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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 Lutra submodule provides a common framework that other submodules in Lutra use and extend. It includes an implementation of OTTR's data model with classes for templates, instances, types, etc.; a system for handling results and error messages; core interfaces and abstract classes for streamed pipelines for parsing and writing templates and instances; as well as implementation of the TemplateStore, a container for templates, with algorithms for checking correctness of templates and expanding and checking correctness of instances. Lutra is the reference implementation of the OTTR framework. For more information about OTTR, see http://ottr.xyz.

DynamicJasper (DJ) is an API that hides the complexity of Jasper Reports, it helps developers to save time when designing simple/medium complexity reports generating the layout of the report elements automatically. It creates reports dynamically, defining at runtime the columns, column width (auto width), groups, variables, fonts, charts, crosstabs, sub reports (that can also be dynamic), page size and everything else that you can define at design time. DJ keeps full compatibility with Jasper Reports since it's a tool that helps create reports programmatically in a easy way (it only interferes with the creation of the report design doing the layout of the elements). You can use the classic .jrxml files as templates while the content and layout of the report elements are handled by the DJ API. http://dynamicjasper.com

Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html

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.