOC contributor will work in close collaboration between helvia.ai (https://helvia.ai/) and the AUEB NLP Group (http://nlp.cs.aueb.gr/software.html), leveraging both industrial and academic expertise to tackle these NLP challenges.<br /> <br /> ===== Expected Results. =====<br /> • Development of a user-friendly Python wrapper based on our best Greeklish-to-Greek NLP model  • Documentation and unit testing of the library • Development of new methods for Greeklish-to-Greek conversion, utilizing more recent and promising Large Language Models (LLMs), such as Meta’s LLaMA2 or Mistral models<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> While the proposed Greeklish toolkit can be developed independently, it could also be integrated as an extra functionality to the mentors’ affiliated current state-of-the-art Greek NLP toolkit: &lt;nowiki&gt;https://github.com/nlpaueb/gr-nlp-toolkit&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Advanced Python Programming and Software Engineering • Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)<br /> <br /> ==== Mentors: ====<br /> John Pavlopoulos (AUEB), Ion Androutsopoulos (AUEB), Stavros Vassos (helvia.ai), Lefteris Loukas (helvia.ai &amp;amp;amp;amp; AUEB)<br /> <br /> <br /> <br /> <br /> == Label Buddy 3.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021 and extended during GSoC 2022. It aims to make the annotation process easy, and simple with the help of AI Assistance tools and at the same time offers a well-defined manager-annotator-reviewer system. The purpose of this project is to investigate and integrate Multi-Modal Annotation Support in data other than only sound with the use of Large Language Models (LLMs) and Active Learning for Model Improvement on the already existing models.<br /> <br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/gsoc2022-Label-buddy<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI<br /> <br /> <br /> ==== Mentors: ====<br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> <br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ===== Background =====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ===== Project Description =====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://en.wikipedia.org/wiki/Triplestore Triplestore]<br /> - [https://en.wikipedia.org/wiki/Semantic_triple Triples]<br /> - [https://en.wikipedia.org/wiki/SPARQL Query language]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras, TBD<br /> <br /> <br /> == Font Validator: A System for Quality Control of Digital Typefaces Containing Greek Characters ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to develop a comprehensive set of tests for quality control on digital typefaces, with a particular focus on Greek characters. The project aims to ensure the consistency of typeface design, especially in terms of height, shape, composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters.<br /> <br /> ===== Background =====<br /> Digital typefaces play a crucial role in the readability and aesthetics of digital content. However, inconsistencies in typeface design, especially between different scripts like Latin and Greek, can lead to visual discomfort and confusion. Quality control in digital typefaces is therefore essential to ensure a consistent and pleasant reading experience.<br /> <br /> ===== Project Description =====<br /> This project will involve the development of a series of tests using Font Bakery or a similar tool. These tests will compare the height and shape of similarly shaped Latin and Greek letters, such as Latin A and Greek Alpha.<br /> <br /> Further tests will be performed on composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters. The goal is to identify any inconsistencies and provide recommendations for improvements.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Study the design principles of Latin and Greek typefaces. Identify the key parameters for comparison, such as height, shape, composites, ascenders, descenders, spacing, and kerning.<br /> <br /> ''Test Development:'' Develop a series of tests using Font Bakery or a similar tool. These tests should be able to compare the identified parameters between Latin and Greek characters.<br /> <br /> ''Testing:'' Perform the developed tests on a variety of digital typefaces.<br /> <br /> ''Analysis:'' Analyze the test results to identify any inconsistencies in the design of Latin and Greek characters.<br /> <br /> ''Recommendations:'' Based on the analysis, provide recommendations for improving the consistency of typeface design.<br /> <br /> ''Documentation:'' Document the findings, the developed tests, and the recommendations.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a comprehensive set of tests for quality control on digital typefaces, especially for Greek characters. These tests will help typeface designers and developers ensure the consistency of their designs, thereby improving the readability and aesthetics of digital content.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the quality of digital typefaces but also contribute to the body of knowledge in the field of typeface design. It will provide valuable insights into the design principles of Latin and Greek characters and help ensure their consistency in digital typefaces.<br /> <br /> ==== Duration of the Project ====<br /> Can be either short (175 hours) or long (350 hours), depending on the agreed-upon scope.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://font-bakery.readthedocs.io/en/latest/ Font Bakery documentation]<br /> - [https://github.com/fonttools/fontbakery Font Bakery source repo]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, font technologies, understanding of Greek characters<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Emilios Theofanous, Alexios Zavras<br /> <br /> <br /> == Development and Enhancement of the Cloud-Based FOSSBot Platform ==<br /> <br /> ==== Brief Explanation ====<br /> The evolution of the DIY robot kit for educators, initiated in GSoC 2019 with Christos Chronis, has significantly progressed over the years, culminating in a 3D printable, modular, and low-cost educational robot. Over the last five years, contributors from GSOC and the open source community have enriched this project with a programming stack compatible with Google Blockly, native Python through Monaco, and a Docker-based deployment system. At the same time, the initial DIY robot kit from GSOC 2019 transformed into the FOSSBot, and more educators understand and use this open-source solution to teach STEM.<br /> <br /> For GSoC 2023, the focus was on shifting the programming stack to the cloud, addressing issues like connectivity, updates, and the rising costs of Single Board Computers. Another notable addition was the web-based simulator, which allowed educators to test and use the robot virtually, reducing cost barriers and hardware requirements. In the following months, the platform will be available to all through the infrastructure of GFOSS, and at the same time, organized workshops will help make more people aware of the robot. Finally, in the last two years, two scientific publications were released, and the project started to attract the interest of the academic community.<br /> <br /> GSoC 2024 Objectives<br /> <br /> In 2024, our goal is to further enhance this cloud-based platform by:<br /> <br /> Performance Optimization: Improving both front-end and back-end performance for a seamless user experience.<br /> Development of Cooperative Modes: Enabling multiple robots to interact and collaborate in shared tasks or scenarios, fostering teamwork and advanced programming skills.<br /> Educator's Content Creation and Upload System: Developing a system where educators can create, share, and upload educational material, facilitating a dynamic and evolving educational environment.<br /> Custom Scene Creation for Godot Simulator: Integrating an interface for creating new, customized scenes in the existing Godot-based simulator, enabling tailored educational experiences.<br /> Platform Support for Physical Hardware: Introducing programming support for physical devices like the FOSSBot, Arduino, and MicroPython-supported microcontrollers, broadening the scope of practical applications and hands-on learning.<br /> Continuous Integration and Deployment (CI/CD) Enhancements: Streamlining updates and maintenance through advanced GitHub automation and cloud deployment strategies.<br /> Extensive Documentation: Ensuring comprehensive documentation to facilitate ease of use and adaptability for educators and developers.<br /> <br /> ===== Expected Results. =====<br /> A robust, cloud-based platform offering a wide range of educational and programming possibilities.<br /> Enhanced user experience with improved performance and new features.<br /> Greater accessibility and cost-effectiveness for educators worldwide.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> <br /> ==== Related repositories ==== <br /> https://github.com/eellak/gsoc2019-diyrobot<br /> https://github.com/eellak/fossbot<br /> https://github.com/chronis10/fossbot-app<br /> https://github.com/eellak/fossbot-platform<br /> https://github.com/eellak/fossbot-source<br /> https://github.com/eellak/fossbot-web-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js<br /> Python<br /> Flask API / FastAPI<br /> SQLAlchemy<br /> Godot (not mandatory)<br /> JavaScript<br /> Docker<br /> Git<br /> <br /> ==== Related publications ==== <br /> Chronis C., &amp; Varlamis I. (2022). FOSSBot: An Open Source and Open Design Educational Robot. Electronics, 11(16), 2606. https://www.mdpi.com/2079-9292/11/16/2606<br /> Kazazis G., Chronis C., Diou C. &amp; Varlamis I. Development and evaluation of Reinforcement Learning models for the FOSSBot Open-Source educational robot, Pan-Hellenic Conference on Progress in Computing and Informatics, ACM 2023 (Under Publication) <br /> <br /> <br /> ==== Mentors: ====<br /> Christos Chronis, Iraklow Varlamis<br /> <br /> <br /> <br /> == Enabling Apothesis to support atomic layer deposition and etching processes ==<br /> <br /> ==== Brief Explanation ====<br /> Αpothesis is an open source software for designing, simulating and analyzing deposition processes. It is based on Kinetic Monte Carlo method and its two main components are the lattice (simple cubic, HPC etc) where particular processes (adsorption, desorption, surface rection and diffusion) are performed. Up until now Apothesis has been used in various applications (see for example https://iopscience.iop.org/article/10.1088/1361-651X/ace276/meta). However, there is a lack of generalized software for atomic layer deposition/etching (ALD/ALE) processes. ALD/ALE are based on pulses performing over sequentially over a certain period of time (see https://www.frontiersin.org/articles/10.3389/fphy.2021.631918/full for more details). That said, the purpose of this project is to enable Apothesis to handle ALD/ALE cases. For that, there various parts of Apothesis that need to be changed with the most basic being: <br /> 1. Enabling Apothesis to read the lattice from a file. Thus creating a generalized reader for hard copy lattices. <br /> 2. Creating the sequentially calls to simulate ALD/ALE processes. <br /> 3. Design the output of the ALD/ALE process. <br /> <br /> <br /> ===== Expected Results. =====<br /> The results from a simple case of ALD in a simple cubic lattice. <br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, desing patters (factory pattern) and basic physics in deposition processes <br /> <br /> ==== Mentors: ====<br /> Cheimarios Nikolaos, Vissarion Fysikopoulos <br /> <br /> <br /> <br /> == eCodeOrama, an educational interactive flow visualization tool for mit scratch programs ==<br /> <br /> ==== Brief Explanation ====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ==== Expected Results ====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> git@github.com:sarantos40/eCodeOrama.git<br /> <br /> ==== Knowledge Prerequisites ====<br /> python, mit scratch, gui development<br /> <br /> ==== Mentors: ====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> <br /> == Enhancement of SmAuto DSL and integration into HomeAssistant ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto is a Domain Specific Language (DSL) that enables users to program complex automation scenarios, for connected IoT devices in smart environments, that go beyond simple tasks. SmAuto lacks extra features like invocation of REST/MQTT services, time delays etc., thus it should be expanded towards this direction. Furthermore, HomeAssistant would benefit from the integration of a DSL like SMAuto for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> ==== Expected Results ====<br /> In the context of this project, we desire to expand the SmAuto DSL with the following features: a) adding virtual entities, like REST calls and MQTT RPCs, so as for the automations to be able to access information from external services, b) incorporate auxiliary concepts like Delay, Conditions/Switches, or Compute nodes and c) generalize the language’s Condition concept. Furthermore, the integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA plugin, where each user can declare SmAuto automations and deploy them locally in HA.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python, Software engineering, IoT concepts, Unix/Linux. Desired: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> <br /> ==== Mentors: ====<br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == Creating a factory pattern for handling lattices in Apothesis. ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is a generalized software for designing, simulating and analyzing deposition processes. It is based on the kinetic Monte Carlo method. That said, it consists of two main components; a lattice (e.g. simple cubic, HPC etc) and the processes (adsorption, desorption, diffusion and surface reactions) performed in this lattice. Currently, the lattices are hard coded inside Apothesis making it difficult to add new ones easily. This proposal is focuses on creating a factory pattern which be used as a guide for adding lattices in Apothesis. Then this will be used to incorporate 2D lattices (like graphene) inside Apothesis.<br /> <br /> ==== Expected Results ====<br /> The factory pattern source code and a simple example for incorporating a graphene lattice. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, design (factory pattern)<br /> <br /> <br /> ==== Mentors: ====<br /> Nikolaos Cheimarios, Christina-Anna Gatsiou <br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ===== Expected Results. =====<br /> 1. We want to add Sanity CMS(https://www.sanity.io/) for data changing frequently. 2. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter. 3. Rewrite in Typescript for maintainability<br /> <br /> Whoever is interested in talking with the initial core contributors can also find us on https://my.uom.gr/about and https://opensource.uom.gr to join our discord and follow us on social media.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js, Java(Spring Boot),Typescript, Next.js(Optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> [[Κατηγορία:GSOC2024]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2024_proposed_ideas&diff=2244 2024-02-05T07:02:14Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> <br /> == EDGAR-CRAWLER: Democratizing accessibility to Financial NLP documents ==<br /> <br /> ==== Brief Explanation ====<br /> Natural Language Processing (NLP) faces big challenges in the field of business and finance since financial text data is often locked behind paywalls, making it hard to get the information we need. This problem highlights why it is so important to automate data collection from free sources, like EDGAR, the U.S. Securities and Exchange Commission's public database. EDGAR contains documents about publicly traded stocks from companies in the U.S., like Microsoft, Google, or Amazon. Our existing software, EDGAR-CRAWLER, tackles this problem by automatically downloading and “cleaning” financial documents, mainly through the use of regular expressions, and then making such data available in an easy-to-use JSON format for NLP pipelines and applications. EDGAR-CRAWLER, with over 190 stars on Github, is the go-to toolkit for making financial data accessible to everyone. However, currently, it is only limited to one type of company filing, the annual reports (10-K filings). By adding support for more types of documents, EDGAR-CRAWLER plans to make financial information even more accessible, playing a vital role in the progress of financial NLP and open data.<br /> <br /> ===== Expected Results. =====<br /> • Extend EDGAR-CRAWLER to fetch more types of US company filings like quarterly reports (10-Q) and current reports (8-K), using string-searching algorithms like regular expressions. • Write documentation for these new features. • Develop unit tests for these new features.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nlpaueb/edgar-crawler/<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Proficiency in Python and Software Development • Familiarity with regular expressions and Natural Language Processing (NLP) • Experience in web scraping and data processing (beautifulsoup, pandas). • Interest in Machine Learning (ML) <br /> <br /> ==== Mentors: ====<br /> <br /> Lefteris Loukas, Ion Androutsopoulos<br /> <br /> == Greeklish-to-Greek: Development of an open-source and state-of-the-art toolkit ==<br /> <br /> ==== Brief Explanation ====<br /> Greeklish” is a way of writing Greek with the commonly used Latin alphabet instead of the Greek alphabet (think of “kalimera” instead of “καλημέρα”), and has been widely used by Greek speakers for decades due to early technological limitations in supporting Greek characters. Its ease of use for informal digital communication, such as on forums or in comments, allows for quick language switching without changing any keyboard input, and hides spelling errors. However, Greeklish complicates the development of Natural Language Processing (NLP) tools for the Greek language because Machine Learning (ML) models are trained on standard Greek, not Greeklish. Most existing Greeklish-to-Greek toolkits are limited, rule-based, and closed-source. Our team has researched and developed state-of-the-art Greeklish-to-Greek methods utilizing Transformer NLP models. The purpose of the project is to develop an open-source, user-friendly Python toolkit, based on our existing research so that the community can benefit from it.<br /> The GSOC contributor will work in close collaboration between helvia.ai (https://helvia.ai/) and the AUEB NLP Group (http://nlp.cs.aueb.gr/software.html), leveraging both industrial and academic expertise to tackle these NLP challenges.<br /> <br /> ===== Expected Results. =====<br /> • Development of a user-friendly Python wrapper based on our best Greeklish-to-Greek NLP model  • Documentation and unit testing of the library • Development of new methods for Greeklish-to-Greek conversion, utilizing more recent and promising Large Language Models (LLMs), such as Meta’s LLaMA2 or Mistral models<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> While the proposed Greeklish toolkit can be developed independently, it could also be integrated as an extra functionality to the mentors’ affiliated current state-of-the-art Greek NLP toolkit: &lt;nowiki&gt;https://github.com/nlpaueb/gr-nlp-toolkit&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Advanced Python Programming and Software Engineering • Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)<br /> <br /> ==== Mentors: ====<br /> John Pavlopoulos (AUEB), Ion Androutsopoulos (AUEB), Stavros Vassos (helvia.ai), Lefteris Loukas (helvia.ai &amp;amp;amp;amp; AUEB)<br /> <br /> <br /> <br /> <br /> == Label Buddy 3.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021 and extended during GSoC 2022. It aims to make the annotation process easy, and simple with the help of AI Assistance tools and at the same time offers a well-defined manager-annotator-reviewer system. The purpose of this project is to investigate and integrate Multi-Modal Annotation Support in data other than only sound with the use of Large Language Models (LLMs) and Active Learning for Model Improvement on the already existing models.<br /> <br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/gsoc2022-Label-buddy<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI<br /> <br /> <br /> ==== Mentors: ====<br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> <br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ===== Background =====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ===== Project Description =====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://en.wikipedia.org/wiki/Triplestore Triplestore]<br /> - [https://en.wikipedia.org/wiki/Semantic_triple Triples]<br /> - [https://en.wikipedia.org/wiki/SPARQL Query language]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras, TBD<br /> <br /> <br /> == Font Validator: A System for Quality Control of Digital Typefaces Containing Greek Characters ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to develop a comprehensive set of tests for quality control on digital typefaces, with a particular focus on Greek characters. The project aims to ensure the consistency of typeface design, especially in terms of height, shape, composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters.<br /> <br /> ===== Background =====<br /> Digital typefaces play a crucial role in the readability and aesthetics of digital content. However, inconsistencies in typeface design, especially between different scripts like Latin and Greek, can lead to visual discomfort and confusion. Quality control in digital typefaces is therefore essential to ensure a consistent and pleasant reading experience.<br /> <br /> ===== Project Description =====<br /> This project will involve the development of a series of tests using Font Bakery or a similar tool. These tests will compare the height and shape of similarly shaped Latin and Greek letters, such as Latin A and Greek Alpha.<br /> <br /> Further tests will be performed on composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters. The goal is to identify any inconsistencies and provide recommendations for improvements.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Study the design principles of Latin and Greek typefaces. Identify the key parameters for comparison, such as height, shape, composites, ascenders, descenders, spacing, and kerning.<br /> <br /> ''Test Development:'' Develop a series of tests using Font Bakery or a similar tool. These tests should be able to compare the identified parameters between Latin and Greek characters.<br /> <br /> ''Testing:'' Perform the developed tests on a variety of digital typefaces.<br /> <br /> ''Analysis:'' Analyze the test results to identify any inconsistencies in the design of Latin and Greek characters.<br /> <br /> ''Recommendations:'' Based on the analysis, provide recommendations for improving the consistency of typeface design.<br /> <br /> ''Documentation:'' Document the findings, the developed tests, and the recommendations.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a comprehensive set of tests for quality control on digital typefaces, especially for Greek characters. These tests will help typeface designers and developers ensure the consistency of their designs, thereby improving the readability and aesthetics of digital content.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the quality of digital typefaces but also contribute to the body of knowledge in the field of typeface design. It will provide valuable insights into the design principles of Latin and Greek characters and help ensure their consistency in digital typefaces.<br /> <br /> ==== Duration of the Project ====<br /> Can be either short (175 hours) or long (350 hours), depending on the agreed-upon scope.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://font-bakery.readthedocs.io/en/latest/ Font Bakery documentation]<br /> - [https://github.com/fonttools/fontbakery Font Bakery source repo]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, font technologies, understanding of Greek characters<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Emilios Theofanous, Alexios Zavras<br /> <br /> <br /> == Development and Enhancement of the Cloud-Based FOSSBot Platform ==<br /> <br /> ==== Brief Explanation ====<br /> The evolution of the DIY robot kit for educators, initiated in GSoC 2019 with Christos Chronis, has significantly progressed over the years, culminating in a 3D printable, modular, and low-cost educational robot. Over the last five years, contributors from GSOC and the open source community have enriched this project with a programming stack compatible with Google Blockly, native Python through Monaco, and a Docker-based deployment system. At the same time, the initial DIY robot kit from GSOC 2019 transformed into the FOSSBot, and more educators understand and use this open-source solution to teach STEM.<br /> <br /> For GSoC 2023, the focus was on shifting the programming stack to the cloud, addressing issues like connectivity, updates, and the rising costs of Single Board Computers. Another notable addition was the web-based simulator, which allowed educators to test and use the robot virtually, reducing cost barriers and hardware requirements. In the following months, the platform will be available to all through the infrastructure of GFOSS, and at the same time, organized workshops will help make more people aware of the robot. Finally, in the last two years, two scientific publications were released, and the project started to attract the interest of the academic community.<br /> <br /> GSoC 2024 Objectives<br /> <br /> In 2024, our goal is to further enhance this cloud-based platform by:<br /> <br /> Performance Optimization: Improving both front-end and back-end performance for a seamless user experience.<br /> Development of Cooperative Modes: Enabling multiple robots to interact and collaborate in shared tasks or scenarios, fostering teamwork and advanced programming skills.<br /> Educator's Content Creation and Upload System: Developing a system where educators can create, share, and upload educational material, facilitating a dynamic and evolving educational environment.<br /> Custom Scene Creation for Godot Simulator: Integrating an interface for creating new, customized scenes in the existing Godot-based simulator, enabling tailored educational experiences.<br /> Platform Support for Physical Hardware: Introducing programming support for physical devices like the FOSSBot, Arduino, and MicroPython-supported microcontrollers, broadening the scope of practical applications and hands-on learning.<br /> Continuous Integration and Deployment (CI/CD) Enhancements: Streamlining updates and maintenance through advanced GitHub automation and cloud deployment strategies.<br /> Extensive Documentation: Ensuring comprehensive documentation to facilitate ease of use and adaptability for educators and developers.<br /> <br /> ===== Expected Results. =====<br /> A robust, cloud-based platform offering a wide range of educational and programming possibilities.<br /> Enhanced user experience with improved performance and new features.<br /> Greater accessibility and cost-effectiveness for educators worldwide.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> <br /> ==== Related repositories ==== <br /> https://github.com/eellak/gsoc2019-diyrobot<br /> https://github.com/eellak/fossbot<br /> https://github.com/chronis10/fossbot-app<br /> https://github.com/eellak/fossbot-platform<br /> https://github.com/eellak/fossbot-source<br /> https://github.com/eellak/fossbot-web-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js<br /> Python<br /> Flask API / FastAPI<br /> SQLAlchemy<br /> Godot (not mandatory)<br /> JavaScript<br /> Docker<br /> Git<br /> <br /> ==== Related publications ==== <br /> Chronis C., &amp; Varlamis I. (2022). FOSSBot: An Open Source and Open Design Educational Robot. Electronics, 11(16), 2606. https://www.mdpi.com/2079-9292/11/16/2606<br /> Kazazis G., Chronis C., Diou C. &amp; Varlamis I. Development and evaluation of Reinforcement Learning models for the FOSSBot Open-Source educational robot, Pan-Hellenic Conference on Progress in Computing and Informatics, ACM 2023 (Under Publication) <br /> <br /> <br /> ==== Mentors: ====<br /> Christos Chronis, Iraklow Varlamis<br /> <br /> <br /> <br /> == Enabling Apothesis to support atomic layer deposition and etching processes ==<br /> <br /> ==== Brief Explanation ====<br /> Αpothesis is an open source software for designing, simulating and analyzing deposition processes. It is based on Kinetic Monte Carlo method and its two main components are the lattice (simple cubic, HPC etc) where particular processes (adsorption, desorption, surface rection and diffusion) are performed. Up until now Apothesis has been used in various applications (see for example https://iopscience.iop.org/article/10.1088/1361-651X/ace276/meta). However, there is a lack of generalized software for atomic layer deposition/etching (ALD/ALE) processes. ALD/ALE are based on pulses performing over sequentially over a certain period of time (see https://www.frontiersin.org/articles/10.3389/fphy.2021.631918/full for more details). That said, the purpose of this project is to enable Apothesis to handle ALD/ALE cases. For that, there various parts of Apothesis that need to be changed with the most basic being: <br /> 1. Enabling Apothesis to read the lattice from a file. Thus creating a generalized reader for hard copy lattices. <br /> 2. Creating the sequentially calls to simulate ALD/ALE processes. <br /> 3. Design the output of the ALD/ALE process. <br /> <br /> <br /> ===== Expected Results. =====<br /> The results from a simple case of ALD in a simple cubic lattice. <br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, desing patters (factory pattern) and basic physics in deposition processes <br /> <br /> ==== Mentors: ====<br /> Cheimarios Nikolaos, Vissarion Fysikopoulos <br /> <br /> <br /> <br /> == eCodeOrama, an educational interactive flow visualization tool for mit scratch programs ==<br /> <br /> ==== Brief Explanation ====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ==== Expected Results ====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> git@github.com:sarantos40/eCodeOrama.git<br /> <br /> ==== Knowledge Prerequisites ====<br /> python, mit scratch, gui development<br /> <br /> ==== Mentors: ====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> <br /> == Enhancement of SmAuto DSL and integration into HomeAssistant ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto is a Domain Specific Language (DSL) that enables users to program complex automation scenarios, for connected IoT devices in smart environments, that go beyond simple tasks. SmAuto lacks extra features like invocation of REST/MQTT services, time delays etc., thus it should be expanded towards this direction. Furthermore, HomeAssistant would benefit from the integration of a DSL like SMAuto for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> ==== Expected Results ====<br /> In the context of this project, we desire to expand the SmAuto DSL with the following features: a) adding virtual entities, like REST calls and MQTT RPCs, so as for the automations to be able to access information from external services, b) incorporate auxiliary concepts like Delay, Conditions/Switches, or Compute nodes and c) generalize the language’s Condition concept. Furthermore, the integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA plugin, where each user can declare SmAuto automations and deploy them locally in HA.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python, Software engineering, IoT concepts, Unix/Linux. Desired: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> <br /> ==== Mentors: ====<br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == Creating a factory pattern for handling lattices in Apothesis. ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is a generalized software for designing, simulating and analyzing deposition processes. It is based on the kinetic Monte Carlo method. That said, it consists of two main components; a lattice (e.g. simple cubic, HPC etc) and the processes (adsorption, desorption, diffusion and surface reactions) performed in this lattice. Currently, the lattices are hard coded inside Apothesis making it difficult to add new ones easily. This proposal is focuses on creating a factory pattern which be used as a guide for adding lattices in Apothesis. Then this will be used to incorporate 2D lattices (like graphene) inside Apothesis.<br /> <br /> ==== Expected Results ====<br /> The factory pattern source code and a simple example for incorporating a graphene lattice. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, design (factory pattern)<br /> <br /> <br /> ==== Mentors: ====<br /> Nikolaos Cheimarios, Christina-Anna Gatsiou <br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ===== Expected Results. =====<br /> 1. We want to add Sanity CMS(https://www.sanity.io/) for data changing frequently. 2. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter. 3. Rewrite in Typescript for maintainability<br /> <br /> Whoever is interested in talking with the initial core contributors can also find us on https://my.uom.gr/about and https://opensource.uom.gr to join our discord and follow us on social media.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js, Java(Spring Boot),Typescript, Next.js(Optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> [[Κατηγορία:GSOC2024]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2024_proposed_ideas&diff=2243 2024-02-05T06:55:43Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> <br /> == Greeklish-to-Greek: Development of an open-source and state-of-the-art toolkit ==<br /> <br /> ==== Brief Explanation ====<br /> Greeklish” is a way of writing Greek with the commonly used Latin alphabet instead of the Greek alphabet (think of “kalimera” instead of “καλημέρα”), and has been widely used by Greek speakers for decades due to early technological limitations in supporting Greek characters. Its ease of use for informal digital communication, such as on forums or in comments, allows for quick language switching without changing any keyboard input, and hides spelling errors. However, Greeklish complicates the development of Natural Language Processing (NLP) tools for the Greek language because Machine Learning (ML) models are trained on standard Greek, not Greeklish. Most existing Greeklish-to-Greek toolkits are limited, rule-based, and closed-source. Our team has researched and developed state-of-the-art Greeklish-to-Greek methods utilizing Transformer NLP models. The purpose of the project is to develop an open-source, user-friendly Python toolkit, based on our existing research so that the community can benefit from it.<br /> The GSOC contributor will work in close collaboration between helvia.ai (https://helvia.ai/) and the AUEB NLP Group (http://nlp.cs.aueb.gr/software.html), leveraging both industrial and academic expertise to tackle these NLP challenges.<br /> <br /> ===== Expected Results. =====<br /> • Development of a user-friendly Python wrapper based on our best Greeklish-to-Greek NLP model  • Documentation and unit testing of the library • Development of new methods for Greeklish-to-Greek conversion, utilizing more recent and promising Large Language Models (LLMs), such as Meta’s LLaMA2 or Mistral models<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> While the proposed Greeklish toolkit can be developed independently, it could also be integrated as an extra functionality to the mentors’ affiliated current state-of-the-art Greek NLP toolkit: &lt;nowiki&gt;https://github.com/nlpaueb/gr-nlp-toolkit&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> <br /> • Advanced Python Programming and Software Engineering • Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)<br /> <br /> ==== Mentors: ====<br /> John Pavlopoulos (AUEB), Ion Androutsopoulos (AUEB), Stavros Vassos (helvia.ai), Lefteris Loukas (helvia.ai &amp;amp;amp;amp; AUEB)<br /> <br /> <br /> <br /> <br /> == Label Buddy 3.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021 and extended during GSoC 2022. It aims to make the annotation process easy, and simple with the help of AI Assistance tools and at the same time offers a well-defined manager-annotator-reviewer system. The purpose of this project is to investigate and integrate Multi-Modal Annotation Support in data other than only sound with the use of Large Language Models (LLMs) and Active Learning for Model Improvement on the already existing models.<br /> <br /> <br /> ===== Expected Results. =====<br /> 1.) Add OpenAPI support; the ability to upload an OpenApi document and convert it to Flexbench test scenarios (ML can help!) 2.) Anonymization feature for exchanged data in requests and responses (ML can help!) 3.) Aesthetic and UX/UI fixes in desktop app 4.) Fixes to existing bugs 5.) Improve documentation<br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/eellak/gsoc2022-Label-buddy<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI<br /> <br /> <br /> ==== Mentors: ====<br /> Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)<br /> <br /> <br /> <br /> == Exploring and Abstracting Triplestore Alternatives ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to explore, analyze, and abstract various triplestore alternatives. The project aims to provide young programmers with a comprehensive understanding of different back-end alternatives that allow for storing data in triple format, commonly known as triplestores.<br /> <br /> ===== Background =====<br /> Triplestores are a type of database specialized in storing triples, a data structure for representing information in a subject-predicate-object format. They are crucial in semantic web technologies, such as RDF, SPARQL, and OWL. However, there are numerous triplestore alternatives available, each with its own strengths and weaknesses.<br /> <br /> ===== Project Description =====<br /> This project will involve a detailed exploration of various triplestore alternatives. The participants will perform rudimentary tests and benchmarks on these alternatives to understand their performance, scalability, and other key features.<br /> <br /> The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will &quot;hide&quot; the underlying implementation, allowing developers to switch between different triplestores without changing their application code. This abstraction layer can be compared to a library abstracting various specific relational database management systems, all providing very similar functionality, like supporting SQL.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.<br /> <br /> ''Testing:'' Perform rudimentary tests and benchmarks on the identified triplestore alternatives.<br /> <br /> ''Analysis:'' Analyze the test results to understand the performance and scalability of each alternative.<br /> <br /> ''Development:'' Develop an abstraction layer that can interface with the various triplestore alternatives.<br /> <br /> ''Documentation:'' Document the findings and the usage of the developed library.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a well-documented library that can act as an abstraction layer for various triplestore alternatives. This will provide developers with the flexibility to choose the most suitable triplestore for their specific needs without having to modify their application code.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the understanding of participants about triplestore alternatives but also equip them with the skills to develop an abstraction layer, thereby broadening their programming skills and knowledge.<br /> <br /> ==== Duration of the Project ====<br /> Long (350 hours)<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://en.wikipedia.org/wiki/Triplestore Triplestore]<br /> - [https://en.wikipedia.org/wiki/Semantic_triple Triples]<br /> - [https://en.wikipedia.org/wiki/SPARQL Query language]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras, TBD<br /> <br /> <br /> == Font Validator: A System for Quality Control of Digital Typefaces Containing Greek Characters ==<br /> <br /> ==== Brief Explanation ====<br /> <br /> ===== Objective =====<br /> The primary objective of this project is to develop a comprehensive set of tests for quality control on digital typefaces, with a particular focus on Greek characters. The project aims to ensure the consistency of typeface design, especially in terms of height, shape, composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters.<br /> <br /> ===== Background =====<br /> Digital typefaces play a crucial role in the readability and aesthetics of digital content. However, inconsistencies in typeface design, especially between different scripts like Latin and Greek, can lead to visual discomfort and confusion. Quality control in digital typefaces is therefore essential to ensure a consistent and pleasant reading experience.<br /> <br /> ===== Project Description =====<br /> This project will involve the development of a series of tests using Font Bakery or a similar tool. These tests will compare the height and shape of similarly shaped Latin and Greek letters, such as Latin A and Greek Alpha.<br /> <br /> Further tests will be performed on composites, ascenders, descenders, spacing, and kerning between Latin and Greek characters. The goal is to identify any inconsistencies and provide recommendations for improvements.<br /> <br /> ===== Methodology =====<br /> <br /> ''Research:'' Study the design principles of Latin and Greek typefaces. Identify the key parameters for comparison, such as height, shape, composites, ascenders, descenders, spacing, and kerning.<br /> <br /> ''Test Development:'' Develop a series of tests using Font Bakery or a similar tool. These tests should be able to compare the identified parameters between Latin and Greek characters.<br /> <br /> ''Testing:'' Perform the developed tests on a variety of digital typefaces.<br /> <br /> ''Analysis:'' Analyze the test results to identify any inconsistencies in the design of Latin and Greek characters.<br /> <br /> ''Recommendations:'' Based on the analysis, provide recommendations for improving the consistency of typeface design.<br /> <br /> ''Documentation:'' Document the findings, the developed tests, and the recommendations.<br /> <br /> ===== Expected Outcome =====<br /> By the end of the project, we expect to have a comprehensive set of tests for quality control on digital typefaces, especially for Greek characters. These tests will help typeface designers and developers ensure the consistency of their designs, thereby improving the readability and aesthetics of digital content.<br /> <br /> ===== Conclusion =====<br /> This project will not only enhance the quality of digital typefaces but also contribute to the body of knowledge in the field of typeface design. It will provide valuable insights into the design principles of Latin and Greek characters and help ensure their consistency in digital typefaces.<br /> <br /> ==== Duration of the Project ====<br /> Can be either short (175 hours) or long (350 hours), depending on the agreed-upon scope.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ===== Information links =====<br /> - [https://font-bakery.readthedocs.io/en/latest/ Font Bakery documentation]<br /> - [https://github.com/fonttools/fontbakery Font Bakery source repo]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, font technologies, understanding of Greek characters<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Emilios Theofanous, Alexios Zavras<br /> <br /> <br /> == Development and Enhancement of the Cloud-Based FOSSBot Platform ==<br /> <br /> ==== Brief Explanation ====<br /> The evolution of the DIY robot kit for educators, initiated in GSoC 2019 with Christos Chronis, has significantly progressed over the years, culminating in a 3D printable, modular, and low-cost educational robot. Over the last five years, contributors from GSOC and the open source community have enriched this project with a programming stack compatible with Google Blockly, native Python through Monaco, and a Docker-based deployment system. At the same time, the initial DIY robot kit from GSOC 2019 transformed into the FOSSBot, and more educators understand and use this open-source solution to teach STEM.<br /> <br /> For GSoC 2023, the focus was on shifting the programming stack to the cloud, addressing issues like connectivity, updates, and the rising costs of Single Board Computers. Another notable addition was the web-based simulator, which allowed educators to test and use the robot virtually, reducing cost barriers and hardware requirements. In the following months, the platform will be available to all through the infrastructure of GFOSS, and at the same time, organized workshops will help make more people aware of the robot. Finally, in the last two years, two scientific publications were released, and the project started to attract the interest of the academic community.<br /> <br /> GSoC 2024 Objectives<br /> <br /> In 2024, our goal is to further enhance this cloud-based platform by:<br /> <br /> Performance Optimization: Improving both front-end and back-end performance for a seamless user experience.<br /> Development of Cooperative Modes: Enabling multiple robots to interact and collaborate in shared tasks or scenarios, fostering teamwork and advanced programming skills.<br /> Educator's Content Creation and Upload System: Developing a system where educators can create, share, and upload educational material, facilitating a dynamic and evolving educational environment.<br /> Custom Scene Creation for Godot Simulator: Integrating an interface for creating new, customized scenes in the existing Godot-based simulator, enabling tailored educational experiences.<br /> Platform Support for Physical Hardware: Introducing programming support for physical devices like the FOSSBot, Arduino, and MicroPython-supported microcontrollers, broadening the scope of practical applications and hands-on learning.<br /> Continuous Integration and Deployment (CI/CD) Enhancements: Streamlining updates and maintenance through advanced GitHub automation and cloud deployment strategies.<br /> Extensive Documentation: Ensuring comprehensive documentation to facilitate ease of use and adaptability for educators and developers.<br /> <br /> ===== Expected Results. =====<br /> A robust, cloud-based platform offering a wide range of educational and programming possibilities.<br /> Enhanced user experience with improved performance and new features.<br /> Greater accessibility and cost-effectiveness for educators worldwide.<br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> <br /> ==== Related repositories ==== <br /> https://github.com/eellak/gsoc2019-diyrobot<br /> https://github.com/eellak/fossbot<br /> https://github.com/chronis10/fossbot-app<br /> https://github.com/eellak/fossbot-platform<br /> https://github.com/eellak/fossbot-source<br /> https://github.com/eellak/fossbot-web-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js<br /> Python<br /> Flask API / FastAPI<br /> SQLAlchemy<br /> Godot (not mandatory)<br /> JavaScript<br /> Docker<br /> Git<br /> <br /> ==== Related publications ==== <br /> Chronis C., &amp; Varlamis I. (2022). FOSSBot: An Open Source and Open Design Educational Robot. Electronics, 11(16), 2606. https://www.mdpi.com/2079-9292/11/16/2606<br /> Kazazis G., Chronis C., Diou C. &amp; Varlamis I. Development and evaluation of Reinforcement Learning models for the FOSSBot Open-Source educational robot, Pan-Hellenic Conference on Progress in Computing and Informatics, ACM 2023 (Under Publication) <br /> <br /> <br /> ==== Mentors: ====<br /> Christos Chronis, Iraklow Varlamis<br /> <br /> <br /> <br /> == Enabling Apothesis to support atomic layer deposition and etching processes ==<br /> <br /> ==== Brief Explanation ====<br /> Αpothesis is an open source software for designing, simulating and analyzing deposition processes. It is based on Kinetic Monte Carlo method and its two main components are the lattice (simple cubic, HPC etc) where particular processes (adsorption, desorption, surface rection and diffusion) are performed. Up until now Apothesis has been used in various applications (see for example https://iopscience.iop.org/article/10.1088/1361-651X/ace276/meta). However, there is a lack of generalized software for atomic layer deposition/etching (ALD/ALE) processes. ALD/ALE are based on pulses performing over sequentially over a certain period of time (see https://www.frontiersin.org/articles/10.3389/fphy.2021.631918/full for more details). That said, the purpose of this project is to enable Apothesis to handle ALD/ALE cases. For that, there various parts of Apothesis that need to be changed with the most basic being: <br /> 1. Enabling Apothesis to read the lattice from a file. Thus creating a generalized reader for hard copy lattices. <br /> 2. Creating the sequentially calls to simulate ALD/ALE processes. <br /> 3. Design the output of the ALD/ALE process. <br /> <br /> <br /> ===== Expected Results. =====<br /> The results from a simple case of ALD in a simple cubic lattice. <br /> <br /> <br /> ==== Duration of the Project ====<br /> (350 hours).<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, desing patters (factory pattern) and basic physics in deposition processes <br /> <br /> ==== Mentors: ====<br /> Cheimarios Nikolaos, Vissarion Fysikopoulos <br /> <br /> <br /> <br /> == eCodeOrama, an educational interactive flow visualization tool for mit scratch programs ==<br /> <br /> ==== Brief Explanation ====<br /> The project will create a interactive tool to extract, visualize graphically and edit (to improve the presentation of) the layout of the flow of code in blocks / scripts in a mit scratch program and their interaction with any messages or other external events. The tool will use rules to decide on many layout parameters (e.g. the position of the code blocks in the layout, the colors used, etc) but the user will be able to overwrite the default choices. The presentation will be compatible with the codeOrama code layout specification.<br /> The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids.<br /> The students can use this flow to better visualize and understand their program, to explain it to others, to debug it and to design extensions and modifications.<br /> <br /> <br /> ==== Expected Results ====<br /> A tool to visualize and edit the layout of the event based script flow of a scratch program, keeping it compatible with the codeOrama code layout specification.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> git@github.com:sarantos40/eCodeOrama.git<br /> <br /> ==== Knowledge Prerequisites ====<br /> python, mit scratch, gui development<br /> <br /> ==== Mentors: ====<br /> Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis<br /> <br /> <br /> == Enhancement of SmAuto DSL and integration into HomeAssistant ==<br /> <br /> ==== Brief Explanation ====<br /> Smart environments are becoming quite popular in the home setting consisting of a broad range of connected devices. While offering a novel set of possibilities, this also contributes to the complexity of the environment, posing new challenges to allowing the full potential of a sensorized home to be made available to users. SmAuto is a Domain Specific Language (DSL) that enables users to program complex automation scenarios, for connected IoT devices in smart environments, that go beyond simple tasks. SmAuto lacks extra features like invocation of REST/MQTT services, time delays etc., thus it should be expanded towards this direction. Furthermore, HomeAssistant would benefit from the integration of a DSL like SMAuto for rapidly developing and deploying automations, using the entities existing in a smart environment.<br /> ==== Expected Results ====<br /> In the context of this project, we desire to expand the SmAuto DSL with the following features: a) adding virtual entities, like REST calls and MQTT RPCs, so as for the automations to be able to access information from external services, b) incorporate auxiliary concepts like Delay, Conditions/Switches, or Compute nodes and c) generalize the language’s Condition concept. Furthermore, the integration of SmAuto and HomeAssistant should occur, by creating a new open-source HA plugin, where each user can declare SmAuto automations and deploy them locally in HA.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python, Software engineering, IoT concepts, Unix/Linux. Desired: Model Driven Engineering, HomeAssistant, Docker<br /> <br /> <br /> ==== Mentors: ====<br /> Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis<br /> <br /> == Creating a factory pattern for handling lattices in Apothesis. ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is a generalized software for designing, simulating and analyzing deposition processes. It is based on the kinetic Monte Carlo method. That said, it consists of two main components; a lattice (e.g. simple cubic, HPC etc) and the processes (adsorption, desorption, diffusion and surface reactions) performed in this lattice. Currently, the lattices are hard coded inside Apothesis making it difficult to add new ones easily. This proposal is focuses on creating a factory pattern which be used as a guide for adding lattices in Apothesis. Then this will be used to incorporate 2D lattices (like graphene) inside Apothesis.<br /> <br /> ==== Expected Results ====<br /> The factory pattern source code and a simple example for incorporating a graphene lattice. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, design (factory pattern)<br /> <br /> <br /> ==== Mentors: ====<br /> Nikolaos Cheimarios, Christina-Anna Gatsiou <br /> <br /> <br /> == MyUni ==<br /> <br /> ==== Brief Explanation ====<br /> Currently there is a University App called MyUoM for Greek universities in https://my.uom.gr/ (followed by an effort in University of West Attica, https://iam.uniwa.gr/. This app is official but it lacks features(e.g. login) and a proper backend with an architecture that will allow different implementations for Universities. In this project we want to add a CMS for info that is changing frequently and a backend that fetches realtime info from the official websites. We want to unify those efforts and make it easier for other universities to join.<br /> <br /> <br /> <br /> ===== Expected Results. =====<br /> 1. We want to add Sanity CMS(https://www.sanity.io/) for data changing frequently. 2. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter. 3. Rewrite in Typescript for maintainability<br /> <br /> Whoever is interested in talking with the initial core contributors can also find us on https://my.uom.gr/about and https://opensource.uom.gr to join our discord and follow us on social media.<br /> <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope<br /> <br /> ==== Related repositories ====<br /> https://github.com/Open-Source-UoM/MyUoM<br /> <br /> ==== Knowledge Prerequisites ====<br /> React.js, Java(Spring Boot),Typescript, Next.js(Optional)<br /> <br /> <br /> ==== Mentors: ====<br /> Efstathios Iosifidis eiosifidis@gmail.com<br /> <br /> [[Κατηγορία:GSOC2024]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%A0%CF%81%CE%BF%CF%84%CE%AC%CF%83%CE%B5%CE%B9%CF%82_%CE%AD%CF%81%CE%B3%CF%89%CE%BD&diff=2187 2023-03-14T12:12:54Z

