[{"language":[{"iso":"eng"}],"keyword":["Schrödinger equation","Fractional Laplacian","Dispersive estimates","Strichartz estimates","Real hyperbolic spaces","Homogeneous trees"],"external_id":{"arxiv":["2412.00780"]},"abstract":[{"lang":"eng","text":"We investigate dispersive and Strichartz estimates for the Schrödinger equation involving the fractional Laplacian in real hyperbolic spaces and their discrete analogues, homogeneous trees. Due to the Knapp phenomenon, the Strichartz estimates on Euclidean spaces for the fractional Laplacian exhibit loss of derivatives. A similar phenomenon appears on real hyperbolic spaces. However, such a loss disappears on homogeneous trees, due to the triviality of the estimates for small times."}],"publication":"Journal of Differential Equations","title":"The Schrödinger equation with fractional Laplacian on hyperbolic spaces and homogeneous trees","date_created":"2024-12-04T16:21:38Z","publisher":"Elsevier","year":"2026","user_id":"109467","department":[{"_id":"10"},{"_id":"548"}],"project":[{"_id":"356","name":"TRR 358 - B02: TRR 358 - Spektraltheorie in höherem Rang und unendlichem Volumen (Teilprojekt B02)"}],"_id":"57580","status":"public","type":"journal_article","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1016/j.jde.2025.114065"}],"doi":"10.1016/j.jde.2025.114065","author":[{"first_name":"Guendalina","id":"109467","full_name":"Palmirotta, Guendalina","last_name":"Palmirotta"},{"first_name":"Yannick","full_name":"Sire, Yannick","last_name":"Sire"},{"last_name":"Anker","full_name":"Anker, Jean-Philippe","first_name":"Jean-Philippe"}],"date_updated":"2026-03-30T12:03:37Z","oa":"1","citation":{"ieee":"G. Palmirotta, Y. Sire, and J.-P. Anker, “The Schrödinger equation with fractional Laplacian on hyperbolic spaces and homogeneous trees,” <i>Journal of Differential Equations</i>, 2026, doi: <a href=\"https://doi.org/10.1016/j.jde.2025.114065\">10.1016/j.jde.2025.114065</a>.","chicago":"Palmirotta, Guendalina, Yannick Sire, and Jean-Philippe Anker. “The Schrödinger Equation with Fractional Laplacian on Hyperbolic Spaces and Homogeneous Trees.” <i>Journal of Differential Equations</i>, 2026. <a href=\"https://doi.org/10.1016/j.jde.2025.114065\">https://doi.org/10.1016/j.jde.2025.114065</a>.","apa":"Palmirotta, G., Sire, Y., &#38; Anker, J.-P. (2026). The Schrödinger equation with fractional Laplacian on hyperbolic spaces and homogeneous trees. <i>Journal of Differential Equations</i>. <a href=\"https://doi.org/10.1016/j.jde.2025.114065\">https://doi.org/10.1016/j.jde.2025.114065</a>","ama":"Palmirotta G, Sire Y, Anker J-P. The Schrödinger equation with fractional Laplacian on hyperbolic spaces and homogeneous trees. <i>Journal of Differential Equations</i>. Published online 2026. doi:<a href=\"https://doi.org/10.1016/j.jde.2025.114065\">10.1016/j.jde.2025.114065</a>","short":"G. Palmirotta, Y. Sire, J.-P. Anker, Journal of Differential Equations (2026).","mla":"Palmirotta, Guendalina, et al. “The Schrödinger Equation with Fractional Laplacian on Hyperbolic Spaces and Homogeneous Trees.” <i>Journal of Differential Equations</i>, Elsevier, 2026, doi:<a href=\"https://doi.org/10.1016/j.jde.2025.114065\">10.1016/j.jde.2025.114065</a>.","bibtex":"@article{Palmirotta_Sire_Anker_2026, title={The Schrödinger equation with fractional Laplacian on hyperbolic spaces and homogeneous trees}, DOI={<a href=\"https://doi.org/10.1016/j.jde.2025.114065\">10.1016/j.jde.2025.114065</a>}, journal={Journal of Differential Equations}, publisher={Elsevier}, author={Palmirotta, Guendalina and Sire, Yannick and Anker, Jean-Philippe}, year={2026} }"},"related_material":{"link":[{"url":"https://www.sciencedirect.com/science/article/pii/S0022039625010927?via%3Dihub","relation":"confirmation"}]},"publication_status":"published"},{"user_id":"72849","department":[{"_id":"196"}],"_id":"60680","language":[{"iso":"eng"}],"keyword":["Causal Machine Learning","Causality in Time Series","Causal Discovery","Human-Machine  Collaboration"],"type":"conference","status":"public","abstract":[{"lang":"eng","text":"Classical machine learning techniques often struggle with overfitting and unreliable predictions when exposed to novel conditions. Introducing causality into the modelling process offers a promising way to mitigate these challenges by enhancing predictive robustness. However, constructing an initial causal graph manually using domain knowledge is time-consuming, particularly in complex time series with numerous variables. To address this, causal discovery algorithms can provide a preliminary causal structure that domain experts can refine. This study investigates causal feature selection with domain knowledge using a data center system as an example. We use simulated time-series data to compare \r\ndifferent causal feature selection with traditional machine-learning feature selection methods. Our results show that predictions based on causal features are more robust compared to those derived from traditional methods. These findings underscore the potential of combining causal discovery algorithms with human expertise to improve machine learning applications."}],"date_created":"2025-07-21T07:52:03Z","author":[{"id":"105506","full_name":"Zapata Gonzalez, David Ricardo","last_name":"Zapata Gonzalez","first_name":"David Ricardo"},{"full_name":"Meyer, Marcel","id":"105120","last_name":"Meyer","first_name":"Marcel"},{"last_name":"Müller","id":"72849","full_name":"Müller, Oliver","first_name":"Oliver"}],"date_updated":"2025-07-22T06:30:37Z","main_file_link":[{"url":"https://aisel.aisnet.org/ecis2025/bus_analytics/bus_analytics/2/"}],"conference":{"name":"European Conference on Information Systems","start_date":"16.06.2025","end_date":"18.06.2025","location":"Amman, Jordan"},"title":"Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems","citation":{"ieee":"D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems,” presented at the European Conference on Information Systems, Amman, Jordan, 2025.","chicago":"Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Bridging the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems,” 2025.","ama":"Zapata Gonzalez DR, Meyer M, Müller O. Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems. In: ; 2025.","apa":"Zapata Gonzalez, D. R., Meyer, M., &#38; Müller, O. (2025). <i>Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems</i>. European Conference on Information Systems, Amman, Jordan.","short":"D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: 2025.","mla":"Zapata Gonzalez, David Ricardo, et al. <i>Bridging the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative Modelling of Dynamical Systems</i>. 2025.","bibtex":"@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Bridging the gap between data-driven and theory-driven modelling – leveraging causal machine learning for integrative modelling of dynamical systems}, author={Zapata Gonzalez, David Ricardo and Meyer, Marcel and Müller, Oliver}, year={2025} }"},"year":"2025"},{"has_accepted_license":"1","citation":{"ieee":"T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks,” presented at the 57th Asilomar Conference on Signals, Systems, and Computers, 2023.","chicago":"Gburrek, Tobias, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks.” In <i>Proc. Asilomar Conference on Signals, Systems, and Computers</i>, 2023.","ama":"Gburrek T, Schmalenstroeer J, Haeb-Umbach R. Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks. In: <i>Proc. Asilomar Conference on Signals, Systems, and Computers</i>. ; 2023.","mla":"Gburrek, Tobias, et al. “Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks.” <i>Proc. Asilomar Conference on Signals, Systems, and Computers</i>, 2023.","bibtex":"@inproceedings{Gburrek_Schmalenstroeer_Haeb-Umbach_2023, title={Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks}, booktitle={Proc. Asilomar Conference on Signals, Systems, and Computers}, author={Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2023} }","short":"T. Gburrek, J. Schmalenstroeer, R. Haeb-Umbach, in: Proc. Asilomar Conference on Signals, Systems, and Computers, 2023.","apa":"Gburrek, T., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2023). Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks. <i>Proc. Asilomar Conference on Signals, Systems, and Computers</i>. 57th Asilomar Conference on Signals, Systems, and Computers."