@inbook{19521, author = {{Pfannschmidt, Karlson and Hüllermeier, Eyke}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030582845}}, issn = {{0302-9743}}, title = {{{Learning Choice Functions via Pareto-Embeddings}}}, doi = {{10.1007/978-3-030-58285-2_30}}, year = {{2020}}, } @inproceedings{19606, abstract = {{Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is is still a lack in the runtime configuration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product configuration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Configurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a configuration model, the Product Configurator is an Android client that uses the configuration model to place different product configurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering. HCSE 2020}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, keywords = {{Product Configuration, Augmented Reality, Model-based, Tool Support}}, location = {{Eindhoven}}, publisher = {{Springer}}, title = {{{ProConAR: A Tool Support for Model-based AR Product Configuration}}}, doi = {{10.1007/978-3-030-64266-2_14}}, volume = {{12481}}, year = {{2020}}, } @inproceedings{19607, abstract = {{Modern services consist of modular, interconnected components, e.g., microservices forming a service mesh. To dynamically adjust to ever-changing service demands, service components have to be instantiated on nodes across the network. Incoming flows requesting a service then need to be routed through the deployed instances while considering node and link capacities. Ultimately, the goal is to maximize the successfully served flows and Quality of Service (QoS) through online service coordination. Current approaches for service coordination are usually centralized, assuming up-to-date global knowledge and making global decisions for all nodes in the network. Such global knowledge and centralized decisions are not realistic in practical large-scale networks. To solve this problem, we propose two algorithms for fully distributed service coordination. The proposed algorithms can be executed individually at each node in parallel and require only very limited global knowledge. We compare and evaluate both algorithms with a state-of-the-art centralized approach in extensive simulations on a large-scale, real-world network topology. Our results indicate that the two algorithms can compete with centralized approaches in terms of solution quality but require less global knowledge and are magnitudes faster (more than 100x).}}, author = {{Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{distributed management, service coordination, network coordination, nfv, softwarization, orchestration}}, publisher = {{IEEE}}, title = {{{Every Node for Itself: Fully Distributed Service Coordination}}}, year = {{2020}}, } @inproceedings{19609, abstract = {{Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks.}}, author = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}}, publisher = {{IEEE}}, title = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}}, year = {{2020}}, } @inproceedings{19632, author = {{Jovanovikj, Ivan and Yigitbas, Enes and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Proceedings of the 8th International Working Conference on Human-Centered Software Engineering (HCSE'20)}}, pages = {{216--224}}, publisher = {{Springer}}, title = {{{Augmented and Virtual Reality Object Repository for Rapid Prototyping }}}, year = {{2020}}, } @inproceedings{19656, author = {{Sharma, Arnab and Wehrheim, Heike}}, booktitle = {{Proceedings of the 32th IFIP International Conference on Testing Software and Systems (ICTSS)}}, publisher = {{Springer}}, title = {{{Automatic Fairness Testing of Machine Learning Models}}}, year = {{2020}}, } @article{19864, author = {{Meyer, Maurice and Frank, Maximilian and Massmann, Melina and Dumitrescu, Roman}}, journal = {{Proceedings of The 11th International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC 2020)}}, title = {{{Research and Consulting in Data-Driven Strategic Product Planning}}}, year = {{2020}}, } @article{19866, author = {{Meyer, Maurice and Frank, Maximilian and Massmann, Melina and Dumitrescu, Roman}}, journal = {{Journal of Systemics, Cybernetics and Informatics}}, number = {{2}}, pages = {{55--61}}, title = {{{Research and Consulting in