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<p>Performing updates on routers is quite different from full Linux distribution. It's not sustainable to do release upgrade via a packet manager. Instead it's usually required to re-flash the system image. Depending on the installed packages an image rebuild may be to complex for regular users. A more convenient way is needed.</p> <p>This project will implement an "image as a service" server side which provides custom build images depending on installed packages. A notification in the web interface will notify about the new release. After image creation a one-click installation is offered within the web interface.</p> <p>The server side implementation will use established tools like LEDE's ImageBuilder to provide an generic approach for image creating. In this way the entire OpenWrt/LEDE community including several community-mesh firmware projects will benefit from that new update routine.</p>

Currently, the backend of the journal policy tracker is on Flask and SQLite3. The goal for this project is to create a full-fledged functioning backend with Express, GraphQL, and MongoDB write extensive tests for that backend and then deploy it online. Switching to a no-SQL database for this use case will remove unneeded complexity from the database and make it more friendly to new contributors. Using GraphQL will eliminate a good amount of server load as the frontend will be able to precisely pick and choose the data that it wants to request. The main objectives of this project are: 1. Building a user Authentication API that will let people register and log in on the website as well as implementing the function of forgot-password. 2. Building Journal CRUD API which will allow our users to Create, Read, Update and Delete journal requests. 3. Writing proper documentation for all the implemented APIs. 4. Deploying this server online and making it fully functional.

<p>Knative Functions ships an MCP (Model Context Protocol) server that exposes its toolchain to AI agents. This project builds on that foundation: deepening the MCP server to cover initial environment setup and CI/CD integration, and authoring a companion skill that walks agents through the full lifecycle of a Function. Together these enable end-to-end agentic usage of Serverless Functions; from initial scaffolding through deployment with CI/CD.</p><p><br></p><p>- Expected Outcome:</p><p>&nbsp;- New agentic installer</p><p>&nbsp;- New MCP operations covering prerequisite checks with guidance.</p><p>&nbsp;- A companion skill that composes initialization, CI/CD setup, and deployment into a guided workflow.</p><p>&nbsp;- End-to-end demonstration.</p><p>&nbsp;- User-facing and agent-facing documentation.</p><p><br></p>

<p>The Sastix-CMS is a CMS solution based on the robust Spring Boot framework. Up until now the CMS provided a REST API offering functionality for managing resources, using a distributed cache system to consume them. The goal of this project is consisted of creating a frontend client to consume resources from the CMS server and providing the monitoring capabilities to handle high volumes of traffic. The frontend implementation should provide a common interface for Administrators and Content Creators/Consumers of the Sastix CMS and extend the current capabilities of the server to support multitenant API calls. The provided solution will support multirole access and authorization, simple, secure flows and multi-device deployment using Flutter. The solution will provide monitoring solutions for the application and scalability using Docker and Kubernetes. The work includes tools to benchmark the deployments, deployment artifacts to easily deploy the stack and thorough documentation of the functionality using Docusaurus.</p>

<p>The main goal of the project is to provide the user of DeepPavlov Framework with an out-of-box solution for relation extraction. I would consider relation extraction as a multi-label classification task and design a pipeline that could reuse the existing DeepPavlov components (such as NER with incorporated tokenizer, already implemented MT-BERT, and others) as much as possible. I would train several relation extraction models with different parameters (for example, different training data, classifier, classifier inputs, inputs encoding, number of relations to be extracted, etc), add them to the DeepPavlov storage, and give the user an opportunity to load them (and, occasionally, additionally train with his/her own training data) and find the one that would suit his/her data the best way. The user's input would be a text and a config with RE parameters, and relational triples would be the output. Thus, the whole preprocessing, data encoding and model application would happen inside the module, while the overall relation extraction component remains clear and convenient for the user.</p>

<p>I want to extend the tutorial offered by LabLua for the Ceu language. As it is, when you write code on their website and press submit, the code is sent to their server, compiled and the result is sent back. The problem with this, is that it doesn't show the entire capabilities of the language. For instance if you write a program that prints a message every second for 10 seconds, it will return that message printed ten times in an instant. What I want to do by using Emscripten is to have a real time tutorial, so that after the server sends back the result, it will actually print the message every one second, for ten seconds. I will also want to create a second tutorial that implements SDL as well. SDL is a language that can create shapes and interacts with keyboard inputs and the mouse. There already is compatibility between Ceu and SDL and there is also compatibility between SDL and Emscripten. The challenge is to create a synergy with all three languages. I believe this to be feasible, because you can transform a Ceu program into a C program that can then be compiled with Emscripten.</p>

My proposal aims to enhance VouchMe, a blockchain-based testimonial platform, and revamp the UI of TNT (Trust Network Tokens), an ERC721 token system for verifiable trust relationships, to create a robust decentralized trust ecosystem. For VouchMe, I will improve user experience and security by integrating user profiles with Decentralized Identity (DID), enabling direct testimonial sharing via Waku, adding one-click social media sharing, and implementing a burn function to allow users to remove testimonials. Additionally, I will integrate comprehensive smart contract tests to ensure security and reliability. For TNT, I will redesign the landing page and create user-friendly interfaces for creating, managing, and viewing TNTs, ensuring a clear and seamless experience. The deliverables include a fully functional VouchMe with these enhanced features: profiles, DID, Waku-based sharing, and shareable testimonial cards, along with a polished TNT UI for improved usability and accessibility.

