@article{50458, abstract = {{AbstractConsider a set of jobs connected to a directed acyclic task graph with a fixed source and sink. The edges of this graph model precedence constraints and the jobs have to be scheduled with respect to those. We introduce the server cloud scheduling problem, in which the jobs have to be processed either on a single local machine or on one of infinitely many cloud machines. For each job, processing times both on the server and in the cloud are given. Furthermore, for each edge in the task graph, a communication delay is included in the input and has to be taken into account if one of the two jobs is scheduled on the server and the other in the cloud. The server processes jobs sequentially, whereas the cloud can serve as many as needed in parallel, but induces costs. We consider both makespan and cost minimization. The main results are an FPTAS for the makespan objective for graphs with a constant source and sink dividing cut and strong hardness for the case with unit processing times and delays.}}, author = {{Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon}}, issn = {{0178-4617}}, journal = {{Algorithmica}}, keywords = {{Applied Mathematics, Computer Science Applications, General Computer Science}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Server Cloud Scheduling}}}, doi = {{10.1007/s00453-023-01189-x}}, year = {{2023}}, } @inproceedings{50460, author = {{Deppert, Max A. and Jansen, Klaus and Maack, Marten and Pukrop, Simon and Rau, Malin}}, booktitle = {{2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)}}, publisher = {{IEEE}}, title = {{{Scheduling with Many Shared Resources}}}, doi = {{10.1109/ipdps54959.2023.00049}}, year = {{2023}}, } @unpublished{43439, abstract = {{This preprint makes the claim of having computed the $9^{th}$ Dedekind Number. This was done by building an efficient FPGA Accelerator for the core operation of the process, and parallelizing it on the Noctua 2 Supercluster at Paderborn University. The resulting value is 286386577668298411128469151667598498812366. This value can be verified in two steps. We have made the data file containing the 490M results available, each of which can be verified separately on CPU, and the whole file sums to our proposed value.}}, author = {{Van Hirtum, Lennart and De Causmaecker, Patrick and Goemaere, Jens and Kenter, Tobias and Riebler, Heinrich and Lass, Michael and Plessl, Christian}}, booktitle = {{arXiv:2304.03039}}, title = {{{A computation of D(9) using FPGA Supercomputing}}}, year = {{2023}}, } @unpublished{51159, abstract = {{Sparsity is a highly desired feature in deep neural networks (DNNs) since it ensures numerical efficiency, improves the interpretability of models (due to the smaller number of relevant features), and robustness. In machine learning approaches based on linear models, it is well known that there exists a connecting path between the sparsest solution in terms of the $\ell^1$ norm,i.e., zero weights and the non-regularized solution, which is called the regularization path. Very recently, there was a first attempt to extend the concept of regularization paths to DNNs by means of treating the empirical loss and sparsity ($\ell^1$ norm) as two conflicting criteria and solving the resulting multiobjective optimization problem. However, due to the non-smoothness of the $\ell^1$ norm and the high number of parameters, this approach is not very efficient from a computational perspective. To overcome this limitation, we present an algorithm that allows for the approximation of the entire Pareto front for the above-mentioned objectives in a very efficient manner. We present numerical examples using both deterministic and stochastic gradients. We furthermore demonstrate that knowledge of the regularization path allows for a well-generalizing network parametrization.}}, author = {{Amakor, Augustina Chidinma and Sonntag, Konstantin and Peitz, Sebastian}}, booktitle = {{arXiv}}, title = {{{A multiobjective continuation method to compute the regularization path of deep neural networks}}}, year = {{2023}}, } @unpublished{51158, abstract = {{Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the Koopman operator for deterministic and stochastic (control) systems. This operator is linear and encompasses full information on the (expected stochastic) dynamics. In this paper, we analyze a kernel-based EDMD algorithm, known as kEDMD, where the dictionary consists of the canonical kernel features at the data points. The latter are acquired by i.i.d. samples from a user-defined and application-driven distribution on a compact set. We prove bounds on the prediction error of the kEDMD estimator when sampling from this (not necessarily ergodic) distribution. The error analysis is further extended to control-affine systems, where the considered invariance of the Reproducing Kernel Hilbert Space is significantly less restrictive in comparison to invariance assumptions on an a-priori chosen dictionary.