},"author":[{"first_name":"Tobias","id":"44006","full_name":"Gburrek, Tobias","last_name":"Gburrek"},{"first_name":"Joerg","full_name":"Schmalenstroeer, Joerg","id":"460","last_name":"Schmalenstroeer"},{"full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"oa":"1","date_updated":"2023-11-22T07:58:49Z","conference":{"name":"57th Asilomar Conference on Signals, Systems, and Computers","start_date":"2023-10-31","end_date":"2023-11-01"},"type":"conference","status":"public","user_id":"460","department":[{"_id":"54"}],"_id":"49109","file_date_updated":"2023-11-22T07:58:49Z","quality_controlled":"1","year":"2023","date_created":"2023-11-22T07:52:29Z","title":"Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks","publication":"Proc. Asilomar Conference on Signals, Systems, and Computers","file":[{"relation":"main_file","content_type":"application/pdf","file_name":"asilomar.pdf","access_level":"open_access","file_id":"49110","file_size":212317,"creator":"schmalen","date_created":"2023-11-22T07:51:18Z","date_updated":"2023-11-22T07:58:49Z"}],"abstract":[{"lang":"eng","text":"We propose a diarization system, that estimates “who spoke when” based on spatial information, to be used as a front-end of a meeting transcription system running on the signals gathered from an acoustic sensor network (ASN). Although the\r\nspatial distribution of the microphones is advantageous, exploiting the spatial diversity for diarization and signal enhancement is challenging, because the microphones’ positions are typically unknown, and the recorded signals are initially unsynchronized in general. Here, we approach these issues by first blindly synchronizing the signals and then estimating time differences of arrival (TDOAs). The TDOA information is exploited to estimate the speakers’ activity, even in the presence of multiple speakers being simultaneously active. This speaker activity information serves as a guide for a spatial mixture model, on which basis the individual speaker’s signals are extracted via beamforming. Finally, the extracted signals are forwarded to a speech recognizer. Additionally, a novel initialization scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments conducted on real recordings from the LibriWASN data set have shown that our proposed system is advantageous compared to a system using a spatial mixture model, which does not make use\r\nof external diarization information."}],"language":[{"iso":"eng"}],"ddc":["004"],"keyword":["Diarization","time difference of arrival","ad-hoc acoustic sensor network","meeting transcription"]},{"keyword":["Algorithm aversion","Time series","Decision making","Advice taking","Forecasting"],"language":[{"iso":"eng"}],"_id":"37312","department":[{"_id":"196"}],"user_id":"51271","abstract":[{"lang":"eng","text":"Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners."}],"status":"public","publication":"Hawaii International Conference on System Sciences","type":"conference","title":"Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time","conference":{"name":"Hawaii International Conference on System Sciences"},"main_file_link":[{"open_access":"1","url":"https://scholarspace.manoa.hawaii.edu/items/62b58ddc-895c-48c3-8194-522a1758a26f"}],"oa":"1","date_updated":"2024-01-10T09:52:59Z","author":[{"first_name":"Dirk","last_name":"Leffrang","orcid":"0000-0001-9004-2391","full_name":"Leffrang, Dirk","id":"51271"},{"first_name":"Kevin","full_name":"Bösch, Kevin","last_name":"Bösch"},{"first_name":"Oliver","id":"72849","full_name":"Müller, Oliver","last_name":"Müller"}],"date_created":"2023-01-18T10:53:51Z","year":"2023","citation":{"ama":"Leffrang D, Bösch K, Müller O. Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time. In: <i>Hawaii International Conference on System Sciences</i>. ; 2023.","ieee":"D. Leffrang, K. Bösch, and O. Müller, “Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time,” presented at the Hawaii International Conference on System Sciences, 2023.","chicago":"Leffrang, Dirk, Kevin Bösch, and Oliver Müller. “Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” In <i>Hawaii International Conference on System Sciences</i>, 2023.","apa":"Leffrang, D., Bösch, K., &#38; Müller, O. (2023). Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time. <i>Hawaii International Conference on System Sciences</i>. Hawaii International Conference on System Sciences.","mla":"Leffrang, Dirk, et al. “Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” <i>Hawaii International Conference on System Sciences</i>, 2023.","bibtex":"@inproceedings{Leffrang_Bösch_Müller_2023, title={Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}, booktitle={Hawaii International Conference on System Sciences}, author={Leffrang, Dirk and Bösch, Kevin and Müller, Oliver}, year={2023} }","short":"D. Leffrang, K. Bösch, O. Müller, in: Hawaii International Conference on System Sciences, 2023."}},{"_id":"50479","project":[{"grant_number":"860801","_id":"410","name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale"}],"department":[{"_id":"34"}],"user_id":"83392","series_title":" Lecture Notes in Computer Science","file_date_updated":"2024-01-13T11:25:48Z","type":"conference","editor":[{"last_name":"R. Payne","full_name":"R. Payne, Terry","first_name":"Terry"},{"full_name":"Presutti, Valentina","last_name":"Presutti","first_name":"Valentina"},{"first_name":"Guilin","full_name":"Qi, Guilin","last_name":"Qi"},{"first_name":"María","full_name":"Poveda-Villalón, María","last_name":"Poveda-Villalón"},{"first_name":"Giorgos","last_name":"Stoilos","full_name":"Stoilos, Giorgos"},{"full_name":"Hollink, Laura","last_name":"Hollink","first_name":"Laura"},{"full_name":"Kaoudi, Zoi","last_name":"Kaoudi","first_name":"Zoi"},{"first_name":"Gong","full_name":"Cheng, Gong","last_name":"Cheng"},{"first_name":"Juanzi","last_name":"Li","full_name":"Li, Juanzi"}],"status":"public","date_updated":"2024-01-13T11:48:28Z","volume":14265,"author":[{"full_name":"Qudus, Umair","last_name":"Qudus","first_name":"Umair"},{"first_name":"Michael","full_name":"Röder, Michael","last_name":"Röder"},{"full_name":"Kirrane, Sabrina","last_name":"Kirrane","first_name":"Sabrina"},{"last_name":"Ngomo","full_name":"Ngomo, Axel-Cyrille Ngonga","first_name":"Axel-Cyrille Ngonga"}],"doi":"10.1007/978-3-031-47240-4_25","conference":{"end_date":"2023-11-10","location":"Athens, Greece","name":"The Semantic Web – ISWC 2023","start_date":"2023-11-06"},"has_accepted_license":"1","publication_identifier":{"issn":["0302-9743","1611-3349"],"isbn":["9783031472398","9783031472404"]},"publication_status":"published","place":"Cham","intvolume":"     14265","page":"465–483","jel":["C"],"citation":{"apa":"Qudus, U., Röder, M., Kirrane, S., &#38; Ngomo, A.-C. N. (2023). TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs. In T. R. Payne, V. Presutti, G. Qi, M. Poveda-Villalón, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, &#38; J. Li (Eds.), <i>The Semantic Web – ISWC 2023</i> (Vol. 14265, pp. 465–483). Springer, Cham. <a href=\"https://doi.org/10.1007/978-3-031-47240-4_25\">https://doi.org/10.1007/978-3-031-47240-4_25</a>","bibtex":"@inproceedings{Qudus_Röder_Kirrane_Ngomo_2023, place={Cham}, series={ Lecture Notes in Computer Science}, title={TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs}, volume={14265}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-47240-4_25\">10.1007/978-3-031-47240-4_25</a>}, booktitle={The Semantic Web – ISWC 2023}, publisher={Springer, Cham}, author={Qudus, Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga}, editor={R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón, María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong and Li, Juanzi}, year={2023}, pages={465–483}, collection={ Lecture Notes in Computer Science} }","short":"U. Qudus, M. Röder, S. Kirrane, A.-C.N. Ngomo, in: T. R. Payne, V. Presutti, G. Qi, M. Poveda-Villalón, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, J. Li (Eds.), The Semantic Web – ISWC 2023, Springer, Cham, Cham, 2023, pp. 465–483.","mla":"Qudus, Umair, et al. “TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs.” <i>The Semantic Web – ISWC 2023</i>, edited by Terry R. Payne et al., vol. 14265, Springer, Cham, 2023, pp. 465–483, doi:<a href=\"https://doi.org/10.1007/978-3-031-47240-4_25\">10.1007/978-3-031-47240-4_25</a>.","ama":"Qudus U, Röder M, Kirrane S, Ngomo A-CN. TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs. In: R. Payne T, Presutti V, Qi G, et al., eds. <i>The Semantic Web – ISWC 2023</i>. Vol 14265.  Lecture Notes in Computer Science. Springer, Cham; 2023:465–483. doi:<a href=\"https://doi.org/10.1007/978-3-031-47240-4_25\">10.1007/978-3-031-47240-4_25</a>","chicago":"Qudus, Umair, Michael Röder, Sabrina Kirrane, and Axel-Cyrille Ngonga Ngomo. “TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs.” In <i>The Semantic Web – ISWC 2023</i>, edited by Terry R. Payne, Valentina Presutti, Guilin Qi, María Poveda-Villalón, Giorgos Stoilos, Laura Hollink, Zoi Kaoudi, Gong Cheng, and Juanzi Li, 14265:465–483.  Lecture Notes in Computer Science. Cham: Springer, Cham, 2023. <a href=\"https://doi.org/10.1007/978-3-031-47240-4_25\">https://doi.org/10.1007/978-3-031-47240-4_25</a>.","ieee":"U. Qudus, M. Röder, S. Kirrane, and A.