Download JAR files tagged by relying with all dependencies

The Eclipse Lyo project is focused on providing an SDK to enable adoption of OSLC specifications. OSLC (Open Services for Lifecycle Collaboration) is an open community dedicated to reducing barriers for lifecycle tool integration. The community authors specifications for exposing lifecycle artifacts through uniform (REST) interfaces and relying on Internet and Linked Data standards.

jfuzzylite(TM) is a free and open-source fuzzy logic control library programmed in Java. The goal of jfuzzylite is to easily design and efficiently operate fuzzy logic controllers following an object-oriented model without relying on external libraries. jfuzzylite is the Java equivalent of the fuzzylite(R) library. jfuzzylite is a trademark of FuzzyLite Limited. fuzzylite is a registered trademark of FuzzyLite Limited.

Shades is an Object/Relational Mapping (ORM) framework that works in conjunction with JDBC APIs. Instead of relying on XML or annotations for configuration, Shades relies on an interface called ORMapping. This makes shades simple and flexible. Shades has no new transaction model nor Connection management which you must learn. You control the transactions, and the Connection that Shades uses, through plain JDBC interfaces. Shades is designed to work well with modern web development frameworks like Tapestry and Wicket.

Module that defines the HttpChannel API. HttpChannels abstract complex download and upload steps into a simple and easy to use NIO Channel. NIO Channels can be wrapped into an InputStream or OutputStream and used in any way you may find possible to. Aside from that, Channels can be used natively in most next-gen libraries, meaning that you don't even need to wrap anything, just start writing or reading data to or from the channel wth a ByteBuffer. Anyone using the library should try to rely on code from this module only and, only if necessary, on configuration classes that are implementation specific. Relying on any other resource or class is considered an error and should NOT be done. One of the most interesting usages of channels for uploads and download is that you can easily copy data straight from one channel to the other, with less than 10 lines of code! Also, channels allows the implementation of a "tee" mechanism, in which data redden from a single channel can be copied to several other channels on the fly!

This module contains a more general approach to construct AlignmentAlgorithms by relying on the theoretical concept of Algebraic Dynamic Programming (ADP) as developed by Giegerich et al. ADP defines four ingredients for an alignment algorithm: 1.) A signature that defines the permitted alignment operations. Operations are just function templates with an associated arity, meaning the number of arguments it takes from the left sequence and from the right sequence. In the TCSAlignmentToolbox we have a fixed signature with the following operations: REPLACEMENT(1, 1), DELETION(1, 0), INSERTION(0, 1), SKIPDELETION(1, 0) and SKIPINSERTION(0, 1) 2.) A regular tree grammar that produces alignments, that is: sequences of operations, in a restricted fashion. 3.) An algebra that can translate such trees to a cost. In the TCSAlignmentToolbox this is a Comparator. 4.) A choice function, in case of the TCSAlignmentToolbox: the strict minimum or the soft minimum. An alignment algorithm in the TCSAlignmentToolbox sense of the word then is the combination of choice function and grammar. While we provide hardcoded versions of these combinations in the main package, the adp package allows you to create your own grammars. You can combine them with a choice function by instantiating one of the Algorithm classes provided in this package with a grammar of your choice. For example: AlignmentAlgorithm algo = new SoftADPScoreAlgorithm(my_grammar, comparator); creates an alignment algorithm that implicitly produces all possible alignments your grammar can construct with the given input, translates them to a cost using the algebra/comparator you provided and applies the soft minimum to return the score. This all gets efficient by dynamic programming. Note that there is runtime overhead when using this method in comparison with the hardcoded algorithms. But for complicated grammars this is a much easier way to go. For more information on the theory, please refer to my master's thesis: "Adaptive Affine Sequence Alignment using Algebraic Dynamic Programming"