@inproceedings{64823, abstract = {{Current legal frameworks enforce that Android developers accurately report the data their apps collect. However, large codebases can make this reporting challenging. This paper employs an empirical approach to understand developers' experience with Google Play Store's Data Safety Section (DSS) form. We first survey 41 Android developers to understand how they categorize privacy-related data into DSS categories and how confident they feel when completing the DSS form. To gain a broader and more detailed view of the challenges developers encounter during the process, we complement the survey with an analysis of 172 online developer discussions, capturing the perspectives of 642 additional developers. Together, these two data sources represent insights from 683 developers. Our findings reveal that developers often manually classify the privacy-related data their apps collect into the data categories defined by Google-or, in some cases, omit classification entirely-and rely heavily on existing online resources when completing the form. Moreover, developers are generally confident in recognizing the data their apps collect, yet they lack confidence in translating this knowledge into DSS-compliant disclosures. Key challenges include issues in identifying privacy-relevant data to complete the form, limited understanding of the form, and concerns about app rejection due to discrepancies with Google's privacy requirements. These results underscore the need for clearer guidance and more accessible tooling to support developers in meeting privacy-aware reporting obligations. }}, author = {{Khedkar, Mugdha and Schlichtig, Michael and Soliman, Mohamed Aboubakr Mohamed and Bodden, Eric}}, booktitle = {{Proceedings of the IEEE/ACM 13th International Conference on Mobile Software Engineering and Systems (MOBILESoft '26). Association for Computing Machinery, New York, NY, USA, 65–68.}}, keywords = {{static analysis, data collection, data protection, privacy-aware reporting}}, location = {{Rio de Janeiro, Brazil}}, title = {{{Challenges in Android Data Disclosure: An Empirical Study.}}}, year = {{2026}}, } @inproceedings{63754, abstract = {{Data spaces are receiving an emerging interest in Information Systems Research and industry practice. They are central to many European research initiatives and shape the data economy in Industry 4.0. Generally, they aim to create secure environments for cross-organizational data management and sharing. Currently, there is considerable interest in developing new data spaces in Industry 4.0, also accelerated through regulatory changes. However, key questions about what precisely characterizes a data space in Industry 4.0 remain unresolved. Against this backdrop, we build a taxonomy of data spaces in the Industry 4.0 context. We identified nine distinctive dimensions and 40 corresponding characteristics among the 19 data spaces analyzed. The taxonomy enables clearer classification and nomenclature of data spaces in this context. This short paper will ignite planned further research on data spaces in Industry 4.0 and contribute to a conceptualization of a taxonomic theory for interested researchers.}}, author = {{Werth, Oliver and Koldewey, Christian and Uslar, Mathias and Zerbin, Julian}}, booktitle = {{Lecture Notes in Business Information Processing}}, isbn = {{9783032145178}}, issn = {{1865-1348}}, keywords = {{Industry 4.0, Taxonomy, Data spaces, Characterization}}, location = {{Stuttgart, Germany}}, publisher = {{Springer Nature Switzerland}}, title = {{{What Characterizes Data Spaces in Industry 4.0? Towards a Better Understanding}}}, doi = {{10.1007/978-3-032-14518-5_3}}, year = {{2026}}, } @inproceedings{64787, abstract = {{This study proposes a fault diagnostics methodology that addresses the challenges posed by highly imbalanced datasets typical of railway applications, where faulty conditions constitute the minority class. Fault diagnostics is performed from the component level upward, considering each sensor’s proximity to its respective critical component. Advanced signal analysis, feature engineering, and automated data-driven model generation techniques were explored to achieve comprehensive diagnostics, such that the model development process accounts for variations in the operating conditions and differing levels of information availability. The proposed methodology is evaluated on datasets from the MONOCAB, for scenarios with limited faulty instances and on the Beijing 2024 IEEE PHM Conference data challenge, which focused on fault diagnostics of railway systems under various fault modes and operating conditions.}}, author = {{Aimiyekagbon, Osarenren Kennedy and Löwen, Alexander and Hanselle, Raphael and Rief, Thomas and Beck, Maximilian and Sextro, Walter}}, booktitle = {{PHM Society Asia-Pacific Conference}}, keywords = {{MONOCAB, Beijing Data Challenge, Diagnostics of railway systems}}, title = {{{Multilevel fault diagnostics for railway applications using limited historical data}}}, doi = {{10.36001/phmap.2025.v5i1.4449}}, volume = {{5}}, year = {{2025}}, } @article{58650, abstract = {{Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of "hybrid decision support" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation.}}, author = {{Gräßler, Iris and Pottebaum, Jens and Nyhuis, Peter and Stark, Rainer and Thoben, Klaus-Dieter and Wiederkehr, Petra}}, issn = {{2942-6170}}, journal = {{Industry 4.0 Science}}, keywords = {{AI, artificial intelligence, Data Science, decision support, extreme data, Künstliche Intelligenz, product creation, product development}}, number = {{1}}, publisher = {{GITO mbH Verlag}}, title = {{{Hybrid Decision Support in Product Creation - Improving performance with data science and artificial intelligence}}}, doi = {{10.30844/i4sd.25.1.18}}, volume = {{2025}}, year = {{2025}}, } @inproceedings{60497, abstract = {{Despite the advantages that the virtual knowledge graph paradigm has brought to many application domains, state-of-the-art systems still do not support popular graph database management systems like Neo4j. Their query rewriting algorithms focus on languages like conjunctive queries and their unions, which were developed for relational data and are poorly suited for graph data. Moreover, they also limit the expressiveness of the ontology languages that admit rewritings, restricting them to those that enjoy the so-called FO-rewritability property. Rewritings have thus focused on the DL-Lite family of Description Logics. In this paper, we propose a technique for rewriting a family of navigational queries for a suitably tailored fragment of ELHI. Leveraging navigational features in the target query language, we can include some widely-used axiom shapes not supported by DL-Lite. We implemented a proof-of-concept prototype that rewrites into Cypher queries, and tested it on a real-world cognitive neuroscience use case with promising results.}}, author = {{Löhnert, Bianca and Augsten, Nikolaus and Okulmus, Cem and Ortiz, Magdalena}}, booktitle = {{The Semantic Web - 22nd European Semantic Web Conference, {ESWC} 2025, Portoroz, Slovenia, June 1-5, 2025, Proceedings, Part {I}}}, isbn = {{9783031945748}}, issn = {{0302-9743}}, keywords = {{Ontology-based Data Access, Property Graphs, Navigational Queries}}, location = {{Portorož, Slovenia}}, pages = {{342----361}}, publisher = {{Springer Nature Switzerland}}, title = {{{Towards Practicable Algorithms for Rewriting Graph Queries Beyond DL-Lite}}}, doi = {{10.1007/978-3-031-94575-5_19}}, volume = {{15718}}, year = {{2025}}, } @article{61013, author = {{Rüther, Torben N. and Rasche, David B. and Schmid, Hans-Joachim}}, issn = {{0021-8502}}, journal = {{Journal of Aerosol Science}}, keywords = {{POCS, Projection onto convex sets, Data inversion, 2D distribution, CDMA, Centrifugal Differential Mobility Analyzer}}, publisher = {{Elsevier BV}}, title = {{{The POCS-Algorithm—An effective tool for calculating 2D particle property distributions via data inversion of exemplary CDMA measurement data}}}, doi = {{10.1016/j.jaerosci.2025.106606}}, volume = {{188}}, year = {{2025}}, } @article{62643, author = {{Schwabe, Tobias and Kress, Christian and Kruse, Stephan and Weizel, Maxim and Rhee, Hanjo and Scheytt, J. Christoph}}, journal = {{Journal of Lightwave Technology}}, keywords = {{Integrated circuit modeling, Capacitance, Silicon, Modulation, Adaptation models, Semiconductor device modeling, Bandwidth, Data communication, electrooptical transmitter, equalization, free-carrier-plasma dispersion effect, modelling, optical modulator, phase shifter, silicon photonics}}, number = {{1}}, pages = {{255--270}}, title = {{{Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation in a 250 nm EPIC BiCMOS Technology}}}, doi = {{10.1109/JLT.2024.3450949}}, volume = {{43}}, year = {{2025}}, } @article{62644, author = {{Schwabe, Tobias and Kress, Christian and Sadiye, Babak and Kruse, Stephan and Scheytt, J. Christoph}}, journal = {{IEEE Access}}, keywords = {{Optical attenuators, Equalizers, Phase shifters, Optical modulation, Electro-optic modulators, Optical amplifiers, Circuits, Silicon photonics, Optical saturation, Integrated circuit modeling, Data communication, equalization, electro-optical transmitter, silicon photonics, phase shifter, optical modulator, free-carrier plasma dispersion effect, driver architectures, biasing schemes}}, pages = {{192433--192450}}, title = {{{Analysis and Design of Forward Biased Silicon Photonics Phase Shifter Equalizer Circuits}}}, doi = {{10.1109/ACCESS.2025.3629385}}, volume = {{13}}, year = {{2025}}, } @techreport{62697, abstract = {{Urged by the European Energy Crisis and the threatening consequences of severe natural gas shortages, energy providers launched gas-saving initiatives incor- porating financial incentives to reduce residential natural gas consumption. In collaboration with one of Germany’s largest energy providers, we conducted a natural field experiment (N = 2,598) to evaluate the effectiveness of a behaviorally-guided co-design of such a gas-saving initiative by implementing two established behavioral instruments – reminders of gas saving intentions and descriptive norm feedback. Our findings show limited effectiveness of the behav- ioral instruments during the high-price period. The feedback risks a “boomerang effect” among households with above-average initial savings, who reduce their conservation efforts in response. The reminder does not significantly enhance sav- ings in our main specifications, yet, realizes 1 percentage point savings in alternate models refining for outliers. Potential mechanisms include a significant intention- action gap and misperceived effectiveness of energy-saving actions, which are not alleviated by the reminder.