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Pwndbg has supported LLDB internally since 2024, and in general availability as of the 2025.01.20 release. That support is, however, still in its infancy. Currently, all the test code in Pwndbg is still strongly tied to GDB, meaning that, aside from manual checks, there is nothing keeping changes to the codebase from silently breaking the LLDB subsystem. And, while there is support for attaching to and debugging programs running on platforms other than Linux, there are not as many features catering to the specifics of those platforms as there are for traditional Linux distributions. In particular, the process by which mobile debugging is done is still poorly documented, intricate, and not thoroughly tested. These shortcomings hold back LLDB Pwndbg from being as useful to its users as it can be.

gprMax, an open-source FDTD electromagnetic simulator used in published research across GPR infrastructure assessment, landmine detection, archaeology, glaciology, and NASA Mars exploration (RIMFAX/Perseverance), currently lacks comprehensive automated testing. This proposal delivers a three-tier test framework: (1) unit tests covering every core Python and Cython module in isolation; (2) integration tests verifying CPU, GPU, and MPI backend consistency and HDF5 I/O correctness; and (3) physics validation tests that compare simulation outputs against known analytical solutions including plane-wave propagation, PML reflection coefficient, energy conservation, and the reciprocity theorem. All tests run automatically on Ubuntu, Windows, and macOS via GitHub Actions on every push to devel. The end-of-summer deliverable is a production-grade, CI-integrated test suite ready for merger into the main gprMax repository.

<p>UNU.RAN is a performant C library with methods to sample from continuous, discrete, multivariate, and empirical distributions. It has been used by CERN in its ROOT project and an R interface called Runuran also exists. It will be a very nice addition to scipy.stats. The default method used by SciPy to sample from any distribution requires integrating the PDF and then numerically inverting the CDF. The implementation in SciPy is too slow to be relied on for practical purposes and custom methods for sampling random variates need to be implemented for the distributions in SciPy. In contrast UNU.RAN offers several methods (e.g. TDR, PINV) that offer very good performance to sample from wide classes of distributions.</p> <p>The goal of this project is to provide an object-oriented interface for methods present in UNU.RAN to sample from univariate continuous and discrete distributions using NumPy’s BitGenerator as the Uniform Random Number Sampler (URNG). My proposal also includes writing a comprehensive test and documentation suite with tutorials. If time permits, I also propose to write benchmarks for the added methods.</p>

This project aims to expand support for serious games within the Cormas platform, enabling easier interaction between players and environment, and autonomous agents. By extending Cormas with “playable” components the project will contribute to the development of a generic solution for designing, playing, and analyzing serious games. The planned development includes three phases, covering initial implementation, testing, and system optimization, with the ultimate goal of creating a first working prototype with a user interface accessible via a smartphone.

<p>JBoss Web Servers consists of Apache Tomcat 7, Apache Tomcat 8 and Apache Tomcat 8.5 servers. JBoss Core Services consists of Apache HTTP server and its dependencies. The project’s goal is to develop a tool that will scan and parse the upstream security advisories of Apache Tomcat and Apache HTTP Server along with their dependencies. This tool’s purpose is to populate Bugzilla by tracing down any new public CVE on every JWS and JBCS related upstream project. The tool will be built in Python3, scripts will be fired via Jenkins job and scripts will be running as a shell script.</p>

My project aims to enhance the PyAfipWs project under the Python Argentina (PyAr) organization, focusing on two main objectives: significantly improving test coverage, followed by implementing documentation using Sphinx and refining GitHub Actions workflows. Additionally, if time permits, I aim to build a simulator for key AFIP web services using Python and Flask. Problem Statement: PyAfipWs currently faces dependency conflicts, lacks extensive test coverage, comprehensive documentation, and efficient GitHub Actions workflows. These issues hinder development, reliability, and collaboration within the project. Proposed Solution: Dependency Conflict Resolution: I'll address conflicts with pysimplesoap to ensure seamless integration and operation. Test Coverage Enhancement: Will add comprehensive unit tests for key modules such as wsfev1.py, utils.py, padron.py, and others, aiming for close to 100% coverage. Documentation Implementation: Utilizing Sphinx, I will create detailed usage guides, API references, and contribution guidelines, ensuring clarity and accessibility for developers. GitHub Actions Workflow Improvement: l will also work to enhance workflows by adding linting, type checking, and test automation, along with implementing PR automation for reviewer assignment and auto-merging. Simulator Development (if time permits): A Flask-based simulator mimicking AFIP web service endpoints will be built, facilitating controlled testing environments and streamlining development. Deliverables: Resolve dependency conflicts and initial Pull Requests addressing the issue. Implementing Extensive unit tests for key modules, along with Pull Requests showcasing high test coverage. Completing Sphinx documentation framework integrated with GitHub Actions for automated updates. Enhancing GitHub Actions workflows covering linting, testing, and PR automation, reflected in Pull Requests.

