@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{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}}, } @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}}, } @inproceedings{16219, abstract = {{Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation.}}, author = {{Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Machine Learning for Dynamic Resource Allocation in Network Function Virtualization}}}, year = {{2020}}, } @inproceedings{16222, author = {{Zafeiropoulos, A. and Fotopoulou, E. and Peuster, Manuel and Schneider, Stefan Balthasar and Gouvas, P. and Behnke, D. and Müller, M. and Bök, P. and Trakadas, P. and Karkazis, P. and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, title = {{{Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case}}}, year = {{2020}}, } @inproceedings{16274, author = {{Jovanovikj, Ivan and Nagaraj, Achyuth and Yigitbas, Enes and Anjorin, Anthony and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST}}, location = {{Seoul}}, pages = {{31--40}}, publisher = {{ACM}}, title = {{{Validating Test Case Migration via Mutation Analysis }}}, year = {{2020}}, } @article{16278, abstract = {{Currently, the coexistence of multiple users and devices challenges the network's ability to reliably connect them. This article proposes a novel communication architecture that satisfies the requirements of fifth-generation (5G) mobile network applications. In particular, this architecture extends and combines ultra-dense networking (UDN), multi-access edge computing (MEC), and virtual infrastructure manager (VIM) concepts to provide a flexible network of moving radio access (RA) nodes, flying or moving to areas where users and devices struggle for connectivity and data rate. Furthermore, advances in radio communications and non-orthogonal multiple access (NOMA), virtualization technologies and energy-awareness mechanisms are integrated towards a mobile UDN that not only allows RA nodes to follow the user but also enables the virtualized network functions (VNFs) to adapt to user mobility by migrating from one node to another. Performance evaluation shows that the underlying network improves connectivity of users and devices through the flexible deployment of moving RA nodes and the use of NOMA.}}, author = {{Nomikos, Nikolaos and Michailidis, Emmanouel T. and Trakadas, Panagiotis and Vouyioukas, Demosthenes and Karl, Holger and Martrat, Josep and Zahariadis, Theodore and Papadopoulos, Konstantinos and Voliotis, Stamatis}}, issn = {{2214-2096}}, journal = {{Vehicular Communications}}, title = {{{A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices}}}, doi = {{10.1016/j.vehcom.2020.100250}}, year = {{2020}}, } @article{16280, abstract = {{Assigning bands of the wireless spectrum as resources to users is a common problem in wireless networks. Typically, frequency bands were assumed to be available in a stable manner. Nevertheless, in recent scenarios where wireless networks may be deployed in unknown environments, spectrum competition is considered, making it uncertain whether a frequency band is available at all or at what quality. To fully exploit such resources with uncertain availability, the multi-armed bandit (MAB) method, a representative online learning technique, has been applied to design spectrum scheduling algorithms. This article surveys such proposals. We describe the following three aspects: how to model spectrum scheduling problems within the MAB framework, what the main thread is following which prevalent algorithms are designed, and how to evaluate algorithm performance and complexity. We also give some promising directions for future research in related fields.}}, author = {{Li, Feng and Yu, Dongxiao and Yang, Huan and Yu, Jiguo and Karl, Holger and Cheng, Xiuzhen}}, issn = {{1536-1284}}, journal = {{IEEE Wireless Communications}}, pages = {{24--30}}, title = {{{Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey}}}, doi = {{10.1109/mwc.001.1900280}}, year = {{2020}}, } @article{16299, author = {{Castenow, Jannik and Fischer, Matthias and Harbig, Jonas and Jung, Daniel and Meyer auf der Heide, Friedhelm}}, issn = {{0304-3975}}, journal = {{Theoretical Computer Science}}, pages = {{289--309}}, title = {{{Gathering Anonymous, Oblivious Robots on a Grid}}}, doi = {{10.1016/j.tcs.2020.02.018}}, volume = {{815}}, year = {{2020}}, } @inproceedings{16363, author = {{Hansmeier, Tim and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{GECCO '20: Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-7127-8}}, location = {{Cancún, Mexico}}, pages = {{125--126}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold}}}, doi = {{10.1145/3377929.3389968}}, year = {{2020}}, }