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Talend ESB SE bundles Service Factory and Integration Factory, Service Locator, Service Activity Monitoring and a demo application to a stable, production ready distribution based on the industry leading open source frameworks Apache CXF and Apache Camel. It includes Web Service support based on JAX-WS and enables simple and scalable message based systems using well known Enterprise Integration Patterns. The Service Locator maintains a repository of service endpoints which can be used by Service Consumers to support simple failover and load balancing scenarios. Service Activity Monitoring allows to log and monitor service calls. A documented Car Rental demo application illustrates how to use these features.

Talend ESB SE bundles Service Factory and Integration Factory, Service Locator, Service Activity Monitoring and a demo application to a stable, production ready distribution based on the industry leading open source frameworks Apache CXF and Apache Camel. It includes Web Service support based on JAX-WS and enables simple and scalable message based systems using well known Enterprise Integration Patterns. The Service Locator maintains a repository of service endpoints which can be used by Service Consumers to support simple failover and load balancing scenarios. Service Activity Monitoring allows to log and monitor service calls. A documented Car Rental demo application illustrates how to use these features.

Closure Compiler is a JavaScript optimizing compiler. It parses your JavaScript, analyzes it, removes dead code and rewrites and minimizes what's left. It also checks syntax, variable references, and types, and warns about common JavaScript pitfalls. It is used in many of Google's JavaScript apps, including Gmail, Google Web Search, Google Maps, and Google Docs. This distribution is slightly modified from the original for easier use with J2CL outside of Bazel by Vertispan LLC. The links and references in this pom.xml will reference the Vertispan fork, to ensure that any error introduced by our changes are not incorrectly blamed on the upstream Google repository.

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

Pdf2Dom is a PDF parser that converts the documents to a HTML DOM representation. The obtained DOM tree may be then serialized to a HTML file or further processed. The inline CSS definitions contained in the resulting document are used for making the HTML page as similar as possible to the PDF input. A command-line utility for converting the PDF documents to HTML is included in the distribution package. Pdf2Dom may be also used as an independent Java library with a standard DOM interface for your DOM-based applications or as an alternative parser for the CSSBox rendering engine in order to add the PDF processing capability to CSSBox.

This module provides algorithms to compare sets, that is, order-invariant lists. These algorithms are implementations of the AlignmentAlgorithm interface defined in the algorithms module. In particular, this module contains the StrictSetAlignmentScoreAlgorithm for computing the cost of the optimal unordered alignment of two sets, the StrictSetAlignmentFullAlgorithm which provides the Alignment itself as well, and the GreedySetAlignmentScoreAlgorithm as well as the GreedySetAlignmentFullAlgorithm for computing a potentially sub-optimal but faster alignment of two sets. The optimal alignments rely on the HungarianAlgorithm for solving the assignment problem in bipartite graphs. Here, we rely on the implementation provided by Kevin L. Stern which is provided within this distribution.

--------------------------------------------------------------------------- Version for VIVO 1.9 This uses the source and test code from the Version for VIVO 1.10, edited to make it compatible with VIVO 1.9. It also borrows some code from VIVO 1.10 itself, changes the package name to avoid conflicts, and adds it here. * Vitro 1.9 uses Jena 2, not Jena 3, and Commons Lang 2, not 3, so modify the "import" statements in the Data Distribution code accordingly. * Vitro 1.9 has an earlier version of "...webapp.utils.sparqlrunner" package, so borrow the source from Vitro 1.10, move it to a different package so it won't conflict with the 1.9 version, modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this sparqlrunner package. * Vitro 1.9 has an earlier version of "...webapp.utils.configuration" package, so borrow the source from Vitro 1.10, move it to a different package so it won't conflict with the 1.9 version, modify it to use Jena 2 and Commons Lang 2, and modify the Data Distribution API code to use this configuration package. Make these modifications, compile, JAR it up, and deploy. ---------------------------------------------------------------------------