@inproceedings{1620, author = {{Aktas, Ismet and Ansari, Junaid and Auroux, Sebastien and Parruca, Donald and Perez Guirao, Maria Dolores and Holfeld, Bernd}}, publisher = {{Proceedings of 23th European Wireless Conference}}, title = {{{A Coordination Architecture for Wireless Industrial Automation}}}, year = {{2017}}, } @inproceedings{16338, abstract = {{To detect errors or find potential for improvement during the CAD-supported development of a complex technical system like modern industrial machines, the system’s virtual prototype can be examined in virtual reality (VR) in the context of virtual design reviews. Besides exploring the static shape of the examined system, observing the machines’ mechanics (e.g., motor-driven mechanisms) and transport routes for the material transport (e.g., via conveyor belts or chains, or rail-based transport systems) can play an equally important role in such a review. In practice it is often the case, that the relevant information about transport routes, or kinematic properties is either not consequently modeled in the CAD data or is lost during conversion processes. To significantly reduce the manual effort and costs for creating animations of the machines complex behavior with such limited input data for a design review, we present a set of algorithms to automatically determine geometrical properties of machine parts based only on their triangulated surfaces. The algorithms allow to detect the course of transport systems, the orientation of objects in 3d space, rotation axes of cylindrical objects and holes, the number of tooth of gears, as well as the tooth spacing of toothed racks. We implemented the algorithms in the VR system PADrend and applied them to animate virtual prototypes of real machines.}}, author = {{Brandt, Sascha and Fischer, Matthias and Gerges, Maria and Jähn, Claudius and Berssenbrügge, Jan}}, booktitle = {{Volume 1: 37th Computers and Information in Engineering Conference}}, isbn = {{9780791858110}}, location = {{Cleveland, USA}}, pages = {{91:1--91:10}}, title = {{{Automatic Derivation of Geometric Properties of Components From 3D Polygon Models}}}, doi = {{10.1115/detc2017-67528}}, volume = {{1}}, year = {{2017}}, } @inproceedings{16339, abstract = {{In der CAD-unterstützten Entwicklung von technischen Systemen (Maschinen, Anlagen etc.) werden virtuelle Prototypen im Rahmen eines virtuellen Design-Reviews mit Hilfe eines VR-Systems gesamtheitlich betrachtet, um frühzeitig Fehler und Verbesserungsbedarf zu erkennen. Ein wichtiger Untersuchungsgegenstand ist dabei die Analyse von Transportwegen für den Materialtransport mittels Fließbändern, Förderketten oder schienenbasierten Transportsystemen. Diese Transportwege werden im VR-System animiert. Problematisch dabei ist, dass derartige Transportsysteme im zugrundeliegenden CAD-Modell in der Praxis oft nicht modelliert und nur exemplarisch angedeutet werden, da diese für die Konstruktion nicht relevant sind (z.B. der Fördergurt eines Förderbandes, oder die Kette einer Förderkette), oder die Informationen über den Verlauf bei der Konvertierung der Daten in das VR-System verloren gehen. Bei der Animation dieser Transportsysteme in einem VR-System muss der Transportweg also aufwändig, manuell nachgearbeitet werden. Das Ziel dieser Arbeit ist die Reduzierung des notwendigen manuellen Nachbearbeitungsaufwandes für das Design-Review durch eine automatische Berechnung der Animationspfade entlang eines Transportsystems. Es wird ein Algorithmus vorgestellt, der es ermöglicht mit nur geringem zeitlichem Benutzeraufwand den Animationspfad aus den reinen polygonalen dreidimensionalen Daten eines Transportsystems automatisch zu rekonstruieren.}}, author = {{Brandt, Sascha and Fischer, Matthias}}, booktitle = {{Wissenschaftsforum Intelligente Technische Systeme (WInTeSys) 2017}}, location = {{Paderborn}}, pages = {{415--427}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Automatische Ableitung der Transportwege von Transportsystemen aus dem 3D-Polygonmodell}}}, volume = {{369}}, year = {{2017}}, } @inproceedings{16347, author = {{Fischer, Matthias and Jung, Daniel and Meyer auf der Heide, Friedhelm}}, booktitle = {{Algorithms for Sensor Systems - 13th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, {ALGOSENSORS}}}, editor = {{Fernández Anta, Antonio and Jurdzinski, Tomasz and Mosteiro, Miguel A. and Zhang, Yanyong}}, pages = {{168--181}}, publisher = {{Springer}}, title = {{{Gathering Anonymous, Oblivious Robots on a Grid}}}, doi = {{10.1007/978-3-319-72751-6_13}}, volume = {{10718}}, year = {{2017}}, } @inproceedings{16348, author = {{Biermeier, Felix and Feldkord, Björn and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 15th Workshop on Approximation and Online Algorithms (WAOA)}}, pages = {{285 -- 300}}, publisher = {{Springer}}, title = {{{A Communication-Efficient Distributed Data Structure for Top-k and k-Select Queries}}}, doi = {{10.1007/978-3-319-89441-6_21}}, year = {{2017}}, } @inproceedings{16349, author = {{Podlipyan, Pavel and Li, Shouwei and Markarian, Christine and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 13th International Symposium on Algorithms and Experiments for Wireless Networks (ALGOSENSORS)}}, pages = {{182--197}}, title = {{{A Continuous Strategy for Collisionless Gathering}}}, doi = {{10.1007/978-3-319-72751-6_14 }}, year = {{2017}}, } @phdthesis{102, author = {{Becker, Matthias}}, publisher = {{Universität Paderborn}}, title = {{{Engineering Self-Adaptive Systems