-C. N. Ngomo, “TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs,” in <i>The Semantic Web – ISWC 2023</i>, Athens, Greece, 2023, vol. 14265, pp. 465–483, doi: <a href=\"https://doi.org/10.1007/978-3-031-47240-4_25\">10.1007/978-3-031-47240-4_25</a>."},"keyword":["temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs"],"ddc":["006"],"language":[{"iso":"eng"}],"publication":"The Semantic Web – ISWC 2023","abstract":[{"lang":"eng","text":"Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TEMPORALFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC."}],"file":[{"content_type":"application/pdf","success":1,"relation":"main_file","date_updated":"2024-01-13T11:25:48Z","date_created":"2024-01-13T11:25:48Z","creator":"uqudus","file_size":1944818,"access_level":"closed","file_id":"50480","file_name":"ISWC 2023 TemporalFC-A Temporal Fact Checking approach over Knowledge Graphs.pdf"}],"publisher":"Springer, Cham","date_created":"2024-01-13T11:22:15Z","title":"TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs","year":"2023"},{"language":[{"iso":"eng"}],"keyword":["Explainable AI (XAI)","machine learning","interpretability","real estate appraisal","framework","taxonomy"],"user_id":"77066","department":[{"_id":"195"},{"_id":"196"}],"_id":"45299","status":"public","abstract":[{"text":"Many applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system’s explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity.","lang":"eng"}],"type":"journal_article","publication":"Journal of Decision Systems","main_file_link":[{"url":"https://www.tandfonline.com/doi/full/10.1080/12460125.2023.2207268"}],"doi":"10.1080/12460125.2023.2207268","title":"HIEF: a holistic interpretability and explainability framework","author":[{"last_name":"Kucklick","full_name":"Kucklick, Jan-Peter","id":"77066","first_name":"Jan-Peter"}],"date_created":"2023-05-26T05:04:45Z","publisher":"Taylor & Francis","date_updated":"2023-05-26T05:08:36Z","citation":{"mla":"Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability Framework.” <i>Journal of Decision Systems</i>, Taylor &#38; Francis, 2023, pp. 1–41, doi:<a href=\"https://doi.org/10.1080/12460125.2023.2207268\">10.1080/12460125.2023.2207268</a>.","short":"J.-P. Kucklick, Journal of Decision Systems (2023) 1–41.","bibtex":"@article{Kucklick_2023, title={HIEF: a holistic interpretability and explainability framework}, DOI={<a href=\"https://doi.org/10.1080/12460125.2023.2207268\">10.1080/12460125.2023.2207268</a>}, journal={Journal of Decision Systems}, publisher={Taylor &#38; Francis}, author={Kucklick, Jan-Peter}, year={2023}, pages={1–41} }","apa":"Kucklick, J.-P. (2023). HIEF: a holistic interpretability and explainability framework. <i>Journal of Decision Systems</i>, 1–41. <a href=\"https://doi.org/10.1080/12460125.2023.2207268\">https://doi.org/10.1080/12460125.2023.2207268</a>","chicago":"Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability Framework.” <i>Journal of Decision Systems</i>, 2023, 1–41. <a href=\"https://doi.org/10.1080/12460125.2023.2207268\">https://doi.org/10.1080/12460125.2023.2207268</a>.","ieee":"J.-P. Kucklick, “HIEF: a holistic interpretability and explainability framework,” <i>Journal of Decision Systems</i>, pp. 1–41, 2023, doi: <a href=\"https://doi.org/10.1080/12460125.2023.2207268\">10.1080/12460125.2023.2207268</a>.","ama":"Kucklick J-P. HIEF: a holistic interpretability and explainability framework. <i>Journal of Decision Systems</i>. Published online 2023:1-41. doi:<a href=\"https://doi.org/10.1080/12460125.2023.2207268\">10.1080/12460125.2023.2207268</a>"},"page":"1-41","year":"2023","publication_status":"published","publication_identifier":{"issn":["1246-0125","2116-7052"]}},{"title":"Visual Interpretability of Image-based Real Estate Appraisal","conference":{"start_date":"2022-01-03","name":"Hawaii International Conference on System Science (HICSS)","location":"Virtual","end_date":"2022-01-07"},"main_file_link":[{"url":"https://scholarspace.manoa.hawaii.edu/bitstream/10125/79519/0149.pdf","open_access":"1"}],"oa":"1","date_updated":"2022-01-06T06:57:40Z","date_created":"2021-11-17T07:08:15Z","author":[{"id":"77066","full_name":"Kucklick, Jan-Peter","last_name":"Kucklick","first_name":"Jan-Peter"}],"year":"2022","citation":{"apa":"Kucklick, J.-P. (2022). Visual Interpretability of Image-based Real Estate Appraisal. <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>. Hawaii International Conference on System Science (HICSS), Virtual.","mla":"Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.” <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>, 2022.","bibtex":"@inproceedings{Kucklick_2022, title={Visual Interpretability of Image-based Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference on System Sciences (HICSS-55)}, author={Kucklick, Jan-Peter}, year={2022} }","short":"J.-P. Kucklick, in: 55th Annual Hawaii International Conference on System Sciences (HICSS-55), 2022.","ama":"Kucklick J-P. Visual Interpretability of Image-based Real Estate Appraisal. In: <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>. ; 2022.","chicago":"Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.” In <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>, 2022.","ieee":"J.-P. Kucklick, “Visual Interpretability of Image-based Real Estate Appraisal,” presented at the Hawaii International Conference on System Science (HICSS), Virtual, 2022."},"keyword":["Explainable Artificial Intelligence (XAI)","Regression Activation Maps","Real Estate Appraisal","Convolutional Block Attention Module","Computer Vision"],"language":[{"iso":"eng"}],"_id":"27506","department":[{"_id":"195"},{"_id":"196"}],"user_id":"77066","abstract":[{"lang":"eng","text":"Explainability for machine learning gets more and more important in high-stakes decisions like real estate appraisal. While traditional hedonic house pricing models are fed with hard information based on housing attributes, recently also soft information has been incorporated to increase the predictive performance. This soft information can be extracted from image data by complex models like Convolutional Neural Networks (CNNs). However, these are intransparent which excludes their use for high-stakes financial decisions. To overcome this limitation, we examine if a two-stage modeling approach can provide explainability. We combine visual interpretability by Regression Activation Maps (RAM) for the CNN and a linear regression for the overall prediction. Our experiments are based on 62.000 family homes in Philadelphia and the results indicate that the CNN learns aspects related to vegetation and quality aspects of the house from exterior images, improving the predictive accuracy of real estate appraisal by up to 5.4%."}],"status":"public","publication":"55th Annual Hawaii International Conference on System Sciences (HICSS-55)","type":"conference"},{"citation":{"chicago":"Heuwinkel, Tim, Jan-Peter Kucklick, and Oliver Müller. “Using Geolocated Text to Quantify Location in Real Estate Appraisal.” In <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>, 2022.","ieee":"T. Heuwinkel, J.-P. Kucklick, and O. Müller, “Using Geolocated Text to Quantify Location in Real Estate Appraisal,” presented at the Hawaii International Conference on System Science (HICSS), Virtual, 2022.","ama":"Heuwinkel T, Kucklick J-P, Müller O. Using Geolocated Text to Quantify Location in Real Estate Appraisal. In: <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>. ; 2022.","apa":"Heuwinkel, T., Kucklick, J.-P., &#38; Müller, O. (2022). Using Geolocated Text to Quantify Location in Real Estate Appraisal. <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>. Hawaii International Conference on System Science (HICSS), Virtual.","bibtex":"@inproceedings{Heuwinkel_Kucklick_Müller_2022, title={Using Geolocated Text to Quantify Location in Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference on System Sciences (HICSS-55)}, author={Heuwinkel, Tim and Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }","short":"T. Heuwinkel, J.-P. Kucklick, O. Müller, in: 55th Annual Hawaii International Conference on System Sciences (HICSS-55), 2022.","mla":"Heuwinkel, Tim, et al. “Using Geolocated Text to Quantify Location in Real Estate Appraisal.” <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>, 2022."