<p>Pkst-1: </p> <hr /> <div><br /> <br /> === Fossbot ===<br /> Η ΕΕΛΛΑΚ σε συνεργασία με το Χαροκόπειο Πανεπιστήμιο έχουν σχεδιάσει και αναπτύξει το Fossbot, ένα Εκπαιδευτικό Ρομπότ Ανοικτού Κώδικα και Σχεδίων. Όλα τα 3D σχέδια και ο κώδικας για τη λειτουργία του ρομπότ είναι ελεύθερα διαθέσιμα στο GitHub της ΕΕΛΛΑΚ (&lt;nowiki&gt;https://github.com/eellak/fossbot&lt;/nowiki&gt;) και επιτρέπουν στον καθένα να τα κατεβάσει, να τυπώσει το ρομπότ, να συναρμολογήσει τα ηλεκτρονικά του τμήματα, να εγκαταστήσει το λογισμικό λειτουργίας του αλλά και να το βελτιώσει.<br /> <br /> Η λειτουργία του ρομπότ βασίζεται σε Raspberry Pi και μια στοίβα λογισμικού σε Python που καταλήγει σε μια γραφική διεπαφή προγραμματισμού του ρομπότ με το Blockly. <br /> <br /> '''https://github.com/eellak/fossbot'''&lt;p&gt;<br /> Υποέργο 1) Ανασχεδιασμός της ηλεκτρονικής διάταξης του Fossbot για μαζική παραγωγή&lt;/p&gt;&lt;p&gt;<br /> Υποέργο 2) Ανασχεδιασμός της ηλεκτρονικής διάταξης του Fossbot για DIY παραγωγή&lt;/p&gt;<br /> <br /> '''Επιβλέπων: Ηρακλής Βαρλάμης'''<br /> <br /> === Επέκταση του Αlexandria3k ===<br /> '''https://github.com/dspinellis/alexandria3k'''&lt;p&gt;<br /> Πρόσθετες ανοιχτές βάσεις δεδομένων (διπλώματα ευρεσιτεχνίας, MEDLINE/PubMed), αλγορίθμους κατηγοριοποίησης με βάση το θέμα, ταίριασμα συγγραφέων και ιδρυμάτων και πρόσθετα παραδείγματα χρήσης.&lt;/p&gt;<br /> <br /> '''Επιβλέπων: Διομήδης Σπινέλης'''<br /> <br /> === MD Guide ===<br /> '''https://github.com/md-guide/md-guide'''&lt;p&gt;Το MD Guide πρόκειται για ένα εργαλείο συγγραφής τεχνικής τεκμηρίωσης και οδηγών (technical documentation) το οποίο βασίζεται στη γλώσσα περιγραφής Markdown. Το Markdown είναι ιδιαίτερα δημοφιλές τα τελευταία χρόνια, ειδικά μετά την κυριάρχηση του Github και του Open source τη δεκαετία του 2010-2020 και έχει γίνει πλέον η κατ'εξοχήν γλώσσα επιλογής για προγραμματιστές που θέλουν να μορφοποιήσουν εύκολα και γρήγορα τα κείμενα τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Με βάση αυτό, το MD Guide υλοποιεί ένα εργαλείο στο οποίο τα παραδείγματα κώδικα που εμπεριέχονται σε αρχεία markdown γίνονται πλέον διαδρασικά για τους αναγνώστες του περιεχομένου. Σε αντίθεση με άλλα εργαλεία που περιορίζονται μόνο σε Javascript/Typescript υλοποιήσεις και παραδείγματα, το MD Guide σκοπεύει να «πακετάρει» λειτουργικότητα ανεξαρτήτως της γλώσσας στην οποία είναι το παράδειγμα και να τρέξει το runtime σε Docker (containerized runtime) και να επιστρέφει σε πραγματικό χρόνο το αποτέλεσμα των διεργασίων (stdout) στον web browser των χρηστών. Τέλος, μπορεί να βοηθάει τους μηχανικούς λογισμικού να έχουν πρόσβαση σε προγραμματιστικούς τύπους (types &amp;amp;amp; interfaces) του πηγαίου κώδικα τους και να τους παρεμβάλουν εντός των τεκμηριώσεων που συγγράφουν.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Αυτή τη στιγμή το project βρίσκεται σε πρώιμο στάδιο ανάπτυξης και σχεδιασμού, και σκοπεύουμε η ανάπτυξη να γίνει σε ανοιχτά hackathons με τη συμμετοχή ατόμων με καθόλου, λίγη ή πολυετή εμπειρία στην ανάπτυξη λογισμικού με σκοπό την επιμόρφωση και την χρήση συλλογικών πρακτικών ανάπτυξης.&lt;/p&gt;<br /> <br /> '''Επιβλέπων: Βασίλειος Κεφαλληνός'''<br /> <br /> === Επέκταση του ανοιχτού λογισμικού Dspace (v7) για ανάρτηση Ανοιχτών Εκπαιδευτικών Πόρων (OERs) ===<br /> <br /> <br /> Το Dspace αποτελεί ένα από τα πιο δημοφιλή λογισμικά ανοιχτού κώδικα για διατήρηση και φιλοξενεία ψηφιακής πληροφορίας κάθε είδους (repositorial infrastructures). Ήδη το Dspace χρησιμοποιείται από διάφορους φορείς σε εθνικό (π.χ. ΥΠΑΙΠΘ, ΕΚΤ κ.λπ.) αλλά κυρίως σε παγκόσμιο επίπεδο. Σκοπός της παρούσας πρότασης είναι η επέκταση του Dspace ώστε να μπορεί να παρέχει δυνατότητα ανάρτησης OERs (pdfs, words, html5, multimedia κ.λπ.) οποιουδήποτε μορφότυπου, με συνοδεία κατάλληλου εμπλουτισμού/τεκμηρίωσης με αξιοποίηση και επέκταση εκπαιδευτικών προτύπων (π.χ. IEEE LOM) και ενσωμάτωση τρίτων εργαλείων (π.χ. IIIF viewer, Video Streaming, e-pub viewers κ.λπ,). Οι επεκτάσεις που θα γίνουν, θα υλοποιηθούν στην τελευταία έκδοση του Dspace (7.x.), η οποία αυτή τη στιγμή χρησιμοποιείται ελάχιστα σε εθνικό επίπεδο (π.χ. το ΥΠΑΙΠΘ χρησιμοποιεί ακόμα την έκδοση 1.8 του Dspace για την πλειοψηφία των εφαρμογών του). Συγκεκριμένα, το Dspace θα επεκταθεί ώστε να υποστηρίξει:&lt;p&gt;- Ανάρτηση και διαμοιρασμό OERs από εγγεγραμμένους χρήστες. Το περιεχόμενο αυτό θα διαμοιράζεται ανοιχτά με βάση συγκεκριμένης προτυποποίησης (π.χ. REST, OAI-PMH κ.λπ.)&lt;/p&gt;&lt;p&gt;- Ενωσμάτωση σχήματος μεταδεδομένων OERs&lt;/p&gt;&lt;p&gt;- Προσαρμογή περιβάλλοντος χρήστη, πλοήγησης και αναζήτησης με χρήση φίλτρων και προηγμένης αναζήτησης&lt;/p&gt;&lt;p&gt;- Ολοκλήρωση με τρίτα εργαλεία για βέλτιστη προεπισκόπηση περιεχομένου&lt;/p&gt;&lt;p&gt;- Ενσωμάτωση επιλογής διαφορετικών αδειών χρήσης με βάση τις ανάγκες του εκάστοτε δημιουργού OER&lt;/p&gt;&lt;p&gt;- Δυνατότητα ενσωμάτωσης μαζικού περιεχομένου με χρήση τεχνολογιών harvesting&lt;/p&gt;&lt;p&gt;- Δυνατότητα προβολής και διαμοιρασμού των OERs με χρήση κατάλληλων προτύπων και τεχνολογιών.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Η παρούσα πρόταση εντάσσεται και στις 2 κατηγορίες Ανοιχτού Λογισμικού και Ανοιχτού Περιεχομένου, δεδομένου ότι στηρίζεται τόσο σε χρήση και επέκταση ανοιχτού λογισμικού, όσο και στην παραγωγή ανοιχτών εκπαιδευτικών πόρων που μπορούν να χρησιμοποιηθούν στην ευρύτερη εκπαιδευτική (και όχι μόνο) κοινότητα (μαθητές, εκπαιδευτικούς, γονείς κ.α.).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> '''Επιβλέπων: Κωνσταντίνος Αλεξανδρής'''<br /> <br /> <br /> === SciDavis, SciLab ===<br /> https://scidavis.sourceforge.net/, https://www.scilab.org/|&lt;p&gt;SciDAVis is a free application for Scientific Data Analysis and Visualization.&lt;/p&gt;&lt;p&gt;Sci Lab is Open source software for numerical computation&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> '''Επιβλέπων: Ιωάννης Βελονάκης'''<br /> <br /> <br /> === Open Robotic Manipulator for Remote Labs in Science and Technology Education ===<br /> <br /> <br /> |https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/,<br /> <br /> https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225|<br /> <br /> Description:&lt;p&gt;Remote teaching, for all educational levels, is one of the most added value concepts, during the last years, for both education and industry. The covid boosted high technologies, i.e IoT, 5G, computing power/cost, VR, AR, and robotics are now mature and there are plenty of DIY and OPEN approaches that could be used for enhancing hands-on teaching for pupils, students, and professionals under the concept of lifelong learning. Robotics can augment STEAM education by supporting a laboratory for remote teaching based on physics experiments allowing one to handle and manipulate activities of basic electrical, electronics, and mechatronics labs. During the proposed project a robotic manipulator (4 or 6 DoF) will be developed (based on available open-source 3D printed robot arms), constructed (assemble of 3d printed parts, electronics, motors, sensors), and programmed (using Python and/or Blockly code). The robot should, autonomously, perform experiments, using IoT sensors, actuators, controllers, etc. The DIY IoT sensors, actuators, and controllers are part of another project and are designed to facilitate their handling by the robotic manipulator. Therefore, it could be programmed and remotely controlled by a trainee for developing and implementing an experiment with the DIY IoT sensors, actuators, and controllers that are made available in a university or school laboratory. Hence, an appropriate open-source gripping system should be developed. The robot-gripper system should be designed for “easy for assembly” with a user-friendly interface while trying to minimize the cost using only open-source resources. It should be composed of only two main parts: 3D printed components (PLA) and electronics (motors, sensors, controller, etc.) as well as minimum connective mechanical parts.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;References:&lt;p&gt;- https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/&lt;/p&gt;&lt;p&gt;- https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225&lt;/p&gt;&lt;p&gt;- https://hackaday.io/search?term=arm&lt;/p&gt;&lt;p&gt;- https://all3dp.com/2/3d-printed-robot-arm-diy-robotic/&lt;/p&gt;&lt;p&gt;&lt;/p&gt;Expected Results:&lt;p&gt;The expected results of the three months project are:&lt;/p&gt;&lt;p&gt;-Understanding of how 6DoF robotic manipulators are designed, constructed, and programmed&lt;/p&gt;&lt;p&gt;-Designs of the shape and basic components of the robot using CAD software ready for 3D printing&lt;/p&gt;&lt;p&gt;-Simulation of the robot’s forward and inverse kinematics&lt;/p&gt;&lt;p&gt;-List of 3D printed components (cad, stl files) and list of required electronic components&lt;/p&gt;&lt;p&gt;-Demonstration of three case studies (remotely executed experiments, i.e., gripping of an IoT sensor, placement to the POI for monitoring, and returning it to the storage area)&lt;/p&gt;&lt;p&gt;-Github repositories development&lt;/p&gt;&lt;p&gt;-Documentation and assembly instructions&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Knowledge Prerequisites:&lt;/p&gt;&lt;p&gt;Required: Robotics, CAD software&lt;/p&gt;&lt;p&gt;Desired: Python, Real-time 3D (RT3D) platforms&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated development budget:&lt;/p&gt;&lt;p&gt;12000 € personnel&lt;/p&gt;&lt;p&gt;5000 € electronics, controllers, motors, 3d printing, consumables&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated reproduction cost of the robot:&lt;/p&gt;&lt;p&gt;The final robotic manipulator will be designed to be massively reproducible by anyone at an estimated cost of 1000-2000€ while following the concept of open source.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> '''Επιβλέποντες: Panagiotis Koustoumpardis and Hariton Polatoglou'''<br /> <br /> === Βιβλιοθήκη φιγούρων και σκηνικών ψηφιακού θεάτρου σκιών ===<br /> http://eshadow.gr/<br /> <br /> To εργαστήριο TUC-MUSIC της Σχολής Ηλεκτρολόγων Μηχανικών και Μηχανικών του Πολυτεχνείου Κρήτης έχει αναπτύξει και διανέμει ελεύθερα το ψηφιακό θέατρο σκιών http://eshadow.gr/ το οποίο συνοδεύεται από μια βιβλιοθήκη ψηφιακών φιγούρων και σκηνικών που μπορούν να χρησιμοποιούν για τη δημιουργία ψηφιακών παραστάσεων. Μας ενδιαφέρει η συνεργασία με πρόσωπα που έχουν τη δυνατότητα να δημιουργήσουν νέες φιγούρες και σκηνικά ώστε να αξιοποιηθούν από τους χρήστες του λογισμικού αυτού (κυρίως σχολεία αλλά και μεμονωμένοι χρήστες). Ενδεικτικά παραπέμπουμε σε μια τέτοια συλλογή υλικό: https://www.dropbox.com/sh/kj0xl32ntly5sbi/AAAt6ftq87goHeTpcKULzrV8a?dl=0&lt;p&gt;Κάθε ψηφιακή φιγούρα αποτελείται από δύο μέρη: Μια εικόνα που αποτελείται από τα επιμέρους κομμάτια που την αρθρώνουν και ένα αρχείο json που περιγράφει τον τρόπο άρθρωσής τους. Αυτό μας ενδιαφέρει είναι η παραγωγή των αρχείων εικόνας, τα αρχεία json μπορούμε να τα δημιουργήσουμε στη συνέχεια εμείς. Ιδιαίτερα θα μας ενδιέφερε, μεταξύ των άλλων, η δημιουργία φιγούρων με βάση παραδοσιακές ιστορίες, μύθους κ.λ.π. καθώς και φιγούρων που μπορούν να βασίζονται σε εικαστικές απαπαραστάσεις από έργα που είναι ελεύθερα (για παράδειγμα πίνακες ζωγραφικής, εικονογραφημένες ιστορίες, αρχαία αγγεία η νωπογραφίες κ.λ.π.).&lt;/p&gt;|<br /> |Βελτιώσεις για το διαλογικό σύστημα Rasa|&lt;https://github.com/kosniaz/rasa-improvements&gt;|&lt;p&gt;Επίλυση προβλημάτων με την πλατφόρμα του Rasa όπως:&lt;/p&gt;&lt;p&gt;\* επίλυση bug με το memoization policy (περιγράφεται εδώ: https://forum.rasa.com/t/there-is-no-memorised-next-action )&lt;/p&gt;&lt;p&gt;\* δημιουργία open source λύσης για data collection/annotation/training, αντί του κλειστού εργαλείου Rasa X (περιγράφεται εδώ: https://forum.rasa.com/t/alternatives-to-rasa-x/ )&lt;/p&gt;&lt;p&gt;\* βελτίωση του logging&lt;/p&gt;&lt;p&gt;\* προσθήκη περισσοτερων dialogue policies ή και βελτίωση υπάρχοντων.&lt;/p&gt;<br /> <br /> '''Επιβλέπων: Νεκτάριος Μουμουτζής'''<br /> <br /> <br /> === DMCRM ===<br /> https://github.com/daniilidisK/crm-application<br /> <br /> Το έργο αποτελεί λογισμικό διαχείρισης λειτουργιών μίας εταιρίας, από την έκδοση τιμολογίων, στην οργάνωση βάσης δεδομένων, τη διαχείριση εγγράφων σε συνάρτηση των εργασιών που αποθηκεύονται στη βάση δεδομένων.|<br /> |opendesign4publiconstructions|&lt;https://github.com/nataliskordou/opendesignpubliconstructions&gt;|Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.|<br /> <br /> '''Επιβλέπων: Θεόδωρος Καραγιάννης'''<br /> <br /> <br /> === BCL ===<br /> https://github.com/clavisound/feather-LoRa32u4-sketches&lt;p&gt;Συσκευή ιχνηλάτησης (tracking) IoT ανοιχτού υλικού ( hardware) και ανοιχτού λογισμικού που δοκιμαστηκε με το δίκτυο TTN V2.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Χρησιμοποιεί ελάχιστα δεδομένα (μόνο 7 bytes) με πολύ περισσότερα δεδομένα (ταχύτητα, κατεύθυνση, κατάσταση) από τις υπάρχουσες εμπορικές προτάσεις ΚΑΙ δυο προεπιλεγμένα τυχαία SF ώστε να είναι χρήσιμη σε πραγματικές συνθήκες IoT εκατομμυρίων συσκευών, μικρότερο airtime για αποφυγή παρεμβολών (collision).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Για την νέα εποχή (TTN V3 ή helium) πρέπει να αναβαθμιστεί με&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1. OTAA&lt;/p&gt;&lt;p&gt;2. Downlinks (MAC και custom. εντολές)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Επιπλέον θα μπορούσαν να οριστούν λευκές ζώνες για ακόμη λιγότερες παρεμβολές.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> '''Επιβλέπων: Μιχάλης Μιχαλούδης'''<br /> <br /> <br /> === ThanCad, εκτύπωση σε Windows ===<br /> &lt;https://thancad.sourceforge.net/&gt;|&lt;p&gt;Ελεύθερο (άδεια GPL v2 ή νεώτερη) λογισμικό CAD για μηχανικούς. Διδάσκεται στη Σχολή Πολ. Μηχανικών ΕΜΠ. Χρησιμοποιείται από τη Σχολή Τοπογράφων Ε.Μ.Π., Τμήμα Πολ. Μηχανικών Πανεπιστημίου Θεσσαλίας, και τμήμα πολ. Μηχανικών ΠΑ.Δ.Α. Τρέχει σε Linux, FreeBSD, OpenBSD, και Windows. Έχει δοκιμαστεί και τρέχει σε παλαιότερη έκδοση του MACOS X.&lt;/p&gt;&lt;p&gt;Πολλοί/ες φοιτητές/τριες είναι ακόμα εγκλωβισμένοι σε Windows λόγω του CAD. Το ThanCad είναι command compatible με AutoCAD και έτσι αποσκοπεί στην εξοικείωση των νέων μηχανικών με ελεύθερο λογισμικό χωρίς να χρειάζεται να καταβάλλουν μεγάλη προσπάθεια.&lt;/p&gt;&lt;p&gt;Παρόλο που το ThanCad μπορεί να τυπώσει και σε Windows μέσω postscript, θα ήταν πολύ βοηθητικό να χρησιμοποιηθεί το native (και χαοτικό στον προγραμματισμό) σύστημα εκτύπωσης των Windows, χωρίς να απαιτείται εκτυπωτής postscript, κατ' αναλογία με το λογισμικό libreoffice.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> '''Επιβλέπων: Θανάσης Στάμος'''<br /> <br /> <br /> === grifobot adventure ===<br /> &lt;https://github.com/vacilos/grifobot&gt;|&lt;p&gt;Το ΓΡΙΦΟΜΠΟΤ (grifobot.gr) είναι ένα διαδικτυακό παιχνίδι που αναπτύχθηκε από το ΓΑΒ LAB προκειμένου να μπορούν παιδιά δημοτικού να απασχοληθούν δημιουργικά συνδυάζοντας ασκήσεις γλώσσας και μαθηματικών με κώδικα. Η αρχική του έκδοση έχει εμπλουτιστεί ώστε να καλύπτει ηλικίες από 5 έως 12 ετών. To Γριφομπότ είναι ένα παιχνίδι που συνδυάζει τη μάθηση σε οποιοδήποτε αντικείμενο μέσα από χρήση αλγορίθμων και αλληλουχιών βημάτων που παραπέμπουν σε κώδικα.&lt;/p&gt;&lt;p&gt;Η έκδοσή του, &amp;quot;ΓΡΙΦΟΜΠΟΤ Quiz&amp;quot;, έχει πιστοποιηθεί από το υπουργείο παιδείας ως εκπαιδευτικό πρόγραμμα και μπορεί να εξυπηρετήσει εκπαιδευτικούς ώστε να εφαρμόσουν διαδικασίες παιχνιδοποίησης στην εκπαίδευση.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Το προτεινόμενο έργο εστιάζεται σε μια επέκταση του Γριφομπότ η οποία μέσα από ένα adventure game (με βάση το Γριφομπότ Quiz) θα δώσει τη δυνατότητα στους εκπαιδευτικούς να παρακολουθούν την εξέλιξη των μαθητών σε συγκεκριμένες θεματικές ενότητες. Παρακολουθώντας την εξέλιξη των μαθητών στο παιχνίδι ο εκπαιδευτικός θα μπορεί να καταλάβει σε ποια σημεία πρέπει να εστιάσει περισσότερο.&lt;/p&gt;&lt;p&gt;Η ιδέα επικεντρώνεται σε μια διαδικασία που θα ξεκινά από ένα gamified assessment ώστε να εντοπιστεί το επίπεδο του κάθε μαθητή και της κάθε μαθήτριας. Στη συνέχεια μέσα από αλληλουχίες διαφορετικών επιπέδων δυσκολίας του παιχνιδιού ο σκοπός θα είναι η μαθήτρια και ο μαθητής να κατακτήσουν μεγαλύτερα επίπεδα γνώσης.&lt;/p&gt;&lt;p&gt;Η εκπαιδευτικός αρκεί να εισάγει ερωτήσεις (και απαντήσεις) για μια θεματική ενότητα χωρίζοντας σε επίπεδα δυσκολίας (και κατάκτησης γνώσης). Στη συνέχεια μέσα από το adventure game ο μαθητής θα πρέπει να κατακτήσει τη γνώση για να ολοκληρώσει το παιχνίδι.&lt;/p&gt;&lt;p&gt;Ειδικότερα για το κομμάτι της βιολογίας υπάρχουν έτοιμα σενάρια χρήσης για την ύλη του Γυμνασίου οπότε η επέκταση θα παρέχει και επαρκές υλικό τουλάχιστον για το συγκεκριμένο μάθημα. Η δε συνεργασία με την εκπαιδευτική κοινότητα θα δημιουργήσει και περιεχόμενο που θα μπορεί να διαμοιραστεί στους εκπαιδευτικούς ώστε να καλύψει κι άλλα μαθήματα.&lt;/p&gt;&lt;p&gt;Για τη συγκεκριμένη επέκταση, σε συνεργασία με τους Μανόλη Wallace (Αν. Καθηγητή), Τάσο Θεοδωρόπουλο (Επικ. Καθηγητή) και Παν. Κόκκινο (Επικ. Καθηγητή) καθώς και με την Υπ. Διδάκτορα, Εκπαιδευτικό Βιολόγο Μαρίνα Λαντζούνη, έχουν γίνει οι κατάλληλοι σχεδιασμοί για τον τρόπο δημιουργίας (σενάρια, χαρακτήρες, εκπαιδευτικά σενάρια, προσωποποίηση σε μαθητές, προσαρμοσμένη μάθηση), συνεπώς η επέκταση έχει ωριμότητα στη σχεδίαση, ενώ υπάρχει και ένα υπόβαθρο (γριφομπότ κουιζ) το οποίο παρέχει την υποδομή πάνω στην οποία μπορεί να χτιστεί η επέκταση.&lt;/p&gt;<br /> <br /> <br /> '''Επιβλέπων: Βασίλης Πουλόπουλος'''<br /> <br /> <br /> === gisola ===<br /> https://github.com/nikosT/Gisola&lt;p&gt;Το λογισμικό Gisola είναι ένα λογισμικό ανοικτού κώδικα για την άμεση επίλυση του Τανυστή Σεισμικής Ροπής (ΤΣΡ) σε πραγματικό χρόνο, υλοποιημένο σε αρχιτεκτονικές CPU και/ή GPU. Είναι υλοποιημένο κυρίως στην προγραμματιστική γλώσσα Python χρησιμοποιώντας ανοικτού κώδικα βασικές σεισμολογικές και υπολογιστικές βιβλιοθήκες όπως τις ObsPy, Matplotlib, Numpy κ.α., καθώς και την παράλληλης επεξεργασίας βιβλιοθήκη (multiprocessing). Ωστόσο, αρκετά τμήματα του βασικού του πυρήνα είναι γραμμένα σε Fortran, ενώ της οπτικοποίησης των αποτελεσμάτων σε τεχνολογίες διαδικτύου (π.χ. Leaflet Maps, Jinja2). Στην επιστήμη της σεισμολογίας, o ΤΣΡ είναι μια μαθηματική αναπαράσταση που σχετίζεται άμεσα με τη γεωμετρία του ρήγματος και το μέγεθος του σεισμού. Οι ΤΣΡ χρησιμοποιούνται σε ένα ευρύ φάσμα ερευνητικών θεμάτων όπως τη σεισμοτεκτονική, τη μοντελοποίηση θαλασσίων κυμάτων βαρύτητας (tsunami), την άμεση αντίδραση σε ένα σεισμό, την εκτίμηση των καταστροφών κτλ. και ως εκ τούτου είναι σημαντικός ο γρήγορος και αξιόπιστος υπολογισμός τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Ο κώδικας μαζί με οδηγίες (Wiki) εγκατάστασης και χρήσης είναι ελεύθερος για χρήση, τροποποίηση και αναδιανομή από οποιονδήποτε, και είναι προσβάσιμος για λήψη από το αποθετήριο GitHub (με mirroring στο GitLab). Επιπλέον, η παρουσίαση της εργασίας βραβεύτηκε στο συνέδριο European Geosciences Union (EGU) General Assembly 2021 με το βραβείο Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Award 2021, παρουσιάστηκε, επίσης, στο ετήσιο πανελλήνιο συνέδριο κοινοτήτων ελεύθερου λογισμικού και λογισμικού ανοικτού κώδικα FOSSCOMM 2021, ενώ δημοσιεύθηκε στο καταξιωμένο σεισμολογικό περιοδικό Seismological Research Letters.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Στο προτεινόμενο έργο, ο κώδικας θα επεκταθεί ως προς τα παρακάτω:&lt;/p&gt;&lt;p&gt;a) χρήση 3Δ μοντέλων εδάφους, b) βελτίωση του υπολογιστικού χρόνου, c) εκτίμηση της αβεβαιότητας της λύσης.&lt;/p&gt;<br /> <br /> '''Επιβλέπων: Ευθύμιος Σώκος'''<br /> <br /> <br /> === Globaleaks ===<br /> &lt;https://github.com/globaleaks/GlobaLeaks&gt;|&lt;p&gt;Το GlobaLeaks είναι μια πλατφόρμα ανοικτού λογισμικού για το whistleblowing, δηλαδή την ασφαλή και ανώνυμη αναφορά εμπιστευτικών πληροφοριών για παράνομες ή μη ηθικές συμπεριφορές από εργαζόμενους, εξωτερικούς συνεργάτες αλλά και πολίτες χωρίς τον φόβο αντιποίνων.&lt;/p&gt;&lt;p&gt;Οι αναφορές αποστέλλονται σε καθορισμένους παραλήπτες εντός των επιχειρήσεων και των οργανισμών που επιλαμβάνονται των περιστατικών. Οι πλατφόρμες whistleblowing είναι επίκαιρες εξαιτίας της πρόσφατης νομοθεσίας για την προστασία των whistleblowers Ν4990/2020.&lt;/p&gt;&lt;p&gt;Η πλατφόρμα είναι security/privacy by design και έχει σχεδιαστεί για να προστατεύει την ταυτότητα τόσο του αναφέροντος όσο και του αναφερόμενου και χρησιμοποιείται από χιλιάδες οργανώσεις και εταιρίες στον κόσμο για την προώθηση της διαφάνειας και της λογοδοσίας.&lt;/p&gt;&lt;p&gt;Χρησιμοποιούμε το GlobaLeaks apo to 2017 και έχουμε βασίσει πάνω του μια υπηρεσία SaaS για την παροχή ολοκληρωμένων υπηρεσιών whistleblowing. Το πελατολόγιό μας περιλαμβάνει εταιρίες με ηγετικά χαρακτηριστικά όπως μεταξύ άλλων Lamda developnet, Kaizen Gaming (stoiximan), Παπαδοπούλου, όπως επίσης και Δημόσιους οργανισμούς όπως η Γενική Γραμματεία Αθλητισμού και το Γεωπονικό Πανεπιστήμιο. Η πλατφόρμα Whistleblowing έχει συμπεριληφθεί στο πλαίσιο της Εθνικής Πλατφόρμας Αθλητικής Ακεραιότητάς ως ένα από τα έργα της Βίβλου Ψηφιακού Μετασχηματισμού του αθλητισμού μετά από συμμέτοχή μας στο open call του Υπουργείου Ψηφιακού Μετασχηματισμού σε συνεργασία με την ΓΓΑ.&lt;/p&gt;&lt;p&gt;Είμαστε ένα από τα πιο ενεργά μέλη τη κοινότητας του Globaleaks από το 2017 και προτείνουμε και αξιολογούμε νέα feature, ελέγχουμε το λογισμικό για αναφέρουμε σφάλματα. Το 2022 έχουμε συνεισφέρουμε και κώδικα σε συνεργασία με την εταιρία Genesis Technologies μία νεοφυή εταιρία με έδρα το Πακιστάν.&lt;/p&gt;&lt;p&gt;Θα χρειαστούμε την χρηματοδότηση για να αναπτύξουμε νέα features τα όποια ζητούν οι πελάτες μας ή εμείς κρίνουμε ότι θα ήταν χρήσιμα για αυτούς για να γίνουμε πιο ανταγωνιστικοί. Τα features θα ενσωματωθούν στο ανοικτό λογισμικό προς όφελος των εκατοντάδων χιλιάδων δυνητικών χρηστών.&lt;/p&gt;<br /> <br /> <br /> '''Επιβλέπων: Βασίλης Σταματόπουλος'''<br /> <br /> <br /> === opendesign4publiconstructions ===<br /> <br /> https://github.com/nataliskordou/opendesignpubliconstructions<br /> <br /> Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.<br /> <br /> '''Επιβλέπουσα: Ναταλία Σκόρδου'''<br /> <br /> === Sandstorm / Cloud in a Box ===<br /> Πρωτότυπο μιας λύσης που θα παρέχεται σε μία εταιρεία, ένα σχολείο, μια μικρή ερευνητική ομάδα, με χαμηλό κόστος, αλλά θα αξιοποιεί κατανεμημένους πόρους στο βαθμό που μια κρίσιμη μάζα χρήστων διαθέτει την ίδια λύση.<br /> <br /> Ως προς τις εφαρμογές που θα παρέχονται στον τελικό χρήστη προκρίνουμε το περιβάλλον χρήστη [https://news.ycombinator.com/from?site=sandstorm.io Sandstorm] το οποίο επιτρέπει εύκολη και ασφαλή σύνδεση των οργανωσιακών μελών, αλλά και ευκολή εγκατάσταση και απεγκατάσταση εφαρμογών σε sandboxed περιβάλλοντα.<br /> Μέσα από το περιβάλλον του Sandstorm η ομάδα θα μπορεί να χρησιμοποιεί εργαλεία ομαδικής συνεργασίας, από το marketplace των εφαρμογών της πλατφόρμας.<br /> <br /> Αντί να εγκαθίσταται σαν self-hosted λύση, προκρίνουμε την ολοκληρωμένη παροχή του σε &quot;ένα κουτί&quot;, με την ελάχιστη απαιτούμενη ενέργεια από τη μεριά του τελικού χρήστη.<br /> Η ίδια η ομάδα του Sandstorm προκρίνει τη λύση του αφιερωμένου κουτιού, όπως επίσης και προτείνει τα [https://www.minnowboard.org/ Minnowboard] αντί για την φτηνότερη και δημοφιλέστερη λύση των Raspberry Pi.<br /> Υπάρχουν θέματα συμβατότητας/ελαχίστων απαιτήσεων του Sandstorm με τα Raspberry, ή τουλάχιστον υπήρχαν στο παρελθόν.<br /> <br /> Σε ένα βαθμό αυτή η λύση μοιάζει με το [https://internet-in-a-box.org/ Internet in a Box], και θα μπορούσε να λέγεται Sandstorm in a Box.<br /> Εμείς όμως θέλουμε να εξασφαλίζονται οι πόροι για να χρησιμοποιηθεί αυτό το περιβάλλον και από σχετικά πολυπληθέστερες ομάδες, αξιοποιώντας τις αρχές της cloud infrastructure (on-demand-self-service, broad network access, resource pooling, rapid elasticity, measuring service), για να διασυνδέσουμε αυτά τα κουτιά μεταξύ τους.<br /> <br /> Καθώς οι πόροι αυτοί θα είναι απομακρυσμένοι μεταξύ τους, θέλουμε να λύσουμε το πρόβλημα του επιπέδου δικτύου μέσα από ένα peer-to-peer πρωτόκολο, είτε αυτοσχέδιο, είτε επανάχρηση υπάρχοντος, έτσι ώστε τα κουτιά να συνδέονται μεταξύ τους σε ένα δικό τους δίκτυο, και να μπορούν να ανακαλύψει και να επαληθεύσει το ένα το άλλο.<br /> Ταυτόχρονα, θα πρέπει να παρέχονται σημεία εξόδου από αυτό το δίκτυο προς το διαδίκτυο.<br /> Ένα παράδειγμα τέτοιου πρωτοκόλου θα μπορούσε να είναι το [https://en.wikipedia.org/wiki/I2P i2p] αλλά δεν θέλουμε τα κουτιά να συνδέονται στο ίδιο το i2p αλλά σε ένα δικό τους i2p, ή κάτι ισοδύναμο.<br /> <br /> Για την επίτευξη αυτού προκρίνουμε μία λύση που θα αξιοποιεί το Sandstorm σε περιβάλλον Docker και μέσα από το Kubernetes bootstrapping ([https://mirailabs.io/blog/building-a-microcloud/ όπως εδώ]) θα διαχειρίζεται τα κουτιά, από τα οποία κάποια θα πρέπει να έχουν ρόλο master. Έτσι, υποθέτουμε, θα μπορεί να τρέχει αποτελεσματικά το περιβάλλον διαχείρισης εφαρμογών του τελικού χρήστη, αξιοποιώντας όμως τους πόρους όλου του δικτύου.<br /> <br /> Παραδοτέο είναι ένα λειτουργικό πρωτότυπο που θα αποτελείται από (ενδεικτικά) <br /> <br /> * 4-6 boards εφοδιασμένα με storage devices ~4T έκαστο<br /> * μία προσαρμοσμένη διανομή Linux με όλα τα απαραίτητα boot media<br /> * standalone scripts μέσα από τα οποία θα επιτυγχάνεται η συμπεριφορά του συστήματος που περιγράφεται πιό πάνω<br /> * τεκμηρίωση για διαχειριστές συστήματος<br /> * οδηγός χρήστη<br /> <br /> '''Επιβλέπουσα: Νίνα Γιαλλούση'''<br /> <br /> ==Zotero==<br /> <br /> Το [https://github.com/zotero/zotero Zotero] είναι ένας διαχειριστής βιβλιογραφίας που παρέχει πολύτιμες λειτουργίες σε ακαδημαϊκού τύπου έρευνα, αλλά μπορεί να χρησιμοποιηθεί σε κάθε είδος έργου που απαιτεί ευέλικτη τεκμηρίωση μεγάλου όγκου πηγών, αφού για παράδειγμα υποστηρίζει και αρχεία ήχου, βίντεο, εξωτερικούς συνδέσμους σε διαδικτυακά φόρουμ και άλλα είδη πηγών.<br /> <br /> Χαρακτηρίζεται από μια ενεργή κοινότητα χρήστων που ενημερώνει τακτικά τους [https://github.com/zotero/translators &quot;μεταφραστές&quot;], δηλαδή τα scripts που αποκτούν από κάθε σχετική σελίδα, όπως τους ιστοτόπους των ακαδημαϊκών εκδοτών ή της φιλοξενίας βίντεο, τα σχετικά μεταδεδομένα.<br /> <br /> Υπολείπεται όμως σαν μέσο ομαδικής συνεργασίας, καθώς εξ αρχής απευθύνεται σε μεμονωμένους ερευνητές και φαίνεται πως η ομαδική συνεργασία είναι μια εξ των υστέρων σκέψη.<br /> Στην παρούσα μορφή του η ομαδική συνεργασία γίνεται μέσω σύνδεσης στο δικό τους ιστότοπο με περιορισμένο αποθηκευτικό χώρο, και πέραν αυτού με πληρωμή για απεριόριστο χώρο.<br /> <br /> Αν και [https://github.com/zotero/dataserver η saas λύση τους είναι ανοιχτού κώδικα], δεν είναι στις προτεραιότητές των συντελεστων να διανέμουν, να συντηρούν, ή να τεκμηριώσουν μια self-hosted λύση, [https://github.com/zotero/dataserver/issues/105 όπως προκύπτει και από αυτήν τη συζήτηση].<br /> <br /> Υπάρχουν [https://github.com/foxsen/zotero-selfhost ορισμένα contributions στο github], αλλά δεν διασφαλίζεται ότι αυτά επικαιροποιούνται ανάλογα με τις νέες εκδόσεις του Zotero, ή με τις αλλαγές που συμβαίνουν στους μεταφραστές.<br /> <br /> Επιπλέον, δεν είναι βελτιστοποιημένη η λειτουργικότητα της λήψης αντιγράφων ασφαλείας.<br /> Αυτό γίνεται σημαντικό πρόβλημα όταν γίνεται πολυετής-πολυπρόσωπη έρευνα και η βάση δεδομένων αποκτά μεγάλο όγκο.<br /> Η αναφορά των εσωτερικών αναπαραστάσεων του Zotero στα αρχεία στηρίζεται σε έναν αδιαφανή κωδικό, και για την εξαγωγή αυτού ένας χρήστης πρέπει να εμβαθύνει στη SQLITE3 βάση δεδομένων του προγράμματος.<br /> <br /> Παραδοτέο είναι: <br /> <br /> * Μία εύχρηστη λύση εγκατάστασης του Zotero ως self-hosted εφαρμογής (μπορεί να είναι για οποιαδήποτε κύρια διανομή Linux, Docker, Sandstorm, Appimage, Flatpak)<br /> * Επίλυση των παραπάνω ελλειμμάτων λειτουργικότητας ως προς τους πολλαπλούς χρήστες<br /> * Επίλυση των παραπάνω ελλειμμάτων λειτουργικότητας ως προς τη λήψη-μεταφόρτωση αντιγράφων ασφαλείας<br /> * Τεκμηρίωση για διαχειριστές συστημάτων<br /> * Οδηγός χρήστη<br /> * Συντήρηση για εύλογο χρονικό διάστημα<br /> <br /> Συνολικά το έργο μπορεί να είναι fork του Zotero και δεν απαιτείται να γίνει PR από αυτό.<br /> <br /> '''Επιβλέπουσα: Νίνα Γιαλλούση'''</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%A0%CF%81%CE%BF%CF%84%CE%AC%CF%83%CE%B5%CE%B9%CF%82_%CE%AD%CF%81%CE%B3%CF%89%CE%BD&diff=2182 2023-03-09T07:49:41Z

