@misc{54382, author = {{Walker, Patrick Simon}}, title = {{{Bewertung der Fließeigenschaften von Lasersinterpulver (Sinterpulver)}}}, year = {{2020}}, } @inproceedings{22007, author = {{Schütte, Jan and Sextro, Walter}}, booktitle = {{Lecture Notes in Mechanical Engineering}}, isbn = {{9783030380762}}, issn = {{2195-4356}}, title = {{{Model-Based Investigation of the Influence of Wheel Suspension Characteristics on Tire Wear}}}, doi = {{10.1007/978-3-030-38077-9_201}}, year = {{2020}}, } @inbook{54518, author = {{Meyer-Hamme, Johannes and Albers, Helene}}, booktitle = {{Handbuch Diversität im Geschichtsunterricht – Zugänge zu einer inklusiven Geschichtsdidaktik}}, editor = {{Barsch, Sebastian}}, isbn = {{978-3734408779}}, pages = {{236--249}}, title = {{{Historische Identitäten im inklusiven Geschichtsunterricht}}}, year = {{2020}}, } @article{49465, author = {{Brendel, B. and Greulich, S. and Trang, Simon Thanh-Nam and Niederman, F.}}, journal = {{AIS Transactions on Replication Research}}, number = {{1}}, pages = {{1--15}}, title = {{{Towards a Greater Diversity of Replication Studies}}}, volume = {{6}}, year = {{2020}}, } @article{49466, author = {{Masuch, K. and Hengstler, S. and Trang, Simon Thanh-Nam and Brendel, A.}}, journal = {{AIS Transactions on Replication Research}}, number = {{1}}, pages = {{1--16}}, title = {{{Replication of the Unified Model of Information Security Policy Compliance}}}, volume = {{6}}, year = {{2020}}, } @inproceedings{45848, author = {{Mahmood, Yasir and Meier, Arne}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030399504}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Parameterised Complexity of Model Checking and Satisfiability in Propositional Dependence Logic}}}, doi = {{10.1007/978-3-030-39951-1_10}}, year = {{2020}}, } @inproceedings{45818, author = {{Mahmood, Yasir and Meier, Arne and Schmidt, Johannes}}, booktitle = {{Logical Foundations of Computer Science}}, isbn = {{9783030367541}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Parameterised Complexity of Abduction in Schaefer’s Framework}}}, doi = {{10.1007/978-3-030-36755-8_13}}, 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}}, } @misc{54663, author = {{Eikermann, Larissa and Hansell, Friederike}}, title = {{{Unterrichtsmaterial Erzgebirge - World Heritage Education}}}, year = {{2020}}, } @misc{54662, author = {{Eikermann, Larissa and Hansell, Friederike}}, title = {{{Unterrichtsmaterial Erzgebirge - Technology}}}, 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}}, } @misc{54658, author = {{Eikermann, Larissa and Hansell, Friederike}}, keywords = {{Welterbe}}, title = {{{Unterrichtsmaterial Erzgebirge - History}}}, year = {{2020}}, }