@article{48836, author = {{Bartz-Beielstein, Thomas and Doerr, Carola and van den Berg, Daan and Bossek, Jakob and Chandrasekaran, Sowmya and Eftimov, Tome and Fischbach, Andreas and Kerschke, Pascal and Cava, William La and Lopez-Ibanez, Manuel and Malan, Katherine M. and Moore, Jason H. and Naujoks, Boris and Orzechowski, Patryk and Volz, Vanessa and Wagner, Markus and Weise, Thomas}}, journal = {{Corr}}, title = {{{Benchmarking in Optimization: Best Practice and Open Issues}}}, year = {{2020}}, } @inproceedings{17397, author = {{Amjad, Muhammad Sohaib and Hardes, Tobias and Schettler, Max and Sommer, Christoph and Dressler, Falko}}, booktitle = {{2019 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781728145716}}, location = {{Los Angeles, CA}}, title = {{{Using Full Duplex Relaying to Reduce Physical Layer Latency in Platooning}}}, doi = {{10.1109/vnc48660.2019.9062784}}, year = {{2020}}, } @inproceedings{17399, abstract = {{Platooning promises to solve worldwide traffic problems by using wireless communication for tight control of convoys of vehicles to reduce their inter-vehicle gaps. To date, however, most research on platooning has focused on freeway scenarios. In this paper, we provide a realistic exploration of platooning in urban environments: We consider traffic lights and buildings, realistic vehicle dynamics, platooning controllers, and network communication. We highlight the challenges that urban platooning faces because of the particularities of the Radio Frequency (RF) channel, specifically near the centers of intersections. We demonstrate that using Visible Light Communication (VLC) as an alternative can alleviate some problems, but causes others. Using extensive simulations, we show how a situation-aware combination of VLC and RF communication can be used as a solution: It reduces the overall amount of lost information by approx. 85% compared to traditional approaches.}}, author = {{Hardes, Tobias and Sommer, Christoph}}, booktitle = {{2019 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781728145716}}, location = {{Los Angeles, CA}}, title = {{{Towards Heterogeneous Communication Strategies for Urban Platooning at Intersections}}}, doi = {{10.1109/vnc48660.2019.9062835}}, year = {{2020}}, } @article{52930, author = {{Baader, Franz and Borgwardt, Stefan and Koopmann, Patrick and Thost, Veronika and Turhan, Anni-Yasmin}}, journal = {{Künstliche Intell.}}, number = {{4}}, pages = {{543–550}}, title = {{{Semantic Technologies for Situation Awareness}}}, doi = {{10.1007/S13218-020-00694-3}}, volume = {{34}}, year = {{2020}}, } @inproceedings{35152, author = {{Lösch, Achim and Platzner, Marco}}, booktitle = {{2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}}, pages = {{6--16}}, title = {{{MigHEFT: DAG-based Scheduling of Migratable Tasks on Heterogeneous Compute Nodes}}}, doi = {{10.1109/IPDPSW50202.2020.00012}}, year = {{2020}}, } @inbook{47261, author = {{Haney, Julie M. and Furman, Susanne M. and Acar, Yasemin}}, booktitle = {{HCI for Cybersecurity, Privacy and Trust}}, isbn = {{9783030503086}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Smart Home Security and Privacy Mitigations: Consumer Perceptions, Practices, and Challenges}}}, doi = {{10.1007/978-3-030-50309-3_26}}, year = {{2020}}, } @inproceedings{47260, author = {{Wermke, Dominik and Huaman, Nicolas and Stransky, Christian and Busch, Niklas and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{Sixteenth Symposium on Usable Privacy and Security, SOUPS 2020, August 7-11, 2020}}, editor = {{Lipford, Heather Richter and Chiasson, Sonia}}, pages = {{359–377}}, publisher = {{USENIX Association}}, title = {{{Cloudy with a Chance of Misconceptions: Exploring Users’ Perceptions and Expectations of Security and Privacy in Cloud Office Suites}}}, year = {{2020}}, } @inproceedings{47262, author = {{Gorski, Peter Leo and Acar, Yasemin and Lo Iacono, Luigi and Fahl, Sascha}}, booktitle = {{Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems}}, publisher = {{ACM}}, title = {{{Listen to Developers! A Participatory Design Study on Security Warnings for Cryptographic APIs}}}, doi = {{10.1145/3313831.3376142}}, year = {{2020}}, } @inproceedings{47879, author = {{Haney, Julie and Furman, Susanne and Acar, Yasemin}}, publisher = {{International Conference on Human-Computer Interaction, Copenhagen, -1}}, title = {{{Smart Home Security and Privacy Mitigations: Consumer Perceptions, Practices, and Challenges}}}, year = {{2020}}, } @inproceedings{46331, abstract = {{Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually - been rather difficult.In this work, we introduce a method for generating strong adversaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.}}, author = {{Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, booktitle = {{Proceedings of the International Joint Conference on Neural Networks (IJCNN)}}, pages = {{1–8}}, title = {{{Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries}}}, doi = {{10.1109/IJCNN48605.2020.9207338}}, year = {{2020}}, } @inproceedings{46330, abstract = {{In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with 1000 nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.