TY - CONF AU - Hüsing, Sven AU - Schulte, Carsten AU - Sparmann, Sören AU - Bolte, Mario ID - 52379 T2 - Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 TI - Using Worked Examples for Engaging in Epistemic Programming Projects ER - TY - CONF AU - Hu, Lijie AU - Habernal, Ivan AU - Shen, Lei AU - Wang, Di ED - Graham, Yvette ED - Purver, Matthew ID - 52827 T2 - Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024 TI - Differentially Private Natural Language Models: Recent Advances and Future Directions ER - TY - CONF AB - Neural machine translation (NMT) is a widely popular text generation task, yet there is a considerable research gap in the development of privacy-preserving NMT models, despite significant data privacy concerns for NMT systems. Differentially private stochastic gradient descent (DP-SGD) is a popular method for training machine learning models with concrete privacy guarantees; however, the implementation specifics of training a model with DP-SGD are not always clarified in existing models, with differing software libraries used and code bases not always being public, leading to reproducibility issues. To tackle this, we introduce DP-NMT, an open-source framework for carrying out research on privacy-preserving NMT with DP-SGD, bringing together numerous models, datasets, and evaluation metrics in one systematic software package. Our goal is to provide a platform for researchers to advance the development of privacy-preserving NMT systems, keeping the specific details of the DP-SGD algorithm transparent and intuitive to implement. We run a set of experiments on datasets from both general and privacy-related domains to demonstrate our framework in use. We make our framework publicly available and welcome feedback from the community. AU - Igamberdiev, Timour AU - Vu, Doan Nam Long AU - Kuennecke, Felix AU - Yu, Zhuo AU - Holmer, Jannik AU - Habernal, Ivan ED - Aletras, Nikolaos ED - De Clercq, Orphee ID - 52842 T2 - Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations TI - DP-NMT: Scalable Differentially Private Machine Translation ER - TY - CONF AU - Razavi, Kamran AU - Ghafouri, Saeid AU - Mühlhäuser, Max AU - Jamshidi, Pooyan AU - Wang, Lin ID - 53095 T2 - Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys), colocated with EuroSys 2024 TI - Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling ER - TY - CONF AU - Dann, Andreas Peter AU - Hermann, Ben AU - Bodden, Eric ID - 35083 TI - UpCy: Safely Updating Outdated Dependencies ER - TY - JOUR AB - As in almost every other branch of science, the major advances in data science and machine learning have also resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays possible to make accurate medium to long-term predictions of highly complex systems such as the weather, the dynamics within a nuclear fusion reactor, of disease models or the stock market in a very efficient manner. In many cases, predictive methods are advertised to ultimately be useful for control, as the control of high-dimensional nonlinear systems is an engineering grand challenge with huge potential in areas such as clean and efficient energy production, or the development of advanced medical devices. However, the question of how to use a predictive model for control is often left unanswered due to the associated challenges, namely a significantly higher system complexity, the requirement of much larger data sets and an increased and often problem-specific modeling effort. To solve these issues, we present a universal framework (which we call QuaSiModO: Quantization-Simulation-Modeling-Optimization) to transform arbitrary predictive models into control systems and use them for feedback control. The advantages of our approach are a linear increase in data requirements with respect to the control dimension, performance guarantees that rely exclusively on the accuracy of the predictive model, and only little prior knowledge requirements in control theory to solve complex control problems. In particular the latter point is of key importance to enable a large number of researchers and practitioners to exploit the ever increasing capabilities of predictive models for control in a straight-forward and systematic fashion. AU - Peitz, Sebastian AU - Bieker, Katharina ID - 21199 JF - Automatica TI - On the Universal Transformation of Data-Driven Models to Control Systems VL - 149 ER - TY - CONF AU - Schrader, Elena AU - Bernijazov, Ruslan AU - Foullois, Marc AU - Hillebrand, Michael AU - Kaiser, Lydia AU - Dumitrescu, Roman ID - 37553 T2 - 2022 IEEE International Symposium on Systems Engineering (ISSE) TI - Examples of AI-based Assistance Systems in context of Model-Based Systems Engineering ER - TY - CONF AU - Richter, Cedric AU - Haltermann, Jan Frederik