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Physicists at the LHC need to perform ML inference on massive amounts of data. Currently, the bookkeeping of ML model files is an unsolved problem. The goal of this project is to evaluate the CernVM File System (CVMFS) as a platform to store, organize, and distribute model files. The two primary issues to tackle are latency and infrastructure. We know there is a latency with using CVMFS versus local storage, so I will rigorously benchmark the performance of CVMFS and determine ways to minimize this overhead. For infrastructure, I will test integrating different services with CVMFS such as Cern Document Server (CDS) or Kubeflow's KServe. The final deliverables will include extensive documentation on benchmarks and best-practices for using CVMFS, and an overall evaluation of CVMFS and the possible integrations like KServe. An example deployment of ML models from the ML4EP project using CVMFS will also be demonstrated.

1. Adding Filters in the Matrix View: Updating the YAML file, and adding the tags title in task-description. After this, adding chip style filter system in the matrix view, that'll encompass the list of activities visible in the matrix view. 2. Adding Team-Based Assessment: Creating a node server that'll save the activity data of multiple teams on the local system. Adding a team selection menu, and creating an updated view of the team-based heatmap. 3. Comprehensive Activity View: Alongside the heatmap, details of activities need to be shown. This will be achieved by creating an overlay screen on the heatmap which can be opened on the selection of activity. This will display the activity detail from generated.yaml file. 4. Enhancement of Diagram in Documentation: Flow charts and a detailed summary of the project will be updated on the Usage page.

<ol> <li>Adding feedback feature to Susi’s functionality.</li> <li>Integration of Susi assistant to Viber, Skype and Gitter.</li> </ol> <p>Feedback Feature: I will change the UI of the Susi assistant to inculcate the option of feedback by the user. This feedback after review will be saved in a file on the server. Thus, this file can be included in the main repository and change the behavior of Susi.</p> <p>Integration to various platforms: Currently, Susi assistant is integrated to Telegram, Facebook, Slack and Kik. My proposed project aims to integrate Susi messenger to Viber, Skype and Gitter. The user can add Susi assistant as a friend, and have a conversation with it, through these platforms.</p> <p>The improvement achieved in the website of asksusi (enabling feedback), the same can be introduced to the Susi assistant present on these messenger platforms, if I am left with time.</p>

This project focuses on extending the R interface of the torchvision library by implementing support for core computer vision datasets and model architectures. The scope includes adding native loaders for widely used datasets such as COCO and VOC, along with support for key models including Faster R-CNN, Mask R-CNN, FCN, Keypoint R-CNN, and quantized ResNet variants. The proposed work enhances the torch ecosystem in R by enabling advanced computer vision workflows with consistent APIs, GPU compatibility, and clean documentation. The implementation will follow package development best practices with robust testing, reusable components, and user-friendly examples. The resulting contributions aim to reduce the gap between R and Python in deep learning tools, enabling R users to build scalable, efficient, and modern vision pipelines.

The project aims to develop a web-based administrative interface for istSOS, focussed on simplify the management and monitoring of SensorThings API components. The project will provide an intuitive method for log new sensors and devices, configuring metadata, and real-time tracking key sensor metrics, including received data, transmission delays, and overall system performance. This will also enhance usability, getting rid of comple API interactions, making istSOS more accessible to researchers, engineers, and IoT enthusiasts. Currently, the istSOS’ graphical interface appears outdated and not aligned with modern software standards. My project will also focus on updating the visual appearance and usability of the interface to enhance the user experience and make the system more accessible and simple to use. All the code will be followed with detailed comments and documentation, making future maintenance easier.

This project is about implementing a JDBC driver for Apache Wayang so that users can interact with it using standard SQL-based tools. Right now, Wayang provides SQL support through its APIs, but it does not have a JDBC interface, which makes it difficult to connect with external tools like database clients or BI platforms. The goal is to build core JDBC components such as Driver, Connection, Statement, and ResultSet, and connect them with Wayang’s SQL API. This will allow users to establish a connection, execute SQL queries, and get results in a familiar way without relying only on programmatic APIs. The implementation will follow a clean and modular design so it is easy to extend in the future. Proper testing will also be done to make sure it works correctly with real tools. Overall, this project will make Wayang easier to use and integrate with existing systems.