Regolith's Wayland session still depends on GNOME for session startup and settings plumbing. That makes the COSMIC path awkward to install and harder to maintain. I want to build regolith-session-cosmic, an experimental Regolith session that starts through cosmic-session, runs Sway, and removes GNOME from the bootstrap path wherever the packaging and runtime audit shows it can go. The project also includes packaging the required COSMIC components through Regolith's Voulage pipeline so the session can be installed without depending on Pop!_OS repositories, finishing the remaining COSMIC-to-Sway keyboard integration in cosmolith, and validating the core session behavior: display, lock and idle, OSD, and settings persistence. The main deliverables are a bootable experimental COSMIC session for Regolith, packaged COSMIC runtime components with reproducible offline builds for the supported target distros, the remaining input integration work in cosmolith, and a documented validation matrix for the supported session path.

<p>Currently, the FreeBSD virtual terminal driver vt(4) does not support inputting CJK characters directly in the virtual terminal, so our project idea is to provide an environment that can run IME (input method editor) in the console.</p> <p>A common usage scenario is when a user wants to create a user with the user info description in Chinese during the system installation. Since the installation process must proceed in a virtual console, there is no way to open an IME window to input CJK characters.</p> <p>Another example is hosting a FreeBSD server on a cloud platform or a virtual machine. When one wants to do some simple admin routines on the machine or disconnects from the SSH session, he/she needs to do some configurations and keeps records or takes notes on the server. It's sometimes quite inconvenient for those CJK writing-system users to write texts in their most familiar languages.</p> <p>And we want to implement similar features of those IME (fcitx, ibus) but in the console.</p>

The objective is to develop an open-source, social voice platform that promotes collaboration and community engagement. To achieve this objective, the project architecture involves the development of a Flutter App and a NodeJs + Express Server, both powered by the Appwrite backend. By integrating Appwrite, the platform will benefit from its robust features and functionalities, replacing the previously planned Firebase backend. The Flutter App will serve as the primary user interface, offering a seamless and intuitive experience for users. The Node.js + Express Server will handle the backend logic, providing essential APIs for communication with the Appwrite backend. This integration will enable efficient management of rooms/spaces and storage of user-generated content. Additionally, the project will leverage Livekit, an open-source WebRTC Stack, to provide real-time audio communication infrastructure. By incorporating Livekit into the platform, users will have the means to engage in live discussions, enabling effective collaboration and fostering a sense of community.

My project mainly aims at testing files in the DML folder in the AMDGPU Linux kernel driver. The AMDGPU driver is the largest one in the kernel, with the DML component being particularly nasty as it doesn't adhere to many of the codebase's standards. KUnit is a framework for performing unit tests in a manageable way, without requiring basically any setup at all, and that would be used for testing the files under the DML component. Thus, my project encompasses (1) making patches for testing those files, as well as refactoring and documenting them. (2) Producing blog posts about my experiences with the community and the project, tutorials on what I learned, and explanations that are less technical than the proper documentation, so as to reach more people. (3) Generating proper coverage reports and documenting the process for doing so. (4) I also plan on contributing to KWorkflow and IGT GPU tools to some extent, so as to make the project, all around, more useful for the kernel's engineers and also integrable in their CI tools. Please check out the X.Org official project page: https://www.x.org/wiki/AMDGPU2022/

<p>GNU Taler is a new electronic online payment system which provides privacy for customers and accountability for merchants. To pay with Taler, customers install an electronic wallet on their device. Before the first payment, the desired currency must be added to the wallet's balance by some other means of payment. Once the wallet is charged, payments on websites take only one click, are never falsely rejected by fraud detection and do not pose any risk of phishing or identity theft. The goal of this project is to create a component that sits between the seller's frontend and the GNU Taler merchant backend. This component should have a web interface, where payment buttons or payment forms will be configured. Additional goals include inventory management, where the seller can configure the available stock for an item and will get notified when their stock runs low. Currently, to accept payments with GNU Taler, people have to write their own code. What I propose to change is this mechanism. People will be able to communicate with the merchant’s backend via a simple API.</p>

