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<p>Multithreaded programming is becoming a mainstream programming practice, while multithreaded programming is difficult and error prone. jpf-trace-server, an extension of Java PathFinder (JPF), can store and analyze traces. The error traces caused by deadlock can also be viewed in a new shell panel from jpf-shell.</p> <p>In this project, we will develop jpf-trace-server to trace the execution of the threads and to provide feedback to the programmer to help them fix the bugs. Inspired by previous work on visualization in JPF, we would like to show the thread interleavings as traces in a new panel of jpf-shell. We would like to show the source code in the panel along with the transition information. The users should be able to walk through each transition of a selected error trace and the corresponding source code where it is executed.</p>

<p>The aim of this project is to allow users to quickly edit the content of different components and modules of their websites without having to leave the front-end or going to a different page. This will save time when small changes need to be made. Normally if there is a typo on the page, one has to first check whether it is in the article's heading, its content, module title, module's content, banner, etc. And then open the corresponding edit page of the component.</p> <p>Upon the completion of this project, it will be much easier to edit the text of almost any part of the website. This project will also be helpful when the user wants to see the end product while modifying the page.</p> <p><strong>To conclude, we want to minimize the use of pop-ups and page-reloads involved in editing the content.</strong></p>

This project aims to integrate visual testing into the Kolibri Design System's (KDS) CI workflows to streamline the validation process for UI modifications. Currently, KDS lacks a visual testing service leading to manual effort in validating UI modifications. Leveraging tools like Puppeteer and Percy, the project proposes to automate the process of capturing and comparing screenshots of UI components before and after modifications, ensuring consistency and correctness. Key deliverables of the project are: 1. Integration of Percy into the existing Jest test suite, 2. Implementing visual testing for at least one UI component, 3. Establishing a mechanism for surfacing visual differences in pull requests. This will help enhance the efficiency and reliability of KDS development, fostering a consistent and polished user experience across Kolibri products.

<p>This project aims to improve Ceph’s Python infrastructure, making the codebase clean, modern, and maintainable. A major part of this improvement would be migrating to advanced Pythonic idioms and porting all the components written in Python 2 to the new, and more feature-rich Python 3 version. This will be done while also maintaining support for Python 2 using a project-wide compatibility layer.</p> <p>With Ceph’s growing codebase and complexity, it is imperative to structure the Python code into independent packages for easy maintenance. The build system of Cython bindings for Ceph libraries like RADOS, RBD, and Ceph FS will be improved, making them ready to be deployed to PyPI. Some other open-ended goals of the project are to refactor and rewrite components of Ceph which have seen very little maintenance in the past.</p>

In SageMath, tensors are presently stored within the Components class as a dictionary, with each key representing indices and values indicating the tensor's corresponding values. This project seeks to advance tensor handling within SageMath by leveraging Python libraries such as PyTorch, NumPy, and SymPy. The primary objective is to develop classes similar to the existing Components class and its subclasses, storing tensors as numpy.ndarray and torch.tensor objects. This change promises memory-efficient storage of tensors and performance enhancements in tensor operations. Additionally, methods like sympy and sympysage_tensor will be implemented to streamline tensor porting between Sage and SymPy. The project's development stages will include code implementation and modification, testing across a variety of inputs, comprehensive code documentation, and building robust tests to ensure functionality and reliability.

As of now, Kanidm doesn't provide a way to allow two users to mutually authenticate each other. I would like to design and implement such feature, building upon and further polishing the design already discussed between the maintainers and me (see pull request #1416). The idea is to use ECDH to compute a unique TOTP for each user, make the users exchange said TOTPs with an out of band channel and then verify if they are correct, with no online communication required between the involved kanidm servers. To begin with I'll have to implement a feature that allows kanidm to store ECDH keys for its users, as they are required for the Identity verification system to authenticate subjects. Afterwards I will work on the core server-side logic of the system, and finally, if time will allow it, I am going to implement a frontend interface in the kanidm client for the identity verification system. I also plan on writing a journal of my experience with this project.

