@misc{GlavanBelyaevaShamoninChamonineetal., author = {Glavan, Gašper and Belyaeva, Inna A. and Shamonin (Chamonine), Mikhail and Ruwisch, Kevin and Wollschl{\"a}ger, Joachim}, title = {Magnetoelectric Response of Laminated Cantilevers Comprising a Magnetoactive Elastomer and a Piezoelectric Polymer, in Pulsed Uniform Magnetic Fields [Data set]}, doi = {10.5281/zenodo.14652152}, language = {en} } @incollection{DeutelMallahWissingetal., author = {Deutel, Mark and Mallah, Maen and Wissing, Julio and Scheele, Stephan}, title = {Recent Trends in Edge AI: Efficient Design, Training and Deployment of Machine Learning Models}, series = {Charting the Intelligence Frontiers - Edge AI Systems Nexus}, booktitle = {Charting the Intelligence Frontiers - Edge AI Systems Nexus}, editor = {Vermesan, Ovidiu and Pagani, Alain and Meloni, Paolo}, publisher = {River Publishers}, address = {New York}, isbn = {9788743808862}, doi = {10.1201/9788743808862-9}, pages = {181 -- 220}, abstract = {With a rising demand for ubiquitous smart systems, processing and interpreting large quantities of data generated on the edge at a high velocity is becoming an increasingly important challenge. Machine learning (ML) models such as Deep Neural Networks (DNNs) are an essential tool of today's artificial intelligence due to their ability to make accurate predictions given complex tasks and environments. However, Deep Learning is computationally complex and energy intensive. This seems to contradict the characteristics of many edge devices, which have only limited memory, computational resources, and energy budget available. To overcome this challenge, an efficient ML model design is crucial that incorporates available optimization techniques from hardware, software, and methodological perspective to enable energy-efficient deployment and operation on the edge. This work comprehensively summarizes recent techniques for training, optimizing, and deploying ML models targeting edge devices. We discuss different strategies for finding deployable ML models, scalable DNN architectures, neural architecture search, and multi-objective optimization approaches, to enable feasible trade-offs considering available resources and latency. Furthermore, we give insight into DNN compression methods such 182as quantization and pruning. We conclude by investigating different forms of cascaded processing, from simple multi-level approaches to highly branched compute graphs and early-exit DNNs.}, language = {en} } @article{KuettnerFischerLaumer, author = {Kuettner, Andreas and Fischer, Samuel and Laumer, Tobias}, title = {Mechanical and structural characterization of heat-staked parts realized by selective laser sintering of polyamide 12}, series = {The International Journal of Advanced Manufacturing Technology}, journal = {The International Journal of Advanced Manufacturing Technology}, publisher = {Springer}, doi = {10.1007/s00170-025-17319-4}, pages = {20}, abstract = {Heat staking is a joining process in which thermoplastic pins are formed by heat and pressure in a form-fitting and insoluble way. This study evaluates the mechanical performance and microstructure of selective laser sintered (SLS) polyamide 12 (PA 12) components before and after heat staking, compared with conventionally turned reference specimens. The components were characterized using tensile tests, micrographs, microscopy, and micro-CT measurements. For the tests, the forces and temperatures during heat staking were varied to determine the best process parameters. Tensile tests revealed that SLS joints achieved strengths of up to 33.6 MPa, approaching the 39.9 MPa of the turned references. Microstructural analysis showed a marked reduction in porosity due to heat staking. Porosity decreased from 3.9\% to 1.56\% at a staking force of 300 N and from 4.29\% to 0.81\% at 1000 N, highlighting the beneficial effect of increased force. These results demonstrate that heat staking parameters significantly influence local densification and mechanical performance, and that, under suitable conditions, SLS components can achieve joint strengths comparable to conventionally manufactured parts. The study shows that the heat staking process parameters have a significant influence on the local microstructure and thus on the mechanical performance and provides a basis for optimizing SLS components for new heat staking applications.