@article{63611, abstract = {{When humans interact with artificial intelligence (AI), one desideratum is appropriate trust. Typically, appropriate trust encompasses that humans trust AI except for instances in which they either explicitly notice AI errors or are suspicious that errors could be present. So far, appropriate trust or related notions have mainly been investigated by assessing trust and reliance. In this contribution, we argue that these assessments are insufficient to measure the complex aim of appropriate trust and the related notion of healthy distrust. We introduce and test the perspective of covert visual attention as an additional indicator for appropriate trust and draw conceptual connections to the notion of healthy distrust. To test the validity of our conceptualization, we formalize visual attention using the Theory of Visual Attention and measure its properties that are potentially relevant to appropriate trust and healthy distrust in an image classification task. Based on temporal-order judgment performance, we estimate participants' attentional capacity and attentional weight toward correct and incorrect mock-up AI classifications. We observe that misclassifications reduce attentional capacity compared to correct classifications. However, our results do not indicate that this reduction is beneficial for a subsequent judgment of the classifications. The attentional weighting is not affected by the classifications' correctness but by the difficulty of categorizing the stimuli themselves. We discuss these results, their implications, and the limited potential for using visual attention as an indicator of appropriate trust and healthy distrust.}}, author = {{Peters, Tobias Martin and Biermeier, Kai and Scharlau, Ingrid}}, issn = {{1664-1078}}, journal = {{Frontiers in Psychology}}, keywords = {{appropriate trust, healthy distrust, visual attention, Theory of Visual Attention, human-AI interaction, Bayesian cognitive model, image classification}}, publisher = {{Frontiers Media SA}}, title = {{{Assessing healthy distrust in human-AI interaction: interpreting changes in visual attention}}}, doi = {{10.3389/fpsyg.2025.1694367}}, volume = {{16}}, year = {{2026}}, } @inproceedings{66072, abstract = {{Data Spaces are establishing themselves as a decentralized alternative for sovereign data sharing across organizations. However, their adoption faces significant challenges, particularly the lack of proven business models that encourage participation and value creation. A data space business model captures value differently than either traditional business models or platform business models. This paper addresses this challenge by developing the Data Space Canvas, a structured framework for designing and aligning business models in data spaces consisting of nine building blocks. The canvas was developed following the design science research methodology. The modular and role-specific structure of the Data Space Canvas facilitates shared understanding and supports business model development across participants. This research contributes a practical tool that enables organizations to conceptualize, communicate, and refine their role and business model in data spaces.}}, author = {{Zerbin, Julian and Koldewey, Christian and Dumitrescu, Roman}}, booktitle = {{ECIS 2026 Proceedings}}, keywords = {{data ecosystems, business model, design science, resource orchestration theory, data sharing}}, location = {{Mailand}}, title = {{{The Data Space Canvas: A Framework For Structuring Business Models In Data Spaces}}}, year = {{2026}}, } @techreport{66449, abstract = {{This paper evaluates the forecasting performance of an expanded class of (semi-)parametric GARCH models belonging to the EGARCH family (EGF), including recently introduced long and short memory specifications and their semiparametric extensions. The semiparametric variants employ a multiplicative volatility decomposition into conditional and slowly varying unconditional components, where the latter is estimated via a data-driven local polynomial smoother to accommodate non-stationarities commonly observed in financial time series. Based on the revised Basel Committee framework for market-risk assessment, all models are capable of producing rolling one-day-ahead forecasts for Value at Risk (VaR) and Expected Shortfall (ES) under a wide range of symmetric and skewed innovation distributions. Their forecasting accuracy is examined using the regulatory traffic light tests for VaR and the recently developed ES-specific traffic light procedure, complemented by the regulatory loss function. In addition, model selection incorporates both a recently proposed corrected firm-oriented loss function that accounts for opportunity costs and the Weighted Absolute Deviation (WAD) criterion. The empirical comparison demonstrates that (semiparametric) long memory GARCH models - particularly those combining fractional dynamics with nonparametric scale adjustments - can serve as valuable alternatives to traditional parametric short memory models, offering more stable volatility estimates and improved tail-risk forecasts for practical risk management applications.