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A secure, REST administrative connector that can be used locally or remotely using any Java developer kit. This connector enables remote access from a Java client or directly through an HTTPS call. The administrator and reader roles protect the connector. The connector requires TLS to keep the communication confidential. The HTTPS port used by the connector is defined through the default HTTP endpoint. This feature supersedes the restConnector-1.0 feature. It enables the REST handler configuration APIs. When this feature is combined with features supporting configuration validation, the corresponding REST handler validation APIs are enabled.

日志模块，集成log4j2, 提供工具类可静态打印日志, 业务可import通用日志配置(中间件配置和业务基础配置)开箱即用， 另外还提供http接口出入参打印和rpc提供者和消费者的出入参打印(有开关可以热启动和热关闭，同时可在线修改日志级别)， traceId在日志中进行输出以及普通文件日志之外的结构化日志( 供运维采集分析可视化)，提供全链路信息（包括tid）跨线程跨线程池的能力 Log module, integrates log4j2, provides tool classes that can print logs statically, business can import common log configurations (middleware configuration and business basic configuration) out of the box, and also provides http interface input and output parameter printing and rpc providers and consumers. Input and output parameter printing (there is a switch for hot start and hot shutdown, and the log level can be modified online), traceId is output in the log and structured logs other than ordinary file logs (for operation and maintenance collection and analysis visualization). The ability to provide full link information (including tid) across threads across thread pools.

Set of various Utils: stacktrace noise filter, String to/from unicode sequence converter, simple Http client JSON parser/serializer, Silent String parsing to Integer and other numeric types, Parsing String to time intervals with support for time unit suffixes, JSON parser that provides serialization/deserialization of classes to JSON, Version comparator and Version ranges operations, Self-throttling binary reader from Http request, File reader, A utility that automatically initiates a Factory with instances of all classes that implement user provided interface. An infrastructure for writing Scheduled Task classes where time interval for task execution is provided in humanly readable format (such as "9h" for 9 hours)

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.