Smart Detection of Deficiencies and Faults in Automotive Software Releases

A novel approach for evaluating Electric and Electronic automotive software development processes is introduced. The aim is to provide transparency among stakeholders and deliver feedback throughout the development cycles, preventing software defects and conflicts. The proposed model allows stakeholders to assess the status, performance, and quality of software packages by posing targeted questions and evaluating the answers to draw conclusions. These questions, derived from literature as metrics and principles, are selectively proposed by a Reinforcement Learning agent using Contextual Multi-Armed Bandits (CMAB) as a recommendation system. The questions serve as software packages with key evaluation information, enabling a complete system assessment. Each question has a pre-evaluated weight, and each answer has a value. These parameters define the agent's reward, balancing exploration and exploitation. The model is scalable in terms of the target software or component's complexity, allowing continuous performance improvement as the algorithm learns over time. The evaluation results confirm the concept's functionality in various circumstances, addressing challenges of cold start, partial feedback, and data parsing. In summary, this thesis contributes to automotive software development by enhancing transparency and enabling timely detection of process deficiencies and software faults.:1. Introduction 2. Literature Review for Automotive Software Development 3. Concept and Model Description for Software Evaluation 4. Design and Implementation 5. Results and Validation Analysis of the Software Evaluation Model 6. Comprehensive Evaluation Summary and Proof of Concept 7. Conclusion and Future Work Appendi

Design of an Embedded System for Fast Classification of Bimetallic Coins by Impedance Spectroscopy

A novel embedded impedance measurement system for counterfeit detection of bimetallic coins is presented. The system integrates Eddy current sensors with inductive spectroscopy, facilitating the detection of hidden inlay security features and the identification of surface minting. A salient feature of the system is the use of a single Eddy current sensor that operates across multiple frequencies. This feature eliminates the need for extensive calibration procedures typically required when multiple sensor coils are used for each excitation signal frequency. The system employs an undersampling technique, facilitating impedance measurements over a wide frequency in MHz range using a simple microcontroller. The employment of machine learning-based classification further enhances the system's accuracy, enabling precise coin classification. The system's high-speed classification capability of 203 coins per second leads to a substantial enhancement in counterfeit detection while reducing the system footprint, cost, and power consumption. The classification algorithm has been rigorously tested on multiple datasets with varying difficulty levels, ensuring its robustness and reliability under different conditions. The compact, real-time, and cost-effective design of a system represents a significant breakthrough in modern coin counterfeit detection, setting new standards for accuracy and efficiency.:1. Introduction 2. Theoretical Background 3. State of the Art 4. Bimetallic Coin Investigation 5. Embedded Bimetallic Coin Classification System 6. Conclusion and Outloo

Road Construction Site Detection using Low-Level Sensor Fusion for Self-Driving Cars

Navigating through road work zones remains a challenge for the development of autonomous driving technology. While HD maps are essential for accurate local- ization and navigation in autonomous vehicles, they face issues when encountering dynamic and constantly changing situations on the road such as road construction sites. As a result, autonomous vehicles need to rely on their onboard sensor data for safe navigation through construction zones. This thesis focuses on low-level fusion-based methods, using both point cloud and image data for the detection of road construction sites. The primary objective is to identify temporary traffic control devices like delineator posts, safety barriers, and traffic cones which play a role in ensuring road safety while maintaining smooth traffic flow throughout construction areas. To achieve this, the CARLA simulator is used to generate an autonomous driving dataset that represents various road construction sites that are frequently observed in German regions. This dataset forms the basis for evaluating four state-of-the- art low-level LiDAR-camera fusion-based methods. By establishing a benchmark, this thesis presents a proof of concept for successful road work zone detection. The results demonstrate the effectiveness of low-level fusion-based methods in identifying road construction sites and open the door for further developments, emphasizing its potential impact on advancing autonomous driving technology within work zones

textil trainer. the free digital lerning platform: ADD International Textile Conference

