@misc{AppelhansKampmannMottoketal., author = {Appelhans, Marie-Luise and Kampmann, Matthias and Mottok, J{\"u}rgen and Riederer, Michael and Nagl, Klaus and Steffens, Oliver and D{\"u}nnweber, Jan and Wildgruber, Markus and Roth, Julius and Stadler, Timo and Palm, Christoph and Weiß, Martin Georg and Rochholz, Sandra and Bierl, Rudolf and Gschossmann, Andreas and Haug, Sonja and Schmidbauer, Simon and Koch, Anna and Westner, Markus and Bary, Benedikt von and Ellermeier, Andreas and V{\"o}gele, Daniel and Maiwald, Frederik and Hierl, Stefan and Schlamp, Matthias and Ehrlich, Ingo and Siegl, Marco and H{\"u}ntelmann, Sven and Wildfeuer, Matthias and Br{\"u}ckl, Oliver and Sterner, Michael and Hofrichter, Andreas and Eckert, Fabian and Bauer, Franz and Dawoud, Belal and Rabl, Hans-Peter and Gamisch, Bernd and Schmidt, Ottfried and Heberl, Michael and Thema, Martin and Mayer, Ulrike and Eller, Johannes and Sippenauer, Thomas and Adelt, Christian and Haslbeck, Matthias and Vogl, Bettina and Mauerer, Wolfgang and Ramsauer, Ralf and Lohmann, Daniel and Sax, Irmengard and Gabor, Thomas and Feld, Sebastian and Linnhoff-Popien, Claudia and Ławrowski, Robert Damian and Langer, Christoph and Schreiner, Rupert and Sellmair, Josef}, title = {Forschung 2019}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-9818209-7-3}, doi = {10.35096/othr/pub-789}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-7890}, pages = {72}, abstract = {Bericht mit Forschungsprojekten aus verschiedenen Bereichen der OTH Regensburg mit dem Schwerpunktthema "K{\"u}nstliche Intelligenz" und einem Gespr{\"a}ch zur "Medizin der Zukunft"}, subject = {Forschung}, language = {de} } @inproceedings{BockMottokKoehler, author = {Bock, T. and Mottok, J{\"u}rgen and K{\"o}hler, Thorsten}, title = {A gesture-based human-machine interface using stereoscopic depth images}, series = {2nd Applied Research Conference 2012 (ARC 2012), 25./26. June 2012, Nuremberg}, booktitle = {2nd Applied Research Conference 2012 (ARC 2012), 25./26. June 2012, Nuremberg}, editor = {Mottok, J{\"u}rgen and Ziemann, Olaf}, publisher = {Shaker}, address = {Aachen}, pages = {166 -- 169}, abstract = {Reasonable integration of gesture-based automotive HMI-functionality offers potential safety benefits by reducing driver distractions and glance times to operate tertiary in-car devices. Stereo camera systems are a well investigated choice to perform the task of generating depth data for spatial gesture recognition. This paper describes the functionality of our stereo vision software, which is intended for application in a target system based on CMOS wafer-level cameras. The retrieved point cloud data was passed to a gesture-based sample application.}, language = {en} } @inproceedings{MaierMottok, author = {Maier, Robert and Mottok, J{\"u}rgen}, title = {BayesianSafety - an Open-Source Package for Causality-Guided, Multi-Model Safety Analysis}, series = {Computer Safety, Reliability, and Security, 41st International Conference (SAFECOMP 2022): Munich, Germany, September 6-9, 2022, Proceedings}, booktitle = {Computer Safety, Reliability, and Security, 41st International Conference (SAFECOMP 2022): Munich, Germany, September 6-9, 2022, Proceedings}, editor = {Trapp, Mario and Saglietti, Francesca and Spisl{\"a}nder, Marc and Bitsch, Friedemann}, publisher = {Springer Nature}, address = {Cham}, doi = {10.1007/978-3-031-14835-4_2}, pages = {17 -- 30}, abstract = {Development and verification of modern, dependable automotive systems require appropriate modelling approaches. Classic automotive safety is described by the normative regulations ISO 26262, its relative ISO/PAS 21448, and their respective methodologies. In recent publications, an emerging demand to combine environmental influences, machine learning, or reasoning under uncertainty with standard-compliant analysis techniques can be noticed. Therefore, adapting established methods like FTA and proper tool support is necessary. We argue that Bayesian Networks (BNs) can be used as a central component to address and merge these demands. In this paper, we present our Open-Source Python package BayesianSafety. First, we review how BNs relate to data-driven