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

<p>The idea of this project is to use Haskell as another way of reasoning about the typing rules of Typed Lua. Project involves implementing parser and typechecker for Typed Lua Core - Typed Lua dialect which does not contain any syntactic sugar what simplifies reasoning about typing rules.</p>

<p>Python 3.5 has syntax features that are unavailable in the previous versions. They grant improved support for asynchronous programming (PEP 492), matrix multiplications (PEP 465) and the unpacking operator (PEP 448). The goal of this project is to implement these features in PyPy3. This way support for Python 3 code gets improved. Implementing coroutines will be the main feature of this project, as this feature offers more new methods than the other features. PEP 465 shows that Matrix multiplication is already used more often than bitwise operations in numeric libraries. Also, having coroutines with async and await syntax as well as enhanced unpacking options, the possibility that Python 3.5 syntax will be used in upcoming code rises. Therefore, an implementation of these features in PyPy3 will definitely increase compatibility to future Python 3 programs. In order to support PyPy’s Python 3 compatibility even further, other new features of Python 3.5 and 3.4 will be implemented after finishing the three PEPs if there is enough time, starting with the enum module of 3.4 and continuing with PEP 461 to allow support of the % formatting operator to bytes and bytearrays.</p>

<p>Pallene is a statically typed sister of the Lua programming language. This proposal aims to add support for Closures in Pallene.</p> <p>Closures are functions that are capable of capturing variables from outer scopes and retain references to them even after their scope has been exited. The addition of closures and higher order functions will allow programmers to take a functional approach when authoring libraries and scripts.</p>

<p>DIPY is an open source software mainly focusing on diffusion Magnetic Resonance Imaging (dMRI) analysis. For nonrigid image registration, it implements a Symmetric Normalization framework. The major goal of this framework is to calculate an affine or deffeomorphic transformation to register two 2D images or 3D volumes. As a well-known problem, registration is a non-convex optimization problem. It is very important to initialize as close to the solution as possible. So in order to avoid getting stuck at local optima, it uses a multi-resolution strategy by building a Gaussian Pyramid. That is to say we can register our images in as many stages as we want, providing previous results as initialization for the next. As this way, we need to do a lot of iterations, thus it slow down the optimization process. To speed up the calculation, one solution is to convert to a parallel computing algorithm. The OpenMP API supports multi-platform shared-memory parallel programming in C/C++ and Fortran. So in this project, I will implement a multi-thread algorithm using OpenMP to improve the performance of nonrigid image registration in DIPY.</p>

<p>OpenLex is a legal management system developed in web2py this system has the advantage that it can be run on any operating system thanks to the fact that it is a web browser application, however, for this reason it represents mistrust among users due to ignorance about the security and system integrity because this is used by a browser and not as a desktop application. In order to satisfy these users, the development of the installer in the different versions of linux, windows and apple is proposed in the project issues.</p>

<p>Memory access is one of the most important operation done in modern processors thus it plays an important role in implementation of trusted execution environment. Recent secure processors and trusted environment encrypt memory contents to protect those important memory operations. However, sometimes just encrypting data is not enough since there are intelligent attackers who can see where the memory access goes to and infer where the real important data lies in. Those important data includes the private data, key, or OS fingerprint and else. These kind of attacks lead to concept of “memory obliviousness” to secure memory address itself. ORAM refers to Oblivious memory which can leverage obliviousness using specialized data structures and obfuscate memory access so even an attacker see or probe the memory access itself, he never learns about where important data lies on or even where the memory requests are headed to. Initially proposed by Goldreich and Ostrovsky, ORAM obfuscates memory access using random permutation, shuffling and encryption of contents. In this proposal, I’ll implement ORAM interface to support obliviousness on RISC-V’s trusted execution environment.</p>

The aim of this project is to extend LPython's OOP capabilities to enable the porting of CPython modules. The proposal is divided into three main parts: improving the utility of classes in LPython using general imports and dunder methods, porting CPython's re and json modules and addressing some miscellaneous goals like adding advanced data structures and improving string attributes.

