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MassBank of America (MoNA), is an auto curating repository for storing, comparing and querying mass spectra of chemical compounds. It is metadata centric and it was designed to allow easy integration into other tools by utilize its REST based application programming interface. At the current time it contains over 200k predicted and 40k unique experimental mass spectra and their associated metadata. The predicted spectra were obtained by utilizing the lipid blast library and the experimental spectra were acquired from 30 different facilities all over the world, including several major research facilities in the United States and Japan. MoNA is utilizing the InChI Key as unique identifier for chemicals and is designed for easy scalability and expendability. This is realized by utilizing common applications like nginx, grails, AngularJS, postgresSQL and tomcat. MoNA is currently integrated in applications like MSDial, BinBase, MZMine and the statistics package R. This was accomplished by utilizing its REST based API, which is also utilized by its main AngularJS based web interface. We consider MoNA to be highly useful for crosslinking mass spectra in publications, identification of unknowns and integration in data acquisition software. This package provides you with access to the REST api of the main MoNA installation.

XApi is a dependency injection, service-oriented, polyglot java framework. It is comprised of standalone modules for use in web, desktop and mobile java applications. This codebase enables complete reuse of all code across your java stack, as well as the ability to easily define your own Platform, so you can build your own customized runtime environment to extend or replace existing platforms. It is currently geared towards servlets and gwt clients, but the target we are currently developing include: java, gwt [webkit, firefox, ie], javafx, playn [android, ios, flash], appengine & vert.x For Google Web Toolkit, we also include reflection support, more complete emulation of java.lang.Class, zero-overhead dependency injection, a range of code generation utilities, and even "magic-method injection" (for production compiles any method in the app can be swapped out with others based on configuration properties, or any AST mangling / code generating methods you wish to write). There is also a bytecode transformer in the works which will replace calls into the DI utility with direct references to static final factories, to allow the java compiler to completely erase all injection overhead in all java runtimes.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

The Video SDKs are app development kits provided to enrich apps with video collaboration features to connect customers and communities. Use these SDKs to build apps with highly customized user interfaces along with access to raw video and audio data. Video SDKs are designed to be: * Easy to use: Import libraries, call required functions, and all video conferencing will be handled for you. * Lightweight: Video SDKs are streamlined toolkits with an enormous reduction in size compared to Meeting SDKs with all the power of Zoom's video and audio solutions. * Highly customizable: Raw video and audio data is available to you, allowing your chosen renderer to customize the video experience. Video sessions created by the Video SDKs are launched instantly, bringing a delightful video communication experience to your users with high-quality video and audio. Direct access to raw video and audio data allows improved interaction between users and the app video stream. Imagine a gaming video streaming app with direct interaction between the player and viewers based on in-game events or prompts from the community. Or, imagine an AR streaming platform with direct viewer access to the on-screen video. As with our Meeting SDKs, Video SDKs allow screen-sharing from devices, in-session chat messages, and high-quality video and audio streams similar to Zoom's core capabilities. The Video SDKs enable the following functionality in your app: * Launch a video communication session instantly * Share screen directly from your device * Send instant chat messages during the session * Capture and review raw data locally * Test different rendering schema and enjoy high-quality video and audio streams * Broadcast the video session to third-party live streaming providers To know more, see: https://developers.zoom.us/docs/video-sdk/

The Video SDKs are app development kits provided to enrich apps with video collaboration features to connect customers and communities. Use these SDKs to build apps with highly customized user interfaces along with access to raw video and audio data. Video SDKs are designed to be: * Easy to use: Import libraries, call required functions, and all video conferencing will be handled for you. * Lightweight: Video SDKs are streamlined toolkits with an enormous reduction in size compared to Meeting SDKs with all the power of Zoom's video and audio solutions. * Highly customizable: Raw video and audio data is available to you, allowing your chosen renderer to customize the video experience. Video sessions created by the Video SDKs are launched instantly, bringing a delightful video communication experience to your users with high-quality video and audio. Direct access to raw video and audio data allows improved interaction between users and the app video stream. Imagine a gaming video streaming app with direct interaction between the player and viewers based on in-game events or prompts from the community. Or, imagine an AR streaming platform with direct viewer access to the on-screen video. As with our Meeting SDKs, Video SDKs allow screen-sharing from devices, in-session chat messages, and high-quality video and audio streams similar to Zoom's core capabilities. The Video SDKs enable the following functionality in your app: * Launch a video communication session instantly * Share screen directly from your device * Send instant chat messages during the session * Capture and review raw data locally * Test different rendering schema and enjoy high-quality video and audio streams * Broadcast the video session to third-party live streaming providers To know more, see: https://developers.zoom.us/docs/video-sdk/

We have deprecated ZoomVideoSDK maven repository. Since Video SDK features have been modularized, we are deprecating this repository and publishing new releases into the following repositories. See the feature libraries documentation for details. ZoomVideoSDK Core — core Video SDK features: https://mvnrepository.com/artifact/us.zoom.videosdk/zoomvideosdk-core ZoomVideoSDK Annotation — Screen share annotation feature: https://mvnrepository.com/artifact/us.zoom.videosdk/zoomvideosdk-annotation ZoomVideoSDK Videoeffects — Virtual background feature: https://mvnrepository.com/artifact/us.zoom.videosdk/zoomvideosdk-videoeffects The Video SDKs are app development kits provided to enrich apps with video collaboration features to connect customers and communities. Use these SDKs to build apps with highly customized user interfaces along with access to raw video and audio data. Video SDKs are designed to be: * Easy to use: Import libraries, call required functions, and all video conferencing will be handled for you. * Lightweight: Video SDKs are streamlined toolkits with an enormous reduction in size compared to Meeting SDKs with all the power of Zoom's video and audio solutions. * Highly customizable: Raw video and audio data is available to you, allowing your chosen renderer to customize the video experience. Video sessions created by the Video SDKs are launched instantly, bringing a delightful video communication experience to your users with high-quality video and audio. Direct access to raw video and audio data allows improved interaction between users and the app video stream. Imagine a gaming video streaming app with direct interaction between the player and viewers based on in-game events or prompts from the community. Or, imagine an AR streaming platform with direct viewer access to the on-screen video. As with our Meeting SDKs, Video SDKs allow screen-sharing from devices, in-session chat messages, and high-quality video and audio streams similar to Zoom's core capabilities. The Video SDKs enable the following functionality in your app: * Launch a video communication session instantly * Share screen directly from your device * Send instant chat messages during the session * Capture and review raw data locally * Test different rendering schema and enjoy high-quality video and audio streams * Broadcast the video session to third-party live streaming providers To know more, see: https://developers.zoom.us/docs/video-sdk/

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.