},"year":"2022","main_file_link":[{"url":"https://scholarspace.manoa.hawaii.edu/bitstream/10125/80039/0561.pdf","open_access":"1"}],"conference":{"start_date":"2022-01-03","name":"Hawaii International Conference on System Science (HICSS)","location":"Virtual","end_date":"2022-01-07"},"title":"Using Geolocated Text to Quantify Location in Real Estate Appraisal","date_created":"2021-11-17T07:12:03Z","author":[{"first_name":"Tim","full_name":"Heuwinkel, Tim","last_name":"Heuwinkel"},{"first_name":"Jan-Peter","full_name":"Kucklick, Jan-Peter","id":"77066","last_name":"Kucklick"},{"first_name":"Oliver","last_name":"Müller","full_name":"Müller, Oliver","id":"72849"}],"date_updated":"2022-01-06T06:57:40Z","oa":"1","status":"public","abstract":[{"lang":"eng","text":"Accurate real estate appraisal is essential in decision making processes of financial institutions, governments, and trending real estate platforms like Zillow. One of the most important factors of a property’s value is its location. However, creating accurate quantifications of location remains a challenge. While traditional approaches rely on Geographical Information Systems (GIS), recently unstructured data in form of images was incorporated in the appraisal process, but text data remains an untapped reservoir. Our study shows that using text data in form of geolocated Wikipedia articles can increase predictive performance over traditional GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to automatically extract geographically weighted vector representations for text is established and used alongside traditional structural housing features to make predictions and to uncover local patterns on sale price for real estate transactions between 2015 and 2020 in Allegheny County, Pennsylvania."}],"type":"conference","publication":"55th Annual Hawaii International Conference on System Sciences (HICSS-55)","language":[{"iso":"eng"}],"keyword":["Real Estate Appraisal","Text Regression","Natural Language Processing (NLP)","Location Intelligence","Wikipedia"],"user_id":"77066","department":[{"_id":"195"}],"_id":"27507"},{"abstract":[{"text":"Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.","lang":"eng"}],"status":"public","type":"journal_article","publication":"ACM Transactions on Management Information Systems","article_type":"original","keyword":["Interpretability","Convolutional Neural Network","Accuracy-Interpretability Trade-Of","Real Estate Appraisal","Hedonic Pricing","Grad-Ram"],"language":[{"iso":"eng"}],"_id":"35620","user_id":"77066","department":[{"_id":"195"},{"_id":"196"}],"year":"2022","citation":{"chicago":"Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate Appraisal.” <i>ACM Transactions on Management Information Systems</i>, 2022. <a href=\"https://doi.org/10.1145/3567430\">https://doi.org/10.1145/3567430</a>.","ieee":"J.-P. Kucklick and O. Müller, “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,” <i>ACM Transactions on Management Information Systems</i>, 2022, doi: <a href=\"https://doi.org/10.1145/3567430\">10.1145/3567430</a>.","ama":"Kucklick J-P, Müller O. Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal. <i>ACM Transactions on Management Information Systems</i>. Published online 2022. doi:<a href=\"https://doi.org/10.1145/3567430\">10.1145/3567430</a>","bibtex":"@article{Kucklick_Müller_2022, title={Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal}, DOI={<a href=\"https://doi.org/10.1145/3567430\">10.1145/3567430</a>}, journal={ACM Transactions on Management Information Systems}, publisher={Association for Computing Machinery (ACM)}, author={Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }","mla":"Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate Appraisal.” <i>ACM Transactions on Management Information Systems</i>, Association for Computing Machinery (ACM), 2022, doi:<a href=\"https://doi.org/10.1145/3567430\">10.1145/3567430</a>.","short":"J.-P. Kucklick, O. Müller, ACM Transactions on Management Information Systems (2022).","apa":"Kucklick, J.-P., &#38; Müller, O. (2022). Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal. <i>ACM Transactions on Management Information Systems</i>. <a href=\"https://doi.org/10.1145/3567430\">https://doi.org/10.1145/3567430</a>"},"publication_status":"published","publication_identifier":{"issn":["2158-656X","2158-6578"]},"title":"Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal","main_file_link":[{"url":"https://dl.acm.org/doi/pdf/10.1145/3567430"}],"doi":"10.1145/3567430","date_updated":"2023-01-10T05:20:18Z","publisher":"Association for Computing Machinery (ACM)","date_created":"2023-01-10T05:16:02Z","author":[{"first_name":"Jan-Peter","id":"77066","full_name":"Kucklick, Jan-Peter","last_name":"Kucklick"},{"first_name":"Oliver","id":"72849","full_name":"Müller, Oliver","last_name":"Müller"}]},{"title":"Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments","publisher":"Springer International Publishing","date_created":"2022-10-20T15:06:39Z","year":"2022","quality_controlled":"1","keyword":["Traffic control","Traffic estimation","Real-time","MPC","Fuzzy","Isolated intersection","Networked intersection","Sensor fusion"],"language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed."}],"publication":"Communications in Computer and Information Science","doi":"10.1007/978-3-031-17098-0_12","date_updated":"2026-01-26T08:49:52Z","author":[{"first_name":"Kevin","full_name":"Malena, Kevin","id":"36303","orcid":"0000-0003-1183-4679","last_name":"Malena"},{"first_name":"Christopher","last_name":"Link","id":"38249","full_name":"Link, Christopher"},{"last_name":"Bußemas","id":"51118","full_name":"Bußemas, Leon","first_name":"Leon"},{"id":"17793","full_name":"Gausemeier, Sandra","last_name":"Gausemeier","first_name":"Sandra"},{"first_name":"Ansgar","id":"552","full_name":"Trächtler, Ansgar","last_name":"Trächtler"}],"volume":1612,"place":"Cham","citation":{"chicago":"Malena, Kevin, Christopher Link, Leon Bußemas, Sandra Gausemeier, and Ansgar Trächtler. “Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments.” In <i>Communications in Computer and Information Science</i>, edited by Cornel Klein, Mathias Jarke, Markus Helfert, Karsten Berns, and Oleg Gusikhin, 1612:232–254. Communications in Computer and Information Science. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-031-17098-0_12\">https://doi.org/10.1007/978-3-031-17098-0_12</a>.","ieee":"K. Malena, C. Link, L. Bußemas, S. Gausemeier, and A. Trächtler, “Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments,” in <i>Communications in Computer and Information Science</i>, vol. 1612, C. Klein, M. Jarke, M. Helfert, K. Berns, and O. Gusikhin, Eds. Cham: Springer International Publishing, 2022, pp. 232–254.","ama":"Malena K, Link C, Bußemas L, Gausemeier S, Trächtler A. Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments. In: Klein C, Jarke M, Helfert M, Berns K, Gusikhin O, eds. <i>Communications in Computer and Information Science</i>. Vol 1612. Communications in Computer and Information Science. Springer International Publishing; 2022:232–254. doi:<a href=\"https://doi.org/10.1007/978-3-031-17098-0_12\">10.1007/978-3-031-17098-0_12</a>","apa":"Malena, K., Link, C., Bußemas, L., Gausemeier, S., &#38; Trächtler, A. (2022). Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments. In C. Klein, M. Jarke, M. Helfert, K. Berns, &#38; O. Gusikhin (Eds.), <i>Communications in Computer and Information Science</i> (Vol. 1612, pp. 232–254). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-17098-0_12\">https://doi.org/10.1007/978-3-031-17098-0_12</a>","short":"K. Malena, C. Link, L. Bußemas, S. Gausemeier, A. Trächtler, in: C. Klein, M. Jarke, M. Helfert, K. Berns, O. Gusikhin (Eds.), Communications in Computer and Information Science, Springer International Publishing, Cham, 2022, pp. 232–254.","bibtex":"@inbook{Malena_Link_Bußemas_Gausemeier_Trächtler_2022, place={Cham}, series={Communications in Computer and Information Science}, title={Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments}, volume={1612}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-17098-0_12\">10.1007/978-3-031-17098-0_12</a>}, booktitle={Communications in Computer and Information Science}, publisher={Springer International Publishing}, author={Malena, Kevin and Link, Christopher and Bußemas, Leon and Gausemeier, Sandra and Trächtler, Ansgar}, editor={Klein, Cornel and Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}, year={2022}, pages={232–254}, collection={Communications in Computer and Information Science} }","mla":"Malena, Kevin, et al. “Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments.” <i>Communications in Computer and Information Science</i>, edited by Cornel Klein et al., vol. 1612, Springer International Publishing, 2022, pp. 232–254, doi:<a href=\"https://doi.org/10.1007/978-3-031-17098-0_12\">10.1007/978-3-031-17098-0_12</a>."