Google Summer of Code 2024 proposed ideas

Contributors interested to participate should check which of the following projects fits their interests and skills.

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

For practical information, developers should visit this page.

EDGAR-CRAWLER: Democratizing accessibility to Financial NLP documents

Brief Explanation

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.

Expected Results.

• 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.

Duration of the Project

(350 hours).

Related repositories

https://github.com/nlpaueb/edgar-crawler/

Knowledge Prerequisites

• 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)

Mentors:

Lefteris Loukas, Ion Androutsopoulos

Greeklish-to-Greek: Development of an open-source and state-of-the-art toolkit

Brief Explanation

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. 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.

Expected Results.

• 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

Duration of the Project

(350 hours).

Related repositories

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: https://github.com/nlpaueb/gr-nlp-toolkit

Knowledge Prerequisites

• Advanced Python Programming and Software Engineering • Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)

Mentors:

John Pavlopoulos (AUEB), Ion Androutsopoulos (AUEB), Stavros Vassos (helvia.ai), Lefteris Loukas (helvia.ai & AUEB)

Label Buddy 3.0: Automated audio-tagging using transfer learning

Brief Explanation

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.

Expected Results.

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

Duration of the Project

(350 hours).

Related repositories

https://github.com/eellak/gsoc2022-Label-buddy

Knowledge Prerequisites

NodeJS, js, typescript, Html, Css, React, Electron, docker, Machine Learning, OpenAPI

Mentors:

Ioannis Skitsas (iskitsas@gmail.com), Marios Karagiannopoulos (mariosk@gmail.com)

Exploring and Abstracting Triplestore Alternatives

Brief Explanation

Objective

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.

Background

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.

Project Description

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.

The ultimate goal is to develop a library that can act as an abstraction layer for these triplestore alternatives. This library will "hide" 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.

Methodology

Research: Identify and study various triplestore alternatives. Understand their architecture, features, and limitations.

Testing: Perform rudimentary tests and benchmarks on the identified triplestore alternatives.

Analysis: Analyze the test results to understand the performance and scalability of each alternative.

Development: Develop an abstraction layer that can interface with the various triplestore alternatives.

Documentation: Document the findings and the usage of the developed library.

Expected Outcome

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.

Conclusion

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.

Duration of the Project

Long (350 hours)

Related repositories

New project, no existing repo available.

Information links

- Triplestore - Triples - Query language

Knowledge Prerequisites

Python (mandatory). Other programming languages like C, Go, Rust, Java, might prove useful.

Mentors:

Alexios Zavras, TBD

Font Validator: A System for Quality Control of Digital Typefaces Containing Greek Characters

Brief Explanation

Objective

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.

Background

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.

Project Description

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.

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.

Methodology

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.

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.

Testing: Perform the developed tests on a variety of digital typefaces.

Analysis: Analyze the test results to identify any inconsistencies in the design of Latin and Greek characters.

Recommendations: Based on the analysis, provide recommendations for improving the consistency of typeface design.

Documentation: Document the findings, the developed tests, and the recommendations.

Expected Outcome

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.

Conclusion

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.

Duration of the Project

Can be either short (175 hours) or long (350 hours), depending on the agreed-upon scope.

Related repositories

New project, no existing repo available.

Information links

- Font Bakery documentation - Font Bakery source repo

Knowledge Prerequisites

Python, font technologies, understanding of Greek characters

Mentors:

Irene Vlachou, Emilios Theofanous, Alexios Zavras

Development and Enhancement of the Cloud-Based FOSSBot Platform

Brief Explanation

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.

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.

GSoC 2024 Objectives

In 2024, our goal is to further enhance this cloud-based platform by:

Performance Optimization: Improving both front-end and back-end performance for a seamless user experience. Development of Cooperative Modes: Enabling multiple robots to interact and collaborate in shared tasks or scenarios, fostering teamwork and advanced programming skills. 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. Custom Scene Creation for Godot Simulator: Integrating an interface for creating new, customized scenes in the existing Godot-based simulator, enabling tailored educational experiences. 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. Continuous Integration and Deployment (CI/CD) Enhancements: Streamlining updates and maintenance through advanced GitHub automation and cloud deployment strategies. Extensive Documentation: Ensuring comprehensive documentation to facilitate ease of use and adaptability for educators and developers.

Expected Results.

A robust, cloud-based platform offering a wide range of educational and programming possibilities. Enhanced user experience with improved performance and new features. Greater accessibility and cost-effectiveness for educators worldwide.

Duration of the Project

(350 hours).

Related repositories

https://github.com/eellak/gsoc2019-diyrobot https://github.com/eellak/fossbot https://github.com/chronis10/fossbot-app https://github.com/eellak/fossbot-platform https://github.com/eellak/fossbot-source https://github.com/eellak/fossbot-web-simulator

Knowledge Prerequisites

React.js Python Flask API / FastAPI SQLAlchemy Godot (not mandatory) JavaScript Docker Git

Related publications

Chronis C., & 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 Kazazis G., Chronis C., Diou C. & 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)

Mentors:

Christos Chronis, Iraklow Varlamis

Enabling Apothesis to support atomic layer deposition and etching processes

Brief Explanation

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: 1. Enabling Apothesis to read the lattice from a file. Thus creating a generalized reader for hard copy lattices. 2. Creating the sequentially calls to simulate ALD/ALE processes. 3. Design the output of the ALD/ALE process.

Expected Results.

The results from a simple case of ALD in a simple cubic lattice.

Duration of the Project

(350 hours).

Related repositories

https://github.com/nixeimar/Apothesis

Knowledge Prerequisites

C++, desing patters (factory pattern) and basic physics in deposition processes

Mentors:

Cheimarios Nikolaos, Vissarion Fysikopoulos

eCodeOrama, an educational interactive flow visualization tool for mit scratch programs

Brief Explanation

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. The tool will also promote code understanding, especially to young students that use scratch, and will include debugging aids. 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.

Expected Results

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.

Duration of the Project

350 hours

Related repositories

git@github.com:sarantos40/eCodeOrama.git

Knowledge Prerequisites

python, mit scratch, gui development

Mentors:

Sarantos Kapidakis (sarantos.kapidakis@gmail.com), Chrysovalantis Sfyrakis

Enhancement of SmAuto DSL and integration into HomeAssistant

Brief Explanation

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.

Expected Results

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.

Duration of the Project

350 hours

Related repositories

https://github.com/robotics-4-all/smauto, https://www.home-assistant.io/

Knowledge Prerequisites

Required: Python, Software engineering, IoT concepts, Unix/Linux. Desired: Model Driven Engineering, HomeAssistant, Docker

Mentors:

Konstantinos Panayiotou, Emmanouil Tsardoulias, Andreas Symeonidis

Creating a factory pattern for handling lattices in Apothesis.

Brief Explanation

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.

Expected Results

The factory pattern source code and a simple example for incorporating a graphene lattice.

Duration of the Project

350 hours

Related repositories

https://github.com/nixeimar/Apothesis

Knowledge Prerequisites

C++, design (factory pattern)

Mentors:

Nikolaos Cheimarios, Christina-Anna Gatsiou

MyUni

Brief Explanation

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.

Expected Results.

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

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.

Duration of the Project

Depending on the scope

Related repositories

https://github.com/Open-Source-UoM/MyUoM

Knowledge Prerequisites

React.js, Java(Spring Boot),Typescript, Next.js(Optional)

Mentors:

Efstathios Iosifidis eiosifidis@gmail.com