<p>Pkst-1: </p> <hr /> <div><br /> <br /> === Fossbot ===<br /> '''https://github.com/eellak/fossbot'''&lt;p&gt;<br /> Ανασχεδιασμός της ηλεκτρονικής διάταξης του Fossbot.&lt;/p&gt;<br /> <br /> Επιβλέπων: Ηρακλής Βαρλάμης<br /> <br /> === Επέκταση του Αlexandria3k ===<br /> '''https://github.com/dspinellis/alexandria3k'''&lt;p&gt;<br /> Πρόσθετες ανοιχτές βάσεις δεδομένων (διπλώματα ευρεσιτεχνίας, MEDLINE/PubMed), αλγορίθμους κατηγοριοποίησης με βάση το θέμα, ταίριασμα συγγραφέων και ιδρυμάτων και πρόσθετα παραδείγματα χρήσης.&lt;/p&gt;<br /> <br /> Επιβλέπων: Διομήδης Σπινέλης<br /> <br /> === MD Guide ===<br /> '''https://github.com/md-guide/md-guide'''&lt;p&gt;Το MD Guide πρόκειται για ένα εργαλείο συγγραφής τεχνικής τεκμηρίωσης και οδηγών (technical documentation) το οποίο βασίζεται στη γλώσσα περιγραφής Markdown. Το Markdown είναι ιδιαίτερα δημοφιλές τα τελευταία χρόνια, ειδικά μετά την κυριάρχηση του Github και του Open source τη δεκαετία του 2010-2020 και έχει γίνει πλέον η κατ'εξοχήν γλώσσα επιλογής για προγραμματιστές που θέλουν να μορφοποιήσουν εύκολα και γρήγορα τα κείμενα τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Με βάση αυτό, το MD Guide υλοποιεί ένα εργαλείο στο οποίο τα παραδείγματα κώδικα που εμπεριέχονται σε αρχεία markdown γίνονται πλέον διαδρασικά για τους αναγνώστες του περιεχομένου. Σε αντίθεση με άλλα εργαλεία που περιορίζονται μόνο σε Javascript/Typescript υλοποιήσεις και παραδείγματα, το MD Guide σκοπεύει να «πακετάρει» λειτουργικότητα ανεξαρτήτως της γλώσσας στην οποία είναι το παράδειγμα και να τρέξει το runtime σε Docker (containerized runtime) και να επιστρέφει σε πραγματικό χρόνο το αποτέλεσμα των διεργασίων (stdout) στον web browser των χρηστών. Τέλος, μπορεί να βοηθάει τους μηχανικούς λογισμικού να έχουν πρόσβαση σε προγραμματιστικούς τύπους (types &amp;amp;amp; interfaces) του πηγαίου κώδικα τους και να τους παρεμβάλουν εντός των τεκμηριώσεων που συγγράφουν.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Αυτή τη στιγμή το project βρίσκεται σε πρώιμο στάδιο ανάπτυξης και σχεδιασμού, και σκοπεύουμε η ανάπτυξη να γίνει σε ανοιχτά hackathons με τη συμμετοχή ατόμων με καθόλου, λίγη ή πολυετή εμπειρία στην ανάπτυξη λογισμικού με σκοπό την επιμόρφωση και την χρήση συλλογικών πρακτικών ανάπτυξης.&lt;/p&gt;<br /> <br /> Επιβλέπων: Βασίλειος Κεφαλληνός<br /> <br /> === Επέκταση του ανοιχτού λογισμικού Dspace (v7) για ανάρτηση Ανοιχτών Εκπαιδευτικών Πόρων (OERs) ===<br /> <br /> <br /> Το Dspace αποτελεί ένα από τα πιο δημοφιλή λογισμικά ανοιχτού κώδικα για διατήρηση και φιλοξενεία ψηφιακής πληροφορίας κάθε είδους (repositorial infrastructures). Ήδη το Dspace χρησιμοποιείται από διάφορους φορείς σε εθνικό (π.χ. ΥΠΑΙΠΘ, ΕΚΤ κ.λπ.) αλλά κυρίως σε παγκόσμιο επίπεδο. Σκοπός της παρούσας πρότασης είναι η επέκταση του Dspace ώστε να μπορεί να παρέχει δυνατότητα ανάρτησης OERs (pdfs, words, html5, multimedia κ.λπ.) οποιουδήποτε μορφότυπου, με συνοδεία κατάλληλου εμπλουτισμού/τεκμηρίωσης με αξιοποίηση και επέκταση εκπαιδευτικών προτύπων (π.χ. IEEE LOM) και ενσωμάτωση τρίτων εργαλείων (π.χ. IIIF viewer, Video Streaming, e-pub viewers κ.λπ,). Οι επεκτάσεις που θα γίνουν, θα υλοποιηθούν στην τελευταία έκδοση του Dspace (7.x.), η οποία αυτή τη στιγμή χρησιμοποιείται ελάχιστα σε εθνικό επίπεδο (π.χ. το ΥΠΑΙΠΘ χρησιμοποιεί ακόμα την έκδοση 1.8 του Dspace για την πλειοψηφία των εφαρμογών του). Συγκεκριμένα, το Dspace θα επεκταθεί ώστε να υποστηρίξει:&lt;p&gt;- Ανάρτηση και διαμοιρασμό OERs από εγγεγραμμένους χρήστες. Το περιεχόμενο αυτό θα διαμοιράζεται ανοιχτά με βάση συγκεκριμένης προτυποποίησης (π.χ. REST, OAI-PMH κ.λπ.)&lt;/p&gt;&lt;p&gt;- Ενωσμάτωση σχήματος μεταδεδομένων OERs&lt;/p&gt;&lt;p&gt;- Προσαρμογή περιβάλλοντος χρήστη, πλοήγησης και αναζήτησης με χρήση φίλτρων και προηγμένης αναζήτησης&lt;/p&gt;&lt;p&gt;- Ολοκλήρωση με τρίτα εργαλεία για βέλτιστη προεπισκόπηση περιεχομένου&lt;/p&gt;&lt;p&gt;- Ενσωμάτωση επιλογής διαφορετικών αδειών χρήσης με βάση τις ανάγκες του εκάστοτε δημιουργού OER&lt;/p&gt;&lt;p&gt;- Δυνατότητα ενσωμάτωσης μαζικού περιεχομένου με χρήση τεχνολογιών harvesting&lt;/p&gt;&lt;p&gt;- Δυνατότητα προβολής και διαμοιρασμού των OERs με χρήση κατάλληλων προτύπων και τεχνολογιών.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Η παρούσα πρόταση εντάσσεται και στις 2 κατηγορίες Ανοιχτού Λογισμικού και Ανοιχτού Περιεχομένου, δεδομένου ότι στηρίζεται τόσο σε χρήση και επέκταση ανοιχτού λογισμικού, όσο και στην παραγωγή ανοιχτών εκπαιδευτικών πόρων που μπορούν να χρησιμοποιηθούν στην ευρύτερη εκπαιδευτική (και όχι μόνο) κοινότητα (μαθητές, εκπαιδευτικούς, γονείς κ.α.).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> Επιβλέπων: Κωνσταντίνος Αλεξανδρής<br /> <br /> <br /> === SciDavis, SciLab ===<br /> https://scidavis.sourceforge.net/, https://www.scilab.org/|&lt;p&gt;SciDAVis is a free application for Scientific Data Analysis and Visualization.&lt;/p&gt;&lt;p&gt;Sci Lab is Open source software for numerical computation&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> Επιβλέπων:Ιωάννης Βελονάκης<br /> <br /> <br /> === Open Robotic Manipulator for Remote Labs in Science and Technology Education ===<br /> <br /> <br /> |https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/,<br /> <br /> https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225|<br /> <br /> Description:&lt;p&gt;Remote teaching, for all educational levels, is one of the most added value concepts, during the last years, for both education and industry. The covid boosted high technologies, i.e IoT, 5G, computing power/cost, VR, AR, and robotics are now mature and there are plenty of DIY and OPEN approaches that could be used for enhancing hands-on teaching for pupils, students, and professionals under the concept of lifelong learning. Robotics can augment STEAM education by supporting a laboratory for remote teaching based on physics experiments allowing one to handle and manipulate activities of basic electrical, electronics, and mechatronics labs. During the proposed project a robotic manipulator (4 or 6 DoF) will be developed (based on available open-source 3D printed robot arms), constructed (assemble of 3d printed parts, electronics, motors, sensors), and programmed (using Python and/or Blockly code). The robot should, autonomously, perform experiments, using IoT sensors, actuators, controllers, etc. The DIY IoT sensors, actuators, and controllers are part of another project and are designed to facilitate their handling by the robotic manipulator. Therefore, it could be programmed and remotely controlled by a trainee for developing and implementing an experiment with the DIY IoT sensors, actuators, and controllers that are made available in a university or school laboratory. Hence, an appropriate open-source gripping system should be developed. The robot-gripper system should be designed for “easy for assembly” with a user-friendly interface while trying to minimize the cost using only open-source resources. It should be composed of only two main parts: 3D printed components (PLA) and electronics (motors, sensors, controller, etc.) as well as minimum connective mechanical parts.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;References:&lt;p&gt;- https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/&lt;/p&gt;&lt;p&gt;- https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225&lt;/p&gt;&lt;p&gt;- https://hackaday.io/search?term=arm&lt;/p&gt;&lt;p&gt;- https://all3dp.com/2/3d-printed-robot-arm-diy-robotic/&lt;/p&gt;&lt;p&gt;&lt;/p&gt;Expected Results:&lt;p&gt;The expected results of the three months project are:&lt;/p&gt;&lt;p&gt;-Understanding of how 6DoF robotic manipulators are designed, constructed, and programmed&lt;/p&gt;&lt;p&gt;-Designs of the shape and basic components of the robot using CAD software ready for 3D printing&lt;/p&gt;&lt;p&gt;-Simulation of the robot’s forward and inverse kinematics&lt;/p&gt;&lt;p&gt;-List of 3D printed components (cad, stl files) and list of required electronic components&lt;/p&gt;&lt;p&gt;-Demonstration of three case studies (remotely executed experiments, i.e., gripping of an IoT sensor, placement to the POI for monitoring, and returning it to the storage area)&lt;/p&gt;&lt;p&gt;-Github repositories development&lt;/p&gt;&lt;p&gt;-Documentation and assembly instructions&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Knowledge Prerequisites:&lt;/p&gt;&lt;p&gt;Required: Robotics, CAD software&lt;/p&gt;&lt;p&gt;Desired: Python, Real-time 3D (RT3D) platforms&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated development budget:&lt;/p&gt;&lt;p&gt;12000 € personnel&lt;/p&gt;&lt;p&gt;5000 € electronics, controllers, motors, 3d printing, consumables&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated reproduction cost of the robot:&lt;/p&gt;&lt;p&gt;The final robotic manipulator will be designed to be massively reproducible by anyone at an estimated cost of 1000-2000€ while following the concept of open source.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> &lt;p&gt;Επιβλέποντες: Panagiotis Koustoumpardis and Hariton Polatoglou&lt;/p&gt;<br /> <br /> === Βιβλιοθήκη φιγούρων και σκηνικών ψηφιακού θεάτρου σκιών ===<br /> http://eshadow.gr/<br /> <br /> To εργαστήριο TUC-MUSIC της Σχολής Ηλεκτρολόγων Μηχανικών και Μηχανικών του Πολυτεχνείου Κρήτης έχει αναπτύξει και διανέμει ελεύθερα το ψηφιακό θέατρο σκιών http://eshadow.gr/ το οποίο συνοδεύεται από μια βιβλιοθήκη ψηφιακών φιγούρων και σκηνικών που μπορούν να χρησιμοποιούν για τη δημιουργία ψηφιακών παραστάσεων. Μας ενδιαφέρει η συνεργασία με πρόσωπα που έχουν τη δυνατότητα να δημιουργήσουν νέες φιγούρες και σκηνικά ώστε να αξιοποιηθούν από τους χρήστες του λογισμικού αυτού (κυρίως σχολεία αλλά και μεμονωμένοι χρήστες). Ενδεικτικά παραπέμπουμε σε μια τέτοια συλλογή υλικό: https://www.dropbox.com/sh/kj0xl32ntly5sbi/AAAt6ftq87goHeTpcKULzrV8a?dl=0&lt;p&gt;Κάθε ψηφιακή φιγούρα αποτελείται από δύο μέρη: Μια εικόνα που αποτελείται από τα επιμέρους κομμάτια που την αρθρώνουν και ένα αρχείο json που περιγράφει τον τρόπο άρθρωσής τους. Αυτό μας ενδιαφέρει είναι η παραγωγή των αρχείων εικόνας, τα αρχεία json μπορούμε να τα δημιουργήσουμε στη συνέχεια εμείς. Ιδιαίτερα θα μας ενδιέφερε, μεταξύ των άλλων, η δημιουργία φιγούρων με βάση παραδοσιακές ιστορίες, μύθους κ.λ.π. καθώς και φιγούρων που μπορούν να βασίζονται σε εικαστικές απαπαραστάσεις από έργα που είναι ελεύθερα (για παράδειγμα πίνακες ζωγραφικής, εικονογραφημένες ιστορίες, αρχαία αγγεία η νωπογραφίες κ.λ.π.).&lt;/p&gt;|<br /> |Βελτιώσεις για το διαλογικό σύστημα Rasa|&lt;https://github.com/kosniaz/rasa-improvements&gt;|&lt;p&gt;Επίλυση προβλημάτων με την πλατφόρμα του Rasa όπως:&lt;/p&gt;&lt;p&gt;\* επίλυση bug με το memoization policy (περιγράφεται εδώ: https://forum.rasa.com/t/there-is-no-memorised-next-action )&lt;/p&gt;&lt;p&gt;\* δημιουργία open source λύσης για data collection/annotation/training, αντί του κλειστού εργαλείου Rasa X (περιγράφεται εδώ: https://forum.rasa.com/t/alternatives-to-rasa-x/ )&lt;/p&gt;&lt;p&gt;\* βελτίωση του logging&lt;/p&gt;&lt;p&gt;\* προσθήκη περισσοτερων dialogue policies ή και βελτίωση υπάρχοντων.&lt;/p&gt;<br /> <br /> Επιβλέπων: Νεκτάριος Μουμουτζής<br /> <br /> <br /> === DMCRM ===<br /> https://github.com/daniilidisK/crm-application<br /> <br /> Το έργο αποτελεί λογισμικό διαχείρισης λειτουργιών μίας εταιρίας, από την έκδοση τιμολογίων, στην οργάνωση βάσης δεδομένων, τη διαχείριση εγγράφων σε συνάρτηση των εργασιών που αποθηκεύονται στη βάση δεδομένων.|<br /> |opendesign4publiconstructions|&lt;https://github.com/nataliskordou/opendesignpubliconstructions&gt;|Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.|<br /> <br /> Επιβλέπων: Θεόδωρος Καραγιάννης<br /> <br /> <br /> === BCL ===<br /> https://github.com/clavisound/feather-LoRa32u4-sketches&lt;p&gt;Συσκευή ιχνηλάτησης (tracking) IoT ανοιχτού υλικού ( hardware) και ανοιχτού λογισμικού που δοκιμαστηκε με το δίκτυο TTN V2.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Χρησιμοποιεί ελάχιστα δεδομένα (μόνο 7 bytes) με πολύ περισσότερα δεδομένα (ταχύτητα, κατεύθυνση, κατάσταση) από τις υπάρχουσες εμπορικές προτάσεις ΚΑΙ δυο προεπιλεγμένα τυχαία SF ώστε να είναι χρήσιμη σε πραγματικές συνθήκες IoT εκατομμυρίων συσκευών, μικρότερο airtime για αποφυγή παρεμβολών (collision).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Για την νέα εποχή (TTN V3 ή helium) πρέπει να αναβαθμιστεί με&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1. OTAA&lt;/p&gt;&lt;p&gt;2. Downlinks (MAC και custom. εντολές)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Επιπλέον θα μπορούσαν να οριστούν λευκές ζώνες για ακόμη λιγότερες παρεμβολές.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> Επιβλέπων:Μιχάλης Μιχαλούδης<br /> <br /> <br /> === ThanCad, εκτύπωση σε Windows ===<br /> &lt;https://thancad.sourceforge.net/&gt;|&lt;p&gt;Ελεύθερο (άδεια GPL v2 ή νεώτερη) λογισμικό CAD για μηχανικούς. Διδάσκεται στη Σχολή Πολ. Μηχανικών ΕΜΠ. Χρησιμοποιείται από τη Σχολή Τοπογράφων Ε.Μ.Π., Τμήμα Πολ. Μηχανικών Πανεπιστημίου Θεσσαλίας, και τμήμα πολ. Μηχανικών ΠΑ.Δ.Α. Τρέχει σε Linux, FreeBSD, OpenBSD, και Windows. Έχει δοκιμαστεί και τρέχει σε παλαιότερη έκδοση του MACOS X.&lt;/p&gt;&lt;p&gt;Πολλοί/ες φοιτητές/τριες είναι ακόμα εγκλωβισμένοι σε Windows λόγω του CAD. Το ThanCad είναι command compatible με AutoCAD και έτσι αποσκοπεί στην εξοικείωση των νέων μηχανικών με ελεύθερο λογισμικό χωρίς να χρειάζεται να καταβάλλουν μεγάλη προσπάθεια.&lt;/p&gt;&lt;p&gt;Παρόλο που το ThanCad μπορεί να τυπώσει και σε Windows μέσω postscript, θα ήταν πολύ βοηθητικό να χρησιμοποιηθεί το native (και χαοτικό στον προγραμματισμό) σύστημα εκτύπωσης των Windows, χωρίς να απαιτείται εκτυπωτής postscript, κατ' αναλογία με το λογισμικό libreoffice.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> Επιβλέπων: Θανάσης Στάμος<br /> <br /> <br /> === grifobot adventure ===<br /> &lt;https://github.com/vacilos/grifobot&gt;|&lt;p&gt;Το ΓΡΙΦΟΜΠΟΤ (grifobot.gr) είναι ένα διαδικτυακό παιχνίδι που αναπτύχθηκε από το ΓΑΒ LAB προκειμένου να μπορούν παιδιά δημοτικού να απασχοληθούν δημιουργικά συνδυάζοντας ασκήσεις γλώσσας και μαθηματικών με κώδικα. Η αρχική του έκδοση έχει εμπλουτιστεί ώστε να καλύπτει ηλικίες από 5 έως 12 ετών. To Γριφομπότ είναι ένα παιχνίδι που συνδυάζει τη μάθηση σε οποιοδήποτε αντικείμενο μέσα από χρήση αλγορίθμων και αλληλουχιών βημάτων που παραπέμπουν σε κώδικα.&lt;/p&gt;&lt;p&gt;Η έκδοσή του, &amp;quot;ΓΡΙΦΟΜΠΟΤ Quiz&amp;quot;, έχει πιστοποιηθεί από το υπουργείο παιδείας ως εκπαιδευτικό πρόγραμμα και μπορεί να εξυπηρετήσει εκπαιδευτικούς ώστε να εφαρμόσουν διαδικασίες παιχνιδοποίησης στην εκπαίδευση.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Το προτεινόμενο έργο εστιάζεται σε μια επέκταση του Γριφομπότ η οποία μέσα από ένα adventure game (με βάση το Γριφομπότ Quiz) θα δώσει τη δυνατότητα στους εκπαιδευτικούς να παρακολουθούν την εξέλιξη των μαθητών σε συγκεκριμένες θεματικές ενότητες. Παρακολουθώντας την εξέλιξη των μαθητών στο παιχνίδι ο εκπαιδευτικός θα μπορεί να καταλάβει σε ποια σημεία πρέπει να εστιάσει περισσότερο.&lt;/p&gt;&lt;p&gt;Η ιδέα επικεντρώνεται σε μια διαδικασία που θα ξεκινά από ένα gamified assessment ώστε να εντοπιστεί το επίπεδο του κάθε μαθητή και της κάθε μαθήτριας. Στη συνέχεια μέσα από αλληλουχίες διαφορετικών επιπέδων δυσκολίας του παιχνιδιού ο σκοπός θα είναι η μαθήτρια και ο μαθητής να κατακτήσουν μεγαλύτερα επίπεδα γνώσης.&lt;/p&gt;&lt;p&gt;Η εκπαιδευτικός αρκεί να εισάγει ερωτήσεις (και απαντήσεις) για μια θεματική ενότητα χωρίζοντας σε επίπεδα δυσκολίας (και κατάκτησης γνώσης). Στη συνέχεια μέσα από το adventure game ο μαθητής θα πρέπει να κατακτήσει τη γνώση για να ολοκληρώσει το παιχνίδι.&lt;/p&gt;&lt;p&gt;Ειδικότερα για το κομμάτι της βιολογίας υπάρχουν έτοιμα σενάρια χρήσης για την ύλη του Γυμνασίου οπότε η επέκταση θα παρέχει και επαρκές υλικό τουλάχιστον για το συγκεκριμένο μάθημα. Η δε συνεργασία με την εκπαιδευτική κοινότητα θα δημιουργήσει και περιεχόμενο που θα μπορεί να διαμοιραστεί στους εκπαιδευτικούς ώστε να καλύψει κι άλλα μαθήματα.&lt;/p&gt;&lt;p&gt;Για τη συγκεκριμένη επέκταση, σε συνεργασία με τους Μανόλη Wallace (Αν. Καθηγητή), Τάσο Θεοδωρόπουλο (Επικ. Καθηγητή) και Παν. Κόκκινο (Επικ. Καθηγητή) καθώς και με την Υπ. Διδάκτορα, Εκπαιδευτικό Βιολόγο Μαρίνα Λαντζούνη, έχουν γίνει οι κατάλληλοι σχεδιασμοί για τον τρόπο δημιουργίας (σενάρια, χαρακτήρες, εκπαιδευτικά σενάρια, προσωποποίηση σε μαθητές, προσαρμοσμένη μάθηση), συνεπώς η επέκταση έχει ωριμότητα στη σχεδίαση, ενώ υπάρχει και ένα υπόβαθρο (γριφομπότ κουιζ) το οποίο παρέχει την υποδομή πάνω στην οποία μπορεί να χτιστεί η επέκταση.&lt;/p&gt;<br /> <br /> <br /> Επιβλέπων: Βασίλης Πουλόπουλος<br /> <br /> <br /> === gisola ===<br /> https://github.com/nikosT/Gisola&lt;p&gt;Το λογισμικό Gisola είναι ένα λογισμικό ανοικτού κώδικα για την άμεση επίλυση του Τανυστή Σεισμικής Ροπής (ΤΣΡ) σε πραγματικό χρόνο, υλοποιημένο σε αρχιτεκτονικές CPU και/ή GPU. Είναι υλοποιημένο κυρίως στην προγραμματιστική γλώσσα Python χρησιμοποιώντας ανοικτού κώδικα βασικές σεισμολογικές και υπολογιστικές βιβλιοθήκες όπως τις ObsPy, Matplotlib, Numpy κ.α., καθώς και την παράλληλης επεξεργασίας βιβλιοθήκη (multiprocessing). Ωστόσο, αρκετά τμήματα του βασικού του πυρήνα είναι γραμμένα σε Fortran, ενώ της οπτικοποίησης των αποτελεσμάτων σε τεχνολογίες διαδικτύου (π.χ. Leaflet Maps, Jinja2). Στην επιστήμη της σεισμολογίας, o ΤΣΡ είναι μια μαθηματική αναπαράσταση που σχετίζεται άμεσα με τη γεωμετρία του ρήγματος και το μέγεθος του σεισμού. Οι ΤΣΡ χρησιμοποιούνται σε ένα ευρύ φάσμα ερευνητικών θεμάτων όπως τη σεισμοτεκτονική, τη μοντελοποίηση θαλασσίων κυμάτων βαρύτητας (tsunami), την άμεση αντίδραση σε ένα σεισμό, την εκτίμηση των καταστροφών κτλ. και ως εκ τούτου είναι σημαντικός ο γρήγορος και αξιόπιστος υπολογισμός τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Ο κώδικας μαζί με οδηγίες (Wiki) εγκατάστασης και χρήσης είναι ελεύθερος για χρήση, τροποποίηση και αναδιανομή από οποιονδήποτε, και είναι προσβάσιμος για λήψη από το αποθετήριο GitHub (με mirroring στο GitLab). Επιπλέον, η παρουσίαση της εργασίας βραβεύτηκε στο συνέδριο European Geosciences Union (EGU) General Assembly 2021 με το βραβείο Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Award 2021, παρουσιάστηκε, επίσης, στο ετήσιο πανελλήνιο συνέδριο κοινοτήτων ελεύθερου λογισμικού και λογισμικού ανοικτού κώδικα FOSSCOMM 2021, ενώ δημοσιεύθηκε στο καταξιωμένο σεισμολογικό περιοδικό Seismological Research Letters.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Στο προτεινόμενο έργο, ο κώδικας θα επεκταθεί ως προς τα παρακάτω:&lt;/p&gt;&lt;p&gt;a) χρήση 3Δ μοντέλων εδάφους, b) βελτίωση του υπολογιστικού χρόνου, c) εκτίμηση της αβεβαιότητας της λύσης.&lt;/p&gt;<br /> <br /> Επιβλέπων: Ευθύμιος Σώκος<br /> <br /> <br /> === Globaleaks ===<br /> &lt;https://github.com/globaleaks/GlobaLeaks&gt;|&lt;p&gt;Το GlobaLeaks είναι μια πλατφόρμα ανοικτού λογισμικού για το whistleblowing, δηλαδή την ασφαλή και ανώνυμη αναφορά εμπιστευτικών πληροφοριών για παράνομες ή μη ηθικές συμπεριφορές από εργαζόμενους, εξωτερικούς συνεργάτες αλλά και πολίτες χωρίς τον φόβο αντιποίνων.&lt;/p&gt;&lt;p&gt;Οι αναφορές αποστέλλονται σε καθορισμένους παραλήπτες εντός των επιχειρήσεων και των οργανισμών που επιλαμβάνονται των περιστατικών. Οι πλατφόρμες whistleblowing είναι επίκαιρες εξαιτίας της πρόσφατης νομοθεσίας για την προστασία των whistleblowers Ν4990/2020.&lt;/p&gt;&lt;p&gt;Η πλατφόρμα είναι security/privacy by design και έχει σχεδιαστεί για να προστατεύει την ταυτότητα τόσο του αναφέροντος όσο και του αναφερόμενου και χρησιμοποιείται από χιλιάδες οργανώσεις και εταιρίες στον κόσμο για την προώθηση της διαφάνειας και της λογοδοσίας.&lt;/p&gt;&lt;p&gt;Χρησιμοποιούμε το GlobaLeaks apo to 2017 και έχουμε βασίσει πάνω του μια υπηρεσία SaaS για την παροχή ολοκληρωμένων υπηρεσιών whistleblowing. Το πελατολόγιό μας περιλαμβάνει εταιρίες με ηγετικά χαρακτηριστικά όπως μεταξύ άλλων Lamda developnet, Kaizen Gaming (stoiximan), Παπαδοπούλου, όπως επίσης και Δημόσιους οργανισμούς όπως η Γενική Γραμματεία Αθλητισμού και το Γεωπονικό Πανεπιστήμιο. Η πλατφόρμα Whistleblowing έχει συμπεριληφθεί στο πλαίσιο της Εθνικής Πλατφόρμας Αθλητικής Ακεραιότητάς ως ένα από τα έργα της Βίβλου Ψηφιακού Μετασχηματισμού του αθλητισμού μετά από συμμέτοχή μας στο open call του Υπουργείου Ψηφιακού Μετασχηματισμού σε συνεργασία με την ΓΓΑ.&lt;/p&gt;&lt;p&gt;Είμαστε ένα από τα πιο ενεργά μέλη τη κοινότητας του Globaleaks από το 2017 και προτείνουμε και αξιολογούμε νέα feature, ελέγχουμε το λογισμικό για αναφέρουμε σφάλματα. Το 2022 έχουμε συνεισφέρουμε και κώδικα σε συνεργασία με την εταιρία Genesis Technologies μία νεοφυή εταιρία με έδρα το Πακιστάν.&lt;/p&gt;&lt;p&gt;Θα χρειαστούμε την χρηματοδότηση για να αναπτύξουμε νέα features τα όποια ζητούν οι πελάτες μας ή εμείς κρίνουμε ότι θα ήταν χρήσιμα για αυτούς για να γίνουμε πιο ανταγωνιστικοί. Τα features θα ενσωματωθούν στο ανοικτό λογισμικό προς όφελος των εκατοντάδων χιλιάδων δυνητικών χρηστών.&lt;/p&gt;<br /> <br /> <br /> Επιβλέπων:Βασίλης Σταματόπουλος<br /> <br /> <br /> === opendesign4publiconstructions ===<br /> <br /> https://github.com/nataliskordou/opendesignpubliconstructions<br /> <br /> Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.<br /> <br /> Επιβλέπουσα: Ναταλία Σκόρδου<br /> <br /> === Ιδέες λογισμικού για βελτίωση ===<br /> {| class=&quot;wikitable&quot;<br /> |Wikidata<br /> |&lt;nowiki&gt;https://www.wikidata.org/wiki/Wikidata:Contribute&lt;/nowiki&gt;<br /> |Γνωσιακή βάση διασυνδεδεμένων ανοιχτών δεδομένων<br /> |-<br /> |LibP2P<br /> |&lt;nowiki&gt;https://github.com/libp2p&lt;/nowiki&gt;<br /> |Peer to peer content-addressing library<br /> |-<br /> |CKAN<br /> |&lt;nowiki&gt;https://github.com/ckan/ckan&lt;/nowiki&gt;<br /> |Σύστημα διαχείρισης ανοιχτών δεδομένων<br /> |-<br /> |Minnowboard<br /> |&lt;nowiki&gt;https://www.minnowboard.org/&lt;/nowiki&gt;<br /> |Open source single board computers<br /> |-<br /> |QualCoder<br /> |&lt;nowiki&gt;https://github.com/ccbogel/QualCoder&lt;/nowiki&gt;<br /> |Περιβάλλον ανάλυσης περιεχομένου - κωδικοποίησης αδόμητου περιεχομένου σε Python 3<br /> |-<br /> |R Studio<br /> |&lt;nowiki&gt;https://github.com/rstudio/rstudio&lt;/nowiki&gt;<br /> |Περιβάλλον εργασίας για τη γλώσσα στατιστικού προγραμματισμού R<br /> |-<br /> |Zotero<br /> |&lt;nowiki&gt;https://github.com/zotero/zotero&lt;/nowiki&gt;<br /> |Διαχειριστής βιβλιογραφίας και τεκμηρίωσης, απευθύνεται σε ερευνητές μεταπτυχιακού επιπέδου και πάνω<br /> |}</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%A0%CF%81%CE%BF%CF%84%CE%AC%CF%83%CE%B5%CE%B9%CF%82_%CE%AD%CF%81%CE%B3%CF%89%CE%BD&diff=2171 2023-03-03T11:44:00Z

<p>Pkst-1: </p> <hr /> <div><br /> <br /> === MD Guide ===<br /> '''https://github.com/md-guide/md-guide'''&lt;p&gt;Το MD Guide πρόκειται για ένα εργαλείο συγγραφής τεχνικής τεκμηρίωσης και οδηγών (technical documentation) το οποίο βασίζεται στη γλώσσα περιγραφής Markdown. Το Markdown είναι ιδιαίτερα δημοφιλές τα τελευταία χρόνια, ειδικά μετά την κυριάρχηση του Github και του Open source τη δεκαετία του 2010-2020 και έχει γίνει πλέον η κατ'εξοχήν γλώσσα επιλογής για προγραμματιστές που θέλουν να μορφοποιήσουν εύκολα και γρήγορα τα κείμενα τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Με βάση αυτό, το MD Guide υλοποιεί ένα εργαλείο στο οποίο τα παραδείγματα κώδικα που εμπεριέχονται σε αρχεία markdown γίνονται πλέον διαδρασικά για τους αναγνώστες του περιεχομένου. Σε αντίθεση με άλλα εργαλεία που περιορίζονται μόνο σε Javascript/Typescript υλοποιήσεις και παραδείγματα, το MD Guide σκοπεύει να «πακετάρει» λειτουργικότητα ανεξαρτήτως της γλώσσας στην οποία είναι το παράδειγμα και να τρέξει το runtime σε Docker (containerized runtime) και να επιστρέφει σε πραγματικό χρόνο το αποτέλεσμα των διεργασίων (stdout) στον web browser των χρηστών. Τέλος, μπορεί να βοηθάει τους μηχανικούς λογισμικού να έχουν πρόσβαση σε προγραμματιστικούς τύπους (types &amp;amp;amp; interfaces) του πηγαίου κώδικα τους και να τους παρεμβάλουν εντός των τεκμηριώσεων που συγγράφουν.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Αυτή τη στιγμή το project βρίσκεται σε πρώιμο στάδιο ανάπτυξης και σχεδιασμού, και σκοπεύουμε η ανάπτυξη να γίνει σε ανοιχτά hackathons με τη συμμετοχή ατόμων με καθόλου, λίγη ή πολυετή εμπειρία στην ανάπτυξη λογισμικού με σκοπό την επιμόρφωση και την χρήση συλλογικών πρακτικών ανάπτυξης.&lt;/p&gt;<br /> <br /> === Επέκταση του ανοιχτού λογισμικού Dspace (v7) για ανάρτηση Ανοιχτών Εκπαιδευτικών Πόρων (OERs) ===<br /> <br /> <br /> Το Dspace αποτελεί ένα από τα πιο δημοφιλή λογισμικά ανοιχτού κώδικα για διατήρηση και φιλοξενεία ψηφιακής πληροφορίας κάθε είδους (repositorial infrastructures). Ήδη το Dspace χρησιμοποιείται από διάφορους φορείς σε εθνικό (π.χ. ΥΠΑΙΠΘ, ΕΚΤ κ.λπ.) αλλά κυρίως σε παγκόσμιο επίπεδο. Σκοπός της παρούσας πρότασης είναι η επέκταση του Dspace ώστε να μπορεί να παρέχει δυνατότητα ανάρτησης OERs (pdfs, words, html5, multimedia κ.λπ.) οποιουδήποτε μορφότυπου, με συνοδεία κατάλληλου εμπλουτισμού/τεκμηρίωσης με αξιοποίηση και επέκταση εκπαιδευτικών προτύπων (π.χ. IEEE LOM) και ενσωμάτωση τρίτων εργαλείων (π.χ. IIIF viewer, Video Streaming, e-pub viewers κ.λπ,). Οι επεκτάσεις που θα γίνουν, θα υλοποιηθούν στην τελευταία έκδοση του Dspace (7.x.), η οποία αυτή τη στιγμή χρησιμοποιείται ελάχιστα σε εθνικό επίπεδο (π.χ. το ΥΠΑΙΠΘ χρησιμοποιεί ακόμα την έκδοση 1.8 του Dspace για την πλειοψηφία των εφαρμογών του). Συγκεκριμένα, το Dspace θα επεκταθεί ώστε να υποστηρίξει:&lt;p&gt;- Ανάρτηση και διαμοιρασμό OERs από εγγεγραμμένους χρήστες. Το περιεχόμενο αυτό θα διαμοιράζεται ανοιχτά με βάση συγκεκριμένης προτυποποίησης (π.χ. REST, OAI-PMH κ.λπ.)&lt;/p&gt;&lt;p&gt;- Ενωσμάτωση σχήματος μεταδεδομένων OERs&lt;/p&gt;&lt;p&gt;- Προσαρμογή περιβάλλοντος χρήστη, πλοήγησης και αναζήτησης με χρήση φίλτρων και προηγμένης αναζήτησης&lt;/p&gt;&lt;p&gt;- Ολοκλήρωση με τρίτα εργαλεία για βέλτιστη προεπισκόπηση περιεχομένου&lt;/p&gt;&lt;p&gt;- Ενσωμάτωση επιλογής διαφορετικών αδειών χρήσης με βάση τις ανάγκες του εκάστοτε δημιουργού OER&lt;/p&gt;&lt;p&gt;- Δυνατότητα ενσωμάτωσης μαζικού περιεχομένου με χρήση τεχνολογιών harvesting&lt;/p&gt;&lt;p&gt;- Δυνατότητα προβολής και διαμοιρασμού των OERs με χρήση κατάλληλων προτύπων και τεχνολογιών.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Η παρούσα πρόταση εντάσσεται και στις 2 κατηγορίες Ανοιχτού Λογισμικού και Ανοιχτού Περιεχομένου, δεδομένου ότι στηρίζεται τόσο σε χρήση και επέκταση ανοιχτού λογισμικού, όσο και στην παραγωγή ανοιχτών εκπαιδευτικών πόρων που μπορούν να χρησιμοποιηθούν στην ευρύτερη εκπαιδευτική (και όχι μόνο) κοινότητα (μαθητές, εκπαιδευτικούς, γονείς κ.α.).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === SciDavis, SciLab ===<br /> https://scidavis.sourceforge.net/, https://www.scilab.org/|&lt;p&gt;SciDAVis is a free application for Scientific Data Analysis and Visualization.&lt;/p&gt;&lt;p&gt;Sci Lab is Open source software for numerical computation&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === Open Robotic Manipulator for Remote Labs in Science and Technology Education ===<br /> <br /> <br /> |https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/,<br /> <br /> https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225|<br /> <br /> Description:&lt;p&gt;Remote teaching, for all educational levels, is one of the most added value concepts, during the last years, for both education and industry. The covid boosted high technologies, i.e IoT, 5G, computing power/cost, VR, AR, and robotics are now mature and there are plenty of DIY and OPEN approaches that could be used for enhancing hands-on teaching for pupils, students, and professionals under the concept of lifelong learning. Robotics can augment STEAM education by supporting a laboratory for remote teaching based on physics experiments allowing one to handle and manipulate activities of basic electrical, electronics, and mechatronics labs. During the proposed project a robotic manipulator (4 or 6 DoF) will be developed (based on available open-source 3D printed robot arms), constructed (assemble of 3d printed parts, electronics, motors, sensors), and programmed (using Python and/or Blockly code). The robot should, autonomously, perform experiments, using IoT sensors, actuators, controllers, etc. The DIY IoT sensors, actuators, and controllers are part of another project and are designed to facilitate their handling by the robotic manipulator. Therefore, it could be programmed and remotely controlled by a trainee for developing and implementing an experiment with the DIY IoT sensors, actuators, and controllers that are made available in a university or school laboratory. Hence, an appropriate open-source gripping system should be developed. The robot-gripper system should be designed for “easy for assembly” with a user-friendly interface while trying to minimize the cost using only open-source resources. It should be composed of only two main parts: 3D printed components (PLA) and electronics (motors, sensors, controller, etc.) as well as minimum connective mechanical parts.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;References:&lt;p&gt;- https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/&lt;/p&gt;&lt;p&gt;- https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225&lt;/p&gt;&lt;p&gt;- https://hackaday.io/search?term=arm&lt;/p&gt;&lt;p&gt;- https://all3dp.com/2/3d-printed-robot-arm-diy-robotic/&lt;/p&gt;&lt;p&gt;&lt;/p&gt;Expected Results:&lt;p&gt;The expected results of the three months project are:&lt;/p&gt;&lt;p&gt;-Understanding of how 6DoF robotic manipulators are designed, constructed, and programmed&lt;/p&gt;&lt;p&gt;-Designs of the shape and basic components of the robot using CAD software ready for 3D printing&lt;/p&gt;&lt;p&gt;-Simulation of the robot’s forward and inverse kinematics&lt;/p&gt;&lt;p&gt;-List of 3D printed components (cad, stl files) and list of required electronic components&lt;/p&gt;&lt;p&gt;-Demonstration of three case studies (remotely executed experiments, i.e., gripping of an IoT sensor, placement to the POI for monitoring, and returning it to the storage area)&lt;/p&gt;&lt;p&gt;-Github repositories development&lt;/p&gt;&lt;p&gt;-Documentation and assembly instructions&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Knowledge Prerequisites:&lt;/p&gt;&lt;p&gt;Required: Robotics, CAD software&lt;/p&gt;&lt;p&gt;Desired: Python, Real-time 3D (RT3D) platforms&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated development budget:&lt;/p&gt;&lt;p&gt;12000 € personnel&lt;/p&gt;&lt;p&gt;5000 € electronics, controllers, motors, 3d printing, consumables&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated reproduction cost of the robot:&lt;/p&gt;&lt;p&gt;The final robotic manipulator will be designed to be massively reproducible by anyone at an estimated cost of 1000-2000€ while following the concept of open source.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Proposed Mentors:&lt;/p&gt;&lt;p&gt;Panagiotis Koustoumpardis and Hariton Polatoglou&lt;/p&gt;<br /> <br /> === Βιβλιοθήκη φιγούρων και σκηνικών ψηφιακού θεάτρου σκιών ===<br /> http://eshadow.gr/<br /> <br /> To εργαστήριο TUC-MUSIC της Σχολής Ηλεκτρολόγων Μηχανικών και Μηχανικών του Πολυτεχνείου Κρήτης έχει αναπτύξει και διανέμει ελεύθερα το ψηφιακό θέατρο σκιών http://eshadow.gr/ το οποίο συνοδεύεται από μια βιβλιοθήκη ψηφιακών φιγούρων και σκηνικών που μπορούν να χρησιμοποιούν για τη δημιουργία ψηφιακών παραστάσεων. Μας ενδιαφέρει η συνεργασία με πρόσωπα που έχουν τη δυνατότητα να δημιουργήσουν νέες φιγούρες και σκηνικά ώστε να αξιοποιηθούν από τους χρήστες του λογισμικού αυτού (κυρίως σχολεία αλλά και μεμονωμένοι χρήστες). Ενδεικτικά παραπέμπουμε σε μια τέτοια συλλογή υλικό: https://www.dropbox.com/sh/kj0xl32ntly5sbi/AAAt6ftq87goHeTpcKULzrV8a?dl=0&lt;p&gt;Κάθε ψηφιακή φιγούρα αποτελείται από δύο μέρη: Μια εικόνα που αποτελείται από τα επιμέρους κομμάτια που την αρθρώνουν και ένα αρχείο json που περιγράφει τον τρόπο άρθρωσής τους. Αυτό μας ενδιαφέρει είναι η παραγωγή των αρχείων εικόνας, τα αρχεία json μπορούμε να τα δημιουργήσουμε στη συνέχεια εμείς. Ιδιαίτερα θα μας ενδιέφερε, μεταξύ των άλλων, η δημιουργία φιγούρων με βάση παραδοσιακές ιστορίες, μύθους κ.λ.π. καθώς και φιγούρων που μπορούν να βασίζονται σε εικαστικές απαπαραστάσεις από έργα που είναι ελεύθερα (για παράδειγμα πίνακες ζωγραφικής, εικονογραφημένες ιστορίες, αρχαία αγγεία η νωπογραφίες κ.λ.π.).&lt;/p&gt;|<br /> |Βελτιώσεις για το διαλογικό σύστημα Rasa|&lt;https://github.com/kosniaz/rasa-improvements&gt;|&lt;p&gt;Επίλυση προβλημάτων με την πλατφόρμα του Rasa όπως:&lt;/p&gt;&lt;p&gt;\* επίλυση bug με το memoization policy (περιγράφεται εδώ: https://forum.rasa.com/t/there-is-no-memorised-next-action )&lt;/p&gt;&lt;p&gt;\* δημιουργία open source λύσης για data collection/annotation/training, αντί του κλειστού εργαλείου Rasa X (περιγράφεται εδώ: https://forum.rasa.com/t/alternatives-to-rasa-x/ )&lt;/p&gt;&lt;p&gt;\* βελτίωση του logging&lt;/p&gt;&lt;p&gt;\* προσθήκη περισσοτερων dialogue policies ή και βελτίωση υπάρχοντων&lt;/p&gt;<br /> <br /> === DMCRM ===<br /> https://github.com/daniilidisK/crm-application<br /> <br /> Το έργο αποτελεί λογισμικό διαχείρισης λειτουργιών μίας εταιρίας, από την έκδοση τιμολογίων, στην οργάνωση βάσης δεδομένων, τη διαχείριση εγγράφων σε συνάρτηση των εργασιών που αποθηκεύονται στη βάση δεδομένων.|<br /> |opendesign4publiconstructions|&lt;https://github.com/nataliskordou/opendesignpubliconstructions&gt;|Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.|<br /> <br /> === BCL ===<br /> https://github.com/clavisound/feather-LoRa32u4-sketches&lt;p&gt;Συσκευή ιχνηλάτησης (tracking) IoT ανοιχτού υλικού ( hardware) και ανοιχτού λογισμικού που δοκιμαστηκε με το δίκτυο TTN V2.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Χρησιμοποιεί ελάχιστα δεδομένα (μόνο 7 bytes) με πολύ περισσότερα δεδομένα (ταχύτητα, κατεύθυνση, κατάσταση) από τις υπάρχουσες εμπορικές προτάσεις ΚΑΙ δυο προεπιλεγμένα τυχαία SF ώστε να είναι χρήσιμη σε πραγματικές συνθήκες IoT εκατομμυρίων συσκευών, μικρότερο airtime για αποφυγή παρεμβολών (collision).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Για την νέα εποχή (TTN V3 ή helium) πρέπει να αναβαθμιστεί με&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1. OTAA&lt;/p&gt;&lt;p&gt;2. Downlinks (MAC και custom. εντολές)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Επιπλέον θα μπορούσαν να οριστούν λευκές ζώνες για ακόμη λιγότερες παρεμβολές.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === ThanCad, εκτύπωση σε Windows ===<br /> &lt;https://thancad.sourceforge.net/&gt;|&lt;p&gt;Ελεύθερο (άδεια GPL v2 ή νεώτερη) λογισμικό CAD για μηχανικούς. Διδάσκεται στη Σχολή Πολ. Μηχανικών ΕΜΠ. Χρησιμοποιείται από τη Σχολή Τοπογράφων Ε.Μ.Π., Τμήμα Πολ. Μηχανικών Πανεπιστημίου Θεσσαλίας, και τμήμα πολ. Μηχανικών ΠΑ.Δ.Α. Τρέχει σε Linux, FreeBSD, OpenBSD, και Windows. Έχει δοκιμαστεί και τρέχει σε παλαιότερη έκδοση του MACOS X.&lt;/p&gt;&lt;p&gt;Πολλοί/ες φοιτητές/τριες είναι ακόμα εγκλωβισμένοι σε Windows λόγω του CAD. Το ThanCad είναι command compatible με AutoCAD και έτσι αποσκοπεί στην εξοικείωση των νέων μηχανικών με ελεύθερο λογισμικό χωρίς να χρειάζεται να καταβάλλουν μεγάλη προσπάθεια.&lt;/p&gt;&lt;p&gt;Παρόλο που το ThanCad μπορεί να τυπώσει και σε Windows μέσω postscript, θα ήταν πολύ βοηθητικό να χρησιμοποιηθεί το native (και χαοτικό στον προγραμματισμό) σύστημα εκτύπωσης των Windows, χωρίς να απαιτείται εκτυπωτής postscript, κατ' αναλογία με το λογισμικό libreoffice.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === grifobot adventure ===<br /> &lt;https://github.com/vacilos/grifobot&gt;|&lt;p&gt;Το ΓΡΙΦΟΜΠΟΤ (grifobot.gr) είναι ένα διαδικτυακό παιχνίδι που αναπτύχθηκε από το ΓΑΒ LAB προκειμένου να μπορούν παιδιά δημοτικού να απασχοληθούν δημιουργικά συνδυάζοντας ασκήσεις γλώσσας και μαθηματικών με κώδικα. Η αρχική του έκδοση έχει εμπλουτιστεί ώστε να καλύπτει ηλικίες από 5 έως 12 ετών. To Γριφομπότ είναι ένα παιχνίδι που συνδυάζει τη μάθηση σε οποιοδήποτε αντικείμενο μέσα από χρήση αλγορίθμων και αλληλουχιών βημάτων που παραπέμπουν σε κώδικα.&lt;/p&gt;&lt;p&gt;Η έκδοσή του, &amp;quot;ΓΡΙΦΟΜΠΟΤ Quiz&amp;quot;, έχει πιστοποιηθεί από το υπουργείο παιδείας ως εκπαιδευτικό πρόγραμμα και μπορεί να εξυπηρετήσει εκπαιδευτικούς ώστε να εφαρμόσουν διαδικασίες παιχνιδοποίησης στην εκπαίδευση.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Το προτεινόμενο έργο εστιάζεται σε μια επέκταση του Γριφομπότ η οποία μέσα από ένα adventure game (με βάση το Γριφομπότ Quiz) θα δώσει τη δυνατότητα στους εκπαιδευτικούς να παρακολουθούν την εξέλιξη των μαθητών σε συγκεκριμένες θεματικές ενότητες. Παρακολουθώντας την εξέλιξη των μαθητών στο παιχνίδι ο εκπαιδευτικός θα μπορεί να καταλάβει σε ποια σημεία πρέπει να εστιάσει περισσότερο.&lt;/p&gt;&lt;p&gt;Η ιδέα επικεντρώνεται σε μια διαδικασία που θα ξεκινά από ένα gamified assessment ώστε να εντοπιστεί το επίπεδο του κάθε μαθητή και της κάθε μαθήτριας. Στη συνέχεια μέσα από αλληλουχίες διαφορετικών επιπέδων δυσκολίας του παιχνιδιού ο σκοπός θα είναι η μαθήτρια και ο μαθητής να κατακτήσουν μεγαλύτερα επίπεδα γνώσης.&lt;/p&gt;&lt;p&gt;Η εκπαιδευτικός αρκεί να εισάγει ερωτήσεις (και απαντήσεις) για μια θεματική ενότητα χωρίζοντας σε επίπεδα δυσκολίας (και κατάκτησης γνώσης). Στη συνέχεια μέσα από το adventure game ο μαθητής θα πρέπει να κατακτήσει τη γνώση για να ολοκληρώσει το παιχνίδι.&lt;/p&gt;&lt;p&gt;Ειδικότερα για το κομμάτι της βιολογίας υπάρχουν έτοιμα σενάρια χρήσης για την ύλη του Γυμνασίου οπότε η επέκταση θα παρέχει και επαρκές υλικό τουλάχιστον για το συγκεκριμένο μάθημα. Η δε συνεργασία με την εκπαιδευτική κοινότητα θα δημιουργήσει και περιεχόμενο που θα μπορεί να διαμοιραστεί στους εκπαιδευτικούς ώστε να καλύψει κι άλλα μαθήματα.&lt;/p&gt;&lt;p&gt;Για τη συγκεκριμένη επέκταση, σε συνεργασία με τους Μανόλη Wallace (Αν. Καθηγητή), Τάσο Θεοδωρόπουλο (Επικ. Καθηγητή) και Παν. Κόκκινο (Επικ. Καθηγητή) καθώς και με την Υπ. Διδάκτορα, Εκπαιδευτικό Βιολόγο Μαρίνα Λαντζούνη, έχουν γίνει οι κατάλληλοι σχεδιασμοί για τον τρόπο δημιουργίας (σενάρια, χαρακτήρες, εκπαιδευτικά σενάρια, προσωποποίηση σε μαθητές, προσαρμοσμένη μάθηση), συνεπώς η επέκταση έχει ωριμότητα στη σχεδίαση, ενώ υπάρχει και ένα υπόβαθρο (γριφομπότ κουιζ) το οποίο παρέχει την υποδομή πάνω στην οποία μπορεί να χτιστεί η επέκταση.&lt;/p&gt;<br /> <br /> === gisola ===<br /> https://github.com/nikosT/Gisola&lt;p&gt;Το λογισμικό Gisola είναι ένα λογισμικό ανοικτού κώδικα για την άμεση επίλυση του Τανυστή Σεισμικής Ροπής (ΤΣΡ) σε πραγματικό χρόνο, υλοποιημένο σε αρχιτεκτονικές CPU και/ή GPU. Είναι υλοποιημένο κυρίως στην προγραμματιστική γλώσσα Python χρησιμοποιώντας ανοικτού κώδικα βασικές σεισμολογικές και υπολογιστικές βιβλιοθήκες όπως τις ObsPy, Matplotlib, Numpy κ.α., καθώς και την παράλληλης επεξεργασίας βιβλιοθήκη (multiprocessing). Ωστόσο, αρκετά τμήματα του βασικού του πυρήνα είναι γραμμένα σε Fortran, ενώ της οπτικοποίησης των αποτελεσμάτων σε τεχνολογίες διαδικτύου (π.χ. Leaflet Maps, Jinja2). Στην επιστήμη της σεισμολογίας, o ΤΣΡ είναι μια μαθηματική αναπαράσταση που σχετίζεται άμεσα με τη γεωμετρία του ρήγματος και το μέγεθος του σεισμού. Οι ΤΣΡ χρησιμοποιούνται σε ένα ευρύ φάσμα ερευνητικών θεμάτων όπως τη σεισμοτεκτονική, τη μοντελοποίηση θαλασσίων κυμάτων βαρύτητας (tsunami), την άμεση αντίδραση σε ένα σεισμό, την εκτίμηση των καταστροφών κτλ. και ως εκ τούτου είναι σημαντικός ο γρήγορος και αξιόπιστος υπολογισμός τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Ο κώδικας μαζί με οδηγίες (Wiki) εγκατάστασης και χρήσης είναι ελεύθερος για χρήση, τροποποίηση και αναδιανομή από οποιονδήποτε, και είναι προσβάσιμος για λήψη από το αποθετήριο GitHub (με mirroring στο GitLab). Επιπλέον, η παρουσίαση της εργασίας βραβεύτηκε στο συνέδριο European Geosciences Union (EGU) General Assembly 2021 με το βραβείο Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Award 2021, παρουσιάστηκε, επίσης, στο ετήσιο πανελλήνιο συνέδριο κοινοτήτων ελεύθερου λογισμικού και λογισμικού ανοικτού κώδικα FOSSCOMM 2021, ενώ δημοσιεύθηκε στο καταξιωμένο σεισμολογικό περιοδικό Seismological Research Letters.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Στο προτεινόμενο έργο, ο κώδικας θα επεκταθεί ως προς τα παρακάτω:&lt;/p&gt;&lt;p&gt;a) χρήση 3Δ μοντέλων εδάφους, b) βελτίωση του υπολογιστικού χρόνου, c) εκτίμηση της αβεβαιότητας της λύσης.&lt;/p&gt;<br /> <br /> === Globaleaks ===<br /> &lt;https://github.com/globaleaks/GlobaLeaks&gt;|&lt;p&gt;Το GlobaLeaks είναι μια πλατφόρμα ανοικτού λογισμικού για το whistleblowing, δηλαδή την ασφαλή και ανώνυμη αναφορά εμπιστευτικών πληροφοριών για παράνομες ή μη ηθικές συμπεριφορές από εργαζόμενους, εξωτερικούς συνεργάτες αλλά και πολίτες χωρίς τον φόβο αντιποίνων.&lt;/p&gt;&lt;p&gt;Οι αναφορές αποστέλλονται σε καθορισμένους παραλήπτες εντός των επιχειρήσεων και των οργανισμών που επιλαμβάνονται των περιστατικών. Οι πλατφόρμες whistleblowing είναι επίκαιρες εξαιτίας της πρόσφατης νομοθεσίας για την προστασία των whistleblowers Ν4990/2020.&lt;/p&gt;&lt;p&gt;Η πλατφόρμα είναι security/privacy by design και έχει σχεδιαστεί για να προστατεύει την ταυτότητα τόσο του αναφέροντος όσο και του αναφερόμενου και χρησιμοποιείται από χιλιάδες οργανώσεις και εταιρίες στον κόσμο για την προώθηση της διαφάνειας και της λογοδοσίας.&lt;/p&gt;&lt;p&gt;Χρησιμοποιούμε το GlobaLeaks apo to 2017 και έχουμε βασίσει πάνω του μια υπηρεσία SaaS για την παροχή ολοκληρωμένων υπηρεσιών whistleblowing. Το πελατολόγιό μας περιλαμβάνει εταιρίες με ηγετικά χαρακτηριστικά όπως μεταξύ άλλων Lamda developnet, Kaizen Gaming (stoiximan), Παπαδοπούλου, όπως επίσης και Δημόσιους οργανισμούς όπως η Γενική Γραμματεία Αθλητισμού και το Γεωπονικό Πανεπιστήμιο. Η πλατφόρμα Whistleblowing έχει συμπεριληφθεί στο πλαίσιο της Εθνικής Πλατφόρμας Αθλητικής Ακεραιότητάς ως ένα από τα έργα της Βίβλου Ψηφιακού Μετασχηματισμού του αθλητισμού μετά από συμμέτοχή μας στο open call του Υπουργείου Ψηφιακού Μετασχηματισμού σε συνεργασία με την ΓΓΑ.&lt;/p&gt;&lt;p&gt;Είμαστε ένα από τα πιο ενεργά μέλη τη κοινότητας του Globaleaks από το 2017 και προτείνουμε και αξιολογούμε νέα feature, ελέγχουμε το λογισμικό για αναφέρουμε σφάλματα. Το 2022 έχουμε συνεισφέρουμε και κώδικα σε συνεργασία με την εταιρία Genesis Technologies μία νεοφυή εταιρία με έδρα το Πακιστάν.&lt;/p&gt;&lt;p&gt;Θα χρειαστούμε την χρηματοδότηση για να αναπτύξουμε νέα features τα όποια ζητούν οι πελάτες μας ή εμείς κρίνουμε ότι θα ήταν χρήσιμα για αυτούς για να γίνουμε πιο ανταγωνιστικοί. Τα features θα ενσωματωθούν στο ανοικτό λογισμικό προς όφελος των εκατοντάδων χιλιάδων δυνητικών χρηστών.&lt;/p&gt;</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%A0%CF%81%CE%BF%CF%84%CE%AC%CF%83%CE%B5%CE%B9%CF%82_%CE%AD%CF%81%CE%B3%CF%89%CE%BD&diff=2170 2023-03-03T11:43:46Z

