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<p>This GSOC will add support for the solution of a much wider class of bilevel optimization problems to JuMP. Currently, support for bilevel optimization is through BilevelJuMP.jl, which is primarily capable of solving problems for which there is a compact, strong dual for the lower-level problem. The aim of this project is to allow the integer variables and possibly even stochasticity to be included at both upper and lower levels. For this purpose, we propose two approaches. 1- Integrate the well-developed open-source solver into BilevelJuMP.jl. 2- As a stretch goal, extend Dualization.jl to include concepts from the duality of integer programming.</p>

The current SPDX conformance tools do not support the upcoming 2025 CISA SBOM minimum elements draft or the newly introduced AI and Dataset profiles in the SPDX v3 specification. This project bridges that compliance gap. The solution involves generating updated Python bindings for the SPDX Python model and implementing a dedicated, decoupled CISA 2025 compliance checker class. The core deliverables include: updated Python bindings supporting new AI profiles, a fully integrated CISA 2025 conformance checker, and a robust Pytest suite using real-world SBOMs for regression testing.

<p>This proposal describes a project to build a set of p5.js tools around mapping with the specific purpose of being able to quickly and smoothly add a spatial layer to creative, interactive or visual sketches. The 2016 Processing Foundation Community Survey revealed that one of the most popular interests, when people used p5.js or Processing was for data visualization. Therefore, expanding this topic with more easy to use, capabilities, extensions, and examples could potentially augment even more the number of people interested in this environments and contribute to the Foundation's original purpose of making coding accessible for artists, designers, educators and other communities.</p>

<p>FLINT is a modular tool that supports better land management and helps reduce greenhouse gas (GHG) emissions worldwide, and allows multiple models and data required to better understand land management to be brought together in one place. This project idea is for implementation of new models to monitor land sector GHG emissions and support sustainable land management. There are already several existing models that have been attached, but many more are required to make the system complete. This project involves specifically implementing the agricultural soils model that are used to estimate non-CO2 GHG emissions from agricultural practices.</p>

Users need to communicate between the cloud and the edge. For example, the cloud calls the rest interface of the edge service. In this case, the routing management function of KubeEdge can be used. Currently, routing management function of KubeEdge has some problems in the case of multiple CloudCore copies. The main problem is that when there are multiple copies of CloudCore, whether the cloud sends messages to the edge or reports the message to the cloud, it is not known which CloudCore is sent to it for processing, and there is confusion in message management in the cloud. In this project, we hope router manager can be optimized to support multi-CloudCore scenario. Expected Outcome: Support using router manager in multi-CloudCore scenario.

Perception metrics is a unified toolkit for evaluating perception models across datasets and frameworks. Its foundation was built through prior GSoC efforts designed to unify and formalized in a stable release published in Sensors. This platform was recently modernized to support LiDAR, images segmentation, and object detection. This proposal focuses on scaling PerceptionMetrics from off-road focused workflows to industry-standard benchmarking. The core objectives are: • Integrate and prioritize standard image and Lidar datasets. • Extend GUI support to segmentation visualization (image and Lidar). • Deliver comprehensive tutorials/documentations for practical usage. • Strengthen reliability through expanded tests and CI-friendly validation.

The RK3588 chip, developed by Rockchip, is widely used in edge computing devices due to its balanced computational power, rich interface options, and low power consumption. Supporting RK3588 edge devices is crucial for expanding the KubeEdge ecosystem. However, it has not yet been fully validated whether RK3588-based edge nodes can be seamlessly integrated with KubeEdge. This project aims to establish complete compatibility between RK3588 and KubeEdge. Expected Outcome: 1. Debug and Support KubeEdge EdgeNode running on RK3588 Chip. 2. Successfully deploy edge pods on edge node based on RK3588. 3. Achieve node management and metrics for nodes and pods. 4. Complete hardware compatibility testing and output documentation or a blog.

The torch ecosystem is rapidly growing in R, but it still lacks support for several key computer vision capabilities available in Python's torchvision package. This proposal aims to close the gap by implementing a wide range of torchvision datasets and models directly in R. Specifically, the project will: Implement all torchvision datasets. Add support for 5 key computer vision tasks: Object Detection, Instance Segmentation, Keypoint Detection, Semantic Segmentation, and Quantized Models. Provide native R implementations that don't rely on Python bindings, making it easier to install and use. Deliverables include tested R implementations of datasets and models, documented APIs, and example use cases. This will make R a more viable option for computer vision research and production.

