Google Summer of Code 2024 proposed ideas - Ιστορικό εκδόσεων

Ellak-editor: /* Related repositories */

2024-05-02T17:51:29Z

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← Παλαιότερη αναθεώρηση		Αναθεώρηση της 17:51, 2 Μαΐου 2024
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	==== Related repositories ====		==== 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: ~~<nowiki>~~https://github.com/nlpaueb/gr-nlp-toolkit~~</nowiki>~~		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 ====		==== Knowledge Prerequisites ====

Ellak-editor: /* Related repositories */

2024-05-02T17:48:55Z

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← Παλαιότερη αναθεώρηση		Αναθεώρηση της 17:48, 2 Μαΐου 2024
Γραμμή 340:		Γραμμή 340:
	https://github.com/chronis10/fossbot-app		https://github.com/chronis10/fossbot-app
	https://github.com/eellak/fossbot-platform		https://github.com/eellak/fossbot-platform
	~~https://github.com/eellak/fossbot-source~~
	https://github.com/eellak/fossbot-web-simulator		https://github.com/eellak/fossbot-web-simulator

Ellak-editor: /* Related repositories */

2024-05-02T17:43:00Z

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← Παλαιότερη αναθεώρηση		Αναθεώρηση της 17:43, 2 Μαΐου 2024
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	==== Related repositories ====		==== Related repositories ====
	~~git@~~github.com:sarantos40/eCodeOrama~~.git~~		https://github.com/sarantos40/eCodeOrama

	==== Knowledge Prerequisites ====		==== Knowledge Prerequisites ====

Pkst στις 08:10, 2 Απριλίου 2024

2024-04-02T08:10:12Z

← Παλαιότερη αναθεώρηση		Αναθεώρηση της 08:10, 2 Απριλίου 2024
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			== LearniTron ==

			====Brief Explanation====

			LearniTron is an educational 3D printed mars rover style robot, with AI-Computer vision capabilities, such as: Face detection,
			People counting, Hand Tracking, Object Recognition, Traffic Signs Recognition, etc.
			Robotic-sensor capabilities, such as: object avoidance and finally NLP capabilities, such as voice commands recognition, chatbot
			responses. All data will be uploaded to a web server along side with the chatbot.
			People like students can interact with it, reprogram it and experiment, to lean and enhance their knowledge on technologies,
			as AI, Computer Vision, Robotics
			====Expected Results====

			By the end of the program,the design of the 3d models of the robot, code some AI programs if not all, and
			assemble the robot’s parts and sensors, and finally code the web server to store data. The robot to be able to interact with
			people and achieve an amount of autonomy via AI and sensors


			====Duration of the Project====
			Large Size - 350 hours

			====Related Repositories====




			====Knowledge Prerequisites====
			The technologies are: Python, OpenCV, Tensorflow, Keras, C, Arduino, Raspberry

			Pi, Flask, HTML5, CSS3, JS

			====Mentors====

			Kostas Papadimas and Alexandros Melidis
	[[Κατηγορία:GSOC2024]]		[[Κατηγορία:GSOC2024]]
	[[Κατηγορία:GSOC]]		[[Κατηγορία:GSOC]]

Lefterisloukas: Fixed formatting and added open research paper for Greeklish-to-Greek paper

2024-03-22T18:09:22Z

Fixed formatting and added open research paper for Greeklish-to-Greek paper

← Παλαιότερη αναθεώρηση		Αναθεώρηση της 18:09, 22 Μαρτίου 2024
Γραμμή 10:		Γραμμή 10:
	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.		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. ====		==== 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.		• 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 ====		==== Duration of the Project ====
Γραμμή 21:		Γραμμή 25:
	==== Knowledge Prerequisites ====		==== 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)~~		• Proficiency in Python and Software Development

	==== Mentors: ====		• 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		Lefteris Loukas, Ion Androutsopoulos
Γραμμή 74:		Γραμμή 84:

	==== Brief Explanation ====		==== 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.		"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 (paper available at: https://drive.google.com/file/d/1mmCaEyue8xWcEEgDTrRv9gzvAGEFgohl/view?usp=sharing). 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.		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 ====		==== 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		• 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 ====		==== Duration of the Project ====
Γραμμή 88:		Γραμμή 102:
	==== Knowledge Prerequisites ====		==== Knowledge Prerequisites ====

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

			• Deep Learning for NLP and familiarity with related frameworks (e.g., PyTorch, HuggingFace)

