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10546 Publications


2020 | Book Chapter | LibreCat-ID: 35911
Lankeit, E., Bauer, T., & Biehler, R. (2020). Votingfragen in den Übungen zur Analysis – Wirkung verschiedener Einsatzszenarien. In H.-S. Siller, W. Weigel, & J. F. Wörler (Eds.), Beiträge zum Mathematikunterricht 2020 (pp. 1361–1364). WTM-Verlag. https://doi.org/10.17877/DE290R-21396
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2020 | Journal Article | LibreCat-ID: 35712
Schürmann, M., Gildehaus, L., Liebendörfer, M., Schaper, N., Biehler, R., Hochmuth, R., Kuklinski, C., & Lankeit, E. (2020). Mathematics learning support centres in Germany—an overview. Teaching Mathematics and Its Applications: An International Journal of the IMA, 40(2), 99–113. https://doi.org/10.1093/teamat/hraa007
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2020 | Book Chapter | LibreCat-ID: 35820
Biehler, R., Liebendörfer, M., Schmitz, A., Fleischmann, Y., Krämer, S., Ostsieker, L., & Schlüter, S. (2020). studiVEMINTvideos – Mathematische Lernvideos zur Studienvorbereitung und Unterstützung im ersten Studienjahr. In H.-S. Siller, W. Weigel, & J. F. Wörler (Eds.), Beiträge zum Mathematikunterricht 2020. WTM. https://doi.org/10.17877/DE290R-21234
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2020 | Conference Paper | LibreCat-ID: 35152
Lösch, A., & Platzner, M. (2020). MigHEFT: DAG-based Scheduling of Migratable Tasks on Heterogeneous Compute Nodes. 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 6–16. https://doi.org/10.1109/IPDPSW50202.2020.00012
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2020 | Journal Article | LibreCat-ID: 16961
Liebendörfer, M., Göller, R., Biehler, R., Hochmuth, R., Kortemeyer, J., Ostsieker, L., Rode, J., & Schaper, N. (2020). LimSt – Ein Fragebogen zur Erhebung von Lernstrategien im mathematikhaltigen Studium. Journal für Mathematik-Didaktik. https://doi.org/10.1007/s13138-020-00167-y
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2020 | Journal Article | LibreCat-ID: 24784
Schürmann, M., Liebendörfer, M., Gildehaus, L., Schaper, N., Hochmuth, R., Biehler, R., Lankeit, E., Kuklinski, C., & Ruge, J. (2020). Chancen und Möglichkeiten eines Netzwerks von Mathematischen Lernzentren in Deutschland - Bericht zum digitalen Lernzentrumsworkshop am 22.10.2020. Dghd-Newsletter, 4/2020, 24–26.
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2020 | Journal Article | LibreCat-ID: 16965
Schürmann, M., Schaper, N., Liebendörfer, M., Biehler, R., Lankeit, E., Hochmuth, R., Ruge, J., & Kuklinski, C. (2020). Ein Kurzbericht aus dem Forschungsprojekt WiGeMath-Lernzentren als Unterstützungsmaßnahme für mathematikbezogenes Lernen in der Studieneingangsphase. Dghd-Newsletter, 01/2020, 25–29.
LibreCat
 

2020 | Book Chapter | LibreCat-ID: 47261
Haney, J. M., Furman, S. M., & Acar, Y. (2020). Smart Home Security and Privacy Mitigations: Consumer Perceptions, Practices, and Challenges. In HCI for Cybersecurity, Privacy and Trust. Springer International Publishing. https://doi.org/10.1007/978-3-030-50309-3_26
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2020 | Conference Paper | LibreCat-ID: 47260
Wermke, D., Huaman, N., Stransky, C., Busch, N., Acar, Y., & Fahl, S. (2020). Cloudy with a Chance of Misconceptions: Exploring Users’ Perceptions and Expectations of Security and Privacy in Cloud Office Suites. In H. R. Lipford & S. Chiasson (Eds.), Sixteenth Symposium on Usable Privacy and Security, SOUPS 2020, August 7-11, 2020 (pp. 359–377). USENIX Association.
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2020 | Conference Paper | LibreCat-ID: 47262
Gorski, P. L., Acar, Y., Lo Iacono, L., & Fahl, S. (2020). Listen to Developers! A Participatory Design Study on Security Warnings for Cryptographic APIs. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3313831.3376142
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2020 | Conference Paper | LibreCat-ID: 47879
Haney, J., Furman, S., & Acar, Y. (2020). Smart Home Security and Privacy Mitigations: Consumer Perceptions, Practices, and Challenges.
LibreCat
 

2020 | Conference Paper | LibreCat-ID: 46331
Seiler, M., Trautmann, H., & Kerschke, P. (2020). Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207338
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2020 | Conference Paper | LibreCat-ID: 46330
Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI) (pp. 48–64). https://doi.org/10.1007/978-3-030-58112-1_4
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2020 | Journal Article | LibreCat-ID: 46334
Bossek, J., Kerschke, P., & Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Applied Soft Computing, 88, 105901. https://doi.org/10.1016/j.asoc.2019.105901
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2020 | Conference Paper | LibreCat-ID: 46322
Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778
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2020 | Conference Paper | LibreCat-ID: 46324
Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8.
LibreCat
 

2020 | Conference Paper | LibreCat-ID: 46323
Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), 166–174.
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2020 | Journal Article | LibreCat-ID: 55288
Baier, St., & Technau, M. (2020). On the distribution of αp modulo one in imaginary quadratic number fields with class number one. J. Théor. Nombres Bordx., 32(2), 719–760. https://doi.org/10.5802/jtnb.1141
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2020 | Journal Article | LibreCat-ID: 55286
Technau, M. (2020). Modular hyperbolas and Beatty sequences. J. Number Theory, 208, 148–167. https://doi.org/10.1016/j.jnt.2019.07.022
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2020 | Journal Article | LibreCat-ID: 55287
Shparlinski, I. E., & Technau, M. (2020). Kloosterman sums with twice-differentiable functions. Funct. Approximatio, Comment. Math., 63(1), 113–124. https://doi.org/10.7169/facm/1845
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