AF_XDP is an address family that is optimized for high performance packet processing. AF_XDP redirects raw network packets to user mode through the XDP program. AF_XDP, like DPDK, can be used to bypass the Linux kernel to gain high-performance processing network packets. DNSDist acts as a load balancer and often needs to process and forward packets. Therefore, in order to forward DNS requests DNSDist needs the raw network packets processed by the Linux kernel network protocol stack, read its request, and send the same request to the downstream DNS server again through the Linux kernel network protocol stack. The repeated processing of the same or similar data by the Linux kernel network stack is an unnecessary overhead for the server deploying DNSDist. This overhead can be effectively avoided by bypassing the Linux kernel through AF_XDP.

<p>Boost.uBLAS employs numerous compile-time optimizations which improve the performance of matrix operations. However, there is no implementation of any application on matrices which can exploit this performance - like mean, variance, factorization etc. Providing some such fundamental functionalities for vectors and matrices in a single package would be a great addition to the uBLAS library. The goal of this project is to lay out the foundation of uBLAS to support basic statistical analysis methods and machine learning algorithms. More specifically, I am interested in implementing the following fundamental components:</p> <ul> <li>Basic statistics</li> <li>Running statistics</li> <li>Histograms - with custom number of bins and edges</li> <li>KMeans clustering</li> <li>Principal Component Analysis (PCA)</li> <li>Gaussian Mixture Model - Expectation Maximization</li> </ul>

Meshery Models are declarative representations of infrastructure and applications. Within these models, Relationships define how different Components (e.g., Kubernetes resources, Cloud services) interact and depend on each other. These relationships are crucial for visualizing, understanding, and managing complex cloud native systems. This internship focuses on significantly expanding the breadth and depth of Meshery Relationships across a wide array of technologies supported by Meshery. As Meshery continues to integrate with more cloud-native technologies (Kubernetes, public clouds, and all CNCF projects), there's a growing need to accurately model the intricate relationships between their components - vital for providing users with comprehensive insights and control over their deployments. Expected Outcome: - A multitude of new relationships defined both intra and inter Azure services. - Policy Contribution: For advanced interns, there may be opportunities to contribute to the Rego policies that evaluate and enforce these relationships.

Meshery Models are declarative representations of infrastructure and applications. Within these models, Relationships define how different Components (e.g., Kubernetes resources, Cloud services) interact and depend on each other. These relationships are crucial for visualizing, understanding, and managing complex cloud native systems. This internship focuses on significantly expanding the breadth and depth of Meshery Relationships across a wide array of technologies supported by Meshery. As Meshery continues to integrate with more cloud-native technologies (Kubernetes, public clouds, and all CNCF projects), there's a growing need to accurately model the intricate relationships between their components - vital for providing users with comprehensive insights and control over their deployments. Expected Outcome: - A multitude of new relationships defined both intra and inter GCP services. - Policy Contribution: For advanced interns, there may be opportunities to contribute to the Rego policies that evaluate and enforce these relationships.

NeoRoo currently talks to DHIS2 through raw HTTP calls with no offline support, so if a nurse loses connectivity while entering baby data, that data is just gone. I'm proposing to integrate the d2 Android SDK as the primary sync engine and build a Flutter abstraction layer on top of it that works across both Android and iOS. The main deliverables are: a Pigeon-based platform bridge to d2 (Android) and DHIS2 REST API (iOS), an offline-first sync queue using Drift/SQLite with automatic retry, a server-wins conflict resolution system with a review log, real-time sync status indicators in the UI, background sync with battery optimization, push notifications for server-side changes, and a testing + monitoring toolkit. By the end, healthcare workers should be able to use NeoRoo reliably regardless of connectivity.

Meshery Models are declarative representations of infrastructure and applications. Within these models, Relationships define how different Components (e.g., Kubernetes resources, Cloud services) interact and depend on each other. These relationships are crucial for visualizing, understanding, and managing complex cloud native systems. This internship focuses on significantly expanding the breadth and depth of Meshery Relationships across a wide array of technologies supported by Meshery. As Meshery continues to integrate with more cloud-native technologies (Kubernetes, public clouds, and all CNCF projects), there's a growing need to accurately model the intricate relationships between their components - vital for providing users with comprehensive insights and control over their deployments. Expected Outcome: - A multitude of new relationships defined both intra and inter AWS services. - Policy Contribution: For advanced interns, there may be opportunities to contribute to the Rego policies that evaluate and enforce these relationships.