methods, model-to-model transformations, and causal reasoning. Together with FTA and ETA, these models form the core functionality of our software. After describing currently implemented features and possibilities of combining individual modelling approaches, we provide an informal view of the tool's architecture and of the resulting software ecosystem. By comparing selected publicly available safety and reliability analysis libraries, we outline that many relevant methodologies yield specialized implementations. Finally, we show that there is a demand for a flexible, unifying analysis tool that allows researching system safety by using multi-model and multi-domain approaches.}, language = {en} } @inproceedings{MaierGrabingerUrlhartetal., author = {Maier, Robert and Grabinger, Lisa and Urlhart, David and Mottok, J{\"u}rgen}, title = {Towards Causal Model-Based Engineering in Automotive System Safety}, series = {Model-Based Safety and Assessment, 8th International Symposium, IMBSA 2022: Munich, Germany, September 5-7, 2022, Proceedings}, booktitle = {Model-Based Safety and Assessment, 8th International Symposium, IMBSA 2022: Munich, Germany, September 5-7, 2022, Proceedings}, editor = {Seguin, Christel and Zeller, Marc and Prosvirnova, Tatiana}, publisher = {Springer Nature}, address = {Cham}, doi = {10.1007/978-3-031-15842-1_9}, pages = {116 -- 129}, abstract = {Engineering is based on the understanding of causes and effects. Thus, causality should also guide the safety assessment of complex systems such as autonomous driving cars. To ensure the safety of the intended functionality of these systems, normative regulations like ISO 21448 recommend scenario-based testing. An important task here is to identify critical scenarios, so-called edge and corner cases. Data-driven approaches to this task (e.g. based on machine learning) cannot adequately address a constantly changing operational design domain. Model-based approaches offer a remedy - they allow including different sources of knowledge (e.g. data, human experts) into safety considerations. With this paper, we outline a novel approach for ensuring automotive system safety. We propose to use structural causal models as a probabilistic modelling language to combine knowledge about an open-context environment from different sources. Based on these models, we investigate parameter configurations that are candidates for critical scenarios. In this paper, we first discuss some aspects of scenario-based testing. We then provide an informal introduction to causal models and relate their development lifecycle to the established V-model. Finally, we outline a generic workflow for using causal models to identify critical scenarios and highlight some challenges that arise in the process.}, language = {en} } @article{MaierGrabingerUrlhartetal., author = {Maier, Robert and Grabinger, Lisa and Urlhart, David and Mottok, J{\"u}rgen}, title = {Causal Models to Support Scenario-Based Testing of ADAS}, series = {IEEE Transactions on Intelligent Transportation Systems}, journal = {IEEE Transactions on Intelligent Transportation Systems}, publisher = {IEEE}, issn = {1524-9050}, doi = {10.1109/TITS.2023.3317475}, pages = {1 -- 17}, abstract = {In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.}, language = {en} } @inproceedings{MaierMottok, author = {Maier, Robert and Mottok, J{\"u}rgen}, title = {Causality and Functional Safety - How Causal Models Relate to the Automotive Standards ISO 26262, ISO/PAS 21448, and UL 4600}, series = {27th International Conference on Applied Electronics (AE): 06-07 September 2022, Pilsen, CZ}, booktitle = {27th International Conference on Applied Electronics (AE): 06-07 September 2022, Pilsen, CZ}, publisher = {IEEE}, doi = {10.1109/AE54730.2022.9920053}, pages = {6}, abstract = {With autonomous driving, the system complexity of vehicles will increase drastically. This requires new approaches to ensure system safety. Looking at standards like ISO 26262 or ISO/PAS 21448 and their suggested methodologies, an increasing trend in the recent literature can be noticed to incorporate uncertainty. Often this is done by using Bayesian Networks as a framework to enable probabilistic