<p>Pkst-1: </p> <hr /> <div><br /> <br /> === '''MD Guide''' ===<br /> '''https://github.com/md-guide/md-guide'''&lt;p&gt;Το MD Guide πρόκειται για ένα εργαλείο συγγραφής τεχνικής τεκμηρίωσης και οδηγών (technical documentation) το οποίο βασίζεται στη γλώσσα περιγραφής Markdown. Το Markdown είναι ιδιαίτερα δημοφιλές τα τελευταία χρόνια, ειδικά μετά την κυριάρχηση του Github και του Open source τη δεκαετία του 2010-2020 και έχει γίνει πλέον η κατ'εξοχήν γλώσσα επιλογής για προγραμματιστές που θέλουν να μορφοποιήσουν εύκολα και γρήγορα τα κείμενα τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Με βάση αυτό, το MD Guide υλοποιεί ένα εργαλείο στο οποίο τα παραδείγματα κώδικα που εμπεριέχονται σε αρχεία markdown γίνονται πλέον διαδρασικά για τους αναγνώστες του περιεχομένου. Σε αντίθεση με άλλα εργαλεία που περιορίζονται μόνο σε Javascript/Typescript υλοποιήσεις και παραδείγματα, το MD Guide σκοπεύει να «πακετάρει» λειτουργικότητα ανεξαρτήτως της γλώσσας στην οποία είναι το παράδειγμα και να τρέξει το runtime σε Docker (containerized runtime) και να επιστρέφει σε πραγματικό χρόνο το αποτέλεσμα των διεργασίων (stdout) στον web browser των χρηστών. Τέλος, μπορεί να βοηθάει τους μηχανικούς λογισμικού να έχουν πρόσβαση σε προγραμματιστικούς τύπους (types &amp;amp;amp; interfaces) του πηγαίου κώδικα τους και να τους παρεμβάλουν εντός των τεκμηριώσεων που συγγράφουν.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Αυτή τη στιγμή το project βρίσκεται σε πρώιμο στάδιο ανάπτυξης και σχεδιασμού, και σκοπεύουμε η ανάπτυξη να γίνει σε ανοιχτά hackathons με τη συμμετοχή ατόμων με καθόλου, λίγη ή πολυετή εμπειρία στην ανάπτυξη λογισμικού με σκοπό την επιμόρφωση και την χρήση συλλογικών πρακτικών ανάπτυξης.&lt;/p&gt;<br /> <br /> === Επέκταση του ανοιχτού λογισμικού Dspace (v7) για ανάρτηση Ανοιχτών Εκπαιδευτικών Πόρων (OERs) ===<br /> <br /> <br /> Το Dspace αποτελεί ένα από τα πιο δημοφιλή λογισμικά ανοιχτού κώδικα για διατήρηση και φιλοξενεία ψηφιακής πληροφορίας κάθε είδους (repositorial infrastructures). Ήδη το Dspace χρησιμοποιείται από διάφορους φορείς σε εθνικό (π.χ. ΥΠΑΙΠΘ, ΕΚΤ κ.λπ.) αλλά κυρίως σε παγκόσμιο επίπεδο. Σκοπός της παρούσας πρότασης είναι η επέκταση του Dspace ώστε να μπορεί να παρέχει δυνατότητα ανάρτησης OERs (pdfs, words, html5, multimedia κ.λπ.) οποιουδήποτε μορφότυπου, με συνοδεία κατάλληλου εμπλουτισμού/τεκμηρίωσης με αξιοποίηση και επέκταση εκπαιδευτικών προτύπων (π.χ. IEEE LOM) και ενσωμάτωση τρίτων εργαλείων (π.χ. IIIF viewer, Video Streaming, e-pub viewers κ.λπ,). Οι επεκτάσεις που θα γίνουν, θα υλοποιηθούν στην τελευταία έκδοση του Dspace (7.x.), η οποία αυτή τη στιγμή χρησιμοποιείται ελάχιστα σε εθνικό επίπεδο (π.χ. το ΥΠΑΙΠΘ χρησιμοποιεί ακόμα την έκδοση 1.8 του Dspace για την πλειοψηφία των εφαρμογών του). Συγκεκριμένα, το Dspace θα επεκταθεί ώστε να υποστηρίξει:&lt;p&gt;- Ανάρτηση και διαμοιρασμό OERs από εγγεγραμμένους χρήστες. Το περιεχόμενο αυτό θα διαμοιράζεται ανοιχτά με βάση συγκεκριμένης προτυποποίησης (π.χ. REST, OAI-PMH κ.λπ.)&lt;/p&gt;&lt;p&gt;- Ενωσμάτωση σχήματος μεταδεδομένων OERs&lt;/p&gt;&lt;p&gt;- Προσαρμογή περιβάλλοντος χρήστη, πλοήγησης και αναζήτησης με χρήση φίλτρων και προηγμένης αναζήτησης&lt;/p&gt;&lt;p&gt;- Ολοκλήρωση με τρίτα εργαλεία για βέλτιστη προεπισκόπηση περιεχομένου&lt;/p&gt;&lt;p&gt;- Ενσωμάτωση επιλογής διαφορετικών αδειών χρήσης με βάση τις ανάγκες του εκάστοτε δημιουργού OER&lt;/p&gt;&lt;p&gt;- Δυνατότητα ενσωμάτωσης μαζικού περιεχομένου με χρήση τεχνολογιών harvesting&lt;/p&gt;&lt;p&gt;- Δυνατότητα προβολής και διαμοιρασμού των OERs με χρήση κατάλληλων προτύπων και τεχνολογιών.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Η παρούσα πρόταση εντάσσεται και στις 2 κατηγορίες Ανοιχτού Λογισμικού και Ανοιχτού Περιεχομένου, δεδομένου ότι στηρίζεται τόσο σε χρήση και επέκταση ανοιχτού λογισμικού, όσο και στην παραγωγή ανοιχτών εκπαιδευτικών πόρων που μπορούν να χρησιμοποιηθούν στην ευρύτερη εκπαιδευτική (και όχι μόνο) κοινότητα (μαθητές, εκπαιδευτικούς, γονείς κ.α.).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === SciDavis, SciLab ===<br /> https://scidavis.sourceforge.net/, https://www.scilab.org/|&lt;p&gt;SciDAVis is a free application for Scientific Data Analysis and Visualization.&lt;/p&gt;&lt;p&gt;Sci Lab is Open source software for numerical computation&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === Open Robotic Manipulator for Remote Labs in Science and Technology Education ===<br /> <br /> <br /> |https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/,<br /> <br /> https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225|<br /> <br /> Description:&lt;p&gt;Remote teaching, for all educational levels, is one of the most added value concepts, during the last years, for both education and industry. The covid boosted high technologies, i.e IoT, 5G, computing power/cost, VR, AR, and robotics are now mature and there are plenty of DIY and OPEN approaches that could be used for enhancing hands-on teaching for pupils, students, and professionals under the concept of lifelong learning. Robotics can augment STEAM education by supporting a laboratory for remote teaching based on physics experiments allowing one to handle and manipulate activities of basic electrical, electronics, and mechatronics labs. During the proposed project a robotic manipulator (4 or 6 DoF) will be developed (based on available open-source 3D printed robot arms), constructed (assemble of 3d printed parts, electronics, motors, sensors), and programmed (using Python and/or Blockly code). The robot should, autonomously, perform experiments, using IoT sensors, actuators, controllers, etc. The DIY IoT sensors, actuators, and controllers are part of another project and are designed to facilitate their handling by the robotic manipulator. Therefore, it could be programmed and remotely controlled by a trainee for developing and implementing an experiment with the DIY IoT sensors, actuators, and controllers that are made available in a university or school laboratory. Hence, an appropriate open-source gripping system should be developed. The robot-gripper system should be designed for “easy for assembly” with a user-friendly interface while trying to minimize the cost using only open-source resources. It should be composed of only two main parts: 3D printed components (PLA) and electronics (motors, sensors, controller, etc.) as well as minimum connective mechanical parts.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;References:&lt;p&gt;- https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/&lt;/p&gt;&lt;p&gt;- https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225&lt;/p&gt;&lt;p&gt;- https://hackaday.io/search?term=arm&lt;/p&gt;&lt;p&gt;- https://all3dp.com/2/3d-printed-robot-arm-diy-robotic/&lt;/p&gt;&lt;p&gt;&lt;/p&gt;Expected Results:&lt;p&gt;The expected results of the three months project are:&lt;/p&gt;&lt;p&gt;-Understanding of how 6DoF robotic manipulators are designed, constructed, and programmed&lt;/p&gt;&lt;p&gt;-Designs of the shape and basic components of the robot using CAD software ready for 3D printing&lt;/p&gt;&lt;p&gt;-Simulation of the robot’s forward and inverse kinematics&lt;/p&gt;&lt;p&gt;-List of 3D printed components (cad, stl files) and list of required electronic components&lt;/p&gt;&lt;p&gt;-Demonstration of three case studies (remotely executed experiments, i.e., gripping of an IoT sensor, placement to the POI for monitoring, and returning it to the storage area)&lt;/p&gt;&lt;p&gt;-Github repositories development&lt;/p&gt;&lt;p&gt;-Documentation and assembly instructions&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Knowledge Prerequisites:&lt;/p&gt;&lt;p&gt;Required: Robotics, CAD software&lt;/p&gt;&lt;p&gt;Desired: Python, Real-time 3D (RT3D) platforms&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated development budget:&lt;/p&gt;&lt;p&gt;12000 € personnel&lt;/p&gt;&lt;p&gt;5000 € electronics, controllers, motors, 3d printing, consumables&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated reproduction cost of the robot:&lt;/p&gt;&lt;p&gt;The final robotic manipulator will be designed to be massively reproducible by anyone at an estimated cost of 1000-2000€ while following the concept of open source.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Proposed Mentors:&lt;/p&gt;&lt;p&gt;Panagiotis Koustoumpardis and Hariton Polatoglou&lt;/p&gt;<br /> <br /> === Βιβλιοθήκη φιγούρων και σκηνικών ψηφιακού θεάτρου σκιών ===<br /> http://eshadow.gr/<br /> <br /> To εργαστήριο TUC-MUSIC της Σχολής Ηλεκτρολόγων Μηχανικών και Μηχανικών του Πολυτεχνείου Κρήτης έχει αναπτύξει και διανέμει ελεύθερα το ψηφιακό θέατρο σκιών http://eshadow.gr/ το οποίο συνοδεύεται από μια βιβλιοθήκη ψηφιακών φιγούρων και σκηνικών που μπορούν να χρησιμοποιούν για τη δημιουργία ψηφιακών παραστάσεων. Μας ενδιαφέρει η συνεργασία με πρόσωπα που έχουν τη δυνατότητα να δημιουργήσουν νέες φιγούρες και σκηνικά ώστε να αξιοποιηθούν από τους χρήστες του λογισμικού αυτού (κυρίως σχολεία αλλά και μεμονωμένοι χρήστες). Ενδεικτικά παραπέμπουμε σε μια τέτοια συλλογή υλικό: https://www.dropbox.com/sh/kj0xl32ntly5sbi/AAAt6ftq87goHeTpcKULzrV8a?dl=0&lt;p&gt;Κάθε ψηφιακή φιγούρα αποτελείται από δύο μέρη: Μια εικόνα που αποτελείται από τα επιμέρους κομμάτια που την αρθρώνουν και ένα αρχείο json που περιγράφει τον τρόπο άρθρωσής τους. Αυτό μας ενδιαφέρει είναι η παραγωγή των αρχείων εικόνας, τα αρχεία json μπορούμε να τα δημιουργήσουμε στη συνέχεια εμείς. Ιδιαίτερα θα μας ενδιέφερε, μεταξύ των άλλων, η δημιουργία φιγούρων με βάση παραδοσιακές ιστορίες, μύθους κ.λ.π. καθώς και φιγούρων που μπορούν να βασίζονται σε εικαστικές απαπαραστάσεις από έργα που είναι ελεύθερα (για παράδειγμα πίνακες ζωγραφικής, εικονογραφημένες ιστορίες, αρχαία αγγεία η νωπογραφίες κ.λ.π.).&lt;/p&gt;|<br /> |Βελτιώσεις για το διαλογικό σύστημα Rasa|&lt;https://github.com/kosniaz/rasa-improvements&gt;|&lt;p&gt;Επίλυση προβλημάτων με την πλατφόρμα του Rasa όπως:&lt;/p&gt;&lt;p&gt;\* επίλυση bug με το memoization policy (περιγράφεται εδώ: https://forum.rasa.com/t/there-is-no-memorised-next-action )&lt;/p&gt;&lt;p&gt;\* δημιουργία open source λύσης για data collection/annotation/training, αντί του κλειστού εργαλείου Rasa X (περιγράφεται εδώ: https://forum.rasa.com/t/alternatives-to-rasa-x/ )&lt;/p&gt;&lt;p&gt;\* βελτίωση του logging&lt;/p&gt;&lt;p&gt;\* προσθήκη περισσοτερων dialogue policies ή και βελτίωση υπάρχοντων&lt;/p&gt;<br /> <br /> === DMCRM ===<br /> https://github.com/daniilidisK/crm-application<br /> <br /> Το έργο αποτελεί λογισμικό διαχείρισης λειτουργιών μίας εταιρίας, από την έκδοση τιμολογίων, στην οργάνωση βάσης δεδομένων, τη διαχείριση εγγράφων σε συνάρτηση των εργασιών που αποθηκεύονται στη βάση δεδομένων.|<br /> |opendesign4publiconstructions|&lt;https://github.com/nataliskordou/opendesignpubliconstructions&gt;|Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.|<br /> <br /> === BCL ===<br /> https://github.com/clavisound/feather-LoRa32u4-sketches&lt;p&gt;Συσκευή ιχνηλάτησης (tracking) IoT ανοιχτού υλικού ( hardware) και ανοιχτού λογισμικού που δοκιμαστηκε με το δίκτυο TTN V2.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Χρησιμοποιεί ελάχιστα δεδομένα (μόνο 7 bytes) με πολύ περισσότερα δεδομένα (ταχύτητα, κατεύθυνση, κατάσταση) από τις υπάρχουσες εμπορικές προτάσεις ΚΑΙ δυο προεπιλεγμένα τυχαία SF ώστε να είναι χρήσιμη σε πραγματικές συνθήκες IoT εκατομμυρίων συσκευών, μικρότερο airtime για αποφυγή παρεμβολών (collision).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Για την νέα εποχή (TTN V3 ή helium) πρέπει να αναβαθμιστεί με&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1. OTAA&lt;/p&gt;&lt;p&gt;2. Downlinks (MAC και custom. εντολές)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Επιπλέον θα μπορούσαν να οριστούν λευκές ζώνες για ακόμη λιγότερες παρεμβολές.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === ThanCad, εκτύπωση σε Windows ===<br /> &lt;https://thancad.sourceforge.net/&gt;|&lt;p&gt;Ελεύθερο (άδεια GPL v2 ή νεώτερη) λογισμικό CAD για μηχανικούς. Διδάσκεται στη Σχολή Πολ. Μηχανικών ΕΜΠ. Χρησιμοποιείται από τη Σχολή Τοπογράφων Ε.Μ.Π., Τμήμα Πολ. Μηχανικών Πανεπιστημίου Θεσσαλίας, και τμήμα πολ. Μηχανικών ΠΑ.Δ.Α. Τρέχει σε Linux, FreeBSD, OpenBSD, και Windows. Έχει δοκιμαστεί και τρέχει σε παλαιότερη έκδοση του MACOS X.&lt;/p&gt;&lt;p&gt;Πολλοί/ες φοιτητές/τριες είναι ακόμα εγκλωβισμένοι σε Windows λόγω του CAD. Το ThanCad είναι command compatible με AutoCAD και έτσι αποσκοπεί στην εξοικείωση των νέων μηχανικών με ελεύθερο λογισμικό χωρίς να χρειάζεται να καταβάλλουν μεγάλη προσπάθεια.&lt;/p&gt;&lt;p&gt;Παρόλο που το ThanCad μπορεί να τυπώσει και σε Windows μέσω postscript, θα ήταν πολύ βοηθητικό να χρησιμοποιηθεί το native (και χαοτικό στον προγραμματισμό) σύστημα εκτύπωσης των Windows, χωρίς να απαιτείται εκτυπωτής postscript, κατ' αναλογία με το λογισμικό libreoffice.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === grifobot adventure ===<br /> &lt;https://github.com/vacilos/grifobot&gt;|&lt;p&gt;Το ΓΡΙΦΟΜΠΟΤ (grifobot.gr) είναι ένα διαδικτυακό παιχνίδι που αναπτύχθηκε από το ΓΑΒ LAB προκειμένου να μπορούν παιδιά δημοτικού να απασχοληθούν δημιουργικά συνδυάζοντας ασκήσεις γλώσσας και μαθηματικών με κώδικα. Η αρχική του έκδοση έχει εμπλουτιστεί ώστε να καλύπτει ηλικίες από 5 έως 12 ετών. To Γριφομπότ είναι ένα παιχνίδι που συνδυάζει τη μάθηση σε οποιοδήποτε αντικείμενο μέσα από χρήση αλγορίθμων και αλληλουχιών βημάτων που παραπέμπουν σε κώδικα.&lt;/p&gt;&lt;p&gt;Η έκδοσή του, &amp;quot;ΓΡΙΦΟΜΠΟΤ Quiz&amp;quot;, έχει πιστοποιηθεί από το υπουργείο παιδείας ως εκπαιδευτικό πρόγραμμα και μπορεί να εξυπηρετήσει εκπαιδευτικούς ώστε να εφαρμόσουν διαδικασίες παιχνιδοποίησης στην εκπαίδευση.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Το προτεινόμενο έργο εστιάζεται σε μια επέκταση του Γριφομπότ η οποία μέσα από ένα adventure game (με βάση το Γριφομπότ Quiz) θα δώσει τη δυνατότητα στους εκπαιδευτικούς να παρακολουθούν την εξέλιξη των μαθητών σε συγκεκριμένες θεματικές ενότητες. Παρακολουθώντας την εξέλιξη των μαθητών στο παιχνίδι ο εκπαιδευτικός θα μπορεί να καταλάβει σε ποια σημεία πρέπει να εστιάσει περισσότερο.&lt;/p&gt;&lt;p&gt;Η ιδέα επικεντρώνεται σε μια διαδικασία που θα ξεκινά από ένα gamified assessment ώστε να εντοπιστεί το επίπεδο του κάθε μαθητή και της κάθε μαθήτριας. Στη συνέχεια μέσα από αλληλουχίες διαφορετικών επιπέδων δυσκολίας του παιχνιδιού ο σκοπός θα είναι η μαθήτρια και ο μαθητής να κατακτήσουν μεγαλύτερα επίπεδα γνώσης.&lt;/p&gt;&lt;p&gt;Η εκπαιδευτικός αρκεί να εισάγει ερωτήσεις (και απαντήσεις) για μια θεματική ενότητα χωρίζοντας σε επίπεδα δυσκολίας (και κατάκτησης γνώσης). Στη συνέχεια μέσα από το adventure game ο μαθητής θα πρέπει να κατακτήσει τη γνώση για να ολοκληρώσει το παιχνίδι.&lt;/p&gt;&lt;p&gt;Ειδικότερα για το κομμάτι της βιολογίας υπάρχουν έτοιμα σενάρια χρήσης για την ύλη του Γυμνασίου οπότε η επέκταση θα παρέχει και επαρκές υλικό τουλάχιστον για το συγκεκριμένο μάθημα. Η δε συνεργασία με την εκπαιδευτική κοινότητα θα δημιουργήσει και περιεχόμενο που θα μπορεί να διαμοιραστεί στους εκπαιδευτικούς ώστε να καλύψει κι άλλα μαθήματα.&lt;/p&gt;&lt;p&gt;Για τη συγκεκριμένη επέκταση, σε συνεργασία με τους Μανόλη Wallace (Αν. Καθηγητή), Τάσο Θεοδωρόπουλο (Επικ. Καθηγητή) και Παν. Κόκκινο (Επικ. Καθηγητή) καθώς και με την Υπ. Διδάκτορα, Εκπαιδευτικό Βιολόγο Μαρίνα Λαντζούνη, έχουν γίνει οι κατάλληλοι σχεδιασμοί για τον τρόπο δημιουργίας (σενάρια, χαρακτήρες, εκπαιδευτικά σενάρια, προσωποποίηση σε μαθητές, προσαρμοσμένη μάθηση), συνεπώς η επέκταση έχει ωριμότητα στη σχεδίαση, ενώ υπάρχει και ένα υπόβαθρο (γριφομπότ κουιζ) το οποίο παρέχει την υποδομή πάνω στην οποία μπορεί να χτιστεί η επέκταση.&lt;/p&gt;<br /> <br /> === gisola ===<br /> https://github.com/nikosT/Gisola&lt;p&gt;Το λογισμικό Gisola είναι ένα λογισμικό ανοικτού κώδικα για την άμεση επίλυση του Τανυστή Σεισμικής Ροπής (ΤΣΡ) σε πραγματικό χρόνο, υλοποιημένο σε αρχιτεκτονικές CPU και/ή GPU. Είναι υλοποιημένο κυρίως στην προγραμματιστική γλώσσα Python χρησιμοποιώντας ανοικτού κώδικα βασικές σεισμολογικές και υπολογιστικές βιβλιοθήκες όπως τις ObsPy, Matplotlib, Numpy κ.α., καθώς και την παράλληλης επεξεργασίας βιβλιοθήκη (multiprocessing). Ωστόσο, αρκετά τμήματα του βασικού του πυρήνα είναι γραμμένα σε Fortran, ενώ της οπτικοποίησης των αποτελεσμάτων σε τεχνολογίες διαδικτύου (π.χ. Leaflet Maps, Jinja2). Στην επιστήμη της σεισμολογίας, o ΤΣΡ είναι μια μαθηματική αναπαράσταση που σχετίζεται άμεσα με τη γεωμετρία του ρήγματος και το μέγεθος του σεισμού. Οι ΤΣΡ χρησιμοποιούνται σε ένα ευρύ φάσμα ερευνητικών θεμάτων όπως τη σεισμοτεκτονική, τη μοντελοποίηση θαλασσίων κυμάτων βαρύτητας (tsunami), την άμεση αντίδραση σε ένα σεισμό, την εκτίμηση των καταστροφών κτλ. και ως εκ τούτου είναι σημαντικός ο γρήγορος και αξιόπιστος υπολογισμός τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Ο κώδικας μαζί με οδηγίες (Wiki) εγκατάστασης και χρήσης είναι ελεύθερος για χρήση, τροποποίηση και αναδιανομή από οποιονδήποτε, και είναι προσβάσιμος για λήψη από το αποθετήριο GitHub (με mirroring στο GitLab). Επιπλέον, η παρουσίαση της εργασίας βραβεύτηκε στο συνέδριο European Geosciences Union (EGU) General Assembly 2021 με το βραβείο Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Award 2021, παρουσιάστηκε, επίσης, στο ετήσιο πανελλήνιο συνέδριο κοινοτήτων ελεύθερου λογισμικού και λογισμικού ανοικτού κώδικα FOSSCOMM 2021, ενώ δημοσιεύθηκε στο καταξιωμένο σεισμολογικό περιοδικό Seismological Research Letters.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Στο προτεινόμενο έργο, ο κώδικας θα επεκταθεί ως προς τα παρακάτω:&lt;/p&gt;&lt;p&gt;a) χρήση 3Δ μοντέλων εδάφους, b) βελτίωση του υπολογιστικού χρόνου, c) εκτίμηση της αβεβαιότητας της λύσης.&lt;/p&gt;<br /> <br /> === Globaleaks ===<br /> &lt;https://github.com/globaleaks/GlobaLeaks&gt;|&lt;p&gt;Το GlobaLeaks είναι μια πλατφόρμα ανοικτού λογισμικού για το whistleblowing, δηλαδή την ασφαλή και ανώνυμη αναφορά εμπιστευτικών πληροφοριών για παράνομες ή μη ηθικές συμπεριφορές από εργαζόμενους, εξωτερικούς συνεργάτες αλλά και πολίτες χωρίς τον φόβο αντιποίνων.&lt;/p&gt;&lt;p&gt;Οι αναφορές αποστέλλονται σε καθορισμένους παραλήπτες εντός των επιχειρήσεων και των οργανισμών που επιλαμβάνονται των περιστατικών. Οι πλατφόρμες whistleblowing είναι επίκαιρες εξαιτίας της πρόσφατης νομοθεσίας για την προστασία των whistleblowers Ν4990/2020.&lt;/p&gt;&lt;p&gt;Η πλατφόρμα είναι security/privacy by design και έχει σχεδιαστεί για να προστατεύει την ταυτότητα τόσο του αναφέροντος όσο και του αναφερόμενου και χρησιμοποιείται από χιλιάδες οργανώσεις και εταιρίες στον κόσμο για την προώθηση της διαφάνειας και της λογοδοσίας.&lt;/p&gt;&lt;p&gt;Χρησιμοποιούμε το GlobaLeaks apo to 2017 και έχουμε βασίσει πάνω του μια υπηρεσία SaaS για την παροχή ολοκληρωμένων υπηρεσιών whistleblowing. Το πελατολόγιό μας περιλαμβάνει εταιρίες με ηγετικά χαρακτηριστικά όπως μεταξύ άλλων Lamda developnet, Kaizen Gaming (stoiximan), Παπαδοπούλου, όπως επίσης και Δημόσιους οργανισμούς όπως η Γενική Γραμματεία Αθλητισμού και το Γεωπονικό Πανεπιστήμιο. Η πλατφόρμα Whistleblowing έχει συμπεριληφθεί στο πλαίσιο της Εθνικής Πλατφόρμας Αθλητικής Ακεραιότητάς ως ένα από τα έργα της Βίβλου Ψηφιακού Μετασχηματισμού του αθλητισμού μετά από συμμέτοχή μας στο open call του Υπουργείου Ψηφιακού Μετασχηματισμού σε συνεργασία με την ΓΓΑ.&lt;/p&gt;&lt;p&gt;Είμαστε ένα από τα πιο ενεργά μέλη τη κοινότητας του Globaleaks από το 2017 και προτείνουμε και αξιολογούμε νέα feature, ελέγχουμε το λογισμικό για αναφέρουμε σφάλματα. Το 2022 έχουμε συνεισφέρουμε και κώδικα σε συνεργασία με την εταιρία Genesis Technologies μία νεοφυή εταιρία με έδρα το Πακιστάν.&lt;/p&gt;&lt;p&gt;Θα χρειαστούμε την χρηματοδότηση για να αναπτύξουμε νέα features τα όποια ζητούν οι πελάτες μας ή εμείς κρίνουμε ότι θα ήταν χρήσιμα για αυτούς για να γίνουμε πιο ανταγωνιστικοί. Τα features θα ενσωματωθούν στο ανοικτό λογισμικό προς όφελος των εκατοντάδων χιλιάδων δυνητικών χρηστών.&lt;/p&gt;</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%A0%CF%81%CE%BF%CF%84%CE%AC%CF%83%CE%B5%CE%B9%CF%82_%CE%AD%CF%81%CE%B3%CF%89%CE%BD&diff=2169 2023-03-03T11:43:24Z

<p>Pkst-1: </p> <hr /> <div><br /> <br /> === '''MD Guide''' ===<br /> '''https://github.com/md-guide/md-guide'''&lt;p&gt;Το MD Guide πρόκειται για ένα εργαλείο συγγραφής τεχνικής τεκμηρίωσης και οδηγών (technical documentation) το οποίο βασίζεται στη γλώσσα περιγραφής Markdown. Το Markdown είναι ιδιαίτερα δημοφιλές τα τελευταία χρόνια, ειδικά μετά την κυριάρχηση του Github και του Open source τη δεκαετία του 2010-2020 και έχει γίνει πλέον η κατ'εξοχήν γλώσσα επιλογής για προγραμματιστές που θέλουν να μορφοποιήσουν εύκολα και γρήγορα τα κείμενα τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Με βάση αυτό, το MD Guide υλοποιεί ένα εργαλείο στο οποίο τα παραδείγματα κώδικα που εμπεριέχονται σε αρχεία markdown γίνονται πλέον διαδρασικά για τους αναγνώστες του περιεχομένου. Σε αντίθεση με άλλα εργαλεία που περιορίζονται μόνο σε Javascript/Typescript υλοποιήσεις και παραδείγματα, το MD Guide σκοπεύει να «πακετάρει» λειτουργικότητα ανεξαρτήτως της γλώσσας στην οποία είναι το παράδειγμα και να τρέξει το runtime σε Docker (containerized runtime) και να επιστρέφει σε πραγματικό χρόνο το αποτέλεσμα των διεργασίων (stdout) στον web browser των χρηστών. Τέλος, μπορεί να βοηθάει τους μηχανικούς λογισμικού να έχουν πρόσβαση σε προγραμματιστικούς τύπους (types &amp;amp;amp; interfaces) του πηγαίου κώδικα τους και να τους παρεμβάλουν εντός των τεκμηριώσεων που συγγράφουν.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Αυτή τη στιγμή το project βρίσκεται σε πρώιμο στάδιο ανάπτυξης και σχεδιασμού, και σκοπεύουμε η ανάπτυξη να γίνει σε ανοιχτά hackathons με τη συμμετοχή ατόμων με καθόλου, λίγη ή πολυετή εμπειρία στην ανάπτυξη λογισμικού με σκοπό την επιμόρφωση και την χρήση συλλογικών πρακτικών ανάπτυξης.&lt;/p&gt;<br /> <br /> === Επέκταση του ανοιχτού λογισμικού Dspace (v7) για ανάρτηση Ανοιχτών Εκπαιδευτικών Πόρων (OERs) ===<br /> |&lt;https://github.com/DSpace/&gt;|<br /> <br /> <br /> Το Dspace αποτελεί ένα από τα πιο δημοφιλή λογισμικά ανοιχτού κώδικα για διατήρηση και φιλοξενεία ψηφιακής πληροφορίας κάθε είδους (repositorial infrastructures). Ήδη το Dspace χρησιμοποιείται από διάφορους φορείς σε εθνικό (π.χ. ΥΠΑΙΠΘ, ΕΚΤ κ.λπ.) αλλά κυρίως σε παγκόσμιο επίπεδο. Σκοπός της παρούσας πρότασης είναι η επέκταση του Dspace ώστε να μπορεί να παρέχει δυνατότητα ανάρτησης OERs (pdfs, words, html5, multimedia κ.λπ.) οποιουδήποτε μορφότυπου, με συνοδεία κατάλληλου εμπλουτισμού/τεκμηρίωσης με αξιοποίηση και επέκταση εκπαιδευτικών προτύπων (π.χ. IEEE LOM) και ενσωμάτωση τρίτων εργαλείων (π.χ. IIIF viewer, Video Streaming, e-pub viewers κ.λπ,). Οι επεκτάσεις που θα γίνουν, θα υλοποιηθούν στην τελευταία έκδοση του Dspace (7.x.), η οποία αυτή τη στιγμή χρησιμοποιείται ελάχιστα σε εθνικό επίπεδο (π.χ. το ΥΠΑΙΠΘ χρησιμοποιεί ακόμα την έκδοση 1.8 του Dspace για την πλειοψηφία των εφαρμογών του). Συγκεκριμένα, το Dspace θα επεκταθεί ώστε να υποστηρίξει:&lt;p&gt;- Ανάρτηση και διαμοιρασμό OERs από εγγεγραμμένους χρήστες. Το περιεχόμενο αυτό θα διαμοιράζεται ανοιχτά με βάση συγκεκριμένης προτυποποίησης (π.χ. REST, OAI-PMH κ.λπ.)&lt;/p&gt;&lt;p&gt;- Ενωσμάτωση σχήματος μεταδεδομένων OERs&lt;/p&gt;&lt;p&gt;- Προσαρμογή περιβάλλοντος χρήστη, πλοήγησης και αναζήτησης με χρήση φίλτρων και προηγμένης αναζήτησης&lt;/p&gt;&lt;p&gt;- Ολοκλήρωση με τρίτα εργαλεία για βέλτιστη προεπισκόπηση περιεχομένου&lt;/p&gt;&lt;p&gt;- Ενσωμάτωση επιλογής διαφορετικών αδειών χρήσης με βάση τις ανάγκες του εκάστοτε δημιουργού OER&lt;/p&gt;&lt;p&gt;- Δυνατότητα ενσωμάτωσης μαζικού περιεχομένου με χρήση τεχνολογιών harvesting&lt;/p&gt;&lt;p&gt;- Δυνατότητα προβολής και διαμοιρασμού των OERs με χρήση κατάλληλων προτύπων και τεχνολογιών.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Η παρούσα πρόταση εντάσσεται και στις 2 κατηγορίες Ανοιχτού Λογισμικού και Ανοιχτού Περιεχομένου, δεδομένου ότι στηρίζεται τόσο σε χρήση και επέκταση ανοιχτού λογισμικού, όσο και στην παραγωγή ανοιχτών εκπαιδευτικών πόρων που μπορούν να χρησιμοποιηθούν στην ευρύτερη εκπαιδευτική (και όχι μόνο) κοινότητα (μαθητές, εκπαιδευτικούς, γονείς κ.α.).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === SciDavis, SciLab ===<br /> https://scidavis.sourceforge.net/, https://www.scilab.org/|&lt;p&gt;SciDAVis is a free application for Scientific Data Analysis and Visualization.&lt;/p&gt;&lt;p&gt;Sci Lab is Open source software for numerical computation&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === Open Robotic Manipulator for Remote Labs in Science and Technology Education ===<br /> <br /> <br /> |https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/,<br /> <br /> https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225|<br /> <br /> Description:&lt;p&gt;Remote teaching, for all educational levels, is one of the most added value concepts, during the last years, for both education and industry. The covid boosted high technologies, i.e IoT, 5G, computing power/cost, VR, AR, and robotics are now mature and there are plenty of DIY and OPEN approaches that could be used for enhancing hands-on teaching for pupils, students, and professionals under the concept of lifelong learning. Robotics can augment STEAM education by supporting a laboratory for remote teaching based on physics experiments allowing one to handle and manipulate activities of basic electrical, electronics, and mechatronics labs. During the proposed project a robotic manipulator (4 or 6 DoF) will be developed (based on available open-source 3D printed robot arms), constructed (assemble of 3d printed parts, electronics, motors, sensors), and programmed (using Python and/or Blockly code). The robot should, autonomously, perform experiments, using IoT sensors, actuators, controllers, etc. The DIY IoT sensors, actuators, and controllers are part of another project and are designed to facilitate their handling by the robotic manipulator. Therefore, it could be programmed and remotely controlled by a trainee for developing and implementing an experiment with the DIY IoT sensors, actuators, and controllers that are made available in a university or school laboratory. Hence, an appropriate open-source gripping system should be developed. The robot-gripper system should be designed for “easy for assembly” with a user-friendly interface while trying to minimize the cost using only open-source resources. It should be composed of only two main parts: 3D printed components (PLA) and electronics (motors, sensors, controller, etc.) as well as minimum connective mechanical parts.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;References:&lt;p&gt;- https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/&lt;/p&gt;&lt;p&gt;- https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225&lt;/p&gt;&lt;p&gt;- https://hackaday.io/search?term=arm&lt;/p&gt;&lt;p&gt;- https://all3dp.com/2/3d-printed-robot-arm-diy-robotic/&lt;/p&gt;&lt;p&gt;&lt;/p&gt;Expected Results:&lt;p&gt;The expected results of the three months project are:&lt;/p&gt;&lt;p&gt;-Understanding of how 6DoF robotic manipulators are designed, constructed, and programmed&lt;/p&gt;&lt;p&gt;-Designs of the shape and basic components of the robot using CAD software ready for 3D printing&lt;/p&gt;&lt;p&gt;-Simulation of the robot’s forward and inverse kinematics&lt;/p&gt;&lt;p&gt;-List of 3D printed components (cad, stl files) and list of required electronic components&lt;/p&gt;&lt;p&gt;-Demonstration of three case studies (remotely executed experiments, i.e., gripping of an IoT sensor, placement to the POI for monitoring, and returning it to the storage area)&lt;/p&gt;&lt;p&gt;-Github repositories development&lt;/p&gt;&lt;p&gt;-Documentation and assembly instructions&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Knowledge Prerequisites:&lt;/p&gt;&lt;p&gt;Required: Robotics, CAD software&lt;/p&gt;&lt;p&gt;Desired: Python, Real-time 3D (RT3D) platforms&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated development budget:&lt;/p&gt;&lt;p&gt;12000 € personnel&lt;/p&gt;&lt;p&gt;5000 € electronics, controllers, motors, 3d printing, consumables&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated reproduction cost of the robot:&lt;/p&gt;&lt;p&gt;The final robotic manipulator will be designed to be massively reproducible by anyone at an estimated cost of 1000-2000€ while following the concept of open source.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Proposed Mentors:&lt;/p&gt;&lt;p&gt;Panagiotis Koustoumpardis and Hariton Polatoglou&lt;/p&gt;<br /> <br /> === Βιβλιοθήκη φιγούρων και σκηνικών ψηφιακού θεάτρου σκιών ===<br /> http://eshadow.gr/<br /> <br /> To εργαστήριο TUC-MUSIC της Σχολής Ηλεκτρολόγων Μηχανικών και Μηχανικών του Πολυτεχνείου Κρήτης έχει αναπτύξει και διανέμει ελεύθερα το ψηφιακό θέατρο σκιών http://eshadow.gr/ το οποίο συνοδεύεται από μια βιβλιοθήκη ψηφιακών φιγούρων και σκηνικών που μπορούν να χρησιμοποιούν για τη δημιουργία ψηφιακών παραστάσεων. Μας ενδιαφέρει η συνεργασία με πρόσωπα που έχουν τη δυνατότητα να δημιουργήσουν νέες φιγούρες και σκηνικά ώστε να αξιοποιηθούν από τους χρήστες του λογισμικού αυτού (κυρίως σχολεία αλλά και μεμονωμένοι χρήστες). Ενδεικτικά παραπέμπουμε σε μια τέτοια συλλογή υλικό: https://www.dropbox.com/sh/kj0xl32ntly5sbi/AAAt6ftq87goHeTpcKULzrV8a?dl=0&lt;p&gt;Κάθε ψηφιακή φιγούρα αποτελείται από δύο μέρη: Μια εικόνα που αποτελείται από τα επιμέρους κομμάτια που την αρθρώνουν και ένα αρχείο json που περιγράφει τον τρόπο άρθρωσής τους. Αυτό μας ενδιαφέρει είναι η παραγωγή των αρχείων εικόνας, τα αρχεία json μπορούμε να τα δημιουργήσουμε στη συνέχεια εμείς. Ιδιαίτερα θα μας ενδιέφερε, μεταξύ των άλλων, η δημιουργία φιγούρων με βάση παραδοσιακές ιστορίες, μύθους κ.λ.π. καθώς και φιγούρων που μπορούν να βασίζονται σε εικαστικές απαπαραστάσεις από έργα που είναι ελεύθερα (για παράδειγμα πίνακες ζωγραφικής, εικονογραφημένες ιστορίες, αρχαία αγγεία η νωπογραφίες κ.λ.π.).&lt;/p&gt;|<br /> |Βελτιώσεις για το διαλογικό σύστημα Rasa|&lt;https://github.com/kosniaz/rasa-improvements&gt;|&lt;p&gt;Επίλυση προβλημάτων με την πλατφόρμα του Rasa όπως:&lt;/p&gt;&lt;p&gt;\* επίλυση bug με το memoization policy (περιγράφεται εδώ: https://forum.rasa.com/t/there-is-no-memorised-next-action )&lt;/p&gt;&lt;p&gt;\* δημιουργία open source λύσης για data collection/annotation/training, αντί του κλειστού εργαλείου Rasa X (περιγράφεται εδώ: https://forum.rasa.com/t/alternatives-to-rasa-x/ )&lt;/p&gt;&lt;p&gt;\* βελτίωση του logging&lt;/p&gt;&lt;p&gt;\* προσθήκη περισσοτερων dialogue policies ή και βελτίωση υπάρχοντων&lt;/p&gt;<br /> <br /> === DMCRM ===<br /> https://github.com/daniilidisK/crm-application<br /> <br /> Το έργο αποτελεί λογισμικό διαχείρισης λειτουργιών μίας εταιρίας, από την έκδοση τιμολογίων, στην οργάνωση βάσης δεδομένων, τη διαχείριση εγγράφων σε συνάρτηση των εργασιών που αποθηκεύονται στη βάση δεδομένων.|<br /> |opendesign4publiconstructions|&lt;https://github.com/nataliskordou/opendesignpubliconstructions&gt;|Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.|<br /> <br /> === BCL ===<br /> https://github.com/clavisound/feather-LoRa32u4-sketches&lt;p&gt;Συσκευή ιχνηλάτησης (tracking) IoT ανοιχτού υλικού ( hardware) και ανοιχτού λογισμικού που δοκιμαστηκε με το δίκτυο TTN V2.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Χρησιμοποιεί ελάχιστα δεδομένα (μόνο 7 bytes) με πολύ περισσότερα δεδομένα (ταχύτητα, κατεύθυνση, κατάσταση) από τις υπάρχουσες εμπορικές προτάσεις ΚΑΙ δυο προεπιλεγμένα τυχαία SF ώστε να είναι χρήσιμη σε πραγματικές συνθήκες IoT εκατομμυρίων συσκευών, μικρότερο airtime για αποφυγή παρεμβολών (collision).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Για την νέα εποχή (TTN V3 ή helium) πρέπει να αναβαθμιστεί με&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1. OTAA&lt;/p&gt;&lt;p&gt;2. Downlinks (MAC και custom. εντολές)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Επιπλέον θα μπορούσαν να οριστούν λευκές ζώνες για ακόμη λιγότερες παρεμβολές.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === ThanCad, εκτύπωση σε Windows ===<br /> &lt;https://thancad.sourceforge.net/&gt;|&lt;p&gt;Ελεύθερο (άδεια GPL v2 ή νεώτερη) λογισμικό CAD για μηχανικούς. Διδάσκεται στη Σχολή Πολ. Μηχανικών ΕΜΠ. Χρησιμοποιείται από τη Σχολή Τοπογράφων Ε.Μ.Π., Τμήμα Πολ. Μηχανικών Πανεπιστημίου Θεσσαλίας, και τμήμα πολ. Μηχανικών ΠΑ.Δ.Α. Τρέχει σε Linux, FreeBSD, OpenBSD, και Windows. Έχει δοκιμαστεί και τρέχει σε παλαιότερη έκδοση του MACOS X.&lt;/p&gt;&lt;p&gt;Πολλοί/ες φοιτητές/τριες είναι ακόμα εγκλωβισμένοι σε Windows λόγω του CAD. Το ThanCad είναι command compatible με AutoCAD και έτσι αποσκοπεί στην εξοικείωση των νέων μηχανικών με ελεύθερο λογισμικό χωρίς να χρειάζεται να καταβάλλουν μεγάλη προσπάθεια.&lt;/p&gt;&lt;p&gt;Παρόλο που το ThanCad μπορεί να τυπώσει και σε Windows μέσω postscript, θα ήταν πολύ βοηθητικό να χρησιμοποιηθεί το native (και χαοτικό στον προγραμματισμό) σύστημα εκτύπωσης των Windows, χωρίς να απαιτείται εκτυπωτής postscript, κατ' αναλογία με το λογισμικό libreoffice.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === grifobot adventure ===<br /> &lt;https://github.com/vacilos/grifobot&gt;|&lt;p&gt;Το ΓΡΙΦΟΜΠΟΤ (grifobot.gr) είναι ένα διαδικτυακό παιχνίδι που αναπτύχθηκε από το ΓΑΒ LAB προκειμένου να μπορούν παιδιά δημοτικού να απασχοληθούν δημιουργικά συνδυάζοντας ασκήσεις γλώσσας και μαθηματικών με κώδικα. Η αρχική του έκδοση έχει εμπλουτιστεί ώστε να καλύπτει ηλικίες από 5 έως 12 ετών. To Γριφομπότ είναι ένα παιχνίδι που συνδυάζει τη μάθηση σε οποιοδήποτε αντικείμενο μέσα από χρήση αλγορίθμων και αλληλουχιών βημάτων που παραπέμπουν σε κώδικα.&lt;/p&gt;&lt;p&gt;Η έκδοσή του, &amp;quot;ΓΡΙΦΟΜΠΟΤ Quiz&amp;quot;, έχει πιστοποιηθεί από το υπουργείο παιδείας ως εκπαιδευτικό πρόγραμμα και μπορεί να εξυπηρετήσει εκπαιδευτικούς ώστε να εφαρμόσουν διαδικασίες παιχνιδοποίησης στην εκπαίδευση.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Το προτεινόμενο έργο εστιάζεται σε μια επέκταση του Γριφομπότ η οποία μέσα από ένα adventure game (με βάση το Γριφομπότ Quiz) θα δώσει τη δυνατότητα στους εκπαιδευτικούς να παρακολουθούν την εξέλιξη των μαθητών σε συγκεκριμένες θεματικές ενότητες. Παρακολουθώντας την εξέλιξη των μαθητών στο παιχνίδι ο εκπαιδευτικός θα μπορεί να καταλάβει σε ποια σημεία πρέπει να εστιάσει περισσότερο.&lt;/p&gt;&lt;p&gt;Η ιδέα επικεντρώνεται σε μια διαδικασία που θα ξεκινά από ένα gamified assessment ώστε να εντοπιστεί το επίπεδο του κάθε μαθητή και της κάθε μαθήτριας. Στη συνέχεια μέσα από αλληλουχίες διαφορετικών επιπέδων δυσκολίας του παιχνιδιού ο σκοπός θα είναι η μαθήτρια και ο μαθητής να κατακτήσουν μεγαλύτερα επίπεδα γνώσης.&lt;/p&gt;&lt;p&gt;Η εκπαιδευτικός αρκεί να εισάγει ερωτήσεις (και απαντήσεις) για μια θεματική ενότητα χωρίζοντας σε επίπεδα δυσκολίας (και κατάκτησης γνώσης). Στη συνέχεια μέσα από το adventure game ο μαθητής θα πρέπει να κατακτήσει τη γνώση για να ολοκληρώσει το παιχνίδι.&lt;/p&gt;&lt;p&gt;Ειδικότερα για το κομμάτι της βιολογίας υπάρχουν έτοιμα σενάρια χρήσης για την ύλη του Γυμνασίου οπότε η επέκταση θα παρέχει και επαρκές υλικό τουλάχιστον για το συγκεκριμένο μάθημα. Η δε συνεργασία με την εκπαιδευτική κοινότητα θα δημιουργήσει και περιεχόμενο που θα μπορεί να διαμοιραστεί στους εκπαιδευτικούς ώστε να καλύψει κι άλλα μαθήματα.&lt;/p&gt;&lt;p&gt;Για τη συγκεκριμένη επέκταση, σε συνεργασία με τους Μανόλη Wallace (Αν. Καθηγητή), Τάσο Θεοδωρόπουλο (Επικ. Καθηγητή) και Παν. Κόκκινο (Επικ. Καθηγητή) καθώς και με την Υπ. Διδάκτορα, Εκπαιδευτικό Βιολόγο Μαρίνα Λαντζούνη, έχουν γίνει οι κατάλληλοι σχεδιασμοί για τον τρόπο δημιουργίας (σενάρια, χαρακτήρες, εκπαιδευτικά σενάρια, προσωποποίηση σε μαθητές, προσαρμοσμένη μάθηση), συνεπώς η επέκταση έχει ωριμότητα στη σχεδίαση, ενώ υπάρχει και ένα υπόβαθρο (γριφομπότ κουιζ) το οποίο παρέχει την υποδομή πάνω στην οποία μπορεί να χτιστεί η επέκταση.&lt;/p&gt;<br /> <br /> === gisola ===<br /> https://github.com/nikosT/Gisola&lt;p&gt;Το λογισμικό Gisola είναι ένα λογισμικό ανοικτού κώδικα για την άμεση επίλυση του Τανυστή Σεισμικής Ροπής (ΤΣΡ) σε πραγματικό χρόνο, υλοποιημένο σε αρχιτεκτονικές CPU και/ή GPU. Είναι υλοποιημένο κυρίως στην προγραμματιστική γλώσσα Python χρησιμοποιώντας ανοικτού κώδικα βασικές σεισμολογικές και υπολογιστικές βιβλιοθήκες όπως τις ObsPy, Matplotlib, Numpy κ.α., καθώς και την παράλληλης επεξεργασίας βιβλιοθήκη (multiprocessing). Ωστόσο, αρκετά τμήματα του βασικού του πυρήνα είναι γραμμένα σε Fortran, ενώ της οπτικοποίησης των αποτελεσμάτων σε τεχνολογίες διαδικτύου (π.χ. Leaflet Maps, Jinja2). Στην επιστήμη της σεισμολογίας, o ΤΣΡ είναι μια μαθηματική αναπαράσταση που σχετίζεται άμεσα με τη γεωμετρία του ρήγματος και το μέγεθος του σεισμού. Οι ΤΣΡ χρησιμοποιούνται σε ένα ευρύ φάσμα ερευνητικών θεμάτων όπως τη σεισμοτεκτονική, τη μοντελοποίηση θαλασσίων κυμάτων βαρύτητας (tsunami), την άμεση αντίδραση σε ένα σεισμό, την εκτίμηση των καταστροφών κτλ. και ως εκ τούτου είναι σημαντικός ο γρήγορος και αξιόπιστος υπολογισμός τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Ο κώδικας μαζί με οδηγίες (Wiki) εγκατάστασης και χρήσης είναι ελεύθερος για χρήση, τροποποίηση και αναδιανομή από οποιονδήποτε, και είναι προσβάσιμος για λήψη από το αποθετήριο GitHub (με mirroring στο GitLab). Επιπλέον, η παρουσίαση της εργασίας βραβεύτηκε στο συνέδριο European Geosciences Union (EGU) General Assembly 2021 με το βραβείο Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Award 2021, παρουσιάστηκε, επίσης, στο ετήσιο πανελλήνιο συνέδριο κοινοτήτων ελεύθερου λογισμικού και λογισμικού ανοικτού κώδικα FOSSCOMM 2021, ενώ δημοσιεύθηκε στο καταξιωμένο σεισμολογικό περιοδικό Seismological Research Letters.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Στο προτεινόμενο έργο, ο κώδικας θα επεκταθεί ως προς τα παρακάτω:&lt;/p&gt;&lt;p&gt;a) χρήση 3Δ μοντέλων εδάφους, b) βελτίωση του υπολογιστικού χρόνου, c) εκτίμηση της αβεβαιότητας της λύσης.&lt;/p&gt;<br /> <br /> === Globaleaks ===<br /> &lt;https://github.com/globaleaks/GlobaLeaks&gt;|&lt;p&gt;Το GlobaLeaks είναι μια πλατφόρμα ανοικτού λογισμικού για το whistleblowing, δηλαδή την ασφαλή και ανώνυμη αναφορά εμπιστευτικών πληροφοριών για παράνομες ή μη ηθικές συμπεριφορές από εργαζόμενους, εξωτερικούς συνεργάτες αλλά και πολίτες χωρίς τον φόβο αντιποίνων.&lt;/p&gt;&lt;p&gt;Οι αναφορές αποστέλλονται σε καθορισμένους παραλήπτες εντός των επιχειρήσεων και των οργανισμών που επιλαμβάνονται των περιστατικών. Οι πλατφόρμες whistleblowing είναι επίκαιρες εξαιτίας της πρόσφατης νομοθεσίας για την προστασία των whistleblowers Ν4990/2020.&lt;/p&gt;&lt;p&gt;Η πλατφόρμα είναι security/privacy by design και έχει σχεδιαστεί για να προστατεύει την ταυτότητα τόσο του αναφέροντος όσο και του αναφερόμενου και χρησιμοποιείται από χιλιάδες οργανώσεις και εταιρίες στον κόσμο για την προώθηση της διαφάνειας και της λογοδοσίας.&lt;/p&gt;&lt;p&gt;Χρησιμοποιούμε το GlobaLeaks apo to 2017 και έχουμε βασίσει πάνω του μια υπηρεσία SaaS για την παροχή ολοκληρωμένων υπηρεσιών whistleblowing. Το πελατολόγιό μας περιλαμβάνει εταιρίες με ηγετικά χαρακτηριστικά όπως μεταξύ άλλων Lamda developnet, Kaizen Gaming (stoiximan), Παπαδοπούλου, όπως επίσης και Δημόσιους οργανισμούς όπως η Γενική Γραμματεία Αθλητισμού και το Γεωπονικό Πανεπιστήμιο. Η πλατφόρμα Whistleblowing έχει συμπεριληφθεί στο πλαίσιο της Εθνικής Πλατφόρμας Αθλητικής Ακεραιότητάς ως ένα από τα έργα της Βίβλου Ψηφιακού Μετασχηματισμού του αθλητισμού μετά από συμμέτοχή μας στο open call του Υπουργείου Ψηφιακού Μετασχηματισμού σε συνεργασία με την ΓΓΑ.&lt;/p&gt;&lt;p&gt;Είμαστε ένα από τα πιο ενεργά μέλη τη κοινότητας του Globaleaks από το 2017 και προτείνουμε και αξιολογούμε νέα feature, ελέγχουμε το λογισμικό για αναφέρουμε σφάλματα. Το 2022 έχουμε συνεισφέρουμε και κώδικα σε συνεργασία με την εταιρία Genesis Technologies μία νεοφυή εταιρία με έδρα το Πακιστάν.&lt;/p&gt;&lt;p&gt;Θα χρειαστούμε την χρηματοδότηση για να αναπτύξουμε νέα features τα όποια ζητούν οι πελάτες μας ή εμείς κρίνουμε ότι θα ήταν χρήσιμα για αυτούς για να γίνουμε πιο ανταγωνιστικοί. Τα features θα ενσωματωθούν στο ανοικτό λογισμικό προς όφελος των εκατοντάδων χιλιάδων δυνητικών χρηστών.&lt;/p&gt;</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%A0%CF%81%CE%BF%CF%84%CE%AC%CF%83%CE%B5%CE%B9%CF%82_%CE%AD%CF%81%CE%B3%CF%89%CE%BD&diff=2168 2023-03-03T11:42:58Z

