@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}}, } @inproceedings{52816, abstract = {{Manufacturing companies face the challenge of reaching required quality standards. Using optical sensors and deep learning might help. However, training deep learning algorithms require large amounts of visual training data. Using domain randomization to generate synthetic image data can alleviate this bottleneck. This paper presents the application of synthetic image training data for optical quality inspections using visual sensor technology. The results show synthetically generated training data are appropriate for visual quality inspections.}}, author = {{Gräßler, Iris and Hieb, Michael}}, booktitle = {{Lectures}}, keywords = {{synthetic training data, machine vision quality gates, deep learning, automated inspection and quality control, production control}}, location = {{Nuremberg}}, pages = {{253--524}}, publisher = {{AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany}}, title = {{{Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing}}}, doi = {{10.5162/smsi2023/d7.4}}, year = {{2023}}, } @misc{55850, abstract = {{This release covers the state of this prototype app at the end of the funding phase for the Paderborn University part of the Beethoven in the House project. It uses https://api.domestic-beethoven.eu/ld/BithCollection.jsonld as starting point for traversing the LOD graph, and reads data from the project pod available from https://bith.solidcommunity.net/public/bith.ttl (which has no content at the time of the release).}}, author = {{Kepper, Johannes}}, keywords = {{MEI, Edirom, Music Encoding Initiative, Linked Open Data, MELD}}, publisher = {{Zenodo}}, title = {{{DomesticBeethoven/bith-annotator: Release 2023-04}}}, doi = {{10.5281/ZENODO.7877741}}, year = {{2023}}, } @inproceedings{55833, abstract = {{We present a new multi-layered, conceptual model for associating musical source materials to musicological arguments. We describe our proposal for operationalizing these concepts through a framework for musical annotation which we have implemented using RDF. Briefly stated, this model shows how portions of digitized data in various files and formats can be identified, selected, labelled, and compared.}}, author = {{Shibata, Elisabete and Lewis, David and Saccomano, Mark and Kepper, Johannes and Page, Kevin}}, booktitle = {{Proceedings of the Music Encoding Conference 2022}}, editor = {{Weigl, David and Bain, Jennifer and Ang, Ailynn}}, keywords = {{BitH, Linked Data}}, pages = {{145–150}}, title = {{{A New Conceptual Model for Musical Sources and Musicological Studies}}}, doi = {{https://doi.org/10.17613/8p2c-1q77}}, year = {{2023}}, } @inproceedings{55832, abstract = {{Digital musicology research often proceeds by extending and enriching its evidence base as it progresses, rather than starting with a complete corpus of data and metadata, as a consequence of an emergent research need. In this paper, we consider a research workflow which assumes an incremental approach to data gathering and annotation. We describe tooling which implements parts of this workflow, developed to support the study of nineteenth-century music arrangements, and evaluate the applicability of our approach through interviews with musicologists and music editors who have used the tools. We conclude by considering extensions of this approach and the wider implications for digital musicology and music information retrieval.}}, author = {{Lewis, David and Shibata, Elisabete and Hankinson, Andrew and Kepper, Johannes and Page, Kevin R. and Rosendahl, Lisa and Saccomano, Mark and Siegert, Christine}}, keywords = {{BitH, Linked Data}}, title = {{{Supporting Musicological Investigations With Information Retrieval Tools: An Iterative Approach to Data Collection}}}, doi = {{10.5281/ZENODO.10265407}}, year = {{2023}}, } @inproceedings{37497, abstract = {{Since historical times, cartographic maps have revealed spatial relations and enabled decisions and processes. Geographic Information Systems (GIS) allow for acquisition, management, analysis, and presentation of geospatial objects. With free geospatial data becoming available through open data policies and an increasing amount of digitally connected objects in the Internet of Things (IoT), GIS are becoming indispensable to Information Systems (IS) research. However, the consideration and relevance of GIS has only been investigated rarely. We examine, how and in which fields of application GIS have been studied in the IS literature and elicit the importance of GIS regarding their design and usage. A systematic literature review leads us to develop four research propositions. Our results indicate that GIS are still an undeservedly underrepresented discipline in IS and should be more theorized, put center-stage in design-oriented research, and considered for creating superior value co-creation in service systems.}}, author = {{Priefer, Jennifer}}, booktitle = {{Proceedings of the 56th Hawaii International Conference on System Sciences}}, editor = {{Bui, T.X. and Sprague, R.H.