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<p>This is a draft proposal.</p>

The Multiple Flightpath Docking Widget in MSS is crucial for visualizing atmospheric data but lacks customization options hindering effective comparison of flight paths. This project aims to enhance the widget's functionality by introducing centralized line controls, adjustable transparency, Matplotlib linestyle options, automatic color assignment, regex filtering, select all/deselect all buttons, integrated legend, operation labeling, and saving user preferences using QSettings. (Deliverables): Enhanced UI with centralized controls. Improved backend filtering with regex. Comprehensive legend design with synchronization. Annotation feature for operation labeling. QSettings implementation for saving/loading preferences. Test cases, documentation, and future work suggestions.

<p>Many Data Scientists have their data saved in a PersistentVolumeClaim accessed by their Notebooks. So it would be helpful and greatly improve Kubefow's UX if the user could also visualize data with Tensorboard that lives inside of a PersistentVolumeClaim. This way, they wouldn't need to save their visualization data on external services, which would also require additional code and effort when working inside of a Jupyter Notebook. I would love to contribute to such a project since my work could be part of real world workflows and help organizations perform their day to day Machine Learning tasks more efficiently.</p>

<p>HDMI (High-Definition Multimedia Interface) is a proprietary audio/video interface for transferring uncompressed video data and compressed or uncompressed digital audio data from an HDMI-compliant source device to an HDMI-compliant sink device. The Project deals with the creation of HDMI Source and Sink IP cores in Python Using MyHDL. These IP cores makes it easy for anyone to build a HDMI Transmitter and Receiver using FPGA. Python's power and clarity make MyHDL an ideal solution for high level modeling. The project will also serve as an useful example for the Python MyHDL project to show a non‐trivial example of an IP core.</p> <p>RISC-V is an open ISA freely available for all types of use. The RISC-V ISA has been designed with small, fast, and low-power real-world implementations in mind, but without "over-architecting" for a particular microarchitecture style. The Project deals with the implementation of RISC-V processor in Python using MyHDL. The project demonstrates the advantages of MyHDL and Python in the field of CPU design.</p>

<p>This project aims to create a Graphical User Interface (GUI) for big gridded geospatial data visualization in the browser interface backed by the full power of the Python ecosystem. This GUI would allow controlled data points selection, massive rendering, data display, custom interaction, selection of fields for plotting and layout of widgets in the browser using Intake, Xarray and Pyviz collection of tools. Currently majority of geospatial data exploration happens in stand-alone applications like Panoply and NcView, tools that have limited functionality, do not provide complex analysis methods and can only be reasonably extended by the software developers on those projects. This new tool, written in Python, but presented as a dashboard in the notebook environment, will be extendable directly by researchers by using it in conjunction with tools like Dask and will also provide complex analysis methods on the data being visualized. It holds the promise of saving Earth Science and other researchers significant amounts of time since they can directly focus on visual data analysis and research rather than writing custom code to explore data.</p>

<p>In many cases once you have exploited a device you don’t know exactly what to do or how to go on. Maybe you have many payloads available and after trying all of them they don't work because of the architecture of the device. Even more in limited environments like embedded devices, for instance. The result is a waste of time and results. Post-exploitation is a critical phase and that’s why I would like to create a generic interface to make it easy, automatic and direct for anyone to execute postexploitation functions in whatever device they are trying to pentest. And it will not depend on the architecture of the device. At the same time I will try to make a project very scalable and modular to ease future improvements on it and avoid some of the current problems.</p>

<p>Apache VXQuery will be a standard compliant Xquery processor implemented in java. JSONiq is a query and processing language specifically designed for the popular JSON data model. It is much similar to XQuery burrowing a large numbers of ideas from XQuery. The "JSONiq extension to XQuery" allows processing XML and JSON natively and with a single language. This project aims at adding objects to the data model of VXquery allowing it to query JSON objects.</p>

<p>Since SBML Level 3 is being developed as a modular format with optional SBML Level 3 packages are available to extend the focus of the core SBML representation. Deviser is a new code generation system developed to facilitate the development of SBML L3 packages by helping to automate the generation of specifications, UML diagrams and library code. The code generator currently targets libSBML only. The goal is to extend Deviser's code generator to produce Java code that can be integrated into JSBML, the pure Java API library for SBML. This will allow Deviser to target both libSBML and JSBML. By contributing to Deviser and also being an active spectator, I hope to develop the skills I already have and also incorporate some new ones.</p>

My proposal is to extend the Python package dingo with two Markov Chain Monte Carlo sampling methods, which will enable exponential sampling from the set of flux vectors for constraint-based metabolic modelling. The sampling methods - NUTS Reflective Hamiltonian Monte Carlo, Reflective Exact HMC, and Riemannian Hamiltonian Monte Carlo - have already been implemented in C++ and integrated into the volesti codebase. The extended C++ bindings and Python wrappers will enable access to these methods through dingo. This will involve running experiments on benchmark metabolic networks to compare the results obtained from the new random walks and the existing Multiphase Monte Carlo Sampling method based on Billiard walk. The experiments will generate new biological insights by providing an exponential sampling of flux vectors, which will allow for an unbiased characterization of the metabolic capabilities of biochemical networks. The final deliverables of the project will be the extended dingo package, the results of the experiments, and a brief report discussing the findings.

