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The project aims to build a real-time monitoring dashboard and terminal UI (TUI) for Crankshaft, a genomics workflow engine. Using gRPC for streaming and Ratatui for visualization, it will allow users to view live task statuses and logs directly in the terminal. The system will instrument task events (Queued, Started, Logs, Completed, Failed) without impacting performance and stream them efficiently. A stretch goal includes enabling job control (e.g., cancel tasks) through the TUI. The project will significantly improve observability and debugging in Crankshaft workflows.

DBpedia is a collaborative initiative focused on extracting structured information from Wikipedia and presenting it as Linked Open Data. While semantic web resourceful languages like English and German have dedicated DBpedia chapters, there is a need for more representation of low-resourced languages like Amharic. This project endeavors to create an Amharic DBpedia Chapter, aiming to be the first sub-Saharan African language to join the internationalization efforts of DBpedia. Our goal is to extend the existing extractor framework for Amharic to allow knowledge graph extraction from Amharic Wikipedia. We will make the extracted knowledge graph queryable and available to end users via a web page .

As ABMs are simulations and often have phase transitions (periods of rapid change to new stable states), being able to go back in time and replay key results would be a great addition to Mesa. Critically, no computation would be needed as the results are stored. This project aims to implement caching for Mesa, with options for users to select the critical results, so that the critical parts of the simulation can be replayed. For long simulations, they can access interesting points in the simulation quickly and easily.

Although Apache Custos is currently deployed on a Kubernetes cluster - its size and complexity may not be ideal for a relatively small application like Custos. This project aims to develop an alternative deployment architecture for Custos that aims to simplify its deployment and backup process.

<p>Tarantool’s team has released a new C++ connector, which is based on compile-time MsgPack encoder/decoder. Reaching the limits of C++, this library is really state of the art, eliminating any possible overheads. The project idea aims related to it is: Complete providing support of Tarantool features (pushes and SQL statements)</p>

<p>The Buddhist Digital Resource Center has a well-maintained database. The entities present on Wikidata often lack a label in Tibetan language. By adding a Tibetan label during the import we can dramatically improve the usability of both Google and Wikidata for Tibetan natives and community. The project will focus on various aspects of data engineering to provide the best solution to help the Tibetan community.</p> <p>Wikidata has proved to be the most efficient open source of knowledge having structured data which is extracted directly by google and affects the google search result directly. Knowledge Graphs pull in data from a variety of sources such as Wikidata. This data is used to understand user search intent and to answer search queries. The project will focus on importing data of places and persons from BDRC database, as well as the addition of new data on existing Wikidata records from BDRC. All the shortcomings present in the database will be dealt with and improved.</p> <p>Upon successful completion of this project: The Tibetan community members will be able to get better search results. The labels added will result in efficient use of Wikidata for the members of the community.</p>

<p>This project would involve adapting pathology annotation tools to prefer following edges in the base image when close and also add other smart techniques that will make it look perfect.</p> <p>With the current technology, it is possible to aid the process of annotation with computers. This tool will help the user to draw almost 100% accurate drawing/ annotation free-hand with the help of CV (namely, Canny Edge Detection and other algorithms). This feature is similar to the magnetic lasso option in Photoshop.</p> <p>This project would also extend other annotation related facilities.</p>

<p>The mlpack library boasts an extensive set of objective optimizers, almost all of which focus on single-objective problems. Previous works by Sayan Goswami paved the way for multiobjective optimizers. This was further complemented by the addition of Schaffer-N1 and Fonseca-Flemming test suites.</p> <p>This project aims to add optimizers, expand the test framework and make the library more accepting of multi-objective problems in general. Building from existing work, the end product would enrich the MOO module and package these features to fit into our codebase providing familiar public-interface.</p>

<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>

<p>The current HPX Parallel Algorithms do no support vectorization. This project aims at providing vectorization support to Parallel Algorithms with OpenMP as backend.</p>