Data-Driven Strategic Product Planning}}}, volume = {{18}}, year = {{2020}}, } @inproceedings{19899, abstract = {{Most existing robot formation problems seek a target formation of a certain minimal and, thus, efficient structure. Examples include the Gathering and the Chain-Formation problem. In this work, we study formation problems that try to reach a maximal structure, supporting for example an efficient coverage in exploration scenarios. A recent example is the NASA Shapeshifter project, which describes how the robots form a relay chain along which gathered data from extraterrestrial cave explorations may be sent to a home base. As a first step towards understanding such maximization tasks, we introduce and study the Max-Chain-Formation problem, where $n$ robots are ordered along a winding, potentially self-intersecting chain and must form a connected, straight line of maximal length connecting its two endpoints. We propose and analyze strategies in a discrete and in a continuous time model. In the discrete case, we give a complete analysis if all robots are initially collinear, showing that the worst-case time to reach an $\varepsilon$-approximation is upper bounded by $\mathcal{O}(n^2 \cdot \log (n/\varepsilon))$ and lower bounded by $\Omega(n^2 \cdot~\log (1/\varepsilon))$. If one endpoint of the chain remains stationary, this result can be extended to the non-collinear case. If both endpoints move, we identify a family of instances whose runtime is unbounded. For the continuous model, we give a strategy with an optimal runtime bound of $\Theta(n)$. Avoiding an unbounded runtime similar to the discrete case relies crucially on a counter-intuitive aspect of the strategy: slowing down the endpoints while all other robots move at full speed. Surprisingly, we can show that a similar trick does not work in the discrete model.}}, author = {{Castenow, Jannik and Kling, Peter and Knollmann, Till and Meyer auf der Heide, Friedhelm}}, booktitle = {{Stabilization, Safety, and Security of Distributed Systems - 22nd International Symposium, SSS 2020, Austin, Texas, USA, November 18-21, 2020, Proceedings}}, editor = {{Devismes , Stéphane and Mittal, Neeraj }}, isbn = {{978-3-030-64347-8}}, pages = {{65--80}}, publisher = {{Springer}}, title = {{{A Discrete and Continuous Study of the Max-Chain-Formation Problem – Slow Down to Speed Up}}}, doi = {{10.1007/978-3-030-64348-5_6}}, volume = {{12514}}, year = {{2020}}, } @inproceedings{19953, abstract = {{Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs.}}, author = {{Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)}}, editor = {{Jialin Pan, Sinno and Sugiyama, Masashi}}, keywords = {{graph neural networks, Weisfeiler-Lehman test, cycle detection}}, location = {{Bangkok, Thailand}}, pages = {{49--64}}, publisher = {{PMLR}}, title = {{{A Novel Higher-order Weisfeiler-Lehman Graph Convolution}}}, volume = {{129}}, year = {{2020}}, } @misc{19999, author = {{Mayer, Stefan}}, publisher = {{Universität Paderborn}}, title = {{{Optimierung von JMCTest beim Testen von Inter Method Contracts}}}, year = {{2020}}, } @inproceedings{20116, author = {{Nouri, Zahra and Wachsmuth, Henning and Engels, Gregor}}, booktitle = {{Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics}}, location = {{Barcelona, Spain}}, pages = {{6264--6276}}, title = {{{Mining Crowdsourcing Problems from Discussion Forums of Workers}}}, year = {{2020}}, } @inproceedings{20122, author = {{El Baff, Roxanne and Al-Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES 2020)}}, pages = {{29--40}}, title = {{{Persuasiveness of News Editorials depending on Ideology and Personality}}}, year = {{2020}}, } @inproceedings{20139, author = {{Spliethöver, Maximilian and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020)}}, pages = {{76--87}}, title = {{{Argument from Old Man's View: Assessing Social Bias in Argumentation}}}, year = {{2020}}, } @inproceedings{20140, author = {{Dorsch, Jonas and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020)}}, pages = {{19--29}}, title = {{{Semi-Supervised Cleansing of Web Argument Corpora}}}, year = {{2020}}, } @inproceedings{20159, abstract = {{Let G = (V,E) be an undirected graph on n vertices with non-negative capacities on its edges. The mincut sensitivity problem for the insertion of an edge is defined as follows. Build a compact data structure for G and a given set S ⊆ V of vertices that, on receiving any edge (x,y) ∈ S×S of positive capacity as query input, can efficiently report the set of all pairs from S× S whose mincut value increases upon insertion of the edge (x,y) to G. The only result that exists for this problem is for a single pair of vertices (Picard and Queyranne, Mathematical Programming Study, 13 (1980), 8-16). We present the following results for the single source and the all-pairs versions of this problem. 