Download JAR files tagged by final with all dependencies

This bill of materials (BOM) POM covers all the Hibernate ORM artifacts related to a particular version. By importing this BOM, you fix the version of all of Hibernate-related artifacts to the versions associated with a particular release. This prevents the overriding of Hibernate transitive dependencies which in turn could cause version conflicts between libraries. To use this BOM, add the following in your POM: <dependencyManagement> <dependencies> <dependency> <groupId>com.covisint.core</groupId> <artifactId>hibernate-orm-bom</artifactId> <version>4.3.1.Final</version> <type>pom</type> <scope>import</scope> </dependency> </dependencies> </dependencyManagement> And then declare your dependency on the Hibernate artifacts *without* a version.

Implements a least median squared linear regression utilizing the existing weka LinearRegression class to form predictions. Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model. The basis of the algorithm is Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection.

Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking. For more information see: I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422.

This project produces the Mojo that can build a proper release.xml for the SQuirreLSQL update site, given a directory where the updated jar artifacts are located. This plugin accepts a directory in it's configuration and expects to find three sub-directories there : core, plugins and i18n. It then categorizes the artifacts found in those directories accordingly in the final release.xml file, that is generated in the specified folder.

Contains the main Multiverse STM Implementation: the Alpha Engine. The Alpha engine supports readonly and update transactions. It also supports the retry/or else mechanism. This not the jar you want to have in your final distribution because it isn't instrumented (hence the name unborn). The jar you eventually want to have in your distribution is the multiverse-alpha jar. What this package does, is test the instrumentation for the Javaagent. The compiletime instrumentation is tested in the multiverse-alpha package (where all tests are copied and reexecuted but now with compiletime instrumentation).

Fontys Venlo best maven practices. "Up with your competencies" The 'testeasypom' Parent pom that provides all dependencies that you need for modern testing, generating good looking Javadoc and a set of static test reports using spotbugs, pmd, and checkstyle. This is NOT cargo-cult, because NONE of the dependencies in this pom will end up in your final product. This is a continuation of sebipom. Version 4.0.7: update dep and plugin versions. Version 4.0.8: jacaco 0.8.8 for java 17 and 18 support Version 4.0.11: default to java 17 Version 5.1.0: default to java 21 Version 5.2.0: include testfx stuff in fx profile

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

This module exists solely to package all other jre modules into a single uber jar. This makes deploying to non-mavenized targets much easier. Of course, you would be wise to inherit your dependencies individually; the uber jar is intended for projects like collide, which have complex configuration, and adding many jars would be a pain. It also allows dependent modules to discover new features, as modules graduate from labs to final, they will be added here. As you type X_, autocomplete will expose newly added services.