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<p>Mapping free text trait names to ontologies so that they can be processed is an important task that is not yet standardized. The goal of this project is to create an easy to use web application so that the curators can do this job efficiently.</p>

MusicBrainz contains data about upcoming and past events. This data is not surfaced, nor available to ListenBrainz users in any way. This data should be utilized in ListenBrainz to provide it's users with an easy way to discover events. This will be achieved by copying over, and caching the event data from MusicBrainz in a suitable format, and creating endpoints, as well as adequate frontend to display this information. The user should be able to discover events, gather event data from ListenBrainz itself, and find events relevant to them based on their preferred artists.

<p>The aim of this project is to investigate and propose a plan for <em>separation of Servo script crate</em>. Script crate lays in the core of the Servo and requires a large amount of memory to build. Large memory requirements make it impossible to build on 32-bit computers and slowdown development.</p>

<p>Teiid is a data virtualization tool. This tool enables querying multiple data sources with a single query. My project covers the following 3 major tasks:</p> <ol> <li>Implementing a translator for parquet files that would enable reading of parquet files from different sources.</li> <li>Implementing data source support for s3 would enable the reading of various files in S3 sources.</li> <li>Implementing data source support for HDFS would enable the reading of various files in the HDFS source.</li> </ol>

Concerto is the modeling language of the Accord Project. It supports code generation for 16 target languages and schema formats such as Java, TypeScript, Go, Rust, C#, GraphQL, and Protobuf. At present, CI only checks that the generator runs without crashing. It does not verify that the generated code compiles or validates in its target environment. This allows broken code to reach users without detection. This project adds full compilation and validation checks across all 16 targets through three deliverables. The first deliverable is a set of Docker images, one for each target. Each image includes the Concerto CLI and the required language toolchain. These images are published to the GitHub Container Registry with layer caching enabled. The second deliverable is a GitHub Actions matrix workflow. It generates code from a test corpus, runs it inside the correct image, and executes the compiler or validator for every pull request. The workflow fails immediately if the output is invalid. The third deliverable is a versioned test corpus of .cto model files. These files cover all Concerto language constructs such as primitives, optionals, arrays, inheritance, enumerations, and relationships. This ensures full coverage of code generation features.

My proposal is centered around integrating GitHub discussions seamlessly into the leaderboard repository while concurrently enhancing the scraper's type safety. This comprehensive approach will involve a meticulous refactoring of the existing Python-based scraper to TypeScript, ensuring a robust and type-safe codebase. Leveraging the power of Octokit and GraphQL, I intend to optimize the process of scraping GitHub data, enhancing efficiency and reliability. Furthermore, I aim to introduce an innovative reward system for contributors, incentivizing active participation and fostering a collaborative environment. This system will recognize and reward contributors when their discussions are marked as answered, along with receiving an empathy badge as an acknowledgment of their valuable contribution. Overall, this proposal represents a strategic amalgamation of technical refinement and community enrichment, aligning with the overarching objectives of the project while enhancing its functionality and user experience.

<p>The goal of this project is to enhance SUSI, integrating different APIs to make it more responsive and smart and implementing SUSI AI messenger chatbots. SUSI will learn from every other interaction with user and will implement machine learning techniques relating with an option of enabling user to give feedback on answers.</p>

Currently, the KCL IDE plug-in based on Jetbrains LSP cannot support all versions of Jetbrains IDE, so migrate the KCL IDE plug-in to Lsp4IJ to support all versions of Jetbrains IDE. Expected Outcome: KCL IDE plug-in is migrated to Lsp4IJ

<p>Markup handling has been a problem in Apertium for a long time. It was done using superblanks that encapsulate markup information inside them during the translation process. This works well to protect the formatting of the document. However, languages represent information differently and during translation, words/phrases move around, get deleted, split, merge, etc. The markup information on the words needs to stick with the words, otherwise we end up with erroneous markup in the translation, which is what happened:</p> <pre><code>Spanish Input: &lt;i&gt;El perro&lt;/i&gt; &lt;b&gt;blanco&lt;/b&gt; English Output: &lt;i&gt;The white&lt;/i&gt; &lt;b&gt;dog&lt;/b&gt; </code></pre> <p>As part of this project, a new kind of blank was proposed - <strong>a wordbound blank.</strong> It contains any information that needs to stay attached to a word/phrase during the translation process. After modifying most modules in the pipeline to work with these wblanks, writing new de/reformatters, and adding markup in wblanks, the translation we have:</p> <pre><code>Spanish Input: &lt;i&gt;El perro&lt;/i&gt; &lt;b&gt;blanco&lt;/b&gt; English Output: &lt;i&gt;The&lt;/i&gt; &lt;b&gt;white&lt;/b&gt; &lt;i&gt;dog&lt;/i&gt; </code></pre> <p>It should prove immensely useful for users of Apertium MT system to translate html or any formatted documents such as odt, docx, pptx.</p>

This proposal describes a thorough implementation strategy for streamlining Zarr data straight from cloud storage using Ice Chunk and Zarr 3 technologies, thereby optimizing machine learning training workflows. The project's main goal is to incorporate these technologies into PVNet's training pipeline for processing NWP and satellite data. Creating a strong data access layer, intelligent prefetching techniques, flexible caching plans, and a smooth integration with PyTorch's data loading pipeline are all part of the implementation. A thorough benchmarking framework is also included in the project to gauge cost effectiveness and performance gains. Four stages make up the 175-hour schedule: preliminary setup and research, core cloud access mechanism implementation, performance benchmarking and optimization, and thorough documentation. The implementation of a cloud-aware PyTorch dataset, an adaptive prefetcher, and optimized chunk configuration based on access patterns are important technical advancements. By removing the requirement to locally download sizable datasets prior to training, the project seeks to revolutionize OpenClimatefix's use of cloud resources for machine learning training.