I will migrate the eos-icons.com website to TypeScript and Next.js and also implement proper file management and state management in the project ensuring a cleaner and more scalable codebase. I will also do certain changes to improve the website's UI. I plan to add a navigable menu to the landing page showing all categories of the icons. Also, adding a new and improved HowTo section which contains information about the usage of icons in all places like React, Vue2/3, & HTML, and moving it into a side drawer panel along with the icon editor. I plan to add unit tests by using Jest & React-testing-library, and end-to-end(e2e) testing with Cypress.js. I will integrate these tests with Github CI/CD pipelines and ensure proper development workflow using es-lint, prettier and husky.

<p>ALICE (A Large Ion Collider Experiment) is a heavy-ion detector on the Large Hadron Collider (LHC) ring. It is designed to study the physics of strongly interacting matter at extreme energy densities, where a phase of matter called quark-gluon plasma forms. The new ALICE synchronous data reconstruction facility for Run 3 needs a real-time conditions and calibration data distribution mechanism. New calibration objects are produced at up to 50Hz and have to be propagated to about 2000 servers. For efficient data distribution in this environment a network multicast delivery mechanism has to be used. There will be two sides to be implemented for this project: a library to send the newly produced objects and a caching service to run on each of the 2000 servers to receive and keep in memory the objects, making them available to the localhost running processes via a REST API.</p>

When a Cassandra node shuts down, clients experience timeout errors because drivers cannot distinguish planned shutdown from a crash. CEP-59 introduces a GRACEFUL_DISCONNECT event in the native binary protocol that lets the server signal drivers before closing connections. My implementation uses a self-draining mechanism that hooks into the existing cleanup() callback in CQLMessageHandler — each connection closes itself when its last response flushes out, after a grace period that accounts for network-latent requests. Three conditions added to an existing callback, no new threads or timers. Deliverables: server-side four-phase shutdown (SEAL, SIGNAL, DRAIN, DEADLINE), Python driver support with DRAINING host state, configuration and observability metrics, protocol spec update, and unit/integration tests. Java driver support as a stretch goal. JIRA: CASSANDRA-21265 (7 sub-tasks).

Meshery Models are declarative representations of infrastructure and applications. Within these models, Relationships define how different Components (e.g., Kubernetes resources, Cloud services) interact and depend on each other. These relationships are crucial for visualizing, understanding, and managing complex cloud native systems. This internship focuses on significantly expanding the breadth and depth of Meshery Relationships across a wide array of technologies supported by Meshery. As Meshery continues to integrate with more cloud-native technologies (Kubernetes, public clouds, and all CNCF projects), there's a growing need to accurately model the intricate relationships between their components - vital for providing users with comprehensive insights and control over their deployments. Expected Outcome: 1. A multitude of new relationships defined both intra and inter AWS services. 2. Policy Contribution: For advanced interns, there may be opportunities to contribute to the Rego policies that evaluate and enforce these relationships.

Meshery Models are declarative representations of infrastructure and applications. Within these models, Relationships define how different Components (e.g., Kubernetes resources, Cloud services) interact and depend on each other. These relationships are crucial for visualizing, understanding, and managing complex cloud native systems. This internship focuses on significantly expanding the breadth and depth of Meshery Relationships across a wide array of technologies supported by Meshery. As Meshery continues to integrate with more cloud-native technologies (Kubernetes, public clouds, and all CNCF projects), there's a growing need to accurately model the intricate relationships between their components - vital for providing users with comprehensive insights and control over their deployments. Expected Outcome: 1. A multitude of new relationships defined both intra and inter AWS services. 2. Policy Contribution: For advanced interns, there may be opportunities to contribute to the Rego policies that evaluate and enforce these relationships.