}}, author = {{Hanke, Dominik Christian and Uhde, André and Feng, Yuanhua}}, keywords = {{semiparametric GARCH extension, data-driven local polynomial smoother, long memory, GARCH models, Value at Risk, Expected Shortfall, traffic light test, backtesting, Basel III, market risk}}, title = {{{Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements of Basel III}}}, year = {{2026}}, } @techreport{66447, abstract = {{Volatility modeling is utilized across numerous fields including finance, environmental studies, and social sciences. It is particularly relevant in scenarios where understanding and predicting conditional variability is crucial, such as when dealing with incremental or time-dependent data. In this paper, novel short and long memory volatility models of the EGARCH family are introduced and analyzed, which are closely related to the well-established EGARCH model proposed by Nelson (1991) but share desirable theoretical properties in several dimensions. Recently developed members of the so-called EGARCH family, which introduces a modulus-log transformation proposed by John and Draper (1980) and a power transformation for the size and magnitude effect to tackle the problem with near-zero innovations and the asymmetric impact of positive and negative shocks on the volatility, are discussed. After a theoretical discussion of the proposed and related volatility models, the practical performance of the elaborated volatility models is compared to well-established and traditional GARCH approaches. A general QMLE algorithm is proposed to estimate the model parameters. The practical relevance of the advanced models is illustrated through a comparative study. By applying these volatility models to a variety of international stock index returns, this paper identifies market-specific characteristics as well as unique strengths and weaknesses of discussed volatility models. Although the practical performance of the recently introduced models is comparable to those obtained by the traditional EGARCH model, they generally outperform traditional non-exponential volatility models used as benchmarks and thus provide a useful alternative to existing short and long memory volatility models. }}, author = {{Hanke, Dominik Christian and Feng, Yuanhua and Uhde, André}}, keywords = {{Modulus Log-GARCH, Modified (FI)EGARCH, Modulus asymmetric (FI)Log-GARCH, (FI)EGARCH, long memory, modulus-log transformation, QMLE, model selection, implementation in R}}, title = {{{Comparing the behaviors of some original short and long memory exponential volatility models}}}, year = {{2026}}, } @inproceedings{67174, abstract = {{Approval of products in safety-sensitive areas, such as medical technology, requires considerable effort. Companies must comply with numerous regulations for such product approvals. This results in requirements for processes, methods, tools and organizations throughout the product life cycle. A proven approach to handling regulatory requirements is the application of product lifecycle management (PLM) platforms. Applying PLM platforms (e.g. Teamcenter, 3DExperience) poses challenges in designing compliant processes as well as adapting methods and tools. For example, in medical technology, ISO 13485 specifies an industry-specific product engineering process. The exemplary adaptations of requirements engineering methods and related support – e.g. MBSE including Requirements Diagrams – are not intuitively apparent. Existing literature primarily regards these regulation-related challenges as boundary conditions, offering only vague operational solutions. Companies struggle to align PLM processes with regulatory requirements, yet few methods exist to support this integration. In this paper, a method is proposed that supports the application of regulation-centered PLM platforms and reduces effort in product approvals. The method is developed by applying a five-step approach. Existing literature on process models for applying PLM platforms is identified (1). The identified