Download JAR files tagged by issue with all dependencies

Apache Rat is a release audit tool. It improves accuracy and efficiency when checking releases. It is heuristic in nature: making guesses about possible problems. It will produce false positives and cannot find every possible issue with a release. Its reports require interpretation. In response to demands from project quality tool developers, Rat is available as a library suitable for inclusion in tools. This POM describes that library. Note that binary compatibility is not guaranteed between 0.x releases. Apache Rat is developed by the Apache Creadur project, a language and build agnostic home for software distribution comprehension and audit tools.

Provide interactive issue reporting in your Android application. Application users can take record the screen and speak their issues, take screenshots, making it easier to submit issues. Issues are created in your Jira or Azure Boards. The created issues are actionable as it has all the context required to understand and fix them. E.g. screenshot, screen recording, device diagnostics (logs, OS version, device manufacturer and so on. Visit https://support.redpen.ai/hc/en-us/articles/5579906622093-How-to-integrate-Redpen-Android-SDK-into-your-application for the instructions on how to integrate Redpen Capture Widget into your Android application.

This small library provides an optimized solution to a problem where tasks for a particular key need to be processed sequentially as they arrive. This kind of problem can be solved by a SingleThreadExecutor, however it is not efficient. The issue is that the tasks for unrelated keys are not being processed in parallel, instead they are put into a queue common to all keys and wait for the single thread to execute them. This library allows them to be executed concurrently. Moreover this library works well in a situation where all the possible keys and their number is not known upfront.

Burningwave core is an advanced, free and open source Java library that contains THE MOST POWERFUL CLASSPATH SCANNER for criteria based classes search. It’s possible to search classes by every criteria that your immagination can made by using lambda expressions. Burningwave core scan engine is highly optimized using direct allocated ByteBuffers to avoid heap saturation; searches are executed in multithreading context and are not affected by “the issue of the same class loaded by different classloaders” (normally if you try to execute "isAssignableFrom" method on a same class loaded from different classloader it return false). This library is useful also for creating classes during runtime, facilitate the use of reflection and much more...

The MAJ API was written by Richard Cartwright, with the help and support of Guillaume Belrose and Fang Ren. To contact the author, please raise an issue. The software is provided AS IS with no warranty whatsoever. The MAJ API (pronounced madge) is a pure Java API for creating, reading, manipulating and writing MXF (SMPTE ST 377), AAF structured storage and Reg-XML (SMPTE ST 2001) files. MXF files are commonly used as a container for professional media file formats and AAF is supported by a number of professional video editing packages. MXF and Reg-XML are used as part of the Interoperable Mastering Format suite of specifications.

This is an endorsed version of the original Deployment Client for Oracle GlassFish Open Source Edition. The purpose of the endorsement is to provide a shaded JAR which has the OSGI meta data removed because it causes the original Deployment Client to fail when used within a module running within a GlassFish container due to some class loader configuration conflicts between the container and the deployed module. This issue has been reported to the GlassFish project at https://java.net/jira/browse/GLASSFISH-20856 . It is hoped that it can get fixed so that this workaround project can get deprecated soon.

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.

DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests. Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets. For more details see: P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003; P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems.