@misc{42839, author = {{Mehlich, Florian}}, publisher = {{Paderborn University}}, title = {{{An Evaluation of XCS on the OpenAI Gym}}}, year = {{2023}}, } @misc{53930, author = {{Tadakamalla, Yashwanth }}, publisher = {{Paderborn University}}, title = {{{A Comparison of Algorithms for the Generation of Layouts based on Reconfigurable Slots on FPGAs}}}, year = {{2023}}, } @misc{54295, author = {{Middeke, Sorel Horst}}, publisher = {{Paderborn University}}, title = {{{Design and Realization of Optimized Intra-FPGA ROS 2 Communication}}}, year = {{2023}}, } @misc{54294, author = {{Thiele, Simon}}, publisher = {{Paderborn University}}, title = {{{A Hardware/Software Co-designed ORB-SLAM2 Algorithm for FPGA}}}, year = {{2023}}, } @misc{54297, author = {{Abooof, Alhussain}}, publisher = {{Paderborn University}}, title = {{{Implementation and Evaluation of a ReconROS-based Obstacle Avoidance Architecture for Autonomous Robots}}}, year = {{2023}}, } @misc{54296, author = {{Rao, Aniruddh P}}, publisher = {{Paderborn University}}, title = {{{Multithreaded Software/Hardware Programming with ReconOS/Zephyr on a RISC-V-based System-on-Chip}}}, year = {{2023}}, } @misc{45762, author = {{Simon-Mertens, Florian}}, publisher = {{Paderborn University}}, title = {{{Effizienzanalyse leichtgewichtiger Neuronaler Netze für FPGA-basierte Modulationsklassifikation}}}, year = {{2023}}, } @misc{54246, author = {{Hamm, Robin}}, publisher = {{Paderborn University}}, title = {{{Verarbeitung von Sensordaten auf eingebetteten heterogenen FPGA-Systemen}}}, year = {{2023}}, } @misc{52480, author = {{Klassen, Alexander}}, publisher = {{Paderborn University}}, title = {{{Fast Partial Reconfiguration for ReconOS64 on Xilinx MPSoC Devices}}}, year = {{2023}}, } @misc{54298, author = {{Tsague Dingo, Jorian}}, publisher = {{Paderborn University}}, title = {{{Ein Simulator für Schedulability-Experimente mit periodischen Tasks auf FPGAs}}}, year = {{2023}}, } @misc{54299, author = {{Brede, Mathis}}, publisher = {{Paderborn University}}, title = {{{Evaluation of Classifier Migration Between Multiple XCS Populations}}}, year = {{2023}}, } @misc{54300, author = {{Nowosad, Alexander}}, publisher = {{Paderborn University}}, title = {{{Design and Realization of an Intra-FPGA ROS 2 Communication Infrastructure for the ReconROS Executor}}}, year = {{2023}}, } @misc{54242, author = {{Evers, Gerrit}}, publisher = {{Paderborn University}}, title = {{{Bewertung der Xilinx Runtime Library zur Hardware/Software-Kommunikation}}}, year = {{2023}}, } @misc{46075, author = {{Raeisi Nafchi, Masood}}, publisher = {{Paderborn University}}, title = {{{Reconfigurable Random Forest Implementation on FPGA}}}, year = {{2023}}, } @inproceedings{54352, author = {{Urbaneck, Daniel and Böcker, Joachim and Schafmeister, Frank}}, booktitle = {{PCIM Europe 2023; IEEE International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management }}, isbn = {{978-3-8007-6091-6}}, location = {{Nuremberg}}, title = {{{Advanced Synchronous Rectification for an IGBT-Based ZCS LLC Converter with High Output Currents for a 2 kW Automotive DC-DC Stage}}}, year = {{2023}}, } @inbook{47421, abstract = {{Class expression learning in description logics has long been regarded as an iterative search problem in an infinite conceptual space. Each iteration of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approaches for class expression learning become prohibitively slow. Current neural class expression synthesis (NCES) approaches investigate the use of neural networks for class expression learning in the attributive language with complement (ALC). While they show significant improvements over search-based approaches in runtime and quality of the computed solutions, they rely on the availability of pretrained embeddings for the input knowledge base. Moreover, they are not applicable to ontologies in more expressive description logics. In this paper, we propose a novel NCES approach which extends the state of the art to the description logic ALCHIQ(D). Our extension, dubbed NCES2, comes with an improved training data generator and does not require pretrained embeddings for the input knowledge base as both the embedding model and the class expression synthesizer are trained jointly. Empirical results on benchmark datasets suggest that our approach inherits the scalability capability of current NCES instances with the additional advantage that it supports more complex learning problems. NCES2 achieves the highest performance overall when compared to search-based approaches and to its predecessor NCES. We provide our source code, datasets, and pretrained models at https://github.com/dice-group/NCES2.}}, author = {{Kouagou, N'Dah Jean and Heindorf, Stefan and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Machine Learning and Knowledge Discovery in Databases: Research Track}}, isbn = {{9783031434204}}, issn = {{0302-9743}}, location = {{Turin}}, publisher = {{Springer Nature Switzerland}}, title = {{{Neural Class Expression Synthesis in ALCHIQ(D)}}}, doi = {{10.1007/978-3-031-43421-1_12}}, year = {{2023}}, } @unpublished{37937, abstract = {{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.}}, author = {{Sieger, Leonie Nora and Heindorf, Stefan and Blübaum, Lukas and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{arXiv:2301.05109}}, title = {{{Explaining ELH Concept Descriptions through Counterfactual Reasoning}}}, year = {{2023}}, } @inproceedings{54506, author = {{Lick, Jonas and Schreckenberg, Felix and Sahrhage, Philipp and Wohlers, Benedict and Klöcker, Susanne and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Artificial Intelligence, Social Computing and Wearable Technologies, Vol. 113}}, editor = {{Karwowsk, Waldemar and Ahram, Tareq }}, location = {{Hawaii}}, title = {{{Integrating Domain Expertise and Artificial Intelligence for Effective Supply Chain Management Planning Tasks: A Collaborative Approach}}}, year = {{2023}}, } @inproceedings{54503, author = {{Günther, Matthias and Göllner, Denis and Heihoff-Schwede, Jörg and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{Tag des Systems Engineering 2023}}, editor = {{Wilke, Daria and Koch, Walter and Kaffenberger, Rüdiger and Dreiseitel, Stefan}}, location = {{Würzburg}}, title = {{{Engineering und Management von System of Systems–Gestaltungskonzepte im SoS-Engineering}}}, year = {{2023}}, } @inproceedings{54505, author = {{Wilke, Daria and Heitmann, Rebecca and Tekaat, Julian and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{Tag des Systems Engineering 2023}}, editor = {{Wilke, Daria and Koch, Walter and Kaffenberger, Rüdiger and Dreiseitel, Stefan}}, location = {{Würzburg}}, title = {{{Reifegradmodell zur Einführung von Systems Engineering–Systemdenken als Handlungsfeld}}}, year = {{2023}}, }