@article{20834, author = {{Webb, Mary E and Fluck, Andrew and Magenheim, Johannes and Malyn-Smith, Joyce and Waters, Juliet and Deschênes , Michelle and Zagami, Jason}}, journal = {{Educational Technology Research and Development}}, pages = {{1--22}}, publisher = {{Springer}}, title = {{{Machine learning for human learners: opportunities, issues, tensions and threats}}}, doi = {{10.1007/s11423-020-09858-2}}, year = {{2020}}, } @article{20835, author = {{Magenheim, Johannes}}, journal = {{MedienPädagogik: Zeitschrift für Theorie und Praxis der Medienbildung}}, pages = {{139--163}}, title = {{{< Big Data> aus der Perspektive von Informatischer Bildung und Medienpädagogik}}}, doi = {{10.21240/mpaed/37/2020.07.08.X}}, volume = {{37}}, year = {{2020}}, } @article{20836, author = {{Magenheim, Johannes and Schulte, Carsten}}, journal = {{Encyclopedia of Education and Information Technologies. Cham: Springer}}, title = {{{Data science education}}}, doi = {{10.1007/978-3-030-10576-1_253}}, year = {{2020}}, } @inbook{20840, author = {{Schulte, Carsten and Budde, Lea and Winkelnkemper, Felix}}, booktitle = {{Mobile Medien im Schulkontext}}, pages = {{215--240}}, publisher = {{Springer}}, title = {{{Programmieren - Lehren und Lernen mit und über Medien}}}, doi = {{10.1007/978-3-658-29039-9}}, year = {{2020}}, } @article{20888, author = {{Blömer, Johannes and Brauer, Sascha and Bujna, Kathrin}}, issn = {{1549-6325}}, journal = {{ACM Transactions on Algorithms}}, number = {{4}}, pages = {{1--25}}, title = {{{A Complexity Theoretical Study of Fuzzy K-Means}}}, doi = {{10.1145/3409385}}, volume = {{16}}, year = {{2020}}, } @inbook{20891, abstract = {{Today, software systems are rarely developed monolithically, but may be composed of numerous individually developed features. Their modularization facilitates independent development and verification. While feature-based strategies to verify features in isolation have existed for years, they cannot address interactions between features. The problem with feature interactions is that they are typically unknown and may involve any subset of the features. Contrary, a family-based verification strategy captures feature interactions, but does not scale well when features evolve frequently. To the best of our knowledge, there currently exists no approach with focus on evolving features that combines both strategies and aims at eliminating their respective drawbacks. To fill this gap, we introduce Fefalution, a feature-family-based verification approach based on abstract contracts to verify evolving features and their interactions. Fefalution builds partial proofs for each evolving feature and then reuses the resulting partial proofs in verifying feature interactions, yielding a full verification of the complete software system. Moreover, to investigate whether a combination of both strategies is fruitful, we present the first empirical study for the verification of evolving features implemented by means of feature-oriented programming and by comparing Fefalution with another five family-based approaches varying in a set of optimizations. Our results indicate that partial proofs based on abstract contracts exhibit huge reuse potential, but also come with a substantial overhead for smaller evolution scenarios. }}, author = {{Knüppel, Alexander and Krüger, Stefan and Thüm, Thomas and Bubel, Richard and Krieter, Sebastian and Bodden, Eric and Schaefer, Ina}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030643539}}, issn = {{0302-9743}}, title = {{{Using Abstract Contracts for Verifying Evolving Features and Their Interactions}}}, doi = {{10.1007/978-3-030-64354-6_5}}, year = {{2020}}, } @inbook{18014, author = {{El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}}, booktitle = {{Learning and Intelligent Optimization. LION 2020.}}, isbn = {{9783030535513}}, issn = {{0302-9743}}, pages = {{216 -- 232}}, publisher = {{Springer}}, title = {{{Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach}}}, doi = {{10.1007/978-3-030-53552-0_22}}, volume = {{12096}}, year = {{2020}}, } @unpublished{18017, abstract = {{We consider an extension of the contextual multi-armed bandit problem, in which, instead of selecting a single alternative (arm), a learner is supposed to make a preselection in the form of a subset of alternatives. More specifically, in each iteration, the learner is presented a set of arms and a context, both described in terms of feature vectors. The task of the learner is to preselect $k$ of these arms, among which a final choice is made in a second step. In our setup, we assume that each arm has a latent (context-dependent) utility, and that feedback on a preselection is produced according to a Plackett-Luce model. We propose the CPPL algorithm, which is inspired by the well-known UCB algorithm, and evaluate this algorithm on synthetic and real data. In particular, we consider an online algorithm selection scenario, which served as a main motivation of our problem setting. Here, an instance (which defines the context) from a certain problem class (such as SAT) can be solved by different algorithms (the arms), but only $k$ of these algorithms can actually be run.