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JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.

"CLIF is Load Injection Framework": open performance testing. Submit light or massive traffic to your application/service under test, and get response time measurements as well as computing and networking resources usage measurements. Support for large scale distributed load injection (up to +1000 load injectors) with unlimited number of virtual users. Multiple protocols and probes available, extendible through Java programming to generate traffic with arbitrary protocols and to measure arbitrary resources usage. Integration to Eclipse and Jenkins.

JARTs are Java-based Asynchronous Real Time sockets. JART uses JNA to build a pure Java TCP/IP stack upon Netmap. It is implemented in imperative style (no TCP state machine) using asynchronous programming with the help of ea-async. JART runs on both Linux (with the proper Netmap kernel module) and FreeBSD with Netmap enabled. FreeBSD 12.1+ has Netmap in the kernel by default so it works "out of the box". While JART has proven fairly robust in limited testing, it is still a work-in-progress and may not be suitable for production use. Pull requests welcome!

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.

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"