}}, author = {{Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)}}, editor = {{Bäck, Thomas and Preuss, Mike and Deutz, André and Wang, Hao and Doerr, Carola and Emmerich, Michael and Trautmann, Heike}}, pages = {{48–64}}, title = {{{Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}}}, doi = {{10.1007/978-3-030-58112-1_4}}, year = {{2020}}, } @article{46334, abstract = {{We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers.}}, author = {{Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{1568-4946}}, journal = {{Applied Soft Computing}}, keywords = {{Algorithm selection, Multi-objective optimization, Performance measurement, Combinatorial optimization, Traveling Salesperson Problem}}, pages = {{105901}}, title = {{{A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms}}}, doi = {{https://doi.org/10.1016/j.asoc.2019.105901}}, volume = {{88}}, year = {{2020}}, } @inproceedings{46322, abstract = {{We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences D and the number of eras n t and analyze all |D| nt combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity.}}, author = {{Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}}, pages = {{1–8}}, title = {{{Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}}}, doi = {{10.1109/CEC48606.2020.9185778}}, year = {{2020}}, } @inproceedings{46324, abstract = {{The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.}}, author = {{Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}}, pages = {{1–8}}, publisher = {{IEEE}}, title = {{{Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection}}}, year = {{2020}}, } @inproceedings{46323, abstract = {{In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.}}, author = {{Bossek, Jakob and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)}}, pages = {{166–174}}, publisher = {{ACM}}, title = {{{Dynamic Bi-Objective Routing of Multiple Vehicles}}}, year = {{2020}}, } @inproceedings{35814, author = {{Biehler, Rolf and Fleischer, Franz Yannik and Budde, Lea and Frischemeier, Daniel and Gerstenberger, Dietrich and Podworny, Susanne and Schulte, Carsten}}, booktitle = {{New Skills in the Changing World of Statistics Education Proceedings of the Roundtable conference of the International Association for Statistical Education (IASE)}}, editor = {{Arnold, P.}}, publisher = {{ISI/IASE}}, title = {{{Data science education in secondary schools: Teaching and learning decision trees with CODAP and Jupyter Notebooks as an example of integrating machine learning into statistics education}}}, year = {{2020}}, } @article{37122, author = {{Niewöhner, Nadine and Asmar, Laban and Röltgen, Daniel and Kühn, Arno and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, keywords = {{General Medicine}}, pages = {{43--48}}, publisher = {{Elsevier BV}}, title = {{{The impact of the 4th industrial revolution on the design fields of innovation management}}}, doi = {{10.1016/j.procir.2020.02.149}}, volume = {{91}}, year = {{2020}}, } @book{37125, author = {{Schuh, Günther and Anderl, Reiner and Dumitrescu, Roman and Krüger, Antonio and ten Hompel, Michael}}, publisher = {{ acatech – Deutsche Akademie der Technikwissenschaften}}, title = {{{Der Industrie 4.0 Maturity Index in der betrieblichen Anwendung. Aktuelle Herausforderungen, Fallbeispiele und Entwicklungstrends (acatech KOOPERATION)}}}, year = {{2020}}, } @inproceedings{16214, author = {{Pauck, Felix and Bodden, Eric and Wehrheim, Heike}}, booktitle = {{Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria}}, editor = {{Felderer, Michael and Hasselbring, Wilhelm and Rabiser, Rick and Jung, Reiner}}, pages = {{123--124}}, publisher = {{Gesellschaft f{\"{u}}r Informatik e.V.}}, title = {{{Reproducing Taint-Analysis Results with ReproDroid}}}, doi = {{10.18420/SE2020_36}}, year = {{2020}}, } @inproceedings{8426, abstract = {{A central tenet of theoretical cryptography is the study of the minimal assumptions required to implement a given cryptographic primitive. One such primitive is the one-time memory (OTM), introduced by Goldwasser, Kalai, and Rothblum [CRYPTO 2008], which is a classical functionality modeled after a non-interactive 1-out-of-2 oblivious transfer, and which is complete for one-time classical and quantum programs. It is known that secure OTMs do not exist in the standard model in both the classical and quantum settings. Here, we propose a scheme for using quantum information, together with the assumption of stateless (i.e., reusable) hardware tokens, to build statistically secure OTMs. Via the semidefinite programming-based quantum games framework of Gutoski and Watrous [STOC 2007], we prove security for a malicious receiver, against a linear number of adaptive queries to the token, in the quantum universal composability framework. We prove stand-alone security against a malicious sender, but leave open the question of composable security against a malicious sender, as well as security against a malicious receiver making a polynomial number of adaptive queries. Compared to alternative schemes derived from the literature on quantum money, our scheme is technologically simple since it is of the "prepare-and measure" type. We also show our scheme is "tight" according to two scenarios.}}, author = {{Broadbent, Anne and Gharibian, Sevag and Zhou, Hong-Sheng}}, booktitle = {{Proceedings of the 15th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC)}}, pages = {{6:1--6:25}}, publisher = {{Leibniz International Proceedings in Informatics (LIPIcs)}}, title = {{{Towards Quantum One-Time Memories from Stateless Hardware}}}, volume = {{158}}, year = {{2020}}, }