<p>Energy system components are modular in nature and interact across multiple scales. There is a need for a modelling framework that can easily construct and configure systems of modular entities (energy demands, sources, converters, storages and network links). These systems should be hierarchically nestable, facilitating the construction of large systems at high levels of detail. The framework should also facilitate the approximation of sub-systems, to aid model solvability. The above framework should be implemented in an object-oriented fashion, making use of the modular &gt;&gt;Open Energy System Modelling Framework&lt;&lt; (Oemof). Components should be specified in a common format that integrates with a database of available entities. The implementation should allow particular systems to be duplicated and reconfigured as well as reused as subsystems in larger models. Configuration parameters of multiple sub-systems should be readable from an external table.</p>

The future is already here - it's just not evenly distributed” - William Gibson We’d like to introduce an idea for a new open-source project: Racklet. It’s a fully-integrated, Raspberry Pi form-factor server rack and software stack that aims to be a scale model of hyperscaler datacenters. All layers of the stack are 100% OSS/OSH, and will be developed together with the community. It’s reproducible through open PCB designs, 3D printed casing, and commodity, off-the-shelf hardware. We want to lower the barrier of entry for becoming cloud native. Racklet aims to inspire users to explore how modern server architectures work, in a tangible and educational way. Emphasis is put on security, knowledge sharing, extensibility, and portability. The goal is to conceptually map to real environments and provide an accessible and well-documented path to welcome future talents to the world of cloud native.

<p>Flow Completion Time (FCT) has been the core metric to optimize via scheduling, congestion control, load balancing in Data Center Networks (DCN). As a user-perceived metric, FCT is one of the most important networks' performance metrics, as the users typically want their flows to complete as quickly as possible during their interactions with the network, e.g., webpage download, file transfer. The past decade has witnessed significant interest in studying the FCT minimization in networked systems research, especially in DCN.</p> <p>Current NS-3 are less friendly to researchers in this domain. This project aims to augment NS-3 with related components to support the simulation in the domain so that researchers would try out with a minimal amount of coding. The framework will supplement the NS-3 ecosystem with components such as Multi-Level Feedback Queue scheduling, topology helper, load balancing algorithms and so on.</p>

Time Series models constitute an integral part of the machine learning stack. Time series tasks can span a wide range of tasks such as classification, regression, forecasting, etc. This project would aim to build elements of the FastAI.jl library, including high-level components for quickly generating state-of-the-art solutions for time-series tasks and low-level components for creating new novel techniques. The project would include adding support to load time series datasets using the FastAI.jl data registry, developing new methods for time series tasks, and training the models using best practices. We would also aim to write detailed tutorials on using, modifying, and creating new time series models for various tasks. We wish to achieve these goals without compromising on the simplicity of use, flexibility or performance that FastAI.jl can offer.

<p>The Akonadi framework is responsible for providing KDE PIM applications with a centralized database to store, index and retrieve the user's personal information. The data can be stored either in local files or can be synced via a host of services like Google Calendar, Microsoft Exchange WebServer (EWS) or a number of Groupware servers like Nextcloud, DAV etc.</p> <p>The proposed project is to add to this list a secure, end-to-end encrypted, FLOSS sync solution for your contacts, calendars and tasks, called EteSync. EteSync clients are available for Android, iOS, the desktop (Cal/CardDAV bridge) and the web, and a Thunderbird plugin is in the works. The server too is open-source and can be self-hosted.</p> <p>The idea is to implement an Akonadi backend resource to enable KDE users to use EteSync to easily end-to-end encrypt and sync their contacts, calendars and tasks.</p>

<p>Migrate Chat, Skill, Account SUSI.AI under a common app SUSI.AI. Create a set of common and modular components. For styling 1 common approach should be used, like styled-components. Enhance Admin section by adding Reported Skills tab, Mail configuration, Email Messages(with reCaptcha configuration), User, Skills Statistics for Admin Panel.</p> <p>Add more functionality to settings for users. Make the whole application mobile responsive. Integrating Redux and removing flux architecture for data flow. Implement stop action, SoundCloud action on chat. Fix preview chat code and scroll interactions.</p> <p>Implement slideshow slider, Google Analytics, multi-search for skills CMS. All the above features will be integrated on frontend as well on the backend. Integrate support for SUSI.AI Speaker with SUSI.AI web client.</p>