}}, author = {{Tinnefeld, Vicky and Kesternich, Martin and Werthschulte, Madeline}}, keywords = {{Residential energy savings, energy crisis, behavioral interventions, survey data, field experiment}}, publisher = {{ZEW Discussion Paper No. 25-60}}, title = {{{Do Energy-Saving Nudges Deliver During High-Price Periods? Field Experimental Evidence From the European Energy Crisis}}}, year = {{2025}}, } @inbook{61237, abstract = {{In diesem Beitrag wird zunächst die historische Entstehung von Open Science kurz skizziert und definiert, was unter diesem Begriff zu verstehen ist. Daran anschließend werden die Open-Science-Praktiken Open Data, Open Access, Open Source, Open Methodology und Open Peer Review dargestellt und diskutiert, welche Forschungserkenntnisse zu Open Science vorhanden sind. Im Schluss werden Forschungsdesiderate aufgegriffen und die Implikationen von Open Science für die Wissenschaft erläutert.}}, author = {{Steinhardt, Isabel and Röwert, Ronny}}, booktitle = {{Hochschulforschung}}, editor = {{Pasternack, Peer and Reinmann, Gabi and Schneijderberg, Christian }}, isbn = {{9783748943334}}, keywords = {{Open Data, Open Access, Open Source, Open Methodology, Open Peer Review}}, pages = {{487--496}}, publisher = {{Nomos}}, title = {{{Open Science}}}, doi = {{10.5771/9783748943334-487}}, year = {{2025}}, } @article{63498, author = {{Kirchgässner, Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid, Oliver}}, journal = {{IEEE Transactions on Power Electronics}}, keywords = {{Mathematical models, Estimation, Data models, Convolutional neural networks, Accuracy, Magnetic hysteresis, Magnetic cores, Temperature measurement, Magnetic domains, Temperature distribution, Convolutional neural network (CNN), machine learning (ML), magnetics}}, number = {{2}}, pages = {{3326--3335}}, title = {{{HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores}}}, doi = {{10.1109/TPEL.2024.3488174}}, volume = {{40}}, year = {{2025}}, } @misc{64894, abstract = {{This dataset contains experimental measurements of the radial dynamic and quasi-static characteristics of four different types of Rubber-Metal Bushings (RMBs) used in the suspension system of a passenger car under harmonic displacement excitation. For each bushing type, 2–3 individual specimens were tested. Quasi-static measurements were performed at a constant excitation frequency of 0.05 Hz with varying displacement amplitudes. Dynamic measurements were conducted with displacement amplitudes ranging from 0.04 mm to 0.3 mm and excitation frequencies of 2, 5, 10, ..., up to 100 Hz. The data is structured by bushing type, measurement mode, amplitude, and frequency, and is provided in *.csv and *.hrm format. It is intended to support further research in modeling rubber-metal bushings and parameter identification techniques.}}, author = {{Schütte, Jan}}, keywords = {{bushing, experimental data, rubber-metal-bushing, Dataset suspension}}, publisher = {{LibreCat University}}, title = {{{Experimental Dataset: Force and Displacement Measurements of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement Excitation}}}, doi = {{10.5281/ZENODO.14851317}}, year = {{2025}}, } @unpublished{53793, abstract = {{We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance.}}, author = {{Harder, Hans and Peitz, Sebastian}}, keywords = {{extreme learning machines, partial differential equations, data-driven prediction, high-dimensional systems}}, title = {{{Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines}}}, year = {{2024}}, } @inproceedings{55336, abstract = {{Predicting the remaining useful life of technical systems has gained significant attention in recent years due to increasing demands for extending the lifespan of degrading system components. Therefore, already used systems are retrofitted by integrating sensors to monitor their performance and functionality, enabling accurate diagnosis of their condition and prediction of their remaining useful life. One of the main challenges in this field is identified in the missing data from the time where the retrofitted system has already run but without being monitored by sensors. In this paper, a novel approach for the combined diagnostics and prognostics of retrofitted systems is proposed. The methodology aims to provide an accurate diagnosis of the system’s health state and estimation of the remaining useful life by a combination of a machine learning and expert knowledge. To evaluate the effectiveness of the proposed methodology, a case study involving a retrofitted system in an industrial setting is selected and applied. It is demonstrated that the approach effectively diagnose the current system’s health state and accurately predict its remaining useful life, thereby enabling predictive maintenance and decision-making. Overall, our research contributes to advancing the field of condition monitoring for retrofitted systems by providing a comprehensive methodology that addresses the challenge of missing data.