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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 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.

A collection of filters for manipulating multi-instance data. Includes PropositionalToMultiInstance, MultiInstanceToPropositional, MILESFilter and RELAGGS. For more information see: M.-A. Krogel, S. Wrobel: Facets of Aggregation Approaches to Propositionalization. In: Work-in-Progress Track at the Thirteenth International Conference on Inductive Logic Programming (ILP), 2003. Y. Chen, J. Bi, J.Z. Wang (2006). MILES: Multiple-instance learning via embedded instance selection. IEEE PAMI. 28(12):1931-1947. James Foulds, Eibe Frank: Revisiting multiple-instance learning via embedded instance selection. In: 21st Australasian Joint Conference on Artificial Intelligence, 300-310, 2008.

The Interaction Model Mapper enables a code-first approach to the development of a skill by abstracting the interaction model. Alexa types such as Intents and Slots are defined as Java classes, and corresponding interaction model data such as sample utterances, prompts, synonyms, etc. are stored in resource bundles. A skill is then constructed from a collection of these entities, and the interaction model is rendered from the CLI. This abstraction enables developers to componentize and share parts of their skill with others and promotes a standard software build/deploy lifecycle for skills.