This poster presents an art-inspired iterative design approach applied to the definition of two experimental scenarios for movement data collection. Scenarios are inspired by warm-up exercises dancers perform to broaden group consciousness. Research focuses on the influence an expert dancer exerts on novice dancers by propagating her own movement patterns (individual motor signatures), for stimulating the emergence of a group movement pattern (group motor signature).

UNLABELLED: The semantic measures library and toolkit are robust open-source and easy to use software solutions dedicated to semantic measures. They can be used for large-scale computations and analyses of semantic similarities between terms/concepts defined in terminologies and ontologies. The comparison of entities (e.g. genes) annotated by concepts is also supported. A large collection of measures is available. Not limited to a specific application context, the library and the toolkit can be used with various controlled vocabularies and ontology specifications (e.g. Open Biomedical Ontology, Resource Description Framework). The project targets both designers and practitioners of semantic measures providing a JAVA library, as well as a command-line tool that can be used on personal computers or computer clusters. AVAILABILITY AND IMPLEMENTATION: Downloads, documentation, tutorials, evaluation and support are available at http://www.semantic-measures-library.org.

Ontologies are successfully used as semantic guides when navigating through the huge and ever increasing quantity of digital documents. Nevertheless, the size of numerous domain ontologies tends to grow beyond the human capacity to grasp information. This growth is problematic for a lot of key applications that require user interactions such as document annotation or ontology modification/evolution. The problem could be partially overcome by providing users with a subontology focused on their current concepts of interest. A subontology restricted to this sole set of concepts is of limited interest since their relationships can generally not be explicit without adding some of their hyponyms and hypernyms. This paper proposes efficient algorithms to identify these additional key concepts based on the closure of two common graph operators: the least common-ancestor (lca) and greatest common descendant (gcd). The resulting method produces ontology excerpts focused on a set of concepts of interest and is fast enough to be used in interactive environments. As an example, we use the resulting program, called OntoFocus (http://www.ontotoolkit.mines-ales.fr/), to restrict, in few seconds, the large Gene Ontology (~30,000 concepts) to a subontology focused on concepts annotating a gene related to breast cancer.

Summary form only given. Differential evolution, is a recently invented global optimization algorithm. Originally proposed as a method for the global continuous optimization differential evolution has been easily modified for handling mixed (continuous and discrete) variables. In order to have a better choice of the differentiation's formula, we introduce a generalization of the differential evolution's strategies. This is done by dividing them into four groups according to their differentiation principle. Such approach leads us to the new universal formula of differentiation. Some examples of strategies are demonstrated and compared on the De Jong test functions.