}}, author = {{Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl and Peitz, Sebastian and Nüske, Feliks}}, booktitle = {{arXiv:2312.10460}}, title = {{{Error analysis of kernel EDMD for prediction and control in the Koopman framework}}}, year = {{2023}}, } @inproceedings{47049, abstract = {{As technology advances, Unmanned Aerial Vehicles ( UAVs) have emerged as an innovative solution to a variety of problems in many fields. Automated control of UAVs is most common in large area operations, but they may also increase the versatility of smart home compositions by acting as a physical helper. For example, a voice- controlled UAV could act as an intelligent aerial assistant that can be seamlessly integrated into smart home systems. In this paper, we present a novel Augmented Reality (AR )-based UAV control that provides high-level control over a UAV by automating common UAV missions. In our work, we enable users to operate a small UAV hands-free using only a small set of voice commands. To help users identify the targets, and to understand the UAV ’s intentions, targets within the user’s field of vision are highlighted in an AR interface. We evaluate our approach in a user study (n=26) regarding usability, physical and mental demand, as well as a focus on the users’ preferences. Our study showed that the use of the proposed control was not only accepted, but some users stated that they would use such a system at home to help with some tasks at home }}, author = {{Helmert, Robin and Hardes, Tobias and Yigitbas, Enes}}, booktitle = {{Proceedings of the ACM Symposium on Spatial User Interaction (SUI 2023)}}, location = {{ Sydney, Australia }}, publisher = {{ACM}}, title = {{{Design and Evaluation of an AR Voice-based Indoor UAV Assistant for Smart Home Scenarios}}}, year = {{2023}}, } @phdthesis{51352, abstract = {{Erfolg und Misserfolg eines Unternehmens werden maßgeblich durch getroffene Entscheidungen beeinflusst. Daher verlassen sich Entscheider oft auf Entscheidungsunterstützungssysteme, die durch Datensimulation, -optimierung und -visualisierung bei der Identifizierung von geeigneten Entscheidungen unterstützen. Für eine optimale Unterstützung muss ein Entscheidungsunterstützungssystem (EUS) jedoch auf den Entscheidungsprozess eines Entscheiders abgestimmt sein und verfügbare Daten, Optimierungsziele, persönliche Präferenzen sowie weitere Einflussfaktoren berücksichtigen. EUS-Entwickler können aufgrund der Komplexität und Volatilität von Geschäftsumgebungen allerdings nicht alle potenziellen Entscheidungsprozesse während des Entwurfs eines EUS vorhersehen, wodurch ein EUS einem Entscheider häufig nur unzureichende Anpassungsmöglichkeiten an den individuellen Entscheidungsprozess bietet. Die Einzelanfertigung eines EUS, das auf einen Entscheidungsprozess zugeschnitten ist, ist ein kosten- und zeitintensives Unterfangen aufgrund der begrenzten Verfügbarkeit von Softwareentwicklern oder Missverständnissen zwischen Entwicklern und Entscheidern während der Entwicklung. Daher geben sich Entscheider möglicherweise mit einem handelsüblichen EUS zufrieden, das nicht vollständig mit ihrem Entscheidungsprozess übereinstimmt, suboptimale Entscheidungen begünstigt und so den Unternehmenserfolg negativ beeinflusst. In dieser Arbeit wird ein Ansatz vorgeschlagen, der es Entscheidern ermöglicht, selbst maßgeschneiderte Entscheidungsunterstützungssysteme zu entwickeln und so die Diskrepanz zwischen benötigter und tatsächlicher Entscheidungsunterstützung zu vermeiden. Dazu stellen EUS-Entwickler einen Teil der EUS-Funktionalität als wiederverwendbare Software-Dienste bereit ...}}, author = {{Kirchhoff, Jonas}}, publisher = {{Universität Paderborn}}, title = {{{Decision Support Ecosystems: Assisted Low-Code Development of Tailored Decision Support Systems}}}, doi = {{10.17619/UNIPB/1-1845}}, year = {{2023}}, } @unpublished{46578, abstract = {{Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control - potentially with non-smoothness both on the level of the objectives or in the system dynamics. This results in new challenges such as dealing with expensive models (e.g., governed by partial differential equations (PDEs)) and developing dedicated algorithms handling the non-smoothness. Since in contrast to single-objective optimization, the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in the field of multiobjective optimization of non-smooth PDE-constrained problems. In particular we report on the advances achieved within Project 2 "Multiobjective Optimization of Non-Smooth PDE-Constrained Problems - Switches, State Constraints and Model Order Reduction" of the DFG Priority Programm 1962 "Non-smooth and Complementarity-based Distributed Parameter Systems: Simulation and Hierarchical Optimization".