When DNS queries fail due to policy-based restrictions (e.g., BLOCKED, CENSORED, FILTERED), DNS resolvers can attach Extended DNS Error (EDE) codes along with structured JSON metadata in the response. This proposal aims to extend Chromium's DNS stack to parse and log Structured DNS Error (SDE) and Public DNS Error (PDE) content embedded in these EDE responses. The project involves extracting JSON metadata from the extra_text field for specific EDE and exposing them through the NetLog system. These improvements will provide more transparent diagnostics and support future UX enhancements. Deliverables: Full support for parsing SDE extra text and PDE, integration with Chromium's DNS resolution pipeline, NetLog exposure of parsed fields, and unit tests to ensure robust and secure behavior.

Kubeflow Pipelines (KFP) currently only supports MySQL/MariaDB databases, creating a significant limitation for many organizations that standardize on PostgreSQL. This project aims to implement comprehensive PostgreSQL support across the KFP stack, including the API server, cache server, and ML Metadata components. The solution involves developing database abstraction layers, SQL dialect adapters, and migration tools that enable users to seamlessly deploy KFP with PostgreSQL backends. By providing this integration, the project will greatly expand deployment options for enterprise environments where MySQL is prohibited by policy, benefit organizations leveraging PostgreSQL's superior data warehousing capabilities, and eliminate the need for unsupported workarounds that currently require substantial effort from users. The end result will be a more flexible, enterprise-ready Kubeflow Pipelines platform that meets diverse organizational requirements.

One pertinent use case of the gcc-python plugin was to provide the infrastructure for cpychecker, a static analysis tool designed for CPython extension modules. The primary objective of cpychecker was to assist programmers in identifying common coding errors in their extensions, which, in turn, could lead to difficult-to-find bugs in their systems. However, the gcc-python-plugin has suffered from bitrot over the years, and as a result, cpychecker ceased functioning several GCC releases ago. The goal of this project is to port the functionalities of cpychecker to a -fanalyzer plugin, offering programmers a reliable tool for conducting static analysis of CPython extension modules once again in a more maintainable manner. The project will be divided into several logical components, such as reference count checking, format string checking, error handling checking, and verification of PyMethodDef tables. Accomplishing this task will involve building a -fanalyzer plugin and extending certain areas of the existing analyzer.

<p>JdeRobot's DetectionSuite is primarily used for fair comparision of different Deep learning models on any framework or dataset preferred. This project proposal aims to expand DetectionSuite’s current functionality to support more deep learning Frameworks such as Tensorflow, keras and Caffe and more datasets such as Imagenet, COCO, etc. It also aims to add Segmentation support to this tool along with various evaluation metrics in order to compare different detection models. For easy installation, this project will also improve current documentation and create debian packages.<br> <a href="https://drive.google.com/open?id=1HSByDVfi6ZOqKVTAJTiUb11ATFTalp-P" target="_blank">Link to Proposal</a><br> <a href="https://jderobot.org/GSoC-2018#Project_.233:_Improving_DetectionSuite_deep_learning_tool_including_segmentation_and_classification_tools" target="_blank">Project Link</a><br> <a href="https://github.com/JdeRobot/dl-DetectionSuite" target="_blank">Github Repo</a></p>

The project tackles inefficiencies in API contract testing for microservices, focusing on schema management, validation, and consistency across distributed systems, which often cause bugs and delays due to fragmented schemas and poor versioning. I propose enhancing Keploy’s contract testing by implementing local schema storage as a mock registry, unifying schemas into a single service contract, adding advanced comparison with dependency resolution and real-time validation, and introducing provider-driven testing with contract publishing and rollback capabilities. My solution builds on a Go-based prototype, utilizing tools like Cobra, YAML, and Docker, with extensibility to S3. Deliverables include an integrated CLI with versioning and multi-format support, schema merging with diffing and real-time validation, and a provider-driven testing framework with rollback, all supported by comprehensive documentation and tests. This will streamline microservice interactions, ensuring scalability and consistency for developers.

