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The Apache Java Enterprise Mail Server (a.k.a. Apache James) is a 100% pure Java SMTP, IMAP, JMAP and POP3 Mail server. We have designed James to be a complete and portable enterprise mail engine solution based on currently available open protocols. James is also a mail application platform. We have developed a Java API to let you write Java code to process emails that we call the mailet API. A mailet can generate an automatic reply, update a database, prevent spam, build a message archive, or whatever you can imagine. A matcher determines whether your mailet should process an email in the server. The James project hosts the Mailet API, and James provides an implementation of this mail application platform API.

This module was created to fix issue DIRSHARED-91 where the embedded Felix instance inside the default LdapCodecService implementation was making it very problematic to deploy the ldap-codec inside an RCP (OSGi) environment and hence Apache Directory Studio could not use it. This module is most likely temporary and will disappear once we are fully OSGi enabled. This module is a plain old jar, not a bundle to avoid accidental reuse as an OSGi module because it contains the version of the LdapCodecService that embeds Felix to make controls and extended ops pluggable in the codec.

rpc快速使用，可自动从配置中心获取注册中心、线程池、超时时间等配置， 在rpc调用异常时将异常封装成标准响应对象返回给调用方，并自定义rpc telnet指令供开发测试使用， 业务可使用提供注解进行rpc服务的消费和提供，可实现消费和提供者服务批量管理，提供泛化调用工具 RPC use quickly, and the underlying implementation is currently dubbo, which can automatically obtain configurations such as registration center, thread pool, and timeout time from the configuration center. When dubbo calls an exception, it will encapsulate the exception into a standard response object and return it to the caller, and customize the dubbo telnet command. For development and testing, businesses can use annotations to consume and provide RPC services, realize batch management of consumption and provider services, and provide generalized calling tools

Unicode Emoji List for Java is a comprehensive library that provides easy access to the vast world of Unicode emojis. This project aims to offer a user-friendly API for retrieving and utilizing emoji characters and their metadata within Java applications. The library covers a wide range of emojis as defined in the latest Unicode standards, ensuring compatibility and up-to-date representation of emojis across different platforms and devices. Whether for enhancing text processing capabilities, developing chat applications, or for any project requiring emoji support, Unicode Emoji List for Java delivers a robust and reliable solution. Its straightforward implementation allows for seamless integration into existing Java projects, fostering creativity and expanding the expressive potential of digital communication.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "minimum description length" (MDL) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "evidence-driven state merging" (EDSM) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

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.