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<p>Understanding actions and interactions of humans from the RGB-D sensor input can significantly improve cognitive functions of robots and help safely and smoothly incorporate them in the world of humans. Human activity recognition component will thus be a significant addition to the functionality of RoboComp. Human activity recognition in the video is an interesting and challenging task and recent research shows that there are different ways to address the spatial properties of human actions and their temporal dynamics. In the course of this project I plan to start with the simpler LSTM architectures and iteratively test and improve the model to achieve state-of-the art results on the selected datasets with the final goal of providing ready-to-use RoboComp components.</p>

<p>We are living in a world where a cognitive robot can act as a human assistant and has the potential to offer social experiences through human-robot interactions. To foster this coexistence, a robot should be able to infer human emotions allowing for a more meaningful and enriched interaction between robots and humans. This project aims to design an emotion recognition component which will help establish an affective loop between humans and robots.</p> <p>In order to achieve this I plan to benchmark the well-known publicly available state-of-the-art image and video datasets for classical machine learning algorithms and deep learning based approaches. Based on my observations and evaluation metrics I propose to integrate a trained model with the emotion recognition component in the robocomp codebase.</p>

<p>Documentation tools have become modern, lighter, easier to use with the use of Sphinx, Github pages/wiki, Mkdocs and many more similar frameworks and applications. It’s easily the first and the most important steps for any open-source projects implemented by almost everybody.</p> <p>Hence, <strong>WikiPort</strong> would be a tool that helps in migrating documentation from wiki-pages in MediaWiki format to rich Markdown/reStructured text with attention to the transfer of all information and media accurately with least redundancies to have a more sustainable form of documentation that is easily available, accessible and editable to all users, developers and enthusiasts of Sugar and Sugar Activities. These converted pages can then be exported to any code sharing website or documentation tools like Github, Gitlab, Sphinx through git repositories or other VCS.</p> <p><strong>Setting up the new Activity Server (ASLOv3)</strong> and help in its deployment with by first finding and then adding/fixing of various files, solving redundancies in data present in Activity Repositories of Sugar Labs to present accurate information on the server front-end for a better UX to all the users and community.</p> <p>Coded with love in Python</p>

<p>To refactor frontend codebase written in Ember to React, including adding tests and setup code standards. Add integration and unit tests in the server for various features like video integration, sessions &amp; exhibitors etc. Add new features like creating a group consisting of events &amp; allow group follow/unfollow feature, allow users to upload multiple event documents etc</p>

<p>The AeroGear UnifiedPush Server sends native push messages to different mobile operating systems, such as Android, iOS and others. It has already developed good logical structures for sending of push notifications using JMS. Moreover, its "sender" module is mainly constructed of different message queues and their producers and consumers. On the other hand, the server DB is currently based on MySQL/PostgreSQL and each data entry lies in one DB scheme. We need the process for sending of push notifications and the evaluation of message metrics to be as fast as possible and as scalable as possible. Therefore, with an increase of our data problems can be caused by slow writes/reads to DB and not scalable enough message queue usage. The idea of the project is to try to overcome these obstacles and rebuild some modules of the system in the way that they perform well even if the data volume they are operating over increases. For that two different approaches can be taken. To speed up the DB access a new distributed, scalable, big data store HBase can be integrated into the project. For more scalable processing of the data, Apache Kafka can be used instead of JMS.</p>

<p>Mozilla is a global non-profit dedicated to keeping the internet open, accessible, and safe for everyone.</p><p>Implement high-resolution DOM mutation profiling and memory footprint tracking inside Firefox Developer Tools.</p><p><br></p><p><strong>Deliverables:</strong></p><ul><li>DevTools panel extension for DOM mutation profiling.</li><li>Performance telemetry collector module.</li></ul><p><br></p><p><strong>Applications closing date: 25-Apr-2026</strong></p>