<p>Dimensionality reduction techniques are useful methods that allow us to gain crucial insights about the given dataset. Unfortunately, such methods become computationally intensive when dealing with large scale dataset. To deal with complexity issues, one possible approach is to implement algorithms in a distributed fashion. Ideally, users of LiberTEM can benefit from implementation of these algorithms that they can run through a simple pipeline called User-defined functions, which allow the users to run functions with their desired functionality without having to worry about parallelization, which is done under the hood by LiberTEM. Therefore, my project will be concerning both the distributed implementation of a dimensionality reduction method as well as improving on the User-defined functions framework.</p>

<p>The CFC mobile application (Educult) project aims to add some specific features to the Mobile Application which will allow students from all over the world to develop their skills and help each other more supportively. The aim of the project is to implement a classroom and doubt forum functionality so that students can gain knowledge by helping others by providing an answer to the doubt questions in the doubt forum and by enrolling in various programming Courses.</p>

<p>I will introduce methods to pgmpy to select Bayesian models based on data sets. First, I will implement support for basic score-based and constraint-based structure learning. Second, I will add common enhancements to the score-based approach, including local score computation + memoization and tabu lists. Finally, I will implement the MMHC algorithm, which combines the score-based and the constraint-based method.</p> <p>I believe that structure learning is <em>the</em> missing feature in pgmpy at the moment.</p>

<p>A Collision Detection system that is an alternate to Bounding Box and Precision Collision Systems. Used In Enigma to detect collisions in a game between objects and sprites.</p>

cve-bin-tool currently uses the National Vulnerability Database as its only source of vulnerabilities. However not all vulnerabilities are captured in the N.V.D and there are other sources which may also be useful in understanding the vulnerability status of a product. This project aims to add data sources for vulnerabilities, by creating a framework that allows for the use of multiple databases, and minimises duplicate reporting of vulnerabilities.

The main objective of this project is to update the existing UI system without impacting the current functionality, while also creating tests for each new change to ensure reliability. The project will focus on several key areas including Layout Management, Creating Scrollbar as an individual element, Completing Tree UI implementation, Spin Box Implementation, and Improving the Border Implementation. Additionally, the project will include fixing multiple UI elements such as ComboBox, TabUI, Filedialog, and Tree UI. Furthermore, the project will involve exploring a new framework with the existing UI element, which will enable selecting them together. Overall, the project aims to enhance the UI system while ensuring that it remains stable and reliable.

This project extends the open-source Cormas simulation platform by integrating a computer vision system that allows real-world interaction with agent-based models. The goal is to enable users to turn Cormas simulations into interactive, game-like experiences by detecting and tracking physical objects (such as game pieces) on a board and linking them to simulation agents. To achieve this, I will develop a lightweight Python-based vision pipeline using YOLO for object detection and tracking, then build a JSON-based interface to communicate with Cormas in real time. On the Pharo/Cormas side, I will implement the necessary APIs and tools to interpret the visual data and control agent behavior accordingly. Deliverables include: - A reusable computer vision module with manual calibration tools. - A JSON-based interface for real-time communication between Python and Pharo. - Integration into Cormas as a plugin or extension. - Documentation and examples for creating interactive physical games with Cormas. - All work will be contributed to the open-source Cormas repository and designed to support future projects involving physical interaction with simulations.