<p>Pkst-1: </p> <hr /> <div><br /> <br /> === '''MD Guide''' ===<br /> '''https://github.com/md-guide/md-guide'''&lt;p&gt;Το MD Guide πρόκειται για ένα εργαλείο συγγραφής τεχνικής τεκμηρίωσης και οδηγών (technical documentation) το οποίο βασίζεται στη γλώσσα περιγραφής Markdown. Το Markdown είναι ιδιαίτερα δημοφιλές τα τελευταία χρόνια, ειδικά μετά την κυριάρχηση του Github και του Open source τη δεκαετία του 2010-2020 και έχει γίνει πλέον η κατ'εξοχήν γλώσσα επιλογής για προγραμματιστές που θέλουν να μορφοποιήσουν εύκολα και γρήγορα τα κείμενα τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Με βάση αυτό, το MD Guide υλοποιεί ένα εργαλείο στο οποίο τα παραδείγματα κώδικα που εμπεριέχονται σε αρχεία markdown γίνονται πλέον διαδρασικά για τους αναγνώστες του περιεχομένου. Σε αντίθεση με άλλα εργαλεία που περιορίζονται μόνο σε Javascript/Typescript υλοποιήσεις και παραδείγματα, το MD Guide σκοπεύει να «πακετάρει» λειτουργικότητα ανεξαρτήτως της γλώσσας στην οποία είναι το παράδειγμα και να τρέξει το runtime σε Docker (containerized runtime) και να επιστρέφει σε πραγματικό χρόνο το αποτέλεσμα των διεργασίων (stdout) στον web browser των χρηστών. Τέλος, μπορεί να βοηθάει τους μηχανικούς λογισμικού να έχουν πρόσβαση σε προγραμματιστικούς τύπους (types &amp;amp;amp; interfaces) του πηγαίου κώδικα τους και να τους παρεμβάλουν εντός των τεκμηριώσεων που συγγράφουν.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Αυτή τη στιγμή το project βρίσκεται σε πρώιμο στάδιο ανάπτυξης και σχεδιασμού, και σκοπεύουμε η ανάπτυξη να γίνει σε ανοιχτά hackathons με τη συμμετοχή ατόμων με καθόλου, λίγη ή πολυετή εμπειρία στην ανάπτυξη λογισμικού με σκοπό την επιμόρφωση και την χρήση συλλογικών πρακτικών ανάπτυξης.&lt;/p&gt;<br /> <br /> === Επέκταση του ανοιχτού λογισμικού Dspace (v7) για ανάρτηση Ανοιχτών Εκπαιδευτικών Πόρων (OERs) ===<br /> |&lt;https://github.com/DSpace/&gt;|<br /> <br /> <br /> Το Dspace αποτελεί ένα από τα πιο δημοφιλή λογισμικά ανοιχτού κώδικα για διατήρηση και φιλοξενεία ψηφιακής πληροφορίας κάθε είδους (repositorial infrastructures). Ήδη το Dspace χρησιμοποιείται από διάφορους φορείς σε εθνικό (π.χ. ΥΠΑΙΠΘ, ΕΚΤ κ.λπ.) αλλά κυρίως σε παγκόσμιο επίπεδο. Σκοπός της παρούσας πρότασης είναι η επέκταση του Dspace ώστε να μπορεί να παρέχει δυνατότητα ανάρτησης OERs (pdfs, words, html5, multimedia κ.λπ.) οποιουδήποτε μορφότυπου, με συνοδεία κατάλληλου εμπλουτισμού/τεκμηρίωσης με αξιοποίηση και επέκταση εκπαιδευτικών προτύπων (π.χ. IEEE LOM) και ενσωμάτωση τρίτων εργαλείων (π.χ. IIIF viewer, Video Streaming, e-pub viewers κ.λπ,). Οι επεκτάσεις που θα γίνουν, θα υλοποιηθούν στην τελευταία έκδοση του Dspace (7.x.), η οποία αυτή τη στιγμή χρησιμοποιείται ελάχιστα σε εθνικό επίπεδο (π.χ. το ΥΠΑΙΠΘ χρησιμοποιεί ακόμα την έκδοση 1.8 του Dspace για την πλειοψηφία των εφαρμογών του). Συγκεκριμένα, το Dspace θα επεκταθεί ώστε να υποστηρίξει:&lt;p&gt;- Ανάρτηση και διαμοιρασμό OERs από εγγεγραμμένους χρήστες. Το περιεχόμενο αυτό θα διαμοιράζεται ανοιχτά με βάση συγκεκριμένης προτυποποίησης (π.χ. REST, OAI-PMH κ.λπ.)&lt;/p&gt;&lt;p&gt;- Ενωσμάτωση σχήματος μεταδεδομένων OERs&lt;/p&gt;&lt;p&gt;- Προσαρμογή περιβάλλοντος χρήστη, πλοήγησης και αναζήτησης με χρήση φίλτρων και προηγμένης αναζήτησης&lt;/p&gt;&lt;p&gt;- Ολοκλήρωση με τρίτα εργαλεία για βέλτιστη προεπισκόπηση περιεχομένου&lt;/p&gt;&lt;p&gt;- Ενσωμάτωση επιλογής διαφορετικών αδειών χρήσης με βάση τις ανάγκες του εκάστοτε δημιουργού OER&lt;/p&gt;&lt;p&gt;- Δυνατότητα ενσωμάτωσης μαζικού περιεχομένου με χρήση τεχνολογιών harvesting&lt;/p&gt;&lt;p&gt;- Δυνατότητα προβολής και διαμοιρασμού των OERs με χρήση κατάλληλων προτύπων και τεχνολογιών.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Η παρούσα πρόταση εντάσσεται και στις 2 κατηγορίες Ανοιχτού Λογισμικού και Ανοιχτού Περιεχομένου, δεδομένου ότι στηρίζεται τόσο σε χρήση και επέκταση ανοιχτού λογισμικού, όσο και στην παραγωγή ανοιχτών εκπαιδευτικών πόρων που μπορούν να χρησιμοποιηθούν στην ευρύτερη εκπαιδευτική (και όχι μόνο) κοινότητα (μαθητές, εκπαιδευτικούς, γονείς κ.α.).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === SciDavis, SciLab ===<br /> https://scidavis.sourceforge.net/, https://www.scilab.org/|&lt;p&gt;SciDAVis is a free application for Scientific Data Analysis and Visualization.&lt;/p&gt;&lt;p&gt;Sci Lab is Open source software for numerical computation&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === Open Robotic Manipulator for Remote Labs in Science and Technology Education ===<br /> <br /> <br /> |https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/,<br /> <br /> https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225|<br /> <br /> ==== Description: ====<br /> &lt;p&gt;Remote teaching, for all educational levels, is one of the most added value concepts, during the last years, for both education and industry. The covid boosted high technologies, i.e IoT, 5G, computing power/cost, VR, AR, and robotics are now mature and there are plenty of DIY and OPEN approaches that could be used for enhancing hands-on teaching for pupils, students, and professionals under the concept of lifelong learning. Robotics can augment STEAM education by supporting a laboratory for remote teaching based on physics experiments allowing one to handle and manipulate activities of basic electrical, electronics, and mechatronics labs. During the proposed project a robotic manipulator (4 or 6 DoF) will be developed (based on available open-source 3D printed robot arms), constructed (assemble of 3d printed parts, electronics, motors, sensors), and programmed (using Python and/or Blockly code). The robot should, autonomously, perform experiments, using IoT sensors, actuators, controllers, etc. The DIY IoT sensors, actuators, and controllers are part of another project and are designed to facilitate their handling by the robotic manipulator. Therefore, it could be programmed and remotely controlled by a trainee for developing and implementing an experiment with the DIY IoT sensors, actuators, and controllers that are made available in a university or school laboratory. Hence, an appropriate open-source gripping system should be developed. The robot-gripper system should be designed for “easy for assembly” with a user-friendly interface while trying to minimize the cost using only open-source resources. It should be composed of only two main parts: 3D printed components (PLA) and electronics (motors, sensors, controller, etc.) as well as minimum connective mechanical parts.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> ==== References: ====<br /> &lt;p&gt;- https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/&lt;/p&gt;&lt;p&gt;- https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225&lt;/p&gt;&lt;p&gt;- https://hackaday.io/search?term=arm&lt;/p&gt;&lt;p&gt;- https://all3dp.com/2/3d-printed-robot-arm-diy-robotic/&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> ==== Expected Results: ====<br /> &lt;p&gt;The expected results of the three months project are:&lt;/p&gt;&lt;p&gt;-Understanding of how 6DoF robotic manipulators are designed, constructed, and programmed&lt;/p&gt;&lt;p&gt;-Designs of the shape and basic components of the robot using CAD software ready for 3D printing&lt;/p&gt;&lt;p&gt;-Simulation of the robot’s forward and inverse kinematics&lt;/p&gt;&lt;p&gt;-List of 3D printed components (cad, stl files) and list of required electronic components&lt;/p&gt;&lt;p&gt;-Demonstration of three case studies (remotely executed experiments, i.e., gripping of an IoT sensor, placement to the POI for monitoring, and returning it to the storage area)&lt;/p&gt;&lt;p&gt;-Github repositories development&lt;/p&gt;&lt;p&gt;-Documentation and assembly instructions&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Knowledge Prerequisites:&lt;/p&gt;&lt;p&gt;Required: Robotics, CAD software&lt;/p&gt;&lt;p&gt;Desired: Python, Real-time 3D (RT3D) platforms&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated development budget:&lt;/p&gt;&lt;p&gt;12000 € personnel&lt;/p&gt;&lt;p&gt;5000 € electronics, controllers, motors, 3d printing, consumables&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated reproduction cost of the robot:&lt;/p&gt;&lt;p&gt;The final robotic manipulator will be designed to be massively reproducible by anyone at an estimated cost of 1000-2000€ while following the concept of open source.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Proposed Mentors:&lt;/p&gt;&lt;p&gt;Panagiotis Koustoumpardis and Hariton Polatoglou&lt;/p&gt;<br /> <br /> === Βιβλιοθήκη φιγούρων και σκηνικών ψηφιακού θεάτρου σκιών ===<br /> http://eshadow.gr/<br /> <br /> <br /> To εργαστήριο TUC-MUSIC της Σχολής Ηλεκτρολόγων Μηχανικών και Μηχανικών του Πολυτεχνείου Κρήτης έχει αναπτύξει και διανέμει ελεύθερα το ψηφιακό θέατρο σκιών http://eshadow.gr/ το οποίο συνοδεύεται από μια βιβλιοθήκη ψηφιακών φιγούρων και σκηνικών που μπορούν να χρησιμοποιούν για τη δημιουργία ψηφιακών παραστάσεων. Μας ενδιαφέρει η συνεργασία με πρόσωπα που έχουν τη δυνατότητα να δημιουργήσουν νέες φιγούρες και σκηνικά ώστε να αξιοποιηθούν από τους χρήστες του λογισμικού αυτού (κυρίως σχολεία αλλά και μεμονωμένοι χρήστες). Ενδεικτικά παραπέμπουμε σε μια τέτοια συλλογή υλικό: https://www.dropbox.com/sh/kj0xl32ntly5sbi/AAAt6ftq87goHeTpcKULzrV8a?dl=0&lt;p&gt;Κάθε ψηφιακή φιγούρα αποτελείται από δύο μέρη: Μια εικόνα που αποτελείται από τα επιμέρους κομμάτια που την αρθρώνουν και ένα αρχείο json που περιγράφει τον τρόπο άρθρωσής τους. Αυτό μας ενδιαφέρει είναι η παραγωγή των αρχείων εικόνας, τα αρχεία json μπορούμε να τα δημιουργήσουμε στη συνέχεια εμείς. Ιδιαίτερα θα μας ενδιέφερε, μεταξύ των άλλων, η δημιουργία φιγούρων με βάση παραδοσιακές ιστορίες, μύθους κ.λ.π. καθώς και φιγούρων που μπορούν να βασίζονται σε εικαστικές απαπαραστάσεις από έργα που είναι ελεύθερα (για παράδειγμα πίνακες ζωγραφικής, εικονογραφημένες ιστορίες, αρχαία αγγεία η νωπογραφίες κ.λ.π.).&lt;/p&gt;|<br /> |Βελτιώσεις για το διαλογικό σύστημα Rasa|&lt;https://github.com/kosniaz/rasa-improvements&gt;|&lt;p&gt;Επίλυση προβλημάτων με την πλατφόρμα του Rasa όπως:&lt;/p&gt;&lt;p&gt;\* επίλυση bug με το memoization policy (περιγράφεται εδώ: https://forum.rasa.com/t/there-is-no-memorised-next-action )&lt;/p&gt;&lt;p&gt;\* δημιουργία open source λύσης για data collection/annotation/training, αντί του κλειστού εργαλείου Rasa X (περιγράφεται εδώ: https://forum.rasa.com/t/alternatives-to-rasa-x/ )&lt;/p&gt;&lt;p&gt;\* βελτίωση του logging&lt;/p&gt;&lt;p&gt;\* προσθήκη περισσοτερων dialogue policies ή και βελτίωση υπάρχοντων&lt;/p&gt;<br /> <br /> === DMCRM ===<br /> https://github.com/daniilidisK/crm-application<br /> <br /> Το έργο αποτελεί λογισμικό διαχείρισης λειτουργιών μίας εταιρίας, από την έκδοση τιμολογίων, στην οργάνωση βάσης δεδομένων, τη διαχείριση εγγράφων σε συνάρτηση των εργασιών που αποθηκεύονται στη βάση δεδομένων.|<br /> |opendesign4publiconstructions|&lt;https://github.com/nataliskordou/opendesignpubliconstructions&gt;|Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.|<br /> <br /> === BCL ===<br /> https://github.com/clavisound/feather-LoRa32u4-sketches&lt;p&gt;Συσκευή ιχνηλάτησης (tracking) IoT ανοιχτού υλικού ( hardware) και ανοιχτού λογισμικού που δοκιμαστηκε με το δίκτυο TTN V2.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Χρησιμοποιεί ελάχιστα δεδομένα (μόνο 7 bytes) με πολύ περισσότερα δεδομένα (ταχύτητα, κατεύθυνση, κατάσταση) από τις υπάρχουσες εμπορικές προτάσεις ΚΑΙ δυο προεπιλεγμένα τυχαία SF ώστε να είναι χρήσιμη σε πραγματικές συνθήκες IoT εκατομμυρίων συσκευών, μικρότερο airtime για αποφυγή παρεμβολών (collision).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Για την νέα εποχή (TTN V3 ή helium) πρέπει να αναβαθμιστεί με&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1. OTAA&lt;/p&gt;&lt;p&gt;2. Downlinks (MAC και custom. εντολές)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Επιπλέον θα μπορούσαν να οριστούν λευκές ζώνες για ακόμη λιγότερες παρεμβολές.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === ThanCad, εκτύπωση σε Windows ===<br /> &lt;https://thancad.sourceforge.net/&gt;|&lt;p&gt;Ελεύθερο (άδεια GPL v2 ή νεώτερη) λογισμικό CAD για μηχανικούς. Διδάσκεται στη Σχολή Πολ. Μηχανικών ΕΜΠ. Χρησιμοποιείται από τη Σχολή Τοπογράφων Ε.Μ.Π., Τμήμα Πολ. Μηχανικών Πανεπιστημίου Θεσσαλίας, και τμήμα πολ. Μηχανικών ΠΑ.Δ.Α. Τρέχει σε Linux, FreeBSD, OpenBSD, και Windows. Έχει δοκιμαστεί και τρέχει σε παλαιότερη έκδοση του MACOS X.&lt;/p&gt;&lt;p&gt;Πολλοί/ες φοιτητές/τριες είναι ακόμα εγκλωβισμένοι σε Windows λόγω του CAD. Το ThanCad είναι command compatible με AutoCAD και έτσι αποσκοπεί στην εξοικείωση των νέων μηχανικών με ελεύθερο λογισμικό χωρίς να χρειάζεται να καταβάλλουν μεγάλη προσπάθεια.&lt;/p&gt;&lt;p&gt;Παρόλο που το ThanCad μπορεί να τυπώσει και σε Windows μέσω postscript, θα ήταν πολύ βοηθητικό να χρησιμοποιηθεί το native (και χαοτικό στον προγραμματισμό) σύστημα εκτύπωσης των Windows, χωρίς να απαιτείται εκτυπωτής postscript, κατ' αναλογία με το λογισμικό libreoffice.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === grifobot adventure ===<br /> &lt;https://github.com/vacilos/grifobot&gt;|&lt;p&gt;Το ΓΡΙΦΟΜΠΟΤ (grifobot.gr) είναι ένα διαδικτυακό παιχνίδι που αναπτύχθηκε από το ΓΑΒ LAB προκειμένου να μπορούν παιδιά δημοτικού να απασχοληθούν δημιουργικά συνδυάζοντας ασκήσεις γλώσσας και μαθηματικών με κώδικα. Η αρχική του έκδοση έχει εμπλουτιστεί ώστε να καλύπτει ηλικίες από 5 έως 12 ετών. To Γριφομπότ είναι ένα παιχνίδι που συνδυάζει τη μάθηση σε οποιοδήποτε αντικείμενο μέσα από χρήση αλγορίθμων και αλληλουχιών βημάτων που παραπέμπουν σε κώδικα.&lt;/p&gt;&lt;p&gt;Η έκδοσή του, &amp;quot;ΓΡΙΦΟΜΠΟΤ Quiz&amp;quot;, έχει πιστοποιηθεί από το υπουργείο παιδείας ως εκπαιδευτικό πρόγραμμα και μπορεί να εξυπηρετήσει εκπαιδευτικούς ώστε να εφαρμόσουν διαδικασίες παιχνιδοποίησης στην εκπαίδευση.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Το προτεινόμενο έργο εστιάζεται σε μια επέκταση του Γριφομπότ η οποία μέσα από ένα adventure game (με βάση το Γριφομπότ Quiz) θα δώσει τη δυνατότητα στους εκπαιδευτικούς να παρακολουθούν την εξέλιξη των μαθητών σε συγκεκριμένες θεματικές ενότητες. Παρακολουθώντας την εξέλιξη των μαθητών στο παιχνίδι ο εκπαιδευτικός θα μπορεί να καταλάβει σε ποια σημεία πρέπει να εστιάσει περισσότερο.&lt;/p&gt;&lt;p&gt;Η ιδέα επικεντρώνεται σε μια διαδικασία που θα ξεκινά από ένα gamified assessment ώστε να εντοπιστεί το επίπεδο του κάθε μαθητή και της κάθε μαθήτριας. Στη συνέχεια μέσα από αλληλουχίες διαφορετικών επιπέδων δυσκολίας του παιχνιδιού ο σκοπός θα είναι η μαθήτρια και ο μαθητής να κατακτήσουν μεγαλύτερα επίπεδα γνώσης.&lt;/p&gt;&lt;p&gt;Η εκπαιδευτικός αρκεί να εισάγει ερωτήσεις (και απαντήσεις) για μια θεματική ενότητα χωρίζοντας σε επίπεδα δυσκολίας (και κατάκτησης γνώσης). Στη συνέχεια μέσα από το adventure game ο μαθητής θα πρέπει να κατακτήσει τη γνώση για να ολοκληρώσει το παιχνίδι.&lt;/p&gt;&lt;p&gt;Ειδικότερα για το κομμάτι της βιολογίας υπάρχουν έτοιμα σενάρια χρήσης για την ύλη του Γυμνασίου οπότε η επέκταση θα παρέχει και επαρκές υλικό τουλάχιστον για το συγκεκριμένο μάθημα. Η δε συνεργασία με την εκπαιδευτική κοινότητα θα δημιουργήσει και περιεχόμενο που θα μπορεί να διαμοιραστεί στους εκπαιδευτικούς ώστε να καλύψει κι άλλα μαθήματα.&lt;/p&gt;&lt;p&gt;Για τη συγκεκριμένη επέκταση, σε συνεργασία με τους Μανόλη Wallace (Αν. Καθηγητή), Τάσο Θεοδωρόπουλο (Επικ. Καθηγητή) και Παν. Κόκκινο (Επικ. Καθηγητή) καθώς και με την Υπ. Διδάκτορα, Εκπαιδευτικό Βιολόγο Μαρίνα Λαντζούνη, έχουν γίνει οι κατάλληλοι σχεδιασμοί για τον τρόπο δημιουργίας (σενάρια, χαρακτήρες, εκπαιδευτικά σενάρια, προσωποποίηση σε μαθητές, προσαρμοσμένη μάθηση), συνεπώς η επέκταση έχει ωριμότητα στη σχεδίαση, ενώ υπάρχει και ένα υπόβαθρο (γριφομπότ κουιζ) το οποίο παρέχει την υποδομή πάνω στην οποία μπορεί να χτιστεί η επέκταση.&lt;/p&gt;<br /> <br /> === gisola ===<br /> https://github.com/nikosT/Gisola&lt;p&gt;Το λογισμικό Gisola είναι ένα λογισμικό ανοικτού κώδικα για την άμεση επίλυση του Τανυστή Σεισμικής Ροπής (ΤΣΡ) σε πραγματικό χρόνο, υλοποιημένο σε αρχιτεκτονικές CPU και/ή GPU. Είναι υλοποιημένο κυρίως στην προγραμματιστική γλώσσα Python χρησιμοποιώντας ανοικτού κώδικα βασικές σεισμολογικές και υπολογιστικές βιβλιοθήκες όπως τις ObsPy, Matplotlib, Numpy κ.α., καθώς και την παράλληλης επεξεργασίας βιβλιοθήκη (multiprocessing). Ωστόσο, αρκετά τμήματα του βασικού του πυρήνα είναι γραμμένα σε Fortran, ενώ της οπτικοποίησης των αποτελεσμάτων σε τεχνολογίες διαδικτύου (π.χ. Leaflet Maps, Jinja2). Στην επιστήμη της σεισμολογίας, o ΤΣΡ είναι μια μαθηματική αναπαράσταση που σχετίζεται άμεσα με τη γεωμετρία του ρήγματος και το μέγεθος του σεισμού. Οι ΤΣΡ χρησιμοποιούνται σε ένα ευρύ φάσμα ερευνητικών θεμάτων όπως τη σεισμοτεκτονική, τη μοντελοποίηση θαλασσίων κυμάτων βαρύτητας (tsunami), την άμεση αντίδραση σε ένα σεισμό, την εκτίμηση των καταστροφών κτλ. και ως εκ τούτου είναι σημαντικός ο γρήγορος και αξιόπιστος υπολογισμός τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Ο κώδικας μαζί με οδηγίες (Wiki) εγκατάστασης και χρήσης είναι ελεύθερος για χρήση, τροποποίηση και αναδιανομή από οποιονδήποτε, και είναι προσβάσιμος για λήψη από το αποθετήριο GitHub (με mirroring στο GitLab). Επιπλέον, η παρουσίαση της εργασίας βραβεύτηκε στο συνέδριο European Geosciences Union (EGU) General Assembly 2021 με το βραβείο Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Award 2021, παρουσιάστηκε, επίσης, στο ετήσιο πανελλήνιο συνέδριο κοινοτήτων ελεύθερου λογισμικού και λογισμικού ανοικτού κώδικα FOSSCOMM 2021, ενώ δημοσιεύθηκε στο καταξιωμένο σεισμολογικό περιοδικό Seismological Research Letters.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Στο προτεινόμενο έργο, ο κώδικας θα επεκταθεί ως προς τα παρακάτω:&lt;/p&gt;&lt;p&gt;a) χρήση 3Δ μοντέλων εδάφους, b) βελτίωση του υπολογιστικού χρόνου, c) εκτίμηση της αβεβαιότητας της λύσης.&lt;/p&gt;<br /> <br /> === Globaleaks ===<br /> &lt;https://github.com/globaleaks/GlobaLeaks&gt;|&lt;p&gt;Το GlobaLeaks είναι μια πλατφόρμα ανοικτού λογισμικού για το whistleblowing, δηλαδή την ασφαλή και ανώνυμη αναφορά εμπιστευτικών πληροφοριών για παράνομες ή μη ηθικές συμπεριφορές από εργαζόμενους, εξωτερικούς συνεργάτες αλλά και πολίτες χωρίς τον φόβο αντιποίνων.&lt;/p&gt;&lt;p&gt;Οι αναφορές αποστέλλονται σε καθορισμένους παραλήπτες εντός των επιχειρήσεων και των οργανισμών που επιλαμβάνονται των περιστατικών. Οι πλατφόρμες whistleblowing είναι επίκαιρες εξαιτίας της πρόσφατης νομοθεσίας για την προστασία των whistleblowers Ν4990/2020.&lt;/p&gt;&lt;p&gt;Η πλατφόρμα είναι security/privacy by design και έχει σχεδιαστεί για να προστατεύει την ταυτότητα τόσο του αναφέροντος όσο και του αναφερόμενου και χρησιμοποιείται από χιλιάδες οργανώσεις και εταιρίες στον κόσμο για την προώθηση της διαφάνειας και της λογοδοσίας.&lt;/p&gt;&lt;p&gt;Χρησιμοποιούμε το GlobaLeaks apo to 2017 και έχουμε βασίσει πάνω του μια υπηρεσία SaaS για την παροχή ολοκληρωμένων υπηρεσιών whistleblowing. Το πελατολόγιό μας περιλαμβάνει εταιρίες με ηγετικά χαρακτηριστικά όπως μεταξύ άλλων Lamda developnet, Kaizen Gaming (stoiximan), Παπαδοπούλου, όπως επίσης και Δημόσιους οργανισμούς όπως η Γενική Γραμματεία Αθλητισμού και το Γεωπονικό Πανεπιστήμιο. Η πλατφόρμα Whistleblowing έχει συμπεριληφθεί στο πλαίσιο της Εθνικής Πλατφόρμας Αθλητικής Ακεραιότητάς ως ένα από τα έργα της Βίβλου Ψηφιακού Μετασχηματισμού του αθλητισμού μετά από συμμέτοχή μας στο open call του Υπουργείου Ψηφιακού Μετασχηματισμού σε συνεργασία με την ΓΓΑ.&lt;/p&gt;&lt;p&gt;Είμαστε ένα από τα πιο ενεργά μέλη τη κοινότητας του Globaleaks από το 2017 και προτείνουμε και αξιολογούμε νέα feature, ελέγχουμε το λογισμικό για αναφέρουμε σφάλματα. Το 2022 έχουμε συνεισφέρουμε και κώδικα σε συνεργασία με την εταιρία Genesis Technologies μία νεοφυή εταιρία με έδρα το Πακιστάν.&lt;/p&gt;&lt;p&gt;Θα χρειαστούμε την χρηματοδότηση για να αναπτύξουμε νέα features τα όποια ζητούν οι πελάτες μας ή εμείς κρίνουμε ότι θα ήταν χρήσιμα για αυτούς για να γίνουμε πιο ανταγωνιστικοί. Τα features θα ενσωματωθούν στο ανοικτό λογισμικό προς όφελος των εκατοντάδων χιλιάδων δυνητικών χρηστών.&lt;/p&gt;</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%A0%CF%81%CE%BF%CF%84%CE%AC%CF%83%CE%B5%CE%B9%CF%82_%CE%AD%CF%81%CE%B3%CF%89%CE%BD&diff=2167 2023-03-03T11:39:25Z

<p>Pkst-1: </p> <hr /> <div><br /> <br /> === '''|MD Guide|&lt;https://github.com/md-guide/md-guide&gt;|''' ===<br /> &lt;p&gt;Το MD Guide πρόκειται για ένα εργαλείο συγγραφής τεχνικής τεκμηρίωσης και οδηγών (technical documentation) το οποίο βασίζεται στη γλώσσα περιγραφής Markdown. Το Markdown είναι ιδιαίτερα δημοφιλές τα τελευταία χρόνια, ειδικά μετά την κυριάρχηση του Github και του Open source τη δεκαετία του 2010-2020 και έχει γίνει πλέον η κατ'εξοχήν γλώσσα επιλογής για προγραμματιστές που θέλουν να μορφοποιήσουν εύκολα και γρήγορα τα κείμενα τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Με βάση αυτό, το MD Guide υλοποιεί ένα εργαλείο στο οποίο τα παραδείγματα κώδικα που εμπεριέχονται σε αρχεία markdown γίνονται πλέον διαδρασικά για τους αναγνώστες του περιεχομένου. Σε αντίθεση με άλλα εργαλεία που περιορίζονται μόνο σε Javascript/Typescript υλοποιήσεις και παραδείγματα, το MD Guide σκοπεύει να «πακετάρει» λειτουργικότητα ανεξαρτήτως της γλώσσας στην οποία είναι το παράδειγμα και να τρέξει το runtime σε Docker (containerized runtime) και να επιστρέφει σε πραγματικό χρόνο το αποτέλεσμα των διεργασίων (stdout) στον web browser των χρηστών. Τέλος, μπορεί να βοηθάει τους μηχανικούς λογισμικού να έχουν πρόσβαση σε προγραμματιστικούς τύπους (types &amp;amp;amp; interfaces) του πηγαίου κώδικα τους και να τους παρεμβάλουν εντός των τεκμηριώσεων που συγγράφουν.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Αυτή τη στιγμή το project βρίσκεται σε πρώιμο στάδιο ανάπτυξης και σχεδιασμού, και σκοπεύουμε η ανάπτυξη να γίνει σε ανοιχτά hackathons με τη συμμετοχή ατόμων με καθόλου, λίγη ή πολυετή εμπειρία στην ανάπτυξη λογισμικού με σκοπό την επιμόρφωση και την χρήση συλλογικών πρακτικών ανάπτυξης.&lt;/p&gt;<br /> <br /> === | |Επέκταση του ανοιχτού λογισμικού Dspace (v7) για ανάρτηση Ανοιχτών Εκπαιδευτικών Πόρων (OERs)|&lt;https://github.com/DSpace/&gt;| ===<br /> Το Dspace αποτελεί ένα από τα πιο δημοφιλή λογισμικά ανοιχτού κώδικα για διατήρηση και φιλοξενεία ψηφιακής πληροφορίας κάθε είδους (repositorial infrastructures). Ήδη το Dspace χρησιμοποιείται από διάφορους φορείς σε εθνικό (π.χ. ΥΠΑΙΠΘ, ΕΚΤ κ.λπ.) αλλά κυρίως σε παγκόσμιο επίπεδο. Σκοπός της παρούσας πρότασης είναι η επέκταση του Dspace ώστε να μπορεί να παρέχει δυνατότητα ανάρτησης OERs (pdfs, words, html5, multimedia κ.λπ.) οποιουδήποτε μορφότυπου, με συνοδεία κατάλληλου εμπλουτισμού/τεκμηρίωσης με αξιοποίηση και επέκταση εκπαιδευτικών προτύπων (π.χ. IEEE LOM) και ενσωμάτωση τρίτων εργαλείων (π.χ. IIIF viewer, Video Streaming, e-pub viewers κ.λπ,). Οι επεκτάσεις που θα γίνουν, θα υλοποιηθούν στην τελευταία έκδοση του Dspace (7.x.), η οποία αυτή τη στιγμή χρησιμοποιείται ελάχιστα σε εθνικό επίπεδο (π.χ. το ΥΠΑΙΠΘ χρησιμοποιεί ακόμα την έκδοση 1.8 του Dspace για την πλειοψηφία των εφαρμογών του). Συγκεκριμένα, το Dspace θα επεκταθεί ώστε να υποστηρίξει:&lt;p&gt;- Ανάρτηση και διαμοιρασμό OERs από εγγεγραμμένους χρήστες. Το περιεχόμενο αυτό θα διαμοιράζεται ανοιχτά με βάση συγκεκριμένης προτυποποίησης (π.χ. REST, OAI-PMH κ.λπ.)&lt;/p&gt;&lt;p&gt;- Ενωσμάτωση σχήματος μεταδεδομένων OERs&lt;/p&gt;&lt;p&gt;- Προσαρμογή περιβάλλοντος χρήστη, πλοήγησης και αναζήτησης με χρήση φίλτρων και προηγμένης αναζήτησης&lt;/p&gt;&lt;p&gt;- Ολοκλήρωση με τρίτα εργαλεία για βέλτιστη προεπισκόπηση περιεχομένου&lt;/p&gt;&lt;p&gt;- Ενσωμάτωση επιλογής διαφορετικών αδειών χρήσης με βάση τις ανάγκες του εκάστοτε δημιουργού OER&lt;/p&gt;&lt;p&gt;- Δυνατότητα ενσωμάτωσης μαζικού περιεχομένου με χρήση τεχνολογιών harvesting&lt;/p&gt;&lt;p&gt;- Δυνατότητα προβολής και διαμοιρασμού των OERs με χρήση κατάλληλων προτύπων και τεχνολογιών.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Η παρούσα πρόταση εντάσσεται και στις 2 κατηγορίες Ανοιχτού Λογισμικού και Ανοιχτού Περιεχομένου, δεδομένου ότι στηρίζεται τόσο σε χρήση και επέκταση ανοιχτού λογισμικού, όσο και στην παραγωγή ανοιχτών εκπαιδευτικών πόρων που μπορούν να χρησιμοποιηθούν στην ευρύτερη εκπαιδευτική (και όχι μόνο) κοινότητα (μαθητές, εκπαιδευτικούς, γονείς κ.α.).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === | |SciDavis, SciLab|https://scidavis.sourceforge.net/, https://www.scilab.org/| ===<br /> &lt;p&gt;SciDAVis is a free application for Scientific Data Analysis and Visualization.&lt;/p&gt;&lt;p&gt;Sci Lab is Open source software for numerical computation&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === Open Robotic Manipulator for Remote Labs in Science and Technology Education ===<br /> <br /> <br /> |https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/,<br /> <br /> https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225|<br /> <br /> ==== Description: ====<br /> &lt;p&gt;Remote teaching, for all educational levels, is one of the most added value concepts, during the last years, for both education and industry. The covid boosted high technologies, i.e IoT, 5G, computing power/cost, VR, AR, and robotics are now mature and there are plenty of DIY and OPEN approaches that could be used for enhancing hands-on teaching for pupils, students, and professionals under the concept of lifelong learning. Robotics can augment STEAM education by supporting a laboratory for remote teaching based on physics experiments allowing one to handle and manipulate activities of basic electrical, electronics, and mechatronics labs. During the proposed project a robotic manipulator (4 or 6 DoF) will be developed (based on available open-source 3D printed robot arms), constructed (assemble of 3d printed parts, electronics, motors, sensors), and programmed (using Python and/or Blockly code). The robot should, autonomously, perform experiments, using IoT sensors, actuators, controllers, etc. The DIY IoT sensors, actuators, and controllers are part of another project and are designed to facilitate their handling by the robotic manipulator. Therefore, it could be programmed and remotely controlled by a trainee for developing and implementing an experiment with the DIY IoT sensors, actuators, and controllers that are made available in a university or school laboratory. Hence, an appropriate open-source gripping system should be developed. The robot-gripper system should be designed for “easy for assembly” with a user-friendly interface while trying to minimize the cost using only open-source resources. It should be composed of only two main parts: 3D printed components (PLA) and electronics (motors, sensors, controller, etc.) as well as minimum connective mechanical parts.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> ==== References: ====<br /> &lt;p&gt;- https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/&lt;/p&gt;&lt;p&gt;- https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225&lt;/p&gt;&lt;p&gt;- https://hackaday.io/search?term=arm&lt;/p&gt;&lt;p&gt;- https://all3dp.com/2/3d-printed-robot-arm-diy-robotic/&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> ==== Expected Results: ====<br /> &lt;p&gt;The expected results of the three months project are:&lt;/p&gt;&lt;p&gt;-Understanding of how 6DoF robotic manipulators are designed, constructed, and programmed&lt;/p&gt;&lt;p&gt;-Designs of the shape and basic components of the robot using CAD software ready for 3D printing&lt;/p&gt;&lt;p&gt;-Simulation of the robot’s forward and inverse kinematics&lt;/p&gt;&lt;p&gt;-List of 3D printed components (cad, stl files) and list of required electronic components&lt;/p&gt;&lt;p&gt;-Demonstration of three case studies (remotely executed experiments, i.e., gripping of an IoT sensor, placement to the POI for monitoring, and returning it to the storage area)&lt;/p&gt;&lt;p&gt;-Github repositories development&lt;/p&gt;&lt;p&gt;-Documentation and assembly instructions&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Knowledge Prerequisites:&lt;/p&gt;&lt;p&gt;Required: Robotics, CAD software&lt;/p&gt;&lt;p&gt;Desired: Python, Real-time 3D (RT3D) platforms&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated development budget:&lt;/p&gt;&lt;p&gt;12000 € personnel&lt;/p&gt;&lt;p&gt;5000 € electronics, controllers, motors, 3d printing, consumables&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated reproduction cost of the robot:&lt;/p&gt;&lt;p&gt;The final robotic manipulator will be designed to be massively reproducible by anyone at an estimated cost of 1000-2000€ while following the concept of open source.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Proposed Mentors:&lt;/p&gt;&lt;p&gt;Panagiotis Koustoumpardis and Hariton Polatoglou&lt;/p&gt;<br /> <br /> === | |Βιβλιοθήκη φιγούρων και σκηνικών ψηφιακού θεάτρου σκιών|&lt;http://eshadow.gr/&gt;| ===<br /> To εργαστήριο TUC-MUSIC της Σχολής Ηλεκτρολόγων Μηχανικών και Μηχανικών του Πολυτεχνείου Κρήτης έχει αναπτύξει και διανέμει ελεύθερα το ψηφιακό θέατρο σκιών http://eshadow.gr/ το οποίο συνοδεύεται από μια βιβλιοθήκη ψηφιακών φιγούρων και σκηνικών που μπορούν να χρησιμοποιούν για τη δημιουργία ψηφιακών παραστάσεων. Μας ενδιαφέρει η συνεργασία με πρόσωπα που έχουν τη δυνατότητα να δημιουργήσουν νέες φιγούρες και σκηνικά ώστε να αξιοποιηθούν από τους χρήστες του λογισμικού αυτού (κυρίως σχολεία αλλά και μεμονωμένοι χρήστες). Ενδεικτικά παραπέμπουμε σε μια τέτοια συλλογή υλικό: https://www.dropbox.com/sh/kj0xl32ntly5sbi/AAAt6ftq87goHeTpcKULzrV8a?dl=0&lt;p&gt;Κάθε ψηφιακή φιγούρα αποτελείται από δύο μέρη: Μια εικόνα που αποτελείται από τα επιμέρους κομμάτια που την αρθρώνουν και ένα αρχείο json που περιγράφει τον τρόπο άρθρωσής τους. Αυτό μας ενδιαφέρει είναι η παραγωγή των αρχείων εικόνας, τα αρχεία json μπορούμε να τα δημιουργήσουμε στη συνέχεια εμείς. Ιδιαίτερα θα μας ενδιέφερε, μεταξύ των άλλων, η δημιουργία φιγούρων με βάση παραδοσιακές ιστορίες, μύθους κ.λ.π. καθώς και φιγούρων που μπορούν να βασίζονται σε εικαστικές απαπαραστάσεις από έργα που είναι ελεύθερα (για παράδειγμα πίνακες ζωγραφικής, εικονογραφημένες ιστορίες, αρχαία αγγεία η νωπογραφίες κ.λ.π.).&lt;/p&gt;|<br /> |Βελτιώσεις για το διαλογικό σύστημα Rasa|&lt;https://github.com/kosniaz/rasa-improvements&gt;|&lt;p&gt;Επίλυση προβλημάτων με την πλατφόρμα του Rasa όπως:&lt;/p&gt;&lt;p&gt;\* επίλυση bug με το memoization policy (περιγράφεται εδώ: https://forum.rasa.com/t/there-is-no-memorised-next-action )&lt;/p&gt;&lt;p&gt;\* δημιουργία open source λύσης για data collection/annotation/training, αντί του κλειστού εργαλείου Rasa X (περιγράφεται εδώ: https://forum.rasa.com/t/alternatives-to-rasa-x/ )&lt;/p&gt;&lt;p&gt;\* βελτίωση του logging&lt;/p&gt;&lt;p&gt;\* προσθήκη περισσοτερων dialogue policies ή και βελτίωση υπάρχοντων&lt;/p&gt;<br /> <br /> === |DMCRM|&lt;https://github.com/daniilidisK/crm-application&gt;| ===<br /> Το έργο αποτελεί λογισμικό διαχείρισης λειτουργιών μίας εταιρίας, από την έκδοση τιμολογίων, στην οργάνωση βάσης δεδομένων, τη διαχείριση εγγράφων σε συνάρτηση των εργασιών που αποθηκεύονται στη βάση δεδομένων.|<br /> |opendesign4publiconstructions|&lt;https://github.com/nataliskordou/opendesignpubliconstructions&gt;|Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.|<br /> <br /> === |BCL|&lt;https://github.com/clavisound/feather-LoRa32u4-sketches&gt;| ===<br /> &lt;p&gt;Συσκευή ιχνηλάτησης (tracking) IoT ανοιχτού υλικού ( hardware) και ανοιχτού λογισμικού που δοκιμαστηκε με το δίκτυο TTN V2.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Χρησιμοποιεί ελάχιστα δεδομένα (μόνο 7 bytes) με πολύ περισσότερα δεδομένα (ταχύτητα, κατεύθυνση, κατάσταση) από τις υπάρχουσες εμπορικές προτάσεις ΚΑΙ δυο προεπιλεγμένα τυχαία SF ώστε να είναι χρήσιμη σε πραγματικές συνθήκες IoT εκατομμυρίων συσκευών, μικρότερο airtime για αποφυγή παρεμβολών (collision).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Για την νέα εποχή (TTN V3 ή helium) πρέπει να αναβαθμιστεί με&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1. OTAA&lt;/p&gt;&lt;p&gt;2. Downlinks (MAC και custom. εντολές)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Επιπλέον θα μπορούσαν να οριστούν λευκές ζώνες για ακόμη λιγότερες παρεμβολές.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === | |ThanCad, εκτύπωση σε Windows|&lt;https://thancad.sourceforge.net/&gt;| ===<br /> &lt;p&gt;Ελεύθερο (άδεια GPL v2 ή νεώτερη) λογισμικό CAD για μηχανικούς. Διδάσκεται στη Σχολή Πολ. Μηχανικών ΕΜΠ. Χρησιμοποιείται από τη Σχολή Τοπογράφων Ε.Μ.Π., Τμήμα Πολ. Μηχανικών Πανεπιστημίου Θεσσαλίας, και τμήμα πολ. Μηχανικών ΠΑ.Δ.Α. Τρέχει σε Linux, FreeBSD, OpenBSD, και Windows. Έχει δοκιμαστεί και τρέχει σε παλαιότερη έκδοση του MACOS X.&lt;/p&gt;&lt;p&gt;Πολλοί/ες φοιτητές/τριες είναι ακόμα εγκλωβισμένοι σε Windows λόγω του CAD. Το ThanCad είναι command compatible με AutoCAD και έτσι αποσκοπεί στην εξοικείωση των νέων μηχανικών με ελεύθερο λογισμικό χωρίς να χρειάζεται να καταβάλλουν μεγάλη προσπάθεια.&lt;/p&gt;&lt;p&gt;Παρόλο που το ThanCad μπορεί να τυπώσει και σε Windows μέσω postscript, θα ήταν πολύ βοηθητικό να χρησιμοποιηθεί το native (και χαοτικό στον προγραμματισμό) σύστημα εκτύπωσης των Windows, χωρίς να απαιτείται εκτυπωτής postscript, κατ' αναλογία με το λογισμικό libreoffice.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === | |grifobot adventure|&lt;https://github.com/vacilos/grifobot&gt;| ===<br /> &lt;p&gt;Το ΓΡΙΦΟΜΠΟΤ (grifobot.gr) είναι ένα διαδικτυακό παιχνίδι που αναπτύχθηκε από το ΓΑΒ LAB προκειμένου να μπορούν παιδιά δημοτικού να απασχοληθούν δημιουργικά συνδυάζοντας ασκήσεις γλώσσας και μαθηματικών με κώδικα. Η αρχική του έκδοση έχει εμπλουτιστεί ώστε να καλύπτει ηλικίες από 5 έως 12 ετών. To Γριφομπότ είναι ένα παιχνίδι που συνδυάζει τη μάθηση σε οποιοδήποτε αντικείμενο μέσα από χρήση αλγορίθμων και αλληλουχιών βημάτων που παραπέμπουν σε κώδικα.&lt;/p&gt;&lt;p&gt;Η έκδοσή του, &amp;quot;ΓΡΙΦΟΜΠΟΤ Quiz&amp;quot;, έχει πιστοποιηθεί από το υπουργείο παιδείας ως εκπαιδευτικό πρόγραμμα και μπορεί να εξυπηρετήσει εκπαιδευτικούς ώστε να εφαρμόσουν διαδικασίες παιχνιδοποίησης στην εκπαίδευση.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Το προτεινόμενο έργο εστιάζεται σε μια επέκταση του Γριφομπότ η οποία μέσα από ένα adventure game (με βάση το Γριφομπότ Quiz) θα δώσει τη δυνατότητα στους εκπαιδευτικούς να παρακολουθούν την εξέλιξη των μαθητών σε συγκεκριμένες θεματικές ενότητες. Παρακολουθώντας την εξέλιξη των μαθητών στο παιχνίδι ο εκπαιδευτικός θα μπορεί να καταλάβει σε ποια σημεία πρέπει να εστιάσει περισσότερο.&lt;/p&gt;&lt;p&gt;Η ιδέα επικεντρώνεται σε μια διαδικασία που θα ξεκινά από ένα gamified assessment ώστε να εντοπιστεί το επίπεδο του κάθε μαθητή και της κάθε μαθήτριας. Στη συνέχεια μέσα από αλληλουχίες διαφορετικών επιπέδων δυσκολίας του παιχνιδιού ο σκοπός θα είναι η μαθήτρια και ο μαθητής να κατακτήσουν μεγαλύτερα επίπεδα γνώσης.&lt;/p&gt;&lt;p&gt;Η εκπαιδευτικός αρκεί να εισάγει ερωτήσεις (και απαντήσεις) για μια θεματική ενότητα χωρίζοντας σε επίπεδα δυσκολίας (και κατάκτησης γνώσης). Στη συνέχεια μέσα από το adventure game ο μαθητής θα πρέπει να κατακτήσει τη γνώση για να ολοκληρώσει το παιχνίδι.&lt;/p&gt;&lt;p&gt;Ειδικότερα για το κομμάτι της βιολογίας υπάρχουν έτοιμα σενάρια χρήσης για την ύλη του Γυμνασίου οπότε η επέκταση θα παρέχει και επαρκές υλικό τουλάχιστον για το συγκεκριμένο μάθημα. Η δε συνεργασία με την εκπαιδευτική κοινότητα θα δημιουργήσει και περιεχόμενο που θα μπορεί να διαμοιραστεί στους εκπαιδευτικούς ώστε να καλύψει κι άλλα μαθήματα.&lt;/p&gt;&lt;p&gt;Για τη συγκεκριμένη επέκταση, σε συνεργασία με τους Μανόλη Wallace (Αν. Καθηγητή), Τάσο Θεοδωρόπουλο (Επικ. Καθηγητή) και Παν. Κόκκινο (Επικ. Καθηγητή) καθώς και με την Υπ. Διδάκτορα, Εκπαιδευτικό Βιολόγο Μαρίνα Λαντζούνη, έχουν γίνει οι κατάλληλοι σχεδιασμοί για τον τρόπο δημιουργίας (σενάρια, χαρακτήρες, εκπαιδευτικά σενάρια, προσωποποίηση σε μαθητές, προσαρμοσμένη μάθηση), συνεπώς η επέκταση έχει ωριμότητα στη σχεδίαση, ενώ υπάρχει και ένα υπόβαθρο (γριφομπότ κουιζ) το οποίο παρέχει την υποδομή πάνω στην οποία μπορεί να χτιστεί η επέκταση.&lt;/p&gt;<br /> <br /> === | |gisola|&lt;https://github.com/nikosT/Gisola&gt;| ===<br /> &lt;p&gt;Το λογισμικό Gisola είναι ένα λογισμικό ανοικτού κώδικα για την άμεση επίλυση του Τανυστή Σεισμικής Ροπής (ΤΣΡ) σε πραγματικό χρόνο, υλοποιημένο σε αρχιτεκτονικές CPU και/ή GPU. Είναι υλοποιημένο κυρίως στην προγραμματιστική γλώσσα Python χρησιμοποιώντας ανοικτού κώδικα βασικές σεισμολογικές και υπολογιστικές βιβλιοθήκες όπως τις ObsPy, Matplotlib, Numpy κ.α., καθώς και την παράλληλης επεξεργασίας βιβλιοθήκη (multiprocessing). Ωστόσο, αρκετά τμήματα του βασικού του πυρήνα είναι γραμμένα σε Fortran, ενώ της οπτικοποίησης των αποτελεσμάτων σε τεχνολογίες διαδικτύου (π.χ. Leaflet Maps, Jinja2). Στην επιστήμη της σεισμολογίας, o ΤΣΡ είναι μια μαθηματική αναπαράσταση που σχετίζεται άμεσα με τη γεωμετρία του ρήγματος και το μέγεθος του σεισμού. Οι ΤΣΡ χρησιμοποιούνται σε ένα ευρύ φάσμα ερευνητικών θεμάτων όπως τη σεισμοτεκτονική, τη μοντελοποίηση θαλασσίων κυμάτων βαρύτητας (tsunami), την άμεση αντίδραση σε ένα σεισμό, την εκτίμηση των καταστροφών κτλ. και ως εκ τούτου είναι σημαντικός ο γρήγορος και αξιόπιστος υπολογισμός τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Ο κώδικας μαζί με οδηγίες (Wiki) εγκατάστασης και χρήσης είναι ελεύθερος για χρήση, τροποποίηση και αναδιανομή από οποιονδήποτε, και είναι προσβάσιμος για λήψη από το αποθετήριο GitHub (με mirroring στο GitLab). Επιπλέον, η παρουσίαση της εργασίας βραβεύτηκε στο συνέδριο European Geosciences Union (EGU) General Assembly 2021 με το βραβείο Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Award 2021, παρουσιάστηκε, επίσης, στο ετήσιο πανελλήνιο συνέδριο κοινοτήτων ελεύθερου λογισμικού και λογισμικού ανοικτού κώδικα FOSSCOMM 2021, ενώ δημοσιεύθηκε στο καταξιωμένο σεισμολογικό περιοδικό Seismological Research Letters.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Στο προτεινόμενο έργο, ο κώδικας θα επεκταθεί ως προς τα παρακάτω:&lt;/p&gt;&lt;p&gt;a) χρήση 3Δ μοντέλων εδάφους, b) βελτίωση του υπολογιστικού χρόνου, c) εκτίμηση της αβεβαιότητας της λύσης.&lt;/p&gt;<br /> <br /> === | |Globaleaks|&lt;https://github.com/globaleaks/GlobaLeaks&gt;| ===<br /> &lt;p&gt;Το GlobaLeaks είναι μια πλατφόρμα ανοικτού λογισμικού για το whistleblowing, δηλαδή την ασφαλή και ανώνυμη αναφορά εμπιστευτικών πληροφοριών για παράνομες ή μη ηθικές συμπεριφορές από εργαζόμενους, εξωτερικούς συνεργάτες αλλά και πολίτες χωρίς τον φόβο αντιποίνων.&lt;/p&gt;&lt;p&gt;Οι αναφορές αποστέλλονται σε καθορισμένους παραλήπτες εντός των επιχειρήσεων και των οργανισμών που επιλαμβάνονται των περιστατικών. Οι πλατφόρμες whistleblowing είναι επίκαιρες εξαιτίας της πρόσφατης νομοθεσίας για την προστασία των whistleblowers Ν4990/2020.&lt;/p&gt;&lt;p&gt;Η πλατφόρμα είναι security/privacy by design και έχει σχεδιαστεί για να προστατεύει την ταυτότητα τόσο του αναφέροντος όσο και του αναφερόμενου και χρησιμοποιείται από χιλιάδες οργανώσεις και εταιρίες στον κόσμο για την προώθηση της διαφάνειας και της λογοδοσίας.&lt;/p&gt;&lt;p&gt;Χρησιμοποιούμε το GlobaLeaks apo to 2017 και έχουμε βασίσει πάνω του μια υπηρεσία SaaS για την παροχή ολοκληρωμένων υπηρεσιών whistleblowing. Το πελατολόγιό μας περιλαμβάνει εταιρίες με ηγετικά χαρακτηριστικά όπως μεταξύ άλλων Lamda developnet, Kaizen Gaming (stoiximan), Παπαδοπούλου, όπως επίσης και Δημόσιους οργανισμούς όπως η Γενική Γραμματεία Αθλητισμού και το Γεωπονικό Πανεπιστήμιο. Η πλατφόρμα Whistleblowing έχει συμπεριληφθεί στο πλαίσιο της Εθνικής Πλατφόρμας Αθλητικής Ακεραιότητάς ως ένα από τα έργα της Βίβλου Ψηφιακού Μετασχηματισμού του αθλητισμού μετά από συμμέτοχή μας στο open call του Υπουργείου Ψηφιακού Μετασχηματισμού σε συνεργασία με την ΓΓΑ.&lt;/p&gt;&lt;p&gt;Είμαστε ένα από τα πιο ενεργά μέλη τη κοινότητας του Globaleaks από το 2017 και προτείνουμε και αξιολογούμε νέα feature, ελέγχουμε το λογισμικό για αναφέρουμε σφάλματα. Το 2022 έχουμε συνεισφέρουμε και κώδικα σε συνεργασία με την εταιρία Genesis Technologies μία νεοφυή εταιρία με έδρα το Πακιστάν.&lt;/p&gt;&lt;p&gt;Θα χρειαστούμε την χρηματοδότηση για να αναπτύξουμε νέα features τα όποια ζητούν οι πελάτες μας ή εμείς κρίνουμε ότι θα ήταν χρήσιμα για αυτούς για να γίνουμε πιο ανταγωνιστικοί. Τα features θα ενσωματωθούν στο ανοικτό λογισμικό προς όφελος των εκατοντάδων χιλιάδων δυνητικών χρηστών.&lt;/p&gt;</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%A0%CF%81%CE%BF%CF%84%CE%AC%CF%83%CE%B5%CE%B9%CF%82_%CE%AD%CF%81%CE%B3%CF%89%CE%BD&diff=2166 2023-03-03T11:38:29Z