},"intvolume":"      1612","page":"232–254","publication_status":"published","publication_identifier":{"issn":["1865-0929","1865-0937"],"isbn":["9783031170973","9783031170980"]},"related_material":{"record":[{"status":"public","relation":"continues","id":"24159"}]},"_id":"33849","series_title":"Communications in Computer and Information Science","user_id":"552","department":[{"_id":"153"}],"editor":[{"last_name":"Klein","full_name":"Klein, Cornel","first_name":"Cornel"},{"first_name":"Mathias","full_name":"Jarke, Mathias","last_name":"Jarke"},{"first_name":"Markus","full_name":"Helfert, Markus","last_name":"Helfert"},{"first_name":"Karsten","last_name":"Berns","full_name":"Berns, Karsten"},{"last_name":"Gusikhin","full_name":"Gusikhin, Oleg","first_name":"Oleg"}],"status":"public","type":"book_chapter"},{"citation":{"ieee":"S. Lange, C. Hedayat, H. Kuhn, and U. Hilleringmann, “Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization in the Near Field,” in <i>2021 Smart Systems Integration (SSI)</i>, Grenoble, France , 2021.","chicago":"Lange, Sven, Christian Hedayat, Harald Kuhn, and Ulrich Hilleringmann. “Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization in the Near Field.” In <i>2021 Smart Systems Integration (SSI)</i>. Grenoble, France: IEEE, 2021. <a href=\"https://doi.org/10.1109/ssi52265.2021.9466958\">https://doi.org/10.1109/ssi52265.2021.9466958</a>.","ama":"Lange S, Hedayat C, Kuhn H, Hilleringmann U. Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization in the Near Field. In: <i>2021 Smart Systems Integration (SSI)</i>. Grenoble, France: IEEE; 2021. doi:<a href=\"https://doi.org/10.1109/ssi52265.2021.9466958\">10.1109/ssi52265.2021.9466958</a>","mla":"Lange, Sven, et al. “Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization in the Near Field.” <i>2021 Smart Systems Integration (SSI)</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/ssi52265.2021.9466958\">10.1109/ssi52265.2021.9466958</a>.","short":"S. Lange, C. Hedayat, H. Kuhn, U. Hilleringmann, in: 2021 Smart Systems Integration (SSI), IEEE, Grenoble, France, 2021.","bibtex":"@inproceedings{Lange_Hedayat_Kuhn_Hilleringmann_2021, place={Grenoble, France}, title={Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization in the Near Field}, DOI={<a href=\"https://doi.org/10.1109/ssi52265.2021.9466958\">10.1109/ssi52265.2021.9466958</a>}, booktitle={2021 Smart Systems Integration (SSI)}, publisher={IEEE}, author={Lange, Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}, year={2021} }","apa":"Lange, S., Hedayat, C., Kuhn, H., &#38; Hilleringmann, U. (2021). Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization in the Near Field. In <i>2021 Smart Systems Integration (SSI)</i>. Grenoble, France: IEEE. <a href=\"https://doi.org/10.1109/ssi52265.2021.9466958\">https://doi.org/10.1109/ssi52265.2021.9466958</a>"},"place":"Grenoble, France","publication_identifier":{"isbn":["9781665440929"]},"publication_status":"published","doi":"10.1109/ssi52265.2021.9466958","conference":{"location":"Grenoble, France ","end_date":"2021-04-29","start_date":"2021-04-27","name":"2021 Smart Systems Integration (SSI)"},"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9466958"}],"author":[{"first_name":"Sven","last_name":"Lange","full_name":"Lange, Sven","id":"38240"},{"full_name":"Hedayat, Christian","last_name":"Hedayat","first_name":"Christian"},{"last_name":"Kuhn","full_name":"Kuhn, Harald","first_name":"Harald"},{"last_name":"Hilleringmann","full_name":"Hilleringmann, Ulrich","first_name":"Ulrich"}],"date_updated":"2022-01-06T06:55:36Z","status":"public","type":"conference","department":[{"_id":"59"},{"_id":"485"}],"user_id":"38240","_id":"22532","year":"2021","title":"Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization in the Near Field","date_created":"2021-07-05T19:31:52Z","publisher":"IEEE","abstract":[{"lang":"eng","text":"In this publication, further elements of the newly developed inductive localization in the near field are presented. The advantage of inductive localization is the usage of the magnetic fields, which have a very low influence of non-metallic materials in the environment and thus follows good applications in the area of medicine and biochemistry. This allows a precise localization of sensor platforms in inhomogeneous mixtures of materials, where classical methods have major problems with inhomogeneous dielectric conductivity or density. The calculation of the localization of the searched object differs from other methods such as ultrasound or electromagnetic waves due to the source-free propagation of the magnetic field. Therefore, new mathematical evaluation methods and systematic adaptations are necessary, which are presented in this paper in circuit analysis. For this purpose, the exact circuit influences of one coil and the influence of another coil are investigated and which resonance circuit should be selected for both coils for a inductive localization with optimized signal strength."}],"publication":"2021 Smart Systems Integration (SSI)","language":[{"iso":"eng"}],"keyword":["Electrotechnical Characteristics of Real Coils","Inductive Localization","Resonant Circuit","Mutual Inductance","Near-Field"]},{"type":"journal_article","publication":"Algorithmica","status":"public","abstract":[{"text":"We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a setting where the number of colors is bounded and we are minimizing the number of conflicts. Iterated local search algorithms use an unbounded color palette and aim to use the smallest colors and, consequently, the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i.e., starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. We further show that tailoring mutation operators to parts of the graph where changes have occurred can significantly reduce the expected reoptimization time. In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges. However, tailored algorithms cannot prevent exponential times in settings where the original algorithm is inefficient.","lang":"eng"}],"user_id":"102979","department":[{"_id":"819"}],"_id":"48854","language":[{"iso":"eng"}],"keyword":["Dynamic optimization","Evolutionary algorithms","Running time analysis"],"issue":"10","publication_identifier":{"issn":["0178-4617"]},"citation":{"apa":"Bossek, J., Neumann, F., Peng, P., &#38; Sudholt, D. (2021). Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. <i>Algorithmica</i>, <i>83</i>(10), 3148–3179. <a href=\"https://doi.org/10.1007/s00453-021-00838-3\">https://doi.org/10.1007/s00453-021-00838-3</a>","short":"J. Bossek, F. Neumann, P. Peng, D. Sudholt, Algorithmica 83 (2021) 3148–3179.","mla":"Bossek, Jakob, et al. “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.” <i>Algorithmica</i>, vol. 83, no. 10, 2021, pp. 3148–3179, doi:<a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>.","bibtex":"@article{Bossek_Neumann_Peng_Sudholt_2021, title={Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}, volume={83}, DOI={<a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>}, number={10}, journal={Algorithmica}, author={Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}, year={2021}, pages={3148–3179} }","ama":"Bossek J, Neumann F, Peng P, Sudholt D. Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. <i>Algorithmica</i>. 2021;83(10):3148–3179. doi:<a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>","chicago":"Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.” <i>Algorithmica</i> 83, no. 10 (2021): 3148–3179. <a href=\"https://doi.org/10.1007/s00453-021-00838-3\">https://doi.org/10.1007/s00453-021-00838-3</a>.","ieee":"J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem,” <i>Algorithmica</i>, vol. 83, no. 10, pp. 3148–3179, 2021, doi: <a href=\"https://doi.org/10.1007/s00453-021-00838-3\">10.1007/s00453-021-00838-3</a>."},"page":"3148–3179","intvolume":"        83","year":"2021","date_created":"2023-11-14T15:58:54Z","author":[{"full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"},{"first_name":"Pan","last_name":"Peng","full_name":"Peng, Pan"},{"first_name":"Dirk","last_name":"Sudholt","full_name":"Sudholt, Dirk"}],"volume":83,"date_updated":"2023-12-13T10:51:34Z","doi":"10.1007/s00453-021-00838-3","title":"Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem"},{"publication":"8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)","type":"conference","status":"public","abstract":[{"lang":"eng","text":"Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time."}],"department":[{"_id":"196"},{"_id":"172"}],"user_id":"60721","_id":"24547","language":[{"iso":"eng"}],"keyword":["expected possession value","handball","tracking data","time series classification","deep learning"],"publication_status":"inpress","citation":{"chicago":"Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” In <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>, n.d.","ieee":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data,” presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.","ama":"Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. In: <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>.","short":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.","bibtex":"@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }","mla":"Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>.","apa":"Müller, O., Caron, M., Döring, M., Heuwinkel, T., &#38; Baumeister, J. (n.d.). PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online."