AU - Jakobs, Marie-Christine AU - Pauck, Felix AU - Schott, Stefan AU - Wehrheim, Heike ID - 35426 T2 - 37th IEEE/ACM International Conference on Automated Software Engineering TI - Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs? ER - TY - CONF AU - Schott, Stefan AU - Pauck, Felix ID - 36848 T2 - 2022 IEEE 22nd International Working Conference on Source Code Analysis and Manipulation (SCAM) TI - Benchmark Fuzzing for Android Taint Analyses ER - TY - CONF AU - Pauck, Felix ID - 35427 T2 - 37th IEEE/ACM International Conference on Automated Software Engineering TI - Scaling Arbitrary Android App Analyses ER - TY - GEN AB - We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation error if the data are collected from long-term ergodic simulations. We derive both an exact expression for the variance of the kernel cross-covariance operator, measured in the Hilbert-Schmidt norm, and probabilistic bounds for the finite-data estimation error. Moreover, we derive a bound on the prediction error of observables in the RKHS using a finite Mercer series expansion. Further, assuming Koopman-invariance of the RKHS, we provide bounds on the full approximation error. Numerical experiments using the Ornstein-Uhlenbeck process illustrate our results. AU - Philipp, Friedrich AU - Schaller, Manuel AU - Worthmann, Karl AU - Peitz, Sebastian AU - Nüske, Feliks ID - 38031 T2 - arXiv:2301.08637 TI - Error bounds for kernel-based approximations of the Koopman operator ER - TY - GEN AU - Pilot, Matthias ID - 40440 TI - Updatable Privacy-Preserving Reputation System based on Blockchain ER - TY - CHAP AU - Hüsing, Sven AU - Schulte, Carsten AU - Winkelnkemper, Felix ID - 40511 SN - 9781350296916 T2 - Computer Science Education TI - Epistemic Programming ER - TY - JOUR AU - Castenow, Jannik AU - Harbig, Jonas AU - Jung, Daniel AU - Knollmann, Till AU - Meyer auf der Heide, Friedhelm ID - 33947 JF - Theoretical Computer Science KW - General Computer Science KW - Theoretical Computer Science SN - 0304-3975 TI - Gathering a Euclidean Closed Chain of Robots in Linear Time and Improved Algorithms for Chain-Formation VL - 939 ER - TY - CONF AU - Luo, Linghui AU - Piskachev, Goran AU - Krishnamurthy, Ranjith AU - Dolby, Julian AU - Schäf, Martin AU - Bodden, Eric ID - 41812 T2 - IEEE International Conference on Software Testing, Verification and Validation (ICST) TI - Model Generation For Java Frameworks ER - TY - CONF AU - Shivarpatna Venkatesh, Ashwin Prasad AU - Wang, Jiawei AU - Li, Li AU - Bodden, Eric ID - 41813 T2 - IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) TI - Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis ER - TY - JOUR AU - Yigitbas, Enes AU - Klauke, Jonas AU - Gottschalk, Sebastian AU - Engels, Gregor ID - 34402 JF - Journal on Computer Languages (COLA) TI - End-User Development of Interactive Web-Based Virtual Reality Scenes ER - TY - CONF AU - Yigitbas, Enes AU - Engels, Gregor ID - 33511 T2 - 56th Hawaii International Conference on System Science (HICSS 2023) TI - Enhancing Robot Programming through Digital Twin and Augmented Reality ER - TY - CONF AU - Yigitbas, Enes AU - Krois, Sebastian AU - Gottschalk, Sebastian AU - Engels, Gregor ID - 34401 T2 - Proceedings of the 7th International Conference on Human Computer Interaction Theory and Applications (HUCAPP'23) TI - Towards Enhanced Guiding Mechanisms in VR Training through Process Mining ER - TY - CONF AU - Castenow, Jannik AU - Harbig, Jonas AU - Jung, Daniel AU - Kling, Peter AU - Knollmann, Till AU - Meyer auf der Heide, Friedhelm ED - Hillel, Eshcar ED - Palmieri, Roberto ED - Riviére, Etienne ID - 34008 SN - 1868-8969 T2 - Proceedings of the 26th International Conference on Principles of Distributed Systems (OPODIS) TI - A Unifying Approach to Efficient (Near-)Gathering of Disoriented Robots with Limited Visibility VL - 253 ER - TY - GEN AB - The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution for the first issue using a data-driven surrogate model in the form of a convolutional LSTM with actuation. We demonstrate that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. Furthermore, we show that iteratively updating the model is of major importance to avoid biases in the RL training. Detailed ablation studies reveal the most important ingredients of the modeling process. We use the chaotic Kuramoto-Sivashinsky equation do demonstarte our findings. AU - Werner, Stefan AU - Peitz, Sebastian ID - 42160 T2 - arXiv:2302.07160 TI - Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs ER - TY - CONF AB - Savitch's theorem states that NPSPACE computations can be simulated in PSPACE. We initiate the study of a quantum analogue of NPSPACE, denoted Streaming-QCMASPACE (SQCMASPACE), where an exponentially long classical proof