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This feature enables support for obtaining the values of the user and password attributes of authData elements from an application, and also allows the use of the com.ibm.websphere.crypto.PasswordUtil class as an API.

This feature enables support for obtaining the values of the user and password attributes of authData elements from an application, and also allows the use of the com.ibm.websphere.crypto.PasswordUtil class as an API.

DefaultJ is a powerful Java library that provides default values for any class, offering multiple strategies for obtaining those values. It can be used as a dependency injection utility, similar to Guice. With 14 built-in strategies, DefaultJ can obtain default values through various means such as explicit bindings, annotations, enum values, factory methods, and more. It also supports custom annotations and offers annotation processors for validation. Available on Maven Central, DefaultJ can be easily integrated into Gradle and Maven projects. Contributions and selective usage of the library are encouraged.

DefaultJ is a powerful Java library that provides default values for any class, offering multiple strategies for obtaining those values. It can be used as a dependency injection utility, similar to Guice. With 14 built-in strategies, DefaultJ can obtain default values through various means such as explicit bindings, annotations, enum values, factory methods, and more. It also supports custom annotations and offers annotation processors for validation. Available on Maven Central, DefaultJ can be easily integrated into Gradle and Maven projects. Contributions and selective usage of the library are encouraged.

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.