approaches are then analyzed to determine whether and to what extent they consider regulations in applying PLM platforms (2). Based on identified partial solutions from literature and findings from industry workshops, a new method is developed (3). The method comprises strategic level, process level, data level, pilot level, and operation level. Validation is conducted within a German medical technology company, containing proof of applicability determined through a pilot project (4) and proof of concept achieved through interviews (5). The method is applied to regulation-centered PLM for the use case ‘requirements engineering’. The concept is confirmed by experts in terms of applicability and transferability. The method supports engineers in applying regulation-centered PLM platforms, leading to reduced effort.}}, author = {{Gräßler, Iris and Pfeifer, Jan Niklas and Trost, Denis}}, booktitle = {{INCOSE International Symposium}}, issn = {{2334-5837}}, keywords = {{Regulations, Product Lifecycle Management, Requirements Engineering, Model-Based Systems Engineering, medical technology}}, location = {{Yokohma, Japan}}, number = {{1}}, pages = {{1641 -- 1655}}, publisher = {{Wiley}}, title = {{{RegPLM – A method for the application of regulation-centered PLM platforms}}}, doi = {{https://doi.org/10.1002/iis2.70264}}, volume = {{36}}, year = {{2026}}, } @inproceedings{61432, abstract = {{This study investigated how action histories – unfolding sequences of actions with objects – provide a context for both attentional allocation and linguistic repair strategies. Building on theories of enactive cognition and sensorimotor contingency theory, we experimentally manipulated action sequences (action history) to create either simple or rich “situational models,” and investigated how these models interact with attention and reflect in linguistic processes during human–robot interaction. Participants (N = 30) engaged in a controlled object placement task with a humanoid robot, where the action (manner) information was either provided or omitted. The omission elicited repair behaviors in participants that were in focus of our investigation. For rich models (competing action possibilities) participants demonstrated: a) increased attentional reorientation, reflecting active engagement with the situational model b) preference for restricted repairs, targeting the specific source of trouble in action selection. Conversely, a simple situational model led to more generalized attention patterns and open repair strategies, suggesting weaker constraints on internal processing. These findings highlight how situational structures emerge externally to scaffold internal cognitive processes, with action histories serving as a crucial context for the interface between perception, action, and language. We discuss how to implement such a tight loop in the assistance of a system.}}, author = {{Singh, Amit and Rohlfing, Katharina J.}}, booktitle = {{IEEE International Conference on Development and Learning (ICDL)}}, keywords = {{Attention, Action, Repairs, Task model, HRI, Eyemovement}}, location = {{Prague}}, title = {{{Manners Matter: Action history guides attention and repair choices during interaction}}}, doi = {{10.31234/osf.io/yn2we_v1}}, year = {{2025}}, } @inbook{62701, abstract = {{Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.}}, author = {{Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, keywords = {{Decision Tree, OWL Class Expression Learning, Description Logic, Knowledge Graph, Large Language Model, Verbalizer}}, location = {{Porto, Portugal}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @article{62937, abstract = {{Sandwich packings are assembled from two conventional structured packings with different geometrical surface areas stacked alternatingly within a separation column. When operated under partially flooded conditions, they provide significant mass transfer improvement compared to common structured packings. In this work, a rate-based model including novel mass transfer correlations is presented and validated using a comprehensive experimental database for the reactive absorption of CO2 into aqueous monoethanolamine. The proposed rate-based approach is capable of accounting for axial dispersion, thereby enabling the evaluation of the effect of liquid-phase backmixing on the mass transfer performance. The validated rate-based model is used to evaluate the separation performance of sandwich packings. Compared with structured packings, up to 10 % higher mass transfer rates are obtained.