The Mission Support System (MSS) facilitates scientific flight planning in atmospheric science, but its existing mssautoplot CLI tool lacks user-friendliness, hindering widespread adoption. To overcome this, we propose developing a Graphical User Interface (GUI) for mssautoplot using PyQt5. This GUI will empower researchers by offering an intuitive platform to configure parameters like layers, resolution, and time steps visually, eliminating the need for complex command-line inputs. Additionally, it will support multilayering from multiple Web Map Service (WMS) servers, enhancing data visualization capabilities. Integration with the MSS UI will ensure seamless interaction, further streamlining workflow processes. Comprehensive documentation and testing using pytest will ensure reliability and usability. Overall, the GUI for mssautoplot aims to democratize access to advanced plotting tools, empowering researchers to efficiently analyze atmospheric data within the MSS ecosystem.

<p>ABioTrans Plus is a web server for transcriptome data analysis and visualization. It supports the analysis of microarray and RNASeq data and performs ten different bio-statistical analyses that cover the common analytics for gene expression data. Furthermore, it gives the users access to several bioinformatics tools to perform 12 different bioinformatics analyses on gene/protein datasets. This project aims to develop ABioTrans Plus v3.0 which will support a wider range of high throughput omics data, namely MS-based proteomics and metabolomics data. Furthermore, developing a detailed report with the performed analysis with figures and data ready to be included in the publications. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning has been used broadly in biological studies for prediction and discovery.</p>

The Hyperledger Web3j-android library is pivotal for developers integrating Android applications with Ethereum blockchain, leveraging the robust capabilities of Hyperledger web3j in a mobile context. Despite its critical role, the current state of web3j-android, last updated to version 4.8.8, faces challenges in coherence, functionality, and modern Android development practices. This project proposes a comprehensive update and restructuring of the web3j-android library to align with the latest Android development standards and Ethereum blockchain technologies. Learning Objectives - To establish a unified and coherent versioning and release strategy for web3j-android that aligns with web3j core releases, ensuring seamless functionality across platforms. - To enhance compatibility by updating the library to support the latest Android SDK versions, ensuring it meets modern app development requirements. - To refactor existing codebase for improving support and compatibility in Android context - To introduce comprehensive testing and documentation, providing a solid foundation for developers to contribute and leverage the library effectively.

Apache NuttX, while offering a Unix-like RTOS environment, currently lacks support for the vast ecosystem of X11-based graphical applications. This limits its usability in embedded GUI scenarios. This project addresses the gap by porting NanoX (also known as Microwindows), a compact X11-compatible server, to NuttX. The port will adapt NanoX’s client-server communication from Unix sockets to NuttX’s message-queue model, and rework the Nano-X server backend to run on top of NuttX’s NX/NXTK window system instead of accessing the framebuffer directly. It will also integrate NuttX’s input subsystem for mouse, keyboard, and touch support while preserving Nano-X/NX11 compatibility. The result will be a Nano-X/NX11 compatibility layer on NuttX that can run applications such as FLTK with minimal changes. Time permitting, I'll also port of FLTK 1.3 and the Dillo web browser to demonstrate real-world application capability.

r.proj and r.param.scale are widely used GRASS raster modules that run entirely on a single core, leaving modern multi-core hardware idle. This project parallelizes both using OpenMP. For r.proj, the core blocker is the legacy readcell tile cache which is not thread-safe. The solution is a two-path architecture: a fast RAM-resident buffer for maps that fit in memory, and a thread-local tile cache fallback for larger maps. For r.param.scale, a sequential sliding buffer blocks row-level parallelism. The fix is the same RAM preload pattern: load the full raster before the parallel region and let threads work on rows independently. Proof of concept PRs show 2.5x speedup for r.proj and 1.7x speedup for r.param.scale on 100M cell rasters. r.geomorphon will be audited during the bonding period as a third target. Deliverables include production-quality parallel implementations of both modules, per-thread PJ_CONTEXT initialization, a fix for issue #5776, pixel parity regression tests, scalability benchmarks, and full documentation.

