Download JAR files tagged by specific with all dependencies

All Roads Lead to ROME. ROME is a set of Atom/RSS Java utilities that make it easy to work in Java with most syndication formats. Today it accepts all flavors of RSS (0.90, 0.91, 0.92, 0.93, 0.94, 1.0 and 2.0), Atom 0.3 and Atom 1.0 feeds. Rome includes a set of parsers and generators for the various flavors of feeds, as well as converters to convert from one format to another. The parsers can give you back Java objects that are either specific for the format you want to work with, or a generic normalized SyndFeed object that lets you work on with the data without bothering about the underlying format.

All Roads Lead to ROME. ROME is a set of Atom/RSS Java utilities that make it easy to work in Java with most syndication formats. Today it accepts all flavors of RSS (0.90, 0.91, 0.92, 0.93, 0.94, 1.0 and 2.0), Atom 0.3 and Atom 1.0 feeds. Rome includes a set of parsers and generators for the various flavors of feeds, as well as converters to convert from one format to another. The parsers can give you back Java objects that are either specific for the format you want to work with, or a generic normalized SyndFeed object that lets you work on with the data without bothering about the underlying format.

A great way to output Xml. Far easier to code with than painful DOM or SAX like solutions and much nicer in terms of speed and memory usage. <br/> <br/> While XmlWriter contains its own xml outputter, it has the ability to sit on top of other core Xml writing products, such as <a href="http://xmlenc.sf.net/">XmlEnc</a>. In addition, the user may layer <a href="Optional.html">other functionalities</a> on top of the core writing, such as on the fly schema checking, date/number formatting, specific empty-element handling and pretty-printing.

rapiddweller 'Benerator' is a software solution to generate, anonymize, pseudonymize and migrate data for development, testing and training purposes. rapiddweller 'Benerator' is easy to learn, integrates into CI Tools like Jenkins and Gitlab CI and allows to create data models on the most abstract level. rapiddweller 'Benerator' allows creating realistic and valid high-volume test data, used for testing (unit/integration/load) and showcase setup. Metadata constraints are imported from systems and/or configuration files. Data can imported from and exported to files and systems, anonymized or generated from scratch. Domain packages provide reusable generators for creating domain-specific data as names and addresses internationalizable in language and region. It is strongly customizable with plugins and configuration options. rapiddweller 'Benerator' is build for Java 1.8 and Java 11. The development is ongoing for Java 11.

R can use RJava or jsr223 to communicate with java. R also has a class system called R6. If you want to use a java library with native rJava or jsr223 in R there is potentially a lot of glue code needed, and R library specific packaging configuration required. However if you don't mind writing an R-centric API in Java you can generate all of this glue code using a few java annotations and the normal javadoc annotations. This plugin aims to provide an annotation processor that writes that glue code and creates a fairly transparent connection between Java code and R code, with a minimum of hard work. The focus of this is streamlining the creation of R libraries by Java developers, rather than allowing access to arbitrary Java code from R. The ultimate aim of this plugin to allow java developers to provide simple APIs for their libraries, package their library using Maven, push it to github and for that to become seamlessly available as an R library, with a minimal amount of fuss. A focus is on trying to produce CI ready libraries tested with Github workflows and ready for CRAN submission.

Luzzu is a Quality Assessment Framework for Linked Open Datasets. It is a generic framework based on the Dataset Quality Ontology (daQ), allowing users to define their own quality metrics. Luzzu is an integrated platform that: - assesses Linked Data quality using a library of generic and user-provided domain specific quality metrics in a scalable manner; - provides queryable quality metadata on the assessed datasets; - assembles detailed quality reports on assessed datasets. Furthermore, the infrastructure: - scales for the assessment of big datasets; - can be easily extended by the users by creating their custom and domain-specific pluggable metrics, either by employing a novel declarative quality metric specification language or conventional imperative plugins; - employs a comprehensive ontology framework for representing and exchanging all quality related information in the assessment workflow; - implements quality-driven dataset ranking algorithms facilitating use-case driven discovery and retrieval.

This module defines the interfaces for Comparators in the TCS Alignment Toolbox. A Comparator has the purpose of defining the dissimilarity between elements in the input sequences of an Alignment. More specific information on Comparators can be found in the 'Comparator' interface. You can find a lot of helpful standard implementations of Comparators in the comparators-lib module. In the TCS Alignment Toolbox we require the output values of Comparators to lie in the range [0,1]. Many natural dissimilarities on value sets do not meet this criterion, such that additional normalization has to be applied. To that end this package also contains a Normalizer interface for functions that map real values from the range [0, infinity) to the range [0,1]. This package also provides a few convenience implementations of the Comparator interface to make the implementation of custom Comparators simpler, namely: SkipExtendedComparator, ParameterLessSkipExtendedComparator, ComparisonBasedSkipExtendedComparator, and ParameterLessComparisonBasedSkipExtendedComparator. Finally the TCS Alignment Toolbox also provides the means to learn parameters of Comparators. To enable that Comparators must implement the DerivableComparator interface to properly define the parameters that can be learned and the gradient of the dissimilarity with respect to these parameters. Gradients are stored using the Gradient interface as well as some convenience implementations of said interface, namely EmptyGradient, SingletonGradient, ArrayGradient and ListGradient.