}}, author = {{Bernreuther, Marco and Dellnitz, Michael and Gebken, Bennet and Müller, Georg and Peitz, Sebastian and Sonntag, Konstantin and Volkwein, Stefan}}, booktitle = {{arXiv:2308.01113}}, title = {{{Multiobjective Optimization of Non-Smooth PDE-Constrained Problems}}}, year = {{2023}}, } @misc{52317, author = {{Beckendorf, Björn}}, title = {{{Self-Stabilizing Skip-Graph with Growth-bounded Metric}}}, year = {{2023}}, } @article{46248, author = {{Demir, Caglar and Wiebesiek, Michel and Lu, Renzhong and Ngonga Ngomo, Axel-Cyrille and Heindorf, Stefan}}, journal = {{ECML PKDD}}, location = {{Torino}}, title = {{{LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals}}}, year = {{2023}}, } @inproceedings{52369, abstract = {{Megatrends, such as digitization or sustainability, are confronting the product management of manufacturing companies with a variety of challenges regarding the design of future products, but also the management of the actual products. To successfully position their products in the market, product managers need to gather and analyze comprehensive information about customers, developments in the products’ environment, product usage, and more. The digitization of all aspects of life is making data on these topics increasingly available – via social media, documents, or the internet of things from the products themselves. The systematic collection and analysis of these data enable the exploitation of new potentials for the adaption of existing products and the creation of the products of tomorrow. However, there are still no insights into the main concepts and cause-effect relationships in exploiting data-driven approaches for product management. Therefore, this paper aims to identify the main concepts and advantages of data-driven product management. To answer the corresponding research questions a comprehensive systematic literature review is conducted. From its results, a detailed description of the main concepts of data-driven product management is derived. Furthermore, a taxonomy for the advantages of data-driven product management is presented. The main concepts and the taxonomy allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.}}, author = {{Fichtler, Timm and Grigoryan, Khoren and Koldewey, Christian and Dumitrescu, Roman}}, booktitle = {{2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}}, keywords = {{Product Lifecyle Management (PLM), Data Analytics, Data-driven Design, Engineering Management, Lifecycle Data}}, location = {{Rabat, Morocco}}, publisher = {{IEEE}}, title = {{{Towards a Data-Driven Product Management – Concepts, Advantages, and Future Research}}}, doi = {{10.1109/ictmod59086.2023.10438135}}, year = {{2023}}, } @inproceedings{52530, author = {{Prager, Raphael Patrick and Trautmann, Heike}}, booktitle = {{Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023}}, editor = {{Silva, Sara and Paquete, Luís}}, pages = {{451–454}}, publisher = {{ACM}}, title = {{{Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems}}}, doi = {{10.1145/3583133.3590757}}, year = {{2023}}, } @inbook{52662, abstract = {{Static analysis tools support developers in detecting potential coding issues, such as bugs or vulnerabilities. Research emphasizes technical challenges of such tools but also mentions severe usability shortcomings. These shortcomings hinder the adoption of static analysis tools, and user dissatisfaction may even lead to tool abandonment. To comprehensively assess the state of the art, we present the first systematic usability evaluation of a wide range of static analysis tools. We derived a set of 36 relevant criteria from the literature and used them to evaluate a total of 46 static analysis tools complying with our inclusion and exclusion criteria - a representative set of mainly non-proprietary tools. The evaluation against the usability criteria in a multiple-raters approach shows that two thirds of the considered tools off er poor warning messages, while about three-quarters provide hardly any fix support. Furthermore, the integration of user knowledge is strongly neglected, which could be used for instance, to improve handling of false positives. Finally, issues regarding workflow integration and specialized user interfaces are revealed. These findings should prove useful in guiding and focusing further research and development in user experience for static code analyses.