[{"date_created":"2023-01-10T10:00:55Z","publisher":"Springer Science and Business Media LLC","title":"Systematizing the lexicon of platforms in information systems: a data-driven study","quality_controlled":"1","year":"2022","language":[{"iso":"eng"}],"ddc":["380"],"keyword":["Platform","Text mining","Machine learning","Data communications","Interpretive research","Systems design and implementation"],"publication":"Electronic Markets","file":[{"file_name":"EM - Lexicon of Platform Terms.pdf","access_level":"closed","file_id":"53573","file_size":1262427,"date_created":"2024-04-18T12:39:00Z","creator":"dabe","date_updated":"2024-04-18T12:39:00Z","relation":"main_file","success":1,"content_type":"application/pdf"}],"abstract":[{"lang":"eng","text":"While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level."}],"author":[{"last_name":"Bartelheimer","full_name":"Bartelheimer, Christian","id":"49160","first_name":"Christian"},{"last_name":"zur Heiden","id":"64394","full_name":"zur Heiden, Philipp","first_name":"Philipp"},{"full_name":"Lüttenberg, Hedda","last_name":"Lüttenberg","first_name":"Hedda"},{"first_name":"Daniel","full_name":"Beverungen, Daniel","id":"59677","last_name":"Beverungen"}],"volume":32,"date_updated":"2024-04-18T12:40:34Z","doi":"10.1007/s12525-022-00530-6","publication_status":"published","publication_identifier":{"issn":["1019-6781","1422-8890"]},"has_accepted_license":"1","citation":{"ieee":"C. Bartelheimer, P. zur Heiden, H. Lüttenberg, and D. Beverungen, “Systematizing the lexicon of platforms in information systems: a data-driven study,” <i>Electronic Markets</i>, vol. 32, pp. 375–396, 2022, doi: <a href=\"https://doi.org/10.1007/s12525-022-00530-6\">10.1007/s12525-022-00530-6</a>.","chicago":"Bartelheimer, Christian, Philipp zur Heiden, Hedda Lüttenberg, and Daniel Beverungen. “Systematizing the Lexicon of Platforms in Information Systems: A Data-Driven Study.” <i>Electronic Markets</i> 32 (2022): 375–96. <a href=\"https://doi.org/10.1007/s12525-022-00530-6\">https://doi.org/10.1007/s12525-022-00530-6</a>.","ama":"Bartelheimer C, zur Heiden P, Lüttenberg H, Beverungen D. Systematizing the lexicon of platforms in information systems: a data-driven study. <i>Electronic Markets</i>. 2022;32:375-396. doi:<a href=\"https://doi.org/10.1007/s12525-022-00530-6\">10.1007/s12525-022-00530-6</a>","apa":"Bartelheimer, C., zur Heiden, P., Lüttenberg, H., &#38; Beverungen, D. (2022). Systematizing the lexicon of platforms in information systems: a data-driven study. <i>Electronic Markets</i>, <i>32</i>, 375–396. <a href=\"https://doi.org/10.1007/s12525-022-00530-6\">https://doi.org/10.1007/s12525-022-00530-6</a>","short":"C. Bartelheimer, P. zur Heiden, H. Lüttenberg, D. Beverungen, Electronic Markets 32 (2022) 375–396.","mla":"Bartelheimer, Christian, et al. “Systematizing the Lexicon of Platforms in Information Systems: A Data-Driven Study.” <i>Electronic Markets</i>, vol. 32, Springer Science and Business Media LLC, 2022, pp. 375–96, doi:<a href=\"https://doi.org/10.1007/s12525-022-00530-6\">10.1007/s12525-022-00530-6</a>.","bibtex":"@article{Bartelheimer_zur Heiden_Lüttenberg_Beverungen_2022, title={Systematizing the lexicon of platforms in information systems: a data-driven study}, volume={32}, DOI={<a href=\"https://doi.org/10.1007/s12525-022-00530-6\">10.1007/s12525-022-00530-6</a>}, journal={Electronic Markets}, publisher={Springer Science and Business Media LLC}, author={Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}, year={2022}, pages={375–396} }"},"jel":["L86"],"page":"375-396","intvolume":"        32","user_id":"59677","department":[{"_id":"526"}],"_id":"35732","file_date_updated":"2024-04-18T12:39:00Z","article_type":"original","type":"journal_article","status":"public"},{"quality_controlled":"1","year":"2022","publisher":"ACM","date_created":"2022-12-21T09:48:43Z","title":"User Involvement in Training Smart Home Agents","publication":"International Conference on Human-Agent Interaction","abstract":[{"lang":"eng","text":"Smart