@article{145, abstract = {{Comparative evaluations of peer-to-peer protocols through simulations are a viable approach to judge the performance and costs of the individual protocols in large-scale networks. In order to support this work, we present the peer-to-peer system simulator PeerfactSim.KOM, which we extended over the last years. PeerfactSim.KOM comes with an extensive layer model to support various facets and protocols of peer-to-peer networking. In this article, we describe PeerfactSim.KOM and show how it can be used for detailed measurements of large-scale peer-to-peer networks. We enhanced PeerfactSim.KOM with a fine-grained analyzer concept, with exhaustive automated measurements and gnuplot generators as well as a coordination control to evaluate sets of experiment setups in parallel. Thus, by configuring all experiments and protocols only once and starting the simulator, all desired measurements are performed, analyzed, evaluated, and combined, resulting in a holistic environment for the comparative evaluation of peer-to-peer systems. An immediate comparison of different configurations and overlays under different aspects is possible directly after the execution without any manual post-processing. }}, author = {{Feldotto, Matthias and Graffi, Kalman}}, journal = {{Concurrency and Computation: Practice and Experience}}, number = {{5}}, pages = {{1655--1677}}, publisher = {{Wiley Online Library}}, title = {{{Systematic evaluation of peer-to-peer systems using PeerfactSim.KOM}}}, doi = {{10.1002/cpe.3716}}, volume = {{28}}, year = {{2016}}, } @inproceedings{13151, author = {{Graf, Tobias and Platzner, Marco}}, booktitle = {{Computer and Games}}, title = {{{Using Deep Convolutional Neural Networks in Monte Carlo Tree Search}}}, year = {{2016}}, } @inproceedings{13152, author = {{Graf, Tobias and Platzner, Marco}}, booktitle = {{IEEE Computational Intelligence and Games}}, title = {{{Monte-Carlo Simulation Balancing Revisited}}}, year = {{2016}}, } @inproceedings{132, abstract = {{Runtime reconfiguration can be used to replace hardware modules in the field and even to continuously improve them during operation. Runtime reconfiguration poses new challenges for validation, since the required properties of newly arriving modules may be difficult to check fast enough to sustain the intended system dynamics. In this paper we present a method for just-in-time verification of the worst-case completion time of a reconfigurable hardware module. We assume so-called run-to-completion modules that exhibit start and done signals indicating the start and end of execution, respectively. We present a formal verification approach that exploits the concept of proof-carrying hardware. The approach tasks the creator of a hardware module with constructing a proof of the worst-case completion time, which can then easily be checked by the user of the module, just prior to reconfiguration. After explaining the verification approach and a corresponding tool flow, we present results from two case studies, a short term synthesis filter and a multihead weigher. The resultsclearly show that cost of verifying the completion time of the module is paid by the creator instead of the user of the module.}}, author = {{Wiersema, Tobias and Platzner, Marco}}, booktitle = {{Proceedings of the 11th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC 2016)}}, pages = {{1----8}}, title = {{{Verifying Worst-Case Completion Times for Reconfigurable Hardware Modules using Proof-Carrying Hardware}}}, doi = {{10.1109/ReCoSoC.2016.7533910}}, year = {{2016}}, } @misc{133, abstract = {{.}}, author = {{Dewender, Markus}}, publisher = {{Universität Paderborn}}, title = {{{Verifikation von Service Kompositionen mit Spin}}}, year = {{2016}}, } @misc{134, abstract = {{.}}, author = {{Heinisch, Philipp}}, publisher = {{Universität Paderborn}}, title = {{{Verifikation von Service Kompositionen mit Prolog}}}, year = {{2016}}, } @phdthesis{10136, author = {{Eikel, Martina}}, publisher = {{Universität Paderborn}}, title = {{{Insider-resistent Distributed Storage Systems}}}, year = {{2016}}, } @inbook{10214, author = {{Fürnkranz, J. and Hüllermeier, Eyke}}, booktitle = {{Encyclopedia of Machine Learning and Data Mining}}, editor = {{Sammut, C. and Webb, G.I.