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

Γραμμή 474:		Γραμμή 490:
	• TypeScript		• TypeScript
	• Next.js (optional)		• Next.js (optional)


	==== Mentors: ====		==== Mentors: ====

Pkst στις 07:37, 20 Μαρτίου 2024

2024-03-20T07:37:48Z

← Παλαιότερη αναθεώρηση		Αναθεώρηση της 07:37, 20 Μαρτίου 2024
Γραμμή 448:		Γραμμή 448:

	==== Expected Results. ====		==== 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		1. Create a structure that can be used by different university structures and can be fully customised based on the needs of each institution.
			2. The application should be fully customisable in terms of interface, content and functionality.
	~~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~~.		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)
			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.
			5. Add an admin panel from where the appearance and content of the institution's application will be defined.
			6. Create a BackEnd system to manage all the data described above.
			7. Setup a backend that fetches information from official sources and static information(e.g. map images) to make the frontend lighter.
			8. Rewrite in Typescript for maintainability
			9. Creation of a custom CMS (consisting of FrontEnd & BackEnd) for data changing frequently
			10. Create a system where the application is shared across domains by creating an instance.


Γραμμή 460:		Γραμμή 467:

	==== Knowledge Prerequisites ====		==== Knowledge Prerequisites ====
	React.js~~, Java~~(~~Spring Boot~~)~~,Typescript,~~ Next.js(~~Optional~~)
			• React.js
			• Express.js (for BackEnd)
			• MySQL (for BackEnd)
			• JavaScript
			• TypeScript
			• Next.js (optional)

Pkst στις 17:22, 27 Φεβρουαρίου 2024

2024-02-27T17:22:43Z

← Παλαιότερη αναθεώρηση		Αναθεώρηση της 17:22, 27 Φεβρουαρίου 2024
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	==== Related repositories ====		==== Related repositories ====
	[https://github.com/~~dspinellis~~/~~alexandria3k~~ https://github.com/flexivian/flexbench/tree/develop ,][https://github.~~com~~/~~dspinellis~~/~~alexandria3k~~ https://flexivian.github.io/flexbench/]		[https://github.com/flexivian/flexbench/tree/develop https://github.com/flexivian/flexbench/tree/develop ,][https://flexivian.github.io/flexbench/ https://flexivian.github.io/flexbench/]
	==== Knowledge Prerequisites ====		==== Knowledge Prerequisites ====

Pkst στις 07:09, 27 Φεβρουαρίου 2024

2024-02-27T07:09:27Z

← Παλαιότερη αναθεώρηση		Αναθεώρηση της 07:09, 27 Φεβρουαρίου 2024
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	==== Mentors: ====		==== Mentors: ====
	Anastasios Tsalmas tsalmanastasios@gmail.com		Anastasios Tsalmas tsalmanastasios@gmail.com, Efstathios Iosifidis eiosifidis@gmail.com

	== DIY IoT Physics Experiments for education ==		== DIY IoT Physics Experiments for education ==

Pkst στις 07:07, 27 Φεβρουαρίου 2024

2024-02-27T07:07:35Z

← Παλαιότερη αναθεώρηση		Αναθεώρηση της 07:07, 27 Φεβρουαρίου 2024
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	==== Mentors: ====		==== Mentors: ====
	Christos Chronis, ~~Iraklow~~ Varlamis		Christos Chronis, Iraklis Varlamis


Γραμμή 464:		Γραμμή 464:

	==== Mentors: ====		==== Mentors: ====
	~~Efstathios Iosifidis eiosifidis~~@gmail.com		Anastasios Tsalmas tsalmanastasios@gmail.com

	== DIY IoT Physics Experiments for education ==		== DIY IoT Physics Experiments for education ==

Pkst στις 09:35, 23 Φεβρουαρίου 2024

2024-02-23T09:35:00Z

← Παλαιότερη αναθεώρηση		Αναθεώρηση της 09:35, 23 Φεβρουαρίου 2024
Γραμμή 147:		Γραμμή 147:

	==== Expected Results. ====		==== Expected Results. ====
	~~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.~~		Implementation of four new data sources added to the eight existing ones

	==== Duration of the Project ====		==== Duration of the Project ====
Γραμμή 177:		Γραμμή 177:

	==== Related repositories ====		==== Related repositories ====
	https://github.com/~~nlpaueb~~/~~edgar~~-~~crawler/~~		https://github.com/sfendourakis/MD-Attractor

	==== Knowledge Prerequisites ====		==== Knowledge Prerequisites ====