is streamed to a poly-space quantum verifier. Besides two main results, we also show that a quantum analogue of Savitch's theorem is unlikely to hold, as SQCMASPACE=NEXP. For completeness, we introduce Streaming-QMASPACE (SQMASPACE) with an exponentially long streamed quantum proof, and show SQMASPACE=QMA_EXP (quantum analogue of NEXP). Our first main result shows, in contrast to the classical setting, the solution space of a quantum constraint satisfaction problem (i.e. a local Hamiltonian) is always connected when exponentially long proofs are permitted. For this, we show how to simulate any Lipschitz continuous path on the unit hypersphere via a sequence of local unitary gates, at the expense of blowing up the circuit size. This shows quantum error-correcting codes can be unable to detect one codeword erroneously evolving to another if the evolution happens sufficiently slowly, and answers an open question of [Gharibian, Sikora, ICALP 2015] regarding the Ground State Connectivity problem. Our second main result is that any SQCMASPACE computation can be embedded into "unentanglement", i.e. into a quantum constraint satisfaction problem with unentangled provers. Formally, we show how to embed SQCMASPACE into the Sparse Separable Hamiltonian problem of [Chailloux, Sattath, CCC 2012] (QMA(2)-complete for 1/poly promise gap), at the expense of scaling the promise gap with the streamed proof size. As a corollary, we obtain the first systematic construction for obtaining QMA(2)-type upper bounds on arbitrary multi-prover interactive proof systems, where the QMA(2) promise gap scales exponentially with the number of bits of communication in the interactive proof. AU - Gharibian, Sevag AU - Rudolph, Dorian ID - 31872 T2 - 14th Innovations in Theoretical Computer Science (ITCS) TI - Quantum space, ground space traversal, and how to embed multi-prover interactive proofs into unentanglement VL - 251 ER - TY - JOUR AB - Regularization is used in many different areas of optimization when solutions are sought which not only minimize a given function, but also possess a certain degree of regularity. Popular applications are image denoising, sparse regression and machine learning. Since the choice of the regularization parameter is crucial but often difficult, path-following methods are used to approximate the entire regularization path, i.e., the set of all possible solutions for all regularization parameters. Due to their nature, the development of these methods requires structural results about the regularization path. The goal of this article is to derive these results for the case of a smooth objective function which is penalized by a piecewise differentiable regularization term. We do this by treating regularization as a multiobjective optimization problem. Our results suggest that even in this general case, the regularization path is piecewise smooth. Moreover, our theory allows for a classification of the nonsmooth features that occur in between smooth parts. This is demonstrated in two applications, namely support-vector machines and exact penalty methods. AU - Gebken, Bennet AU - Bieker, Katharina AU - Peitz, Sebastian ID - 27426 IS - 3 JF - Journal of Global Optimization TI - On the structure of regularization paths for piecewise differentiable regularization terms VL - 85 ER - TY - GEN AU - Lienen, Christian AU - Middeke, Sorel Horst AU - Platzner, Marco ID - 43048 TI - fpgaDDS: An Intra-FPGA Data Distribution Service for ROS 2 Robotics Applications ER - TY - JOUR AU - Götte, Thorsten AU - Kolb, Christina AU - Scheideler, Christian AU - Werthmann, Julian ID - 43109 JF - Theor. Comput. Sci. TI - Beep-and-Sleep: Message and Energy Efficient Set Cover VL - 950 ER - TY - CONF AU - Yigitbas, Enes AU - Nowosad, Alexander AU - Engels, Gregor ID - 43424 T2 - Proceedings of the 19th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2023) TI - Supporting Construction and Architectural Visualization through BIM and AR/VR: A Systematic Literature Review ER - TY - CONF AB - We present an approach for guaranteed constraint satisfaction by means of data-based optimal control, where the model is unknown and has to be obtained from measurement data. To this end, we utilize the Koopman framework and an eDMD-based bilinear surrogate modeling approach for control systems to show an error bound on predicted observables, i.e., functions of the state. This result is then applied to the constraints of the optimal control problem to show that satisfaction of tightened constraints in the purely data-based surrogate model implies constraint satisfaction for the original system. AU - Schaller, Manuel AU - Worthmann, Karl AU - Philipp, Friedrich AU - Peitz, Sebastian AU - Nüske, Feliks ID - 30125 IS - 1 T2 - IFAC-PapersOnLine