reasoning. These models can also be used to represent causal relationships. Many publications claim to model cause-effect relations, yet rarely give a formal introduction of the implications and resulting possibilities such an approach may have. This paper aims to link the domains of causal reasoning and automotive system safety by investigating relations between causal models and approaches like FMEA, FTA, or GSN. First, the famous "Ladder of Causation" and its implications on causality are reviewed. Next, we give an informal overview of common hazard and reliability analysis techniques and associate them with probabilistic models. Finally, we analyse a mixed-model methodology called Hybrid Causal Logic, extend its idea, and build the concept of a causal shell model of automotive system safety.}, language = {en} } @inproceedings{KristenEnglMottok, author = {Kristen, Meret and Engl, Fabian and Mottok, J{\"u}rgen}, title = {Enhancing Phishing Detection: An Eye-Tracking Study on User Interaction and Oversights in Phishing Emails}, series = {SECURWARE 2024 : The Eighteenth International Conference on Emerging Security Information, Systems and Technologies, November 03-07, 2024, Nice, France}, booktitle = {SECURWARE 2024 : The Eighteenth International Conference on Emerging Security Information, Systems and Technologies, November 03-07, 2024, Nice, France}, publisher = {IARIA}, isbn = {978-1-68558-206-7}, pages = {71 -- 80}, abstract = {Phishing remains a significant threat to organizational security, necessitating effective countermeasures. This paper presents findings from an in-depth eye-tracking study with 103 participants, evaluating the effectiveness of phishing awareness tools and trainings. The study examines how a phishing awareness system influences user behavior, efficiency, and the ability to identify phishing attempts. By analyzing eye movements, the study reveals real-time interactions and oversights, providing insights into the decision-making process. Results indicate that while the system improves the efficiency of users already proficient in phishing detection, it does not universally enhance recognition rates. Notably, participants using the tool spent significantly less time looking at attachment-related phishing markers, indicating partial efficiency improvements. Since phishing attempts containing suspicious attachments were successful in 19\% of cases, as compared to an overall phishing success rate of 15\%, the phishing awareness tool is particularly useful here. A usability evaluation revealed that users reporting a higher perceived usability score profited more from the help of the tool. Additionally, no improvement in phishing detection rates was observed in users who had completed prior IT-security training, highlighting the necessity for a paradigm shift in phishing training to adequately prepare users for phishing attempts.}, language = {en} } @misc{WeberDendorferSuessetal., author = {Weber, Karsten and Dendorfer, Sebastian and S{\"u}ß, Franz and Kubowitsch, Simone and Schratzenstaller, Thomas and Haug, Sonja and Mohr, Christa and Kiesl, Hans and Drechsler, J{\"o}rg and Westner, Markus and Kobus, J{\"o}rn and Schubert, Martin J. W. and Zenger, Stefan and Pietsch, Alexander and Weiß, Josef and Hinterseer, Sebastian and Schieck, Roland and Scherzinger, Stefanie and Klettke, Meike and Ringlstetter, Andreas and St{\"o}rl, Uta and Bissyand{\´e}, Tegawend{\´e} F. and Seeburger, Achim and Schindler, Timo and Ramsauer, Ralf and Kiszka, Jan and K{\"o}lbl, Andreas and Lohmann, Daniel and Mauerer, Wolfgang and Maier, Johannes and Scorna, Ulrike and Palm, Christoph and Soska, Alexander and Mottok, J{\"u}rgen and Ellermeier, Andreas and V{\"o}gele, Daniel and Hierl, Stefan and Briem, Ulrich and Buschmann, Knut and Ehrlich, Ingo and Pongratz, Christian and Pielmeier, Benjamin and Tyroller, Quirin and Monkman, Gareth J. and Gut, Franz and Roth, Carina and Hausler, Peter and Bierl, Rudolf and Prommesberger, Christian and Ławrowski, Robert Damian and Langer, Christoph and Schreiner, Rupert and Huang, Yifeng and She, Juncong and Ottl, Andreas and Rieger, Walter and Kraml, Agnes and Poxleitner, Thomas and Hofer, Simon and