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<p>The purpose of this project is to create functions which convert a wxMaxima worksheet to Texinfo. The functions to convert a worksheet will be written in Common Lisp, which is the implementation language for Maxima itself.</p>

<p>A web app that does automatic labeling of radiology images. The backend is in Django and tensorflow-serving whereas the frontend is in Reactjs. The web app will help make dataset bigger and better in radiology domain. The user will be able to visualise deep learning model output such as bounding box and segmentation and modify them in real time to enlarge the dataset and intern further improve the DL models.</p>

<p>Matrix-ircd is a work-in-progress bridge between matrix and IRC. The majority of the code was written in mid 2017 and does not use modern Rust asynchronous programming techniques, namely async/await. Instead, the project currently uses older asynchronous-styled code using manual combinators that have historically made maintaining (and writing) async code very difficult. This project will migrate the older futures code from 0.1.25 to std::future::Future and port all functions to using the newly introduced async/await syntax.</p>

<p>Scylla is an open-source distributed NoSQL data store. It was designed to achieve significantly higher throughputs and lower latencies. Apache Gora is a Object to datastore mapping data persistence framework. This proposal is about to support Scylla DB in Apache Gora</p>

<p>Monumento application at a production ready state for the Android platform. Initially, I had envisioned the project with Flutter completely for cross-platform support as it suited the project idea. But along my way, working out the details and developing the sample prototype for the project idea, I got to know that ‘Landmark Detection’ from the Firebase Vision API in particular wasn’t yet supported with Flutter. To make it work, I needed to write quite good amount of native code for both Android and iOS platforms. If we were use to use on-device processing, that would make the app itself heavy since we would need to train our own custom ML model with a lot of data as an user can try to detect a monument from any angle possible. All these points, got me to reject the idea of Flutter for the complete app development. But at the same time, Flutter really has some great benefits such as extremely good UX/UI development, smooth performance, faster productive development, multiplatform support for future using same codebase. So I thought why not develop a native Android application and integrate Flutter with it for the frontend work and some backend work binding as well if needed.</p>

<p>This project will focus on improving the unit tests as well as creating a more complete unit test coverage for p5.js. This project would also include creating a tutorial for new contributors, covering the basics of unit testing for p5.js and how to write and add them.</p>

<p>In this project, my work will mainly focus on working as a full stack developer for Amahi. I will implement new API endpoints which can be consumed by the Amahi mobile applications. I will convert complete front-end to responsive design using bootstrap-4. There is also a need for proper documentation of Amahi backend APIs. Good documentation will serve as single point reference for all present developers and future contributors, which thus helps in expanding the Amahi community. Also, I will develop new plugins for Amahi 11 as per requirements.</p> <p>This project can be mainly divided into 3 parts:-</p> <p>Part 1: Making front-end responsive using Bootstrap-4 and Upgrading Rails app to version 5.2</p> <p>Part 2: Developing new API endpoints and enhancing Amahi apps system.</p> <p>Part 3: Developing new plugins for Amahi and providing support to existing plugins.</p>

<p>This proposal is made to improve the current LibreCAD 3 plugin system, by adding new features like custom entities or read and write files, to allow more complex plugins to be created. This is also containing rendering improvements, with unit tests and optimizations.</p>

<p>I hope to develop a package, ParallelGraphs, that enables the analysis and manipulation of massive graphs in a distributed environment. The package will adhere to two separate computing models; The first being the vertex centric Pregel model that relies on Bulk Synchronous Parallel infrastructure. The second model is a combinatorial approach that involves matrix operations such multiplication and vector indexing on distributed sparse matrices. ParallelGraphs will enable users to process graphs using sequential algorithms on smaller graphs. This will be accomplished by providing compatibility with LightGraphs.jl. The package will also experiment will CPU/GPU parallel algorithms with an aim of unifying all graph computation models in a single package.</p>

<p>The Google Login Authenticator Module adds an extra layer of security to the Drupal login architecture. The plugin no longer works in Drupal 8 because of the architecture change between Drupal 7 and 8, Drupal 8 being more Symfony rich. The aim of this project is to port the the module over to Drupal 8 as a plugin for Two Factor Authentication Module which works in Drupal 8. Additional functionality like checking for time discrepancy will also be added in this port.</p>

<p><strong>Rule induction</strong> from examples is recognised as a fundamental component of many machine learning systems. We propose to implement supervised rule induction algorithms and rule-based classification methods, established on a more general framework of replaceable individual components that can be fine-tuned to specific needs. For this purpose, the separate-and-conquer (also covering) strategy will be purposed.</p> <p>The addition to the Orange software suite should benefit both novice and expert users looking to advance their knowledge in a particular area of study through a better understanding of given predictions and underlying argumentation.</p>

The Problem When developers write custom webpack plugins, they need reliable API documentation. Currently, the generated docs drop critical function signatures, returning unhelpful {object} types instead of actual parameters. The new documentation infrastructure is also missing webpack's visual identity, essential navigation pages, and a safe review process for automated updates. The Solution I will complete the TypeDoc to doc-kit pipeline to finalize webpack's API documentation redesign. First, I will fix the AST renderers to properly map TypeScript function overloads so no signature data is lost. Then, I will configure the doc-kit UI with webpack brand tokens and build custom React components to cleanly display complex hooks. Finally, I will secure the CI/CD workflow and port the core website pages. Deliverables: Accurate API Output: A fixed TypeDoc theme that correctly renders function parameters and TypeScript overloads. Visual Identity: A custom doc-kit configuration with webpack branding and new React components (HookSignature, PluginInterface, DeprecationBanner). Automated CI/CD: A GitHub Actions workflow that safely opens pull requests for new docs instead of pushing directly. Core Navigation: Fully ported Home, Download, Guides, and About pages.