}}, author = {{El Mesaoudi-Paul, Adil and Bengs, Viktor and Hüllermeier, Eyke}}, booktitle = {{arXiv:2002.04275}}, title = {{{Online Preselection with Context Information under the Plackett-Luce Model}}}, year = {{2020}}, } @inproceedings{18021, author = {{Yigitbas, Enes and Tejedor, Christopher Bernal and Engels, Gregor}}, booktitle = {{Proceedings of the Mensch und Computer 2020 (MuC ’20)}}, title = {{{Experiencing and Programming the ENIAC in VR}}}, year = {{2020}}, } @inproceedings{18022, author = {{Augstein, Mirjam and Buschek, Daniel and Herder, Eelco and Loepp, Benedikt and Yigitbas, Enes and Ziegler, Jürgen}}, booktitle = {{Proceedings of the Mensch und Computer 2020 (MuC ’20)}}, publisher = {{ACM}}, title = {{{UCAI 2020 - 1st International Workshop on User-Centered Artificial Intelligence}}}, year = {{2020}}, } @inproceedings{18038, author = {{Böttcher, Stefan and Hartel, Rita and Peeters, Sven}}, booktitle = {{Proceedings of The International Workshop on Semantic Big Data}}, isbn = {{9781450379748}}, title = {{{QSGG: query simulation in grammar-compressed graphs}}}, doi = {{10.1145/3391274.3393638}}, year = {{2020}}, } @inproceedings{18039, author = {{Böttcher, Stefan and Hartel, Rita and Peeters, Sven}}, booktitle = {{2020 Data Compression Conference (DCC) (Poster)}}, isbn = {{9781728164571}}, title = {{{Pattern Search in Grammar-Compressed Graphs}}}, doi = {{10.1109/dcc47342.2020.00054}}, year = {{2020}}, } @misc{18066, author = {{Skowronek, Michael}}, publisher = {{Universität Paderborn}}, title = {{{Approaches for Competetive Routing through Intersections of Hole Abstractions in Hybrid Communication Networks}}}, year = {{2020}}, } @misc{18085, author = {{Heimann, Jonas}}, title = {{{Distributed Key Generation for Attribute-Based Signatures}}}, year = {{2020}}, } @inproceedings{18109, author = {{Yigitbas, Enes and Jovanovikj, Ivan and Scholand, Janis and Engels, Gregor}}, booktitle = {{Proceedings of the 26th ACM Symposium on Virtual Reality Software and Technology (VRST)}}, publisher = {{ACM}}, title = {{{VR Training for Warehouse Management }}}, year = {{2020}}, } @inproceedings{18249, abstract = {{Augmented Reality (AR) has recently found high attention in mobile shopping apps such as in domains like furniture or decoration. Here, the developers of the apps focus on the positioning of atomic 3D objects in the physical environment. With this focus, they neglect the configuration of multi-faceted 3D object composition according to the user needs and environmental constraints. To tackle these challenges, we present a model-based approach to support AR-assisted product con-figuration based on the concept of Dynamic Software Product Lines. Our approach splits products (e.g. table) into parts (e.g. tabletop, ta-ble legs, funnier) with their 3D objects and additional information (e.g. name, price). The possible products, which can be configured out of these parts, are stored in a feature model. At runtime, this feature model can be used to configure 3D object compositions out of the product parts and adapt to user needs and environmental constraints. The benefits of this approach are demonstrated by a case study of configuring modular kitchens with the help of a prototypical mobile-based implementation.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering. HCSE 2020}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, keywords = {{Product Configuration, Augmented Reality, Runtime Adaptation, Dynamic Software Product Lines}}, location = {{Eindhoven}}, publisher = {{Springer}}, title = {{{Model-based Product Configuration in Augmented Reality Applications}}}, doi = {{10.1007/978-3-030-64266-2_5}}, volume = {{12481}}, year = {{2020}}, } @inproceedings{18276, abstract = {{Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.}}, author = {{Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{ACML 2020}}, location = {{Bangkok, Thailand}}, title = {{{Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}}}, year = {{2020}}, } @article{18350, author = {{Koldewey, Christian and Meyer, Maurice and Stockbrügger, Patrick and Dumitrescu, Roman and Gausemeier, Jürgen}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, number = {{91}}, pages = {{851--857}}, title = {{{Framework and Functionality Patterns for Smart Service Innovation}}}, doi = {{10.1016/j.procir.2020.02.244}}, year = {{2020}}, } @phdthesis{18520, author = {{Setzer, Alexander}}, publisher = {{Universität Paderborn}}, title = {{{Local Graph Transformation Primitives For Some Basic Problems In Overlay Networks}}}, doi = {{10.17619/UNIPB/1-1026}}, year = {{2020}}, } @misc{18637, author = {{Schürmann, Patrick}}, publisher = {{Universität Paderborn}}, title = {{{A Group Signature Scheme from Flexible Public Key Signatures and Structure-Preserving Signatures on Equivalence Classes}}}, year = {{2020}}, }