@inbook{10784, author = {{Fürnkranz, J. and Hüllermeier, Eyke}}, booktitle = {{Encyclopedia of Machine Learning and Data Mining}}, editor = {{Sammut, C. and Webb, G.I.}}, pages = {{1000--1005}}, publisher = {{Springer}}, title = {{{Preference Learning}}}, volume = {{107}}, year = {{2017}}, } @misc{1080, author = {{Bürmann, Jan}}, publisher = {{Universität Paderborn}}, title = {{{Complexity of Signalling in Routing Games under Uncertainty}}}, year = {{2017}}, } @misc{1081, author = {{Vijayalakshmi, Vipin Ravindran}}, publisher = {{Universität Paderborn}}, title = {{{Bounding the Inefficiency of Equilibria in Congestion Games under Taxation}}}, year = {{2017}}, } @misc{109, author = {{Pauck, Felix}}, publisher = {{Universität Paderborn}}, title = {{{Cooperative static analysis of Android applications}}}, year = {{2017}}, } @inproceedings{1094, abstract = {{Many university students struggle with motivational problems, and gamification has the potential to address these problems. However, gamification is hardly used in education, because current approaches to gamification require instructors to engage in the time-consuming preparation of their course contents for use in quizzes, mini-games and the like. Drawing on research on limited attention and present bias, we propose a "lean" approach to gamification, which relies on gamifying learning activities (rather than learning contents) and increasing their salience. In this paper, we present the app StudyNow that implements such a lean gamification approach. With this app, we aim to enable more students and instructors to benefit from the advantages of gamification.}}, author = {{Feldotto, Matthias and John, Thomas and Kundisch, Dennis and Hemsen, Paul and Klingsieck, Katrin and Skopalik, Alexander}}, booktitle = {{Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology (DESRIST)}}, pages = {{462--467}}, title = {{{Making Gamification Easy for the Professor: Decoupling Game and Content with the StudyNow Mobile App}}}, doi = {{10.1007/978-3-319-59144-5_32}}, year = {{2017}}, } @inproceedings{1095, abstract = {{Many university students struggle with motivational problems, and gamification has the potential to address these problems. However, using gamification currently is rather tedious and time-consuming for instructors because current approaches to gamification require instructors to engage in the time-consuming preparation of course contents (e.g., for quizzes or mini-games). In reply to this issue, we propose a “lean” approach to gamification, which relies on gamifying learning activities rather than learning contents. The learning activities that are gamified in the lean approach can typically be drawn from existing course syllabi (e.g., attend certain lectures, hand in assignments, read book chapters and articles). Hence, compared to existing approaches, lean gamification substantially lowers the time requirements posed on instructors for gamifying a given course. Drawing on research on limited attention and the present bias, we provide the theoretical foundation for the lean gamification approach. In addition, we present a mobile application that implements lean gamification and outline a mixed-methods study that is currently under way for evaluating whether lean gamification does indeed have the potential to increase students’ motivation. We thereby hope to allow more students and instructors to benefit from the advantages of gamification. }}, author = {{John, Thomas and Feldotto, Matthias and Hemsen, Paul and Klingsieck, Katrin and Kundisch, Dennis and Langendorf, Mike}}, booktitle = {{Proceedings of the 25th European Conference on Information Systems (ECIS)}}, pages = {{2970--2979}}, title = {{{Towards a Lean Approach for Gamifying Education}}}, year = {{2017}}, } @article{110, abstract = {{We consider an extension of the dynamic speed scaling scheduling model introduced by Yao et al.: A set of jobs, each with a release time, deadline, and workload, has to be scheduled on a single, speed-scalable processor. Both the maximum allowed speed of the processor and the energy costs may vary continuously over time. The objective is to find a feasible schedule that minimizes the total energy costs. Theoretical algorithm design for speed scaling problems often tends to discretize problems, as our tools in the discrete realm are often better developed or understood. Using the above speed scaling variant with variable, continuous maximal processor speeds and energy prices as an example, we demonstrate that a more direct approach via tools from variational calculus can not only lead to a very concise and elegant formulation and analysis, but also avoids the “explosion of variables/constraints” that often comes with discretizing. Using well-known tools from calculus of variations, we derive combinatorial optimality characteristics for our continuous problem and provide a quite concise and simple correctness proof.