Presentation of the free online platform textil trainer at the ADD International Textile Conference from 21 to 22 November 2024 in Stuttgart. The textil trainer is the digital learning standard in basic textile theory training in the federal state of Saxony: in textile companies, in training centres for textile professions and at institutions of higher education with a textile focus

Domain-Specific Knowledge Extraction and Synthesis for Literature Reviews

The social sciences have a knowledge management problem. Researchers need to identify related work to position their research, discover theoretical and practical knowledge gaps, and acquire methodological knowledge about how to conduct research. These tasks have become increasingly resource and time consuming as millions of articles are published each year. The predominant method of identifying related work requires the use of a handful of academic search engines that dominate the market, namely Google Search, Scopus, and Web of Science. However, these products do a poor job of making the available knowledge sufficiently accessible. As a result, hundreds of millions of articles are described by only a handful of metadata points, such as author, title, publication date, or a set of author-defined keywords. Searching for specific topics, research methods, and theories requires crafting database queries that include potential synonyms to check for matching terms in titles, abstracts, and, where available, the full text of articles. With millions of research articles available, current search strategies result in thousands of search results that must be manually reviewed for inclusion in a literature review. The recent rise of general artificial intelligence with tools such as ChatGPT has led to academic search engines attempting to adopt this new technology. However, it has been shown that these tools do not live up to expectations and in particular suffer from the phenomenon of hallucinating information. This requires researchers to carefully review generated search results of such models. Researchers currently rely on tools and technologies owned by private companies or large publishers, which often come with limitations, such as a lack of domain specificity and restricted search and filtering capabilities. But what if researchers could develop their own domain-specific tools and technologies, reducing dependency on these external players and better supporting the literature review process? This thesis consists of eight design science research studies, each of which contributes a building block to answering the overarching research question of how we can design tools and technologies that incorporate domain knowledge to support literature reviews. The research papers describe the design and development of several conceptual and technical artifacts, such as models, methods, and prototypical instantiations, that guide the design of domain-specific solutions for academic knowledge management, exemplified in the information systems discipline. We explore how knowledge representations, such as domain ontologies, can guide the design and functionality of automated knowledge extraction to identify domain-specific knowledge in research articles. Based on the extracted knowledge, we demonstrate how innovative semantic functionalities can be integrated into search engines and literature review processes to support knowledge synthesis and the conduct of literature reviews. This thesis presents a multi-design science research study aiming for generalizing, integrating, and formalizing the results of the individual research papers into design principles and an overarching design theory. The knowledge contributions of this thesis lead to several implications for research and practice. We have developed methods that can support research disciplines in managing domain knowledge more effectively, potentially supporting the development of domain- specific knowledge infrastructures. We have also shown how the developments in this thesis can support the conduct of literature reviews. By generating additional semantic metadata based on domain ontologies, it becomes possible to not only analyze a sample of articles, but the entire population. This thesis also contributes to the evaluation and improvement of Generative AI-based tools and technologies. Based on the semantic metadata extracted from research articles, it becomes possible to create a ground truth that enables the evaluation of Generative AI-based tools by assessing the degree of information hallucination. Furthermore, the approaches outlined in this thesis could be applied to different types of organizations to support knowledge extraction from unstructured data in documents.:Summary of Content ================== Preface and Acknowledgements i Research Papers iii Use of Generative AI v Summary of Content vi Table of Content vii List of Abbreviations xiii List of Figures xv List of Tables xviii Abstract xx Kurzfassung xxii Section A. Research Summary and Synthesis 1 1. Introduction 1 2. Research Approach 9 3. Foundations 19 4. Results 30 5. Discussion 77 Section B. Research Papers of the Dissertation 87 Paper A – Toward an Information Systems Ontology 87 Paper B – A Framework for Ontology-Based Knowledge Synthesis from Research Articles 98 Paper C – Automated Knowledge Extraction from IS Research Articles Combining Sentence Classification and Ontological Annotation 116 Paper D – How Best to Hunt a Mammoth – Toward Automated Knowledge Extraction from Graphical Research Models 130 Paper E – Hey Article, What Are You About? Question Answering for Information Systems Articles through Transformer Models for Long Sequences 144 Paper F – A Method for Performing Ontology-Based Computational Literature Reviews Exemplified for Design Science Research 160 Paper G – Designing Ontology-Based Search Systems for Research Articles 174 Paper H – Enhancing Ontologies with Large Language Models: A Semi-Automated Approach 204 References xxiii Appendix A – Appendices of Paper G xlviii Appendix A.1 – Investigated Functionalities in Literature Databases xlviii Appendix A.2 – Survey Questionnaire xlix Appendix B – Declaration of Contribution

