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<h5>Adding trust anchor functionality to drg.</h5> <p>Drogue Cloud supports the authentication of devices using X.509 certificates. Drg is the command-line client for Drogue cloud. This project aims to implement drg commands to work with certificates and keys to make the trust anchor workflow easier for the user.</p>

<p>The cyclocopter module currently available in MBDyn models the inflow of a cycloidal rotor. It is however limited to 2D aerodynamics models; moreover, the implementation of the double multiple streamtube model is not yet stable. Calculate the inflow velocity vectors over the cyclocopter rotor (at each azimuth location) using the Double Multiple Stream Tube Method (Combination of Blade element theory and Momentum theory). Which will basically consider/capture the effect of induced velocity generated by upper half blades on the lower half blades.</p>

<p>FastTable was integrated into Pharo 5.0 to solve two main problems that the old lists implementations were facing, first the performance issue, old list’s data source was limited to a couple of hundreds of rows only and second the lack of extensibility since old lists don’t handle complex element morphs very well. Since their integration, FT and their subclasses have known several improvements allowing them to render multi cells tables, expandable trees and draggable lists but the development is still in its early stage and there are still a lot of work ahead. The goal of this project is to target the needed improvements and extensions to build a better FT family widgets that could fulfill much bigger variety of applications.</p>

<p>Expand the PFS GSoC blogging platform to include a top level overview of student status grouped by suborg and extend student activity monitoring to include code checkins, chat and mailing list activity.</p>

<p>Panda3D is an open source framework for 3D rendering and games. It combines the speed of C++ with the ease of use of Python in order to provide users a fast rate of development without sacrificing on performance. The project I wish to work upon is Integration of Navigation-mesh Toolset for Games in Panda3D. Recast and Detour are tools/libraries for building navigation meshes and performing pathfinding. Once these tools/libraries are successfully integrated with Panda3D, it would be possible for a Panda3D actor to move from the source to the destination avoiding collisions with the walls and inappropriate paths.</p>

A library management add-on for managing Different online-offline FreeCAD components is a utility application for FreeCAD which allows an easy and structured management of components/models available online or offline and to easily be inserted into the FreeCAD document. Its basic functionalities is to facilitate individual component from the online repository to be able to browsed, downloaded, and extended by already available local components. For a Library management system, a robust repository of data and an local storage structure is must. This is important as user need to brows through the all the existing models which are the part of the repository and download only selective models. A structured local storage will not only also allow users to easily add their own models to the local library management system, but also to the online repository open to public. For maintaining the metadata of these, a well defined storage structure is required. All this need to have an proper interface to be operable. The interface for the above will be developed as both a Graphical Interface and module for python scripting.

Description:Kruize Autotune is a Performance Tuning Tool for Kubernetes. . It uses Hyper Parameter Optimization to tune the language runtime layers of a given application. System component metrics can give a better look into what is happening inside them. Metrics are particularly useful for building dashboards and alerts. Kubernetes components emit metrics in Prometheus format. This format is structured plain text, designed so that people and machines can both read it.Following are four common methods of hyperparameter optimization for machine learning in order of increasing efficiency: 1.Manual 2.Grid search 3.Random search 4.Bayesian model-based optimization The aim of Bayesian reasoning is to become “less wrong” with more data which these approaches do by continually updating the surrogate probability model after each evaluation of the objective function. At a high-level, Bayesian optimization methods are efficient because they choose the next hyperparameters in an informed manner. The basic idea is: spend a little more time selecting the next hyperparameters in order to make fewer calls to the objective function. In practice, the time spent selecting the next hyperparameters is inconsequential compared to the time spent in the objective function. By evaluating hyperparameters that appear more promising from past results, Bayesian methods can find better model settings than random search in fewer iterations.So,this project aims at tuning kubernetes applications using bayesian optimization.

<p>I intend to work on the scrapy benchmarking suite project. I would like to create benchmarks that reflect real-life cases as well as component-specific micro benchmarks.<code>--profile</code> uses cProfile for profiling and the feature is currently undocumented. I intend to investigate other profilers to be used with scrapy and try to improve the performance of the spiders based on the results.</p>

The EOS User Story project is a project that supports the development and testing of other projects. I will be working on improving and adding features to the platform. I will be working on both the frontend and backend of the project. The frontend uses react.js and the backend is made in Strapi. During the program, I will migrate the services to newer versions and add some interesting features in the comment section, notification section, labels , etc. After this program user story will be ready to use by EOS and other organizations. The main objectives of my proposal are- 1. Migration to typescript 2. Migration to Strapi V4 3. Revamping the notification section 4. Making the comment section user friendly 5. Adding more UI/UX solutions 6. Removing existing bugs.