Zplit is a decentralized, privacy-first group expense tracking application designed to give users full control over their financial data without relying on centralized servers. Unlike traditional apps, Zplit operates using peer-to-peer technologies such as Wi-Fi Direct, Bluetooth, NFC, and QR codes, enabling seamless offline-first collaboration.I will implement Deep Link integration for seamless group invitations, integrate USDC/DAI stablepay settlement for expense splitting, and build interactive data visualizations . All user data will be synced peer-to-peer via Wi-Fi Direct, Bluetooth, NFC, and QR codes, ensuring zero cloud dependency. The final deliverables will include a fully functional Flutter app with Deep Link support, stablepay payment flow, expense visualization dashboard

This GSoC project focuses on strengthening SymPy's assumptions system through a two-fold approach. The first component targets relational reasoning by addressing key inference gaps, without needing explicit real assumptions. Additionally, it involves integrating an incremental SAT solver to significantly improve the performance of ask and satask by enabling clause reuse across queries. The second part of the project introduces support for quantified logical reasoning. By implementing universal and existential quantifiers (Q.forall, Q.exists), SymPy users will be able to express and evaluate rich, domain-spanning mathematical statements. This will be realized using a hybrid strategy, combining Z3-style pattern-based instantiation with SymPy's symbolic, declarative assumptions system. Together, these enhancements aim to make SymPy's logic engine more expressive, efficient, and mathematically powerful.

Right now, Mifos Gazelle automatically deploys core components like Payment Hub EE and MifosX. My project will integrate OpenSPP (Open Source Protection Platform) as a new, first-class deployment module inside Gazelle. Instead of just deploying the pods, I will build a fully reproducible end-to-end demo. This will prove that a social welfare program defined in OpenSPP can seamlessly execute payments through the Gazelle infrastructure. I will update the Bash and Python orchestration scripts, add fail-fast dependency checks, and optimize the resource limits for demo environments. Deliverables include: Integrated deployment logic for OpenSPP within the Gazelle CLI. A working end-to-end demo scenario (OpenSPP + Payment Hub EE + MifosX + vNext). Infrastructure optimization and clear runbooks for future maintainers.

The CircuitVerse mobile app faces performance bottlenecks (UI jank, slow rendering), inconsistent UX (broken project cards, poor navigation), and missing features (blog integration, interactive book errors). These issues degrade user experience, especially on low-end devices. Solution: 1. Performance Optimization: >Upgrade Flutter, profile with DevTools, and optimize widgets (RepaintBoundary, const constructors). >Reduce rebuilds and repaints for smoother animations. 2. UX Overhaul: >Revamp project cards with default images, share functionality, and Material 3 alignment. >Fix sidebar navigation and add micro interactions (ripple effects, staggered animations). 3. Feature Integration: >Fix the interactive book’s rendering errors with error boundaries. >Integrate blog/Editor’s Pick sections for seamless web-app parity.

In Bayesian statistics, one way to circumvent positing parametric assumptions is to use Dirichlet Processes (DP) as priors for probability distributions themselves. PyMC is a Python probabilistic programming library for Bayesian modelling and there are ongoing efforts to add a DP functionality to the package. DPs are daunting both theoretically and computationally as their non-trivial implementation would benefit from polished non-user facing methods in Aesara - formerly Theano - and AePPL. The former provides the computational backend to PyMC whereas AePPL converts arbitrary Aesara graphs into equivalent log-probability graphs which are convenient for automatic differentiation and hence sampling. The goals of my proposed Google Summer of Code (GSoC) project are threefold: relax the univariate assumption of components in mixture models, allow the creation of mixture graph via IfElse and Switch statements and continue ongoing efforts to add a DP functionality to PyMC.

