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<p>Beamforming is among the most widely used source localization techniques for MEG and EEG data in neuroscience. This project aims at porting an updated version of the five-dimensional beamformer to analyze time-frequency data in source space (Dalal et al., 2008; implemented in MNE-Python as “tf_lcmv”). The update adopts the Hilbert transform to obtain a continuous amplitude and phase time series per frequency band, in contrast to the previous method that utilizes discrete time windows and does not retain phase information. It also adds a weight normalization option to the LCMV beamformer (neural activity index and unit noise gain), which will be available for other LCMV versions as well. Furthermore, statistical testing of the Hilbert beamformer output using the Wilcoxon rank sum test will be implemented to complete the new Hilbert beamformer pipeline. Due to the size of the MEG data and intensiveness of the calculations, the code will be developed with memory efficiency and parallelization in mind. Interactive visualization of source-localized time frequency representations will complete the Hilbert beamformer routine.</p>

<p><strong>Hibernate Search</strong> is an extension to Hibernate ORM that brings the powers of full-text search via <a href="https://lucene.apache.org" target="_blank">Lucene</a> and <a href="https://www.elastic.co" target="_blank">Elasticsearch</a> to JPA models. Think of it as Google: after processing the input keywords (e.g. ignoring stop words and applying different kinds of normalization), Hibernate Search returns results ordered by relevance. It brings significant benefits comparing to normal SQL for search-related uses cases. Its full-text index can be updated in two ways: Automatically, whenever data changes (using listeners hooked into Hibernate) or by means of the "mass indexe", which rebuilds the entire index for given entity types.</p> <p><strong>This proposal aims to provide an alternative to the current mass indexer</strong>, based on the Java Batch architecture as defined by <a href="https://jcp.org/en/jsr/detail?id=352" target="_blank">JSR 352</a>. This standardized tool <a href="https://jcp.org/en/jsr/detail?id=352" target="_blank">JSR 352</a> provides task-and-chunk oriented processing, parallel execution and many other optimization features. This batch job should accept the entity type(s) to re-index as an input, load the relevant entities from the database and rebuild the full-text index from these.</p>

<p>NMatrix is being re-implemented by SciRuby contributors here at <a href="https://github.com/prasunanand/nmatrix_reloaded" target="_blank">https://github.com/prasunanand/nmatrix_reloaded</a>. This re-implementation is having a fast core written using C-API with Ruby front-end. With this project, we aim to create a faster NMatrix which will replace the original NMatrix later on and the source would be simpler and easier to read and improve. This proposal is to add more features to this newer NMatrix such as adding support for LAPACK and BLAS routines, sparse matrix operations, indexing and broadcasting, matrix decomposition etc.</p>

This project brings autodiff and offload into Rust CI by making them buildable and verifiable in the CI pipeline. Autodiff is currently only partially supported in CI, and offload is not yet enabled there at all. The project will focus on the CI and bootstrap changes needed to validate both areas more consistently before merge, including deeper build-system work such as resolving LLVM linking constraints on macOS where required. Deliverables include CI support for autodiff and offload builds and validation, the required bootstrap integration, and supporting infrastructure improvements to keep the workflow reliable and maintainable over time.

Eye-tracking devices are widely used in neuroscience research. Studies of visual perception and attention in particular are increasingly integrating eye-tracking and EEG/MEG devices, in order to gain a better understanding of these processes and their neural correlates. However, the Python community has yet to coalesce around a well-supported package for analyzing eye-tracking signals. This project proposes to integrate support for reading, visualizing, and analyzing eye-tracking signals in MNE-Python. By incorporating the ability to analyze eye-tracking data into MNE-Python, researchers would be able to analyze both neural and eye movement data within the same software environment, making MNE-Python more versatile and useful for a wider range of research questions. Finally, MNE-Python has a large community of developers and users, making it likely that eye-tracking integration will prove useful for many in the scientific Python community. This project will focus on building a robust I/O routine for eye-tracking data and developing functions for preprocessing, visualization, and analytical routines that are commonly required in eye-tracking research. This includes the ability to interpolate eye-position data during blinks, produce heatmap plots of eye-position across trials, and estimate the deconvolved pupil response.