}}, author = {{Antoniadis, Antonios and Kling, Peter and Ott, Sebastian and Riechers, Sören}}, journal = {{Theoretical Computer Science}}, pages = {{1--13}}, publisher = {{Elsevier}}, title = {{{Continuous Speed Scaling with Variability: A Simple and Direct Approach}}}, doi = {{10.1016/j.tcs.2017.03.021}}, year = {{2017}}, } @misc{117, author = {{Bemmann, Pascal}}, publisher = {{Universität Paderborn}}, title = {{{Attribute-based Signatures using Structure Preserving Signatures}}}, year = {{2017}}, } @inproceedings{1180, abstract = {{These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{27th Workshop Computational Intelligence}}, location = {{Dortmund}}, title = {{{Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}}}, year = {{2017}}, } @article{11984, author = {{Bloessl, Bastian and Segata, Michele and Sommer, Christoph and Dressler, Falko}}, issn = {{1536-1233}}, journal = {{IEEE Transactions on Mobile Computing}}, pages = {{1162--1175}}, title = {{{Performance Assessment of IEEE 802.11p with an Open Source SDR-Based Prototype}}}, doi = {{10.1109/tmc.2017.2751474}}, year = {{2017}}, } @inproceedings{120, abstract = {{Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to identify the user’s needs. We want to partially automate even this first step of requirements elicitation by methods of evolutionary computation. The idea is to enable users to specify their desired software by listing examples of behavioral descriptions. Users initially specify two lists of operation sequences, one with desired behaviors and one with forbidden behaviors. Then, we search for the appropriate formal software specification in the form of a deterministic finite automaton. We solve this problem known as grammatical inference with an active coevolutionary approach following Bongard and Lipson [2]. The coevolutionary process alternates between two phases: (A) additional training data is actively proposed by an evolutionary process and the user is interactively asked to label it; (B) appropriate automata are then evolved to solve this extended grammatical inference problem. Our approach leverages multi-objective evolution in both phases and outperforms the state-of-the-art technique [2] for input alphabet sizes of three and more, which are relevant to our problem domain of requirements specification.}}, author = {{Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}}, pages = {{1327----1334}}, title = {{{Active Coevolutionary Learning of Requirements Specifications from Examples}}}, doi = {{10.1145/3071178.3071258}}, year = {{2017}}, } @inproceedings{12005, author = {{Eckhoff, David and Brummer, Alexander and Sommer, Christoph}}, booktitle = {{2016 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781509051977}}, title = {{{On the impact of antenna patterns on VANET simulation}}}, doi = {{10.1109/vnc.2016.7835925}}, year = {{2017}}, } @inproceedings{12006, author = {{Eckhoff, David and Sommer, Christoph}}, booktitle = {{2016 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781509051977}}, title = {{{Marrying safety with privacy: A holistic solution for location privacy in VANETs}}}, doi = {{10.1109/vnc.2016.7835971}}, year = {{2017}}, } @inproceedings{12014, author = {{Hagenauer, Florian and Sommer, Christoph and Higuchi, Takamasa and Altintas, Onur and Dressler, Falko}}, booktitle = {{2016 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781509051977}}, title = {{{Poster: Using clusters of parked cars as virtual vehicular network infrastructure}}}, doi = {{10.1109/vnc.2016.7835943}}, year = {{2017}}, } @inproceedings{12015, author = {{Hagenauer, Florian and Sommer, Christoph and Higuchi, Takamasa and Altintas, Onur and Dressler, Falko}}, booktitle = {{Proceedings of the 2nd ACM International Workshop on Smart, Autonomous, and Connected Vehicular Systems and Services - CarSys '17}}, isbn = {{9781450351461}}, title = {{{Parked Cars as Virtual Network Infrastructure}}}, doi = {{10.1145/3131944.3131952}}, year = {{2017}}, } @inproceedings{12016, author = {{Hagenauer, Florian and Sommer, Christoph and Higuchi, Takamasa and Altintas, Onur and Dressler, Falko}}, booktitle = {{Proceedings of the 2nd ACM International Workshop on Smart, Autonomous, and Connected Vehicular Systems and Services - CarSys '17}}, isbn = {{9781450351461}}, title = {{{Vehicular Micro Clouds as Virtual Edge Servers for Efficient Data Collection}}}, doi = {{10.1145/3131944.3133937}}, year = {{2017}}, } @inproceedings{12018, author = {{Hardes, Tobias and Dressler, Falko and Sommer, Christoph}}, booktitle = {{2017 International Conference on Networked Systems (NetSys)}}, isbn = {{9781509043941}}, title = {{{Simulating a city-scale community network: From models to first improvements for Freifunk}}}, doi = {{10.1109/netsys.2017.7903954}}, year = {{2017}}, } @inproceedings{12019, author = {{Heinovski, Julian and Klingler, Florian and Dressler, Falko and Sommer, Christoph}}, booktitle = {{2016 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781509051977}}, title = {{{Performance comparison of IEEE 802.11p and ARIB STD-T109}}}, doi = {{10.1109/vnc.2016.7835923}}, year = {{2017}}, } @inproceedings{12031, author = {{Klingler, Florian and Pannu, Gurjashan Singh and Sommer, Christoph and Dressler, Falko}}, booktitle = {{Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking - MobiCom '17}}, isbn = {{9781450349161}}, title = {{{Poster}}}, doi = {{10.1145/3117811.3131265}}, year = {{2017}}, } @inproceedings{12032, author = {{Klingler, Florian and Pannu, Gurjashan Singh and Sommer, Christoph and Bloessl, Bastian and Dressler, Falko}}, booktitle = {{Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services - MobiSys '17}}, isbn = {{9781450349284}}, title = {{{Poster}}}, doi = {{10.1145/3081333.3089322}}, year = {{2017}}, }