University of Paderborn

About: University of Paderborn is a education organization based out in Paderborn, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 6684 authors who have published 16929 publications receiving 323154 citations.

International Olympic Committee consensus statement on the health and fitness of young people through physical activity and sport

Abstract: The International Olympic Committee (IOC) recognises the health and fitness benefits of physical activity (PA) and sport as stated in recommendation #51 from the Olympic Movement in Society Congress held in Copenhagen, 2009: Everyone involved in the Olympic Movement must become more aware of the fundamental importance of Physical Activity and sport for a healthy lifestyle, not least in the growing battle against obesity, and must reach out to parents and schools as part of a strategy to counter the rising inactivity of young people.1 The IOC assembled an expert group (January 2011) to discuss the role of PA and sport on the health and fitness of young people and to critically evaluate the scientific evidence as a basis for decision making. Specifically, the purpose of this consensus paper is to identify potential solutions through collaboration between sport and existing programmes and to review the research gaps in this field. The ultimate aim of the paper is to provide recommendations for young people's sport and PA stakeholders. After an introduction to the scope of the problem, issues addressed include how best to define the current state of …

Symbolic invariant verification for systems with dynamic structural adaptation

Abstract: The next generation of networked mechatronic systems will be characterized by complex coordination and structural adaptation at run-time. Crucial safety properties have to be guaranteed for all potential structural configurations. Testing cannot provide safety guarantees, while current model checking and theorem proving techniques do not scale for such systems. We present a verification technique for arbitrarily large multi-agent systems from the mechatronic domain, featuring complex coordination and structural adaptation. We overcome the limitations of existing techniques by exploiting the local character of structural safety properties. The system state is modeled as a graph, system transitions are modeled as rule applications in a graph transformation system, and safety properties of the system are encoded as inductive invariants (permitting the verification of infinite state systems). We developed a symbolic verification procedure that allows us to perform the computation on an efficient BDD-based graph manipulation engine, and we report performance results for several examples.

SystemC: methodologies and applications

Abstract: Foreword. Preface. 1: A SystemC Based System On Chip Modelling and Design Methodology Y. Vanderperren, M. Pauwels, W. Dehaene, A.Berna, F. OEzdemir. 2: Using Transactional Level Models in a SoC Design Flow A. Clouard, K. Jain, F. Ghenassia, L. Maillet-Contoz, J.-P. Strassen. 3: Refining a High Level SystemC Model B. Niemann, F. Mayer, F.J. Rabano Rubio, M. Speitel. 4: An ASM Based SystemC Simulation Semantics W. Muller, J. Ruf, W. Rosenstiel. 5: SystemC as a Complete Design and Validation Environment A. Fin, F. Fummi, G. Pravadelli. 6: System Level Performance Estimation N. Pazos, W. Brunnbauer, J. Foag, T. Wild. 7: Design of Protocol Dominated Digital Systems R. Siegmund, U. Pross, D. Muller. 8: Object Oriented Hardware Design and Synthesis Based on SystemC 2.0 E. Grimpe, W. Nebel, F. Oppenheimer, T. Schubert. 9: Embedded Software Generation from SystemC for Platform Based Design F. Herrera, V. Fernandez, P. Sanchez, E. Villar. 10: SystemC-AMS: Rationales, State of the Art, and Examples K. Einwich, P. Schwarz, C. Grimm, C. Meise. 11: Modeling and Refinement of Mixed-Signal Systems with SystemC C. Grimm. References. Index.

ML-Plan: Automated machine learning via hierarchical planning

Abstract: Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.