On the one hand, no one international model for quality assurance evaluation of higher education has emerged. On the other hand, as a reference model rather than a prescription, the CDIO initiative proposes a mature integrated framework for creation or continuous improvement of engineering programs. However, institutions developing and managing educational programs have to juggle the expectation of various accreditation and evaluation bodies, which may create consistency and interoperability problems. A need exists to unambiguously specify relations among quality assurance concepts to enable more transparent and comparable descriptions of quality frameworks for educational programs. Following constructive alignment principles, this article creates structural models using some of the CDIO Standards. In doing so it lays the foundations of an architectural meta-model for describing complex educational systems, which will contribute to consistency and interoperability among quality frameworks.

This paper proposes a two-part contribution to the human factor issues raised by the prospect of the deployment of swarms of autonomous robots under the supervision of a human operator. First, we propose a generic framework for system description that allows us to analyze and guide the design of automated systems. Second, we analyze the way some highly adaptive systems such as swarms can be perceived by operators, and especially the way representational gaps between man and machine can impact global performances.

Temporal regularity of itemset appearance can be regarded as an important criterion for measuring the interestingness of itemsets in several applications. A frequent itemset can be said to be regular-frequent in a database if it appears at a regular period. Therefore, the problem of mining a complete set of regular-frequent itemsets requires the specification of a support and a regularity threshold. However, in practice, it is often difficult for users to provide an appropriate support threshold. In addition, the use of a support threshold tends to produce a large number of regular-frequent itemsets and it might be better to ask for the number of desired results. We thus propose an efficient algorithm for mining top-k regular-frequent itemsets without setting a support threshold. Based on database partitioning and support estimation techniques, the proposed algorithm also uses a best-first search strategy with only one database scan. We then compare our algorithm with the state-of-the-art algorithms for mining top-k regular-frequent itemsets. Our experimental studies on both synthetic and real data show that our proposal achieves high performance for small and large values of k.

Diviz est un logiciel libre qui sert à concevoir, à exécuter et à partager des méthodes, des analyses et des expériences issues du domaine de l’Aide MultiCritère à la Décision (AMCD). Ceci se fait en construisant des workflows algorithmiques (séquences d’étapes de calcul) qui reposent sur des ressources de calcul d’AMCD disponibles en ligne (services web). Nous nous concentrons ici sur la méthodologie de travail innovante que génère l'outil diviz. En effet, la vision décomposée des méthodes d’AMCD induite par leur représentation sous la forme de workflows de traitement mène à de nombreux avantages, que nous explicitons dans cette présentation.