with Simulation-Based Performence Prediction}}}, doi = {{10.17619/UNIPB/1-133}}, year = {{2017}}, } @inproceedings{10204, author = {{Ewerth, Ralph and Springstein, M. and Müller, E. and Balz, A. and Gehlhaar, J. and Naziyok, T. and Dembczynski, K. and Hüllermeier, Eyke}}, booktitle = {{Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017)}}, pages = {{919--924}}, title = {{{Estimating relative depth in single images via rankboost}}}, year = {{2017}}, } @inproceedings{10205, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke and Couso, Ines}}, booktitle = {{Proc. 34th Int. Conf. on Machine Learning (ICML 2017)}}, pages = {{1078--1087}}, title = {{{Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening}}}, year = {{2017}}, } @inproceedings{10206, author = {{Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke}}, booktitle = {{Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017)}}, pages = {{193--206}}, title = {{{Planning with Independent Task Networks}}}, doi = {{10.1007/978-3-319-67190-1_15}}, year = {{2017}}, } @inproceedings{10207, author = {{Czech, M. and Hüllermeier, Eyke and Jakobs, M.-C. and Wehrheim, Heike}}, booktitle = {{Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017}}, pages = {{23--26}}, title = {{{Predicting rankings of software verification tools}}}, year = {{2017}}, } @inproceedings{10208, author = {{Couso, Ines and Dubois, D. and Hüllermeier, Eyke}}, booktitle = {{Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017)}}, pages = {{3--16}}, title = {{{Maximum Likelihood Estimation and Coarse Data}}}, year = {{2017}}, } @inproceedings{10209, author = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}}, booktitle = {{Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence}}, title = {{{Learning to Rank based on Analogical Reasoning}}}, year = {{2017}}, } @inproceedings{10212, author = {{Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}}, title = {{{(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017}}}, year = {{2017}}, } @inproceedings{10213, author = {{Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{Proceedings 27. Workshop Computational Intelligence, Dortmund, Germany 2017}}, pages = {{1--12}}, title = {{{Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics}}}, year = {{2017}}, } @inproceedings{10216, author = {{Shaker, Ammar and Heldt, W. and Hüllermeier, Eyke}}, booktitle = {{Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases, Skopje, Macedonia}}, title = {{{Learning TSK Fuzzy Rules from Data Streams}}}, year = {{2017}}, } @article{10267, author = {{Bräuning, M. and Hüllermeier, Eyke and Keller, T. and Glaum, M.}}, journal = {{European Journal of Operational Research}}, number = {{1}}, pages = {{295--306}}, title = {{{Lexicographic preferences for predictive modeling of human decision making. A new machine learning method with an application in accounting}}}, volume = {{258}}, year = {{2017}}, } @article{10268, author = {{Platenius, M.-C. and Shaker, Ammar and Becker, M. and Hüllermeier, Eyke and Schäfer, W.}}, journal = {{IEEE Transactions on Software Engineering}}, number = {{8}}, pages = {{739--759}}, title = {{{Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic}}}, volume = {{43}}, year = {{2017}}, } @article{10269, author = {{Hüllermeier, Eyke}}, journal = {{The Computing Research Repository (CoRR)}}, title = {{{From Knowledge-based to Data-driven Modeling of Fuzzy Rule-based Systems: A Critical Reflection}}}, year = {{2017}}, } @inproceedings{5829, abstract = {{Websites increasingly embed semantic data for search engine optimization. The most common ontology for semantic data, schema.org, is supported by all major search engines and describes over 500 data types, including calendar events, recipes, products, and TV shows. As of today, users wishing to pass this data to their favorite applications, e.g., their calendars, cookbooks, price comparison applications or even smart devices such as TV receivers, rely on cumbersome and error-prone workarounds such as reentering the data or a series of copy and paste operations. In this paper, we present Semantic Data Mediator (SDM), an approach that allows the easy transfer of semantic data to a multitude of services, ranging from web services to applications installed on different devices. SDM extracts semantic data from the currently displayed web page on the client-side, offers suitable services to the user, and by the press of a button, forwards this data to the desired service while doing all the necessary data conversion and service interface adaptation in between. To realize this, we built a reusable repository of service descriptions, data converters, and service adapters, which can be extended by the crowd. Our approach for linking services to websites relies solely on semantic data and does not require any additional support by either website or service developers. We have fully implemented our approach and present a real-world case study demonstrating its feasibility and usefulness.}}, author = {{Wolters, Dennis and Heindorf, Stefan and Kirchhoff, Jonas and Engels, Gregor}}, booktitle = {{2017 IEEE International Conference on Web Services (ICWS)}}, editor = {{Altintas, Ilkay and Chen, Shiping}}, isbn = {{9781538607527}}, keywords = {{Services, Websites, Semantic Data, schema.org, Data Conversion, Interface Adaptation, Mediation}}, publisher = {{IEEE}}, title = {{{Linking Services to Websites by Leveraging Semantic Data}}}, doi = {{10.1109/icws.2017.80}}, year = {{2017}}, }