that the automaton will follow? Social actors, naturally. Thus, there is a social struggle to assign goals to the network. But once the network is programmed, it imposes its logic to all its members (actors). Actors will have to play their strategies within the rules of the network. To assign different goals

verification strategies. Supported by the FWF grant S5302-PHY "Parallel Symbolic Computation". 2 CONTENTS Contents 1 Introduction 4 2 Parallel Program Verification 5 2.1 Organization of the Verification : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.2 Program Model and Implementation

doi:10.4156/ijiip.vol1. issue2.2 Due to the increased complexity of modern embedded applications and integrated circuits there has been a long effort for automated techniques and tools to automatically produce correct hardware and systems from high-level, abstract and executable specifications

behaviour by limits we follow earlier work by Goguen on Categorical Systems Theory [4, 5, 6]. This approach pays particular attention to the hierarchical structure of systems, and provides means of constructing systems from component parts in a way that captures both complex objects and parallel composition

A semantically enhanced weighted tree similarity algorithm for buyer-seller match-making is presented. First, our earlier global, structural similarity measure over (product) partonomy trees is enriched by taxonomic semantics: Inner nodes can be labeled by classes whose partial subsumption order is represented as a taxonomy tree that is used for similarity computation. In particular, the similarity of any two classes can be defined via the weighted length of the shortest path connecting them in the taxonomy. Instead of a taxonomy separate from the partonomy, we encode the taxonomy tree into the partonomy tree (as a “Classification ” branch) in a way that allows us to directly reuse our partonomy similarity algorithm and to permit weighted (or 'fuzzy') taxonomic subsumption with no added effort. Second, leaf nodes can be typed and each type is associated with a local, special-purpose similarity measure realising the semantics to be invoked when computing the similarity of any two of its instances. We illustrate local similarity measures with e-Business types such as “Currency”, “Address”, “Date”, and “Price”. For example, the similarity measure on “Date”-typed leaf node labels translates various notations for date instances into a normal form from which it linearly maps any two to their similarity value. Finally, previous adjustment functions, which prevent similarity degradation for our arbitrarily wide and deep trees, are enhanced by smoother functions that evenly compensate intermediate similarity values. Key Words/Phrases global similarity measure, local similarity measure, arc-labeled and arc-weighted trees, taxonomic class similarity, taxonomy tree, Weighted Object-Oriented RuleML, adjustment function, arithmetic mean 1.

of their reflections (i.e. approx-imations) in the chosen language. The most important issues in this induction process are: – construction of relevant primitive concepts from which approximations of more complex concepts are assembled, – measures of inclusion and similarity (closeness) on concepts, – construction

In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree

I hereby declare that I have created this work completely on my own and used no other sources or tools than the ones listed, and that I have marked any citations accordingly. Hiermit versichere ich, dass ich die vorliegende Arbeit selbständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel benutzt sowie Zitate kenntlich gemacht habe.

Abstract: Online learning initia-tives over the past decade have become increasingly comprehen-sive in their selection of courses and sophisticated in their presen-tation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university education on the internet, free of charge, a real possibility. At this pivotal moment it is appropriate to explore the potential for obtaining comprehensive bioinformatics training with currently existing free video resources. This article pre-sents such a bioinformatics curric-ulum in the form of a virtual course catalog, together with editorial commentary, and an assessment of strengths, weaknesses, and likely future directions for open online learning in this field. Online Learning Comes of Age Online academic ‘‘courseware’ ’ at the university level has now been available to the public for a decade, the earliest concerted effort having originated in 2002 with the Massachusetts Institute of Technology (MIT) and their OpenCour-seWare initiative

Abstract not found