<p>Lua is a tiny language that can do a lot. As a scripting language, it can be embedded into larger projects, used in IoT devices for its lightweight nature and implemented as a user-friendly way to get help from a wide variety of coders thanks to its simple interface and flexible design. It can support both functional and object-oriented approaches, but the amount of support it can supply is limited. Lua has no built-in support for advanced file manipulation, directory traversal, process creation or piping. The goal of Apolo, and my Google Summer of Code project is to build a library that adds these core functions to lua.</p> <p>The major goals of this project are:</p> <ul> <li>To broaden Apolo's current library of commands</li> <li>To add piping and IO redirecting to Apolo</li> <li>To include flexible management of background processes</li> <li>To build a wide variety of tests to ensure that all of lua's functions work as intended</li> </ul>

I will migrate the eos-icons.com website to TypeScript and Next.js and also implement proper file management and state management in the project ensuring a cleaner and more scalable codebase. I will also do certain changes to improve the website's UI. I plan to add a navigable menu to the landing page showing all categories of the icons. Also, adding a new and improved HowTo section which contains information about the usage of icons in all places like React, Vue2/3, & HTML, and moving it into a side drawer panel along with the icon editor. I plan to add unit tests by using Jest & React-testing-library, and end-to-end(e2e) testing with Cypress.js. I will integrate these tests with Github CI/CD pipelines and ensure proper development workflow using es-lint, prettier and husky.

<p>Use shlex to parse Dockerfile RUN instruction commands. On looking at the type of parsing needed for full shell scripts embedded in the run command, we may need to develop a shell script parser to catch all places where software could have been installed.</p>

During my project, I will successfully implement a module for Correlation-Tensor Magnetic Resonance Imaging (CT-MRI) in DIPY, which provides advanced diffusion acquisition techniques for characterizing healthy and pathological brain alterations. This implementation is significant because DIPY previously provided limited modules to process this type of diffusion MRI data. By expanding DIPY's capabilities, researchers and clinicians can now gain unique insights into tissue microstructure, making DIPY a more complete and useful tool for a wider range of MRI data users.

<p>The convolution operation is a core part of the Theano code base. Several variants of the basic convolution operation exist, and more are always being introduced through research papers. Adding new types of convolution operations built-in to Theano will minimise coding coding effort and increase efficiency.</p> <p>The new features to be added are :</p> <ol> <li>Grouped Convolutions</li> <li>Locally connected (unshared) Convolutions</li> <li>Dilated Causal Convolutions</li> <li>Circular Padding for Convolution</li> </ol> <p>In addition to these, the 'meta-optimizer' will be finished.</p>

<p>To introduce open-source IP for components such as an NASTI slave memory mapped DDRx controller.</p>

<p>The way PySAL users interact with geographic data is directly tied to manipulating unlabeled Numpy arrays. With Pandas readily available for most users, this is tedious fait accompli for users, since the core data model for PySAL's API was designed before Pandas existed. While newer projects have attempted to extend Pandas for geographic data, those packages have difficult-to-install dependencies that make them inaccessible for many end users, and make design decisions which the author thinks are somewhat suboptimal for PySAL's use case. Therefore, I propose to specify and implement a tabular spatial data model in Python leveraging Pandas dataframes directly, rather than through subclassing. This follows in the spatialite idea that spatial data is first data, then spatial. Thus, tooling must focus on enabling spatial operations directly on Pandas dataframes by exploiting column dtype information and dispatching appropriately, instead of a subclassing approach. In the end, this project would result in a simpler data model for PySAL.</p>

<p>CVE Binary Tool is running on Linux systems now by taking advantages of bash commands like file and string. Since file and string have already been naively implemented, it is ideal to extend the tool to other operating systems like Windows.</p>

<p>The project aims to parse numbers written in natural language. This needs to be done while keeping incorporation with dateparser and price-parser in mind. We also need to provide support for multiple languages.</p>

This project aims to implement a methodological approach to further improve the visualization of DTI tensors and HARDI ODFs glyphs by using well-established techniques in the field of computer graphics such as billboarding, signed distance functions (SDFs), and ray marching. In addition, it is proposed to visualize the uncertainty associated with the DTI model.