The proposed Personal Finance Management (PFM) system for Mifos Pay Mobile represents a strategic evolution from a transaction-centric mobile wallet into a comprehensive financial intelligence platform. While existing mobile banking solutions primarily focus on enabling payments and account access, they often lack holistic tools that empower users to understand, manage, and optimize their financial behavior. This limitation is particularly significant for users in emerging markets, where financial literacy, fragmented account ownership, and limited access to advanced financial tools hinder effective money management. This project aims to bridge that gap by introducing a unified, intelligent, and extensible PFM layer built on top of the existing Mifos Pay ecosystem.

This project integrates Open Concept Lab (OCL) test concepts into OpenELIS-Global by parsing OCL ZIP files from a Configurations/ocl directory at startup, mapping them to the TEST table, and using checksums to process only modified files. It enhances lab standardization and interoperability, reducing manual setup time (e.g., 2 hours to 10 minutes) for technicians and ensuring consistent updates across sites for admins.

<p>Volcano's `Queue` is a cluster-scoped resource, which means only cluster admins can create or update it. This is a barrier for multi-tenant scenarios, where tenants usually only own their own namespaces and want to leverage Volcano's queue capabilities (resource sharing, capability/guarantee/deserved, hierarchy, etc.) for their own workloads without requesting changes from a cluster admin. This project adds a namespace-scoped `NamespaceQueue` to Volcano. A `NamespaceQueue` is derived from a cluster-scoped `Queue` and behaves consistently with it, so tenants can create and use queues within their own namespace and associate `PodGroup`/`Job` with a `NamespaceQueue` exactly as they would with a cluster `Queue`. The existing cluster `Queue` semantics and APIs remain unchanged for users who do not opt in.</p><p><br></p><p>Expected Outcome:</p><p><br></p><ul><li>A namespace-scoped `NamespaceQueue` CRD derived from cluster-scoped `Queue`, with consistent semantics for fields such as `capability`, `guarantee`, `deserved`, and hierarchy.</li><li>Tenants can create and manage `NamespaceQueue` within their own namespace without cluster-admin permission.</li><li>`PodGroup`/`Job` can reference a `NamespaceQueue` and be scheduled with the same behavior as referencing a cluster `Queue`.</li><li>Resource accounting, status, and events for `NamespaceQueue` work end-to-end through Volcano's existing queue management path.</li><li>Compatibility with the existing cluster `Queue` and the `scheduling.volcano.sh/queue-name` annotation, with a clear migration story for existing users.</li><li>E2E tests covering core `NamespaceQueue` flows, including negative cases.</li><li>User-facing documentation on the Volcano website and the repository.</li></ul><p><br></p>

<p>Port and Integrate DRM ioctl in NetBSD kernel for Linux binaries running in NetBSD. Convert between 32bit and 64bit DRM ioctl calls. Create a test suit to run and test Linux applications in NetBSD.</p>

<p>With beacons being lit and the specification readily being adopted by organizations all over the world, there is a need for a toolkit which guarantees compliance with the defined beacon API. The toolkit should contain an entire compliance test suite for organizations looking to implement the beacon API, as well as adapters (or protocols for creating adapters) for organizations which want to user their current data for the beacon. My proposed project will implement the toolkit as well as generate more community documentation to further simplify the beacon creation process.</p>

This project proposes various new features for the haskell language server(HLS). While HLS already comes with the most important ones, a few haven’t been implemented yet. These important unimplemented features of Language Server Protocol(LSP) have to be implemented. Namely, Semantic Highlighting, Folding ranges, Linked editing, Change Annotation, Document links, and Completion / Code Action / Code Lens resolving.

<p>A major requirement for the current status of the dashboard is the implementation of a download and install pipeline for tables having spatial data in the form of vector images/data. According to the retriever docs, currently the retriever library uses the PostGIS extension for the same. Therefore, the install process to be used for the dashboard will make use of the postgres engine. The spatial datasets are already tested for only download and changes are detected using the md5 hash. This proposal will further that, into proper install of these datasets into Postgres and find out diffs in the databases using PostGIS joins. The results obtained will then be converted into HTML diffs just like we do for non-spatial databases.</p>

Dubbo Proxyless mode refers to Dubbo directly communicating with the control plane and achieving capabilities such as Service Discovery and Service Governance through the xDS protocol.

Performance-focused, production-level machine learning libraries need to leverage the resources at their disposal to the maximum extent to deliver efficient and effective machine learning workflows that ultimately result in improved user experience. SparkNLP, one such library widely adopted and used by 16% of enterprise companies(as of Feb 2019), is currently capable of taking advantage of CPU optimization capabilities using Intel-optimized Tensorflow. This coupled with other optimizations already allows it to run machine learning pipelines orders of magnitude faster than legacy libraries. Such a library would benefit from solutions like OpenVINO that offer extensive integrations in the ML ecosystem and even further optimization capabilities for inferring and deploying models on a range of hardware platforms. Exposing the OpenVINO API bindings in Java will allow integration with SparkNLP to enable the above-mentioned capabilities, and furthermore, open up avenues for a large community of developers to benefit from OpenVINO’s rich feature set in the future.