<h3>The project involves building a native iOS application for the Agora Web application from scratch.</h3> <p>It allows for democratic elections to be held by using multiple algorithms such as Oklahoma, RangeVoting, The application needs to be built from scratch and certain features need to be added to the iOS application while providing a good UI/UX.</p> <h4>Would be working on the following features:</h4> <ul> <li>Facebook/AppleID/Server Authentication and Sign up</li> <li>MVVM Architecture</li> <li>Build a Custom Calendar Library</li> <li>Implement the elections-algorithms</li> <li>Integrate Database with API</li> <li>CI/CD</li> <li>Unit and UI tests</li> <li>iPad support</li> <li>UI/UX with animations</li> <li>Deploy and test in TestFlight</li> </ul>

Currently, Jaeger v1 has an official Helm chart (https://github.com/jaegertracing/helm-charts), but there isn't one yet for Jaeger v2. The goal of this project is to develop a comprehensive Helm chart for Jaeger v2 that allows for easy deployment and management of Jaeger v2 components in Kubernetes environments. This chart should provide flexibility in configuration, support various deployment scenarios, and integrate well with the new architecture of Jaeger v2. More details in the [upstream issue](https://github.com/jaegertracing/jaeger/issues/5767). Expected Outcome: By the end of this project, we aim to have a production-ready Helm chart for Jaeger v2 that is: - Fully functional and tested on the current version of Kubernetes - Well-documented with clear usage instructions and examples - Flexible enough to support a wide range of deployment scenarios - Ready for submission to the official Jaeger Helm chart repository

The proposal aims to address the issue of the expensive and inaccessible research publishing industry by leveraging modern internet-based social technology to create an open reviewing and quality-ranking web portal. The portal will facilitate manuscript submission and an automated, free, community-based open access, peer review, and quality-rating system. The project will develop a proof of concept portal using ReactJS/NextJS and TailwindCSS for the frontend, Django for the backend, and PostgreSQL as the database server. The deliverables include a functional web portal that allows for detailed comments, ratings, and reputation-based filtering, which can improve the quality of research and provide a platform for communities of reviewers to form and manage the reviewing process. The system will eventually be tested with the Aperture Neuro open access publishing platform of the Organization for Human Brain Mapping (OHBM), in consultation with that community.

<p>This project aims to develop a Jupyter notebook plugin which deploys Spark required services to a kubernetes cluster on OpenStack cloud at CERN.</p> <p>Kubernetes provides scaling when the traffic or computation increases by launching a Spark driver pod in the cluster which in turn creates multiple Spark executer pods which executes the application code.</p> <p>The services that will be attached to the Kubernetes cluster are CERN CVMFS, Spark shuffle service, and Spark history server. These services are needed for running Spark on Kubernetes. Physicists can then use Spark running in the background to perform scalable interactive data analysis and visualization.</p> <p>Also, a proper UI will be provided inside the Jupyter notebook so that a user can attach various services to the cluster. This plugin then will be integrated with SWAN notebook service which CERN provides.</p>

<p>Talawa is an open source project helping people with same interest to collaborate together. It deals with managing the members of the group via help of Talawa admin panal. Tawala appilcation helps members to join organizations of their interest via organization's Tawala-api hosted URL.</p> <h4>Talawa Feature:</h4> <ul> <li>[x] International language support.</li> <li>[x] Theming</li> <li>[x] Messaging</li> <li>[x] Organization joining</li> <li>[x] Organization switching on same server, feeds, event creation/registration.</li> <li>[x] Notifications</li> <li>[x] Verified account creation</li> <li>[x] Account password recovery</li> <li>[x] Signup from google and facebook</li> <li>[x] Responsive Layout</li> </ul>

This project proposes an AI-powered accessibility auditing and remediation toolkit tailored for Drupal environments. It integrates automated accessibility scanners (e.g., Deque Axe API), and NLP models (such as GPT-4 or BLIP-2) to suggest intelligent alt-text and identify layout/media accessibility issues. A dynamic dashboard will be built to track scan results, remediation progress, and generate compliance documentation in formats like PDF and CSV. The system will be developed using Symfony (backend), HTML+Twig (frontend templating), and MongoDB (for storing audit results and history). Key components include API communication layers, a feedback system for rating AI suggestions, and tight workflow integration with Drupal, enabling accessibility checks before content is published. The architecture emphasizes modularity, ethical AI use, and compatibility with WCAG standards.

