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This package contains several example programs of the usage of the JavaPermutationTools (JPT) library. JPT is a Java library that enables representing and generating permutations and sequences, as well as performing computation on permutations and sequences. It includes implementations of a variety of permutation distance metrics as well as distance metrics on sequences (i.e., Strings, arrays, and other ordered data types). In addition to programs demonstrating the usage of the JPT library, the jpt-examples package also contains programs for replicating the experiments from a few published papers that utilized the library or implementations on which the library is based. JPT's source code is maintained on GitHub, and the prebuilt jars of the library can be imported from Maven Central using maven or other build tools. The purpose of the package jpt-examples is to demonstrate usage of the major functionality of the JPT library. You can find out more about the JPT library itself from its website: https://jpt.cicirello.org/.

The Limbus System is a small light-weight CDI framework managing the Limbus Core Components. The object graph is represented by an XML configuration file or can be build using the Limbus System API. This module delivers an optional system component that visualizes the object graph and its dependencies after initializing: com.remondis.limbus.system.visualize.LimbusSystemVisualizer This component can be added to the Limbus System. To keep the dependencies of this module transparent and light-weight, the graph renderer is declared as an optional dependency. Add the following dependencies to your project to use the visualisation component: <!-- Graph Stream for Visualization feature This is an optional dependency and only required if using the com.remondis.limbus.system.visualize.LimbusSystemVisualizer --> <dependency> <groupId>org.graphstream</groupId> <artifactId>gs-core</artifactId> <version>1.3</version> </dependency> <dependency> <groupId>org.graphstream</groupId> <artifactId>gs-ui</artifactId> <version>1.3</version> </dependency>

# heatshrink-java Java library used to encode/decode [heatshrink](https://github.com/atomicobject/heatshrink) compressed data. ## Building ```bash ./gradlew build ``` ## Gradle dependency See https://search.maven.org/artifact/io.github.markrileybot/heatshrink-java/ ## Usage ### Java library ```java int windowSize = 9; int lookaheadSize = 8; ByteArrayOutputStream baos = new ByteArrayOutputStream(); try(HsOutputStream out = new HsOutputStream(baos, windowSize, lookaheadSize)) { out.write("ABCABCABCABCABCABC".getBytes()); } try(HsInputStream hsi = new HsInputStream(new ByteArrayInutStream(baos.toByteArray()), windowSize, lookaheadSize)) { byte[] res = new byte[512]; int len = hsi.read(res); System.out.println(new String(res, 0, len)); } ``` ### CLI ```bash $ java -jar heatshrink-java-exe.jar [-h] [-e|-d] [-v] [-w SIZE] [-l BITS] [IN_FILE] [OUT_FILE] ``` ## Status [](https://github.com/markrileybot/heatshrink-java/actions/workflows/gradle-publish.yml) [](https://coveralls.io/github/markrileybot/heatshrink-java?branch=master) [](https://maven-badges.herokuapp.com/maven-central/io.github.markrileybot/geokey)

The AppOptics Java instrumentation agent jar file. This artifact provides a convenient way to download the agent jar from public Maven repository. Please take note that this should NOT be included as a part of the project dependencies. Rather, this artifact should be integrated into the build process by using extra step such as the "copy" goal of "maven-dependency-plugin". For more information, please refer to https://docs.appoptics.com/kb/apm_tracing/java/install/#getting-the-agent-jar

Azure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live videos along with publishing the results, video, and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline. Use the client library for Video Analyzer Edge to simplify interactions with the Microsoft Azure IoT SDKs (https://github.com/azure/azure-iot-sdks) and programmatically construct pipeline topologies and live pipelines.

Leveraging Tencent's 21 years of experience in network and audio/video technologies, Tencent Real-Time Communication (TRTC) offers multi-person audio/video calls and low-latency interactive live streaming solutions. With TRTC, you can quickly develop cost-effective, low-latency, and high-quality interactive audio/video services. Audio/Video Call Solution TRTC provides a client SDK that works across platforms and regions. This solution can be easily used on webpages to enable audio and video call capability for all applications. Low-Latency Interactive Live Streaming Solution TRTC leverages Tencent Cloud's high-quality node resources, industry-leading network and audio/video technologies to helps you build interactive live streaming services with minimal lags and a latency below 1 second, taking live streaming into the era of CDN 2.0.腾讯实时音视频（Tencent Real-Time Communication，TRTC）将腾讯21年来在网络与音视频技术上的深度积累，以多人音视频通话和低延时互动直播两大场景化方案，通过腾讯云服务向开发者开放，致力于帮助开发者快速搭建低成本、低延时、高品质的音视频互动解决方案。

Concourse is a distributed database warehouse for transactions, search, and analytics across time. Developers use Concourse to quickly build mission critical software while also benefiting from real time insight into their most important data. With Concourse, end-to-end data management requires no extra infrastructure, no prior configuration and no additional coding–all of which greatly reduce costs and allow developers to focus on core business problems.