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This library offers components that facilitate validation of arbitrary objects and how they are serialized towards a client. It focuses on the wiring of the proposed patterns and does not contain any concrete validator implementations.

An authorization module for the Fedora Commons Repository framework, implementing the RDF-based WebAccessControl authorization mechanism, as proposed by the W3C. This makes it possible to define fine-grained repository access controls on individual resources or classes of resources to specific users and/or groups.

An authorization module for the Fedora Commons Repository framework, implementing the RDF-based WebAccessControl authorization mechanism, as proposed by the W3C. This makes it possible to define fine-grained repository access controls on individual resources or classes of resources to specific users and/or groups.

Changelogs should be in sync with the corresponding release. It is confusing for your users if the changelog does not reflect the latest changes. An obvious mistake are changes in your changelog tagged as "unreleased". This Maven plugin checks that there are no more unreleased changes in your changelog and breaks the release build otherwise. Your changelog is expected to follow the format proposed by keepachangelog.com.

JCLAL is a software system for Active Learning research, developed in the Java programming language. It provides a high-level software framework and a robust object-oriented design that allows an easy use, extention, modification and reusability. It includes the most relevant query strategies proposed on the single-label and multi-label learning contexts.

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.

We propose two efficient algorithms for exploring topic diversity in large document corpora such as user generated content on the social web, bibliographic data, or other web repositories. Analyzing diversity is useful for obtaining insights into knowledge evolution, trends, periodicities, and topic heterogeneity of such collections. Calculating diversity statistics requires averaging over the similarity of all object pairs, which, for large corpora, is prohibitive from a computational point of view. Our proposed algorithms overcome the quadratic complexity of the average pair-wise similarity computation, and allow for constant time (depending on dataset properties) or linear time approximation with probabilistic guarantees.