JBoss Web Server (JWS) is Red Hat’s enterprise distribution of Apache Tomcat used in production environments where configuration correctness and operational visibility are critical. Many production incidents are caused by configuration errors such as TLS misconfiguration, incorrect connector exposure, proxy misconfiguration, permission issues, and environment drift rather than software defects. This project proposes jws-diag, a read-only Java CLI diagnostic tool designed to help operators and support engineers quickly understand installation state, effective runtime configuration, and potential configuration risks. The tool will provide: • Installation and runtime summary (JWS/Tomcat versions, JVM, OS, container signals) • Effective configuration parsing from server.xml with defaults resolution • Validation rules engine producing INFO/WARN/ERROR findings • Comprehensive test suite with configuration fixtures • Documentation and operational usage guidance • Optional redacted support bundle generation (stretch goal) Key deliverables include: • jws-diag summary command • jws-diag config command • jws-diag validate command with validation rules • Test suite and documentation • Integration into JWS ecosystem tooling The project will initially target: JWS 6 and JWS 7 Tomcat 10.1 and Tomcat 11 The expected outcome is a production-safe diagnostic tool that improves troubleshooting efficiency and configuration reliability for JBoss Web Server deployments.

<ol> <li>Implement Casbin-Server in rust and make it compatible with yaml-adapter, diesel-adapter and sqlx adapter.</li> <li>Make JSON-Adapter for casbin-rs.</li> <li>Make rocket.rs middleware for casbin-rs using Fairings.</li> </ol>

EAT (Eap Additional Testsuite) is a testsuite to develop tests against infinite number of JBoss servers. It’s an innovative tool because it’s creating the test once and testing with any version of the tested software. EAT is available for a number of server configurations and a CI pipeline can be built using the maven tool. The goal of the present project is to extend EAT with new features and tools. The implementation of the project is estimated to last from June to November (Large Size Project) and the timeline is envisaged as follows: During the 1st Phase (June 2022 - July 2022) a snapshot will be added for testing the latest version of JBoss Community Server. The newly added snapshot will be made available for use with the CI builds. Moreover, the extension of EAT with additional multiversion android projects (described at the second phase) will begin. Continuing to the 2nd Phase (July 2022 - September 2022), new multiversion Android projects will be added to EAT. In EAT there is already a basic android multiversion example. This will be extended with other open source android projects demonstrating how EAT can be used to support multiversion real life android projects. Finally, during the 3rd Phase ( September 2022 – November 2022) a multi-version Android application will be developed and added to EAT. This app will enable users to upload and view descriptions and ways of usage for various programs or functions. The users will be able to upload the descriptions of projects related to EAT, how they can be used and other project related information. The documents will be proposed for acceptance and when accepted they will be stored in e.g. google drive, from where they will be accessible for anyone interested.

This project proposes building jws diag, a read only diagnostic CLI for JBoss Web Server that helps operators and SREs to inspect runtime and configuration state more easily. The tool will provide concise installation and deployment summaries, effective connector TLS and proxy configuration reporting, validation checks for common misconfigurations, structured JSON output, and realistic test coverage for troubleshooting and automation use cases.

The Global Alliance for Genomics and Health (GA4GH) defines the Tool Registry Service (TRS) specification for the discovering and accessing research software, packaged as either “tools” (command-line executables) or “workflows” (descriptors of DAG-based analysis pipelines, typically written in specialized DSLs). In this project, I will aim to create components that will help discovering these tools and workflows. This will be done using Svelte framework and Typescript.

<p>Aim of this project is to develop a People Identification System with the following two capabilities.</p> <ol> <li>Given an image identify all the faces in it and return labels of identified people. </li> <li>Classify new persons and use incremental learning techniques to recognize it later.</li> </ol> <p>This component will allow for a more enriched interaction between robots and humans by enabling robots to make better decisions based on the input from different categories of people.</p>

This project transforms API Dash into a next-generation, AI-native API client. I am merging three core objectives: 1. Agentic API Testing, 2. MCP Support 3. Generative UI Visualizations into a unified Dual-Transport Model Context Protocol (MCP) Architecture: 1. Agentic Sandbox & Generative UI (GenUI): Eliminating brittle WebViews, the internal UI will utilize Google's genui SDK and the Open Responses specification. This powers two features: an autonomous, self-healing Agentic Testing Dashboard, and a "Generative UI Previewer" that visualizes rich AI responses for end-users building Flutter/Web apps. 2. Headless MCP Engine: This exposes API Dash as a standard stdio MCP Server for external Agent interfaces (like VS Code, Cursor, or Claude Desktop).

Build custom Skia GLSL shader primitives and reactive motion transitions for smooth 60fps mobile interfaces.