The TUF specification provides explicit guidelines for how artifacts should be hashed and later verified to guarantee their integrity. However, Content Addressable Systems like Git, IPFS (InterPlanetary File System) and OSTree have their own mechanisms for ensuring the integrity of artifacts. When TUF is used with these systems, it is redundant for it to also ensure artifact integrity. This project provides solution to this issue by delegating the responsibility of artifact integrity verification to the underlying content addressable systems themselves, while still using TUF to manage the metadata and provide additional security measures.

<p>The Real-Time Executive for Multiprocessor Systems (RTEMS) is an open-source real-time operating system (RTOS). In the current RTEMS version, a lower-priority ready task must wait if all the processors included in its affinity mask are executing higher-priority tasks. Since a lower-priority task can never “dislodge” a higher-priority task that could also execute elsewhere, this may needlessly prevent some tasks from being scheduled, even if some processors idle as a result[1].</p> <p>This project aims to add the Strong Arbitrary Processor Affinity (Strong APA) scheduler to RTEMS. Strong APA scheduler would allow higher-priority tasks to be ”dislodged” or moved among processors in order to make space for lower priority tasks that are limited by affinity constraints [1]. Consequently, this would allow RTEMS to achieve improved schedulability (i.e., lower response-time bounds).</p> <p>References : [1] Cerqueira, Felipe &amp; Gujarati, Arpan &amp; Brandenburg, Bjorn. (2015). Linux's Processor Affinity API, Refined: Shifting Real-Time Tasks Towards Higher Schedulability. Proceedings - Real-Time Systems Symposium. 2015. 249-259. 10.1109/RTSS.2014.29</p>

This project will create a Kubernetes deployment for the full stack: the OpenTelemetry Demo application, the Jaeger backend components (Collector, Query), and OpenSearch as a storage backend. The environment will be automatically redeployed weekly to ensure it is always fresh and to solve the problem of data retention. The entire stack will be hosted on a managed Kubernetes cluster (Oracle Kubernetes Engine) within an Oracle Cloud environment generously donated to the project. Expected Outcome: 1. A working, publicly accessible Jaeger demo featuring the OpenTelemetry Demo application, deployed on Oracle Cloud. 2. Fully automated, weekly deployments using the existing Helm- based automation and GitHub Actions. 3. The entire environment defined as "Configuration as Code". 4. A secure deployment following best practices for public-facing services. 5. The UIs for Jaeger, the OTel Demo, the load generator, and OpenSearch Dashboards exposed -publicly via the existing demo.jaegertracing.io URL. 6. Public-facing documentation on the Jaeger website explaining the demo architecture and linking to the automation code.

<p>Hyperledger Fabric has been available for several years and there are many production networks using Chaincode. Fabric-X has just been released and it introduces changes to the current way of deploying applications. This brings up the question on how to migrate existing applications and chaincodes to this new environment.</p><p>The goal of this mentorship project is to:</p><ul><li>Explore the feasibility to run existing Fabric Chaincode on Fabric-x;</li><li>Design and develop a proof of concept that showcases Go Chaincode for Fabric-X;</li><li>Implement a prototype of the components identified;</li><li>Identify a demo use case (i.e., Fabric Samples, CC-Tools, Fabric Private Chaincode) that comes with go chaincode;</li><li>Produce documentation that explains the architecture and how it can be used as well as potential avenues of development.</li></ul><h3>Learning Objectives</h3><ul><li>Deep dive into Hyperledger Fabric and Fabric-X.</li><li>Experience an active open-source community.</li><li>Mastering open-source workflow.</li><li>Enhance your designing and implementing skills, testing, documentation, and project management.</li></ul><h3>Expected Outcome and Deliverables</h3><ul><li>Proof of Concept that showcases an alternative on how to migrate classic Fabric go chaincode to Fabric-X.</li><li>Documentation that explains the demo and how to run it.</li><li>Bonus: a technical blog post and meetup talk.</li><li>Having fun.</li></ul><p><br></p><p><br></p><p>Lean more at <a href="https://github.com/LF-Decentralized-Trust-Mentorships/mentorship-program/issues/59" rel="noopener noreferrer" target="_blank">https://github.com/LF-Decentralized-Trust-Mentorships/mentorship-program/issues/59</a></p>

Deploying AWS Lambda functions using AWS SAM (Serverless Application Model) requires manual creation of a deployment descriptor, posing challenges for Swift developers due to learning a new DSL and adopting new tools like SAM CLI and Docker. To simplify this process, the community proposed a Swift-based DSL and a Swift package plugin. However, these solutions rely heavily on accurately mimicking the evolving SAM AWS deployment descriptor. To address this, this project proposes to automatically generate the AWSLambdaDeploymentDescriptor library based on the SAM template definition. That generated code is used by the Swift-based DSL to create SAM YAML templates required to deploy Lambda functions and their dependencies to AWS.