@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{54243, author = {{Oviasogie, Marvin Osaretin}}, publisher = {{Paderborn University}}, title = {{{Demonstrator for Dataflow-based DNN Acceleration for Vision Applications on Platform FPGAs}}}, year = {{2023}}, } @misc{54241, author = {{Reuter, Lucas David}}, publisher = {{Paderborn University}}, title = {{{Development of a Power Analysis Framework for Embedded FPGA Accelerators}}}, 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{54244, author = {{AlAidroos, Salem}}, publisher = {{Paderborn University}}, title = {{{Design and Implementation of a RadioML Demonstrator based on an RFSoC Platform}}}, 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}}, } @misc{50068, author = {{Peckhaus, Volker}}, booktitle = {{Mathematical Reviews, MR4436125}}, title = {{{von Plato, Jan, Chapters from Gödel’s Unfinished Books on Foundational Research, Springer: Cham 2022 (Vienna Circle Institute Library; 6)}}}, year = {{2023}}, } @misc{54389, author = {{Bürckner, Lorena Marie}}, title = {{{Entwicklung eines Verfahrens zur Probenentnahme aus einem Pulverkuchen im SLS-Prozess}}}, year = {{2023}}, } @misc{54391, author = {{Aissani, Soufiane}}, title = {{{Polystyrol Nanopartikel als alternative Fließhilfsmittel für SLS-Materialien (Studienarbeit)}}}, year = {{2023}}, } @inproceedings{49882, abstract = {{Online discussion moderators must make ad-hoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existing research on offensive language and the resulting tools cover only one aspect among many involved in such decisions. The question of what is considered appropriate in a controversial discussion has not yet been systematically addressed. In this paper, we operationalize appropriate language in argumentation for the first time. In particular, we model appropriateness through the absence of flaws, grounded in research on argument quality assessment, especially in aspects from rhetoric. From these, we derive a new taxonomy of 14 dimensions that determine inappropriate language in online discussions. Building on three argument quality corpora, we then create a corpus of 2191 arguments annotated for the 14 dimensions. Empirical analyses support that the taxonomy covers the concept of appropriateness comprehensively, showing several plausible correlations with argument quality dimensions. Moreover, results of baseline approaches to assessing appropriateness suggest that all dimensions can be modeled computationally on the corpus.}}, author = {{Ziegenbein, Timon and Syed, Shahbaz and Lange, Felix and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics}}, location = {{Toronto}}, pages = {{4344--4363}}, title = {{{Modeling Appropriate Language in Argumentation}}}, doi = {{https://doi.org/10.18653/v1/2023.acl-long.238}}, 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}}, }