}}, author = {{Franke, Patrick and Schubert, Markus and Hampel, Uwe and Kenig, Eugeny Y.}}, issn = {{0009-2509}}, journal = {{Chemical Engineering Science}}, keywords = {{Sandwich packings Structured packings Rate-based approach Model validation Ultra-fast X-ray tomography}}, publisher = {{Elsevier BV}}, title = {{{A rate-based model for reactive separation columns with sandwich packings}}}, doi = {{10.1016/j.ces.2025.122681}}, volume = {{321}}, year = {{2025}}, } @inproceedings{63019, author = {{Donner, Johannes Aurelius Tamino and Schlüter, Alexander}}, booktitle = {{SDEWES Conference 2025}}, keywords = {{5GDHC, district heating, DHC, waste heat, AI-Driven}}, location = {{Dubrovnik}}, title = {{{Development of an AI-driven decentralized control for fifth generation district heating and cooling networks}}}, year = {{2025}}, } @inproceedings{60958, abstract = {{Large Language Models (LLMs) excel in understanding, generating, and processing human language, with growing adoption in process mining. Process mining relies on event logs that capture explicit process knowledge; however, knowledge-intensive processes (KIPs) in domains such as healthcare and product development depend on tacit knowledge, which is often absent from event logs. To bridge this gap, this study proposes a LLM-based framework for mobilizing tacit process knowledge and enriching event logs. A proof-of-concept is demonstrated using a KIP-specific LLM-driven conversational agent built on GPT-4o. The results indicate that LLMs can capture tacit process knowledge through targeted queries and systematically integrate it into event logs. This study presents a novel approach combining LLMs, knowledge management, and process mining, advancing the understanding and management of KIPs by enhancing knowledge accessibility and documentation.}}, author = {{Brennig, Katharina}}, booktitle = {{AMCIS 2025 Proceedings. 11.}}, keywords = {{Process Mining, Large Language Model, Knowledge Management, Knowledge-Intensive Process, Tacit Knowledge}}, location = {{Montréal}}, title = {{{Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes}}}, year = {{2025}}, } @inproceedings{62149, abstract = {{The increasing complexity of technical systems requires early, structured verification and validation (V&V). Existing metadata models only map parts of the engineering process and do not enable a continuous chain of effects from requirements to test results. The aim of this publication is to develop a holistic V&V metadata model for the consistent, transparent and machine-processable description and linking of relevant engineering artifacts. In a five-stage research approach, essential model components are derived from literature and are integrated into a holistic model. Initial applications as part of a European research project show the potential of the model for a well-founded effect chain analysis and decision-supporting V&V processes.}}, author = {{Gräßler, Iris and Ebel, Marcel}}, booktitle = {{DS 140: Proceedings of the 36th Symposium Design for X (DFX2025)}}, keywords = {{verification, metadata model, Systems Engineering}}, location = {{Hamburg}}, publisher = {{The Design Society}}, title = {{{Ganzheitliches Metadatenmodel für die Verifikation und Validierung in der Entwicklung komplexer technischer Systeme}}}, doi = {{10.35199/dfx2025.09}}, year = {{2025}}, } @inproceedings{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}}, booktitle = {{Systems theory in data and optimization}}, editor = {{Berberich, J. and Ianelli, A. and Allgöwer, F.}}, isbn = {{978-3-031-83191-1}}, keywords = {{extreme learning machines, partial differential equations, data-driven prediction, high-dimensional systems}}, pages = {{153--168}}, publisher = {{Springer}}, title = {{{Solving partial differential equations with equivariant extreme learning machines}}}, doi = {{10.1007/978-3-031-83191-1_10}}, year = {{2025}}, } @inproceedings{54807, abstract = {{This paper considers the shape formation problem within the 3D hybrid model, where a single agent with a strictly limited viewing range and the computational capacity of a deterministic finite automaton manipulates passive tiles through pick-up, movement, and placement actions. The goal is to reconfigure a set of tiles into a specific shape termed an icicle. The icicle, identified as a dense, hole-free structure, is strategically chosen to function as an intermediate shape for more intricate shape formation tasks. It is designed for easy exploration by a finite state agent, enabling the identification of tiles that can be lifted without breaking connectivity. Compared to the line shape, the icicle presents distinct advantages, including a reduced diameter and the presence of multiple removable tiles. We propose an algorithm that transforms an arbitrary initially connected tile structure into an icicle in 𝒪(n³) steps, matching the runtime of the line formation algorithm from prior work. Our theoretical contribution is accompanied by an extensive experimental analysis, indicating that our algorithm decreases the diameter of tile structures on average.