The Form Engine (FDE) is a specialized module for the OpenMRS healthcare platform. Its primary purpose is to enable a natural and efficient workflow for entering multiple pre-recorded forms at once. It is specifically designed for retrospective data entry tasks and is not intended for use in real-time, point-of-care clinical workflows. For comprehensive details and guidance, please refer to the official FDE Wiki [https://openmrs.atlassian.net/wiki/spaces/docs/pages/150962486/Key+O3+Repositories#Fast-Data-Entry] Documentation. Objectives of the GSoC project: This project will enhance the React Form Engine to natively support the requirements of the Fast Data Entry (FDE) feature. The improved engine will then be used by FDE to deliver faster and more efficient patient data entry. A key objective is to maintain full backwards compatibility with the existing Angular Form Engine, ensuring that current implementations continue to function smoothly while enabling a seamless transition to the React-based workflow.

The current Wikifile Transfer tool is constrained as a single-transfer utility. This creates highly repetitive manual work for contributors migrating non free media across sister projects, lacks durable upload history for failure diagnosis, and misses automated category localization. This project evolves the tool into a robust, job based batch transfer pipeline using Python, Flask, Celery, and React. The solution implements a durable MySQL state machine to track transfer lifecycles and ensure fault tolerance. Key deliverables include: 1) A batch upload API and React UI orchestrator, 2) A persistent History Dashboard with safe, targeted retry logic for failed jobs, 3) Automated, non destructive category localization using MediaWiki APIs, and 4) Comprehensive E2E and backend testing using Cypress and Pytest. This enhancement will eliminate manual bottlenecks and save thousands of volunteer hours across Indic language communities.

This project aims at enhancing the current DRAKVUF Sandbox by: - extracting common TTPs and displaying them in accordance with the MITTRE ATT&CK framework. - Identifying common malware families and extracting their configuration. - Extracting host and network based indicators of compromise (IoCs) using virtual machine introspection (VMI). - Enhancing the analytic pipeline by means of integrating it with FLARE's capa and floss tools. - Generating STIX objects for the newly extracted information. - Refactoring the Flask backend to be able to serve the output of the added tools. - Extending the React frontend to display all the newly-added information. This will be done by adding new modules to the sandbox' post processing script, and serving the output to the Flask backend by means of MinIO. Then, the Flask backend will be updated accordingly to provide endpoints for fetching the newly-added information. Finally, the React frontend will be updated to fetch data from the new version of the backend, and then displaying the information in a concise and informative manner for the malware analysts and threat researchers.

<p>Hyperspectral data is often used to determine what materials are present in a scene. Materials of interest could include roadways, vegetation, and specific targets (i.e. pollutants, hazardous materials, etc.). Trivially, each pixel of a hyperspectral image could be compared to a material database to determine the type of material making up the pixel. However, many hyperspectral imaging platforms have low resolution (&gt;5m per pixel) causing each pixel to be a mixture of several materials. The process of unmixing one of these 'mixed' pixels is called hyperspectral image unmixing or simply hyperspectral unmixing. There is an R package for this purposes however, the already implemented methods N-FINDR and VCA make one crucial assumption: they assume the presence of pure spectra of all components are available somewhere in the data. Obviously, while this assumption is reasonable for some applications, other applications exist where this assumption should not be relied on. ICE algorithm does not need to have pure component spectra for the endmembers in the presented data. Vignettes are also required to provide datailed explainitaions of algorithms and examples.</p>

<p>A changepoint is typically defined as a point in time where the distribution of a data-stream changes in a distinct manner, for example, typically one may look for changepoints in mean, and/or variance. Usually, this is performed in an unsupervised setting where we have no labelled examples of true changepoints. However, in practice, we usually have examples of periods of time where we know no changes should be present, or conversely where changes are expected to exist. When and where such information is available, we can potentially use this to aid our judgement of how to set complexity penalties in the changepoint estimation task, and thus, decide on an appropriate number of changepoints, a task which currently requires time-consuming parameter tuning by domain experts.</p> <p>In this particular project, we seek a simple visualization interface to support, i) human labeling of the data with the aid of several complementary measures on the data-stream such as as mean, trends, min, max, variance, etc. and ii) interactive exploration of the result space suggested by a changepoint detection algorithm. Eventually, any visualization will help communicating the data and respective decisions.</p>

<p>The FLOE Project site is currently using static, hand-coded HTML, while the Fluid Project site is currently built using DocPad. Even though hand-coded websites provide greater flexibility and customization options, it becomes tougher to update each HTML page as the site grows and changes are made. Docpad hasn’t been maintained since 2014. There are currently no active maintainers and its servers were taken down in 2018. There are plenty of bug reports due to its incompatibility with the newer version of ‘things’. Therefore we need to migrate both FLOE project and fluid project websites to a modern and well maintained Static Site Generator (as most of the content is static in nature and dynamic functionalities are not required). In addition to migrating to a modern static site generator, we need a Content Management System (for example Netlify CMS) for automating the process of updating/adding new content and an Automation Server (for example Jenkins) which helps with continuous integration and facilitates technical aspects of continuous delivery to automate the build process.</p>