Heisterkamp, Benjamin and Lerch, Maximilian and Sammer, Nike and Golde, Olivia and Wellnitz, Felix and Schmid, Sandra and Muntschick, Claudia and Kusterle, Wolfgang and Paric, Ivan and Br{\"u}ckl, Oliver and Haslbeck, Matthias and Schmidt, Ottfried and Schwanzer, Peter and Rabl, Hans-Peter and Sterner, Michael and Bauer, Franz and Steinmann, Sven and Eckert, Fabian and Hofrichter, Andreas}, title = {Forschungsbericht 2017}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-9818209-3-5}, doi = {10.35096/othr/pub-1383}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13835}, subject = {Forschung}, language = {de} } @inproceedings{StauferHauserGrabingeretal., author = {Staufer, Susanne and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Nadimpalli, Vamsi Krishna and Mottok, J{\"u}rgen}, title = {Learning elements in online learning management systems}, series = {Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023}, booktitle = {Proceedings of the 16th annual International Conference of Education, Research and Innovation (ICERI 2023), Seville, Spain, 11/13/2023 - 11/14/2023}, editor = {G{\´o}mez Chova, Luis and Gonz{\´a}lez Mart{\´i}nez, Chelo and Lees, Joanna}, publisher = {IATED}, doi = {10.21125/iceri.2023.0815}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-67725}, pages = {3121 -- 3130}, language = {en} } @inproceedings{StauferNadimpalliHauseretal., author = {Staufer, Susanne and Nadimpalli, Vamsi Krishna and Hauser, Florian and Bittner, Dominik and Grabinger, Lisa and Bugert, Flemming and Ezer, Timur and R{\"o}hrl, Simon and Mottok, J{\"u}rgen}, title = {Persistence of Questionnaire Data on Learning Styles, Learning Strategies AND Personality Traits}, series = {ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain}, booktitle = {ICERI2024 Proceedings: 17th Annual International Conference of Education, Research and Innovation, 11th-13th November 2024, Seville, Spain}, publisher = {IATED}, isbn = {978-84-09-63010-3}, issn = {2340-1095}, doi = {10.21125/iceri.2024.1524}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-78246}, pages = {6310 -- 6319}, abstract = {In the context of learning, questionnaires are carried out to gain information about learners. This learner profile has various benefits, like adapting the learning content, promoting of self-reflection, or increasing motivation in learning and therefore better learning results. Some researchers take the results of such questionnaires as ground truth or trust them blindly. Examining how persistent questionnaires are, asking the same questions twice, and comparing the results is a way to prove or falsify this statement. Our research procedure compares the results of three different questionnaires. The first questionnaire is the ILS (index of learning styles) according to the Felder-Silverman Learning Style Model (FSLSM) with about 44 questions. Learning strategies are examined with the LIST-K questionnaire consisting of 39 questions after Klingsieck. The third and last questionnaire is about personality traits. With only ten questions, Rammstedt et al. describe the BFI-10 questionnaire for personality traits. To compare results, the same study with the three named questionnaires is carried out twice with three to four months in between. The sample includes 35 students in their bachelor studies taking the course Software Engineering in a blended learning format. After collecting the questionnaire data from the students, a comparison is made in two different ways. First, we compare the data points based on the given answers. Second, a comparison is made based on the calculated questionnaire results because each of the three questionnaires has its own analysis method. For example, the analysis method of the ILS delivers as a result four integers representing the four characteristics of a learning style, while LIST-K makes a factor analysis. The results of the paper show that both the given answers and the calculated questionnaire results differ. Not one student has given the same answer inside each questionnaire with causes, like a change of learning style, learning strategy, and personality traits, or not filling in conscientiously.}, language = {en} }