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This library contains native facade implementations of the JBrew utility libraries with the Java Native Interface (JNI). This set of libraries features specific optimizations for Unix-based systems in terms of performance and memory. This is achieved through careful tuning using the C programming language to not only control for garbage collection, but also to ensure maximum performance for elected library features.

This library contains native facade implementations of the JBrew utility libraries with the Java Native Interface (JNI). This set of libraries features specific optimizations for Unix-based systems in terms of performance and memory. This is achieved through careful tuning using the C programming language to not only control for garbage collection, but also to ensure maximum performance for elected library features.

Customized version of the ActiveMQ Maven Plugin that registers publishable transport connector addresses in the Maven properties so that dynamic allocation of ports can easily be achieved. This project provides a port of the updates to the currently-released ActiveMQ version 5.10.0; the updates are expected to be released with ActiveMQ version 5.12.0, after which this version will no longer be maintained. See also AMQ-5499 (https://issues.apache.org/jira/browse/AMQ-5499).

The Jadex GPMN (goal oriented process modeling notation) kernel provides a goal-oriented workflow kernel. The idea is that a workflow is specified as declarative goal hierarchy, which describes what has to be achieved to fulfill the workflow. At runtime goals are pursued by executing plans represented by standard BPMN workflows. The execution of goal-based workflows is achieved by conversion to BDI agents.

An SLF4J log provider that directly implements the SLF4J API and provides logging output suitable for console output (i.e. not to a file). The width of the output is restricted to the terminal width, with the possibility to split the width into multiple sections, each which may be a fixed width, or 'auto' which divides all remaining space amongst other automative sections. Any log attribute fields may then be placed in any of the sections with a particular alignment. Text will not overflow out of its section. Sections may be colour or styled with other text attributes, including hyperlinks for supported termins. Emoticons and other graphical sequences may be used in place of log levels. Configuration is achieved via 'ini' type files, which are reload automatically when changed.

This library contains native facade implementations of the JBrew utility libraries with the Java Native Interface (JNI). This set of libraries features specific optimizations for Unix-based systems in terms of performance and memory. This is achieved through careful tuning using the C programming language to not only control for garbage collection, but also to ensure maximum performance for elected library features. This library in particular features example usages of the JNI in order to test hardware for usability.

This library contains native facade implementations of the JBrew utility libraries with the Java Native Interface (JNI). This set of libraries features specific optimizations for Unix-based systems in terms of performance and memory. This is achieved through careful tuning using the C programming language to not only control for garbage collection, but also to ensure maximum performance for elected library features. This library in particular features example usages of the JNI in order to test hardware for usability.

This library contains native facade implementations of the JBrew utility libraries with the Java Native Interface (JNI). This set of libraries features specific optimizations for Unix-based systems in terms of performance and memory. This is achieved through careful tuning using the C programming language to not only control for garbage collection, but also to ensure maximum performance for elected library features. This library in particular features example usages of the JNI in order to test hardware for usability.

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.