Currently, there is no plugin in MariDB that can generate vector embeddings. If the user wishes to store vector embeddings for a document, they have to use a separate solution and then manually insert them into the database. This is inefficient and complicates related pipelines. The goal of this project is twofold: 1. Create a function plugin that implements a function to query OpenAI’s API to generate vector embeddings for a given input. 2. Implement a concept of “expensive” functions in the MariaDB server. When expensive functions are used in stored generated columns, they should not be recomputed whenever possible (for example during ALTER TABLE commands). Instead, the old values should be copied to the new table, just like with regular columns For this project to be successfully completed, the goal would be to create a PR to MariaDB’s server repository that: 1. Implements a function plugin for generating vector embeddings via API calls to OpenAI 2. Introduces and implements the concept of “expensive” functions, to avoid recomputing them whenever possible 3. Ensures the quality of the newly developed features is up to standards, via appropriate testing and documentation

<p>MapMint4ME (MM4ME) is an android application with the capability to record data on the field without internet connectivity. It gives users the facility to store any kind of alphanumeric data. Users are able to take photos, record their position, and view their current location on a map based on the configuration settings of their MapMint server. When the user returns back to a place with access to the internet, the recorded data can be uploaded back to the MapMint server. The aim of the project is to extend Augmented Reality (AR) support in the MapMint4ME application. The motivation for the project was to improve field data acquisition. AR Simulation module provides users capability to visualize 3D objects simulation in real world scenes. AR Area module allows users to export measured "AR measurements" into text format and which can be used for further analysis. MapMint4ME works without the internet too, so adding a GPS Satellite information module which gives detailed analysis about GPS Coordinates and GPS Satellite information improves the information gathering process and makes it more efficient.</p>

<p>Until recently, biodiversity data was scattered in different formats in natural history collections, survey reports, and in literature. In the last fifteen years, lot of efforts are being made to establish standards in the biodiversity database structure and to centralize the data for better accessibility. But the data gathered by such entities does not enforce strong data quality standards. These sources often tend to be prone to many flaws. Thus the data retrieved from centralized sources needs to go through a well formed quality-control process to be used in researches.</p> <p>Bdclean was created for that same purpose. So far we have been able to create numerous quality checks, work-flows, analyses and visualization functionalities in the taxonomical, spatial and temporal aspects. But all these remain as standalone components without much synchronization or connectivity. We propose to refine the overall data cleaning pipeline of bdclean and bring synergy to all the developed components as well as develop new important functionalities. At the end of this project, users will be able go through the quality control process in a very structured, intuitive and effective way.</p>

<p>While KDE Plasma on the desktop has robust calendar, tasks and journal management capabilities thanks to KOrganizer, Plasma Mobile currently lacks such capabilities. Existing applications are hampered by an absence of Akonadi integration, which would allow users to synchronise their data across applications in Plasma.</p> <p>The reason for this is that there is currently no existing Kirigami/QML implementation of a calendar that integrates with Akonadi. Carl Schwan recently began developing a proof-of-concept Kirigami ‘Kalendar’ application that supports a detailed month view and works with Akonadi calendars, but that is currently a work-in-progress project.</p> <p>This proposal puts forward a plan for improvements to this existing proof of concept that would help implement a fully-featured convergent calendar application for Plasma Mobile. This application would allow the user to view, create, modify, and delete calendar events with full Akonadi integration. This will be made possible by creating components written in QML and Kirigami that should allow for the creation of a visually-attractive and intuitive calendar view components that could be used in any Kirigami application.</p>

<p>The Taverna suite is consist of Taverna Engine, which handles both Taverna Workbench and the Taverna Server. Taverna Server is used to execute remote workflows and workbench provides GUI for create, run, import workflows.​This concept allow scientists who has limited knowledge in computing, limited technical resources to do complex process on Data. Also Taverna engine can be run on standard Windows, Linux or Mac OS. At the moment Taverna workflows are constructed using SCULF2 API. So, Tavera Engine can’t execute other workflows other than Taverna workflows. This is where CWL comes in ,The Common Workflow Language (CWL)​. ​CWL allows one description of a workflow to be run on multiple different platforms as long as they all support CWL. " CWL builds on technologies such as JSON­LD and Avro for data modeling and Docker for portable runtime environments ". The project mission is to bring CWL UI support in the Workbench which is a graphical way to build workflows and one of the steps in Add Common Workflow Language support to Taverna.</p>