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This artifact provides the implementation of the L* learning algorithm described in the paper "Learning Regular Sets from Queries and Counterexamples" (https://doi.org/10.1016/0890-5401(87)90052-6) by Dana Angluin including variations and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular Sets" (https://dx.doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata using homing sequences" (http://dx.doi.org/10.1006/inco.1993.1021) by Ronald L. Rivest and Robert E. Schapire.

This artifact provides the implementation of the model checker presented in the paper "M3C: Modal Meta Model Checking" (https://dx.doi.org/10.1007/978-3-030-00244-2_15) by Bernhard Steffen and Alnis Murtovi. The paper is based on "Model Checking for Context-Free Processes" (https://link.springer.com/chapter/10.1007/BFb0084787) by Olaf Burkart and Bernhard Steffen. Note that this implementation requires a runtime dependency to a specific ADDLib backend (see https://add-lib.scce.info/), which is not explicitly included in this artifact due to packaging reasons.

Provides non-free data, including the EPSG geodetic dataset, in a single read-only JAR file. This 'sis-embedded-data' module contains a copy of EPSG geodetic dataset in an embedded Apache Derby database. Having this module on the classpath avoid the need to set the 'SIS_DATA' environment variable for using the Coordinate Reference Systems (CRS) and Coordinate Operations defined by EPSG. EPSG is maintained by the IOGP Surveying & Positioning Committee and reproduced in this module with same content. See https://www.epsg.org/ for more information.

A tool for data backup and consistency. Architecture and technologies: + the classic three-tier architecture is used: Model, DAO, Presentation; + [Spring 6 / Spring Boot 3](https://spring.io/) is the reference framework; + [SQLite](https://sqlite.org/) is the underlying database; + [Spring Data JPA](https://docs.spring.io/spring-data/jpa/docs/current/reference/html/) with [Hibernate](https://hibernate.org/) is used as ORM; + JPA entities are separated objects; + `Finder` and DCI roles from [TheseFoolishThings](http://tidalwave.it/projects/thesefoolishthings) are used; + [SLF4J](https://slf4j.org) and [Logback](https://logback.qos.ch) are used for logging; + [TestNG](https://testng.org) and [Mockito](https://site.mockito.org/) are used for testing; + [Lombok](https://projectlombok.org) is used for cleaner code.

# 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)