},"year":"2021","date_created":"2021-09-16T08:33:04Z","author":[{"last_name":"Müller","full_name":"Müller, Oliver","id":"72849","first_name":"Oliver"},{"first_name":"Matthew","id":"60721","full_name":"Caron, Matthew","last_name":"Caron"},{"first_name":"Michael","full_name":"Döring, Michael","last_name":"Döring"},{"last_name":"Heuwinkel","full_name":"Heuwinkel, Tim","first_name":"Tim"},{"full_name":"Baumeister, Jochen","id":"46","orcid":"0000-0003-2683-5826","last_name":"Baumeister","first_name":"Jochen"}],"date_updated":"2023-02-28T08:58:24Z","conference":{"start_date":"2021-09-13","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021)","location":"Online","end_date":"2021-09-17"},"main_file_link":[{"url":"https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf"}],"title":"PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data"},{"date_updated":"2023-09-22T09:13:01Z","oa":"1","author":[{"last_name":"Aimiyekagbon","full_name":"Aimiyekagbon, Osarenren Kennedy","id":"9557","first_name":"Osarenren Kennedy"},{"first_name":"Lars","full_name":"Muth, Lars","id":"77313","orcid":"0000-0002-2938-5616","last_name":"Muth"},{"first_name":"Meike Claudia","last_name":"Wohlleben","orcid":"0009-0009-9767-7168","full_name":"Wohlleben, Meike Claudia","id":"43991"},{"first_name":"Amelie","full_name":"Bender, Amelie","id":"54290","last_name":"Bender"},{"full_name":"Sextro, Walter","id":"21220","last_name":"Sextro","first_name":"Walter"}],"volume":6,"main_file_link":[{"open_access":"1","url":"http://papers.phmsociety.org/index.php/phme/article/download/3042/1812"}],"doi":"10.36001/phme.2021.v6i1.3042","conference":{"name":"PHM Society European Conference"},"publication_status":"published","citation":{"chicago":"Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In <i>Proceedings of the European Conference of the PHM Society 2021</i>, edited by Phuc Do, Steve King, and Olga Fink, 6:527–36, 2021. <a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">https://doi.org/10.36001/phme.2021.v6i1.3042</a>.","ieee":"O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based Diagnostics of a Production Line,” in <i>Proceedings of the European Conference of the PHM Society 2021</i>, 2021, vol. 6, no. 1, pp. 527–536, doi: <a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>.","ama":"Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics of a Production Line. In: Do P, King S, Fink O, eds. <i>Proceedings of the European Conference of the PHM Society 2021</i>. Vol 6. ; 2021:527-536. doi:<a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>","bibtex":"@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based Diagnostics of a Production Line}, volume={6}, DOI={<a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>}, number={1}, booktitle={Proceedings of the European Conference of the PHM Society 2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve and Fink, Olga}, year={2021}, pages={527–536} }","mla":"Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production Line.” <i>Proceedings of the European Conference of the PHM Society 2021</i>, edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:<a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>.","short":"O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P. Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021, 2021, pp. 527–536.","apa":"Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., &#38; Sextro, W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &#38; O. Fink (Eds.), <i>Proceedings of the European Conference of the PHM Society 2021</i> (Vol. 6, Issue 1, pp. 527–536). <a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">https://doi.org/10.36001/phme.2021.v6i1.3042</a>"},"page":"527-536","intvolume":"         6","_id":"27111","user_id":"9557","department":[{"_id":"151"}],"type":"conference","editor":[{"last_name":"Do","full_name":"Do, Phuc","first_name":"Phuc"},{"last_name":"King","full_name":"King, Steve","first_name":"Steve"},{"first_name":"Olga","full_name":"Fink, Olga","last_name":"Fink"}],"status":"public","date_created":"2021-11-03T12:26:39Z","title":"Rule-based Diagnostics of a Production Line","quality_controlled":"1","issue":"1","year":"2021","keyword":["PHME 2021","Feature Selection Classification","Feature Selection Clustering","Interpretable Model","Transparent Model","Industry 4.0","Real-World Diagnostics","Quality Control","Predictive Maintenance"],"language":[{"iso":"eng"}],"publication":"Proceedings of the European Conference of the PHM Society 2021","abstract":[{"lang":"eng","text":"In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions."}]},{"title":"More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization","doi":"10.1145/3377930.3390174","date_updated":"2023-12-13T10:43:41Z","publisher":"Association for Computing Machinery","date_created":"2023-11-14T15:58:53Z","author":[{"full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"},{"first_name":"Pan","last_name":"Peng","full_name":"Peng, Pan"},{"first_name":"Dirk","full_name":"Sudholt, Dirk","last_name":"Sudholt"}],"place":"New York, NY, USA","year":"2020","page":"1277–1285","citation":{"ama":"Bossek J, Neumann F, Peng P, Sudholt D. More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:1277–1285. doi:<a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>","ieee":"J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 1277–1285, doi: <a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>.","chicago":"Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1277–1285. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390174\">https://doi.org/10.1145/3377930.3390174</a>.","apa":"Bossek, J., Neumann, F., Peng, P., &#38; Sudholt, D. (2020). More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1277–1285. <a href=\"https://doi.org/10.1145/3377930.3390174\">https://doi.org/10.1145/3377930.3390174</a>","bibtex":"@inproceedings{Bossek_Neumann_Peng_Sudholt_2020, place={New York, NY, USA}, series={GECCO ’20}, title={More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}, year={2020}, pages={1277–1285}, collection={GECCO ’20} }","mla":"Bossek, Jakob, et al. “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 1277–1285, doi:<a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>.","short":"J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1277–1285."},"publication_identifier":{"isbn":["978-1-4503-7128-5"]},"publication_status":"published","keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"language":[{"iso":"eng"}],"extern":"1","_id":"48847","department":[{"_id":"819"}],"series_title":"GECCO ’20","user_id":"102979","abstract":[{"lang":"eng","text":"Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization. In our approach the graph instance is given incrementally such that the EA can reoptimize its coloring when a new edge introduces a conflict. We show that, when edges are inserted in a way that preserves graph connectivity, Randomized Local Search (RLS) efficiently finds a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS and other EAs need exponential expected time in a static optimization scenario. We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior. We further show that offspring populations (e. g. a (1 + {$\\lambda$}) RLS) lead to an exponential speedup in {$\\lambda$}. Finally, an island model using 3 islands succeeds in an optimal time of {$\\Theta$}(m) on every m-edge bipartite graph, outperforming offspring populations. This is the first example where an island model guarantees a speedup that is not bounded in the number of islands."