<p>C++ as a programming language comes in handy in case of performance oriented applications for example: a simulator. But when it comes to rapidly prototyping of applications and ease of use of APIs, it lags behind other languages like python which is one of the most widely used languages just because of this reason and also due to the plethora of libraries that it provides. So, binding the C++ APIs and exposing them to the python interface can be helpful in extending the use of innermodel lib to beginner developers with minimal knowledge of programming. By exposing C++ APIs to python we are achieving two things at the same time: getting the performance from C++ and ease of use through python, which can also increase contributions from the dev community and later help in the development of innermodel lib or robocomp in general.</p>

<p>This is a proposal for no. 15 project idea - "Webots integration with RoboComp". As the title indicates, the project aims at integrating RoboComp with Webots and making it possible to open simulations created in RoboComp on Webots simulator. To achieve it we need to get very good understanding of the .proto and .wbt extensions made in Webots and somehow reconstruct the logic from RoboComp to make it understandable for Webots. We can do that using Python classes and objects to interact between the files and programs.</p>

<p>My proposal consists on transform the fixed personal space used actually in Robocomp into an adaptive space, depending of the spatial context (there is difference, for instance, between a narrow corridor and a room). The main idea is to change the values that defines the personal space function in order to adapt the personal space to allow the robot to navigate around the person without problems, adapting it to the spatial context. Besides, I would like to extend the social navigation agent, including other typical social behaviors: crossing people in corridors, approaching to humans in human-robot interaction, etc. In this respect, it would be interesting to study how these algorithms are also integrated into the planner and mission agents, and how define the priority between these missions by defining a social behavior planner.</p>

<p>The detection of risk situations during the navigation of mobile robots is an essential task for future applications. The goal is to create a software agent in Robocomp with the aim of improving vehicle driving, using deep learning and computer vision techniques.</p> <p>The main idea is to use one or several RGB cameras placed in a vehicle for lane detection, pedestrian detection, vehicle detection, sign detection and more elements that affect driving. To perform this task, it is possible to work either with real datasets of cameras placed in vehicles or to use the Carla vehicle driving simulator.</p>

<p>One proposal to redesign Learnbot to solve current problems and make the robot more attractive and expressive</p>

<p>Machines have been socially aware but the efforts to be put into making them socially aware or their behavior to be socially acceptable are tremendous. This is an effort in the same direction to make a machine more socially aware with the help of machine learning techniques using graph data. Graph data is better able to extract the semantics of the environment. This project focuses on the graphical representation of a scenario which helps us to derive the social acceptability score of a robot. Hence, using graph data coupled with the power of machine learning, this project is a step closer to social intelligence embedded in robots.</p>

<p>Robocomp current tutorials are simple and cover just the basics. Improved tutorials and use cases need to be created for Robocomp to be a framework friendly for beginners as well as more advanced users.</p> <p>Working on creating examples of how Robocomp can be implemented, and why it should be used, would make it more accessible to the general public. As will improving the "How to Contribute Page", and making pull requests templates would make it easier to collaborate for more advance developers.</p> <p>Getting Robocomp available in other operating systems it's necessary to making it more well-known, in other Linux distros as well as Windows. This can be done with virtual machines images or making tutorials to download it with their respective dependencies on each OS.</p> <p>In summary, Robocomp would be benefited from offering a better user experience and a more seamlessly transition for collaborators to work on it.</p>

<p>RoboComp’s existing simulator, RCIS, is based on OpenSceneGraph technology and custom made actuators and sensor. This project is to build prototypes of robotics simulation using V-REP and use its APIs to connect them to RoboComp ecosystem. Specifically, the project consists of implementing RoboComp omnirobot, joint motor, laser, RGBD interfaces and create a model of other RoboComp’s robots in V-REP.</p>

<p>This Graphical User Interface is responsible for the Robot's controlling and monitoring. This uses CORTEX architecture which is a cognitive robotics architecture and communicate through a graph structure called Deep State Representation (DSR).</p>

<p>Building an interactive robotic simulator in order to simulate the complex real world. As real worlds are dynamic in nature we need a simulator where these type of environment can be provided to the robot.</p>

<p>The navigation of a robot in an environment with humans is a subject with enormous interest in the last years. To be accepted in these types of scenarios, it is important that the robot navigate respecting social norms, for example, avoiding getting too close to humans, avoiding interrupting a conversation or asking permission to pass through a blocked path. This project aims to describe the dialogue manager, besides the corpus that allows establishing dialogues between the robot and the humans in real situations to improve the behavior of the robot navigation system, making it more socially accepted. The dialogue manager will be a RoboComp agent, which reads information from the robot’s world representation (a graph) and adapts the conversation to the current situation (e.g, according to the age of people, genre, etc).</p>

<p>The existing RCIS simulator is not very efficient when our robot get more complex so we need more advanced functions in RCIS. This project is to improve the current RCIS with new functions dealing with contact physics. Currently RCIS has only collision detection feature. We have to add more functionalities like what happen after collision with the help of collision angle, gravity and contact physics.</p>

<p>Project aims at designing a custom language for getting rid of the burden of textual programming and to provide abstraction and hide the underlying processes from the user and compress huge chunks of code into powerful <em>purpose-specific</em> commands. This set of commands is realized as <strong>Domain Specific Language</strong> and this language will eventually be translated to its <strong>Python</strong> equivalent for processing. The approach is <em>event-based</em> as well as <em>state-based</em>.</p>