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DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples. Comprehensive experiments have demonstrated that this technique is consistently more accurate than the base classifier, Bagging and Random Forests. Decorate also obtains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets. For more details see: P. Melville, R. J. Mooney: Constructing Diverse Classifier Ensembles Using Artificial Training Examples. In: Eighteenth International Joint Conference on Artificial Intelligence, 505-510, 2003; P. Melville, R. J. Mooney (2004). Creating Diversity in Ensembles Using Artificial Data. Information Fusion: Special Issue on Diversity in Multiclassifier Systems.

The Fireblocks Official SDK is a comprehensive software development kit that enables seamless integration and interaction with the Fireblocks platform. Fireblocks is a cutting-edge blockchain infrastructure platform that provides secure and scalable solutions for managing digital assets and transactions. This SDK empowers developers to build robust applications that can interact with the Fireblocks platform's features, including creating and managing vault accounts, initiating secure transactions, managing assets, and more. It abstracts complex interactions with the Fireblocks API, making it easier for developers to leverage the platform's capabilities while adhering to best practices in security and efficiency. Whether you're working on a cryptocurrency exchange, wallet application, or any other financial platform involving digital assets, the Fireblocks Official SDK simplifies the integration process and allows you to harness the power of Fireblocks' secure infrastructure. Stay ahead in the world of digital assets with this powerful SDK at your fingertips.</description>

rapiddweller 'Benerator' is a software solution to generate, anonymize, pseudonymize and migrate data for development, testing and training purposes. rapiddweller 'Benerator' is easy to learn, integrates into CI Tools like Jenkins and Gitlab CI and allows to create data models on the most abstract level. rapiddweller 'Benerator' allows creating realistic and valid high-volume test data, used for testing (unit/integration/load) and showcase setup. Metadata constraints are imported from systems and/or configuration files. Data can imported from and exported to files and systems, anonymized or generated from scratch. Domain packages provide reusable generators for creating domain-specific data as names and addresses internationalizable in language and region. It is strongly customizable with plugins and configuration options. rapiddweller 'Benerator' is build for Java 1.8 and Java 11. The development is ongoing for Java 11.

Luzzu is a Quality Assessment Framework for Linked Open Datasets. It is a generic framework based on the Dataset Quality Ontology (daQ), allowing users to define their own quality metrics. Luzzu is an integrated platform that: - assesses Linked Data quality using a library of generic and user-provided domain specific quality metrics in a scalable manner; - provides queryable quality metadata on the assessed datasets; - assembles detailed quality reports on assessed datasets. Furthermore, the infrastructure: - scales for the assessment of big datasets; - can be easily extended by the users by creating their custom and domain-specific pluggable metrics, either by employing a novel declarative quality metric specification language or conventional imperative plugins; - employs a comprehensive ontology framework for representing and exchanging all quality related information in the assessment workflow; - implements quality-driven dataset ranking algorithms facilitating use-case driven discovery and retrieval.