Download JAR files tagged by java with all dependencies

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.

It helps to quickly make settings for class and its fields of with properties XML. Instead of create a lot of files to describe every class and its fields it use describing by type, by name. E.g. setting "java.lang.Integer"-"INTEGER NOT NULL" will be assigned fo every field of this type. Of course it can be overriden by settings for field name and so on. It is used by beige-web to describe how to render an entity and convert from HTML value. It is used by beige-orm to describe how to map an entity to a DBMS. It make simple settings - Map<String, String>, e.g. "title"-"Beige ORM 1.1", class settings Map<String, Map<String, String>>, e.g. "org.mode.Customer"-["defaultOrder"-"ITSNAME", "wdgFilterOrder"-"filterOrderStd"], fields settings Map<String, Map<String, Map<String, String>>>, e.g. "org.mode.Customer"-["itsId"["wdgNew"-"null", "wdgFilter"-"filterNumber"]]

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.

Alpheus AFP Parser is a library and parser for the IBM Advanced Function Presentation (AFP) document/print stream format. Alpheus covers all AFP specifications: MO:DCA, BCOCA, CMOCA, FOCA, GOCA, IOCA, and PTOCA. It is a complete implementation. Every Structured Field, Repeating Group, and Triplet is fully implemented as Java class. Alpheus AFP Parser was written from scratch and has no external dependencies. Copyright 2015,2016 Rudolf Fiala Alpheus AFP Parser is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This powerful reporting library aims to simplify producing reports using iText. The most demanding and coding intensive tasks when producing reports with iText are how to get from data to report elements, how to style those blocks and how to evaluate (debug) the resulting report layout. Exactly those three tasks are simplified by this library: xml configuration and/or annotations to transform existing Java data objects into report block, provide styling information (css like, syntax independent) seperate from code, a firebug like debug mode to help evaluate your report.

TrueZIP is a Java based virtual file system (VFS) which enables client applications to perform CRUD (Create, Read, Update, Delete) operations on archive files as if they were virtual directories, even with nested archive files in multithreaded environments. As a library, TrueZIP provides simple, uniform, transparent, thread-safe, read/write access to archive files as if they were virtual directories in a file system path. As a framework, TrueZIP provides the interfaces and classes to write file system drivers which plug-in to its federated file system space.

The AppNeta Java instrumentation agent. Contains the required jar, configuration file and native libraries for Windows and Linux. AppNeta provides application performance monitoring for Java web apps through this agent. The files contained here allow for event tracking within your application to identify latency timings between your application layers. More options can be found here: https://docs.appneta.com/java#configuring-instrumentation. For other information on our Java agent please visit https://docs.appneta.com/java.

The Brown-UMBC Reinforcement Learning and Planning (BURLAP) Java code library is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. The library uses a highly flexible state/observation representation where you define states with your own Java classes, enabling support for domains that discrete, continuous, relational, or anything else. Planning and learning algorithms range from classic forward search planning to value-function-based stochastic planning and learning algorithms.