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el from group net.sf.tweety.logics (version 1.10)

Group: net.sf.tweety.logics Artifact: el
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0 downloads
Artifact el
Group net.sf.tweety.logics
Version 1.10
Last update 16. January 2018
Organization not specified
URL Not specified
License not specified
Dependencies amount 1
Dependencies fol,
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tinder from group org.igniterealtime (version 1.3.0)

Tinder is a Java based XMPP library, providing an implementation for XMPP stanzas and components. Tinders origins lie in code that's shared between Jive Software's Openfire and Whack implementations. The implementation that's provided in Tinder hasn't been written again from scratch. Instead, code has been moved from the original projects into Tinder, preserving al of the existing features and functionality. Most of the code that's now in Tinder is based on the org.xmpp package implementation that previously existed in Openfire and Whack. This is the code that defines classes such as Packet, JID, IQ, Component and their extensions. Additionally, some multi-purpose code (such as the DataForm and Result Set Management implementations have been moved to Tinder as well.

Group: org.igniterealtime Artifact: tinder
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35 downloads
Artifact tinder
Group org.igniterealtime
Version 1.3.0
Last update 19. April 2017
Organization Ignite Realtime
URL http://www.igniterealtime.org/projects/tinder/
License Apache License, Version 2.0
Dependencies amount 5
Dependencies dom4j, jcip-annotations, slf4j-api, libidn, concurrentlinkedhashmap-lru,
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burlap from group edu.brown.cs.burlap (version 3.0.1)

The Brown-UMBC Reinforcement Learning and Planning (BURLAP) Java code library is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. The library uses a highly flexible state/observation representation where you define states with your own Java classes, enabling support for domains that discrete, continuous, relational, or anything else. Planning and learning algorithms range from classic forward search planning to value-function-based stochastic planning and learning algorithms.

Group: edu.brown.cs.burlap Artifact: burlap
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50 downloads
Artifact burlap
Group edu.brown.cs.burlap
Version 3.0.1
Last update 03. August 2016
Organization not specified
URL http://burlap.cs.brown.edu
License Apache 2.0
Dependencies amount 9
Dependencies commons-math3, ejml, jcommon, jfreechart, joptimizer, junit, log4j, snakeyaml, jopt-simple,
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ijp-toolkit_2.12.0-M4 from group net.sf.ij-plugins (version 2.1.0)

<html>IJ Plugins Toolkit is a set of ImageJ plugins grouped into:<ul> <li>3D IO - import and export of data in 3D formats.</li> <li>3D Toolkit - operations on stacks interpreted as 3D images, including morphological operations.</li> <li>Color - color space conversion, color edge detection (color and multi-band images).</li> <li>Filters - fast median filters and various anisotropic diffusion filters.</li> <li>Graphics - Texture Synthesis - A plugin to perform texture synthesis using the image quilting algorithm of Efros and Freeman.</li> <li>Segmentation - image segmentation through clustering, thresholding, and region growing.</li></ul></html>

Group: net.sf.ij-plugins Artifact: ijp-toolkit_2.12.0-M4
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Artifact ijp-toolkit_2.12.0-M4
Group net.sf.ij-plugins
Version 2.1.0
Last update 09. May 2016
Organization net.sf.ij-plugins
URL https://ij-plugins.sf.net
License LGPL-2.1
Dependencies amount 4
Dependencies scala-library, commons-math3, jgoodies-binding, ij,
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io.janusproject from group io.janusproject (version 2.0.3.1)

Group: io.janusproject Artifact: io.janusproject
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0 downloads
Artifact io.janusproject
Group io.janusproject
Version 2.0.3.1


ecj from group edu.gmu.cs (version 22)

ECJ, A Java-based Evolutionary Computation Research System. ECJ is a research EC system written in Java. It was designed to be highly flexible, with nearly all classes (and all of their settings) dynamically determined at runtime by a user-provided parameter file. All structures in the system are arranged to be easily modifiable. Even so, the system was designed with an eye toward efficiency. ECJ is developed at George Mason University's ECLab Evolutionary Computation Laboratory. The software has nothing to do with its initials' namesake, Evolutionary Computation Journal. ECJ's sister project is MASON, a multi-agent simulation system which dovetails with ECJ nicely.