TI - Towards reliable data-based optimal and predictive control using extended DMD VL - 56 ER - TY - JOUR AU - Maack, Marten ID - 44077 IS - 3 JF - Operations Research Letters KW - Applied Mathematics KW - Industrial and Manufacturing Engineering KW - Management Science and Operations Research KW - Software SN - 0167-6377 TI - Online load balancing on uniform machines with limited migration VL - 51 ER - TY - GEN AU - Schürmann, Patrick ID - 43374 TI - A Formal Comparison of Advanced Digital Signature Primitives ER - TY - CONF AU - Gharibian, Sevag AU - Watson, James AU - Bausch, Johannes ID - 20841 T2 - Proceedings of the 40th International Symposium on Theoretical Aspects of Computer Science (STACS) TI - The Complexity of Translationally Invariant Problems beyond Ground State Energies VL - 254 ER - TY - CONF AU - Ahmed, Qazi Arbab AU - Awais, Muhammad AU - Platzner, Marco ID - 44194 T2 - The 24th International Symposium on Quality Electronic Design (ISQED'23), San Francisco, Califorina USA TI - MAAS: Hiding Trojans in Approximate Circuits ER - TY - GEN AU - Schweichhart, Jonas ID - 44735 TI - Minimum Edge Cuts in Overlay Networks ER - TY - CHAP AU - Castenow, Jannik AU - Harbig, Jonas AU - Meyer auf der Heide, Friedhelm ID - 44769 SN - 0302-9743 T2 - Lecture Notes in Computer Science TI - Unifying Gathering Protocols for Swarms of Mobile Robots ER - TY - CONF AU - Wolters, Dennis AU - Engels, Gregor ID - 34294 SN - 2184-4348 T2 - MODELSWARD'23 TI - Model-driven Collaborative Design of Professional Education Programmes With Extended Online Whiteboards ER - TY - THES AU - Pauck, Felix ID - 43108 TI - Cooperative Android App Analysis ER - TY - CONF AU - Werthmann, Julian AU - Scheideler, Christian AU - Coy, Sam AU - Czumaj, Artur AU - Schneider, Philipp ID - 45188 TI - Routing Schemes for Hybrid Communication Networks ER - TY - CONF AB - Market transactions are subject to information asymmetry about the delivered value proposition, causing transaction costs and adverse market effects among buyers and sellers. Information systems research has investigated how review systems can reduce information asymmetry in business-to-consumer markets. However, these systems cannot be readily applied to business-to-business markets, are vulnerable to manipulation, and suffer from conceptual weak spots since they use textual data or star ratings. Building on design science research, we conceptualize a new class of reputation systems based on monetary-based payments as quantitative ratings for each transaction stored on a blockchain. Using cryptography, we show that our system assures content confidentiality so that buyers can share and sell their ratings selectively, establishing a reputation ecosystem. Our prescriptive insights advance the design of reputation systems and offer new paths to understanding the antecedents, dynamics, and consequences to reduce information asymmetry in B2B transactions. AU - Hemmrich, Simon AU - Bobolz, Jan AU - Beverungen, Daniel AU - Blömer, Johannes ID - 44855 T2 - ECIS 2023 Research Papers TI - Designing Business Reputation Ecosystems — A Method for Issuing and Trading Monetary Ratings on a Blockchain ER - TY - GEN AU - N., N. ID - 45243 TI - Development and Evaluation of a Model-Based UI Prototyping Experimentation Approach ER - TY - CONF AU - Karakaya, Kadiray AU - Bodden, Eric ID - 45312 T2 - 2023 IEEE Conference on Software Testing, Verification and Validation (ICST) TI - Two Sparsification Strategies for Accelerating Demand-Driven Pointer Analysis ER - TY - GEN AU - Koch, Angelina ID - 43375 TI - Privacy-Preserving Collection and Evaluation of Log Files ER - TY - JOUR AB - AbstractGraffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project (Ingrid) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within Ingrid, the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on Ingrid targeted especially by researchers. In particular, we present IngridKG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update IngridKG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of IngridKG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications. AU - Sherif, Mohamed Ahmed AU - da Silva, Ana Alexandra Morim AU - Pestryakova, Svetlana AU - Ahmed, Abdullah Fathi AU - Niemann, Sven AU - Ngomo, Axel-Cyrille Ngonga ID - 45484 IS - 1 JF - Scientific Data KW - Library and Information Sciences KW - Statistics KW - Probability and Uncertainty KW - Computer Science Applications KW - Education KW - Information Systems KW - Statistics and Probability SN - 2052-4463 TI - IngridKG: A FAIR Knowledge Graph of Graffiti VL - 10 ER - TY - THES AU - Castenow, Jannik ID - 45580 TI - Local Protocols for Contracting and Expanding Robot Formation Problems ER - TY - THES AU - Knollmann, Till ID - 