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An easy-to-implement library that can assist Java developers in using the GeoHash algorithm in order to create geocodes based on custom latitude and longitude values. With the help of jGeohash, Java developers will be able to quickly and easily generate a geohash code using user-defined latitude and longitude values. By using the GeoHash algorithm, the space can be divided into multiple grid shapes. This library is a basic implementation from the GeoHash algorithm written in the java and can be applied to generate from the latitude and longitude a geohash code and reverse. The library is kept simple and have no dependencies to other libraries.

ModelCC is a model-based parser generator (a.k.a. compiler compiler) that decouples language specification from language processing, avoiding some of the problems caused by grammar-driven parser generators. ModelCC receives a conceptual model as input, along with constraints that annotate it. It is then able to create a parser for the desired textual language and the generated parser fully automates the instantiation of the language conceptual model. ModelCC also includes a built-in reference resolution mechanism that results in abstract syntax graphs, rather than mere abstract syntax trees.

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.

Jadex XML is an XML data binding framework for Java and also for other representations. The main idea of Jadex XML is that neither the XML-Schema on the one side nor the Java classes on the other side should define the binding. Instead, a separate mapping between both is used as a mediation. This allows designing the XML representation independent of the Java side but still being able to connect both as desired. This idea was first put forward by the JiBX data binding framework. Jadex XML pushes it further by combining it with the configuration by exception principle. The framework can detect obvious correspondences between both sides automatically and only needs configuration information when translations are necessary. The configuration information is currently specified directly in form of Java configuration classes.

The goal of quality-check is to provide a small Java library for basic runtime code quality checks. It provides similar features to org.springframework.util.Assert or com.google.common.base.Preconditions without the need to include big libraries or frameworks such as Spring or Guava. The package quality-check tries to replace these libraries and provide all the basic code quality checks you need. The checks provided here are typically used to validate method parameters and detect errors during runtime. To detect errors before runtime we use JSR-305 Annotations. With these annotations you are able to detect possible bugs earlier. For more informations look at FindBugs™ JSR-305 support.

The goal of quality-check is to provide a small Java library for basic runtime code quality checks. It provides similar features to org.springframework.util.Assert or com.google.common.base.Preconditions without the need to include big libraries or frameworks such as Spring or Guava. The package quality-check tries to replace these libraries and provide all the basic code quality checks you need. The checks provided here are typically used to validate method parameters and detect errors during runtime. To detect errors before runtime we use JSR-305 Annotations. With these annotations you are able to detect possible bugs earlier. For more informations look at FindBugs™ JSR-305 support.

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).

Scala classes to allow more elegant use of Multiverse from Scala. Atm the functionality is quite limited since not a lot of effort was put in the Scala integration. For the 0.6 release Multiverse should be able to work with in Scala written transactional objects configured with the Multiverse annotations. So that would reduce the need for this library, although in Scala they want to have special Scala 'interfaces' that provide some syntactic sugar to use java collections in Scala. So this module would be the good location for that. If anyone would like to help improving the Multiverse/Scala integration, please don't hesitate to join.