Transforming electrophysiology signals from sensors into their frequency representations has been shown to be a way to increase the power for application of this data; for one example of very many see Swann et al., 2015. This Fourier-transformed sensor data can be used to infer data within the brain in a very similar way as the time-series data but with an added, fifth dimension for frequency. MNE-Python has all the components necessary for time-frequency source estimate viewing and would be greatly improved by integrating them to allow for exploration of this complex data. A graphical user interface (GUI) that allows users to look at regions of high power spectral density, while letting the user dynamically adjust the range of frequencies, would be incredibly helpful for allowing this kind of data to be explored and analyzed. The ability to browse slices has already been implemented in `mne.gui.ieeg_locate` and could be abstracted to be used in this case. Time-frequency plotting of spectrograms, with appropriate units and baseline correction already exists in MNE-Python as well. Putting these elements together into a GUI would be a great improvement for the MNE-Python tool and community.

<p>The current Python's development process is very complicated for core developers. When an external contributor uploads a patch for a certain issue, the core developer needs to download the patch and then apply the patch and run the tests manually to make sure it works properly. Also, the core developers review the code on the forked version of Rietveld code review tool which is not being maintained. Once the patch is correct, the changes are committed manually. This is a manual and burdensome process for core developers. On <a href="https://mail.python.org/pipermail/core-workflow/2016-January/000345.html" target="_blank">1st January 2016</a>, Brett Cannon decided to migrate from Mercurial on hg.python.org to Git on GitHub.</p> <p>So, my project is about migrating to Git/Github. I will be working on creating pull requests on Github whenever a patch is submitted on the issue tracker (<a href="https://bugs.python.org" target="_blank">https://bugs.python.org</a>) and integrating various bots to maintain linear history of commits, auto-close of issues, enforce CLA, etc. Towards the end of the GSoC period, I will work on patch-related improvements.</p>

**Pwndbg** is a debugger extension that enhances the debugging experience. It currently supports only GDB and LLDB on Linux and macOS. This project introduces Windows support for Pwndbg. Initially, only WinDbg will be supported, but a generic interface could enable support for other debuggers (GDB, LLDB, x64dbg, etc.). The goal is to bring Pwndbg enhancements to the WinDbg console — providing a similar experience to using it with GDB or LLDB. End users should be able to access all native WinDbg features while also using Pwndbg commands and context windows. The project leverages WinDbg’s `wdbgexts` or `DbgEng` to interface with Pwndbg. This requires either an RPC client-server protocol or native integration of Pwndbg on Windows. Pwndbg needs to hook into WinDbg states (registers, memory, etc.) and respond to all command calls from WinDbg.

ENIGMA currently lacks third-party integration with various digital distribution platforms such as Steam, Xbox, or Origin. First-class support for these services would make it easier for Indie developers to publish ENIGMA-created games. Developers would like for it to be easier to monetize their games and enable them to release bigger and better updates to their users. An integrated architectural layer will be created between Steamworks ENIGMA's extension APIs and Steamworks SDK. ENIGMA's Asynchronous system will be updated. Currently, ENIGMA's Networking system is not stable so Steam Networking features will be postponed for later for now I will add support to these APIs: Management, General, Overlay, Leaderboards, Achievements, Cloud, and Social.

<p>The goal of the project will be to provide stable and platform independent APIs to the users for accessing features of their desktop and mobile devices. The supported APIs will be for the following platforms: Windows, Linux, OSX, Android and iOS.</p>

<p>I will implement ARM CMSIS library on PULPino using proposed priority, then i will port the implementation to its DSP core and compare results.</p>

The major objectives of the project are: 1. Refactoring the Python code to increase efficiency and readability and increase the overall code quality of the entire codebase of the software. 2. To enhance user engagement and support by creating a comprehensive FAQ dataset which is used to train a custom Chatbot. 3. Investigate and discuss different possibilities for multi-GPU model execution, i.e. allow a model to execute (and share memory) across multiple GPUs.

