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This version uses the Mavenized 1.6/1.7 branch of Maverick. It will probably eventually become the default Maverick provider implementation, but until such a time it will be a separate module. Using this version we pull the Maverick jars from the SSHTools repository and build directly Maverick is the definitive solution for total SSH compatibility on the Java™ platform. The library is designed to support SSH1 and SSH2 using any version of the Java Development Kit from 1.1 and above, and the API is also now J2ME-ready1 in anticipation of the new breed of interconnected mobile devices Downloads and more information about Maverick may be found at http://www.sshtools.co.uk/en/j2ssh-maverick/. This library is the provider bridge for SSHAPI.

This is a plugin to Madura Objects. It provides a rules/constraint engine to assist with validation, deriving new values from user inputs (eg total of invoices entered on this customer) an manipulating metadata (eg because the amount is above X we make some field readonly). Note that the Java that is using the monitored objects is quite unaware of the rules layer implemented here. That means you can change rules without having to go back to your Java code, and it also means you don't have to wonder if everything implemented the same rules. Anything using that Java object has the rules (unless you turn them all off).

DKPro Lab is a lightweight framework for parameter sweeping experiments. It allows to set up experiments consisting of multiple interdependent tasks in a declarative manner with minimal overhead. Parameters are injected into tasks using via annotated class fields. Data produced by a task for any particular parameter configuration is stored and re-used whenever possible to avoid the needless recalculation of results. Reports can be attached to each task to post-process the experimental results and present them in a convenient manner, e.g. as tables or charts.

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.