}}, author = {{Nachtigall, Marcus and Schlichtig, Michael and Bodden, Eric}}, booktitle = {{Software Engineering 2023}}, isbn = {{978-3-88579-726-5}}, keywords = {{Automated static analysis, Software usability}}, pages = {{95–96}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Evaluation of Usability Criteria Addressed by Static Analysis Tools on a Large Scale}}}, year = {{2023}}, } @inbook{52660, abstract = {{Application Programming Interfaces (APIs) are the primary mechanism developers use to obtain access to third-party algorithms and services. Unfortunately, APIs can be misused, which can have catastrophic consequences, especially if the APIs provide security-critical functionalities like cryptography. Understanding what API misuses are, and how they are caused, is important to prevent them, eg, with API misuse detectors. However, definitions for API misuses and related terms in literature vary. This paper presents a systematic literature review to clarify these terms and introduces FUM, a novel Framework for API Usage constraint and Misuse classification. The literature review revealed that API misuses are violations of API usage constraints. To address this, we provide unified definitions and use them to derive FUM. To assess the extent to which FUM aids in determining and guiding the improvement of an API misuses detector’s capabilities, we performed a case study on the state-of the-art misuse detection tool CogniCrypt. The study showed that FUM can be used to properly assess CogniCrypt’s capabilities, identify weaknesses and assist in deriving mitigations and improvements.}}, author = {{Schlichtig, Michael and Sassalla, Steffen and Narasimhan, Krishna and Bodden, Eric}}, booktitle = {{Software Engineering 2023}}, isbn = {{978-3-88579-726-5}}, keywords = {{API misuses API usage constraints, classification framework, API misuse detection, static analysis}}, pages = {{105–106}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Introducing FUM: A Framework for API Usage Constraint and Misuse Classification}}}, year = {{2023}}, } @article{52861, author = {{Gil, Oliver Fernández and Patrizi, Fabio and Perelli, Giuseppe and Turhan, Anni-Yasmin}}, journal = {{CoRR}}, title = {{{Optimal Alignment of Temporal Knowledge Bases}}}, doi = {{10.48550/ARXIV.2307.15439}}, volume = {{abs/2307.15439}}, year = {{2023}}, } @inproceedings{52863, author = {{Ŝkvorc, Urban and Eftimov, Tome and Koro]ec, Peter}}, booktitle = {{2023 IEEE Symposium Series on Computational Intelligence (SSCI)}}, publisher = {{IEEE}}, title = {{{Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization}}}, doi = {{10.1109/ssci52147.2023.10371868}}, year = {{2023}}, } @inproceedings{52913, author = {{Turhan, Anni-Yasmin}}, booktitle = {{Proceedings of the 36th International Workshop on Description Logics {(DL} 2023) co-located with the 20th International Conference on Principles of Knowledge Representation and Reasoning and the 21st International Workshop on Non-Monotonic Reasoning {(KR} 2023 and NMR 2023)., Rhodes, Greece, September 2-4, 2023}}, editor = {{Kutz, Oliver and Lutz, Carsten and Ozaki, Ana}}, publisher = {{CEUR-WS.org}}, title = {{{Brushing-up DLs to Cope with Imperfect Data (Abstract of Joint DL+NMR Invited Talk)}}}, volume = {{3515}}, year = {{2023}}, } @inproceedings{52380, author = {{Sparmann, Sören and Hüsing, Sven and Schulte, Carsten}}, booktitle = {{Proceedings of the 23rd Koli Calling International Conference on Computing Education Research}}, publisher = {{ACM}}, title = {{{JuGaze: A Cell-based Eye Tracking and Logging Tool for Jupyter Notebooks}}}, doi = {{10.1145/3631802.3631824}}, year = {{2023}}, } @inproceedings{46188, author = {{Faj, Jennifer and Kenter, Tobias and Faghih-Naini, Sara and Plessl, Christian and Aizinger, Vadym}}, booktitle = {{Proceedings of the Platform for Advanced Scientific Computing Conference (PASC)}}, publisher = {{ACM}}, title = {{{Scalable Multi-FPGA Design of a Discontinuous Galerkin Shallow-Water Model on Unstructured Meshes}}}, doi = {{10.1145/3592979.3593407}}, year = {{2023}}, } @inproceedings{46189, author = {{Prouveur, Charles and Haefele, Matthieu and Kenter, Tobias and Voss, Nils}}, booktitle = {{Proceedings of the Platform for Advanced Scientific Computing Conference (PASC)}}, publisher = {{ACM}}, title = {{{FPGA Acceleration for HPC Supercapacitor Simulations}}}, doi = {{10.1145/3592979.3593419}}, year = {{2023}}, }