This proposal addresses the lack of debugging tools for pocketpy, a lightweight Python interpreter for game scripting. It will develop a VSCode debugging extension along with Debug Adapter Protocol (DAP) support. The solution includes a debugger core that integrates with pocketpy's interfaces to support breakpoints, variable inspection, and call stack visualization, paired with a VSCode extension providing an intuitive debugging interface. Deliverables include a DAP-compatible debugger core, a VSCode extension with debugging configuration capabilities, and APIs for runtime inspection of variables and stack frames.

<p>SenZ Switch is a high-speed message passing system which can be used to do the communicating between IoT devices. After connecting the devices to the system, they can share data with the other connected devices. The system acts like a message broker and it does not keep or read any data passing through it. All the messages passing through the system are secured with an End-to-end Encryption method. So this system to can be used to connect private devices also without hesitating.</p> <p>The existing problems of the system are going to be solved and It is planned to modify with some extra features to improve its functionalities. And the system is going to be integrated with Akka Streams which is based on Reactive Streams to provide streaming capabilities.</p>

<p>This is my proposal to create an implementation of <em>OpenPGP for XMPP Instant Messaging</em> (OX) for the XMPP client library Smack.</p>

<p>Radare is a useful framework for reverse engineering. It has debugger interface and disassembler and visualizer. To analyze programs more efficiently, it needs a decompiler. Radare has its own decompiler ‘radeco’. Some of its analysis are developed, but it is not still implement pseudo C backend. In this proposal, I will describe a plan to make radeco generate pseudo C Code.</p>

Weaviate is a cloud-native vector search engine and a vector database. Weaviate is completely modularized and the functionalities of the weaviate can be enhanced by using modules. This project will be focusing on creating a custom text2text generator module that could potentially cover a variety of NLP tasks. Tasks range from text summarization, and translation to any other text2text transformations.

Current Mariadb ColumnStore engine doesn’t support vectorized evaluation of SQL functions and expressions. This project want to cover this by adding row/col transformation before the evaluation and evaluate the performance improvement. Furthermore, this project will also try to accomplish a delayed projection into RowGroup, which store data in vertical stored buffers and optimize the transformation part.

<p>Terasology is designed to be a modular voxel engine but as said: “with great power comes great responsibility”. Keeping track of all the modules can be tough. The idea of the proposal is to provide an automation system that aggregates all the modules and display their information on a generated website. Provide a way for the user to download, list dependencies. With a stretch-goal of improving the UX of the website along with speed and accessibility.</p>

The project aims to introduce capa explorer web for capa, a reverse engineering tool developed by Mandiant's FLARE team. Capa automates the process of identifying capabilities of a given malware sample by extracting program features and matching them against predefined rules. The deliverables of this project will be an improved version of capa with better usability, and a browser-based GUI, along with a blog post summarizing the work and improvements.

This project adds two missing features: struct field tag extraction and interface method name recovery. Tags like json:"user_id" tell a reverse engineer how data is serialized over the network; Go stores them in the binary even after stripping, but GoReSym was not reading them. This project extends the parser to recover tags across all supported Go versions (1.5–1.26) and completes interface method name extraction, with test coverage added for both.

<p>The current decompiler widget in Cutter provides only basic features and interaction, and it is far from being as advanced as the IDA’s or Ghidra’s decompiler’s views and features. This project aims to improve the Decompiler Widget in Cutter and <strong>transform it into a fully-featured Decompiler Widget</strong>. After the successful completion of this project, users would be able to do almost everything possible from renaming variables and functions to seeing xrefs in the disassembly widget and more using the decompiler widget. A complete reverse engineering session would be seamlessly possible in Cutter using the decompiler widget alone.</p>

I suggest a deep-learning-based approach to analyze the physical characteristics of GNSS signals and detect abnormal sequences, and consequently recover the tampered location data by using multiple received signals with various features extracted from them. Especially raw data received has more information(such as Pseudorange, Carrier Phase, Doppler, Clock Bias Drift, Carrier-to-Noise Density) than the processed ones, so it shows a possibility to detect abnormal uncorrelated sequences. This can help many systems that require real-time based GPS utilization such as autopilot cars, UAVs, and military equipment.