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Cloudinary is a cloud service that offers a solution to a web application's entire image management pipeline. Easily upload images to the cloud. Automatically perform smart image resizing, cropping and conversion without installing any complex software. Integrate Facebook or Twitter profile image extraction in a snap, in any dimension and style to match your website’s graphics requirements. Images are seamlessly delivered through a fast CDN, and much much more. This Java library allows to easily integrate with Cloudinary in Java applications.

ToroDB is an open source project that turns your RDBMS into a MongoDB-compatible server, supporting the MongoDB query API and MongoDB's replication, but storing your data into a reliable and trusted ACID database. ToroDB currently supports PostgreSQL as a backend, but others will be added in the future. ToroDB natively implements the MongoDB protocol, so you can use it with MongoDB tools and drivers, and features a document-to-relational mapping algorithm that transforms the JSON documents into relational tables. ToroDB also offers a native SQL layer and automatic data normalization and partitioning based on JSON documents' implicit schema.

ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance and scalability, ELKI offers many data index structures such as the R*-tree that can provide major performance gains. ELKI is designed to be easy to extend for researchers and students in this domain, and welcomes contributions in particular of new methods. ELKI aims at providing a large collection of highly parameterizable algorithms, in order to allow easy and fair evaluation and benchmarking of algorithms.

The Infinispan Spring Integration project provides Spring integration for Infinispan, a high performance distributed cache. Its primary features are * An implementation of org.springframework.cache.CacheManager, Spring's central caching abstraction, backed by Infinispan's EmbeddedCacheManager. To be used if your Spring-powered application and Infinispan are colocated, i.e. running within the same VM. * An implementation of org.springframework.cache.CacheManager backed by Infinispan's RemoteCacheManager. To bes used if your Spring-powered application accesses Infinispan remotely, i.e. over the network. * An implementation of org.springframework.cache.CacheManager backed by a CacheContainer reference. To be used if your Spring- powered application needs access to a CacheContainer defined outside the application (e.g. retrieved from JNDI) * Spring namespace support allowing shortcut definitions for all the components above In addition, Infinispan Spring Integration offers various FactoryBeans for facilitating creation of Infinispan core classes - Cache, CacheManager, ... - within a Spring context.

JSci is a set of open source Java packages. The aim is to encapsulate scientific methods/principles in the most natural way possible. As such they should greatly aid the development of scientific based software. It offers: abstract math interfaces, linear algebra (support for various matrix and vector types), statistics (including probability distributions), wavelets, newtonian mechanics, chart/graph components (AWT and Swing), MathML DOM implementation, ... Note: some packages, like javax.comm, for the astro and instruments package aren't listed as dependencies (not available).

Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).