}, language = {en} } @article{Zuerner, author = {Z{\"u}rner, Christian}, title = {Von der „{\"A}sthetischen" zur „Kulturellen Bildung" - (heimlicher) Verlust eines kritischen Selbstverst{\"a}ndnisses}, series = {P{\"a}dagogische Rundschau}, volume = {69}, journal = {P{\"a}dagogische Rundschau}, number = {1}, publisher = {Lang}, pages = {75 -- 89}, language = {de} } @misc{Kriegl2022, author = {Kriegl, Raphael}, title = {Microstructured Magnetoactive Elastomers for Switchable Wettability [Data set]}, doi = {10.5281/zenodo.13124555}, year = {2022}, language = {en} } @misc{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Study: Notation of Causal Graphs [Data set]}, doi = {10.5281/zenodo.7241158}, abstract = {This repository contains the material and obtained data of an eye tracking study on the topic "Notation of Causal Graphs".}, language = {en} } @misc{GrabingerHauserMottok, author = {Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Study: Layout of Causal Graphs [Data set]}, doi = {10.5281/zenodo.7241097}, abstract = {This repository contains the material and obtained data of an eye tracking study on the topic "Layout of Causal Graphs".}, language = {en} } @misc{GrabingerHomannHauseretal., author = {Grabinger, Lisa and Homann, Alexander and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Study: MISRA C coding guidelines [Data set]}, doi = {10.5281/zenodo.7898606}, abstract = {This repository contains the material and obtained data of an eye tracking study on the topic "MISRA C coding guidelines".}, language = {en} } @misc{MoserEberhardtMeyeretal., author = {Moser, Elisabeth and Eberhardt, Matthias and Meyer, Selina and Schmidhuber, Maximilian and Ketterer, Daniel}, title = {ARGO ship classification dataset [Data set]}, doi = {10.5281/zenodo.6058710}, abstract = {The ARGO ship classification dataset holds 1750 labelled images from PlanetScope-4-Band satelites. The dataset creation process and results on the dataset are published in the demo paper: {CITE} The imagery is provided as numpy binary files. All image data is licensed by Planet Labs PBC. The channel ordering is BGRN. The dataset is provided in two folders named "ship" and "non_ship". Those folders correspond to the original labels created during automated dataset creation. The files are numbered. Two additional .csv files are provided. The shipsAIS_2017_Zone17.csv file holds the AIS information on the imagery contained in the ship folder. The data was retrieved from marinecadastre.gov. During the experiments errors in the automatically created dataset emerged which are further described in the paper. The manual relabelling is supplied in the corrected_labels.csv file.}, language = {en} } @misc{RahimSchumm, author = {Rahim, Stefan and Schumm, Leon}, title = {Weather Data Cutouts for the Wasserstoffatlas (Hydrogen Map) [Data set]}, doi = {10.5281/zenodo.8135586}, abstract = {The cutouts are required to calculate the solar PV and wind onshore feed-in profiles of the Wasserstoffatlas. The provided cutouts are spatiotemporal subsets of the European weather data from the ECMWF ERA5 reanalysis dataset and the CMSAF SARAH-2 solar surface radiation dataset for the year 2013. They have been prepared by and are for use with the atlite tool (https://atlite.readthedocs.io/). ECMWF ERA5 Source: https://cds.climate.copernicus.eu/cdsapp\#!/dataset/reanalysis-era5-single-levels?tab=overview Terms of Use: https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf CMSAF SARAH-2 Pfeifroth, Uwe; Kothe, Steffen; M{\"u}ller, Richard; Trentmann, J{\"o}rg; Hollmann, Rainer; Fuchs, Petra; Werscheck, Martin (2017): Surface Radiation Data Set - Heliosat (SARAH) - Edition 2, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/SARAH/V002, https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002 Terms of Use: https://www.eumetsat.int/cs/idcplg?IdcService=GET_FILE\&dDocName=pdf_leg_data_policy\&allowInterrupt=1\&noSaveAs=1\&RevisionSelectionMethod=LatestReleased}, language = {en} } @misc{BittnerEzerGrabingeretal., author = {Bittner, Dominik and Ezer, Timur and Grabinger, Lisa and Hauser, Florian and Mottok, J{\"u}rgen}, title = {Eye Tracking based Learning Style Identification for Learning Management Systems [Data set]}, doi = {10.5281/zenodo.8349468}, abstract = {In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.}, language = {en} } @misc{Staufer, author = {Staufer, Susanne}, title = {Persistence of Learning Style, Learning Strategy, and Personality Traits [Data set]}, doi = {10.5281/zenodo.12743866}, abstract = {This dataset contains the result of the survey to learning styles, learning strategies, and personality traits. The survey was executed in winter term 2023/24 and summer term 2023 in a German university (OTH Regensburg) during the course "Software Engineering". Examined questionnaires are ILS (learning styles), LIST-K (learning strategies), and BFI-10 (personality traits). The same three questionnaires were asked at two different survey periods three to four months apart while each survey period lasts one to two weeks. Pretest data were examined at the start of the term, while posttest data at the end.}, language = {en} }