This project aims to enhance the Mifos X Web App by introducing a comprehensive, three-layered dashboard architecture that provides real-time insights into financial performance, inclusion metrics, and client behavior. By integrating a flexible Dashboard Management framework, the platform will enable decision-makers and analysts to access actionable insights directly within the system, reducing dependency on external tools and improving data-driven decision-making.

The proposed implementation is planned to include building the camera-based warning system, starting from the low-level driver implementation, creating necessary logic inside PipeWire nodes, training AI models for objects detection, and designing the suitable User Interface using Flutter. The final product would be a Warning System that leverages implemented inference models (e.g. Object Detection) and accordingly issues a visual alert in response to critical conditions, such as the detection of an object in the blind spot, or a very low time-to-collision.

At the end of Moore’s law, building domain-specific computer architectures is considered the next step in improving program efficiency. Hls4ml is an open-source project that translates machine learning (ML) models to high-level synthesis (HLS) code for deployment on hardware accelerators. The idea stems from the high-energy physics community at CERN. Google XLS (Accelerated Hardware Synthesis) is a novel framework that implements an HLS toolchain to produce synthesizable code for FPGA and ASIC applications. Thus, XLS can be integrated as one of the backends in hls4ml for transforming ML models into synthesizable code. The goal of this project is to integrate an XLS-based backend for the hls4ml framework. In doing so, hls4ml benefits from improved vendor compatibility and portability while also offering the potential to increase hardware efficiency. During development, we will benchmark performance and resource utilization against the existing backends (e.g., AMD Vivado, Intel Quartus). Furthermore, XLS provides a domain-specific language (DSL) called DSLX and an optimized compiler that can reduce compilation time. The expected results are an XLS backend prototype and benchmarks performed on various metrics for the synthesized code. Everything will be documented to facilitate use and future developments.

The project aims to develop a comprehensive library of predefined force and torque models. These models are crucial for a wide range of scenarios in computational physics and engineering simulations. Traditionally, users have had to manually generate various forces and torques, a process that is often complex and error-prone. Our library will streamline this process by including commonly used models such as the Duffing Spring, Coulomb Friction, Hill-Type Muscle, and Aerodynamic Forces. This will greatly enhance the functionality and efficiency of SymPy.

Web developers have access to a wide array of tools to track web performance and discover ways to optimize their programs. However, that level of support is nearly nonexistent for extension developers. Chrome extensions are a vital part of the unique Chrome experience, making it customizable and tailored to the needs and preferences of each and every user. Thus, this project proposes 3 new features to provide extension developers a way to understand the impact of extensions on web performance, computer resources, and how to optimize their code to work with websites as efficiently as possible.

SkyWalking BanyanDB is an observability database designed to ingest, analyze, and store metrics, tracing, and logging data. The goal of this project is to implement a built-in monitoring feature for the SkyWalking BanyanDB cluster. This will be achieved by developing database functionalities to store and extract node metrics in the cluster and by creating an overview page in the Web App to display the nodes status.

The Music Listening History Dataset is pretty damn impressive; It contains ~27 billion logs of real-world data from last.fm scrobbles distributed into 18 chunks summing up to ~611.39 GB of compressed text files. This results in 583k users, 555k unique artists, 900k albums, and 7M tracks. Here each scrobble is represented in the following format: timestamp, artist-MBID, release-MBID, recording-MBID. (Source: https://simssa.ca/assets/files/gabriel-MLHD-ismir2017.pdf) Unfortunately, this data has some significant fallbacks due to last.fm’s out-of-date matching algorithms with the MusicBrainz DB, resulting in frequent mismatches & errors in the recording-MBID data, affecting the quality of the available dataset. Overall, the goal of this project is to create an updated version of the MLHD in the same format as the original, but with incorrect data resolved and invalid data removed.

At the moment, Unikraft can leverage a single processor only. While there can be multiple threads (i.e., multiprogramming), no two activities actually run at the same time (i.e., multiprocessing). Unikraft introduces support for symmetric multiprocessing (SMP), thereby allowing it to execute code on all processors and cores of a system concurrently. This creates new challenges for synchronizing access to shared resources. On a single-processor machine synchronization primitives like mutexes and semaphores can consider interrupts as the only source of parallel execution. It is thus sufficient to protect a critical section of code by temporarily disabling interrupts. With SMP, however, this is no longer sufficient as other processors may access shared resources at any time. The project will involve studying locking mechanisms used in modern kernels like Linux, FreeBSD, etc, improving the existing locking mechanisms in unikraft, and implementing new mechanisms such as read-write. The important part of the project will be identifying the critical sections present in the kernel and protecting these critical sections.

<p>Adding new layers (Upsample, Group Normalization and ChannelShuffle) to the ANN module of mlpack. Improve the speed of pooling operations of max, mean and LP pooling layers. Improve the speed of un-pooling operation of mean pooling layers.</p>

<p>The project's aim is to develop a chatbot that can help people create spec documents without knowing the specification.To get started with, the bot will consume the spec, JSON schema and serves the user as an expert. So based on a set of questions and answers it will generate an AsyncApi spec document according to the use cases. The bot will be able to build its knowledge really fast, thanks to Wit.AI NLP functionality. We also plan to build an integration functionality which will allow the bot to be available either to the CLI or UI by using the integration functionality.</p>

<p>The objective of this project is to port BaseJump STL to FuseSoC so that new projects can directly reuse these hand-optimized IP cores rather than starting from scratch. BaseJump STL has the hardware primitives defined in the form of SystemVerilog modules. FuseSoC makes use of core files that reference the provider, file sets and default targets allowing for the reuse of IP cores in the process of creating, building and simulating SoC solutions. The major tasks involved in this project are creating cores for all the hardware models in BaseJump STL and porting the testing infrastructure.</p>