<p>When astronauts travels to Mars, or even other planets, they will be exposed to a number of hazards e.g. radiation, microbes in the spacecraft, planetary surface toxic dust. This project mainly revolves around configuring biometric signal sensors.</p> <p>The project has an initial phase of surveying the commercial sensors available, and selecting one, or many, after extensive research. The sensors are selected such that they read the biometrics like Heart Rate, Accelerometer data, Respiration rate, body temperature, Pulsioximetry data,Respiratory Volume, ECG, etc.</p> <p>The second phase involves developing the Device Server itself in Tango, for these selected sensors. This project is part of the current studies on the simulation of Astronaut’s Health Monitor Systems.</p>

<p>Tiled is a general-purpose tile map editor. It supports various tile shapes like square, hexagonal, isometric square. Tiled support in KivEnt will be a very useful tool, given that Tiled is already a very famous and feature-rich platform for creating game worlds and maps. A game developer would appreciate getting to use Tiled to create worlds with kivy than some custom platform in-built into KivEnt. This will also make it easier (at least the map part) for people to port their existing games to KivEnt, from other platforms which use Tiled.</p> <p>The aim of this project is to create a fully-functional Tiled module which supports almost all features which Tiled currently supports, i.e. various types of tile shapes, tile animations, multiple layers, shape definitions in tiles, custom data etc. The project will also require a TMX file format loader/parser.</p>

<p>Adding support in Kubernetes Client for several types of Resources: ServiceCatalog TemplateInstance VolumeAttachment CertificateSigningRequest SelfSubjectAccessReview SelfSubjectRulesReview TokenReview ControllerRevision UserInfo AdmissionConfiguration</p> <p>Providing test coverage for the following resources: Endpoints PersistentVolumeClaim PersistentVolumes SubjectAccessReview PodPreset HorizontalPodAutoscaler Namespace ResourceQuota</p>

<h5>coala website and coala-html</h5> <p><em>coala-html</em> is a console application used to display results from coala-json as an interactive web page. Prototype is available at <a href="https://github.com/coala-analyzer/coala-html/tree/tushar-rishav" target="_blank">Github</a>.</p> <p><em>coala website</em> a main website for coala with coala demo integrated. It’d be written from scratch with improved UI and features.</p> <p>Proposed features are:</p> <ul> <li>coala-html<ul> <li>Improve UI for Logs and Results by implementing Material Design.</li> <li>Implement better search feature. Create a Fuzzy Search module.</li> <li>Add UnitTest</li> </ul> </li> <li>coala-website<ul> <li>Implement a demo of coala within coala website allowing users to run coala on their code snippet from within their web browser.</li> <li>Available at <a href="https://gitlab.com/coala/website" target="_blank">Gitlab</a></li> </ul> </li> </ul>

Pwndbg delivers a powerful debugging experience on traditional Linux systems, but its support for Android environments remains limited. This is largely due to fundamental differences between GNU libc and Android’s Bionic, as well as the lack of tooling for Android-specific components such as Binder. As a result, many existing features either do not work as expected or provide limited insight when analyzing Android binaries. This project aims to address these limitations by introducing structured and practical support for Android within Pwndbg. It will extend existing features to correctly handle Bionic internals, particularly in memory inspection and heap analysis, and improve Binder support by enabling meaningful userspace inspection. Additionally, the project will refine internal abstractions to ensure consistent behavior across LLDB and GDB.

<p>RAMSES is a 3D graphics framework concerned with the efficient distribution of graphics among multiple screens for automotive purposes. While the framework is almost complete, it lacks a tool that can load, modify and export 3D assets in a suitable format so applications developed with RAMSES can use them at runtime. The following is my plan for the duration of the program:</p> <ul> <li>Produce a Blender export plugin capable of exporting 3D information from a Blender scene for use in a RAMSES application in the form of a RAMSES scene file.</li> <li>Expand GENIVI's Navigation Demo - which uses RAMSES - in a way it can use such tool to import 3D data for displaying on screen.</li> </ul>

<p>Rubyplot only supports GR back-end, my project is to extend this support to Magick back-end, adding plot function to Rubyplot and integration with iruby notebooks. This project will greatly enhance the plotting interface of Rubyplot and pave the way for much greater expansion and allow users to test and debug their code easily.</p>

<p>As the student who took the initiative together with the mentors Jeremiah Foster and Gunnar Andersson during GSoC 2018 for Voice Command on IVI Systems, we were successful in developing a conceptual approach called “VCIVING” and implementing a voice command and response system on PCs. This was called “EmulationCore” and was capable of playing music and finding a location on the map based on user’s speech. Somewhat primitive methods were adopted to refine exact information from the user’s speech those of which have a greater possibility to fail. I intend to work on enhancing the speech recognition and the implementation of VCIVING on GDP.</p>