<p>The Aim of this Project is to create and implement an algorithm that figures out the right indentation for as many programming languages as possible. The implementation must be configurable for different projects and must address features like indents based on <code>keywords</code>, <code>Hanging Indents</code> and creating new lines when the length of the line is too long .</p>

<p>In most of the cases around 80% of icons in an iconic font are unused. This reduces the performance of a web page unnecessarily. This project aims to solve that problem by allowing users to Select icons and generate custom font from them. Recurrent users can also import the Icons configuration file to continue building the same font from where they left.</p>

<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>

Exception handling in GCC follows the Itanium ABI, particularly the C++ exception handling ABI. This process involves creating, throwing, and finally destroying the exception object. The strategy employed is known as the Zero Cost strategy, which aims to minimise the impact on the main program execution path. It achieves this by pushing all exception handling operations into separate side tables, thus avoiding any potential negative effects on the instruction cache. When an exception is caught, GCC utilises DWARF information to unwind the stack, allowing the program to resume execution at the appropriate point. However, one current limitation is that GCC does not track the specific type of exception that is thrown. This project aims to improve the nothrow detection in GCC which will allow to eliminate the dead exception handling regions and thus optimise the intermediate code generated.

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.

This proposal aims to enhance CVE-BIN-TOOL by integrating support for different VEX triage functionality. The problem at hand is the limited scope of CVE-BIN-TOOL, which currently lacks extended VEX integration, hindering its ability to provide a holistic risk assessment. The solution involves refactoring the current workflow, implementing support for various VEX formats (such as CSAF, OpenVex, and CycloneDX), establishing linkages between SBOMs and VEX formats, and updating documentation and tests for usability. Through this project, we aim to provide cybersecurity professionals with a comprehensive tool for identifying and mitigating vulnerabilities effectively. The deliverables include a refined workflow, support for multiple VEX formats, documented linkage mechanisms, and updated documentation and tests.

Currently, LPython does not have full implementations of data structures such as list, dict, set. The goal of this project is to improve support for partially implemented data structures, and implementing other data structures, along with useful algorithms. Further, these implementations would be benchmarked with equivalent ones in other language libraries, such as the C++ STL. I plan to cover these: - Add missing list functions - Improve dict by expanding the space of keys and values, and nested dictionaries - Create an implementation of set - Allow passing of data structures as function parameters and return values - Work on ordered and unordered variants of dict and set - Explore improvement of hashing functionality beyond linear-probing and chaining - Work on other data structures such as heap and trie

Documentation is not written or updated as much as it should be because the overhead is too great. This project aims on integrating existing tooling to provide an intuitive and accessible process to write and edit documentation. The implementation of this solution can be broken down into two phases: 1. Hotswapping page content for code editor 2. Generating pull requests based on content delta

<p>DFFML is a machine learning based project which provides APIs for training and testing datasets using various machine learning frameworks such as scikit-learn, tensorflow and more, making it easy to generate datasets, train and use machine learning models, and integrate machine learning into new or existing applications.</p> <p>At present, there is no way of training and testing the existing machine learning models in DFFML on image datasets, so I have selected 2 image processing python libraries OpenCV and Scikit-Image to wrap in DFFML.</p> <p>The project is divided into 2 parts:</p> <ol> <li>Wrapping the Image Processing Libraries namely OpenCV and Scikit-Image.</li> <li>High Level Operation Workflow, i.e., Custom Operations which will act as high level operations implementing a predefined flow of OpenCV and Scikit-Image functions.</li> </ol>

This project aims to implement an in-memory, volatile file system for NVIDIA GPUs using the NVPTX backend in newlib. NVPTX offloading in GCC enables running tests on the GPU, but currently lacks proper file system support. The proposed solution involves two key objectives: (1) developing a lightweight file system driver that operates in the GPU’s VRAM, and (2) redirecting NVPTX system calls in newlib to interface with this in-memory file system. The file system will support basic operations such as file creation and deletion, without requiring recursive directory structures. This work will improve the runtime environment for NVPTX targets and potentially pave the way for future extensions, such as RPC-based host filesystem access or support for AMD GPUs via the AMD GCN backend.

<p>The point of this project is about coverage of branches of parsers as well as corner cases. In other words, there are mainly two parts. At first, add tests for newly added functions, features (new features for the test suite itself, too) , etc. Secondly, improve existing test cases with the aim at corner cases (type of variables, issues related to pointers, signedness, etc).</p>

<p>By comparing k3s k8s and writing related blogs to help technicians make technology selection in software development</p>

<p>Mission Support System is a flight planning software which a researcher can use to analyze predicted atmospheric data, and plan a flight-path with 3D way-points. The software in the present state allows editing by a single user per flight-path. To share this work, one has to export the work as a <code>$name.ftml</code> file and send it to other researchers for further planning. This back and forth communication not only consumes a lot of human efforts and time, but also can be frustrating when the number of researchers involved in a project is bigger, say &gt;=3.</p> <p>I propose a solution to this problem, the development of <code>Mscollab</code> which stands for "Mission Support Collaboration". <code>Mscollab</code> server would facilitate <em>real-time</em>, <em>collaborated</em> editing of flight-paths by <em>authorized</em> users. By design, it will also provide a chat facility for the users who are collaborating on the project. Its UI would be a part of <code>msui</code>, the core User Interface module of <code>mss</code>. It would additionally provide insights about temporal changes related to waypoints and the users who created them, for analytics purpose. <code>Mscollab-server</code> will be a standalone server built with Python, Flask, and python-socketio.</p>

<p>A fully automatic generator of WSDL documents to LuaSOAP composed by a generation module and a parameter based script. This will improve the LuaSOAP library usage by extinguishing users need to fully understand WSDL types and its syntax.</p>