}}, author = {{Bender, Amelie and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter}}, booktitle = {{Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)}}, isbn = {{979-8-3503-6058-5}}, keywords = {{retrofit, diagnosis, prognostics, RUL prediction, missing data, ball bearings}}, location = {{Stockholm, Schweden}}, publisher = {{IEEE Computer Society}}, title = {{{Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment}}}, doi = {{10.1109/PHM61473.2024.00038}}, year = {{2024}}, } @inproceedings{56166, abstract = {{Developing Intelligent Technical Systems (ITS) involves a complex process encompassing planning, analysis, design, production, and maintenance. Model-Based Systems Engineering (MBSE) is a key methodology for systematic systems engineering. Designing models for ITS requires harmonious interaction of various elements, posing a challenge in MBSE. Leveraging Generative Artificial Intelligence, we generated a dataset for modeling, using prompt engineering on large language models. The generated artifacts can aid engineers in MBSE design or serve as synthetic training data for AI assistants.}}, author = {{Kulkarni, Pranav Jayant and Tissen, Denis and Bernijazov, Ruslan and Dumitrescu, Roman}}, booktitle = {{DS 130: Proceedings of NordDesign 2024}}, editor = {{Malmqvist, J. and Candi, M. and Saemundsson, R. and Bystrom, F. and Isaksson, O.}}, keywords = {{Data Driven Design, Design Automation, Systems Engineering (SE), Artificial Intelligence (AI)}}, location = {{Reykjavik}}, pages = {{617--625}}, title = {{{Towards Automated Design: Automatically Generating Modeling Elements with Prompt Engineering and Generative Artificial Intelligence}}}, doi = {{10.35199/NORDDESIGN2024.66}}, year = {{2024}}, } @inproceedings{62078, abstract = {{Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior. To capture this in simulations, intricate models with a variety of parameters are typically used. The identification of values for such parameters is highly challenging and requires in depth understanding of the model itself. Machine learning (ML) is a promising approach for alleviating this challenge by directly predicting parameters based on experimental results. So far, this works mostly for purely artificial data. In this work, two approaches to generalize to experimental data are investigated: a sequential approach, leveraging understanding of the constitutive model and a direct, purely data driven approach. This is exemplary carried out for a highly non-linear strain rate dependent constitutive model for the shear behavior of FRP.The sequential model is found to work better on both artificial and experimental data. It is capable of extracting well suited parameters from the artificial data under realistic conditions. For the experimental data, the model performance depends on the composition of the experimental curves, varying between excellently suiting and reasonable predictions. Taking the expert knowledge into account for ML-model training led to far better results than the purely data driven approach. Robustifying the model predictions on experimental data promises further improvement. }}, author = {{Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}}, booktitle = {{ECCM21 - Proceedings of the 21st European Conference on Composite Materials}}, isbn = {{978-2-912985-01-9}}, keywords = {{Direct parameter identification, Machine learning, Convolutional neural networks, Strain rate dependency, Fiber reinforced plastics, woven composites, segmentation, synthetic training data, x-ray computed tomography}}, pages = {{1252–1259}}, publisher = {{European Society for Composite Materials (ESCM)}}, title = {{{Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning}}}, doi = {{10.60691/yj56-np80}}, volume = {{3}}, year = {{2024}}, } @inproceedings{52235, abstract = {{Android applications collecting data from users must protect it according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the General Data Protection Regulation (GDPR). Since app developers are not legal experts, they find it difficult to write privacy-aware source code. Moreover, they have limited tool support to reason about data protection throughout their app development process. This paper motivates the need for a static analysis approach to diagnose and explain data protection in Android apps. The analysis will recognize personal data sources in the source code, and aims to further examine the data flow originating from these sources. App developers can then address key questions about data manipulation, derived data, and the presence of technical measures. Despite challenges, we explore to what extent one can realize this analysis through static taint analysis, a common method for identifying security vulnerabilities. This is a first step towards designing a tool-based approach that aids app developers and assessors in ensuring data protection in Android apps, based on automated static program analysis. }}, author = {{Khedkar, Mugdha and Bodden, Eric}}, booktitle = {{Proceedings of the IEEE/ACM 11th International Conference on Mobile Software Engineering and Systems (MOBILESoft '24). Association for Computing Machinery, New York, NY, USA, 65–68.