}}, author = {{Hinnenthal, Kristian and Liedtke, David Jan and Scheideler, Christian}}, booktitle = {{3rd Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2024)}}, editor = {{Casteigts, Arnaud and Kuhn, Fabian}}, isbn = {{978-3-95977-315-7}}, issn = {{1868-8969}}, keywords = {{Programmable Matter, Shape Formation, 3D Model, Finite Automaton}}, pages = {{15:1–15:20}}, publisher = {{Schloss Dagstuhl – Leibniz-Zentrum für Informatik}}, title = {{{Efficient Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter Intermediate Structures}}}, doi = {{10.4230/LIPIcs.SAND.2024.15}}, volume = {{292}}, 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}}, } @phdthesis{54552, abstract = {{Die vorliegende Dissertation beschreibt das Konzept und den Entwicklungsprozess eines Lichtsignalanlagenregelungssystems bis zur Realumsetzung. Das Regelungssystem, welches im Rahmen des Pilotprojekts Schlosskreuzung entstanden ist, besteht dabei aus zwei untereinander abgestimmten Methoden zur Echtzeit-Verkehrsrekonstruktion und zur modellprädiktiven Regelung des Verkehrssystems anhand der vorhandenen Lichtsignalanlagen. Die Echtzeit-Verkehrsrekonstruktion approximiert dabei simulationsbasiert den aktuellen Verkehrszustand anhand gegebener Messdaten über dynamische Verkehrszuweisungen. Die entwickelte mehrstufige Lichtsignalanlagenregelung nutzt ein Fuzzy-System zur Phasenvorauswahl, um anschließend über eine modellprädiktive Regelung das nichtlineare Problem mit dem Ergebnis der optimalen Kombinationen von Phasen und Schaltzeitpunkten zu lösen. Das Regelungssystem wird in dieser Arbeit anhand eines ausgewählten Verkehrsgebiets zunächst rein simulativ getestet und mit dem vorliegenden Bestandsverfahren verglichen. Im Anschluss an die prototypische Inbetriebnahme wird dieser Vergleich durch die Feldtests abgeschlossen. Entsprechende Ergebnisse zeigen das große Potential der Entwicklung hinsichtlich der Reduktion von Kriterien wie Emissionen oder Wartezeiten und gleichzeitig den Handlungsbedarf für eine standardmäßige Nutzung.}}, author = {{Malena, Kevin}}, isbn = {{978-3-947647-41-5}}, keywords = {{Traffic Light System Control, Model Predictive Control}}, pages = {{207}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Konzipierung, Analyse und Realumsetzung eines mehrstufigen modellprädiktiven Lichtsignalanlagenregelungssystems}}}, doi = {{10.17619/UNIPB/1-2021}}, volume = {{422}}, year = {{2024}}, } @inproceedings{56660, abstract = {{In a successful dialogue in general and a successful explanation in specific, partners need to account for both, the task model (what is relevant for the task) and the partner model (what one can con- tribute). The phenomenon of coupling between task and the partner model becomes especially interesting in the context of Human– Robot Interaction where humans have to deal with unknown ca- pabilities of the robot, which can momentarily be perceived when the robot is unable to contribute to the task. Following research on the path over manner prominence in an action [31–33], a robot ex- plained actions to a human by emphasizing two aspects – the path ("where" component) and the manner ("how" component). On criti- cal trials, the robot occasionally omitted one of these components where participants sought missing information for the path or the manner. Participants’ information-seeking and gaze behaviour were analysed. Analysis confirms the initial predictions for, a) task model (path over manner prominence), i.e., earlier information-seeking for path-missing than manner-missing trials, and b) partner model, i.e., while information-seeking is predominantly tied to the attention on the robot’s face, when robot fails to provide resolution, attention shifts more often towards its torso – a behavior likely to indicate an exploration of the robot’s capabilities. An individual-level anal- ysis further confirms that the intra-individual variation in the task model is partly influenced by the perceived capability of the robot.}}, author = {{Singh, Amit and Rohlfing, Katharina J.