}}, keywords = {{GIS, Industry 4.0, and Sustainability, geographic information systems, geospatial data, gis, information systems research, literature review}}, title = {{{Geographic Information Systems in Information Systems Research - Review and Research Prospects}}}, year = {{2023}}, } @inproceedings{45793, abstract = {{The global megatrends of digitization and sustainability lead to new challenges for the design and management of technical products in industrial companies. Product management - as the bridge between market and company - has the task to absorb and combine the manifold requirements and make the right product-related decisions. In the process, product management is confronted with heterogeneous information, rapidly changing portfolio components, as well as increasing product, and organizational complexity. Combining and utilizing data from different sources, e.g., product usage data and social media data leads to promising potentials to improve the quality of product-related decisions. In this paper, we reinforce the need for data-driven product management as an interdisciplinary field of action. The state of data-driven product management in practice was analyzed by conducting workshops with six manufacturing companies and hosting a focus group meeting with experts from different industries. We investigate the expectations and derive requirements leading us to open research questions, a vision for data-driven product management, and a research agenda to shape future research efforts.}}, author = {{Grigoryan, Khoren and Fichtler, Timm and Schreiner, Nick and Rabe, Martin and Panzner, Melina and Kühn, Arno and Dumitrescu, Roman and Koldewey, Christian}}, booktitle = {{Procedia CIRP 33}}, keywords = {{Product Management, Data Analytics, Data-Driven Design, Product-related data, Lifecycle Data, Tool-support}}, location = {{Sydney}}, title = {{{Data-Driven Product Management: A Practitioner-Driven Research Agenda}}}, year = {{2023}}, } @inproceedings{44146, abstract = {{Many Android applications collect data from users. When they do, they must protect this collected data 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). App developers have limited tool support to reason about data protection throughout their app development process. Although many Android applications state a privacy policy, privacy policy compliance checks are currently manual, expensive, and prone to error. One of the major challenges in privacy audits is the significant gap between legal privacy statements (in English text) and technical measures that Android apps use to protect their user's privacy. In this thesis, we will explore to what extent we can use static analysis to answer important questions regarding data protection. Our main goal is to design a tool based approach that aids app developers and auditors in ensuring data protection in Android applications, based on automated static program analysis.}}, author = {{Khedkar, Mugdha}}, booktitle = {{2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), Melbourne, Australia, 2023, pp. 197-199}}, keywords = {{static analysis, data protection and privacy, GDPR compliance}}, title = {{{Static Analysis for Android GDPR Compliance Assurance}}}, doi = {{10.1109/ICSE-Companion58688.2023.00054}}, year = {{2023}}, } @inproceedings{34171, abstract = {{State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a selection of those linear combinations is chosen and thus an interpretable model can be extracted. Results indicate a small estimation error compared to a traditional square-root unscented Kalman filter and exhibit the enhancement of physically meaningful models.}}, author = {{Götte, Ricarda-Samantha and Timmermann, Julia}}, booktitle = {{12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)}}, keywords = {{joint estimation, unscented transform, Kalman filter, sparsity, data-driven, compressed sensing}}, location = {{Canberra, Australien}}, number = {{1}}, pages = {{85--90}}, title = {{{Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF}}}, doi = {{https://doi.org/10.1016/j.ifacol.2023.02.015}}, volume = {{56}}, year = {{2023}}, } @inproceedings{48012, abstract = {{3D printing is a well-established technology with rapidly increasing usage scenarios both in the industry and consumer context. The growing popularity of 3D printing has also attracted security researchers, who have analyzed possibilities for weakening 3D models or stealing intellectual property from 3D models. We extend these important aspects and provide the first comprehensive security analysis of 3D printing data formats. We performed our systematic study on the example of the 3D Manufacturing Format (3MF), which offers a large variety of features that could lead to critical attacks. Based on 3MF’s features, we systematized three attack goals: Data Exfiltration (dex), Denial of Service, and UI Spoofing (uis). We achieve these goals by exploiting the complexity of 3MF, which is based on the Open Packaging Conventions (OPC) format and uses XML to define 3D models. In total, our analysis led to 352 tests. To create and run these tests automatically, we implemented an open-source tool named 3MF Analyzer (tool), which helped us evaluate 20 applications.