<p>Pkst-1: </p> <hr /> <div><br /> <br /> === '''|MD Guide|&lt;https://github.com/md-guide/md-guide&gt;|''' ===<br /> &lt;p&gt;Το MD Guide πρόκειται για ένα εργαλείο συγγραφής τεχνικής τεκμηρίωσης και οδηγών (technical documentation) το οποίο βασίζεται στη γλώσσα περιγραφής Markdown. Το Markdown είναι ιδιαίτερα δημοφιλές τα τελευταία χρόνια, ειδικά μετά την κυριάρχηση του Github και του Open source τη δεκαετία του 2010-2020 και έχει γίνει πλέον η κατ'εξοχήν γλώσσα επιλογής για προγραμματιστές που θέλουν να μορφοποιήσουν εύκολα και γρήγορα τα κείμενα τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Με βάση αυτό, το MD Guide υλοποιεί ένα εργαλείο στο οποίο τα παραδείγματα κώδικα που εμπεριέχονται σε αρχεία markdown γίνονται πλέον διαδρασικά για τους αναγνώστες του περιεχομένου. Σε αντίθεση με άλλα εργαλεία που περιορίζονται μόνο σε Javascript/Typescript υλοποιήσεις και παραδείγματα, το MD Guide σκοπεύει να «πακετάρει» λειτουργικότητα ανεξαρτήτως της γλώσσας στην οποία είναι το παράδειγμα και να τρέξει το runtime σε Docker (containerized runtime) και να επιστρέφει σε πραγματικό χρόνο το αποτέλεσμα των διεργασίων (stdout) στον web browser των χρηστών. Τέλος, μπορεί να βοηθάει τους μηχανικούς λογισμικού να έχουν πρόσβαση σε προγραμματιστικούς τύπους (types &amp;amp;amp; interfaces) του πηγαίου κώδικα τους και να τους παρεμβάλουν εντός των τεκμηριώσεων που συγγράφουν.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Αυτή τη στιγμή το project βρίσκεται σε πρώιμο στάδιο ανάπτυξης και σχεδιασμού, και σκοπεύουμε η ανάπτυξη να γίνει σε ανοιχτά hackathons με τη συμμετοχή ατόμων με καθόλου, λίγη ή πολυετή εμπειρία στην ανάπτυξη λογισμικού με σκοπό την επιμόρφωση και την χρήση συλλογικών πρακτικών ανάπτυξης.&lt;/p&gt;<br /> <br /> === | |Επέκταση του ανοιχτού λογισμικού Dspace (v7) για ανάρτηση Ανοιχτών Εκπαιδευτικών Πόρων (OERs)|&lt;https://github.com/DSpace/&gt;| ===<br /> Το Dspace αποτελεί ένα από τα πιο δημοφιλή λογισμικά ανοιχτού κώδικα για διατήρηση και φιλοξενεία ψηφιακής πληροφορίας κάθε είδους (repositorial infrastructures). Ήδη το Dspace χρησιμοποιείται από διάφορους φορείς σε εθνικό (π.χ. ΥΠΑΙΠΘ, ΕΚΤ κ.λπ.) αλλά κυρίως σε παγκόσμιο επίπεδο. Σκοπός της παρούσας πρότασης είναι η επέκταση του Dspace ώστε να μπορεί να παρέχει δυνατότητα ανάρτησης OERs (pdfs, words, html5, multimedia κ.λπ.) οποιουδήποτε μορφότυπου, με συνοδεία κατάλληλου εμπλουτισμού/τεκμηρίωσης με αξιοποίηση και επέκταση εκπαιδευτικών προτύπων (π.χ. IEEE LOM) και ενσωμάτωση τρίτων εργαλείων (π.χ. IIIF viewer, Video Streaming, e-pub viewers κ.λπ,). Οι επεκτάσεις που θα γίνουν, θα υλοποιηθούν στην τελευταία έκδοση του Dspace (7.x.), η οποία αυτή τη στιγμή χρησιμοποιείται ελάχιστα σε εθνικό επίπεδο (π.χ. το ΥΠΑΙΠΘ χρησιμοποιεί ακόμα την έκδοση 1.8 του Dspace για την πλειοψηφία των εφαρμογών του). Συγκεκριμένα, το Dspace θα επεκταθεί ώστε να υποστηρίξει:&lt;p&gt;- Ανάρτηση και διαμοιρασμό OERs από εγγεγραμμένους χρήστες. Το περιεχόμενο αυτό θα διαμοιράζεται ανοιχτά με βάση συγκεκριμένης προτυποποίησης (π.χ. REST, OAI-PMH κ.λπ.)&lt;/p&gt;&lt;p&gt;- Ενωσμάτωση σχήματος μεταδεδομένων OERs&lt;/p&gt;&lt;p&gt;- Προσαρμογή περιβάλλοντος χρήστη, πλοήγησης και αναζήτησης με χρήση φίλτρων και προηγμένης αναζήτησης&lt;/p&gt;&lt;p&gt;- Ολοκλήρωση με τρίτα εργαλεία για βέλτιστη προεπισκόπηση περιεχομένου&lt;/p&gt;&lt;p&gt;- Ενσωμάτωση επιλογής διαφορετικών αδειών χρήσης με βάση τις ανάγκες του εκάστοτε δημιουργού OER&lt;/p&gt;&lt;p&gt;- Δυνατότητα ενσωμάτωσης μαζικού περιεχομένου με χρήση τεχνολογιών harvesting&lt;/p&gt;&lt;p&gt;- Δυνατότητα προβολής και διαμοιρασμού των OERs με χρήση κατάλληλων προτύπων και τεχνολογιών.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Η παρούσα πρόταση εντάσσεται και στις 2 κατηγορίες Ανοιχτού Λογισμικού και Ανοιχτού Περιεχομένου, δεδομένου ότι στηρίζεται τόσο σε χρήση και επέκταση ανοιχτού λογισμικού, όσο και στην παραγωγή ανοιχτών εκπαιδευτικών πόρων που μπορούν να χρησιμοποιηθούν στην ευρύτερη εκπαιδευτική (και όχι μόνο) κοινότητα (μαθητές, εκπαιδευτικούς, γονείς κ.α.).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === | |SciDavis, SciLab|https://scidavis.sourceforge.net/, https://www.scilab.org/| ===<br /> &lt;p&gt;SciDAVis is a free application for Scientific Data Analysis and Visualization.&lt;/p&gt;&lt;p&gt;Sci Lab is Open source software for numerical computation&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === Open Robotic Manipulator for Remote Labs in Science and Technology Education ===<br /> <br /> === |https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/, ===<br /> <br /> === https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225| ===<br /> <br /> ==== Description: ====<br /> &lt;p&gt;Remote teaching, for all educational levels, is one of the most added value concepts, during the last years, for both education and industry. The covid boosted high technologies, i.e IoT, 5G, computing power/cost, VR, AR, and robotics are now mature and there are plenty of DIY and OPEN approaches that could be used for enhancing hands-on teaching for pupils, students, and professionals under the concept of lifelong learning. Robotics can augment STEAM education by supporting a laboratory for remote teaching based on physics experiments allowing one to handle and manipulate activities of basic electrical, electronics, and mechatronics labs. During the proposed project a robotic manipulator (4 or 6 DoF) will be developed (based on available open-source 3D printed robot arms), constructed (assemble of 3d printed parts, electronics, motors, sensors), and programmed (using Python and/or Blockly code). The robot should, autonomously, perform experiments, using IoT sensors, actuators, controllers, etc. The DIY IoT sensors, actuators, and controllers are part of another project and are designed to facilitate their handling by the robotic manipulator. Therefore, it could be programmed and remotely controlled by a trainee for developing and implementing an experiment with the DIY IoT sensors, actuators, and controllers that are made available in a university or school laboratory. Hence, an appropriate open-source gripping system should be developed. The robot-gripper system should be designed for “easy for assembly” with a user-friendly interface while trying to minimize the cost using only open-source resources. It should be composed of only two main parts: 3D printed components (PLA) and electronics (motors, sensors, controller, etc.) as well as minimum connective mechanical parts.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> ==== References: ====<br /> &lt;p&gt;- https://www.bcn3d.com/bcn3d-moveo-the-future-of-learning-robotic-arm/&lt;/p&gt;&lt;p&gt;- https://github.com/NiryoRobotics?\_ga=2.235469446.647048327.1675682225-494338034.1675682225&lt;/p&gt;&lt;p&gt;- https://hackaday.io/search?term=arm&lt;/p&gt;&lt;p&gt;- https://all3dp.com/2/3d-printed-robot-arm-diy-robotic/&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> ==== Expected Results: ====<br /> &lt;p&gt;The expected results of the three months project are:&lt;/p&gt;&lt;p&gt;-Understanding of how 6DoF robotic manipulators are designed, constructed, and programmed&lt;/p&gt;&lt;p&gt;-Designs of the shape and basic components of the robot using CAD software ready for 3D printing&lt;/p&gt;&lt;p&gt;-Simulation of the robot’s forward and inverse kinematics&lt;/p&gt;&lt;p&gt;-List of 3D printed components (cad, stl files) and list of required electronic components&lt;/p&gt;&lt;p&gt;-Demonstration of three case studies (remotely executed experiments, i.e., gripping of an IoT sensor, placement to the POI for monitoring, and returning it to the storage area)&lt;/p&gt;&lt;p&gt;-Github repositories development&lt;/p&gt;&lt;p&gt;-Documentation and assembly instructions&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Knowledge Prerequisites:&lt;/p&gt;&lt;p&gt;Required: Robotics, CAD software&lt;/p&gt;&lt;p&gt;Desired: Python, Real-time 3D (RT3D) platforms&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated development budget:&lt;/p&gt;&lt;p&gt;12000 € personnel&lt;/p&gt;&lt;p&gt;5000 € electronics, controllers, motors, 3d printing, consumables&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Estimated reproduction cost of the robot:&lt;/p&gt;&lt;p&gt;The final robotic manipulator will be designed to be massively reproducible by anyone at an estimated cost of 1000-2000€ while following the concept of open source.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Proposed Mentors:&lt;/p&gt;&lt;p&gt;Panagiotis Koustoumpardis and Hariton Polatoglou&lt;/p&gt;<br /> <br /> === | |Βιβλιοθήκη φιγούρων και σκηνικών ψηφιακού θεάτρου σκιών|&lt;http://eshadow.gr/&gt;| ===<br /> To εργαστήριο TUC-MUSIC της Σχολής Ηλεκτρολόγων Μηχανικών και Μηχανικών του Πολυτεχνείου Κρήτης έχει αναπτύξει και διανέμει ελεύθερα το ψηφιακό θέατρο σκιών http://eshadow.gr/ το οποίο συνοδεύεται από μια βιβλιοθήκη ψηφιακών φιγούρων και σκηνικών που μπορούν να χρησιμοποιούν για τη δημιουργία ψηφιακών παραστάσεων. Μας ενδιαφέρει η συνεργασία με πρόσωπα που έχουν τη δυνατότητα να δημιουργήσουν νέες φιγούρες και σκηνικά ώστε να αξιοποιηθούν από τους χρήστες του λογισμικού αυτού (κυρίως σχολεία αλλά και μεμονωμένοι χρήστες). Ενδεικτικά παραπέμπουμε σε μια τέτοια συλλογή υλικό: https://www.dropbox.com/sh/kj0xl32ntly5sbi/AAAt6ftq87goHeTpcKULzrV8a?dl=0&lt;p&gt;Κάθε ψηφιακή φιγούρα αποτελείται από δύο μέρη: Μια εικόνα που αποτελείται από τα επιμέρους κομμάτια που την αρθρώνουν και ένα αρχείο json που περιγράφει τον τρόπο άρθρωσής τους. Αυτό μας ενδιαφέρει είναι η παραγωγή των αρχείων εικόνας, τα αρχεία json μπορούμε να τα δημιουργήσουμε στη συνέχεια εμείς. Ιδιαίτερα θα μας ενδιέφερε, μεταξύ των άλλων, η δημιουργία φιγούρων με βάση παραδοσιακές ιστορίες, μύθους κ.λ.π. καθώς και φιγούρων που μπορούν να βασίζονται σε εικαστικές απαπαραστάσεις από έργα που είναι ελεύθερα (για παράδειγμα πίνακες ζωγραφικής, εικονογραφημένες ιστορίες, αρχαία αγγεία η νωπογραφίες κ.λ.π.).&lt;/p&gt;|<br /> |Βελτιώσεις για το διαλογικό σύστημα Rasa|&lt;https://github.com/kosniaz/rasa-improvements&gt;|&lt;p&gt;Επίλυση προβλημάτων με την πλατφόρμα του Rasa όπως:&lt;/p&gt;&lt;p&gt;\* επίλυση bug με το memoization policy (περιγράφεται εδώ: https://forum.rasa.com/t/there-is-no-memorised-next-action )&lt;/p&gt;&lt;p&gt;\* δημιουργία open source λύσης για data collection/annotation/training, αντί του κλειστού εργαλείου Rasa X (περιγράφεται εδώ: https://forum.rasa.com/t/alternatives-to-rasa-x/ )&lt;/p&gt;&lt;p&gt;\* βελτίωση του logging&lt;/p&gt;&lt;p&gt;\* προσθήκη περισσοτερων dialogue policies ή και βελτίωση υπάρχοντων&lt;/p&gt;<br /> <br /> === |DMCRM|&lt;https://github.com/daniilidisK/crm-application&gt;| ===<br /> Το έργο αποτελεί λογισμικό διαχείρισης λειτουργιών μίας εταιρίας, από την έκδοση τιμολογίων, στην οργάνωση βάσης δεδομένων, τη διαχείριση εγγράφων σε συνάρτηση των εργασιών που αποθηκεύονται στη βάση δεδομένων.|<br /> |opendesign4publiconstructions|&lt;https://github.com/nataliskordou/opendesignpubliconstructions&gt;|Ο κύριος στόχος του έργου μας είναι να σχεδιάσουμε ελαφριές δομικές μονάδες που αναπαράγονται θα μπορούν να ικανοποιούν τις απαιτήσεις μιας έκθεσης. Η κεντρική ιδέα πίσω από αυτό το έργο βασίζεται στον ανοιχτό σχεδιασμό κατασκευής, ο οποίος θα είναι διαθέσιμος στο κοινό για τροποποίηση. Η παραγωγή μονάδων μπορεί να χρησιμοποιηθεί παντού χωρίς υψηλό κόστος για την κατασκευή ή τη μεταφορά με τις λιγότερες δυνατές τεχνικές γνώσεις. Θέλουμε να δημιουργήσουμε μια «κοινότητα» στην οποία αρκετοί επιστημονικοί και τεχνικοί επαγγελματίες θα συνεργαστούν για την παραγωγή αυτού του έργου. Θεωρητικά, το μέγεθος και η κλίμακα θα είναι απεριόριστα, ανάλογα με το θέμα της έκθεσης. Το έργο περιλαμβάνει 2 τμήματα, το πρώτο χρησιμοποιείται για την καταγραφή και προβολή του υλικού της έκθεσης και το δεύτερο καθοδηγεί τον επισκέπτη. Οι προκατασκευασμένες μονάδες θα παράγονται και θα συναρμολογούνται, σαν παζλ, in situ χρησιμοποιώντας CNC με την καθοδήγηση εγχειριδίων.|<br /> <br /> === |BCL|&lt;https://github.com/clavisound/feather-LoRa32u4-sketches&gt;| ===<br /> &lt;p&gt;Συσκευή ιχνηλάτησης (tracking) IoT ανοιχτού υλικού ( hardware) και ανοιχτού λογισμικού που δοκιμαστηκε με το δίκτυο TTN V2.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Χρησιμοποιεί ελάχιστα δεδομένα (μόνο 7 bytes) με πολύ περισσότερα δεδομένα (ταχύτητα, κατεύθυνση, κατάσταση) από τις υπάρχουσες εμπορικές προτάσεις ΚΑΙ δυο προεπιλεγμένα τυχαία SF ώστε να είναι χρήσιμη σε πραγματικές συνθήκες IoT εκατομμυρίων συσκευών, μικρότερο airtime για αποφυγή παρεμβολών (collision).&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Για την νέα εποχή (TTN V3 ή helium) πρέπει να αναβαθμιστεί με&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1. OTAA&lt;/p&gt;&lt;p&gt;2. Downlinks (MAC και custom. εντολές)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Επιπλέον θα μπορούσαν να οριστούν λευκές ζώνες για ακόμη λιγότερες παρεμβολές.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === | |ThanCad, εκτύπωση σε Windows|&lt;https://thancad.sourceforge.net/&gt;| ===<br /> &lt;p&gt;Ελεύθερο (άδεια GPL v2 ή νεώτερη) λογισμικό CAD για μηχανικούς. Διδάσκεται στη Σχολή Πολ. Μηχανικών ΕΜΠ. Χρησιμοποιείται από τη Σχολή Τοπογράφων Ε.Μ.Π., Τμήμα Πολ. Μηχανικών Πανεπιστημίου Θεσσαλίας, και τμήμα πολ. Μηχανικών ΠΑ.Δ.Α. Τρέχει σε Linux, FreeBSD, OpenBSD, και Windows. Έχει δοκιμαστεί και τρέχει σε παλαιότερη έκδοση του MACOS X.&lt;/p&gt;&lt;p&gt;Πολλοί/ες φοιτητές/τριες είναι ακόμα εγκλωβισμένοι σε Windows λόγω του CAD. Το ThanCad είναι command compatible με AutoCAD και έτσι αποσκοπεί στην εξοικείωση των νέων μηχανικών με ελεύθερο λογισμικό χωρίς να χρειάζεται να καταβάλλουν μεγάλη προσπάθεια.&lt;/p&gt;&lt;p&gt;Παρόλο που το ThanCad μπορεί να τυπώσει και σε Windows μέσω postscript, θα ήταν πολύ βοηθητικό να χρησιμοποιηθεί το native (και χαοτικό στον προγραμματισμό) σύστημα εκτύπωσης των Windows, χωρίς να απαιτείται εκτυπωτής postscript, κατ' αναλογία με το λογισμικό libreoffice.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;<br /> <br /> === | |grifobot adventure|&lt;https://github.com/vacilos/grifobot&gt;| ===<br /> &lt;p&gt;Το ΓΡΙΦΟΜΠΟΤ (grifobot.gr) είναι ένα διαδικτυακό παιχνίδι που αναπτύχθηκε από το ΓΑΒ LAB προκειμένου να μπορούν παιδιά δημοτικού να απασχοληθούν δημιουργικά συνδυάζοντας ασκήσεις γλώσσας και μαθηματικών με κώδικα. Η αρχική του έκδοση έχει εμπλουτιστεί ώστε να καλύπτει ηλικίες από 5 έως 12 ετών. To Γριφομπότ είναι ένα παιχνίδι που συνδυάζει τη μάθηση σε οποιοδήποτε αντικείμενο μέσα από χρήση αλγορίθμων και αλληλουχιών βημάτων που παραπέμπουν σε κώδικα.&lt;/p&gt;&lt;p&gt;Η έκδοσή του, &amp;quot;ΓΡΙΦΟΜΠΟΤ Quiz&amp;quot;, έχει πιστοποιηθεί από το υπουργείο παιδείας ως εκπαιδευτικό πρόγραμμα και μπορεί να εξυπηρετήσει εκπαιδευτικούς ώστε να εφαρμόσουν διαδικασίες παιχνιδοποίησης στην εκπαίδευση.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Το προτεινόμενο έργο εστιάζεται σε μια επέκταση του Γριφομπότ η οποία μέσα από ένα adventure game (με βάση το Γριφομπότ Quiz) θα δώσει τη δυνατότητα στους εκπαιδευτικούς να παρακολουθούν την εξέλιξη των μαθητών σε συγκεκριμένες θεματικές ενότητες. Παρακολουθώντας την εξέλιξη των μαθητών στο παιχνίδι ο εκπαιδευτικός θα μπορεί να καταλάβει σε ποια σημεία πρέπει να εστιάσει περισσότερο.&lt;/p&gt;&lt;p&gt;Η ιδέα επικεντρώνεται σε μια διαδικασία που θα ξεκινά από ένα gamified assessment ώστε να εντοπιστεί το επίπεδο του κάθε μαθητή και της κάθε μαθήτριας. Στη συνέχεια μέσα από αλληλουχίες διαφορετικών επιπέδων δυσκολίας του παιχνιδιού ο σκοπός θα είναι η μαθήτρια και ο μαθητής να κατακτήσουν μεγαλύτερα επίπεδα γνώσης.&lt;/p&gt;&lt;p&gt;Η εκπαιδευτικός αρκεί να εισάγει ερωτήσεις (και απαντήσεις) για μια θεματική ενότητα χωρίζοντας σε επίπεδα δυσκολίας (και κατάκτησης γνώσης). Στη συνέχεια μέσα από το adventure game ο μαθητής θα πρέπει να κατακτήσει τη γνώση για να ολοκληρώσει το παιχνίδι.&lt;/p&gt;&lt;p&gt;Ειδικότερα για το κομμάτι της βιολογίας υπάρχουν έτοιμα σενάρια χρήσης για την ύλη του Γυμνασίου οπότε η επέκταση θα παρέχει και επαρκές υλικό τουλάχιστον για το συγκεκριμένο μάθημα. Η δε συνεργασία με την εκπαιδευτική κοινότητα θα δημιουργήσει και περιεχόμενο που θα μπορεί να διαμοιραστεί στους εκπαιδευτικούς ώστε να καλύψει κι άλλα μαθήματα.&lt;/p&gt;&lt;p&gt;Για τη συγκεκριμένη επέκταση, σε συνεργασία με τους Μανόλη Wallace (Αν. Καθηγητή), Τάσο Θεοδωρόπουλο (Επικ. Καθηγητή) και Παν. Κόκκινο (Επικ. Καθηγητή) καθώς και με την Υπ. Διδάκτορα, Εκπαιδευτικό Βιολόγο Μαρίνα Λαντζούνη, έχουν γίνει οι κατάλληλοι σχεδιασμοί για τον τρόπο δημιουργίας (σενάρια, χαρακτήρες, εκπαιδευτικά σενάρια, προσωποποίηση σε μαθητές, προσαρμοσμένη μάθηση), συνεπώς η επέκταση έχει ωριμότητα στη σχεδίαση, ενώ υπάρχει και ένα υπόβαθρο (γριφομπότ κουιζ) το οποίο παρέχει την υποδομή πάνω στην οποία μπορεί να χτιστεί η επέκταση.&lt;/p&gt;<br /> <br /> === | |gisola|&lt;https://github.com/nikosT/Gisola&gt;| ===<br /> &lt;p&gt;Το λογισμικό Gisola είναι ένα λογισμικό ανοικτού κώδικα για την άμεση επίλυση του Τανυστή Σεισμικής Ροπής (ΤΣΡ) σε πραγματικό χρόνο, υλοποιημένο σε αρχιτεκτονικές CPU και/ή GPU. Είναι υλοποιημένο κυρίως στην προγραμματιστική γλώσσα Python χρησιμοποιώντας ανοικτού κώδικα βασικές σεισμολογικές και υπολογιστικές βιβλιοθήκες όπως τις ObsPy, Matplotlib, Numpy κ.α., καθώς και την παράλληλης επεξεργασίας βιβλιοθήκη (multiprocessing). Ωστόσο, αρκετά τμήματα του βασικού του πυρήνα είναι γραμμένα σε Fortran, ενώ της οπτικοποίησης των αποτελεσμάτων σε τεχνολογίες διαδικτύου (π.χ. Leaflet Maps, Jinja2). Στην επιστήμη της σεισμολογίας, o ΤΣΡ είναι μια μαθηματική αναπαράσταση που σχετίζεται άμεσα με τη γεωμετρία του ρήγματος και το μέγεθος του σεισμού. Οι ΤΣΡ χρησιμοποιούνται σε ένα ευρύ φάσμα ερευνητικών θεμάτων όπως τη σεισμοτεκτονική, τη μοντελοποίηση θαλασσίων κυμάτων βαρύτητας (tsunami), την άμεση αντίδραση σε ένα σεισμό, την εκτίμηση των καταστροφών κτλ. και ως εκ τούτου είναι σημαντικός ο γρήγορος και αξιόπιστος υπολογισμός τους.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Ο κώδικας μαζί με οδηγίες (Wiki) εγκατάστασης και χρήσης είναι ελεύθερος για χρήση, τροποποίηση και αναδιανομή από οποιονδήποτε, και είναι προσβάσιμος για λήψη από το αποθετήριο GitHub (με mirroring στο GitLab). Επιπλέον, η παρουσίαση της εργασίας βραβεύτηκε στο συνέδριο European Geosciences Union (EGU) General Assembly 2021 με το βραβείο Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Award 2021, παρουσιάστηκε, επίσης, στο ετήσιο πανελλήνιο συνέδριο κοινοτήτων ελεύθερου λογισμικού και λογισμικού ανοικτού κώδικα FOSSCOMM 2021, ενώ δημοσιεύθηκε στο καταξιωμένο σεισμολογικό περιοδικό Seismological Research Letters.&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;Στο προτεινόμενο έργο, ο κώδικας θα επεκταθεί ως προς τα παρακάτω:&lt;/p&gt;&lt;p&gt;a) χρήση 3Δ μοντέλων εδάφους, b) βελτίωση του υπολογιστικού χρόνου, c) εκτίμηση της αβεβαιότητας της λύσης.&lt;/p&gt;<br /> <br /> === | |Globaleaks|&lt;https://github.com/globaleaks/GlobaLeaks&gt;| ===<br /> &lt;p&gt;Το GlobaLeaks είναι μια πλατφόρμα ανοικτού λογισμικού για το whistleblowing, δηλαδή την ασφαλή και ανώνυμη αναφορά εμπιστευτικών πληροφοριών για παράνομες ή μη ηθικές συμπεριφορές από εργαζόμενους, εξωτερικούς συνεργάτες αλλά και πολίτες χωρίς τον φόβο αντιποίνων.&lt;/p&gt;&lt;p&gt;Οι αναφορές αποστέλλονται σε καθορισμένους παραλήπτες εντός των επιχειρήσεων και των οργανισμών που επιλαμβάνονται των περιστατικών. Οι πλατφόρμες whistleblowing είναι επίκαιρες εξαιτίας της πρόσφατης νομοθεσίας για την προστασία των whistleblowers Ν4990/2020.&lt;/p&gt;&lt;p&gt;Η πλατφόρμα είναι security/privacy by design και έχει σχεδιαστεί για να προστατεύει την ταυτότητα τόσο του αναφέροντος όσο και του αναφερόμενου και χρησιμοποιείται από χιλιάδες οργανώσεις και εταιρίες στον κόσμο για την προώθηση της διαφάνειας και της λογοδοσίας.&lt;/p&gt;&lt;p&gt;Χρησιμοποιούμε το GlobaLeaks apo to 2017 και έχουμε βασίσει πάνω του μια υπηρεσία SaaS για την παροχή ολοκληρωμένων υπηρεσιών whistleblowing. Το πελατολόγιό μας περιλαμβάνει εταιρίες με ηγετικά χαρακτηριστικά όπως μεταξύ άλλων Lamda developnet, Kaizen Gaming (stoiximan), Παπαδοπούλου, όπως επίσης και Δημόσιους οργανισμούς όπως η Γενική Γραμματεία Αθλητισμού και το Γεωπονικό Πανεπιστήμιο. Η πλατφόρμα Whistleblowing έχει συμπεριληφθεί στο πλαίσιο της Εθνικής Πλατφόρμας Αθλητικής Ακεραιότητάς ως ένα από τα έργα της Βίβλου Ψηφιακού Μετασχηματισμού του αθλητισμού μετά από συμμέτοχή μας στο open call του Υπουργείου Ψηφιακού Μετασχηματισμού σε συνεργασία με την ΓΓΑ.&lt;/p&gt;&lt;p&gt;Είμαστε ένα από τα πιο ενεργά μέλη τη κοινότητας του Globaleaks από το 2017 και προτείνουμε και αξιολογούμε νέα feature, ελέγχουμε το λογισμικό για αναφέρουμε σφάλματα. Το 2022 έχουμε συνεισφέρουμε και κώδικα σε συνεργασία με την εταιρία Genesis Technologies μία νεοφυή εταιρία με έδρα το Πακιστάν.&lt;/p&gt;&lt;p&gt;Θα χρειαστούμε την χρηματοδότηση για να αναπτύξουμε νέα features τα όποια ζητούν οι πελάτες μας ή εμείς κρίνουμε ότι θα ήταν χρήσιμα για αυτούς για να γίνουμε πιο ανταγωνιστικοί. Τα features θα ενσωματωθούν στο ανοικτό λογισμικό προς όφελος των εκατοντάδων χιλιάδων δυνητικών χρηστών.&lt;/p&gt;</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2023_proposed_ideas&diff=2149 2023-01-27T10:49:39Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' list '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''page'''.<br /> <br /> == Epoptes improvements ==<br /> <br /> ==== Brief Explanation ====<br /> Epoptes is an open source computer lab management and monitoring tool. It is used in more than 1000 Greek schools, and in thousands more schools and businesses worldwide. The following improvements and new features have been requested by the community for years and can be implemented as part of a GSoC project:<br /> <br /> - Make Epoptes available on more Linux distributions.<br /> <br /> - Support screen sharing on Wayland.<br /> <br /> - Drop the session service and keep only the system epoptes-client service.<br /> <br /> - Use systemd socket activation and autorestart.<br /> <br /> And if there's enough time left, also improve its firewall compatibility.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Expected Results ====<br /> It is expected that the student will send a pull request on &lt;nowiki&gt;https://github.com/epoptes/epoptes&lt;/nowiki&gt;, suitable for merging upstream. That PR should implement all the aforementioned tasks, which are described in more detail at &lt;nowiki&gt;https://epoptes.org/documentation/gsoc/&lt;/nowiki&gt;<br /> <br /> ==== Related repositories ====<br /> https://epoptes.org/documentation/gsoc/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, GTK, Shell, networking, systemd services, Wayland, XDG desktop portals<br /> <br /> ==== Mentors: ====<br /> Foteini Tsiami (fottsia@gmail.com), Siahos Yiannis (siahos@cti.gr)<br /> <br /> <br /> == Interoperability of GitLab boards with Nextcloud Decks ==<br /> <br /> ==== Brief Explanation ====<br /> Nextcloud is free software for cloud collaboration. Nextcloud consists of core functionality plus installable apps. One of the apps is &quot;Deck&quot;, that provides Kanban boards. GitLab also provides Kanban boards.<br /> <br /> There exists a Nextcloud &quot;GitLab&quot; app which provides some integration of GitLab with Nextcloud, but it does very little. Its only substantial functionality is to show a panel with &quot;GitLab todos&quot; in the Nextcloud dashboard (which serves as the user's home page). That is, it provides a list in the user's Nextcloud dashboard that shows the next things the user has to do in GitLab. Each &quot;todo&quot; is just a title and a link to GitLab.<br /> <br /> This project is about extending the GitLab-Nextcloud integration so that a user can see and manage the GitLab boards in Nextcloud's Deck application.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Expected Results ====<br /> Some organizations use both Nextcloud and GitLab. Some of their kanban boards tend to be on GitLab and some other on Nextcloud. Developers tend to use more GitLab, while non-developers tend to use Nextcloud. This project is expected to reduce duplication and help developers and non-developers co-operate better.<br /> <br /> ==== Related repositories ====<br /> &lt;&lt;nowiki&gt;https://github.com/nextcloud/deck/&lt;/nowiki&gt;&gt;<br /> <br /> &lt;&lt;nowiki&gt;https://github.com/nextcloud/integration\_gitlab&lt;/nowiki&gt;&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> JavaScript, PHP, Vue, Web APIs<br /> <br /> ==== Mentors: ====<br /> Antonis Christofides<br /> <br /> <br /> <br /> == Development of a Robotic Education Platform for the DIY robot kit for educators ==<br /> <br /> ==== Brief Explanation ====<br /> The project started back on the GSoC 2019 when Christos Chronis designed and implemented a 3D printable robot for educational purposes from scratch. The idea was create a new educational robot, that will be modular, come at a low cost and be available to everyone. All of the parts of the modular robot were designed in a way so that they are easily 3D-printable. The 3D-printed parts are combined with basic low-cost electronics and sensors, which can be easily assembled to the final robot following extensive and simple guidelines of how to print and assemble the robot. In this way non-expert staff in robotics, electronics or IoT programming can assemble and use the robot. Alongside the guidelines, a number of demo scenarios for the class, e.g. simple navigation or sensing scenarios, that require basic programming skills are also provided. <br /> <br /> The project has been continued two years later, in GSoC 2021, by Georgios Giannakoulias, who developed a version of the “DIY robot kit for educators” that integrates Node-Red blocks and introduced a new way to program the robot based on blocks. Last year at the GSOC 2022 two more contributors Danai Brilli and Eleftheria Papagergiou worked on a new version of the robot and redesigned the whole programming stack. The robot can now be programmed using native Python code or using a custom version of Google Blockly (a Scratch-like user interface). They also improved the initial core library and developed a completed unified user interface with multiple project management, robot configuration page, a new programming mode for kindergarten learning activities and a Docker based deployment system with CI/CD capabilities.<br /> <br /> '''What must be done in GSOC 2023:'''<br /> <br /> The current robot programming stack runs in the robot itself, allowing for the use and programming of the robot without the need for additional software installation on a local computer. However, this feature has proven to be more of an obstacle than a benefit due to connectivity and update issues. Also the rising cost of Single Board Computers (SBCs), such as the Raspberry Pi, made DIY solutions uncompetitive with commercial alternatives.<br /> <br /> To address these issues, the entire programming stack must be transferred to the cloud. This approach would remove the need for additional computational power within the robot and provide the opportunity for a wider range of SBCs or microcontrollers such as the Raspberry Pi Pico, Microbit, ESP32, or Micropython-enabled Arduino boards to be used. This would result in a reduction in the overall cost of construction for the robot and give users more freedom to choose their own computing components. Additionally, this approach would reduce the need to perform version updates on each robot separately. Based on the feedback we got from educators, the current approach for interaction with the robot is different from that of similar market robotic kits. Thus, it is necessary to adopt an approach that utilizes existing user interaction knowledge. The proposed solution of transferring the programming stack to the cloud addresses these issues and provides a cost-effective and user-friendly solution.<br /> <br /> ==== Expected Results ====<br /> Within the months of the project the expected results are: <br /> <br /> * Understanding previous work <br /> * Transfer the core library to Micropython<br /> * Convert the whole stack to be deployable on the cloud as a platform.<br /> * New programming modes <br /> * Resolve bugs and provide improvements (front end - back end)<br /> * Github automation for deployment<br /> * Extensive documentation<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related Repositories ====<br /> &lt;nowiki&gt;https://github.com/eellak/fossbot&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/chronis10/fossbot_source&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/chronis10/fossbot_simulator&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/cyberbotics/webots&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/bbcmicrobit&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/yiorgosynkl/914e75d0f9ae98bb31f4d8da66ec9908&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/chronis10/9d069c56b3df9c92693ac8d24270a62a&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/isl-org/OpenBot&lt;/nowiki&gt;<br /> <br /> ==== Mentors: ====<br /> Iraklis Varlamis<br /> <br /> Christos Chronis<br /> <br /> == Development of a Web Based robotic simulator for the DIY robot kit for educators ==<br /> Google Summer of Code 2023 - Proposal<br /> <br /> ==== Brief Explanation ====<br /> The project started back on the GSoC 2019 when Christos Chronis designed and implemented a 3D printable robot for educational purposes from scratch. The idea was create a new educational robot, that will be modular, come at a low cost and be available to everyone. All of the parts of the modular robot were designed in a way so that they are easily 3D-printable. The 3D-printed parts are combined with basic low-cost electronics and sensors, which can be easily assembled to the final robot following extensive and simple guidelines of how to print and assemble the robot. In this way non-expert staff in robotics, electronics or IoT programming can assemble and use the robot. Alongside the guidelines, a number of demo scenarios for the class, e.g. simple navigation or sensing scenarios, that require basic programming skills are also provided. <br /> <br /> The project has been continued two years later, in GSoC 2021, by Georgios Giannakoulias, who developed a version of the “DIY robot kit for educators” that integrates Node-Red blocks and introduced a new way to program the robot based on blocks. Last year at the GSOC 2022 two more contributors Danai Brilli and Eleftheria Papagergiou worked on a new version of the robot and redesigned the whole programming stack. The robot can now be programmed using native Python code or using a custom version of Google Blockly (a Scratch-like user interface). They also improved the initial core library and developed a completed unified user interface with multiple project management, robot configuration page, a new programming mode for kindergarten learning activities and a Docker based deployment system with CI/CD capabilities.<br /> <br /> What must be done in GSOC 2023:<br /> <br /> A contributor Manousos Linardakis developed a simulator for the robot that gives the opportunity to everyone to use the previous programming stack without the need of a real robot. The current version is based on Coppelia Simulator and it was a great import to the whole robot interaction experience. Already the simulator has been tested by many educators and  received great comments. Based on that it is important to develop a new simulator. The new simulator must be web based and must provide a way to run different educational scenarios, like line following or obstacle avoidance. In addition the need for an integrated,  lightweight, web based simulator is very important because the existing simulator requires a high end PC and also adds extra steps to the procedure of testing and using the robot. Finally the new simulator must be capable of being integrated in the existing programming stack or in a future cloud based implementation. The proposed solution provides a cost-effective and user-friendly solution and eliminates the need of a real robot removing the cost boundaries and giving the opportunity to any educator to test and use our open source solution.<br /> <br /> ==== Expected Results ====<br /> Within the months of the project the expected results are: <br /> <br /> * Understanding previous work <br /> * Add compatibility to core library for simulator support<br /> * Develop a web based simulator<br /> * Integration of the simulator with the platform<br /> * Create stages for education scenarios<br /> * Resolve bugs and provide improvements<br /> * Github automation for deployment<br /> * Extensive documentation<br /> <br /> ==== Related Repositories ====<br /> &lt;nowiki&gt;https://github.com/eellak/fossbot&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/chronis10/fossbot_source&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/chronis10/fossbot_simulator&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/cyberbotics/webots&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/bbcmicrobit&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/yiorgosynkl/914e75d0f9ae98bb31f4d8da66ec9908&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/chronis10/9d069c56b3df9c92693ac8d24270a62a&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/isl-org/OpenBot&lt;/nowiki&gt;<br /> <br /> ==== Mentors: ====<br /> Iraklis Varlamis<br /> <br /> Christos Chronis</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2023_proposed_ideas&diff=2148 2023-01-25T10:38:00Z

