BitWrk is creating a marketplace where participants can buy or sell computing power like stocks in a stock exchange, using Bitcoin as currency. The client software can be integrated with existing, compute-intensive applications (e.g. rendering software), creating a big boost by harnessing the combined computing power of the BitWrk network. Sellers earn money by putting their hardware to work, offering an alternative to Bitcoin mining.
lindyFrame is a desktop application framework which eliminates the development time needed to create software tools. The framework provides the ability to create applications which support several languages and loading resources from network sources. The core aspect of the tool is a plugin architecture which the developer uses to build the desired functionality in the desktop application. Multiple plugins can be created and loaded which will operate in their own individual threaded environments.
Dr. PortScan is a tool for the automatic analysis of port scans in large and complex network infrastructures. The differences between successive scans of a network can be sent as reports at regular intervals to predefined admins. It uses port scans generated with nmap by default.
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.
ClodHopper is a Java library for high-performance clustering of numerical data. It contains clustering implementations such as K-Means, K-Means++, X-Means, G-Means, Fuzzy C-Means, Jarvis-Patrick, and various forms of hierarchical clustering. ClodHopper's clustering implementations take advantage of the host system's concurrent processing ability to speed clustering. The data structures are also very lean to conserve memory usage. ClodHopper is very extensible. If you are developing a new clustering algorithm, you may save yourself an enormous amount of work by extending a ClodHopper base class.