This proposal introduces an optional, privacy-first AI assistant for OpenSCAD to flatten the learning curve of SCAD syntax and geometric transformations. Integrated as a togglable dock panel, the assistant remains completely inert when closed and requires no cloud subscriptions or external API keys, instead leveraging locally running models via Ollama or any OpenAI-compatible server. Beyond standard Q&A, the project implements agentic behavior where the AI can analyze the active script, propose modifications through an inline diff, and apply approved changes directly to the editor as undoable actions that trigger an automatic preview. The architecture is designed to be vision-model-ready from the start, ensuring future viewport context integration requires no structural refactoring. This creates a transparent, local-only loop that empowers users without compromising the project’s core philosophy of user-controlled development.

<p><strong>Neural AutoRegressive Flows</strong> are one of the most recent addition to the family of autoregressive flows. By using NAFs, probability density estimation in the domain of scientific exploration can yield amazing results. For example if the background data of a particular device is modelled using NAFs, it can lead to the discovery of new phenomena with very little supervision. Main goals of the project include creating basic reusable building blocks for NAFs, <em>implementing NAFs</em> on the given High Energy Physics data obtained from various experimentation devices, <em>hyperparameter tuning</em> of the NAF model for optimum performance, providing <em>API for training plus inference</em> and finally <em>documenting</em> the API and various components of the system. Final product obtained will not only be easy to use but also easy to extend.</p>

The `loo` package is a widely used R package in the Stan ecosystem which exposes a clean API to cross-validate (CV) Bayesian models. Currently, some `loo` functions use parallelization by way of forking processes, which doesn't work on Windows but has some benefits over other parallelization schemes with respect to memory. The `mirai` package is a new approach to cross-platform parallelization which relies on daemons, allowing package developers to write parallel code without any knowledge of where the code will be executed--allowing users to scale up to any number of local or remote cores. `mirai` represents one approach to updating `loo`'s approach to parallelization. After this project, users of `loo` will be able to run some expensive calls in parallel, regardless of the OS they use, and ideally in any environment they would like--be it a SLURM cluster, SSH access to a large server, across a homelab, or just on many cores on their local machine.

DICOMHawk is a honeypot system designed to emulate a vulnerable DICOM (Digital Imaging and Communications in Medicine) server, primarily to detect, analyze, and understand malicious activity targeting healthcare infrastructure. This project proposes a set of enhancements aimed at improving its operational effectiveness, observability, and deployment experience. The plan involves integrating CanaryToken webhooks to enable deception-based detection of unauthorized file access, refining the logging system with structured and enriched logs to support real-time threat monitoring, and optimizing the Docker configuration to follow best practices for security, automation, and resource efficiency. These technical enhancements will be supported by thorough documentation, testing, and monitoring setup using tools like ELK stack. Together, these contributions aim to make DICOMHawk more robust, extensible, and easier to deploy while contributing meaningfully to the field of threat intelligence in healthcare cybersecurity.

<p>The project idea aims at developing a plugin (as GNU Social is true to the Unix-philosophy of small programs to do a small job) that will implement the ActivityPub Protocol in GNU Social.</p> <p>GNU Social is a social communication software used in federated social networks. In a federated social network, user data stays in the user's server instead of a centralized one.</p> <p>In order to achieve said decentralization, a variety of standards on how communication between different pieces of software in a federated context should be done were created, such as OStatus. ActivityPub, however, is the newest and covers parts left out of OStatus's specification, namely the app/client development.</p> <p>Because of this and given that newer software will focus on the implementation of the ActivityPub Protocol, GNU Social is looking forward to supporting it in order to stay updated and relevant in an even larger fediverse.</p>

In an effort to improve current resource utilisation and optimisation, the Tor team is developing a new version of their pipeline (v2.0). This update involves transferring much of the data related to Tor nodes and bridges from files stored on a single server's disk to two separate databases: Postgres and Victoria Metrics. The main objective of this project is to design a RESTful API service using the `actix_web` framework that is going to be integrated in the new pipeline v2.0 to support data retrival from the two databases. In particular, the focus will be on designing the new APIs, its requests, and response formats. The project will also involve defining appropriate endpoints and data models, ensuring scalability, performance, and security. The final goal is to achieve a web service that is going to extend/replace the current onionoo protocol used by stakeholders interested in the status of the Tor network and its individual nodes.