@article{17092, abstract = {{Radiation tolerance in FPGAs is an important field of research particularly for reliable computation in electronics used in aerospace and satellite missions. The motivation behind this research is the degradation of reliability in FPGA hardware due to single-event effects caused by radiation particles. Redundancy is a commonly used technique to enhance the fault-tolerance capability of radiation-sensitive applications. However, redundancy comes with an overhead in terms of excessive area consumption, latency, and power dissipation. Moreover, the redundant circuit implementations vary in structure and resource usage with the redundancy insertion algorithms as well as number of used redundant stages. The radiation environment varies during the operation time span of the mission depending on the orbit and space weather conditions. Therefore, the overheads due to redundancy should also be optimized at run-time with respect to the current radiation level. In this paper, we propose a technique called Dynamic Reliability Management (DRM) that utilizes the radiation data, interprets it, selects a suitable redundancy level, and performs the run-time reconfiguration, thus varying the reliability levels of the target computation modules. DRM is composed of two parts. The design-time tool flow of DRM generates a library of various redundant implementations of the circuit with different magnitudes of performance factors. The run-time tool flow, while utilizing the radiation/error-rate data, selects a required redundancy level and reconfigures the computation module with the corresponding redundant implementation. Both parts of DRM have been verified by experimentation on various benchmarks. The most significant finding we have from this experimentation is that the performance can be scaled multiple times by using partial reconfiguration feature of DRM, e.g., 7.7 and 3.7 times better performance results obtained for our data sorter and matrix multiplier case studies compared with static reliability management techniques. Therefore, DRM allows for maintaining a suitable trade-off between computation reliability and performance overhead during run-time of an application.}}, author = {{Anwer, Jahanzeb and Meisner, Sebastian and Platzner, Marco}}, issn = {{1687-7195}}, journal = {{International Journal of Reconfigurable Computing}}, pages = {{1--19}}, title = {{{Dynamic Reliability Management for FPGA-Based Systems}}}, doi = {{10.1155/2020/2808710}}, year = {{2020}}, } @article{10596, abstract = {{Multi-objective optimization is an active field of research that has many applications. Owing to its success and because decision-making processes are becoming more and more complex, there is a recent trend for incorporating many objectives into such problems. The challenge with such problems, however, is that the dimensions of the solution sets—the so-called Pareto sets and fronts—grow with the number of objectives. It is thus no longer possible to compute or to approximate the entire solution set of a given problem that contains many (e.g. more than three) objectives. On the other hand, the computation of single solutions (e.g. via scalarization methods) leads to unsatisfying results in many cases, even if user preferences are incorporated. In this article, the Pareto Explorer tool is presented—a global/local exploration tool for the treatment of many-objective optimization problems (MaOPs). In the first step, a solution of the problem is computed via a global search algorithm that ideally already includes user preferences. In the second step, a local search along the Pareto set/front of the given MaOP is performed in user specified directions. For this, several continuation-like procedures are proposed that can incorporate preferences defined in decision, objective, or in weight space. The applicability and usefulness of Pareto Explorer is demonstrated on benchmark problems as well as on an application from industrial laundry design.}}, author = {{Schütze, Oliver and Cuate, Oliver and Martín, Adanay and Peitz, Sebastian and Dellnitz, Michael}}, issn = {{0305-215X}}, journal = {{Engineering Optimization}}, number = {{5}}, pages = {{832--855}}, title = {{{Pareto Explorer: a global/local exploration tool for many-objective optimization problems}}}, doi = {{10.1080/0305215x.2019.1617286}}, volume = {{52}}, year = {{2020}}, } @article{10790, author = {{Blömer, Johannes and Brauer, Sascha and Bujna, Kathrin and Kuntze, Daniel}}, issn = {{1862-5347}}, journal = {{Advances in Data Analysis and Classification}}, pages = {{147–173}}, title = {{{How well do SEM algorithms imitate EM algorithms? A non-asymptotic analysis for mixture models}}}, doi = {{10.1007/s11634-019-00366-7}}, volume = {{14}}, year = {{2020}}, } @phdthesis{15482, author = {{Löken, Nils}}, title = {{{Cryptography for the Crowd — A Study of Cryptographic Schemes with Applications to Crowd Work}}}, doi = {{10.17619/UNIPB/1-854}}, year = {{2020}}, } @inproceedings{15490, author = {{Claes, Leander and Baumhögger, Elmar and Rüther, Torben and Gierse, Jan and Tröster, Thomas and Henning, Bernd}}, booktitle = {{Fortschritte der Akustik - DAGA 2020}}, pages = {{1077--1080}}, title = {{{Reduction of systematic measurement deviation in acoustic absorption measurement systems}}}, year = {{2020}}, } @inproceedings{15604, author = {{Jovanovikj, Ivan and Yigitbas, Enes and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD}}, isbn = {{978-989-758-400-8}}, location = {{Valletta}}, title = {{{Concept-based Co-Migration of Test Cases}}}, doi = {{10.5220/0009171404490456}}, year = {{2020}}, } @article{15605, author = {{Jovanovikj, Ivan and Yigitbas, Enes and Sauer, Stefan and Engels, Gregor}}, issn = {{1613-0073}}, journal = {{Software Engineering 2020 Workshopband}}, location = {{Innscbruck}}, title = {{{Test Case Co-Migration Method Patterns}}}, year = {{2020}}, } @inproceedings{15629, abstract = {{In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.