}}, author = {{Rossel, Jost and Mladenov, Vladislav and Somorovsky, Juraj}}, booktitle = {{Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses}}, keywords = {{Data Format Security, 3D Manufacturing Format, 3D Printing, Additive Manufacturing}}, location = {{Hongkong}}, publisher = {{ACM}}, title = {{{Security Analysis of the 3MF Data Format}}}, doi = {{10.1145/3607199.3607216}}, year = {{2023}}, } @inproceedings{29842, abstract = {{To build successful software products, developers continuously have to discover what features the users really need. This discovery can be achieved with continuous experimentation, testing different software variants with distinct user groups, and deploying the superior variant for all users. However, existing approaches do not focus on explicit modeling of variants and experiments, which offers advantages such as traceability of decisions and combinability of experiments. Therefore, our vision is the provision of model-driven continuous experimentation, which provides the developer with a framework for structuring the experimentation process. For that, we introduce the overall concept, apply it to the experimentation on component-based software architectures and point out future research questions. In particular, we show the applicability by combining feature models for modeling the software variants, users, and experiments (i.e., model-driven) with MAPE-K for the adaptation (i.e., continuous experimentation) and implementing the concept based on the component-based Angular framework.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Engels, Gregor}}, booktitle = {{Proceedings of the 18th International Conference on Software Architecture Companion }}, keywords = {{continuous experimentation, model-driven, component-based software architectures, self-adaptation}}, location = {{Hawaii}}, publisher = {{IEEE}}, title = {{{Model-driven Continuous Experimentation on Component-based Software Architectures }}}, doi = {{10.1109/ICSA-C54293.2022.00011}}, year = {{2022}}, } @article{48878, abstract = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}}, author = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{2076-3417}}, journal = {{Applied Sciences}}, keywords = {{big data, data mining, data stream analysis, machine learning, stream classification, supervised learning}}, number = {{18}}, pages = {{9094}}, publisher = {{{Multidisciplinary Digital Publishing Institute}}}, title = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}}, doi = {{10.3390/app12189094}}, volume = {{12}}, year = {{2022}}, } @article{35728, abstract = {{Technological developments such as Cloud Computing, the Internet of Things, Big Data and Artificial Intelligence continue to drive the digital transformation of business and society. With the advent of platform-based ecosystems and their potential to address complex challenges, there is a trend towards greater interconnectedness between different stakeholders to co-create services based on the provision and use of data. While previous research on digital transformation mainly focused on digital transformation within organizations, it is of growing importance to understand the implications for digital transformation on different layers (e.g., interorganizational cooperation and platform ecosystems). In particular, the conceptualization and implications of public data spaces and related ecosystems provide promising research opportunities. This special issue contains five papers on the topic of digital transformation and, with the editorial, further contributes by providing an initial conceptualization of public data spaces' potential to foster innovative progress and digital transformation from a management perspective.}}, author = {{Beverungen, Daniel and Hess, Thomas and Köster, Antonia and Lehrer, Christiane}}, issn = {{1019-6781}}, journal = {{Electronic Markets}}, keywords = {{Digital transformation, Public data spaces, Digital platforms, GAIA-X}}, pages = {{493--501}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{From private digital platforms to public data spaces: implications for the digital transformation}}}, doi = {{10.1007/s12525-022-00553-z}}, volume = {{32}}, year = {{2022}}, } @article{35732, abstract = {{While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.}}, author = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}}, issn = {{1019-6781}}, journal = {{Electronic Markets}}, keywords = {{Platform, Text mining, Machine learning, Data communications, Interpretive research, Systems design and implementation}}, pages = {{375--396}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}}, doi = {{10.1007/s12525-022-00530-6}}, volume = {{32}}, year = {{2022}}, } @inproceedings{26539, abstract = {{In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN and a physics-inspired loss term (-L) to successfully identify the system's dynamics, while maintaining the consistency with physical laws. The proposed method is demonstrated on two real-world nonlinear systems and outperforms existing techniques regarding complexity and reliability.}}, author = {{Götte, Ricarda-Samantha and Timmermann, Julia}}, booktitle = {{2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)}}, keywords = {{data-driven, physics-based, physics-informed, neural networks, system identification, hybrid modelling}}, location = {{Cairo, Egypt}}, pages = {{67--76}}, title = {{{Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering}}}, doi = {{10.1109/AIRC56195.2022.9836982}}, year = {{2022}}, } @inproceedings{31066, abstract = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }}, author = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}}, booktitle = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}}, keywords = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}}, location = {{Casablanca, Morocco}}, number = {{12}}, pages = {{19--24}}, title = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}}, doi = {{https://doi.org/10.1016/j.ifacol.2022.07.282}}, volume = {{55}}, year = {{2022}}, }