Efficient High-Dimensional Approximation: ANOVA Decomposition Meets Wavelets and Random Fourier Features

In this thesis, we focus on the problem of reconstructing a multivariate function from discrete d-dimensional samples. Beyond achieving accurate function recovery, we aim to enhance interpretability by identifying how individual variables and their interactions influence the target function. To this end, we develop several efficient hybrid methods that combine the ANOVA decomposition, wavelet techniques, and random Fourier features. The multi-resolution capabilities of wavelets and the scalability of random Fourier features, paired with the interpretability provided by the ANOVA decomposition, enable a robust framework for high-dimensional function approximation. The approaches in this thesis address both computational efficiency and transparency. The total approximation error is influenced by three main components. First, the ANOVA truncation to a function of low effective dimension is the basis for the construction of ANOVA-boosting algorithms, which exploit the structure of the function. Second, the projection onto a finite-dimensional subspace is determined by the choice of basis functions. To analyze the projection error, we explore and discuss wavelet characterizations of functions in certain function spaces, like Sobolev and Besov spaces. Finally, for the regression from samples, we give error bounds for the least squares approximation, which asymptotically coincides with the behavior of the projection error.:1 Introduction 2 Toolbox for function approximation 3 The ANOVA decomposition and the curse of dimensionality 4 Characterizations in function spaces 5 Function approximation from samples 6 Numerical results and application

Investigation of Wafer-Level Electromagnetic Heating of Metallic Frames at Radio Frequencies: Analysis and Characterization of Standing Waves

The research provides concise insights into the thermal effects induced by standing waves. Through a combination of numerical FE simulations and experimental validations, the study comprehensively analyzes the distribution of EM fields, standing wave patterns, and resulting temperature profiles within the metallic frames. These findings offer crucial insights a lead to practical implications and limitations of the heating phenomena of several metallic structures at wafer-level, facilitating advancements in MEMS packaging processes. Further research regarding the coil, generator and process parameter optimization will be based on the presented results.:1. Introduction 2. Problem description 3. Preliminary results 4. Conclusions 5. References 6. Author

The use of deep fakes for political purposes – from systemic risks to systemic rivalry