}],"status":"public","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference"},{"title":"The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics","publisher":"Association for Computing Machinery","date_created":"2023-11-14T15:58:53Z","year":"2020","keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"language":[{"iso":"eng"}],"abstract":[{"text":"Several important optimization problems in the area of vehicle routing can be seen as variants of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the Traveling Thief Problem (TTP) has gained increasing interest over the last 5 years. In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour. This provides abstractions of important TSP variants such as the Traveling Thief Problem and time dependent TSP variants, and allows to study precisely the increase in difficulty caused by weight dependence. We provide a 3.59-approximation for this weight dependent version of TSP with metric distances and bounded positive weights. Furthermore, we conduct experimental investigations for simple randomized local search with classical mutation operators and two variants of the state-of-the-art evolutionary algorithm EAX adapted to the weighted TSP. Our results show the impact of the node weights on the position of the nodes in the resulting tour.","lang":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","doi":"10.1145/3377930.3390243","date_updated":"2023-12-13T10:43:33Z","author":[{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob"},{"last_name":"Casel","full_name":"Casel, Katrin","first_name":"Katrin"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"}],"place":"New York, NY, USA","citation":{"bibtex":"@inproceedings{Bossek_Casel_Kerschke_Neumann_2020, place={New York, NY, USA}, series={GECCO ’20}, title={The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Casel, Katrin and Kerschke, Pascal and Neumann, Frank}, year={2020}, pages={1286–1294}, collection={GECCO ’20} }","mla":"Bossek, Jakob, et al. “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 1286–1294, doi:<a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>.","short":"J. Bossek, K. Casel, P. Kerschke, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1286–1294.","apa":"Bossek, J., Casel, K., Kerschke, P., &#38; Neumann, F. (2020). The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1286–1294. <a href=\"https://doi.org/10.1145/3377930.3390243\">https://doi.org/10.1145/3377930.3390243</a>","ama":"Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:<a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>","chicago":"Bossek, Jakob, Katrin Casel, Pascal Kerschke, and Frank Neumann. “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1286–1294. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390243\">https://doi.org/10.1145/3377930.3390243</a>.","ieee":"J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 1286–1294, doi: <a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>."},"page":"1286–1294","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-7128-5"]},"extern":"1","_id":"48851","user_id":"102979","series_title":"GECCO ’20","department":[{"_id":"819"}],"status":"public","type":"conference"},{"publication_identifier":{"issn":["2363-7005","1867-0202"]},"has_accepted_license":"1","publication_status":"published","intvolume":"        63","page":"145-156","citation":{"apa":"Beverungen, D., Buijs, J. C. A. M., Becker, J., Di Ciccio, C., van der Aalst, W. M. P., Bartelheimer, C., vom Brocke, J., Comuzzi, M., Kraume, K., Leopold, H., Matzner, M., Mendling, J., Ogonek, N., Post, T., Resinas, M., Revoredo, K., del-Río-Ortega, A., La Rosa, M., Santoro, F. M., … Wolf, V. (2020). Seven Paradoxes of Business Process Management in a Hyper-Connected World. <i>Business &#38; Information Systems Engineering</i>, <i>63</i>, 145–156. <a href=\"https://doi.org/10.1007/s12599-020-00646-z\">https://doi.org/10.1007/s12599-020-00646-z</a>","mla":"Beverungen, Daniel, et al. “Seven Paradoxes of Business Process Management in a Hyper-Connected World.” <i>Business &#38; Information Systems Engineering</i>, vol. 63, SpringerNature, 2020, pp. 145–56, doi:<a href=\"https://doi.org/10.1007/s12599-020-00646-z\">10.1007/s12599-020-00646-z</a>.","bibtex":"@article{Beverungen_Buijs_Becker_Di Ciccio_van der Aalst_Bartelheimer_vom Brocke_Comuzzi_Kraume_Leopold_et al._2020, title={Seven Paradoxes of Business Process Management in a Hyper-Connected World}, volume={63}, DOI={<a href=\"https://doi.org/10.1007/s12599-020-00646-z\">10.1007/s12599-020-00646-z</a>}, journal={Business &#38; Information Systems Engineering}, publisher={SpringerNature}, author={Beverungen, Daniel and Buijs, Joos C. A. M. and Becker, Jörg and Di Ciccio, Claudio and van der Aalst, Wil M. P. and Bartelheimer, Christian and vom Brocke, Jan and Comuzzi, Marco and Kraume, Karsten and Leopold, Henrik and et al.}, year={2020}, pages={145–156} }","short":"D. Beverungen, J.C.A.M. Buijs, J. Becker, C. Di Ciccio, W.M.P. van der Aalst, C. Bartelheimer, J. vom Brocke, M. Comuzzi, K. Kraume, H. Leopold, M. Matzner, J. Mendling, N. Ogonek, T. Post, M. Resinas, K. Revoredo, A. del-Río-Ortega, M. La Rosa, F.M. Santoro, A. Solti, M. Song, A. Stein, M. Stierle, V. Wolf, Business &#38; Information Systems Engineering 63 (2020) 145–156.","ama":"Beverungen D, Buijs JCAM, Becker J, et al. Seven Paradoxes of Business Process Management in a Hyper-Connected World. <i>Business &#38; Information Systems Engineering</i>. 2020;63:145-156. doi:<a href=\"https://doi.org/10.1007/s12599-020-00646-z\">10.1007/s12599-020-00646-z</a>","chicago":"Beverungen, Daniel, Joos C. A. M. Buijs, Jörg Becker, Claudio Di Ciccio, Wil M. P. van der Aalst, Christian Bartelheimer, Jan vom Brocke, et al. “Seven Paradoxes of Business Process Management in a Hyper-Connected World.” <i>Business &#38; Information Systems Engineering</i> 63 (2020): 145–56. <a href=\"https://doi.org/10.1007/s12599-020-00646-z\">https://doi.org/10.1007/s12599-020-00646-z</a>.","ieee":"D. Beverungen <i>et al.</i>, “Seven Paradoxes of Business Process Management in a Hyper-Connected World,” <i>Business &#38; Information Systems Engineering</i>, vol. 63, pp. 145–156, 2020, doi: <a href=\"https://doi.org/10.1007/s12599-020-00646-z\">10.1007/s12599-020-00646-z</a>."},"volume":63,"author":[{"full_name":"Beverungen, Daniel","id":"59677","last_name":"Beverungen","first_name":"Daniel"},{"first_name":"Joos C. A. M.","last_name":"Buijs","full_name":"Buijs, Joos C. A. M."},{"last_name":"Becker","full_name":"Becker, Jörg","first_name":"Jörg"},{"full_name":"Di Ciccio, Claudio","last_name":"Di Ciccio","first_name":"Claudio"},{"first_name":"Wil M. P.","full_name":"van der Aalst, Wil M. P.","last_name":"van der Aalst"},{"full_name":"Bartelheimer, Christian","id":"49160","last_name":"Bartelheimer","first_name":"Christian"},{"full_name":"vom Brocke, Jan","last_name":"vom Brocke","first_name":"Jan"},{"first_name":"Marco","last_name":"Comuzzi","full_name":"Comuzzi, Marco"},{"first_name":"Karsten","last_name":"Kraume","full_name":"Kraume, Karsten"},{"first_name":"Henrik","last_name":"Leopold","full_name":"Leopold, Henrik"},{"first_name":"Martin","full_name":"Matzner, Martin","last_name":"Matzner"},{"full_name":"Mendling, Jan","last_name":"Mendling","first_name":"Jan"},{"last_name":"Ogonek","full_name":"Ogonek, Nadine","first_name":"Nadine"},{"first_name":"Till","full_name":"Post, Till","last_name":"Post"},{"first_name":"Manuel","full_name":"Resinas, Manuel","last_name":"Resinas"},{"full_name":"Revoredo, Kate","last_name":"Revoredo","first_name":"Kate"},{"first_name":"Adela","last_name":"del-Río-Ortega","full_name":"del-Río-Ortega, Adela"},{"first_name":"Marcello","full_name":"La Rosa, Marcello","last_name":"La Rosa"},{"last_name":"Santoro","full_name":"Santoro, Flávia Maria","first_name":"Flávia Maria"},{"first_name":"Andreas","full_name":"Solti, Andreas","last_name":"Solti"},{"first_name":"Minseok","full_name":"Song, Minseok","last_name":"Song"},{"full_name":"Stein, Armin","last_name":"Stein","first_name":"Armin"},{"first_name":"Matthias","full_name":"Stierle, Matthias","last_name":"Stierle"},{"first_name":"Verena","id":"23633","full_name":"Wolf, Verena","last_name":"Wolf"}],"date_updated":"2024-04-18T12:50:57Z","doi":"10.1007/s12599-020-00646-z","type":"journal_article","status":"public","department":[{"_id":"526"}],"user_id":"59677","_id":"17156","project":[{"name":"RISE_BPM: Propelling Business Process Management by Research and Innovation Staff Exchange","_id":"1070","grant_number":"645751","call_identifier":"MSCA-RISE-2014"}],"file_date_updated":"2024-04-18T12:49:25Z","article_type":"original","quality_controlled":"1","year":"2020","date_created":"2020-06-24T10:40:45Z","publisher":"SpringerNature","title":"Seven Paradoxes of Business Process Management in a Hyper-Connected World","publication":"Business & Information Systems Engineering","file":[{"success":1,"relation":"main_file","content_type":"application/pdf","file_size":360869,"file_id":"53574","file_name":"Business_Process_Management_in_a_Hyperconnected_World.pdf","access_level":"closed","date_updated":"2024-04-18T12:49:25Z","date_created":"2024-04-18T12:49:25Z","creator":"dabe"}],"abstract":[{"text":"Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.","lang":"eng"}],"language":[{"iso":"eng"}],"keyword":["Business process management (BPM)","Social computing","Smart devices","Big data analytics","Real-time computing","BPM life-cycle"],"ddc":["380"]},{"type":"conference","publication":"PHM Society European Conference","abstract":[{"lang":"eng","text":"In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach."}],"status":"public","_id":"17810","user_id":"9557","department":[{"_id":"151"}],"keyword":["PHM 2019","crack propagation","forecasting","unevenly spaced time series","step ahead prediction","short time series"],"language":[{"iso":"eng"}],"quality_controlled":"1","issue":"1","year":"2020","citation":{"apa":"Aimiyekagbon, O. K., Bender, A., &#38; Sextro, W. (2020). Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. <i>PHM Society European Conference</i>, <i>5</i>(1).","short":"O.K. Aimiyekagbon, A. Bender, W. Sextro, in: PHM Society European Conference, 2020.","bibtex":"@inproceedings{Aimiyekagbon_Bender_Sextro_2020, title={Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data}, volume={5}, number={1}, booktitle={PHM Society European Conference}, author={Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}, year={2020} }","mla":"Aimiyekagbon, Osarenren Kennedy, et al. “Evaluation of Time Series Forecasting Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates given Insufficient Data.” <i>PHM Society European Conference</i>, vol. 5, no. 1, 2020.","ama":"Aimiyekagbon OK, Bender A, Sextro W. Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. In: <i>PHM Society European Conference</i>. Vol 5. ; 2020.","chicago":"Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Evaluation of Time Series Forecasting Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates given Insufficient Data.” In <i>PHM Society European Conference</i>, Vol. 5, 2020.","ieee":"O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data,” in <i>PHM Society European Conference</i>, 2020, vol. 5, no. 1."},"intvolume":"         5","date_updated":"2023-09-22T09:13:16Z","date_created":"2020-08-11T13:32:40Z","author":[{"first_name":"Osarenren Kennedy","full_name":"Aimiyekagbon, Osarenren Kennedy","id":"9557","last_name":"Aimiyekagbon"},{"full_name":"Bender, Amelie","id":"54290","last_name":"Bender","first_name":"Amelie"},{"id":"21220","full_name":"Sextro, Walter","last_name":"Sextro","first_name":"Walter"}],"volume":5,"title":"Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data"},{"language":[{"iso":"eng"}],"keyword":["Dynamic Time Warping","Feature Extraction","Masking","Neural Networks"],"user_id":"11829","department":[{"_id":"49"}],"_id":"15488","status":"public","abstract":[{"lang":"eng","text":"The continuous refinement of sensor technologies enables the manufacturing industry to capture increasing amounts of data during the production process. As processes take time to complete, sensors register large amounts of time-series-like data for each product. In order to make this data usable, a feature extraction is mandatory. In this work, we discuss and evaluate different network architectures, input pre-processing and cost functions regarding, among other aspects, their suitability for time series of different lengths."}],"type":"conference","publication":"20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019","doi":"10.5162/SENSOREN2019/P2.9","title":"P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press","date_created":"2020-01-10T16:03:58Z","author":[{"full_name":"Thiel, Christian","last_name":"Thiel","first_name":"Christian"},{"full_name":"Steidl, Carolin","last_name":"Steidl","first_name":"Carolin"},{"first_name":"Bernd","last_name":"Henning","id":"213","full_name":"Henning, Bernd"}],"date_updated":"2022-01-06T06:52:27Z","citation":{"bibtex":"@inproceedings{Thiel_Steidl_Henning_2019, place={Von-Münchhausen-Str. 49, 31515 Wunstorf}, title={P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press}, DOI={<a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">10.5162/SENSOREN2019/P2.9</a>}, booktitle={20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019}, author={Thiel, Christian and Steidl, Carolin and Henning, Bernd}, editor={AMA Service GmbHEditor}, year={2019} }","mla":"Thiel, Christian, et al. “P2.9 Comparison of Deep Feature Extraction Techniques for Varying-Length Time Series from an Industrial Piercing Press.” <i>20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019</i>, edited by AMA Service GmbH, 2019, doi:<a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">10.5162/SENSOREN2019/P2.9</a>.","short":"C. Thiel, C. Steidl, B. Henning, in: AMA Service GmbH (Ed.), 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019, Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019.","apa":"Thiel, C., Steidl, C., &#38; Henning, B. (2019). P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press. In AMA Service GmbH (Ed.), <i>20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019</i>. Von-Münchhausen-Str. 49, 31515 Wunstorf. <a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">https://doi.org/10.5162/SENSOREN2019/P2.9</a>","ama":"Thiel C, Steidl C, Henning B. P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press. In: AMA Service GmbH, ed. <i>20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019</i>. Von-Münchhausen-Str. 49, 31515 Wunstorf; 2019. doi:<a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">10.5162/SENSOREN2019/P2.9</a>","ieee":"C. Thiel, C. Steidl, and B. Henning, “P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press,” in <i>20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019</i>, 2019.","chicago":"Thiel, Christian, Carolin Steidl, and Bernd Henning. “P2.9 Comparison of Deep Feature Extraction Techniques for Varying-Length Time Series from an Industrial Piercing Press.” In <i>20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019</i>, edited by AMA Service GmbH. Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019. <a href=\"https://doi.org/10.5162/SENSOREN2019/P2.9\">https://doi.org/10.5162/SENSOREN2019/P2.9</a>."},"corporate_editor":["AMA Service GmbH"],"year":"2019","place":"Von-Münchhausen-Str. 49, 31515 Wunstorf","publication_identifier":{"isbn":["978-3-9819376-0-2"]}},{"abstract":[{"text":"We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. This includes the (1+1) EA and RLS in a setting where the number of colors is bounded and we are minimizing the number of conflicts as well as iterated local search algorithms that use an unbounded color palette and aim to use the smallest colors and - as a consequence - the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i. e. starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. Furthermore, we show how to speed up computations by using problem specific operators concentrating on parts of the graph where changes have occurred.","lang":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","language":[{"iso":"eng"}],"keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"year":"2019","title":"Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring","date_created":"2023-11-14T15:58:52Z","publisher":"Association for Computing Machinery","status":"public","type":"conference","extern":"1","user_id":"102979","series_title":"GECCO ’19","department":[{"_id":"819"}],"_id":"48843","citation":{"apa":"Bossek, J., Neumann, F., Peng, P., &#38; Sudholt, D. (2019). Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1443–1451. <a href=\"https://doi.org/10.1145/3321707.3321792\">https://doi.org/10.1145/3321707.3321792</a>","bibtex":"@inproceedings{Bossek_Neumann_Peng_Sudholt_2019, place={New York, NY, USA}, series={GECCO ’19}, title={Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring}, DOI={<a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}, year={2019}, pages={1443–1451}, collection={GECCO ’19} }","mla":"Bossek, Jakob, et al. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2019, pp. 1443–1451, doi:<a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>.","short":"J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2019, pp. 1443–1451.","ama":"Bossek J, Neumann F, Peng P, Sudholt D. Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’19. Association for Computing Machinery; 2019:1443–1451. doi:<a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>","ieee":"J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2019, pp. 1443–1451, doi: <a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>.","chicago":"Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1443–1451. GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3321707.3321792\">https://doi.org/10.1145/3321707.3321792</a>."},"page":"1443–1451","place":"New York, NY, USA","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-6111-8"]},"doi":"10.1145/3321707.3321792","author":[{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"first_name":"Frank","full_name":"Neumann, Frank","last_name":"Neumann"},{"full_name":"Peng, Pan","last_name":"Peng","first_name":"Pan"},{"first_name":"Dirk","last_name":"Sudholt","full_name":"Sudholt, Dirk"}],"date_updated":"2023-12-13T10:42:37Z"}]