Group: edu.gmu.cs Artifact: ecj
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49 downloads
Artifact ecj
Group edu.gmu.cs
Version 22
Last update 08. September 2014
Organization Evolutionary Computation Laboratory at George Mason University
URL http://cs.gmu.edu/~eclab/projects/ecj/
License The Academic Free License, version 3.0
Dependencies amount 0
Dependencies No dependencies
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snmpAdaptor4j from group net.sf.snmpadaptor4j (version 1.1)

snmpAdaptor4j is an adaptor for Java Management eXtensions (JMX) providing a simple access to MBeans via the SNMP protocol. Thus, this adapter you allow to connect most monitoring tools (like Nagios and Cacti) to your Java applications. For each MBean, an XML mapping file allows to establish the relationships between attributes and the MIB of the SNMP adapter. No additional code is necessary to integrate the MBeans in the SNMP protocol. This adapter can work in a multi-applications environment (such as application servers).

Group: net.sf.snmpadaptor4j Artifact: snmpAdaptor4j
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0 downloads
Artifact snmpAdaptor4j
Group net.sf.snmpadaptor4j
Version 1.1
Last update 12. March 2013
Organization snmpAdaptor4j
URL http://snmpadaptor4j.sourceforge.net
License GNU Lesser General Public License v3.0
Dependencies amount 0
Dependencies No dependencies
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jmdns from group com.github.rickyclarkson (version 3.4.2-r353-1)

JmDNS is a Java implementation of multi-cast DNS and can be used for service registration and discovery in local area networks. JmDNS is fully compatible with Apple's Bonjour. The project was originally started in December 2002 by Arthur van Hoff at Strangeberry. In November 2003 the project was moved to SourceForge, and the name was changed from JRendezvous to JmDNS for legal reasons. Many thanks to Stuart Cheshire for help and moral support.

Group: com.github.rickyclarkson Artifact: jmdns
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2 downloads
Artifact jmdns
Group com.github.rickyclarkson
Version 3.4.2-r353-1
Last update 19. February 2013
Organization JmDNS
URL http://sourceforge.net/projects/jmdns/
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
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jetty-conf-maven-plugin from group net.uvavru.maven.plugin (version 1.0)

Jetty context XML configuration plugin generator helper. This plugin helps you to generate your Context XML file with a dynamic content such as: * classpath entries * web app resources * maven properties Plugin needs a template Jetty context XML file where the dynamic content is injected. Injection of dynamic content is supported as: * altering the DOM with new values * with enabled filtering dynamic values can be replaced as properties (see bellow) Filtering of the template file is supported. Basically it means you can include content from your maven properties in your context XML file. If desired plugin sets two maven properties with the dynamic content: * jetty.conf-plugin.classpath * jetty.conf-plugin.webapp These properties might be used for filtering as well. Plugin resolves project dependency artifacts. These artifacts are transformed into webapp resources and classpath entries. Works with multi-module projects. Works in Eclipse with enabled m2e.

Group: net.uvavru.maven.plugin Artifact: jetty-conf-maven-plugin
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0 downloads
Artifact jetty-conf-maven-plugin
Group net.uvavru.maven.plugin
Version 1.0
Last update 31. May 2012
Organization not specified
URL https://github.com/stepanv/jetty-conf-maven-plugin
License The Apache Software License, Version 2.0
Dependencies amount 2
Dependencies maven-core, maven-filtering,
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multiBoostAB from group nz.ac.waikato.cms.weka (version 1.0.2)

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).

Group: nz.ac.waikato.cms.weka Artifact: multiBoostAB
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0 downloads
Artifact multiBoostAB
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/multiBoostAB
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!



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