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XLcloud aims to define and demonstrate the principles of HPC as a Service (High Performance Computing) for all those applications that involve highly intensive calculations. XLcloud is designed as a collaborative tool that enables users to work together on highly sophisticated software in the Cloud, thus sidestepping the need for individuals to have to purchase expensive software on their own. XLcloud combines the expertise of companies and academics that are innovative in the field of high performance computer architectures and flow visualization HD/3D and video. While many Internet-based applications are now available on the Cloud, high-performance applications still face a number of technological obstacles before they are cloud ready. For example, high performance applications often need large capacity processing, lots of storage and huge network bandwidth, needs that are difficult to meet in today’s Cloud. In developing HPC as a Service (HaaS), XLcloud has identified the following high performance applications involving research and industrial innovation: * Research environments such as universities or technical institutes where high performance computing is a requirement, * Online game applications, * Online streaming video in real time, * Search engines in real time, * Online three-dimensional imaging for medical use.

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://marketplace.zoom.us/docs/sdk/video/introduction

pact-jvm-consumer-specs2 ======================== ## Specs2 Bindings for the pact-jvm library ## Dependency In the root folder of your project in build.sbt add the line: ```scala libraryDependencies += "au.com.dius.pact.consumer" %% "specs2" % "4.0.1" ``` or if you are using Gradle: ```groovy dependencies { testCompile "au.com.dius.pact.consumer:specs2_2.13:4.0.1" } ``` __*Note:*__ `PactSpec` requires spec2 3.x. Also, for spray users there's an incompatibility between specs2 v3.x and spray. Follow these instructions to resolve that problem: https://groups.google.com/forum/#!msg/spray-user/2T6SBp4OJeI/AJlnJuAKPRsJ ## Usage To author a test, mix `PactSpec` into your spec First we define a service client called `ConsumerService`. In our example this is a simple wrapper for `dispatch`, an HTTP client. The source code can be found in the test folder alongside the `ExamplePactSpec`. Here is a simple example: ``` import au.com.dius.pact.consumer.PactSpec class ExamplePactSpec extends Specification with PactSpec { val consumer = "My Consumer" val provider = "My Provider" override def is = uponReceiving("a request for foo") .matching(path = "/foo") .willRespondWith(body = "{}") .withConsumerTest { providerConfig => Await.result(ConsumerService(providerConfig.url).simpleGet("/foo"), Duration(1000, MILLISECONDS)) must beEqualTo(200, Some("{}")) } } ``` This spec will be run along with the rest of your specs2 unit tests and will output your pact json to ``` /target/pacts/<Consumer>_<Provider>.json ``` # Forcing pact files to be overwritten (3.6.5+) By default, when the pact file is written, it will be merged with any existing pact file. To force the file to be overwritten, set the Java system property `pact.writer.overwrite` to `true`. # Test Analytics We are tracking anonymous analytics to gather important usage statistics like JVM version and operating system. To disable tracking, set the 'pact_do_not_track' system property or environment variable to 'true'.

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.

HockeySDK-Android implements support for using HockeyApp in your Android application. The following features are currently supported: Collect crash reports:If your app crashes, a crash log is written to the device's storage. If the user starts the app again, they will be asked asked to submit the crash report to HockeyApp. This works for both beta and live apps, i.e. those submitted to Google Play or other app stores. Crash logs contain viable information for you to help resolve the issue. Furthermore, you as a developer can add additional information to the report as well. Update Alpha/Beta apps: The app will check with HockeyApp if a new version for your alpha/beta build is available. If yes, it will show a dialog to users and let them see the release notes, the version history and start the installation process right away. You can even force the installation of certain updates. User Metrics: Understand user behavior to improve your app. Track usage through daily and monthly active users. Monitor crash impacted users. Measure customer engagement through session count. Add custom tracking calls to learn which features your users are actually using. This feature requires a minimum API level of 14 (Android 4.x Ice Cream Sandwich). Feedback: Besides crash reports, collecting feedback from your users from within your app is a great option to help with improving your app. You act on and answer feedback directly from the HockeyApp backend. Authenticate: Identify and authenticate users against your registered testers with the HockeyApp backend.