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The crypto4j library provides a simple and pluggable crypto abstraction library for Java, including both cryptographic primitives and the artifacts defined in the JOSE standard. This library separates the cryptographic primitives from the JOSE artifacts. The library uses the algorithms available through JCA (JCE). The implementations of the cryptographic primitives provided in this library rely on the standard JCA implementation, and can be configured and extended through the JCA mechanisms. The JOSE portion of the library allows custom extensions by either replacing the provided JOSE artifact implementations with custom implementations, or by adding new JOSE artifacts, for instance in form of additional algorithms, which is particularly useful in case that the JOSE standard is updated or in case custom extensions to the JOSE standard should be implemented. The implementations available in this library generally come in two variants: a standard implementation of the primitives and advanced algorithms, and a high-performance implementation that takes advantage of implementation patterns that do not compromise cryprographic security, but speed up the overall processing time.

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.

Information extraction is the process of identifying specified classes of entities, relations, and events in natural language text – creating structured data from unstructured input. JET, the Java Extraction Toolkit, developed at New York University over the past fifteen years, provides a rich set of tools for research and education in information extraction from English text. These include standard language processing tools such as a tokenizer, sentence segmenter, part-of-speech tagger, name tagger, regular-expression pattern matcher, and dependency parser. Also provided are relation and event extractors based on the specifications of the U.S. Government's ACE [Automatic Content Extraction] program. The program is provided under an Apache 2.0 license.

The Universal Java Matrix Package (UJMP) is an open source library for dense and sparse matrix computations and linear algebra in Java. In addition to the basic operations like matrix multiplication, matrix inverse or decomposition methods, it also supports visualization, JDBC import/export and many other useful functions such as mean, correlation, standard deviation, mutual information, or the replacement of missing values. It's a swiss army knife for data processing in Java, tailored to machine learning applications.

In some data processing tasks we need to use huge maps or sets that are bigger than available JVM heap space or they are loading too slow to standard Java or Scala Maps. We use TSV format (text file with tab separated columns) for persist this kind of Maps or Sets. Some columns are used as a key and rest of columns as a value. Idea of this library is simple. We can prepare these maps once (sort by key), store it to file and then use it as memory mapped file. Searching key in sorted file has log(n) complexity. If more processes uses the same memory mapped file, it exists in memory just once (on Linux). This file can be loaded lazy by OS.

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.

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.

Sass makes CSS fun again. Sass is an extension of CSS3, adding nested rules, variables, mixins, selector inheritance, and more. It's translated to well-formatted, standard CSS using the command line tool or a web-framework plugin. This is a repackaged GEM in a JAR format of the sass-lang.gem package. The sass-gems package version follows the sass-lang.gem versions located http://rubyforge.org/frs/?group_id=9702. Simply change the version of this package to download and repackage the same GEM version.