<p>Pkst-1: Νέα σελίδα με &#039;Contributors interested to participate should check which of the following projects fits their interests and skills. &#039;&#039;&#039;Τo communicate with the mentors and ask questi...&#039;</p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' list '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''page'''.<br /> <br /> == Epoptes improvements ==<br /> <br /> ==== Brief Explanation ====<br /> Epoptes is an open source computer lab management and monitoring tool. It is used in more than 1000 Greek schools, and in thousands more schools and businesses worldwide. The following improvements and new features have been requested by the community for years and can be implemented as part of a GSoC project:<br /> <br /> - Make Epoptes available on more Linux distributions.<br /> <br /> - Support screen sharing on Wayland.<br /> <br /> - Drop the session service and keep only the system epoptes-client service.<br /> <br /> - Use systemd socket activation and autorestart.<br /> <br /> And if there's enough time left, also improve its firewall compatibility.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Expected Results ====<br /> It is expected that the student will send a pull request on &lt;nowiki&gt;https://github.com/epoptes/epoptes&lt;/nowiki&gt;, suitable for merging upstream. That PR should implement all the aforementioned tasks, which are described in more detail at &lt;nowiki&gt;https://epoptes.org/documentation/gsoc/&lt;/nowiki&gt;<br /> <br /> ==== Related repositories ====<br /> https://epoptes.org/documentation/gsoc/<br /> <br /> ==== Knowledge Prerequisites ====<br /> Python, GTK, Shell, networking, systemd services, Wayland, XDG desktop portals<br /> <br /> ==== Mentors: ====<br /> Foteini Tsiami (fottsia@gmail.com), Siahos Yiannis (siahos@cti.gr)<br /> <br /> <br /> == Interoperability of GitLab boards with Nextcloud Decks ==<br /> <br /> ==== Brief Explanation ====<br /> Nextcloud is free software for cloud collaboration. Nextcloud consists of core functionality plus installable apps. One of the apps is &quot;Deck&quot;, that provides Kanban boards. GitLab also provides Kanban boards.<br /> <br /> There exists a Nextcloud &quot;GitLab&quot; app which provides some integration of GitLab with Nextcloud, but it does very little. Its only substantial functionality is to show a panel with &quot;GitLab todos&quot; in the Nextcloud dashboard (which serves as the user's home page). That is, it provides a list in the user's Nextcloud dashboard that shows the next things the user has to do in GitLab. Each &quot;todo&quot; is just a title and a link to GitLab.<br /> <br /> This project is about extending the GitLab-Nextcloud integration so that a user can see and manage the GitLab boards in Nextcloud's Deck application.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Expected Results ====<br /> Some organizations use both Nextcloud and GitLab. Some of their kanban boards tend to be on GitLab and some other on Nextcloud. Developers tend to use more GitLab, while non-developers tend to use Nextcloud. This project is expected to reduce duplication and help developers and non-developers co-operate better.<br /> <br /> ==== Related repositories ====<br /> &lt;&lt;nowiki&gt;https://github.com/nextcloud/deck/&lt;/nowiki&gt;&gt;<br /> <br /> &lt;&lt;nowiki&gt;https://github.com/nextcloud/integration\_gitlab&lt;/nowiki&gt;&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> JavaScript, PHP, Vue, Web APIs<br /> <br /> ==== Mentors: ====<br /> Antonis Christofides<br /> <br /> <br /> <br /> == Development of a DIY robot kit for educators[επεξεργασία | επεξεργασία κώδικα] ==<br /> <br /> ==== Brief Explanation ====<br /> In GSoC 2019, one of the final projects was “A DIY robot kit for educators”. In this project, the contributor, Christos Chronis, designed and implemented a 3D printable robot for educational purposes from scratch. Electronic parts, such as cables, sensors, buttons, camera, accelerometer, LED set, as well as 3D printed parts were used to assemble the robot alongside with the code written in order to program it. Two years later, in GSoC 2021, the contributor developed another version of “A DIY robot kit for educators” with a Node-Red integration, based on the project of GSoC 2019.<br /> <br /> As the contributor of GSOC 2019 mentioned in his wiki page there is still a lot of work to be done, so that the original project (Proteas robot) can be improved and maybe be a direct competitor to any commercial alternative on educational robotics. Based on the work of the two previous contributors, I would like to propose my idea for GSOC 2022, which includes the robotic designs, the guidelines, the integration of the robot with Google Blockly and improvement of the back end code with a custom library for special control of every electronic part and sensor.<br /> <br /> All of the parts of the modular robot are gonna be designed this way so that they are 3D-printable. The 3D-printed parts together with basic low-cost electronics and sensors can be  easily combined to assemble the robot following the guidelines. Extensive and simple guidelines of how to print and assemble the robot will be provided, so that non-expert staff in robotics, electronics or IoT programming, will be able to follow them successfully. Alongside with the guidelines, the staff will also be provided with a number of demo scenarios for the class, e.g. simple navigation or sensing scenarios, that require basic programming skills.<br /> <br /> The development of a desktop application will mainly consist of the open-source Google Blockly. Blockly is a free, open source web-based, visual programming editor, developed by Google. Users can drag blocks together to build programs. It is similar to Scratch, that the contributor of GSoC 2019 proposed, but it can be a lot easier configured, as it is open source and offers a lot more possibilities, such as custom creation of Google Blockly blocks or preferred language selection of the environment. In the GsoC 2021, the contributor made a first approach using Node-red, but Node-red demands specialized knowledge in its usage and it is not suitable for younger ages. So, the kids and adolescents can use our application for learning visual programming by using the custom blocks to build programs and then the robot can execute them in real time. The application and the robot can be used in STEM education for different experiments. Educators already know Scratch, so adjusting to Google Blockly will be an easy process. The desktop application will be wirelessly connected to the robot, so that the robot can execute the programs built and run by the students. The desktop application will be implemented in such a way, so that it is suitable for students, e.g. colorful and interactive design, pop ups for extra information or to notify the student if an action was successful or not. There will also be the option to save or import Blockly code. The already implemented code can be saved/ extracted into an xml file that will be downloaded in a preferred folder in the computer. Such an xml file can be imported into the application for later use.<br /> <br /> In order for the Blockly integration to be possible, it is going to be essential for some parts of the existing code of the GSoC 2019-2021 robot to be rewritten. This means that back end code that runs in the Raspberry Pi and controls all the movements of the robot, will be improved. For that reason, I will create a custom functionality library (e.x. PID Controller) for special control of every electronic part and sensor. Based on the fact that the GSoC 2021 contributor introduced for the first time the logic of microservices, microservices and dockerization can be utilized so that different services run for controlling the robot movements as well as the communication between the Blockly desktop application and the Raspberry Pi.<br /> <br /> ==== Expected Results ====<br /> Within the months of the project the expected results are:<br /> <br /> * Understanding previous work<br /> * Rewrite core library ( Backend code )<br /> * Create microsreliable communication between Frontend - Backend code<br /> * Frontend implementation<br /> * Educational scenarios for the class<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related Repositories ====<br /> &lt;nowiki&gt;https://hackaday.io/project/26007-versatile-educational-2wd-robot&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/yiorgosynkl/914e75d0f9ae98bb31f4d8da66ec9908&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/chronis10/9d069c56b3df9c92693ac8d24270a62a&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/isl-org/OpenBot&lt;/nowiki&gt;</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_Accepted_projects&diff=2143 2022-12-02T09:47:14Z

<p>Pkst-1: </p> <hr /> <div>{| class=&quot;wikitable&quot;<br /> |Student<br /> |Project<br /> |-<br /> |Berke <br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/mr5vN7K5 First Year Student on Parallelised Flows]<br /> |-<br /> |Christina-Anna Gatsiou<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/uA64Hl2A I/O infrastructure for Apothesis]<br /> |-<br /> |CrocHold<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/z9yD2hv3 Development of a backend management system for NodeRed instances]<br /> |-<br /> |Danai Brilli<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/XJ5kKZQ1 DIY Robot]<br /> |-<br /> |Eleftheria Papagergiou<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/hZgV3TVm Development of a DIY robot kit for educators]<br /> |-<br /> |Giannis Prokopiou<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/dBh2T0yh Label Buddy 2.0: Automated audio-tagging using transfer learning]<br /> |-<br /> |Alex Papadopoulos<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/sSPlO4FN Hephaestus: Testing the TypeScript compiler]<br /> |-<br /> |Fotios Valasiadis<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/gt4uN8hd Build Recorder]<br /> |-<br /> |Kanha Agrawal<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/0vJmJgz4 Flexbench]<br /> |}<br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_Accepted_projects&diff=2142 2022-12-02T09:46:02Z

<p>Pkst-1: Νέα σελίδα με &#039;{| class=&quot;wikitable&quot; |Student |Project |- |Berke |[https://summerofcode.withgoogle.com/programs/2022/projects/mr5vN7K5 First Year Student on Parallelised Flows] |- |Ch...&#039;</p> <hr /> <div>{| class=&quot;wikitable&quot;<br /> |Student<br /> |Project<br /> |-<br /> |Berke <br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/mr5vN7K5 First Year Student on Parallelised Flows]<br /> |-<br /> |Christina-Anna Gatsiou<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/uA64Hl2A I/O infrastructure for Apothesis]<br /> |-<br /> |CrocHold<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/z9yD2hv3 Development of a backend management system for NodeRed instances]<br /> |-<br /> |Danai Brilli<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/XJ5kKZQ1 DIY Robot]<br /> |-<br /> |Eleftheria Papagergiou<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/hZgV3TVm Development of a DIY robot kit for educators]<br /> |-<br /> |Giannis Prokopiou<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/dBh2T0yh Label Buddy 2.0: Automated audio-tagging using transfer learning]<br /> |-<br /> |Alex Papadopoulos<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/sSPlO4FN Hephaestus: Testing the TypeScript compiler]<br /> |-<br /> |Fotios Valasiadis<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/gt4uN8hd Build Recorder]<br /> |-<br /> |Kanha Agrawal<br /> |[https://summerofcode.withgoogle.com/programs/2022/projects/0vJmJgz4 Flexbench]<br /> |}</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%A8%CE%B7%CF%86%CE%B9%CE%B1%CE%BA%CF%8C%CF%82_%CE%BC%CE%B5%CF%84%CE%B1%CF%83%CF%87%CE%B7%CE%BC%CE%B1%CF%84%CE%B9%CF%83%CE%BC%CF%8C%CF%82_%CF%84%CE%B7%CF%82_%CE%B4%CE%B7%CE%BC%CF%8C%CF%83%CE%B9%CE%B1%CF%82_%CE%B4%CE%B9%CE%BF%CE%AF%CE%BA%CE%B7%CF%83%CE%B7%CF%82_%CE%BF_%CF%81%CF%8C%CE%BB%CE%BF%CF%82_%CE%BA%CE%B1%CE%B9_%CE%B7_%CE%B1%CE%BE%CE%AF%CE%B1_%CF%84%CF%89%CE%BD_%CE%B1%CE%BD%CE%BF%CE%B9%CF%87%CF%84%CF%8E%CE%BD_%CF%84%CE%B5%CF%87%CE%BD%CE%BF%CE%BB%CE%BF%CE%B3%CE%B9%CF%8E%CE%BD&diff=2140 2022-11-24T07:42:45Z

<p>Pkst-1: </p> <hr /> <div>[https://ellak.gr/wiki/images/b/b9/1_%CE%A8%CE%B7%CF%86%CE%B9%CE%B1%CE%BA%CF%8C%CF%82_%CE%BC%CE%B5%CF%84%CE%B1%CF%83%CF%87%CE%B7%CE%BC%CE%B1%CF%84%CE%B9%CF%83%CE%BC%CF%8C%CF%82_%CF%84%CE%B7%CF%82_%CE%B4%CE%B7%CE%BC%CF%8C%CF%83%CE%B9%CE%B1%CF%82_%CE%B4%CE%B9%CE%BF%CE%AF%CE%BA%CE%B7%CF%83%CE%B7%CF%82_%CE%BF_%CF%81%CF%8C%CE%BB%CE%BF%CF%82_%CE%BA%CE%B1%CE%B9_%CE%B7_%CE%B1%CE%BE%CE%AF%CE%B1_%CF%84%CF%89%CE%BD_%CE%B1%CE%BD%CE%BF%CE%B9%CF%87%CF%84%CF%8E%CE%BD_%CF%84%CE%B5%CF%87%CE%BD%CE%BF%CE%BB%CE%BF%CE%B3%CE%B9%CF%8E%CE%BD.pdf Ψηφιακός μετασχηματισμός της δημόσιας διοίκησης: ο ρόλος και η αξία των ανοιχτών τεχνολογιών]<br /> <br /> [https://ellak.gr/wiki/images/b/b9/1_%CE%A8%CE%B7%CF%86%CE%B9%CE%B1%CE%BA%CF%8C%CF%82_%CE%BC%CE%B5%CF%84%CE%B1%CF%83%CF%87%CE%B7%CE%BC%CE%B1%CF%84%CE%B9%CF%83%CE%BC%CF%8C%CF%82_%CF%84%CE%B7%CF%82_%CE%B4%CE%B7%CE%BC%CF%8C%CF%83%CE%B9%CE%B1%CF%82_%CE%B4%CE%B9%CE%BF%CE%AF%CE%BA%CE%B7%CF%83%CE%B7%CF%82_%CE%BF_%CF%81%CF%8C%CE%BB%CE%BF%CF%82_%CE%BA%CE%B1%CE%B9_%CE%B7_%CE%B1%CE%BE%CE%AF%CE%B1_%CF%84%CF%89%CE%BD_%CE%B1%CE%BD%CE%BF%CE%B9%CF%87%CF%84%CF%8E%CE%BD_%CF%84%CE%B5%CF%87%CE%BD%CE%BF%CE%BB%CE%BF%CE%B3%CE%B9%CF%8E%CE%BD.pdf Μελέτη του Οργανισμού Ανοιχτών Τεχνολογιών ΕΕΛΛΑΚ για το Ινστιτούτο Νίκος Πουλαντζάς]<br /> [[Κατηγορία:Μελέτες]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%91%CF%81%CF%87%CE%B5%CE%AF%CE%BF:1_%CE%A8%CE%B7%CF%86%CE%B9%CE%B1%CE%BA%CF%8C%CF%82_%CE%BC%CE%B5%CF%84%CE%B1%CF%83%CF%87%CE%B7%CE%BC%CE%B1%CF%84%CE%B9%CF%83%CE%BC%CF%8C%CF%82_%CF%84%CE%B7%CF%82_%CE%B4%CE%B7%CE%BC%CF%8C%CF%83%CE%B9%CE%B1%CF%82_%CE%B4%CE%B9%CE%BF%CE%AF%CE%BA%CE%B7%CF%83%CE%B7%CF%82_%CE%BF_%CF%81%CF%8C%CE%BB%CE%BF%CF%82_%CE%BA%CE%B1%CE%B9_%CE%B7_%CE%B1%CE%BE%CE%AF%CE%B1_%CF%84%CF%89%CE%BD_%CE%B1%CE%BD%CE%BF%CE%B9%CF%87%CF%84%CF%8E%CE%BD_%CF%84%CE%B5%CF%87%CE%BD%CE%BF%CE%BB%CE%BF%CE%B3%CE%B9%CF%8E%CE%BD.pdf&diff=2139 2022-11-23T15:00:55Z

<p>Pkst-1: </p> <hr /> <div>Μελέτη του Οργανισμού Ανοιχτών Τεχνολογιών<br /> ΕΕΛΛΑΚ για το Ινστιτούτο Νίκος Πουλαντζάς<br /> <br /> <br /> <br /> <br /> [[Κατηγορία:Μελέτες]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%91%CF%81%CF%87%CE%B5%CE%AF%CE%BF:1_%CE%A8%CE%B7%CF%86%CE%B9%CE%B1%CE%BA%CF%8C%CF%82_%CE%BC%CE%B5%CF%84%CE%B1%CF%83%CF%87%CE%B7%CE%BC%CE%B1%CF%84%CE%B9%CF%83%CE%BC%CF%8C%CF%82_%CF%84%CE%B7%CF%82_%CE%B4%CE%B7%CE%BC%CF%8C%CF%83%CE%B9%CE%B1%CF%82_%CE%B4%CE%B9%CE%BF%CE%AF%CE%BA%CE%B7%CF%83%CE%B7%CF%82_%CE%BF_%CF%81%CF%8C%CE%BB%CE%BF%CF%82_%CE%BA%CE%B1%CE%B9_%CE%B7_%CE%B1%CE%BE%CE%AF%CE%B1_%CF%84%CF%89%CE%BD_%CE%B1%CE%BD%CE%BF%CE%B9%CF%87%CF%84%CF%8E%CE%BD_%CF%84%CE%B5%CF%87%CE%BD%CE%BF%CE%BB%CE%BF%CE%B3%CE%B9%CF%8E%CE%BD.pdf&diff=2138 2022-11-23T14:55:57Z

