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Servlet-specific additions allowing one to more easily deploy the Stormpath SDK in a servlet-container-based web application.

Common parent metaproject for various miscellaneous additions (algorithms, automaton types) to AutomataLib, which are rather specific in their respective use case.

Provides several additions to the Scala standard library. For example an iterator for traversing directory trees including very reusable classes for using Ant-style path expressions.

Java UUID Generator (JUG) is a Java library for generating Universally Unique IDentifiers, UUIDs (see http://en.wikipedia.org/wiki/UUID). It can be used either as a component in a bigger application, or as a standalone command line tool. JUG generates UUIDs according to the IETF UUID draft specification. JUG supports 3 original official UUID generation methods as well as later additions (v6, v7)

HermiT is reasoner for ontologies written using the Web Ontology Language (OWL). Given an OWL file, HermiT can determine whether or not the ontology is consistent, identify subsumption relationships between classes, and much more. This is the maven build of HermiT and is designed for people who wish to use HermiT from within the OWL API. It is not officially supported by the HermiT development team, but was built initially for use with the Clojure-OWL library. It is built using the HermiT source tree without modification. There have been some additions to the test source tree to account for differences between the maven and ant environment; these are small and (hopefully) maintainable. The version number of this package is a composite of the HermiT version and an value representing releases of this packaged version. So, 1.3.7.1 is the first release of the mavenized version of HermiT based on the 1.3.7 release of HermiT. This package includes the Jautomata library (http://jautomata.sourceforge.net/), and builds with it directly. This library appears to be no longer under active development, and so a "fork" seems appropriate. No development is intended or anticipated on this code base.

Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes: forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set. Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions. Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set. Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc. It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes. Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.