<p>OWASP Honeypot : The idea is to: -Test all the modules in the code (currently 4)and if there are bugs found to fix them. -After testing the entire codebase, would remove the duplicated code. -Then I would like to work on packet tracing,currently only the information like IP Address and Country is being extracted from the packets and it's not that useful to classify it as high risk or not.So I would discuss with my mentor as to what all needs to be extracted from the packets and would store in the database.</p> <ul> <li>After that I would like to start with the WEB part of the project,to write new routes and add new visualizations.(this part I am most comfortable with because I already have some industry experience with web) -Then would add new visualizations for the extracted details and also some logic to classify the record in the database as high risk ip because now we would have more information from the packet and not limited to IP Address and country -Also in between testing modules and removing duplicated codes I would work on PR reviews and issues (if there are any raised or found) -Finally at the end would document all the work I did in the wiki section of the project.</li> </ul>

<p>The Gazebo project has a vast set of learning resources in the form of a documentation section, Gazebo tutorials, the QA website, ROS answers and other blogs that developers can refer to for any assistance. All of this information is distributed across the internet with some links joining each other. The aim of this project is to bring all the learning material under one webpage in the form of a documentation index that contains links to the content where the respective information is hosted. Almost all relevant information in such documentation indexes is just a page quick-search away. Such a platform can act as a one-stop place to get all pertinent information about Gazebo. A roadmap has been provided towards the end of the proposal to share information about how the project can be approached and developed in a planned fashion.</p>

<p>This project aims to make OODT deployment and configuration management simple through implementation of a docker-based deployment tool that integrates with the existing distributed configuration management tool in OODT. The target is to first containerize the major components in OODT (file manager, resource manager and workflow manager) using Docker. Each component will then have its own Dockerfile and Maven build execution using the dockerfile-maven plugin. Next, the initial OODT deployment tool will be created using Kubernetes. Once this is done, the existing OODT Distributed Configuration Management feature will be integrated with this tool to handle dynamic configuration management. As a later step, the deployment could be upgraded to work with Helm or KNative.</p> <p>Through this project, OODT will gain the ability to seamlessly manage OODT components regardless of the deployment environment. Technologies including Docker, Kubernetes and Maven will be required to carry out this project.</p>

<p><strong>MapMint4ME</strong> is an android application which allows its users to record alphanumeric data, photo and GPS locations. The data can be recorded even when there is no internet connectivity. When the user returns to a location with the internet connectivity, the data is uploaded to the database. MapMint4ME is very close to <a href="http://mapmint.github.io/userguide-fr/index.html" target="_blank">MapMint</a> web software which is built on the <strong>ZOO-Project</strong>. The <a href="http://zoo-project.org" target="_blank">ZOO-Project</a> is an SDI manager providing the capability to built map and web applications.</p> <p>In this project, I plan to <strong>add audio and video data recording</strong> facility to MapMint4ME. The main idea is to <strong>include SOS input support to MapMint4ME</strong> which will <strong>enable recording of sensor data directly within the app</strong>. The sensor data will be obtained from the sensors embedded on the Android platform. This project will help lots of GIS users, geologists, geographers <em>et cetera</em> who wish to retrieve data from remote areas (where it is not possible to reach) by accessing sensor data directly through the app. Certainly, more functions with extended capabilities could be added in future. I believe that by following a strict timeline as mentioned in the proposal, I will be able to finish the project successfully.</p>

<p>GCompris is an educational software suite comprising of numerous activities for children aged 2 to 10. My project would be to complete the already started activities (oware, computer parts, play piano and note names). These activities will be useful to GCompris which will make it more resourceful, usable and popular among young children and teachers.</p>

<p>Minuet is an application that allows users to play their favorite MIDI files. It helps novices and experts with learning more about music education. Currently, Minuet is available only as a Desktop Application. This project aims to make a mobile application with the same functionalities as the Desktop version.</p>

<p>This proposal seeks to add a visual lane editor to iD. This lane editor must fit in <code>350px</code> wide side pane of the editor and should be similar to the restrictions editor in terms of functionality and UI. The lane editor should be context sensitive and should automatically get invoked whenever a user selects a highway. It should also provide a simple and intuitive way to add or remove lanes, adjust the type of lanes (eg. forward/ reverse, turn left, right and common conditional lanes like ‘bus’ or ‘HOV’. It should also be able to interpret the existing OSM data for the lane and must conform to the guidelines established by OSM.</p>

This project adds OAuth 2.0 support to the Jenkins email-ext plugin, addressing the deprecation of basic SMTP authentication by providers like Microsoft and Google. It integrates with the oauth-credentials framework to securely obtain access tokens and enables XOAUTH2-based SMTP authentication using Jakarta Mail. The solution is provider-agnostic, allowing compatibility with multiple email services. Deliverables include OAuth integration, UI updates for configuration and credential selection, a test email feature, comprehensive test coverage, and full documentation, while maintaining backward compatibility with existing authentication methods.

