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The goal of quality-test is to provide a small Java library for basic code quality checks within unit tests. It is discouraged to use quality-test in production code. The checks provided in this library are designed to be used in unit-tests. The checks and utilities provided in this package check static properties of classes, for example whether classes are marked final or constructors are private. Additionally, there are utils to give additional code coverage for private constructors. Quality-Test often can be used together with Google Reflections (http://code.google.com/p/reflections/) to perform checks such as, assure that every class in package *.dto.* is final or make sure that no class in the package *.controller.* contains any non-final static variables.

This package contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determining the size of the quadratic penalty is used in both cases.

This package supports the parsing of AIS messages in Java. AIS, the Automatic Identification System, is a system aiming at improving maritime safety by exchanging messages between ships, other vehicles in particular aircraft involved in search-and-rescue (SAR), and (fixed) base stations. To be precise, this package support the ITU-R M.1371-4 AIS standard. See our extensive javadoc and in particular the class AISParser for more information on how to use this package. The parser was used in the Poseidon project, and is improved in the Metis project to better handle uncertain information. Both projects were led by the Embedded Systems Institute. In both projects Thales Nederlands was the carrying industrial partner, and multiple Dutch universities participated.

Siena is a persistence abstraction layer made in Java with various implementations: * siena-jdbc: persists objects into a relational database using JDBC (status: deeply tested) * siena-gae: persists objects into the Google App Engine's datastore using its low-level API (status: beta) * siena-sdb: persists objects using Amazon's SimpleDB (very experimental) * siena-remote: persists objects remotely using a remote web application that uses siena. (planning) Siena is also: * Intrusive. You make a class persistent by extending a certain class. Relationships are defined also in an intrusive way. * Simple. Simplicity is the most important thing. * Limited. Due to the simplicity there are several unsupported things such as JOINs. * Extensible. There are two interfaces you can implement on your own way.

A simple templating system for java codegen. This library includes an object-oriented, fluent source writing api, for a chained, intuitive codegen api. It also includes a template processor which can take java source files formatted with "magic comments", to allow simple mapping of GeneratorClass#generatorMethod() to inject dynamic source content. //@generateWith(GeneratorClass)// -Sets the current generator. You may use as many as you please //generatorMethod(arbitrary-string-data-you-may-want-to-parse)// -invokes the named method on the current injector, with any text (between brackets) sent as a string This allows you to markup default source with hooks where you may want to inject arbitrary code. It is especially useful for generating super-source in gwt compiles; changes to the default class are propagated to overrides.

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).

J2EE integrated development framework,for the rapid build enterprise applications.Based on Struts,Spring,Hibernate constructed to provide Web systems development functions commonly used components. Simply configure can be solved the general problems in the process of developing encountered. For example,already has built in login interface,user and role management,base AOP paging,logging feature and a lot more. It's a Java package, a class library for Java programmers. It's not an application for end-users in itself, but something that programmers can embed into their products.

Wicket is a Java web application framework that takes simplicity, separation of concerns and ease of development to a whole new level. Wicket pages can be mocked up, previewed and later revised using standard WYSIWYG HTML design tools. Dynamic content processing and form handling is all handled in Java code using a first-class component model backed by POJO data beans that can easily be persisted using your favorite technology.