1) Single source: Given any designated source vertex s, there exists a data structure of size 𝒪(|S|) that can output all those vertices from S whose mincut value to s increases upon insertion of any given edge. The time taken by the data structure to answer any query is 𝒪(|S|). 2) All-pairs: There exists an 𝒪(|S|²) size data structure that can output all those pairs of vertices from S× S whose mincut value gets increased upon insertion of any given edge. The time taken by the data structure to answer any query is 𝒪(k), where k is the number of pairs of vertices whose mincut increases. For both these versions, we also address the problem of reporting the values of the mincuts upon insertion of any given edge. To derive our results, we use interesting insights into the nearest and the farthest mincuts for a pair of vertices. In addition, a crucial result, that we establish and use in our data structures, is that there exists a directed acyclic graph of 𝒪(n) size that compactly stores the farthest mincuts from all vertices of V to a designated vertex s in the graph. We believe that this result is of independent interest, especially, because it also complements a previously existing result by Hariharan et al. (STOC 2007) that the nearest mincuts from all vertices of V to s is a laminar family, and hence, can be stored compactly in a tree of 𝒪(n) size.}}, author = {{Baswana, Surender and Gupta, Shiv and Knollmann, Till}}, booktitle = {{28th Annual European Symposium on Algorithms (ESA 2020)}}, editor = {{Grandoni, Fabrizio and Herman, Grzegorz and Sanders, Peter}}, isbn = {{978-3-95977-162-7}}, issn = {{1868-8969}}, keywords = {{Mincut, Sensitivity, Data Structure}}, pages = {{12:1--12:14}}, publisher = {{Schloss Dagstuhl -- Leibniz-Zentrum für Informatik}}, title = {{{Mincut Sensitivity Data Structures for the Insertion of an Edge}}}, doi = {{10.4230/LIPIcs.ESA.2020.12}}, volume = {{173}}, year = {{2020}}, } @inproceedings{20166, author = {{Bondarenko, Alexander and Fröbe, Maik and Beloucif, Meriem and Gienapp, Lukas and Ajjour, Yamen and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and Hagen, Matthias}}, booktitle = {{CEUR Workshop Proceedings}}, pages = {{384--395}}, title = {{{Overview of Touché 2020: Argument Retrieval}}}, volume = {{2696}}, year = {{2020}}, } @inproceedings{20185, author = {{Castenow, Jannik and Harbig, Jonas and Jung, Daniel and Knollmann, Till and Meyer auf der Heide, Friedhelm}}, booktitle = {{Stabilization, Safety, and Security of Distributed Systems - 22nd International Symposium, SSS 2020, Austin, Texas, USA, November 18-21, 2020, Proceedings }}, editor = {{Devismes, Stéphane and Mittal, Neeraj}}, isbn = {{978-3-030-64347-8}}, pages = {{60--64}}, publisher = {{Springer}}, title = {{{Brief Announcement: Gathering in Linear Time: A Closed Chain of Disoriented & Luminous Robots with Limited Visibility }}}, doi = {{10.1007/978-3-030-64348-5_5}}, volume = {{12514}}, year = {{2020}}, } @misc{20221, author = {{Yeole, Paresh Kishor}}, title = {{{Plurality Consensus in Hybrid Networks}}}, year = {{2020}}, } @inproceedings{20274, author = {{Bila, Eleni and Doherty, Simon and Dongol, Brijesh and Derrick, John and Schellhorn, Gerhard and Wehrheim, Heike}}, booktitle = {{Formal Techniques for Distributed Objects, Components, and Systems - 40th {IFIP} {WG} 6.1 International Conference, {FORTE} 2020, Held as Part of the 15th International Federated Conference on Distributed Computing Techniques, DisCoTec 2020, Valletta, Malta, June 15-19, 2020, Proceedings}}, editor = {{Gotsman, Alexey and Sokolova, Ana}}, pages = {{39--58}}, publisher = {{Springer}}, title = {{{Defining and Verifying Durable Opacity: Correctness for Persistent Software Transactional Memory}}}, doi = {{10.1007/978-3-030-50086-3\_3}}, volume = {{12136}}, year = {{2020}}, }