Backerup is a robust remote backup system which can operate over relatively slow networks. File transfers are done using rsync, and snapshots are taken using hardlinks. Bandwidth can be controlled per host and per network. Priorities can be set on backup groups, as well as requesting minimum ages for backups.
Dandelion is a 3D graph rendering application which can be controlled across a network. Its main purpose is to allow clear network graphs to be rendered in a window, which can be controlled by a separate application or the user. The Dandelion visualization is actually controlled by issuing simple commands to it across the network (although this could all be happening on a single machine). The Dandelion source includes a set of very simple libraries which can be incorporated into other applications and which can be used to send these commands. Libraries are included for C, C#, Java, and Python. The project was developed at Liverpool John Moores University within the PROTECT Centre.
SqliteJdbcNG is a JDBC driver for SQLite. The overall goal of this project is to start a fresh implementation that leverages newly available technologies in the Java world. For example, any SQLite driver for any language must integrate with the native SQLite library. All of the current Java implementations rely on a custom JNI library to call out to the SQLite library. This extra layer can easily create a headache for the development and deployment of the driver, since it needs to be built for a variety of operating systems. Fortunately, there are technologies like Bridj and JNA that can be used to call native code directly from Java. By leaving the majority of the headaches of integrating with the native library to the Bridj project, more time can be spent on making a high quality driver that is more compliant with the JDBC4 spec.
MLPACK is a C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression and least-angle regression.