Problem: gprMax's devel branch GPU solver (cuda_opencl/ directory) supports CUDA, OpenCL, and Metal via shared kernel templates. But the Plane Wave (TF/SF) source from GSoC 2023 remains CPU-only Cython, forcing CPU-GPU transfers every timestep that negate GPU speedup. solution: I will create new kernel templates in cuda_opencl/ following the existing multi-backend pattern: 1) Create knl_planewave_updates.py: batched kernel updating all six 1D DPW auxiliary arrays in one launch. 2) Create knl_tfsf_injection.py: TF/SF correction injection at six boundary faces, coalescing optimized per-face with shared memory buffers. 3) Optimize boundary storage: SoA layout, 128-byte padding, precomputed index maps. 4) Testing suite: GPU vs Cython comparison across free-space, slab, oblique, and polarization cases. Why me : I built a PyCUDA heat diffusion solver with shared memory tiling, vectorized loads, and occupancy tuning, the same techniques this project needs. I have raised 8+ issues and multiple PRs to gprMax and studied all 13 files in cuda_opencl/ in depth. Deliverables: multi-backend GPU kernels for DPW update and TF/SF injection, optimized memory layout, and automated testing suite. Outcome: TF/SF runs entirely on GPU, zero CPU fallback.

<p><strong>Neural source reconstruction</strong> of MEG/EEG data requires subject specific geometrical data (i.e. electrode locations and anatomical brain data). For many reconstruction techniques, results are volumetric rather than surface-based. A group level analysis for volumetric data has not yet been implemented in MNE Python. The proposed project aims to fill this gap, by implementing necessary tools for group level analyses based on <strong>volumetric data</strong>, among which are: non-linear warping of one volumetric (grid) space to another; creating pseudo-individual anatomical MR images, based on a subject’s head shape; and output preparation, such that it can be used with already built in statistical functions. The results will be a set of Python functions that enable the user to prepare individual volumetric subject data for <strong>group level analyses</strong>. Furthermore the respective visualization will be targeted as well.</p>

<p>The goal of this project is to implement maximum likelihood models for different distribution. Each model require log-likelihood function, first and second derivatives(score and hessian), fit and predict functions, documentation and tests. I’ll plan to implement generalized, zero inflated and zero altered models for count data distributions.</p>

Borg Collective is a well-known organization that offers a range of Python-based backup tools, including Borg, Borgmatic, and Vorta. Borg is a file backup tool that performs tasks like compression, encryption, authentication, and data deduplication. Vorta is a desktop GUI for Borg that operates in the user's taskbar and conducts backups in the background. It is officially supported on Linux and MacOS platforms, with its functionality on Windows currently being tested by the community. Borgmatic serves as a command-line interface (CLI) wrapper for Borg and is designed to help manage Borg repos, settings, and related tasks, including monitoring. I outline three significant projects aimed at enhancing the functionality, usability, and maintainability of backup solutions Vorta and Borgmatic. The first project focuses on Vorta, addressing critical code base issues through refactor, cleanup, and maintenance tasks.. The project aims to lay a robust foundation for future development endeavors by improving the quality, readability, and maintainability of the codebase. The second project aims to enhance the Exclusions tab in Vorta, introducing various features such as expanded exclusion patterns, alphabetical sorting, filtering options, and reinstatement of the Exclude-if-present feature. These enhancements aim to improve user experience and address community feedback. Finally, the third project focuses on Borgmatic, aiming to add support for dumping MySQL/MariaDB databases into directory format, which proved a more granular approach to managing and organizing backups, enabling easier navigation and selective restoration of database objects.

<p>Current strace filtering syntax allows to debug simple applications. But it has no support for advanced filtering. For example, it is impossible to combine filters. Moreover, strace supports very few filters (syscall filtering, path filtering, number of invocation filtering). My implementation of a new filtering mechanism will include:</p> <ul> <li>filtering language support</li> <li>an extensible mechanism for filtering by syscall arguments</li> <li>support of adding new actions</li> <li>new syscall classes, based on filtering aspects.</li> </ul>

<p>Data structures that complex networks (graphs) can model are present almost everywhere. Therefore, it is important to have a software that can visualize and provide insights into such structures. Unfortunately, the vast majority of network visualization and manipulation software currently available has several limitations. For example, Gephi (Java) does not scale well when the number of nodes increases and cannot deal with 3d layout algorithms. Because of those limitations in the currently available network visualization software, we propose to develop a client/server network visualization for FURY. Our proposed system is inspired by the following work: “Harnessing WebGL and WebSockets for a Web-Based Collaborative Graph Exploration Tool”.</p>

<p>Our project come in category of Testing which is EAT(EAP additional Testsuite) by which we can Test infinite software project versions .The best thing of EAT is creating the test once and testing with any version of the tested software . We can firstly applied with jBoss Servers and after that for similar structures .it has ability to merge test from remote.</p>