Die Synopse enthält sechs Artikel, die in renommierten Fachzeitschriften veröffentlicht wurden. Diese Artikel bieten eine umfassende Analyse der Risiken und Herausforderungen, die mit der Verbreitung von Deepfakes verbunden sind. Zu den Zielen gehört die Analyse der Regulierungsansätze der EU im Umgang mit Deepfakes sowie deren Einfluss auf die internationale Ordnung und die demokratische Debatte, was mit der Möglichkeit des „Brüssel-Effekts“ und der Gestaltung internationaler Beziehungen durch normativen Einfluss verbunden ist. Die Dissertation befasst sich mit der politischen Nutzung von Deepfakes und untersucht, wie diese Technologie systemische Risiken und Rivalität in der internationalen Politik beeinflusst. Deepfakes, die seit 2017 zunehmend verbreitet sind, stellen eine große Herausforderung für demokratische Systeme dar, weil sie Manipulationen ermöglichen, die das Vertrauen in öffentliche Informationen und Medien untergraben, zur Diskreditierung einzelner Personen führen, und mehrstufige Manipulationen in unterschiedlichen Bereichen des gesellschaftlichen Lebens ermöglichen. Die Synopse analysiert die tiefgreifenden Auswirkungen von Deepfakes auf politische Prozesse, insbesondere in Wahlkämpfen, und untersucht deren potenziell destabilisierende Effekte auf die Demokratie. Gleichzeitig schlägt sie einen wichtigen Wandel im Denken über die politischen Konsequenzen des Missbrauchs von Deepfakes vor und zeigt eine breite interdisziplinare Perspektive auf die wachsenden Gruppen schädlicher Nutzungen. Ein zentrales Thema der Dissertation ist die europäische Antwort auf die Herausforderungen durch Deepfakes, insbesondere im Rahmen des KI-Gesetzes der Europäischen Union (AI Act). Die Arbeit betont, dass eine stärkere regulatorische Kontrolle notwendig ist, um die negativen Effekte dieser Technologie zu mildern und gleichzeitig demokratische Grundwerte zu schützen. Die Analyse der axiologischen Grundlagen des KI-Gesetzes ermöglicht es, einen teleologischen Interpretationsrahmen zu entwickeln und das Wissen über die Auswirkungen von Deepfakes deutlich zu erweitern. Dies ermöglicht es auch, die Bemühungen der EU mit dem Handeln anderer Akteure in den internationalen Beziehungen zu vergleichen, insbesondere im Kontext des systemischen Wettbewerbs zwischen den USA und China und den konfrontierenden Visionen einer globalen KI-Governance, die aus demokratischen und autoritären Werten resultieren. Diese Überlegungen sind in der Theorie des Konstruktivismus verankert und mit dem Konzept des „Brüssel-Effekts“ verbunden. Die Arbeit ist hochaktuell und bezieht sich auf Probleme, die in der Fachliteratur bisher nicht beschrieben wurden. Darüber hinaus ermöglicht die Doktorarbeit die Analyse des Konzepts der Deepfakes Political Advertising in den Kontext der deliberativen Demokratie. Aufgrund ihrer erheblichen politischen und gesellschaftlichen Relevanz können die Synopsis und einzelne Artikel als Grundlage für weitere Forschungen und als wichtiger Bezugspunkt für die Analyse der mit der Verbreitung von Deepfakes verbundenen Risiken dienen.:List of abbreviations 3 Deutsche Zusammenfassung 4 1. Introduction 5 2. Framework of the PhD thesis 8 3. Methodology of the PhD thesis 9 3.1. Research questions 9 3.2. Key terms and their sources 10 3.3. Methodological framework 10 3.4. Embedding in theories of political science 11 3.5. Potential limitations 12 4. Objectives of the PhD thesis 13 5. Literature review 14 5.1. Political and social science 15 5.2. Technology 19 5.3. Law 20 6. Discussion and findings 21 6.1. History of deep fakes and the path to the AI Act 23 6.2. Defining deep fakes 25 6.3. AI Act and transparency obligations 29 6.4. Deep fakes as a tool of manipulation 33 6.5. Criticism of the theory of “epistemic apocalypse” 36 6.6. Influence on democratic elections – discussion 37 6.7. “Overblown fears” – discussion 41 6.8. Introducing deep fakes political advertising 44 6.9. Influence on democratic deliberation – discussion 47 6.10. Trustworthy AI – the reasons for regulating deep fakes in the AI Act 50 6.11. Deep fakes and their systemic harmfulness 54 6.12. Confronting vision – China’s opportunistic approach to deep fakes 59 6.13. Global technological rivalry – systemic clash of values? 61 6.14. Brussels effect as a response to systemic challenges 67 6.15. Regulating deep fakes in the AI Act – a model to be repaired 69 7. Academic and political impact of the PhD thesis 72 8. Potential for further research 76 9. Conclusion 77 10. Main articles of the PhD thesis 80 11. Bibliography 8

Perceptions of Aging Within and Across Age Groups in the Context of Intergenerational Dynamics