This project aims to enhance fulldome content rendering in ossia score by integrating advanced ray-tracing render pipelines. Traditional fulldome rendering requires multiple render passes, which leads to high computational overhead. By leveraging ray-tracing techniques on modern graphics APIs (e.g. DirectX, Vulkan), this project will enable single-pass fulldome rendering, significantly improving efficiency and render quality. The project will explore and conduct comparative analysis on various rendering pipelines and graphics APIs, including HDR, meshlet-based, volumetric, and SDF-based techniques, with a focus on their suitability and performance for fulldome rendering within ossia score. Final Deliverables: [1] Integration of the ray-tracing fulldome rendering pipeline into ossia score for better performance and quality. [2] Systematic comparison and analysis of different ray-tracing fulldome rendering pipelines. [3] Open-source release of the codebase and benchmarking results of Deliverable [2]. [4] Documentation of rendering pipeline design, challenges faced, and solutions implemented. Mentors: Jean-Michaël Celerier and Manuel Bolduc

Weighted ensemble (WE) simulations enhance the exploration of a molecular system’s phase space by running numerous short, parallel simulations and selectively replicating trajectories that enter under-sampled regions. This approach improves sampling efficiency compared to traditional long, continuous simulations. However, a potential bottleneck arises from the need to analyze each iteration’s trajectories before starting the next round of simulations. Typically, this involves loading and processing trajectory data after each iteration, which can introduce significant delays due to disk I/O. To address this issue, my project focuses on integrating trajectory streaming into WESTPA using MDAnalysis. Instead of waiting for full trajectories to be written and reloaded for analysis, this implementation will enable real-time, on-the-fly analysis of simulation data as it is generated. By eliminating post-processing delays, this modification will streamline the weighted ensemble workflow.

The goal is to implement a functional GNSS receiver that uses INS measurements to improve the position, velocity and time solutions. This will advance research on sensor fusion, enabling reliable high-accuracy navigation solutions in challenging environments such as the urban cannon. I will leverage state-of-the-art AI techniques, such as Bayesian filters (e.g., Kalman filters and particle filters), graph neural networks (GNNs), and transformers for spatiotemporal data modeling. The end product will consist of a functional prototype receiver which will implement the necessary algorithms.

PyTensor is a Python library that allows you to define, optimize/rewrite, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is based on Theano and integrates with NumPy, C/JAX/Numba, and has a tight API documentation. The project aims to integrate PyTorch as a new backend for PyMC, this would bridge the gap between the PyTorch and PyMC ecosystems. This integration will provide users with access to GPU hardware, enhance performance. Key deliverables include implementing a PyTorch Linker, creating dispatch functions for type conversion, and adding support for linear algebra, element-wise, and sparse operations in PyTorch. The goal is to establish a foundation for future contributions and foster community engagement, ultimately improving the accessibility and functionality of PyMC within the PyTorch ecosystem.

Problem: Traditional anomaly detection systems often struggle with limited data scenarios and can lack the ability to explain their detections. Addressing these challenges is crucial for advancing the field and expanding its application range. Solution: This project proposes the integration of Vision Language Models (VLMs) into the anomalib framework using OpenAI's API. The goal is to enhance anomaly detection capabilities through few-shot and zero-shot learning techniques. This integration involves creating a new model class, 'LVM_openAI', as a subclass of 'AnomalyModule', and incorporating unique parameters like API_key and custom prompts. The model will leverage OpenAI’s API for processing and interpreting data, and will handle error catching, response parsing, and anomaly detection. Methodology: The project will start with a deep dive into Anomalib and OpenAI’s ChatGPT API, followed by a design and prototyping phase. The development will be in phases, with each phase focusing on different aspects of the integration, including testing with various datasets (e.g., MVTec AD and LOCO) and comparing against other models. Additionally, a comprehensive documentation, including a detailed notebook for setting up and using the model, will be created. Deliverables: An integrated VLM-based anomaly detection system within anomalib. Complete documentation and a set of experiments demonstrating the efficacy of the approach. An optional user interface for enhanced model interaction.