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jMimeMagic is a Java library for determining the content type of files or streams.

An implementation of a Sudoku solver, with the intent of formalising how easy a particlar sudoku puzzle would be to solve by determining what moves can be made deterministically, rather than just iterating guesses.

Jakarta Authorization defines a low-level SPI for authorization modules, which are repositories of permissions facilitating subject based security by determining whether a given subject has a given permission, and algorithms to transform security constraints for specific containers (such as Jakarta Servlet or Jakarta Enterprise Beans) into these permissions.

Jakarta Authorization defines a low-level SPI for authorization modules, which are repositories of permissions facilitating subject based security by determining whether a given subject has a given permission, and algorithms to transform security constraints for specific containers (such as Jakarta Servlet or Jakarta Enterprise Beans) into these permissions.

Jakarta Authorization defines a low-level SPI for authorization modules, which are repositories of permissions facilitating subject based security by determining whether a given subject has a given permission, and algorithms to transform security constraints for specific containers (such as Jakarta Servlet or Jakarta Enterprise Beans) into these permissions.

A Multi-valued, generic Metadata container class. The class uses an internal Map of string keys pointing to vectors of strings. The data structure looks like the following: [std:string key]⇒std:vector of std:strings The multi-valued nature of the class is handled transparently by this Metadata container. Since all values are stored internally as string vectors, the difference between a scalar value and a non-scalar is handled by determining whether the list of values for a particular key is greater than 1.

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.