}}, keywords = {{static program analysis, data protection and privacy, GDPR compliance}}, location = {{Lisbon, Portugal}}, title = {{{Toward an Android Static Analysis Approach for Data Protection}}}, doi = {{10.1145/3647632.3651389}}, year = {{2024}}, } @inproceedings{57160, abstract = {{Large audio tagging models are usually trained or pre-trained on AudioSet, a dataset that encompasses a large amount of different sound classes and acoustic environments. Knowledge distillation has emerged as a method to compress such models without compromising their effectiveness. There are many different applications for audio tagging, some of which require a specialization to a narrow domain of sounds to be classified. For these scenarios, it is beneficial to distill the large audio tagger with respect to a specific subset of sounds of interest. A method to prune a general dataset with respect to a target dataset is presented. By distilling with such a specialized pruned dataset, we obtain a compressed model with better classification accuracy in the specific target domain than with target-agnostic distillation.}}, author = {{Werning, Alexander and Haeb-Umbach, Reinhold}}, booktitle = {{32nd European Signal Processing Conference (EUSIPCO 2024)}}, keywords = {{data pruning, knowledge distillation, audio tagging}}, location = {{Lyon}}, title = {{{Target-Specific Dataset Pruning for Compression of Audio Tagging Models}}}, year = {{2024}}, } @inproceedings{50118, abstract = {{Despite the widespread use of machine learning algorithms, their effectiveness is limited by a phenomenon known as algorithm aversion. Recent research concluded that unobserved variables can cause algorithm aversion. However, the impact of an unobserved variable on algorithm aversion remains unclear. Previous studies focused on situations where humans had more variables available than algorithms. We extend this research by conducting an online experiment with 94 participants, systematically varying the number of observable variables to the advisor and the advisor type. Surprisingly, our results did not confirm that an unobserved variable had a negative effect on advice-taking. Instead, we found a positive impact in an algorithm appreciation scenario. This study provides new insights into the paradoxical behavior in which people weigh advice more despite having fewer variables, as they correct for the advisor's errors. Practitioners should consider this behavior when designing algorithms and account for user correction behavior.}}, author = {{Leffrang, Dirk}}, booktitle = {{Wirtschaftsinformatik Conference}}, keywords = {{Algorithm aversion, Data, Decision-making, Advice-taking, Human-Computer Interaction}}, location = {{Paderborn}}, number = {{19}}, title = {{{The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect of Unobserved Variables on Advice Utilization}}}, year = {{2023}}, } @inproceedings{52369, abstract = {{Megatrends, such as digitization or sustainability, are confronting the product management of manufacturing companies with a variety of challenges regarding the design of future products, but also the management of the actual products. To successfully position their products in the market, product managers need to gather and analyze comprehensive information about customers, developments in the products’ environment, product usage, and more. The digitization of all aspects of life is making data on these topics increasingly available – via social media, documents, or the internet of things from the products themselves. The systematic collection and analysis of these data enable the exploitation of new potentials for the adaption of existing products and the creation of the products of tomorrow. However, there are still no insights into the main concepts and cause-effect relationships in exploiting data-driven approaches for product management. Therefore, this paper aims to identify the main concepts and advantages of data-driven product management. To answer the corresponding research questions a comprehensive systematic literature review is conducted. From its results, a detailed description of the main concepts of data-driven product management is derived. Furthermore, a taxonomy for the advantages of data-driven product management is presented. The main concepts and the taxonomy allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.}}, author = {{Fichtler, Timm and Grigoryan, Khoren and Koldewey, Christian and Dumitrescu, Roman}}, booktitle = {{2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}}, keywords = {{Product Lifecyle Management (PLM), Data Analytics, Data-driven Design, Engineering Management, Lifecycle Data}}, location = {{Rabat, Morocco}}, publisher = {{IEEE}}, title = {{{Towards a Data-Driven Product Management – Concepts, Advantages, and Future Research}}}, doi = {{10.1109/ictmod59086.2023.10438135}}, year = {{2023}}, }