@inproceedings{21481, author = {{Weidmann, Nils and Fritsche, Lars and Anjorin, Anthony}}, booktitle = {{Proceedings of the 13th ACM SIGPLAN International Conference on Software Language Engineering, SLE 2020}}, editor = {{Lämmel, Ralf and Tratt, Laurcence and de Lara, Juan}}, isbn = {{9781450381765}}, location = {{Virtual Event, USA}}, publisher = {{ACM}}, title = {{{A search-based and fault-tolerant approach to concurrent model synchronisation}}}, doi = {{10.1145/3426425.3426932}}, year = {{2020}}, } @inproceedings{21482, author = {{Weidmann, Nils and Anjorin, Anthony and Cheney, James}}, booktitle = {{Proceedings of the Eleventh International Workshop on Graph Computation Models, GCM@STAF 2020}}, editor = {{Hoffmann, Berthold and Minas, Mark}}, issn = {{2075-2180}}, location = {{Online-Workshop}}, pages = {{1--12}}, publisher = {{EPTCS}}, title = {{{VICToRy: Visual Interactive Consistency Management in Tolerant Rule-based Systems}}}, doi = {{10.4204/eptcs.330.1}}, year = {{2020}}, } @inproceedings{21483, author = {{Jovanovikj, Ivan and Weidmann, Nils and Yigitbas, Enes and Anjorin, Anthony and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Proceedings of the First International Conference on Systems Modelling and Management, ICSMM 2020 }}, editor = {{Babur, Önder and Denil, Joachim and Vogel-Heuser, Birgit}}, isbn = {{9783030581664}}, issn = {{1865-0929}}, location = {{Bergen, Norway}}, publisher = {{Springer}}, title = {{{A Model-Driven Mutation Framework for Validation of Test Case Migration}}}, doi = {{10.1007/978-3-030-58167-1_2}}, year = {{2020}}, } @misc{21486, booktitle = {{ERCIM News}}, editor = {{Bernijazov, Ruslan and Özcan, Leon and Dumitrescu, Roman}}, number = {{122}}, pages = {{36--37}}, title = {{{AI Marketplace – The Ecosystem for Artificial Intelligence in Product Creation }}}, year = {{2020}}, } @inproceedings{21534, author = {{Bengs, Viktor and Hüllermeier, Eyke}}, booktitle = {{International Conference on Machine Learning}}, pages = {{778--787}}, title = {{{Preselection Bandits}}}, year = {{2020}}, } @unpublished{21536, abstract = {{We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corresponding (random) reward, provided the (random) amount of consumed resources remains below the limit. Otherwise, the observation is censored, i.e., no reward is obtained. For this problem setting, we introduce a measure of regret, which incorporates the actual amount of allocated resources of each learning round as well as the optimality of realizable rewards. Thus, to minimize regret, the learner needs to set a resource limit and choose an arm in such a way that the chance to realize a high reward within the predefined resource limit is high, while the resource limit itself should be kept as low as possible. We derive the theoretical lower bound on the cumulative regret and propose a learning algorithm having a regret upper bound that matches the lower bound. In a simulation study, we show that our learning algorithm outperforms straightforward extensions of standard multi-armed bandit algorithms.}}, author = {{Bengs, Viktor and Hüllermeier, Eyke}}, booktitle = {{arXiv:2011.00813}}, title = {{{Multi-Armed Bandits with Censored Consumption of Resources}}}, year = {{2020}}, } @inproceedings{21584, author = {{Gatica, Carlos Paiz and Platzner, Marco}}, booktitle = {{Machine Learning for Cyber Physical Systems (ML4CPS 2017)}}, isbn = {{9783662590836}}, issn = {{2522-8579}}, title = {{{Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Reconfigurable Architectures}}}, doi = {{10.1007/978-3-662-59084-3_9}}, year = {{2020}}, } @inbook{17337, author = {{Jazayeri, Bahar and Schwichtenberg, Simon and Küster, Jochen and Zimmermann, Olaf and Engels, Gregor}}, booktitle = {{Advanced Information Systems Engineering}}, isbn = {{9783030494346}}, issn = {{0302-9743}}, title = {{{Modeling and Analyzing Architectural Diversity of Open Platforms}}}, doi = {{10.1007/978-3-030-49435-3_3}}, year = {{2020}}, } @article{17358, abstract = {{Approximate circuits trade-off computational accuracy against improvements in hardware area, delay, or energy consumption. IP core vendors who wish to create