Das wahrgenommene Alter einer Person ruft Altersstereotype hervor und prägt die Selbstwahrnehmung des Alterns. Ältere Erwachsene, die mehrere Lebensphasen durchlaufen haben, bieten eine einzigartige und oft wenig erforschte Perspektive auf das Altern und andere Altersgruppen. Diese Dissertation kombiniert verschiedene Perspektiven auf das Altern und stellt neue methodische Ansätze vor, um die Forschung zu intergenerationellem Kontakt und Wahrnehmungen sozialer Schichtung voranzutreiben. Die ersten beiden Artikel untersuchen die Beziehungen zwischen altersbezogenen Einstellungen, Stereotypen und Kontakterfahrungen im ersten Teil dieser Dissertation. Artikel 1 erfasst qualitativ die Stereotype, die ältere Erwachsene über jüngere Menschen haben, und zeigt, dass diese stärker mit negativen als positiven Begegnungen korrelieren. Artikel 2 untersucht Beweggründe älterer Erwachsener für das Aufsuchen von generationenübergreifendem Kontakt in Absicht und Verhalten. Die Ergebnisse zeigen, dass der Wunsch, von Jüngeren zu lernen, sowie positive Metastereotype die Kontaktmotivation fördern. Der zweite Teil dieser Dissertation untersucht die Selbstwahrnehmung älterer Erwachsener bezüglich des Alterns, mit Fokus auf die subjektive Wahrnehmung des sozialen Status. Artikel 3 integriert eine zeitliche Status-Dimension und zeigt, dass negative Zukunftserwartungen mit erhöhter Altersangst und negativem Affekt einhergehen. Artikel 4 wendet eine multidimensionale Sichtweise auf sozialen Status an und zeigt, dass sich der Zusammenhang zwischen subjektivem Status und psychischer Belastung über die Lebensspanne verändert. Die Ergebnisse deuten darauf hin, dass Menschen verschiedene Strategien einsetzen, um trotz wechselnder altersbezogener Erwartungen ein positives Selbstbild zu bewahren. Diese Arbeit bietet durch innovative Ansätze wertvolle Einblicke in die Dynamik altersbezogener Interaktionen und Statuswahrnehmungen und liefert Grundlagen für künftige Forschung und Interventionen zur Förderung einer altersgerechten, solidarischen Gesellschaft.A person’s perceived age is a fundamental social cue influencing both stereotypes about other age groups and self-perceptions of aging. Older adults, having experienced multiple life stages, offer a unique perspective on aging and other age groups that has been largely overlooked in research. This dissertation combines multiple perspectives on aging and introduces new methodological approaches to advance research on intergenerational contact and age-based social stratification. The first two articles explore relationships between age-based attitudes, stereotypes, and intergenerational contact in the first part of this dissertation. Article 1 qualitatively captures contemporary stereotypes that older adults hold about younger individuals and shows their high correlation with negative as opposed to positive encounters. Article 2 investigates older adults’ motivations for intergenerational contact in intention and behavior. Results show that a desire to learn from younger people and positive beliefs about how younger people view them (metastereotypes) increase their motivation to engage in contact. The second part of this dissertation examines older adults’ self-perceptions of their own aging, focusing on subjective standing in a social hierarchy. Article 3 introduces a temporal dimension to social status research, revealing associations between negative expectations about future status developments, higher aging anxiety, and negative affect. Article 4 applies a multidimensional lens to social status perceptions and integrates age-based status beliefs and self-perceived status dimensions in a cross-sectional lifespan sample, showing shifting links between status perceptions and psychological distress. Collectively, the findings indicate that individuals adopt self-protective strategies to maintain positive self-perceptions despite changing age-related expectations. By using innovative approaches, this work provides insights into the dynamics of age-based interactions and status perceptions that offer a foundation for future research avenues and practical interventions to foster a more age-inclusive society

Comicsprache – leichte Sprache?

If has frequently been claimed that the language of comics is “bad” language. This paper tests the hypothesis whether the language used in comics is therefore particularly easy to understand. To this end, a 4,900-word corpus of English-language comics, comic strips and cartoons was compiled, and a readability score based on word and sentence length was calculated. While the data on word frequency first seems inconclusive, the combined results suggest that the language of comics is indeed relatively easy to understand, and that reading comics can therefore be particularly recommended to language learners