Download JAR files tagged by building with all dependencies

This project has a lot of business classes to work with genes and related named entities. Genes are part of a GeneDocument and may have gold IDs as well as mapped/predicted IDs. Another large part are species because most (all?) gene ID databases are species specific. So there are also classes or members to represent species mentions and the assignment of species IDs (taxonomy IDs) to genes. There is little to no business logic herein. This project is only useful as building block in other projects dealing with species assignment, gene ID mapping or similar efforts.

# 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)

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.

dyna4JDBC is a JDBC driver, that allows running alternative JVM language scripts (Groovy, JavaScript, Scala, Jython, Clojure, BeanShell, R (Renjin), JRuby etc.) or external console-oriented programs via the JDBC API. Mainly targeted at allowing JDBC-enabled business intelligence applications to run various scripts instead of SQL easily, dyna4JDBC captures and parses the output of scripts/external programs and presents that as a standard JDBC Result Set, allowing the output to be processed further for various purposes like building complex reports quickly and analysis with other tools.

MASSIS is a framework that facilitates the simulation of scenarios with multiple agents (representing people, robots, sensors, etc.) in indoor environments (i.e., in a building). MASSIS provides support for designing spaces and specifying the behavior of the elements and agents in them It is possible to define a great diversity of behaviours, from a simple sensor to the decisions of a person. MASSIS has been designed to keep this flexibility withough hindering performance. The framework is capable of supporting thousands of agents, each one with an specific behavior.

ATG DUST is a framework for building JUnit tests for applications built on the ATG Dynamo platform. This framework allows one to quickly write test code that depends up Nucleus or ATG Repositories. By using this framework one can drastically cut down on development time. It takes only a few seconds to start up a test with a repository, but it may take multiple minutes to start up an application server. To get started with DUST, take a look at http://atgdust.sourceforge.net/first-test.html. This page will walk you through the process of running a basic test which starts Nucleus. After that, read the other getting started guides to describe how to create standalone Junit tests which can startup repositories and use the DynamoHttpServletResponse classes. For only ATG10 and tested.

Class for building and using a decision table/naive bayes hybrid classifier. At each point in the search, the algorithm evaluates the merit of dividing the attributes into two disjoint subsets: one for the decision table, the other for naive Bayes. A forward selection search is used, where at each step, selected attributes are modeled by naive Bayes and the remainder by the decision table, and all attributes are modelled by the decision table initially. At each step, the algorithm also considers dropping an attribute entirely from the model. For more information, see: Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

BRAP is a Java and Android remoting protocol that uses native Java object serialization, encapsulated in HTTP. It aims to be an alternative to Spring HttpInvoker and Spring Security especially when you don't need or want the dependencies of Spring in your client, for example when building a rich client app or an Android app where size is important. The authentication mechanism lets you use your own domain objects as credentials. Server configuration can be expressed solely in web.xml or by subclassing the ProxyServlet but there is also an optional SpringProxyServlet available for seamless Spring Integration. BRAP focuses on being easy to use, small in size (9 kb), yet powerful and extensible. The client uses a pluggable TransportProvider with default implementations for HttpURLConnection and Apache HttpClient.