such circuits need to convince consumers of the resulting approximation quality. As a solution we propose proof-carrying approximate circuits: The vendor creates an approximate IP core together with a certificate that proves the approximation quality. The proof certificate is bundled with the approximate IP core and sent off to the consumer. The consumer can formally verify the approximation quality of the IP core at a fraction of the typical computational cost for formal verification. In this paper, we first make the case for proof-carrying approximate circuits and then demonstrate the feasibility of the approach by a set of synthesis experiments using an exemplary approximation framework.}}, author = {{Witschen, Linus Matthias and Wiersema, Tobias and Platzner, Marco}}, issn = {{1557-9999}}, journal = {{IEEE Transactions On Very Large Scale Integration Systems}}, keywords = {{Approximate circuit synthesis, approximate computing, error metrics, formal verification, proof-carrying hardware}}, number = {{9}}, pages = {{2084 -- 2088}}, publisher = {{IEEE}}, title = {{{Proof-carrying Approximate Circuits}}}, doi = {{10.1109/TVLSI.2020.3008061}}, volume = {{28}}, year = {{2020}}, } @article{17369, author = {{Ho, Nam and Kaufmann, Paul and Platzner, Marco}}, journal = {{International Journal of Hybrid intelligent Systems}}, publisher = {{IOS Press}}, title = {{{Evolution of Application-Specific Cache Mappings}}}, year = {{2020}}, } @inproceedings{17370, abstract = {{ We consider a natural extension to the metric uncapacitated Facility Location Problem (FLP) in which requests ask for different commodities out of a finite set \( S \) of commodities. Ravi and Sinha (SODA 2004) introduced the model as the \emph{Multi-Commodity Facility Location Problem} (MFLP) and considered it an offline optimization problem. The model itself is similar to the FLP: i.e., requests are located at points of a finite metric space and the task of an algorithm is to construct facilities and assign requests to facilities while minimizing the construction cost and the sum over all assignment distances. In addition, requests and facilities are heterogeneous; they request or offer multiple commodities out of $S$. A request has to be connected to a set of facilities jointly offering the commodities demanded by it. In comparison to the FLP, an algorithm has to decide not only if and where to place facilities, but also which commodities to offer at each. To the best of our knowledge we are the first to study the problem in its online variant in which requests, their positions and their commodities are not known beforehand but revealed over time. We present results regarding the competitive ratio. On the one hand, we show that heterogeneity influences the competitive ratio by developing a lower bound on the competitive ratio for any randomized online algorithm of \( \Omega ( \sqrt{|S|} + \frac{\log n}{\log \log n} ) \) that already holds for simple line metrics. Here, \( n \) is the number of requests. On the other side, we establish a deterministic \( \mathcal{O}(\sqrt{|S|} \cdot \log n) \)-competitive algorithm and a randomized \( \mathcal{O}(\sqrt{|S|} \cdot \frac{\log n}{\log \log n} ) \)-competitive algorithm. Further, we show that when considering a more special class of cost functions for the construction cost of a facility, the competitive ratio decreases given by our deterministic algorithm depending on the function.}}, author = {{Castenow, Jannik and Feldkord, Björn and Knollmann, Till and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures}}, isbn = {{9781450369350}}, keywords = {{Online Multi-Commodity Facility Location, Competitive Ratio, Online Optimization, Facility Location Problem}}, title = {{{The Online Multi-Commodity Facility Location Problem}}}, doi = {{10.1145/3350755.3400281}}, year = {{2020}}, } @inproceedings{17371, author = {{Castenow, Jannik and Kling, Peter and Knollmann, Till and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures}}, isbn = {{9781450369350}}, title = {{{Brief Announcement: A Discrete and Continuous Study of the Max-Chain-Formation Problem: Slow Down to Speed up}}}, doi = {{10.1145/3350755.3400263}}, year = {{2020}}, } @inproceedings{17398, author = {{Turcanu, Ion and Engel, Thomas and Sommer, Christoph}}, booktitle = {{2019 IEEE Vehicular Networking Conference (VNC)}}, isbn = {{9781728145716}}, title = {{{Fog Seeding Strategies for Information-Centric Heterogeneous Vehicular Networks}}}, doi = {{10.1109/vnc48660.2019.9062816}}, year = {{2020}}, } @inproceedings{17405, author = {{Frank, Maximilian and Gausemeier, Juergen and Hennig-Cardinal von Widdern, Nils and Koldewey, Christian and Menzefricke, Joern Steffen and Reinhold, Jannik}}, booktitle = {{Proceedings of the ISPIM connects}}, publisher = {{International Society for Professional Innovation Management (ISPIM)}}, title = {{{A reference process for the Smart Service business: development and practical implications}}}, year = {{2020}}, } @inproceedings{17406, author = {{Becker, Julia-Kristin and Joachim, Klemens and Koldewey, Christian and Reinhold, Jannik and Dumitrescu, Roman}}, booktitle = {{Proceedings of the 2020 ISPIM Innovation Conference (Virtual) Event "Innovating in Times of Crisis"}}, publisher = {{ISPIM Innovation Conference}}, title = {{{Scaling Digital Business Models: A Case from the Automotive Industry}}}, year = {{2020}}, } @inproceedings{17407, author = {{Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Discovery Science}}, title = {{{Extreme Algorithm Selection with Dyadic Feature Representation}}}, year = {{2020}}, } @inproceedings{17408, author = {{Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{KI 2020: Advances in Artificial Intelligence}}, title = {{{Hybrid Ranking and Regression for Algorithm Selection}}}, year = {{2020}}, } @inproceedings{17424, author = {{Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the ECMLPKDD 2020}}, title = {{{AutoML for Predictive Maintenance: One Tool to RUL Them All}}}, doi = {{10.1007/978-3-030-66770-2_8}}, year = {{2020}}, } @unpublished{17605, abstract = {{Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data. While the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography. These irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small. In our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.}}, author = {{Heid, Stefan Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Journal of Data Mining and Digital Humanities}}, publisher = {{episciences}}, title = {{{Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction}}}, year = {{2020}}, } @unpublished{17825, abstract = {{Software verification has recently made enormous progress due to the development of novel verification methods and the speed-up of supporting technologies like SMT solving. To keep software verification tools up to date with these advances, tool developers keep on integrating newly designed methods into their tools, almost exclusively by re-implementing the method within their own framework. While this allows for a conceptual re-use of methods, it requires novel implementations for every new technique. In this paper, we employ cooperative verification in order to avoid reimplementation and enable usage of novel tools as black-box components in verification. Specifically, cooperation is employed for the core ingredient of software verification which is invariant generation. Finding an adequate loop invariant is key to the success of a verification run. Our framework named CoVerCIG allows a master verification tool to delegate the task of invariant generation to one or several specialized helper invariant generators. Their results are then utilized within the verification run of the master verifier, allowing in particular for crosschecking the validity of the invariant. We experimentally evaluate our framework on an instance with two masters and three different invariant generators using a number of benchmarks from SV-COMP 2020. The experiments show that the use of CoVerCIG can increase the number of correctly verified tasks without increasing the used resources}}, author = {{Haltermann, Jan Frederik and Wehrheim, Heike}}, booktitle = {{arXiv:2008.04551}}, title = {{{Cooperative Verification via Collective Invariant Generation}}}, year = {{2020}}, }