<p>Pkst-1: </p> <hr /> <div>Μελέτη του Οργανισμού Ανοιχτών Τεχνολογιών<br /> ΕΕΛΛΑΚ για το Ινστιτούτο Νίκος Πουλαντζάς</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2119 2022-02-25T07:13:00Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> 175 hours<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Expected Results ====<br /> <br /> # &lt;u&gt;Machine Learning&lt;/u&gt;<br /> #* Conduct research for the appropriate model architecture<br /> #* Modify the annotation process by integrating the model<br /> #* Test the model by providing evaluation metrics<br /> # &lt;u&gt;Django&lt;/u&gt;<br /> #* Add lazy loading for the audio files: load segments of the file when needed (i.e., YouTube). This will lead to better performance when the audio file is too big.<br /> #* Add Django Testing<br /> #* Dockerization<br /> #* Add documentation<br /> #* Add rar file upload functionality - currently, users can only upload zip files (optional)<br /> #* UI improvements (optional)<br /> <br /> ==== Related repositories ====<br /> https://labelbuddy.io/ (username: demo, password: labelbuddy123)<br /> <br /> https://github.com/eellak/gsoc2021-audio-annotation-tool<br /> <br /> https://www.youtube.com/watch?v=SdbGhrad-GQ<br /> <br /> https://github.com/jordipons/sklearn-audio-transfer-learning<br /> <br /> https://github.com/jordipons/musicnn<br /> <br /> https://prodi.gy/docs/audio-video#model<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> == Development of a backend management system for NodeRed instances ==<br /> <br /> ==== Brief Explanation ====<br /> NodeRed is one of the most well known low-code IoT programming tools, offering a large number of ready-to-use libraries. Nevertheless, it lacks modern aspects of system deployments, like multi-user server functionalities, since one NodeRed deployment can support only one user. In this context we propose a backend system written in Python or NodeJs, that will provide a web-based API (e.g. REST), via which the management (creation, deletion and deployment) of NodeRed instances will be performed. Each NodeRed instance will be deployed either on-system, or even better using containers (e.g. Docker). Furthermore, the system will support saving annotated NodeRed deployments which contain specific nodes (or flows), so as to easily create new deployments that offer personalized/aggregated functionality. E.g. if a user creates flows annotated as &quot;Raspberry Pi GPIO&quot; and another creates &quot;Google Firebase&quot;, the system should be able to create a new NodeRed instance that contains one of these flow sets or both, according to what the end user needs.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Expected Results ====<br /> Creation of a backend system, able to manage annotated NodeRed instances<br /> <br /> ==== Related repositories ====<br /> https://github.com/node-red/node-red<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python or NodeJS, JavaScript, OpenAPI, Containers. Desired: NoSQL databases, Full Stack development<br /> <br /> ==== Mentors: ====<br /> Emmanouil Tsardoulias (etsardou@gmail.com), Konstantinos Panayiotou (klpanagi@issel.ee.auth.gr), Andreas Symeonidis (asymeon@eng.auth.gr)<br /> <br /> <br /> == Testing the type checker of TypeScript ==<br /> <br /> ==== Brief Explanation ====<br /> Despite the substantial progress in compiler testing, research endeavors have mainly focused on detecting compiler crashes and subtle miscompilations caused by bugs in the implementation of compiler optimizations. Surprisingly, this growing body of work neglects other compiler components, most notably the front-end. In statically-typed programming languages with rich and expressive type systems and modern features, such as type inference or a mix of object-oriented with functional programming features, the process of static typing in compiler front-ends is complicated by a high-density of bugs. As a recent study has shown [1], such bugs can lead to the acceptance of incorrect programs (breaking code portability or the type system's soundness), the rejection of correct (e.g. well-typed) programs, and the reporting of misleading errors and warnings. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Expected Results ====<br /> It is expected that the project will deliver an extended version of Hephaestus that is capable of finding real bugs in the compiler of TypeScript<br /> <br /> ==== Related repositories ====<br /> https://github.com/theosotr/hephaestus<br /> <br /> ==== Knowledge Prerequisites ====<br /> Good programming skills (in particular Python), good knowledge of object-oriented programming, familiarity with Java generics<br /> <br /> ==== Mentors: ====<br /> Thodoris Sotiropoulos, Stefanos Chaliasos, Dimitris Mitropoulos, Diomidis Spinellis<br /> <br /> == Real-time Graph-Clustering and Visualization for Architecture Recovery from Class-Dependency Analysis on Very Large Source-Code Bases ==<br /> <br /> ==== Brief Explanation ====<br /> We have already developed the first generation of a tool for architecture recovery from C++ sources by: (i) tracking dependencies, (ii) preparing a global dependency graph; and (iii) applying graph clustering to compute and visualize likely architectural modules. The tool uses Clang (open source, C++) for the frontend and Go.JS (2d) for the backend (graph rendering and GUI). Numerous practical shortcomings were identified when processing very large projects, above the magnitude of many hundreds of source files and classes. <br /> <br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Ogre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Expected Results ====<br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Expected ResultsOgre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> [https://github.com/theosotr/hephaestus '''Clang''' (https://clang.llvm.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''Ogre 3d''' (https://www.ogre3d.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''wxWidgets''' (https://www.wxwidgets.org/)]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Clang, Go.JS ,Ogre 3d, wxWidgets<br /> <br /> ==== Mentors: ====<br /> Antonios Savidis<br /> <br /> == Development of a DIY robot kit for educators ==<br /> <br /> ==== Brief Explanation ====<br /> In GSoC 2019, one of the final projects was “A DIY robot kit for educators”. In this project, the contributor, Christos Chronis, designed and implemented a 3D printable robot for educational purposes from scratch. Electronic parts, such as cables, sensors, buttons, camera, accelerometer, LED set, as well as 3D printed parts were used to assemble the robot alongside with the code written in order to program it. Two years later, in GSoC 2021, the contributor developed another version of “A DIY robot kit for educators” with a Node-Red integration, based on the project of GSoC 2019. <br /> <br /> As the contributor of GSOC 2019 mentioned in his wiki page there is still a lot of work to be done, so that the original project (Proteas robot) can be improved and maybe be a direct competitor to any commercial alternative on educational robotics. Based on the work of the two previous contributors, I would like to propose my idea for GSOC 2022, which includes the robotic designs, the guidelines, the integration of the robot with Google Blockly and improvement of the back end code with a custom library for special control of every electronic part and sensor.<br /> <br /> All of the parts of the modular robot are gonna be designed this way so that they are 3D-printable. The 3D-printed parts together with basic low-cost electronics and sensors can be  easily combined to assemble the robot following the guidelines. Extensive and simple guidelines of how to print and assemble the robot will be provided, so that non-expert staff in robotics, electronics or IoT programming, will be able to follow them successfully. Alongside with the guidelines, the staff will also be provided with a number of demo scenarios for the class, e.g. simple navigation or sensing scenarios, that require basic programming skills.<br /> <br /> The development of a desktop application will mainly consist of the open-source Google Blockly. Blockly is a free, open source web-based, visual programming editor, developed by Google. Users can drag blocks together to build programs. It is similar to Scratch, that the contributor of GSoC 2019 proposed, but it can be a lot easier configured, as it is open source and offers a lot more possibilities, such as custom creation of Google Blockly blocks or preferred language selection of the environment. In the GsoC 2021, the contributor made a first approach using Node-red, but Node-red demands specialized knowledge in its usage and it is not suitable for younger ages. So, the kids and adolescents can use our application for learning visual programming by using the custom blocks to build programs and then the robot can execute them in real time. The application and the robot can be used in STEM education for different experiments. Educators already know Scratch, so adjusting to Google Blockly will be an easy process. The desktop application will be wirelessly connected to the robot, so that the robot can execute the programs built and run by the students. The desktop application will be implemented in such a way, so that it is suitable for students, e.g. colorful and interactive design, pop ups for extra information or to notify the student if an action was successful or not. There will also be the option to save or import Blockly code. The already implemented code can be saved/ extracted into an xml file that will be downloaded in a preferred folder in the computer. Such an xml file can be imported into the application for later use. <br /> <br /> In order for the Blockly integration to be possible, it is going to be essential for some parts of the existing code of the GSoC 2019-2021 robot to be rewritten. This means that back end code that runs in the Raspberry Pi and controls all the movements of the robot, will be improved. For that reason, I will create a custom functionality library (e.x. PID Controller) for special control of every electronic part and sensor. Based on the fact that the GSoC 2021 contributor introduced for the first time the logic of microservices, microservices and dockerization can be utilized so that different services run for controlling the robot movements as well as the communication between the Blockly desktop application and the Raspberry Pi.<br /> <br /> ==== Expected Results ====<br /> Within the months of the project the expected results are: <br /> <br /> * Understanding previous work <br /> * Rewrite core library ( Backend code )<br /> * Create microsreliable communication between Frontend - Backend code <br /> * Frontend implementation<br /> * Educational scenarios for the class <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related Repositories ====<br /> &lt;nowiki&gt;https://hackaday.io/project/26007-versatile-educational-2wd-robot&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/yiorgosynkl/914e75d0f9ae98bb31f4d8da66ec9908&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/chronis10/9d069c56b3df9c92693ac8d24270a62a&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/isl-org/OpenBot&lt;/nowiki&gt;<br /> <br /> ==== Mentors: ====<br /> Iraklis Varlamis, Christos Chronis<br /> <br /> == Greek glyphs in Open Source Fonts: additions and redesign ==<br /> <br /> ==== Brief Explanation ====<br /> Many of the Open Source fonts (e.g., available at https://fonts.google.com), do not include glyphs for Greek letters and are therefore useless for using in a Greek environment.<br /> The aim of this project is to improve this situation and add the missing glyphs in the correct Unicode codepoints. The exact set of fonts to be completed will be determined in discussions between the student and the mentor(s).<br /> <br /> This is not a typical programming project.<br /> If you have never designed fonts before, it is probably not for you.<br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Expected Results ====<br /> Full support for Greek text in a number of Open Source fonts.<br /> <br /> ==== Related repositories ====<br /> Addition of Greek glyphs in:<br /> <br /> - Merriweather (12 upright +12 italics) https://github.com/EbenSorkin/Merriweather<br /> - Spectral (3 upright +3 italics) https://github.com/productiontype/Spectral<br /> - WorkSans (3 upright +3 italics) https://github.com/weiweihuanghuang/Work-Sans<br /> <br /> Redesign of existing Greek glyphs in:<br /> - Fira (all styles) https://github.com/mozilla/Fira<br /> - Vollkorn (all styles) https://github.com/FAlthausen/Vollkorn-Typeface<br /> <br /> ==== Knowledge Prerequisites ====<br /> Type design, font technologies.<br /> Please note that this is a special project, where coding, in the traditional sense, will not be enough.<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Εmilios Τheofanous, Alexios Zavras<br /> <br /> == Apothesis ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is an open source software for simulating deposition processes via the kinetic Monte Carlo method. This is the first step for creating a generalized kinetic Monte Carlo code for studying surface growth phenomena.  Apothesis currently lucks generalized I/O operations. The goal is to develop the I/O part of the software in order to be easy to use by many researchers. For kMC codes this is far from trivial since the I/O depends on the underlying physical system. That said, the input can be different when studying 2D materials compared to 3D systems. Regarding the output, this is defined by the properties of that a researcher wants to study.   <br /> <br /> ==== Expected Results ====<br /> The necessary classes and infrastructure for the I/O operations of Apothesis. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, JSON<br /> <br /> ==== Mentors: ====<br /> Nikos Cheimarios, Vissarion Fisikopoulos<br /> <br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2118 2022-02-25T07:10:25Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> <br /> # &lt;u&gt;Machine Learning&lt;/u&gt;<br /> #* Conduct research for the appropriate model architecture<br /> #* Modify the annotation process by integrating the model<br /> #* Test the model by providing evaluation metrics<br /> # &lt;u&gt;Django&lt;/u&gt;<br /> #* Add lazy loading for the audio files: load segments of the file when needed (i.e., YouTube). This will lead to better performance when the audio file is too big.<br /> #* Add Django Testing<br /> #* Dockerization<br /> #* Add documentation<br /> #* Add rar file upload functionality - currently, users can only upload zip files (optional)<br /> #* UI improvements (optional)<br /> <br /> ==== Related repositories ====<br /> https://labelbuddy.io/ (username: demo, password: labelbuddy123)<br /> <br /> https://github.com/eellak/gsoc2021-audio-annotation-tool<br /> <br /> https://www.youtube.com/watch?v=SdbGhrad-GQ<br /> <br /> https://github.com/jordipons/sklearn-audio-transfer-learning<br /> <br /> https://github.com/jordipons/musicnn<br /> <br /> https://prodi.gy/docs/audio-video#model<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> == Development of a backend management system for NodeRed instances ==<br /> <br /> ==== Brief Explanation ====<br /> NodeRed is one of the most well known low-code IoT programming tools, offering a large number of ready-to-use libraries. Nevertheless, it lacks modern aspects of system deployments, like multi-user server functionalities, since one NodeRed deployment can support only one user. In this context we propose a backend system written in Python or NodeJs, that will provide a web-based API (e.g. REST), via which the management (creation, deletion and deployment) of NodeRed instances will be performed. Each NodeRed instance will be deployed either on-system, or even better using containers (e.g. Docker). Furthermore, the system will support saving annotated NodeRed deployments which contain specific nodes (or flows), so as to easily create new deployments that offer personalized/aggregated functionality. E.g. if a user creates flows annotated as &quot;Raspberry Pi GPIO&quot; and another creates &quot;Google Firebase&quot;, the system should be able to create a new NodeRed instance that contains one of these flow sets or both, according to what the end user needs.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Creation of a backend system, able to manage annotated NodeRed instances<br /> <br /> ==== Related repositories ====<br /> https://github.com/node-red/node-red<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python or NodeJS, JavaScript, OpenAPI, Containers. Desired: NoSQL databases, Full Stack development<br /> <br /> ==== Mentors: ====<br /> Emmanouil Tsardoulias (etsardou@gmail.com), Konstantinos Panayiotou (klpanagi@issel.ee.auth.gr), Andreas Symeonidis (asymeon@eng.auth.gr)<br /> <br /> <br /> == Testing the type checker of TypeScript ==<br /> <br /> ==== Brief Explanation ====<br /> Despite the substantial progress in compiler testing, research endeavors have mainly focused on detecting compiler crashes and subtle miscompilations caused by bugs in the implementation of compiler optimizations. Surprisingly, this growing body of work neglects other compiler components, most notably the front-end. In statically-typed programming languages with rich and expressive type systems and modern features, such as type inference or a mix of object-oriented with functional programming features, the process of static typing in compiler front-ends is complicated by a high-density of bugs. As a recent study has shown [1], such bugs can lead to the acceptance of incorrect programs (breaking code portability or the type system's soundness), the rejection of correct (e.g. well-typed) programs, and the reporting of misleading errors and warnings. <br /> <br /> ==== Duration of the Project ====<br /> 350 hours<br /> <br /> ==== Expected Results ====<br /> It is expected that the project will deliver an extended version of Hephaestus that is capable of finding real bugs in the compiler of TypeScript<br /> <br /> ==== Related repositories ====<br /> https://github.com/theosotr/hephaestus<br /> <br /> ==== Knowledge Prerequisites ====<br /> Good programming skills (in particular Python), good knowledge of object-oriented programming, familiarity with Java generics<br /> <br /> ==== Mentors: ====<br /> Thodoris Sotiropoulos, Stefanos Chaliasos, Dimitris Mitropoulos, Diomidis Spinellis<br /> <br /> == Real-time Graph-Clustering and Visualization for Architecture Recovery from Class-Dependency Analysis on Very Large Source-Code Bases ==<br /> <br /> ==== Brief Explanation ====<br /> We have already developed the first generation of a tool for architecture recovery from C++ sources by: (i) tracking dependencies, (ii) preparing a global dependency graph; and (iii) applying graph clustering to compute and visualize likely architectural modules. The tool uses Clang (open source, C++) for the frontend and Go.JS (2d) for the backend (graph rendering and GUI). Numerous practical shortcomings were identified when processing very large projects, above the magnitude of many hundreds of source files and classes. <br /> <br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Ogre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Expected Results ====<br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Expected ResultsOgre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Related repositories ====<br /> [https://github.com/theosotr/hephaestus '''Clang''' (https://clang.llvm.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''Ogre 3d''' (https://www.ogre3d.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''wxWidgets''' (https://www.wxwidgets.org/)]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Clang, Go.JS ,Ogre 3d, wxWidgets<br /> <br /> ==== Mentors: ====<br /> Antonios Savidis<br /> <br /> == Development of a DIY robot kit for educators ==<br /> <br /> ==== Brief Explanation ====<br /> In GSoC 2019, one of the final projects was “A DIY robot kit for educators”. In this project, the contributor, Christos Chronis, designed and implemented a 3D printable robot for educational purposes from scratch. Electronic parts, such as cables, sensors, buttons, camera, accelerometer, LED set, as well as 3D printed parts were used to assemble the robot alongside with the code written in order to program it. Two years later, in GSoC 2021, the contributor developed another version of “A DIY robot kit for educators” with a Node-Red integration, based on the project of GSoC 2019. <br /> <br /> As the contributor of GSOC 2019 mentioned in his wiki page there is still a lot of work to be done, so that the original project (Proteas robot) can be improved and maybe be a direct competitor to any commercial alternative on educational robotics. Based on the work of the two previous contributors, I would like to propose my idea for GSOC 2022, which includes the robotic designs, the guidelines, the integration of the robot with Google Blockly and improvement of the back end code with a custom library for special control of every electronic part and sensor.<br /> <br /> All of the parts of the modular robot are gonna be designed this way so that they are 3D-printable. The 3D-printed parts together with basic low-cost electronics and sensors can be  easily combined to assemble the robot following the guidelines. Extensive and simple guidelines of how to print and assemble the robot will be provided, so that non-expert staff in robotics, electronics or IoT programming, will be able to follow them successfully. Alongside with the guidelines, the staff will also be provided with a number of demo scenarios for the class, e.g. simple navigation or sensing scenarios, that require basic programming skills.<br /> <br /> The development of a desktop application will mainly consist of the open-source Google Blockly. Blockly is a free, open source web-based, visual programming editor, developed by Google. Users can drag blocks together to build programs. It is similar to Scratch, that the contributor of GSoC 2019 proposed, but it can be a lot easier configured, as it is open source and offers a lot more possibilities, such as custom creation of Google Blockly blocks or preferred language selection of the environment. In the GsoC 2021, the contributor made a first approach using Node-red, but Node-red demands specialized knowledge in its usage and it is not suitable for younger ages. So, the kids and adolescents can use our application for learning visual programming by using the custom blocks to build programs and then the robot can execute them in real time. The application and the robot can be used in STEM education for different experiments. Educators already know Scratch, so adjusting to Google Blockly will be an easy process. The desktop application will be wirelessly connected to the robot, so that the robot can execute the programs built and run by the students. The desktop application will be implemented in such a way, so that it is suitable for students, e.g. colorful and interactive design, pop ups for extra information or to notify the student if an action was successful or not. There will also be the option to save or import Blockly code. The already implemented code can be saved/ extracted into an xml file that will be downloaded in a preferred folder in the computer. Such an xml file can be imported into the application for later use. <br /> <br /> In order for the Blockly integration to be possible, it is going to be essential for some parts of the existing code of the GSoC 2019-2021 robot to be rewritten. This means that back end code that runs in the Raspberry Pi and controls all the movements of the robot, will be improved. For that reason, I will create a custom functionality library (e.x. PID Controller) for special control of every electronic part and sensor. Based on the fact that the GSoC 2021 contributor introduced for the first time the logic of microservices, microservices and dockerization can be utilized so that different services run for controlling the robot movements as well as the communication between the Blockly desktop application and the Raspberry Pi.<br /> <br /> <br /> <br /> Expected Results<br /> <br /> Within the months of the project the expected results are: <br /> <br /> * Understanding previous work <br /> * Rewrite core library ( Backend code )<br /> * Create microsreliable communication between Frontend - Backend code <br /> * Frontend implementation<br /> * Educational scenarios for the class <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Related Repositories ====<br /> &lt;nowiki&gt;https://hackaday.io/project/26007-versatile-educational-2wd-robot&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/yiorgosynkl/914e75d0f9ae98bb31f4d8da66ec9908&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/chronis10/9d069c56b3df9c92693ac8d24270a62a&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/isl-org/OpenBot&lt;/nowiki&gt;<br /> <br /> ==== Mentors: ====<br /> Iraklis Varlamis, Christos Chronis<br /> <br /> == Greek glyphs in Open Source Fonts: additions and redesign ==<br /> <br /> ==== Brief Explanation ====<br /> Many of the Open Source fonts (e.g., available at https://fonts.google.com), do not include glyphs for Greek letters and are therefore useless for using in a Greek environment.<br /> The aim of this project is to improve this situation and add the missing glyphs in the correct Unicode codepoints. The exact set of fonts to be completed will be determined in discussions between the student and the mentor(s).<br /> <br /> This is not a typical programming project.<br /> If you have never designed fonts before, it is probably not for you.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Expected Results ====<br /> Full support for Greek text in a number of Open Source fonts.<br /> <br /> ==== Related repositories ====<br /> Addition of Greek glyphs in:<br /> <br /> - Merriweather (12 upright +12 italics) https://github.com/EbenSorkin/Merriweather<br /> - Spectral (3 upright +3 italics) https://github.com/productiontype/Spectral<br /> - WorkSans (3 upright +3 italics) https://github.com/weiweihuanghuang/Work-Sans<br /> <br /> Redesign of existing Greek glyphs in:<br /> - Fira (all styles) https://github.com/mozilla/Fira<br /> - Vollkorn (all styles) https://github.com/FAlthausen/Vollkorn-Typeface<br /> <br /> ==== Knowledge Prerequisites ====<br /> Type design, font technologies.<br /> Please note that this is a special project, where coding, in the traditional sense, will not be enough.<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Εmilios Τheofanous, Alexios Zavras<br /> <br /> == Apothesis ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is an open source software for simulating deposition processes via the kinetic Monte Carlo method. This is the first step for creating a generalized kinetic Monte Carlo code for studying surface growth phenomena.  Apothesis currently lucks generalized I/O operations. The goal is to develop the I/O part of the software in order to be easy to use by many researchers. For kMC codes this is far from trivial since the I/O depends on the underlying physical system. That said, the input can be different when studying 2D materials compared to 3D systems. Regarding the output, this is defined by the properties of that a researcher wants to study.   <br /> <br /> ==== Expected Results ====<br /> The necessary classes and infrastructure for the I/O operations of Apothesis. <br /> <br /> ==== Duration of the Project ====<br /> depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, JSON<br /> <br /> ==== Mentors: ====<br /> Nikos Cheimarios, Vissarion Fisikopoulos<br /> <br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2115 2022-02-23T14:06:40Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> <br /> # &lt;u&gt;Machine Learning&lt;/u&gt;<br /> #* Conduct research for the appropriate model architecture<br /> #* Modify the annotation process by integrating the model<br /> #* Test the model by providing evaluation metrics<br /> # &lt;u&gt;Django&lt;/u&gt;<br /> #* Add lazy loading for the audio files: load segments of the file when needed (i.e., YouTube). This will lead to better performance when the audio file is too big.<br /> #* Add Django Testing<br /> #* Dockerization<br /> #* Add documentation<br /> #* Add rar file upload functionality - currently, users can only upload zip files (optional)<br /> #* UI improvements (optional)<br /> <br /> ==== Related repositories ====<br /> https://labelbuddy.io/ (username: demo, password: labelbuddy123)<br /> <br /> https://github.com/eellak/gsoc2021-audio-annotation-tool<br /> <br /> https://www.youtube.com/watch?v=SdbGhrad-GQ<br /> <br /> https://github.com/jordipons/sklearn-audio-transfer-learning<br /> <br /> https://github.com/jordipons/musicnn<br /> <br /> https://prodi.gy/docs/audio-video#model<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> == Development of a backend management system for NodeRed instances ==<br /> <br /> ==== Brief Explanation ====<br /> NodeRed is one of the most well known low-code IoT programming tools, offering a large number of ready-to-use libraries. Nevertheless, it lacks modern aspects of system deployments, like multi-user server functionalities, since one NodeRed deployment can support only one user. In this context we propose a backend system written in Python or NodeJs, that will provide a web-based API (e.g. REST), via which the management (creation, deletion and deployment) of NodeRed instances will be performed. Each NodeRed instance will be deployed either on-system, or even better using containers (e.g. Docker). Furthermore, the system will support saving annotated NodeRed deployments which contain specific nodes (or flows), so as to easily create new deployments that offer personalized/aggregated functionality. E.g. if a user creates flows annotated as &quot;Raspberry Pi GPIO&quot; and another creates &quot;Google Firebase&quot;, the system should be able to create a new NodeRed instance that contains one of these flow sets or both, according to what the end user needs.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Creation of a backend system, able to manage annotated NodeRed instances<br /> <br /> ==== Related repositories ====<br /> https://github.com/node-red/node-red<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python or NodeJS, JavaScript, OpenAPI, Containers. Desired: NoSQL databases, Full Stack development<br /> <br /> ==== Mentors: ====<br /> Emmanouil Tsardoulias (etsardou@gmail.com), Konstantinos Panayiotou (klpanagi@issel.ee.auth.gr), Andreas Symeonidis (asymeon@eng.auth.gr)<br /> <br /> <br /> == Testing the type checker of TypeScript ==<br /> <br /> ==== Brief Explanation ====<br /> Despite the substantial progress in compiler testing, research endeavors have mainly focused on detecting compiler crashes and subtle miscompilations caused by bugs in the implementation of compiler optimizations. Surprisingly, this growing body of work neglects other compiler components, most notably the front-end. In statically-typed programming languages with rich and expressive type systems and modern features, such as type inference or a mix of object-oriented with functional programming features, the process of static typing in compiler front-ends is complicated by a high-density of bugs. As a recent study has shown [1], such bugs can lead to the acceptance of incorrect programs (breaking code portability or the type system's soundness), the rejection of correct (e.g. well-typed) programs, and the reporting of misleading errors and warnings. <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Expected Results ====<br /> It is expected that the project will deliver an extended version of Hephaestus that is capable of finding real bugs in the compiler of TypeScript<br /> <br /> ==== Related repositories ====<br /> https://github.com/theosotr/hephaestus<br /> <br /> ==== Knowledge Prerequisites ====<br /> Good programming skills (in particular Python), good knowledge of object-oriented programming, familiarity with Java generics<br /> <br /> ==== Mentors: ====<br /> Thodoris Sotiropoulos, Stefanos Chaliasos, Dimitris Mitropoulos, Diomidis Spinellis<br /> <br /> == Real-time Graph-Clustering and Visualization for Architecture Recovery from Class-Dependency Analysis on Very Large Source-Code Bases ==<br /> <br /> ==== Brief Explanation ====<br /> We have already developed the first generation of a tool for architecture recovery from C++ sources by: (i) tracking dependencies, (ii) preparing a global dependency graph; and (iii) applying graph clustering to compute and visualize likely architectural modules. The tool uses Clang (open source, C++) for the frontend and Go.JS (2d) for the backend (graph rendering and GUI). Numerous practical shortcomings were identified when processing very large projects, above the magnitude of many hundreds of source files and classes. <br /> <br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Ogre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Expected Results ====<br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Expected ResultsOgre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Related repositories ====<br /> [https://github.com/theosotr/hephaestus '''Clang''' (https://clang.llvm.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''Ogre 3d''' (https://www.ogre3d.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''wxWidgets''' (https://www.wxwidgets.org/)]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Clang, Go.JS ,Ogre 3d, wxWidgets<br /> <br /> ==== Mentors: ====<br /> Antonios Savidis<br /> <br /> == Development of a DIY robot kit for educators ==<br /> <br /> ==== Brief Explanation ====<br /> In GSoC 2019, one of the final projects was “A DIY robot kit for educators”. In this project, the contributor, Christos Chronis, designed and implemented a 3D printable robot for educational purposes from scratch. Electronic parts, such as cables, sensors, buttons, camera, accelerometer, LED set, as well as 3D printed parts were used to assemble the robot alongside with the code written in order to program it. Two years later, in GSoC 2021, the contributor developed another version of “A DIY robot kit for educators” with a Node-Red integration, based on the project of GSoC 2019. <br /> <br /> As the contributor of GSOC 2019 mentioned in his wiki page there is still a lot of work to be done, so that the original project (Proteas robot) can be improved and maybe be a direct competitor to any commercial alternative on educational robotics. Based on the work of the two previous contributors, I would like to propose my idea for GSOC 2022, which includes the robotic designs, the guidelines, the integration of the robot with Google Blockly and improvement of the back end code with a custom library for special control of every electronic part and sensor.<br /> <br /> All of the parts of the modular robot are gonna be designed this way so that they are 3D-printable. The 3D-printed parts together with basic low-cost electronics and sensors can be  easily combined to assemble the robot following the guidelines. Extensive and simple guidelines of how to print and assemble the robot will be provided, so that non-expert staff in robotics, electronics or IoT programming, will be able to follow them successfully. Alongside with the guidelines, the staff will also be provided with a number of demo scenarios for the class, e.g. simple navigation or sensing scenarios, that require basic programming skills.<br /> <br /> The development of a desktop application will mainly consist of the open-source Google Blockly. Blockly is a free, open source web-based, visual programming editor, developed by Google. Users can drag blocks together to build programs. It is similar to Scratch, that the contributor of GSoC 2019 proposed, but it can be a lot easier configured, as it is open source and offers a lot more possibilities, such as custom creation of Google Blockly blocks or preferred language selection of the environment. In the GsoC 2021, the contributor made a first approach using Node-red, but Node-red demands specialized knowledge in its usage and it is not suitable for younger ages. So, the kids and adolescents can use our application for learning visual programming by using the custom blocks to build programs and then the robot can execute them in real time. The application and the robot can be used in STEM education for different experiments. Educators already know Scratch, so adjusting to Google Blockly will be an easy process. The desktop application will be wirelessly connected to the robot, so that the robot can execute the programs built and run by the students. The desktop application will be implemented in such a way, so that it is suitable for students, e.g. colorful and interactive design, pop ups for extra information or to notify the student if an action was successful or not. There will also be the option to save or import Blockly code. The already implemented code can be saved/ extracted into an xml file that will be downloaded in a preferred folder in the computer. Such an xml file can be imported into the application for later use. <br /> <br /> In order for the Blockly integration to be possible, it is going to be essential for some parts of the existing code of the GSoC 2019-2021 robot to be rewritten. This means that back end code that runs in the Raspberry Pi and controls all the movements of the robot, will be improved. For that reason, I will create a custom functionality library (e.x. PID Controller) for special control of every electronic part and sensor. Based on the fact that the GSoC 2021 contributor introduced for the first time the logic of microservices, microservices and dockerization can be utilized so that different services run for controlling the robot movements as well as the communication between the Blockly desktop application and the Raspberry Pi.<br /> <br /> <br /> <br /> Expected Results<br /> <br /> Within the months of the project the expected results are: <br /> <br /> * Understanding previous work <br /> * Rewrite core library ( Backend code )<br /> * Create microsreliable communication between Frontend - Backend code <br /> * Frontend implementation<br /> * Educational scenarios for the class <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Related Repositories ====<br /> &lt;nowiki&gt;https://hackaday.io/project/26007-versatile-educational-2wd-robot&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/yiorgosynkl/914e75d0f9ae98bb31f4d8da66ec9908&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/chronis10/9d069c56b3df9c92693ac8d24270a62a&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/isl-org/OpenBot&lt;/nowiki&gt;<br /> <br /> ==== Mentors: ====<br /> Iraklis Varlamis, Christos Chronis<br /> <br /> == Greek glyphs in Open Source Fonts: additions and redesign ==<br /> <br /> ==== Brief Explanation ====<br /> Many of the Open Source fonts (e.g., available at https://fonts.google.com), do not include glyphs for Greek letters and are therefore useless for using in a Greek environment.<br /> The aim of this project is to improve this situation and add the missing glyphs in the correct Unicode codepoints. The exact set of fonts to be completed will be determined in discussions between the student and the mentor(s).<br /> <br /> This is not a typical programming project.<br /> If you have never designed fonts before, it is probably not for you.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Expected Results ====<br /> Full support for Greek text in a number of Open Source fonts.<br /> <br /> ==== Related repositories ====<br /> Addition of Greek glyphs in:<br /> <br /> - Merriweather (12 upright +12 italics) https://github.com/EbenSorkin/Merriweather<br /> - Spectral (3 upright +3 italics) https://github.com/productiontype/Spectral<br /> - WorkSans (3 upright +3 italics) https://github.com/weiweihuanghuang/Work-Sans<br /> <br /> Redesign of existing Greek glyphs in:<br /> - Fira (all styles) https://github.com/mozilla/Fira<br /> - Vollkorn (all styles) https://github.com/FAlthausen/Vollkorn-Typeface<br /> <br /> ==== Knowledge Prerequisites ====<br /> Type design, font technologies.<br /> Please note that this is a special project, where coding, in the traditional sense, will not be enough.<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Εmilios Τheofanous, Alexios Zavras<br /> <br /> == Apothesis ==<br /> <br /> ==== Brief Explanation ====<br /> Apothesis is an open source software for simulating deposition processes via the kinetic Monte Carlo method. This is the first step for creating a generalized kinetic Monte Carlo code for studying surface growth phenomena.  Apothesis currently lucks generalized I/O operations. The goal is to develop the I/O part of the software in order to be easy to use by many researchers. For kMC codes this is far from trivial since the I/O depends on the underlying physical system. That said, the input can be different when studying 2D materials compared to 3D systems. Regarding the output, this is defined by the properties of that a researcher wants to study.   <br /> <br /> ==== Expected Results ====<br /> The necessary classes and infrastructure for the I/O operations of Apothesis. <br /> <br /> ==== Duration of the Project ====<br /> depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> https://github.com/nixeimar/Apothesis<br /> <br /> ==== Knowledge Prerequisites ====<br /> C++, JSON<br /> <br /> ==== Mentors: ====<br /> Nikos Cheimarios, Vissarion Fisikopoulos<br /> <br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2113 2022-02-23T07:08:13Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Expected Results:<br /> <br /> 1. Machine Learning<br /> <br /> - Conduct research for the appropriate model architecture<br /> <br /> - Modify the annotation process by integrating the model<br /> <br /> - Test the model by providing evaluation metrics<br /> <br /> ==== Related repositories ====<br /> &lt;nowiki&gt;https://github.com/eellak/gsoc2021-audio-annotation-tool&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://youtu.be/SdbGhrad-GQ&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/sklearn-audio-transfer-learning&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/musicnn&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://prodi.gy/docs/audio-video#model&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> <br /> <br /> == Development of a backend management system for NodeRed instances ==<br /> <br /> ==== Brief Explanation ====<br /> NodeRed is one of the most well known low-code IoT programming tools, offering a large number of ready-to-use libraries. Nevertheless, it lacks modern aspects of system deployments, like multi-user server functionalities, since one NodeRed deployment can support only one user. In this context we propose a backend system written in Python or NodeJs, that will provide a web-based API (e.g. REST), via which the management (creation, deletion and deployment) of NodeRed instances will be performed. Each NodeRed instance will be deployed either on-system, or even better using containers (e.g. Docker). Furthermore, the system will support saving annotated NodeRed deployments which contain specific nodes (or flows), so as to easily create new deployments that offer personalized/aggregated functionality. E.g. if a user creates flows annotated as &quot;Raspberry Pi GPIO&quot; and another creates &quot;Google Firebase&quot;, the system should be able to create a new NodeRed instance that contains one of these flow sets or both, according to what the end user needs.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Creation of a backend system, able to manage annotated NodeRed instances<br /> <br /> ==== Related repositories ====<br /> https://github.com/node-red/node-red<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python or NodeJS, JavaScript, OpenAPI, Containers. Desired: NoSQL databases, Full Stack development<br /> <br /> ==== Mentors: ====<br /> Emmanouil Tsardoulias (etsardou@gmail.com), Konstantinos Panayiotou (klpanagi@issel.ee.auth.gr), Andreas Symeonidis (asymeon@eng.auth.gr)<br /> <br /> <br /> == Testing the type checker of TypeScript ==<br /> <br /> ==== Brief Explanation ====<br /> Despite the substantial progress in compiler testing, research endeavors have mainly focused on detecting compiler crashes and subtle miscompilations caused by bugs in the implementation of compiler optimizations. Surprisingly, this growing body of work neglects other compiler components, most notably the front-end. In statically-typed programming languages with rich and expressive type systems and modern features, such as type inference or a mix of object-oriented with functional programming features, the process of static typing in compiler front-ends is complicated by a high-density of bugs. As a recent study has shown [1], such bugs can lead to the acceptance of incorrect programs (breaking code portability or the type system's soundness), the rejection of correct (e.g. well-typed) programs, and the reporting of misleading errors and warnings. <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Expected Results ====<br /> It is expected that the project will deliver an extended version of Hephaestus that is capable of finding real bugs in the compiler of TypeScript<br /> <br /> ==== Related repositories ====<br /> https://github.com/theosotr/hephaestus<br /> <br /> ==== Knowledge Prerequisites ====<br /> Good programming skills (in particular Python), good knowledge of object-oriented programming, familiarity with Java generics<br /> <br /> ==== Mentors: ====<br /> Thodoris Sotiropoulos, Stefanos Chaliasos, Dimitris Mitropoulos, Diomidis Spinellis<br /> <br /> == Real-time Graph-Clustering and Visualization for Architecture Recovery from Class-Dependency Analysis on Very Large Source-Code Bases ==<br /> <br /> ==== Brief Explanation ====<br /> We have already developed the first generation of a tool for architecture recovery from C++ sources by: (i) tracking dependencies, (ii) preparing a global dependency graph; and (iii) applying graph clustering to compute and visualize likely architectural modules. The tool uses Clang (open source, C++) for the frontend and Go.JS (2d) for the backend (graph rendering and GUI). Numerous practical shortcomings were identified when processing very large projects, above the magnitude of many hundreds of source files and classes. <br /> <br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Ogre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Expected Results ====<br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Expected ResultsOgre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Related repositories ====<br /> [https://github.com/theosotr/hephaestus '''Clang''' (https://clang.llvm.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''Ogre 3d''' (https://www.ogre3d.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''wxWidgets''' (https://www.wxwidgets.org/)]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Clang, Go.JS ,Ogre 3d, wxWidgets<br /> <br /> ==== Mentors: ====<br /> Antonios Savidis<br /> <br /> == Development of a DIY robot kit for educators ==<br /> <br /> ==== Brief Explanation ====<br /> In GSoC 2019, one of the final projects was “A DIY robot kit for educators”. In this project, the contributor, Christos Chronis, designed and implemented a 3D printable robot for educational purposes from scratch. Electronic parts, such as cables, sensors, buttons, camera, accelerometer, LED set, as well as 3D printed parts were used to assemble the robot alongside with the code written in order to program it. Two years later, in GSoC 2021, the contributor developed another version of “A DIY robot kit for educators” with a Node-Red integration, based on the project of GSoC 2019. <br /> <br /> As the contributor of GSOC 2019 mentioned in his wiki page there is still a lot of work to be done, so that the original project (Proteas robot) can be improved and maybe be a direct competitor to any commercial alternative on educational robotics. Based on the work of the two previous contributors, I would like to propose my idea for GSOC 2022, which includes the robotic designs, the guidelines, the integration of the robot with Google Blockly and improvement of the back end code with a custom library for special control of every electronic part and sensor.<br /> <br /> All of the parts of the modular robot are gonna be designed this way so that they are 3D-printable. The 3D-printed parts together with basic low-cost electronics and sensors can be  easily combined to assemble the robot following the guidelines. Extensive and simple guidelines of how to print and assemble the robot will be provided, so that non-expert staff in robotics, electronics or IoT programming, will be able to follow them successfully. Alongside with the guidelines, the staff will also be provided with a number of demo scenarios for the class, e.g. simple navigation or sensing scenarios, that require basic programming skills.<br /> <br /> The development of a desktop application will mainly consist of the open-source Google Blockly. Blockly is a free, open source web-based, visual programming editor, developed by Google. Users can drag blocks together to build programs. It is similar to Scratch, that the contributor of GSoC 2019 proposed, but it can be a lot easier configured, as it is open source and offers a lot more possibilities, such as custom creation of Google Blockly blocks or preferred language selection of the environment. In the GsoC 2021, the contributor made a first approach using Node-red, but Node-red demands specialized knowledge in its usage and it is not suitable for younger ages. So, the kids and adolescents can use our application for learning visual programming by using the custom blocks to build programs and then the robot can execute them in real time. The application and the robot can be used in STEM education for different experiments. Educators already know Scratch, so adjusting to Google Blockly will be an easy process. The desktop application will be wirelessly connected to the robot, so that the robot can execute the programs built and run by the students. The desktop application will be implemented in such a way, so that it is suitable for students, e.g. colorful and interactive design, pop ups for extra information or to notify the student if an action was successful or not. There will also be the option to save or import Blockly code. The already implemented code can be saved/ extracted into an xml file that will be downloaded in a preferred folder in the computer. Such an xml file can be imported into the application for later use. <br /> <br /> In order for the Blockly integration to be possible, it is going to be essential for some parts of the existing code of the GSoC 2019-2021 robot to be rewritten. This means that back end code that runs in the Raspberry Pi and controls all the movements of the robot, will be improved. For that reason, I will create a custom functionality library (e.x. PID Controller) for special control of every electronic part and sensor. Based on the fact that the GSoC 2021 contributor introduced for the first time the logic of microservices, microservices and dockerization can be utilized so that different services run for controlling the robot movements as well as the communication between the Blockly desktop application and the Raspberry Pi.<br /> <br /> <br /> <br /> Expected Results<br /> <br /> Within the months of the project the expected results are: <br /> <br /> * Understanding previous work <br /> * Rewrite core library ( Backend code )<br /> * Create microsreliable communication between Frontend - Backend code <br /> * Frontend implementation<br /> * Educational scenarios for the class <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Related Repositories ====<br /> &lt;nowiki&gt;https://hackaday.io/project/26007-versatile-educational-2wd-robot&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/yiorgosynkl/914e75d0f9ae98bb31f4d8da66ec9908&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/chronis10/9d069c56b3df9c92693ac8d24270a62a&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/isl-org/OpenBot&lt;/nowiki&gt;<br /> <br /> ==== Mentors: ====<br /> Iraklis Varlamis, Christos Chronis<br /> <br /> == Greek glyphs in Open Source Fonts: additions and redesign ==<br /> <br /> ==== Brief Explanation ====<br /> Many of the Open Source fonts (e.g., available at https://fonts.google.com), do not include glyphs for Greek letters and are therefore useless for using in a Greek environment.<br /> The aim of this project is to improve this situation and add the missing glyphs in the correct Unicode codepoints. The exact set of fonts to be completed will be determined in discussions between the student and the mentor(s).<br /> <br /> This is not a typical programming project.<br /> If you have never designed fonts before, it is probably not for you.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Expected Results ====<br /> Full support for Greek text in a number of Open Source fonts.<br /> <br /> ==== Related repositories ====<br /> Addition of Greek glyphs in:<br /> <br /> - Merriweather (12 upright +12 italics) https://github.com/EbenSorkin/Merriweather<br /> - Spectral (3 upright +3 italics) https://github.com/productiontype/Spectral<br /> - WorkSans (3 upright +3 italics) https://github.com/weiweihuanghuang/Work-Sans<br /> <br /> Redesign of existing Greek glyphs in:<br /> - Fira (all styles) https://github.com/mozilla/Fira<br /> - Vollkorn (all styles) https://github.com/FAlthausen/Vollkorn-Typeface<br /> <br /> ==== Knowledge Prerequisites ====<br /> Type design, font technologies.<br /> Please note that this is a special project, where coding, in the traditional sense, will not be enough.<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Εmilios Τheofanous, Alexios Zavras<br /> <br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2112 2022-02-23T07:07:01Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Expected Results:<br /> <br /> 1. Machine Learning<br /> <br /> - Conduct research for the appropriate model architecture<br /> <br /> - Modify the annotation process by integrating the model<br /> <br /> - Test the model by providing evaluation metrics<br /> <br /> ==== Related repositories ====<br /> &lt;nowiki&gt;https://github.com/eellak/gsoc2021-audio-annotation-tool&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://youtu.be/SdbGhrad-GQ&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/sklearn-audio-transfer-learning&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/musicnn&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://prodi.gy/docs/audio-video#model&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> <br /> <br /> == Development of a backend management system for NodeRed instances ==<br /> <br /> ==== Brief Explanation ====<br /> NodeRed is one of the most well known low-code IoT programming tools, offering a large number of ready-to-use libraries. Nevertheless, it lacks modern aspects of system deployments, like multi-user server functionalities, since one NodeRed deployment can support only one user. In this context we propose a backend system written in Python or NodeJs, that will provide a web-based API (e.g. REST), via which the management (creation, deletion and deployment) of NodeRed instances will be performed. Each NodeRed instance will be deployed either on-system, or even better using containers (e.g. Docker). Furthermore, the system will support saving annotated NodeRed deployments which contain specific nodes (or flows), so as to easily create new deployments that offer personalized/aggregated functionality. E.g. if a user creates flows annotated as &quot;Raspberry Pi GPIO&quot; and another creates &quot;Google Firebase&quot;, the system should be able to create a new NodeRed instance that contains one of these flow sets or both, according to what the end user needs.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Creation of a backend system, able to manage annotated NodeRed instances<br /> <br /> ==== Related repositories ====<br /> https://github.com/node-red/node-red<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python or NodeJS, JavaScript, OpenAPI, Containers. Desired: NoSQL databases, Full Stack development<br /> <br /> ==== Mentors: ====<br /> Emmanouil Tsardoulias (etsardou@gmail.com), Konstantinos Panayiotou (klpanagi@issel.ee.auth.gr), Andreas Symeonidis (asymeon@eng.auth.gr)<br /> <br /> <br /> == Testing the type checker of TypeScript ==<br /> <br /> ==== Brief Explanation ====<br /> Despite the substantial progress in compiler testing, research endeavors have mainly focused on detecting compiler crashes and subtle miscompilations caused by bugs in the implementation of compiler optimizations. Surprisingly, this growing body of work neglects other compiler components, most notably the front-end. In statically-typed programming languages with rich and expressive type systems and modern features, such as type inference or a mix of object-oriented with functional programming features, the process of static typing in compiler front-ends is complicated by a high-density of bugs. As a recent study has shown [1], such bugs can lead to the acceptance of incorrect programs (breaking code portability or the type system's soundness), the rejection of correct (e.g. well-typed) programs, and the reporting of misleading errors and warnings. <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Expected Results ====<br /> It is expected that the project will deliver an extended version of Hephaestus that is capable of finding real bugs in the compiler of TypeScript<br /> <br /> ==== Related repositories ====<br /> https://github.com/theosotr/hephaestus<br /> <br /> ==== Knowledge Prerequisites ====<br /> Good programming skills (in particular Python), good knowledge of object-oriented programming, familiarity with Java generics<br /> <br /> ==== Mentors: ====<br /> Thodoris Sotiropoulos, Stefanos Chaliasos, Dimitris Mitropoulos, Diomidis Spinellis<br /> <br /> == Real-time Graph-Clustering and Visualization for Architecture Recovery from Class-Dependency Analysis on Very Large Source-Code Bases ==<br /> <br /> ==== Brief Explanation ====<br /> We have already developed the first generation of a tool for architecture recovery from C++ sources by: (i) tracking dependencies, (ii) preparing a global dependency graph; and (iii) applying graph clustering to compute and visualize likely architectural modules. The tool uses Clang (open source, C++) for the frontend and Go.JS (2d) for the backend (graph rendering and GUI). Numerous practical shortcomings were identified when processing very large projects, above the magnitude of many hundreds of source files and classes. <br /> <br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Ogre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Expected Results ====<br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Expected ResultsOgre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Related repositories ====<br /> [https://github.com/theosotr/hephaestus '''Clang''' (https://clang.llvm.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''Ogre 3d''' (https://www.ogre3d.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''wxWidgets''' (https://www.wxwidgets.org/)]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Clang, Go.JS ,Ogre 3d, wxWidgets<br /> <br /> ==== Mentors: ====<br /> Antonios Savidis<br /> <br /> == Development of a DIY robot kit for educators ==<br /> <br /> ==== Brief Explanation ====<br /> In GSoC 2019, one of the final projects was “A DIY robot kit for educators”. In this project, the contributor, Christos Chronis, designed and implemented a 3D printable robot for educational purposes from scratch. Electronic parts, such as cables, sensors, buttons, camera, accelerometer, LED set, as well as 3D printed parts were used to assemble the robot alongside with the code written in order to program it. Two years later, in GSoC 2021, the contributor developed another version of “A DIY robot kit for educators” with a Node-Red integration, based on the project of GSoC 2019. <br /> <br /> As the contributor of GSOC 2019 mentioned in his wiki page there is still a lot of work to be done, so that the original project (Proteas robot) can be improved and maybe be a direct competitor to any commercial alternative on educational robotics. Based on the work of the two previous contributors, I would like to propose my idea for GSOC 2022, which includes the robotic designs, the guidelines, the integration of the robot with Google Blockly and improvement of the back end code with a custom library for special control of every electronic part and sensor.<br /> <br /> All of the parts of the modular robot are gonna be designed this way so that they are 3D-printable. The 3D-printed parts together with basic low-cost electronics and sensors can be  easily combined to assemble the robot following the guidelines. Extensive and simple guidelines of how to print and assemble the robot will be provided, so that non-expert staff in robotics, electronics or IoT programming, will be able to follow them successfully. Alongside with the guidelines, the staff will also be provided with a number of demo scenarios for the class, e.g. simple navigation or sensing scenarios, that require basic programming skills.<br /> <br /> <br /> The development of a desktop application will mainly consist of the open-source Google Blockly. Blockly is a free, open source web-based, visual programming editor, developed by Google. Users can drag blocks together to build programs. It is similar to Scratch, that the contributor of GSoC 2019 proposed, but it can be a lot easier configured, as it is open source and offers a lot more possibilities, such as custom creation of Google Blockly blocks or preferred language selection of the environment. In the GsoC 2021, the contributor made a first approach using Node-red, but Node-red demands specialized knowledge in its usage and it is not suitable for younger ages. So, the kids and adolescents can use our application for learning visual programming by using the custom blocks to build programs and then the robot can execute them in real time. The application and the robot can be used in STEM education for different experiments. Educators already know Scratch, so adjusting to Google Blockly will be an easy process. The desktop application will be wirelessly connected to the robot, so that the robot can execute the programs built and run by the students. The desktop application will be implemented in such a way, so that it is suitable for students, e.g. colorful and interactive design, pop ups for extra information or to notify the student if an action was successful or not. There will also be the option to save or import Blockly code. The already implemented code can be saved/ extracted into an xml file that will be downloaded in a preferred folder in the computer. Such an xml file can be imported into the application for later use. <br /> <br /> In order for the Blockly integration to be possible, it is going to be essential for some parts of the existing code of the GSoC 2019-2021 robot to be rewritten. This means that back end code that runs in the Raspberry Pi and controls all the movements of the robot, will be improved. For that reason, I will create a custom functionality library (e.x. PID Controller) for special control of every electronic part and sensor. Based on the fact that the GSoC 2021 contributor introduced for the first time the logic of microservices, microservices and dockerization can be utilized so that different services run for controlling the robot movements as well as the communication between the Blockly desktop application and the Raspberry Pi.<br /> <br /> <br /> ==== Expected Results ====<br /> Within the months of the project the expected results are: <br /> <br /> * Understanding previous work <br /> * Rewrite core library ( Backend code )<br /> * Create microsreliable communication between Frontend - Backend code <br /> * Frontend implementation<br /> * Educational scenarios for the class <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Related Repositories ====<br /> &lt;nowiki&gt;https://hackaday.io/project/26007-versatile-educational-2wd-robot&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/yiorgosynkl/914e75d0f9ae98bb31f4d8da66ec9908&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://gist.github.com/chronis10/9d069c56b3df9c92693ac8d24270a62a&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/isl-org/OpenBot&lt;/nowiki&gt;<br /> <br /> ==== Mentors: ====<br /> Iraklis Varlamis, Christos Chronis<br /> <br /> == Greek glyphs in Open Source Fonts: additions and redesign ==<br /> <br /> ==== Brief Explanation ====<br /> Many of the Open Source fonts (e.g., available at https://fonts.google.com), do not include glyphs for Greek letters and are therefore useless for using in a Greek environment.<br /> The aim of this project is to improve this situation and add the missing glyphs in the correct Unicode codepoints. The exact set of fonts to be completed will be determined in discussions between the student and the mentor(s).<br /> <br /> This is not a typical programming project.<br /> If you have never designed fonts before, it is probably not for you.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Expected Results ====<br /> Full support for Greek text in a number of Open Source fonts.<br /> <br /> ==== Related repositories ====<br /> Addition of Greek glyphs in:<br /> <br /> - Merriweather (12 upright +12 italics) https://github.com/EbenSorkin/Merriweather<br /> - Spectral (3 upright +3 italics) https://github.com/productiontype/Spectral<br /> - WorkSans (3 upright +3 italics) https://github.com/weiweihuanghuang/Work-Sans<br /> <br /> Redesign of existing Greek glyphs in:<br /> - Fira (all styles) https://github.com/mozilla/Fira<br /> - Vollkorn (all styles) https://github.com/FAlthausen/Vollkorn-Typeface<br /> <br /> ==== Knowledge Prerequisites ====<br /> Type design, font technologies.<br /> Please note that this is a special project, where coding, in the traditional sense, will not be enough.<br /> <br /> ==== Mentors: ====<br /> Irene Vlachou, Εmilios Τheofanous, Alexios Zavras<br /> <br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2109 2022-02-21T14:59:14Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Expected Results:<br /> <br /> 1. Machine Learning<br /> <br /> - Conduct research for the appropriate model architecture<br /> <br /> - Modify the annotation process by integrating the model<br /> <br /> - Test the model by providing evaluation metrics<br /> <br /> ==== Related repositories ====<br /> &lt;nowiki&gt;https://github.com/eellak/gsoc2021-audio-annotation-tool&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://youtu.be/SdbGhrad-GQ&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/sklearn-audio-transfer-learning&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/musicnn&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://prodi.gy/docs/audio-video#model&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> <br /> <br /> == Development of a backend management system for NodeRed instances ==<br /> <br /> ==== Brief Explanation ====<br /> NodeRed is one of the most well known low-code IoT programming tools, offering a large number of ready-to-use libraries. Nevertheless, it lacks modern aspects of system deployments, like multi-user server functionalities, since one NodeRed deployment can support only one user. In this context we propose a backend system written in Python or NodeJs, that will provide a web-based API (e.g. REST), via which the management (creation, deletion and deployment) of NodeRed instances will be performed. Each NodeRed instance will be deployed either on-system, or even better using containers (e.g. Docker). Furthermore, the system will support saving annotated NodeRed deployments which contain specific nodes (or flows), so as to easily create new deployments that offer personalized/aggregated functionality. E.g. if a user creates flows annotated as &quot;Raspberry Pi GPIO&quot; and another creates &quot;Google Firebase&quot;, the system should be able to create a new NodeRed instance that contains one of these flow sets or both, according to what the end user needs.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Creation of a backend system, able to manage annotated NodeRed instances<br /> <br /> ==== Related repositories ====<br /> https://github.com/node-red/node-red<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python or NodeJS, JavaScript, OpenAPI, Containers. Desired: NoSQL databases, Full Stack development<br /> <br /> ==== Mentors: ====<br /> Emmanouil Tsardoulias (etsardou@gmail.com), Konstantinos Panayiotou (klpanagi@issel.ee.auth.gr), Andreas Symeonidis (asymeon@eng.auth.gr)<br /> <br /> <br /> == Testing the type checker of TypeScript ==<br /> <br /> ==== Brief Explanation ====<br /> Despite the substantial progress in compiler testing, research endeavors have mainly focused on detecting compiler crashes and subtle miscompilations caused by bugs in the implementation of compiler optimizations. Surprisingly, this growing body of work neglects other compiler components, most notably the front-end. In statically-typed programming languages with rich and expressive type systems and modern features, such as type inference or a mix of object-oriented with functional programming features, the process of static typing in compiler front-ends is complicated by a high-density of bugs. As a recent study has shown [1], such bugs can lead to the acceptance of incorrect programs (breaking code portability or the type system's soundness), the rejection of correct (e.g. well-typed) programs, and the reporting of misleading errors and warnings. <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Expected Results ====<br /> It is expected that the project will deliver an extended version of Hephaestus that is capable of finding real bugs in the compiler of TypeScript<br /> <br /> ==== Related repositories ====<br /> https://github.com/theosotr/hephaestus<br /> <br /> ==== Knowledge Prerequisites ====<br /> Good programming skills (in particular Python), good knowledge of object-oriented programming, familiarity with Java generics<br /> <br /> ==== Mentors: ====<br /> Thodoris Sotiropoulos, Stefanos Chaliasos, Dimitris Mitropoulos, Diomidis Spinellis<br /> <br /> <br /> == Real-time Graph-Clustering and Visualization for Architecture Recovery from Class-Dependency Analysis on Very Large Source-Code Bases ==<br /> <br /> ==== Brief Explanation ====<br /> We have already developed the first generation of a tool for architecture recovery from C++ sources by: (i) tracking dependencies, (ii) preparing a global dependency graph; and (iii) applying graph clustering to compute and visualize likely architectural modules. The tool uses Clang (open source, C++) for the frontend and Go.JS (2d) for the backend (graph rendering and GUI). Numerous practical shortcomings were identified when processing very large projects, above the magnitude of many hundreds of source files and classes. <br /> <br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Ogre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Expected Results ====<br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Ogre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Related repositories ====<br /> [https://github.com/theosotr/hephaestus '''Clang''' (https://clang.llvm.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''Ogre 3d''' (https://www.ogre3d.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''wxWidgets''' (https://www.wxwidgets.org/)]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Clang, Go.JS ,Ogre 3d, wxWidgets<br /> <br /> ==== Mentors: ====<br /> Antonios Savidis<br /> <br /> == Development of a DIY robot kit for educators ==<br /> <br /> ==== Brief Explanation ====<br /> The aim of the project will be to develop all the designs, guidelines and sample code for a starter DIY robot kit that can be 3d-printed, assembled and operated using basic electronics and sensors. This is expected to create a low-cost alternative to commercial robot kits (e.g. Lego Mindstorms) that does not require expert staff in robotics, electronics or IoT programming (e.g. using Arduino/Raspberry kits). The ability to 3D-print everything and combine it with low-cost basic electronics and sensors will allow regional open technologies initiatives to provide schools with starter kits and a full 'Robotic 101' introductory course.<br /> <br /> The kit that will be developed and opened must comprise 3D-designs for all the necessary parts of a modular robot that can be printed and assembled following the assemble guidelines. The target audience of the project can be educators (e.g. high school ICT teachers), with minimum expertise in robotics, electronics, and programming. So the print and assembly guidelines must be detailed and simple. In addition, the project must have a modular structure that allows educators to guide their students to the step-by-step development of the robot and to the implementation of simple navigation or sensing scenarios, that require basic programming skills.<br /> <br /> Deliverables of the project, apart from the robot parts' designs, include a detailed list of the necessary electronics and sensors and the specifications for a Raspberry pi or similar single board computer (SBG).<br /> <br /> Detailed assembly instructions, images, and videos from the assembly process are desirable.<br /> <br /> The open source code that will be installed and run on the SBG and will allow controlling the robot through a simple programming interface, along with installation guidelines must be developed.<br /> <br /> The robot will be operated either manually using a browser that wirelessly connects with the robot, or automatically by uploading robot control scripts through the same environment.<br /> <br /> Some sample control scripts and robot programming scenarios will also be developed. <br /> <br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2108 2022-02-21T07:36:16Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Expected Results:<br /> <br /> 1. Machine Learning<br /> <br /> - Conduct research for the appropriate model architecture<br /> <br /> - Modify the annotation process by integrating the model<br /> <br /> - Test the model by providing evaluation metrics<br /> <br /> ==== Related repositories ====<br /> &lt;nowiki&gt;https://github.com/eellak/gsoc2021-audio-annotation-tool&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://youtu.be/SdbGhrad-GQ&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/sklearn-audio-transfer-learning&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/musicnn&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://prodi.gy/docs/audio-video#model&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> <br /> <br /> == Development of a backend management system for NodeRed instances ==<br /> <br /> ==== Brief Explanation ====<br /> NodeRed is one of the most well known low-code IoT programming tools, offering a large number of ready-to-use libraries. Nevertheless, it lacks modern aspects of system deployments, like multi-user server functionalities, since one NodeRed deployment can support only one user. In this context we propose a backend system written in Python or NodeJs, that will provide a web-based API (e.g. REST), via which the management (creation, deletion and deployment) of NodeRed instances will be performed. Each NodeRed instance will be deployed either on-system, or even better using containers (e.g. Docker). Furthermore, the system will support saving annotated NodeRed deployments which contain specific nodes (or flows), so as to easily create new deployments that offer personalized/aggregated functionality. E.g. if a user creates flows annotated as &quot;Raspberry Pi GPIO&quot; and another creates &quot;Google Firebase&quot;, the system should be able to create a new NodeRed instance that contains one of these flow sets or both, according to what the end user needs.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Creation of a backend system, able to manage annotated NodeRed instances<br /> <br /> ==== Related repositories ====<br /> https://github.com/node-red/node-red<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python or NodeJS, JavaScript, OpenAPI, Containers. Desired: NoSQL databases, Full Stack development<br /> <br /> ==== Mentors: ====<br /> Emmanouil Tsardoulias (etsardou@gmail.com), Konstantinos Panayiotou (klpanagi@issel.ee.auth.gr), Andreas Symeonidis (asymeon@eng.auth.gr)<br /> <br /> <br /> == Testing the type checker of TypeScript ==<br /> <br /> ==== Brief Explanation ====<br /> Despite the substantial progress in compiler testing, research endeavors have mainly focused on detecting compiler crashes and subtle miscompilations caused by bugs in the implementation of compiler optimizations. Surprisingly, this growing body of work neglects other compiler components, most notably the front-end. In statically-typed programming languages with rich and expressive type systems and modern features, such as type inference or a mix of object-oriented with functional programming features, the process of static typing in compiler front-ends is complicated by a high-density of bugs. As a recent study has shown [1], such bugs can lead to the acceptance of incorrect programs (breaking code portability or the type system's soundness), the rejection of correct (e.g. well-typed) programs, and the reporting of misleading errors and warnings. <br /> <br /> ==== Duration of the Project ====<br /> Large Size Project<br /> <br /> ==== Expected Results ====<br /> It is expected that the project will deliver an extended version of Hephaestus that is capable of finding real bugs in the compiler of TypeScript<br /> <br /> ==== Related repositories ====<br /> https://github.com/theosotr/hephaestus<br /> <br /> ==== Knowledge Prerequisites ====<br /> Good programming skills (in particular Python), good knowledge of object-oriented programming, familiarity with Java generics<br /> <br /> ==== Mentors: ====<br /> Thodoris Sotiropoulos, Stefanos Chaliasos, Dimitris Mitropoulos, Diomidis Spinellis<br /> <br /> <br /> == Real-time Graph-Clustering and Visualization for Architecture Recovery from Class-Dependency Analysis on Very Large Source-Code Bases ==<br /> <br /> ==== Brief Explanation ====<br /> We have already developed the first generation of a tool for architecture recovery from C++ sources by: (i) tracking dependencies, (ii) preparing a global dependency graph; and (iii) applying graph clustering to compute and visualize likely architectural modules. The tool uses Clang (open source, C++) for the frontend and Go.JS (2d) for the backend (graph rendering and GUI). Numerous practical shortcomings were identified when processing very large projects, above the magnitude of many hundreds of source files and classes. <br /> <br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Ogre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Expected Results ====<br /> This project concerns the implementation of the second generation of our tool, still involving Clang, but with the extension to allow incremental analysis of large projects with progress monitoring. Users will be capable to suspend the analysis process at any point, possibly exit the system, and resume it latter or restart it, even on a different machine.<br /> <br /> Concerning the backend, we will adopt Ogre 3d (open source, C++) and fully implement from scratch the visualizer for improved (visually rich) and faster (GPU accelerated) rendering, while fully exploring with alternative implementations of layouts and topologies in 3d for the graph itself and for the architectural clusters denoting components. The GUI will be implemented using wxWidgets, and all clustering algorithms will be implemented in C++. <br /> <br /> ==== Related repositories ====<br /> [https://github.com/theosotr/hephaestus '''Clang''' (https://clang.llvm.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''Ogre 3d''' (https://www.ogre3d.org/)]<br /> <br /> [https://github.com/theosotr/hephaestus '''wxWidgets''' (https://www.wxwidgets.org/)]<br /> <br /> ==== Knowledge Prerequisites ====<br /> Clang, Go.JS ,Ogre 3d, wxWidgets<br /> <br /> ==== Mentors: ====<br /> Antonios Savidis<br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2107 2022-02-21T07:29:54Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Expected Results:<br /> <br /> 1. Machine Learning<br /> <br /> - Conduct research for the appropriate model architecture<br /> <br /> - Modify the annotation process by integrating the model<br /> <br /> - Test the model by providing evaluation metrics<br /> <br /> ==== Related repositories ====<br /> &lt;nowiki&gt;https://github.com/eellak/gsoc2021-audio-annotation-tool&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://youtu.be/SdbGhrad-GQ&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/sklearn-audio-transfer-learning&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/musicnn&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://prodi.gy/docs/audio-video#model&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> <br /> <br /> == Development of a backend management system for NodeRed instances ==<br /> <br /> ==== Brief Explanation ====<br /> NodeRed is one of the most well known low-code IoT programming tools, offering a large number of ready-to-use libraries. Nevertheless, it lacks modern aspects of system deployments, like multi-user server functionalities, since one NodeRed deployment can support only one user. In this context we propose a backend system written in Python or NodeJs, that will provide a web-based API (e.g. REST), via which the management (creation, deletion and deployment) of NodeRed instances will be performed. Each NodeRed instance will be deployed either on-system, or even better using containers (e.g. Docker). Furthermore, the system will support saving annotated NodeRed deployments which contain specific nodes (or flows), so as to easily create new deployments that offer personalized/aggregated functionality. E.g. if a user creates flows annotated as &quot;Raspberry Pi GPIO&quot; and another creates &quot;Google Firebase&quot;, the system should be able to create a new NodeRed instance that contains one of these flow sets or both, according to what the end user needs.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Creation of a backend system, able to manage annotated NodeRed instances<br /> <br /> ==== Related repositories ====<br /> https://github.com/node-red/node-red<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python or NodeJS, JavaScript, OpenAPI, Containers. Desired: NoSQL databases, Full Stack development<br /> <br /> ==== Mentors: ====<br /> Emmanouil Tsardoulias (etsardou@gmail.com), Konstantinos Panayiotou (klpanagi@issel.ee.auth.gr), Andreas Symeonidis (asymeon@eng.auth.gr)<br /> <br /> <br /> == Testing the type checker of TypeScript ==<br /> <br /> ==== Brief Explanation ====<br /> Despite the substantial progress in compiler testing, research endeavors have mainly focused on detecting compiler crashes and subtle miscompilations caused by bugs in the implementation of compiler optimizations. Surprisingly, this growing body of work neglects other compiler components, most notably the front-end. In statically-typed programming languages with rich and expressive type systems and modern features, such as type inference or a mix of object-oriented with functional programming features, the process of static typing in compiler front-ends is complicated by a high-density of bugs. As a recent study has shown [1], such bugs can lead to the acceptance of incorrect programs (breaking code portability or the type system's soundness), the rejection of correct (e.g. well-typed) programs, and the reporting of misleading errors and warnings. <br /> <br /> ==== Expected Results ====<br /> It is expected that the project will deliver an extended version of Hephaestus that is capable of finding real bugs in the compiler of TypeScript<br /> <br /> ==== Related repositories ====<br /> https://github.com/theosotr/hephaestus<br /> <br /> ==== Knowledge Prerequisites ====<br /> Good programming skills (in particular Python), good knowledge of object-oriented programming, familiarity with Java generics<br /> <br /> ==== Mentors: ====<br /> Thodoris Sotiropoulos, Stefanos Chaliasos, Dimitris Mitropoulos, Diomidis Spinellis<br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2106 2022-02-21T07:27:56Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Expected Results:<br /> <br /> 1. Machine Learning<br /> <br /> - Conduct research for the appropriate model architecture<br /> <br /> - Modify the annotation process by integrating the model<br /> <br /> - Test the model by providing evaluation metrics<br /> <br /> ==== Related repositories ====<br /> &lt;nowiki&gt;https://github.com/eellak/gsoc2021-audio-annotation-tool&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://youtu.be/SdbGhrad-GQ&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/sklearn-audio-transfer-learning&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/musicnn&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://prodi.gy/docs/audio-video#model&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> Machine Learning (ML) frameworks, Python, Django, VanillaJS, HTML<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> <br /> <br /> == Development of a backend management system for NodeRed instances ==<br /> <br /> ==== Brief Explanation ====<br /> NodeRed is one of the most well known low-code IoT programming tools, offering a large number of ready-to-use libraries. Nevertheless, it lacks modern aspects of system deployments, like multi-user server functionalities, since one NodeRed deployment can support only one user. In this context we propose a backend system written in Python or NodeJs, that will provide a web-based API (e.g. REST), via which the management (creation, deletion and deployment) of NodeRed instances will be performed. Each NodeRed instance will be deployed either on-system, or even better using containers (e.g. Docker). Furthermore, the system will support saving annotated NodeRed deployments which contain specific nodes (or flows), so as to easily create new deployments that offer personalized/aggregated functionality. E.g. if a user creates flows annotated as &quot;Raspberry Pi GPIO&quot; and another creates &quot;Google Firebase&quot;, the system should be able to create a new NodeRed instance that contains one of these flow sets or both, according to what the end user needs.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Creation of a backend system, able to manage annotated NodeRed instances<br /> <br /> ==== Related repositories ====<br /> https://github.com/node-red/node-red<br /> <br /> ==== Knowledge Prerequisites ====<br /> Required: Python or NodeJS, JavaScript, OpenAPI, Containers. Desired: NoSQL databases, Full Stack development<br /> <br /> ==== Mentors: ====<br /> Emmanouil Tsardoulias (etsardou@gmail.com), Konstantinos Panayiotou (klpanagi@issel.ee.auth.gr), Andreas Symeonidis (asymeon@eng.auth.gr)<br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2105 2022-02-21T07:24:19Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> <br /> == Label Buddy 2.0: Automated audio-tagging using transfer learning ==<br /> <br /> ==== Brief Explanation ====<br /> Label Buddy is an open-source audio annotation tool created during GSoC 2021. It aims to make the annotation process easy, simple and at the same time offer a well-defined manager-annotator-reviewer system. The purpose of this project is to integrate Transfer Learning (TL) techniques (taking advantage of knowledge gained for one problem and applying it to this problem) to make the annotation process less tedious by providing label predictions for the user. This approach will allow us to do more with less data and effort.<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Expected Results:<br /> <br /> 1. Machine Learning<br /> <br /> - Conduct research for the appropriate model architecture<br /> <br /> - Modify the annotation process by integrating the model<br /> <br /> - Test the model by providing evaluation metrics<br /> <br /> ==== Related repositories ====<br /> &lt;nowiki&gt;https://github.com/eellak/gsoc2021-audio-annotation-tool&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://youtu.be/SdbGhrad-GQ&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/sklearn-audio-transfer-learning&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://github.com/jordipons/musicnn&lt;/nowiki&gt;<br /> <br /> &lt;nowiki&gt;https://prodi.gy/docs/audio-video#model&lt;/nowiki&gt;<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ioannis Sina (sinaioannis@gmail.com), Agisilaos Kounelis (kounelisagis@gmail.com), Pantelis Vikatos (pantelis@orfium.com)<br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2104 2022-02-16T07:38:40Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors: ====<br /> George Kousiouris, Christos Diou<br /> <br /> == Build recorder: A system to record what goes on when a software is being built. ==<br /> <br /> ==== Brief Explanation ====<br /> The purpose of a project is to fully record the interactions between assets (files and tools) when a software component is being built (compiled).<br /> For example, when compiling a software written in C,the system will record the source files being compiled, the generated object files being linked, the final executable,<br /> as well as the compiler used, the options given, and the environment.<br /> <br /> ==== Duration of the Project ====<br /> Can be either a short or a long-term project, depending on the scope and the technologies tackled.<br /> <br /> ==== Related repositories ====<br /> New project, no existing repo available.<br /> <br /> ==== Knowledge Prerequisites ====<br /> one or (preferably) more of compiled languages: C, C++, Go, Rust, Java, ...<br /> <br /> ==== Mentors: ====<br /> Alexios Zavras<br /> <br /> <br /> == Flexbench ==<br /> <br /> ==== Brief Explanation ====<br /> Flexbench is a benchmarking tool used mostly for stress and performance testing of web application servers. It is a forked project from the http-traffic-simulator npm package, which was developed to provide simulated throttled http traffic for testing purposes, towards specific http endpoints. It utilizes the nodejs cluster module to generate http requests, by spawning multiple workers (balanced over the system cores) in the cluster, responsible to create clients that generate requests. This architecture enables the simulator to scale in really big throughputs. The main supported features are:<br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> - Create a GUI using flutter - Offer it as desktop app (package it and make it run as a standalone app in Windows, macOS and Linux) - Offer it as a web server exposing REST APIs - Support authentication and authorization for REST APIs, preferably with OpenIDC. - Support GraphQL - Migrate to typescript - Dockerize, produce the required artifacts to deploy to kubernetes. - Integrate Nginx proxy - Implement a DSL language to describe scenarios that can be executed by flexbench (eg. store a scenario under a .flxb file ) - Implement an editor for .flxb files with syntax highlighting support<br /> <br /> ==== Related repositories ====<br /> https://github.com/flexivian/flexbench<br /> https://www.npmjs.com/package/http-traffic-simulator, <br /> https://github.com/iskitsas/http-traffic-simulator<br /> <br /> ==== Knowledge Prerequisites ====<br /> NodeJS, js, typescript, Html, Css, flutter, docker<br /> <br /> ==== Mentors: ====<br /> Ilias Kyrannas (iliaskyrannas@gmail.com), Giannis Skitsas (iskitsas@gmail.com)<br /> <br /> <br /> == SciDavis data Analysis and Visualization Program ==<br /> <br /> ==== Brief Explanation ====<br /> SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis runs on GNU/Linux, Windows and MacOS X; possibly also on other platforms like *BSD, although this is untested. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.More information, including screenshots, reviews, current contributers can be found on the SourceForge project webpage. <br /> <br /> SciDAVis has been started as a fork off of QtiPlot with the aim of introducing some changes in design and establishing an open and friendly community. The versions labelled SciDAVis 0.1.0 to 0.1.4 are still very close to QtiPlot 0.9.×. But many new features and a revision of existing ones have been introduced in release 0.2.0. This is especially true for tables and matrices which have internally been rewritten almost completely.<br /> <br /> At some time in 2008, the developer teams of LabPlot and SciDAVis found their project goals to be very similar (and since LabPlot 2.x is based on the same library as SciDAVis, i.e., Qt4.x) decided to start a close cooperation. The current plans are to use a common backend with two frontends, one with full KDE4 integration (called LabPlot 2.x) and one with no KDE dependencies (pure Qt so to say) for easier cross-platform use (called SciDAVis). This promises a faster development speed for both projects while focussing on slightly different audiences. From the user's point of view, there will still be two different applications.<br /> <br /> At the time of writing, SciDAVis is completely independent of LabPlot, whether the collaboration mentioned in the previous paragraph will ever happen is a moot point.<br /> <br /> By 2011, the original development team had moved on to other things, and development of SciDAVis stalled. Stewardship of SciDAVis has passed over to Russell Standish, aka High Performance Coder, an experienced SourceForge project manager. The immediate plan is to focus on bug fixes reported in the SourceForge ticket system, and creating regression tests, whilst the new development team get up to speed with the code base. Russell uses the Aegis source code repository system for managing the releases, so this has meant bumping the version number to 1. Releases within a version number are denoted by the delta number assigned to the code when it is successfully committed to Aegis. At the time of writing the current version is 1.D4, and the plans are for roughly 6 monthly releases, unless a critical bug is found and fixed to bring the release forward, or no development activity has taken place since the last release. <br /> <br /> ==== Duration of the Project ====<br /> Depending on the scope and the technologies tackled<br /> <br /> ==== Expected Results ====<br /> Extending the features of Scidavis open software to adapt the needs of scientific community , creating useful tutorial for the community, contributing to program documentation and translating program features and documentation in Greek<br /> <br /> ==== Related repositories ====<br /> http://scidavis.sourceforge.net/ <br /> <br /> ==== Knowledge Prerequisites ====<br /> C/C++, Python, Latex<br /> <br /> ==== Mentors: ====<br /> <br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code&diff=2098 2022-02-09T09:44:47Z

<p>Pkst-1: </p> <hr /> <div><br /> [[Αρχείο:Gfoss2.png ‎|432x432px]] [[Αρχείο:400px-GSoC2016Logo.jpg]]<br /> == Welcome to the GFOSS Google Summer of Code page ==<br /> <br /> * See [[Google Summer of Code 2022 proposed ideas]]<br /> * [[Google Summer of Code 2021 Accepted Projects|See Google Summer of Code 2021 Accepted projects]]<br /> * See [[Google Summer of Code 2021 proposed ideas]]<br /> * See [https://ellak.gr/wiki/index.php?title=Google_Summer_of_Code_2019_Accepted_projects Google Summer of Code 2019 Accepted projects]<br /> <br /> * See [[Google Summer of Code 2019 proposed ideas]]<br /> <br /> * See [[Google Summer of Code 2018 Accepted projects]]<br /> <br /> * See [https://ellak.gr/wiki/index.php?title=GSOC2018_Projects Google Summer of Code 2018 proposed ideas ]<br /> <br /> * See [[Google Summer of Code 2017 Accepted projects]]<br /> <br /> * See [https://ellak.gr/wiki/index.php?title=GSOC2017_Ideas Google Summer of Code 2017 proposed ideas ]<br /> <br /> == Open Technologies Alliance at Google Summer of Code Archive ==<br /> <br /> [https://summerofcode.withgoogle.com/archive/2019/organizations/6491213031538688/ GFOSS at GSOC 2019] <br /> <br /> [https://summerofcode.withgoogle.com/archive/2018/organizations/5472477677355008/ GFOSS at GSOC 2018]<br /> <br /> [https://summerofcode.withgoogle.com/archive/2017/organizations/6266968780832768/ GFOSS at GSOC 2017]</div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=%CE%9A%CE%B1%CF%84%CE%B7%CE%B3%CE%BF%CF%81%CE%AF%CE%B1:GSOC2022&diff=2097 2022-02-09T09:44:01Z

<p>Pkst-1: Δημιουργήθηκε κενή σελίδα</p> <hr /> <div></div>

Pkst-1 https://wiki-staging.ellak.gr/index.php?title=Google_Summer_of_Code_2022_proposed_ideas&diff=2096 2022-02-09T09:43:47Z

<p>Pkst-1: </p> <hr /> <div>Contributors interested to participate should check which of the following projects fits their interests and skills.<br /> <br /> '''Τo communicate with the mentors and ask questions about the projects, students should subscribe to this''' [https://lists.ellak.gr/gsoc-developers/listinfo.html list] '''and post relevant questions. Please follow the [[Proposal Template]]'''<br /> <br /> For practical information, developers should visit this '''[https://summerofcode.withgoogle.com/how-it-works page]'''.<br /> <br /> == Workflow and Parallelization patterns for Node-RED ==<br /> <br /> ==== Brief Explanation ====<br /> Node-RED (&lt;nowiki&gt;https://nodered.org/&lt;/nowiki&gt;) is a popular low code programming environment for event driven applications, particularly in the IoT world. It is based on a visual, browser based editor in which developers can write functions, wire them together to form a workflow, group a subset of functions into reusable and parametric subflows etc. Node-RED can interact with any API or service, thus its usage can be extended to be used in collaboration with other services, e.g. Function as a Service platforms, as an orchestrator. <br /> <br /> ==== Expected Results ====<br /> The purpose of the work is to exploit Node-RED's subflow and workflow features in order to implement reusable flows that can be shared through Node-RED's repository. The initial scope of the flows is around workflow primitives for easier workflow creation, parallelization patterns (e.g. migrating logic and patterns from MPI to node-red, implementation of orchestration logic for parallel AI operations and learning etc). This can be coupled with function creation for executing the workflows in state of the art function as a service environments like Openwhisk, for actual computational parallelism.  <br /> <br /> ==== Related repositories ====<br /> https://flows.nodered.org/,<br /> <br /> https://github.com/node-red,<br /> <br /> https://flows.nodered.org/flow/7a5acfc999b1ad47bb32b5d37419c777,<br /> <br /> https://gist.github.com/gkousiouris/7a5acfc999b1ad47bb32b5d37419c777<br /> <br /> ==== Knowledge Prerequisites ====<br /> Javascript,  Knowledge of Node-RED or FaaS is a plus<br /> <br /> ==== Mentors:[επεξεργασία | επεξεργασία κώδικα] ====<br /> George Kousiouris, Christos Diou<br /> <br /> [[Κατηγορία:GSOC2022]]<br /> [[Κατηγορία:GSOC]]</div>

Pkst-1