45579 TI - Online Algorithms for Allocating Heterogeneous Resources ER - TY - CONF AU - Hotegni, Sedjro Salomon AU - Mahabadi, Sepideh AU - Vakilian, Ali ID - 45695 KW - Fair range clustering T2 - Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023. TI - Approximation Algorithms for Fair Range Clustering ER - TY - CONF AU - Hebrok, Sven Niclas AU - Nachtigall, Simon AU - Maehren, Marcel AU - Erinola, Nurullah AU - Merget, Robert AU - Somorovsky, Juraj AU - Schwenk, Jörg ID - 43060 T2 - 32nd USENIX Security Symposium TI - We Really Need to Talk About Session Tickets: A Large-Scale Analysis of Cryptographic Dangers with TLS Session Tickets ER - TY - GEN AU - Simon-Mertens, Florian ID - 45762 TI - Effizienzanalyse leichtgewichtiger Neuronaler Netze für FPGA-basierte Modulationsklassifikation ER - TY - CONF AB - The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts. AU - Grigoryan, Khoren AU - Fichtler, Timm AU - Schreiner, Nick AU - Rabe, Martin AU - Panzner, Melina AU - Kühn, Arno AU - Dumitrescu, Roman AU - Koldewey, Christian ID - 45793 KW - Product Management KW - Data Analytics KW - Data-Driven Design KW - Product-related data KW - Lifecycle Data KW - Tool-support T2 - Procedia CIRP 33 TI - Data-Driven Product Management: A Practitioner-Driven Research Agenda ER - TY - CONF AU - Özcan, Leon AU - Fichtler, Timm AU - Kasten, Benjamin AU - Koldewey, Christian AU - Dumitrescu, Roman ID - 45812 KW - Digital Platform KW - Platform Strategy KW - Strategic Management KW - Platform Life Cycle KW - Interview Study KW - Business Model KW - Business-to-Business KW - Two-sided Market KW - Multi-sided Market TI - Interview Study on Strategy Options for Platform Operation in B2B Markets ER - TY - CONF AB - Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning as a translation problem and propose a new family of class expression learning approaches which we dub neural class expression synthesizers. Training examples are ``translated'' into class expressions in a fashion akin to machine translation. Consequently, our synthesizers are not subject to the runtime limitations of search-based approaches. We study three instances of this novel family of approaches based on LSTMs, GRUs, and set transformers, respectively. An evaluation of our approach on four benchmark datasets suggests that it can effectively synthesize high-quality class expressions with respect to the input examples in approximately one second on average. Moreover, a comparison to state-of-the-art approaches suggests that we achieve better F-measures on large datasets. For reproducibility purposes, we provide our implementation as well as pretrained models in our public GitHub repository at https://github.com/dice-group/NeuralClassExpressionSynthesis AU - KOUAGOU, N'Dah Jean AU - Heindorf, Stefan AU - Demir, Caglar AU - Ngonga Ngomo, Axel-Cyrille ED - Pesquita, Catia ED - Jimenez-Ruiz, Ernesto ED - McCusker, Jamie ED - Faria, Daniel ED - Dragoni, Mauro ED - Dimou, Anastasia ED - Troncy, Raphael ED - Hertling, Sven ID - 33734 KW - Neural network KW - Concept learning KW - Description logics T2 - The Semantic Web - 20th Extended Semantic Web Conference (ESWC 2023) TI - Neural Class Expression Synthesis VL - 13870 ER - TY - GEN AB - Knowledge bases are widely used for information management on the web, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g. for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify fair decisions, a number of explanation approaches have been proposed using concepts in description logics. However, the learned concepts can become long and difficult to fathom for non-experts, even when verbalized. Moreover, long concepts do not immediately provide a clear path of action to change one's situation. Counterfactuals answering the question "How must feature values be changed to obtain a different classification?" have been proposed as short, human-friendly explanations for tabular data. In this paper, we transfer the notion of counterfactuals to description logics and propose the first algorithm for generating counterfactual explanations in the description logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts and the candidates with fewest feature changes are selected as counterfactuals. In case of multiple counterfactuals, we rank them according to the likeliness of their feature combinations. For evaluation, we conduct a user survey to investigate which of the generated counterfactual candidates are preferred for explanation by participants. In a second study, we explore possible use cases for counterfactual explanations. AU - Sieger, Leonie Nora AU - Heindorf, Stefan AU - Blübaum, Lukas AU - Ngonga Ngomo, Axel-Cyrille ID - 37937 T2 - arXiv:2301.05109 TI - Counterfactual Explanations for Concepts in ELH ER -