Java-Ranger is a path merging extension of Symbolic Pathfinder, that collapses multiple execution paths into a single region with a disjunctive constraint. Currently, it handles only integer constrains, which means, floating-point (FP) operations are either approximated unsoundly or cause solver errors. In this project, the aim is to add sound IEEE-754 floating-point support to Java ranger, enabling the correct summarization of regions containing FP arithmetic, comparisons, conversions, and library calls. The work heavily involves refactoring the underlying FP support in SPF (Modelling special values, rounding, comparisons, and conversions), then extending Java Ranger’s AST visitors, merging heuristics, and solver integration to handle the new FP constraints. The result will be an engine capable of analyzing FP-Java programs. Deliverables include: - New floating‑point expression nodes in the numeric package. - Updated bytecode handlers for all FP instructions. - Solver integration for Z3bitvector using the floating‑point theory. - Constant folding and simplification passes for FP expressions. - A test suite validating the correctness and coverage of FP handling. - Documentation of the new API and usage.

This project aims to introduce a scalable and maintainable internationalization (i18n) system for applications built using MIT App Inventor. Currently, App Inventor does not provide a structured approach for handling multi-language support, making it difficult for developers to create globally accessible apps. The proposed solution introduces a key-based translation system that decouples user-visible text from UI components. The system integrates across multiple layers of App Inventor, including the Designer, Blockly, runtime, and build system, enabling automatic translation handling in generated applications. Key features include: - Automatic detection of user-visible text from component properties and Blockly blocks - A centralized translation editor for managing multi-language content - Context-based key generation to ensure stability and avoid ambiguity - Runtime translation using JSON for flexibility during development (Companion) - Build-time generation of native Android/iOS resources for efficient production builds The system is designed to be fully backward compatible and supports progressive adoption, allowing existing projects to continue working while enabling developers to gradually migrate to the new translation model. Deliverables include: - A working runtime translation system for App Inventor apps - Integration with Blockly for key-based text handling - Designer-level synchronization and key management - Build-time resource generation for native localization - End-to-end tested implementation with documentation

This project focuses on adding full 3MF export and import support to InVesalius, enabling efficient handling of multi-surface medical models with accurate color (RGBA) representation and proper coordinate transformations. At present, InVesalius does not support modern 3D formats like 3MF, which offer better structure, metadata handling, and color capabilities compared to formats such as STL and OBJ. Through this project, I aim to integrate 3MF support into the existing export and import pipeline using lib3mf, ensuring compatibility with tools like OrcaSlicer while preserving geometry, scaling, and visual attributes. The implementation will handle coordinate transformations (LPS ↔ Z-up), support multi-surface models, include alpha channel handling, and integrate cleanly with the current architecture. This work builds on my prior contributions to InVesalius, where I have worked on surface pipelines, coordinate transformations, and UI workflows, allowing me to integrate this feature effectively within the existing system. The goal is to improve interoperability with modern 3D tools, ensure accurate representation of models, and provide a solid foundation for future extensions in 3D medical imaging workflows.

<p>Firefox Developer Tools offers good tooling for monitoring HTTP traffic between the current page and the server. The existing Network panel allows intercepting and inspecting all data transferred over the wire including headers, GET/POST data, responses, detailed timings, etc. Unfortunately, Firefox Developer Tools doesn’t offer a way to inspect WebSocket (WS) traffic in Firefox Quantum.</p> <p>This project aims at providing support for WebSocket monitoring and inspection in Firefox Developer Tools. The feature should be built on top of the existing Network panel user interface (UI) and be responsible for visualizing data (i.e. WS frames) sent through a WebSocket connection. Users should be able to perform common tasks such as pausing/resuming monitoring, clearing frames, searching/filtering, looking at summary data etc. Light and Dark Themes should be supported.</p> <p>Furthermore, support for popular protocols such as Socket.IO, SockJS, plain JSON, and MQTT will be implemented. There is also ongoing work for supporting binary message viewer and showing WebSocket messages in HAR exports.</p>

<p><strong>Galaxy climate to support local/regional initiatives and concrete actions to fight climate change</strong></p><p>The deadline to create an initial application has passed for the May 2022 Outreachy internship cohort. We are no longer accepting initial applications for internships. We encourage you to sign up for the announcements mailing list to get an email when the next round opens. Why apply to Outreachy? &gt; Start your initial application &gt; May 2022 Outreachy internship cohort &gt; Galaxy Community Community details are hidden until you are approved to participate as a mentor or coordinator.</p><p><br></p><p><strong>Mentorship Cohort:</strong> 2022</p>