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iServe is what we refer to as service warehouse which unifies service publication, analysis, and discovery through the use of lightweight semantics as well as advanced discovery and analytic capabilities. iServe provides the typical features of service registries and additional functionality that exploits service descriptions, service annotations and further data gathered and derived from the analysis of these descriptions, data crawled from the Web, periodic monitoring and user activities. iServe builds upon lessons learnt from research and development on the Web and on service discovery algorithms to provide a generic semantic service registry able to support advanced discovery over both Web APIs and WSDL services described using heterogeneous formalisms.

NoHR (Nova Hybrid Reasoner) is a plug-in for the ontology editor Protégé that allows its users to query knowledge bases composed of both an Ontology in OWL 2 EL or QL and a set of Reasoning Rules. Using a top-down reasoning approach, which means that only the part of the ontology and rules that is relevant for the query is actually evaluated, NoHR respectively combines the capabilities of ELK for OWL 2 EL and a dedicated direct translation for OWL 2 QL with the rule engine XSB Prolog to deliver very fast interactive response times. NoHR is the first hybrid reasoner of its kind for Protégé. NoHR is also distributed as an API.

This is a demo program that illustrates some of the capabilities of Madura working with Vaadin. Specifically it shows the use of the JPAontainer. The JPAContainer shown here is extended from the one you can find in Vaadin Addons to support Spring's @Transactional notation. It is also using Madura Objects, but that is an interesting incidental because you could use Madura Objects with both. So the objective here is to show a complete working demo of how to configure everything to show a table of data fetched from a database using JPA, including popup edit forms.

Customizable cruft-free Scala archetype. Options: -sourceFolders: [all-in-src-java, scala-only, both-split-src]. DEFAULT: all-in-src-java. --all-in-src-java: Scala and Java code are both in src/main/java (same for test). Plays nicely with IDEs. --scala-only: only src/main/scala set up, and set as source folder. --both-split-src: both src/main/java and src/main/scala. Can play merry havock with IDEs, so not the default. -testLibrary: [scalatest, specs2, scalacheck-only, junit-only]. DEFAULT: scalatest. --scalatest: adds Scalatest AND Scalacheck AND JUnit for most of your testing needs. --specs2: adds specs2 AND Scalacheck AND JUnit. --scalacheck-junit: adds Scalacheck AND JUnit. --junit-only: adds JUnit ONLY. -scalaVersion: 2.10.x+. DEFAULT: 2.11.2. *The Scala tool version is generated automatically. *Some Scala versions may not be compatible with selected test library versions. Adjust as necessary.

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

This plugin provides integration between Spring and Maven. Plugin goals support loading a Spring context XML file as part of the Maven build lifecycle. The XML file can be on the local file system or be accessible via any URL Spring's resource loading mechanism can understand. Spring's "classpath:context.xml" style notation is supported. Annotated Java classes can also be used to load a Spring context. Maven properties are injected into the Spring context (both XML and annotation style) as a java.util.Properties bean named "mavenProperties". Maven properties are also registered as a top level PropertySource so that Spring's placeholder resolution framework automatically considers them. See Project Reports -> Plugin Documentation for details on plugin goals. By default, the profile "maven" is set as an active Spring profile along with any other active Maven profiles.