}}, publisher = {{Springer}}, title = {{{Preference Learning}}}, year = {{2016}}, } @proceedings{10221, editor = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, title = {{{ Proceedings 26. Workshop Computational Intelligence KIT Scientific Publishing, Karlsruhe, Germany}}}, year = {{2016}}, } @inproceedings{10222, author = {{Jasinska, K. and Dembczynski, K. and Busa-Fekete, Robert and Klerx, Timo and Hüllermeier, Eyke}}, booktitle = {{Proceedings ICML-2016, 33th International Conference on Machine Learning, New York, USA}}, editor = {{Balcan, M.F. and Weinberger, K.Q.}}, title = {{{Extreme F-measure maximization using sparse probability estimates }}}, year = {{2016}}, } @inproceedings{10223, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{European Conference on Machine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy}}, pages = {{756--771}}, title = {{{Learning to aggregate using uninorms, in Proceedings ECML/PKDD-2016}}}, year = {{2016}}, } @inproceedings{10224, author = {{Dembczynski, K. and Kotlowski, W. and Waegeman, W. and Busa-Fekete, Robert and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ECML/PKDD European Conference on Maschine Learning and Knowledge Discovery in Databases, Part II, Riva del Garda, Italy}}, pages = {{511--526}}, title = {{{Consistency of probalistic classifier trees}}}, year = {{2016}}, } @inproceedings{10225, author = {{Shabani, Aulon and Paul, Adil and Platon, R. and Hüllermeier, Eyke}}, booktitle = {{In Proceedings ICCBR, 24th International Conference on Case-Based Reasoning, Atlanta, GA, USA}}, pages = {{356--369}}, title = {{{Predicting the electricity consumption of buildings: An improved CBR approach}}}, year = {{2016}}, } @inproceedings{10226, author = {{Pfannschmidt, Karlson and Hüllermeier, Eyke and Held, S. and Neiger, R.}}, booktitle = {{In Proceedings IPMU 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part 1, Eindhoven, The Netherlands}}, pages = {{450--461}}, publisher = {{Springer}}, title = {{{Evaluating tests in medical diagnosis-Combining machine learning with game-theoretical concepts}}}, year = {{2016}}, } @inproceedings{10227, author = {{Labreuche, C. and Hüllermeier, Eyke and Vojtas, P. and Fallah Tehrani, A.}}, booktitle = {{Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning}}, editor = {{Busa-Fekete, Robert and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{On the Identifiability of models in multi-criteria preference learning }}}, year = {{2016}}, } @inproceedings{10228, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning}}, editor = {{Busa-Fekete, Robert and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{Preference-Based Reinforcement Learning Using Dyad Ranking}}}, year = {{2016}}, } @inproceedings{10229, author = {{Couso, Ines and Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Proceedings DA2PL ´2016, Euro Mini Conference from Multiple Criteria Decision Aid to Preference Learning}}, editor = {{Busa-Fekete, Robert and Hüllermeier, Eyke and Mousseau, V. and Pfannschmidt, Karlson}}, title = {{{Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators}}}, year = {{2016}}, } @inproceedings{10230, author = {{Lu, S. and Hüllermeier, Eyke}}, booktitle = {{Proceedings 26. Workshop Computational Intelligence, KIT Scientific Publishing}}, editor = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, pages = {{1--8}}, title = {{{Support vector classification on noisy data using fuzzy supersets losses}}}, year = {{2016}}, } @inproceedings{10231, author = {{Schäfer, Dirk and Hüllermeier, Eyke}}, booktitle = {{In Workshop LWDA "Lernen, Wissen, Daten, Analysen"}}, title = {{{Plackett-Luce networks for dyad ranking}}}, year = {{2016}}, } @proceedings{10263, editor = {{Kaminka, G.A. and Fox, M. and Bouquet, P. and Hüllermeier, Eyke and Dignum, V. and Dignum, F. and van Harmelen, F.}}, publisher = {{IOS Press}}, title = {{{ECAI 2016, 22nd European Conference on Artificial Intelligence, including PAIS 2016, Prestigious Applications of Artificial Intelligence}}}, volume = {{285}}, year = {{2016}}, }