}}, author = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}}, location = {{Konstanz, Germany}}, publisher = {{Springer}}, title = {{{LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification}}}, year = {{2020}}, } @phdthesis{15631, author = {{Feldkord, Björn}}, title = {{{Mobile Resource Allocation}}}, doi = {{10.17619/UNIPB/1-869}}, year = {{2020}}, } @misc{15770, author = {{Warner, Daniel}}, publisher = {{Universität Paderborn}}, title = {{{On the complexity of local transformations in SDN overlays}}}, year = {{2020}}, } @inproceedings{15820, author = {{Al-Khatib, Khalid and Hou, Yufang and Wachsmuth, Henning and Jochim, Charles and Bonin, Francesca and Stein, Benno}}, booktitle = {{Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)}}, pages = {{7367 -- 7374}}, title = {{{End-to-End Argumentation Knowledge Graph Construction}}}, year = {{2020}}, } @inproceedings{15821, author = {{Bondarenko, Alexander and Hagen, Matthias and Potthast, Martin and Wachsmuth, Henning and Beloucif, Meriem and Biemann, Chris and Panchenko, Alexander and Stein, Benno}}, booktitle = {{Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020)}}, pages = {{517--523}}, title = {{{Touché: First Shared Task on Argument Retrieval}}}, year = {{2020}}, } @inproceedings{15825, author = {{Kiesel, Johannes and Lang, Kevin and Wachsmuth, Henning and Hornecker, Eva and Stein, Benno}}, booktitle = {{Proceedings of the 2020 ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2020)}}, pages = {{53--62}}, title = {{{Investigating Expectations for Voice-based and Conversational Argument Search on the Web}}}, year = {{2020}}, } @article{15836, author = {{Bellman, K. and Dutt, N. and Esterle, L. and Herkersdorf, A. and Jantsch, A. and Landauer, C. and R. Lewis, P. and Platzner, Marco and TaheriNejad, N. and Tammemäe, K.}}, journal = {{ACM Transactions on Cyber-Physical Systems}}, pages = {{1--24}}, title = {{{Self-aware Cyber-Physical Systems}}}, volume = {{Accepted for Publication}}, year = {{2020}}, } @article{15025, abstract = {{In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.}}, author = {{Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}}, journal = {{Evolutionary Computation}}, number = {{2}}, pages = {{165–193}}, publisher = {{MIT Press Journals}}, title = {{{Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets}}}, doi = {{10.1162/evco_a_00266}}, volume = {{28}}, year = {{2020}}, } @inproceedings{15163, author = {{Feldmann, Nadine and Schulze, Veronika and Jurgelucks, Benjamin and Henning, Bernd}}, booktitle = {{Fortschritte der Akustik - DAGA 2020}}, pages = {{1125--1128}}, title = {{{Solving piezoelectric inverse problems using Algorithmic Differentiation}}}, year = {{2020}}, } @inproceedings{15169, author = {{Castenow, Jannik and Kolb, Christina and Scheideler, Christian}}, booktitle = {{Proceedings of the 21st International Conference on Distributed Computing and Networking (ICDCN)}}, location = {{Kolkata, Indien}}, publisher = {{ACM}}, title = {{{A Bounding Box Overlay for Competitive Routing in Hybrid Communication Networks}}}, year = {{2020}}, } @inproceedings{15264, author = {{Johannesmann, Sarah and Becker, Sebastian and Webersen, Manuel and Henning, Bernd}}, booktitle = {{SMSI 2020 - Measurement Science}}, isbn = {{978-3-9819376-2-6}}, location = {{Nuremberg}}, title = {{{Determination of Murnaghan constants of plate-shaped polymers under uniaxial tensile load}}}, doi = {{10.5162/SMSI2020/D6.1}}, year = {{2020}}, } @inbook{15267, author = {{Yigitbas, Enes and Jovanovikj, Ivan and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes - IFIP WG 13.2/13.5}}, publisher = {{Springer, LNCS}}, title = {{{On the Development of Context-aware Augmented Reality Applications }}}, year = {{2020}}, } @inproceedings{16213, abstract = {{Automated synthesis of approximate circuits via functional approximations is of prominent importance to provide efficiency in energy, runtime, and chip area required to execute an application. Approximate circuits are usually obtained either through analytical approximation methods leveraging approximate transformations such as bit-width scaling or via iterative search-based optimization methods when a library of approximate components, e.g., approximate adders and multipliers, is available. For the latter, exploring the extremely large design space is challenging in terms of both computations and quality of results. While the combination of both methods can create more room for further approximations, the \textit{Design Space Exploration}~(DSE) becomes a crucial issue. In this paper, we present such a hybrid synthesis methodology that applies a low-cost analytical method followed by parallel stochastic search-based optimization. We address the DSE challenge through efficient pruning of the design space and skipping unnecessary expensive testing and/or verification steps. The experimental results reveal up to 10.57x area savings in comparison with both purely analytical or search-based approaches. }}, author = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}}, booktitle = {{Proceedings of the 30th ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020}}, location = {{Beijing, China}}, pages = {{421--426}}, publisher = {{ACM}}, title = {{{A Hybrid Synthesis Methodology for Approximate Circuits}}}, doi = {{10.1145/3386263.3406952}}, year = {{2020}}, }