}}, booktitle = {{Proceedings of 26th ACM International Conference on Multimodal Interaction (ICMI 2024)}}, keywords = {{Explanation, Scaffolding, Eyetracking, Partner Model, HRI}}, location = {{San Jose, Costa Rica}}, title = {{{Coupling of Task and Partner Model: Investigating the Intra-Individual Variability in Gaze during Human–Robot Explanatory Dialogue}}}, doi = {{10.1145/3686215.3689202}}, year = {{2024}}, } @inproceedings{56918, abstract = {{Joint value creation of organizations in ecosystems have a high failure rate, stressing the need for tools that enable the alignment of business models through visual inquiry. However, existing visual inquiry tools rarely consider recent design knowledge or ecosystem understanding. This leads to dissatisfied users and impedes the full realization of ecosystems’ potential. This short paper proposes an archaeological design science approach for enhancing the design of visual inquiry tools (e.g., a canvas) for ecosystems. Preliminary findings reveal 24 relevant artifacts, and shortcomings in the creation of conceptual models and rigorous evaluations. The proposed research process aims to develop design principles for more effective tools to bridge the gap between visual inquiry tools and ecosystems. This research contributes to design science research by reutilizing design knowledge and further developing the archaeological design approach. It also offers valuable information to practitioners about existing business model tools for the creation of ecosystems.}}, author = {{Vorbohle, Christian}}, booktitle = {{Proceedings of the Thirty-Second European Conference on Information Systems (ECIS 2024)}}, keywords = {{Design Science Research, Design Archaeology, Canvas Analysis, Business Model Tools}}, location = {{Paphos, Cyprus}}, title = {{{Bridging Boundaries: Enhancing Visual Inquiry Tools for Ecosystems through Design Archaeology}}}, year = {{2024}}, } @inproceedings{57085, abstract = {{We propose an approach for simultaneous diarization and separation of meeting data. It consists of a complex Angular Central Gaussian Mixture Model (cACGMM) for speech source separation, and a von-Mises-Fisher Mixture Model (VMFMM) for diarization in a joint statistical framework. Through the integration, both spatial and spectral information are exploited for diarization and separation. We also develop a method for counting the number of active speakers in a segment of a meeting to support block-wise processing. While the total number of speakers in a meeting may be known, it is usually not known on a per-segment level. With the proposed speaker counting, joint diarization and source separation can be done segment-by-segment, and the permutation problem across segments is solved, thus allowing for block-online processing in the future. Experimental results on the LibriCSS meeting corpus show that the integrated approach outperforms a cascaded approach of diarization and speech enhancement in terms of WER, both on a per-segment and on a per-meeting level.}}, author = {{Cord-Landwehr, Tobias and Boeddeker, Christoph and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, keywords = {{diarization, source separation, mixture model, meeting}}, location = {{Hyderabad, India}}, title = {{{Simultaneous Diarization and Separation of Meetings through the Integration of Statistical Mixture Models}}}, doi = {{10.1109/ICASSP49660.2025.10888445}}, year = {{2024}}, } @inproceedings{53811, abstract = {{Persistent security challenges plague DevOps teams due to a deficiency in expertise regarding security tools and methods, as evidenced by frequent security incidents. Existing maturity models fail to adequately address the specific needs of DevOps teams. In response, this paper proposes "Security Belts," a novel maturity model inspired by martial arts ranking systems. This model aims to assist DevOps teams in enhancing their security capabilities by providing a structured approach, starting with fundamental activities and progressing to more advanced techniques. Drawing from the experiences of monitoring 21 teams, the paper presents lessons learned and offers actionable advice for refining maturity models tailored to software quality improvement.}}, author = {{Taaibi, Samira and Dziwok, Stefan and Hermerschmidt, Lars and Koch, Thorsten and Merschjohann, Sven and Vollmary, Mark}}, booktitle = {{AMCIS 2024 Proceedings. 13.}}, keywords = {{Software security, maturity model}}, location = {{Salt Lake City}}, title = {{{Security Belts: A Maturity Model for DevOps Teams to Increase the Software Security of their Product - An Experience Report}}}, year = {{2024}}, } @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}}, }