<p>With the advent of Machine Learning, one can learn such heuristics and use them as cost functions in conventional path planners. SNGNN-1D is one such Graph Neural Network based Machine Learning algorithm which evaluates the static scenes generated randomly from the point of view of the robot. In short, it outputs a score between 0-100 which shows how good the robot’s position is with respect to its surroundings. This score, for every state in the room, is used to generate a heat map that is used by A* algorithm to plan a path to the goal position. The problem with this implementation is that it is computationally expensive and is relatively slow, also SNGNN has been trained using static scenes and does not factor in dynamic settings for example, walking humans, which is a very common scenario in a social setting. A more cost effective way to carry out this task would be to train a policy to reach the goal. However, it is difficult to structure a reward function for complex environments. In such cases, Reinforcement Learning is a promising pursuit that can make use of SNGNN-1D as a reward function because it scores the robot’s presence in the room with respect to its surroundings.</p>

<p>Create-React-App provides some convenience features, such as: a live development environment, boilerplate code, and standard dependencies pre-installed. The idea is to make Plone-react package as a dependency(library) to other packages and make ( use ) a create-react-app like app generator to generate the example boilerplate which fits with the package. By doing this it doesn't need to fork the repo everytime, just execute the command create-plone-react-app like so and we’ll be good to go.</p>

<p>“Htsget retrieval API spec” [1] is a specification by the <a href="http://www.genomicsandhealth.org/" target="_blank">Global Alliance for Genomics and Health (GA4GH)</a> for genomics bulk data transfers. This is a Streaming API that allows users to query remote services for regions of interest of genomic data, instead of having to download gigabytes of data and filter afterwards. The results are retrieved in formats that can be consumed by widely used bioinformatics tools. The specification currently supports BAM and CRAM formats.<br> Variant Call Format (VCF) is the standard for describing genomic variation in bioinformatics. The goal of the project is to extend the specification to support the VCF format and provide an implementation.</p>

<p>This project will center around the development of additional optimization techniques to enable interactive visualization of very large networks, as well as new interactive graph features, including node/edge selection/addition/removal and node/edge styling.</p> <p>Specific components of this project include:</p> <p>Optimalization of already implemented interactivity features: node / edge (straight line) selection. Implementation of curve edges selection. Text rendering using SDF (signed distance field). Editable texts. Possibility to visualize multiple values per single node using colors / styles / textures. Advanced styling for nodes and edges (like using textures for edges to achieve dotted / dashed lines). Demo page to preview all implemented features.</p>

<p>My project is based on the fact that the offline component to be created for EBO gives it the ability to speak through a TTS modulating the voice depending on their mood and also this contains a dictionary of phrases created by the child and thus offer the possibility that the robot has a wide range of phrases with which the child feels identified and that he does not have the obligation to specify a phrase whenever he wants to speak.</p>

<p>The goal of this project is to produce a Docker image that contains everything that an end-user needs to run Reactome on his/her own workstation. Provisions will be made to update the database in the image with the latest centralized database of Reactome.</p>

Extend the Markdoc abstract syntax tree engine with dynamic custom tags and AST validation hooks.

<p>The task of recognizing and predicting human daily activities is a trending topic nowadays, and a lot of research has been developed around it, accompanied with the creation of algorithms that achieve state-of-the-art results on different human activity data sets: CAD-60 or CAD-120, UTKinect-Action, Florence3D-Action data sets, etc. The application of a machine that can detect a person’s actions is broad. It has been developed for gaming, Human-Computer interac-tion or Active and Assistive Living.</p> <p>For the development of RoboComp's framework, my work will be based on the article of Premebida, Souza and Faria (2017) where the proposed algorithm reached an accuracy of 94.74% and a recall of 94.74 % on CAD-60, which is considered as a state-of-the-art result. These articles are mainly based on Dynamic Bayesian Networks and a Dynamic Bayesian Mixture Model (Faria,Premebida,Nunes(2014)). Other works will be taken in account to take advantage of what we have available, combining machine learning approaches with mathematics to get robust results, such as the introduction of Partial Differential Equations or Lie groups, explained, for example, in Vemulapalli et al. (2014)</p>

Socially aware path planning enables a robot to navigate through a crowded environment causing the least amount of discomfort to the surrounding people. Building upon the work done in GSoC '21, the aim of the project is to account for the interactions between the different entities in the environment by adding interaction nodes between entities in the graph representation. A way in which the robot can also come up with interaction nodes would also be looked into.Finally, the project also aims to compare SNGNN-RL's performance with the current baseline.