home systems contain plenty of features that enhance wellbeing in everyday life through artificial intelligence (AI). However, many users feel insecure because they do not understand the AI’s functionality and do not feel they are in control of it. Combining technical, psychological and philosophical views on AI, we rethink smart homes as interactive systems where users can partake in an intelligent agent’s learning. Parallel to the goals of explainable AI (XAI), we explored the possibility of user involvement in supervised learning of the smart home to have a first approach to improve acceptance, support subjective understanding and increase perceived control. In this work, we conducted two studies: In an online pre-study, we asked participants about their attitude towards teaching AI via a questionnaire. In the main study, we performed a Wizard of Oz laboratory experiment with human participants, where participants spent time in a prototypical smart home and taught activity recognition to the intelligent agent through supervised learning based on the user’s behaviour. We found that involvement in the AI’s learning phase enhanced the users’ feeling of control, perceived understanding and perceived usefulness of AI in general. The participants reported positive attitudes towards training a smart home AI and found the process understandable and controllable. We suggest that involving the user in the learning phase could lead to better personalisation and increased understanding and control by users of intelligent agents for smart home automation."}],"file":[{"creator":"heindorf","date_created":"2024-05-30T18:04:31Z","date_updated":"2024-05-30T18:04:31Z","access_level":"closed","file_id":"54524","file_name":"User_Involvement_in_Training_Smart_Home_Agents_public.pdf","file_size":1151728,"content_type":"application/pdf","relation":"main_file","success":1}],"ddc":["000"],"keyword":["human-agent interaction","smart homes","supervised learning","participation"],"language":[{"iso":"eng"}],"publication_status":"published","has_accepted_license":"1","citation":{"short":"L.N. Sieger, J. Hermann, A. Schomäcker, S. Heindorf, C. Meske, C.-C. Hey, A. Doğangün, in: International Conference on Human-Agent Interaction, ACM, 2022.","bibtex":"@inproceedings{Sieger_Hermann_Schomäcker_Heindorf_Meske_Hey_Doğangün_2022, title={User Involvement in Training Smart Home Agents}, DOI={<a href=\"https://doi.org/10.1145/3527188.3561914\">10.1145/3527188.3561914</a>}, booktitle={International Conference on Human-Agent Interaction}, publisher={ACM}, author={Sieger, Leonie Nora and Hermann, Julia and Schomäcker, Astrid and Heindorf, Stefan and Meske, Christian and Hey, Celine-Chiara and Doğangün, Ayşegül}, year={2022} }","mla":"Sieger, Leonie Nora, et al. “User Involvement in Training Smart Home Agents.” <i>International Conference on Human-Agent Interaction</i>, ACM, 2022, doi:<a href=\"https://doi.org/10.1145/3527188.3561914\">10.1145/3527188.3561914</a>.","apa":"Sieger, L. N., Hermann, J., Schomäcker, A., Heindorf, S., Meske, C., Hey, C.-C., &#38; Doğangün, A. (2022). User Involvement in Training Smart Home Agents. <i>International Conference on Human-Agent Interaction</i>. HAI ’22: International Conference on Human-Agent Interaction, Christchurch, New Zealand. <a href=\"https://doi.org/10.1145/3527188.3561914\">https://doi.org/10.1145/3527188.3561914</a>","ama":"Sieger LN, Hermann J, Schomäcker A, et al. User Involvement in Training Smart Home Agents. In: <i>International Conference on Human-Agent Interaction</i>. ACM; 2022. doi:<a href=\"https://doi.org/10.1145/3527188.3561914\">10.1145/3527188.3561914</a>","ieee":"L. N. Sieger <i>et al.</i>, “User Involvement in Training Smart Home Agents,” presented at the HAI ’22: International Conference on Human-Agent Interaction, Christchurch, New Zealand, 2022, doi: <a href=\"https://doi.org/10.1145/3527188.3561914\">10.1145/3527188.3561914</a>.","chicago":"Sieger, Leonie Nora, Julia Hermann, Astrid Schomäcker, Stefan Heindorf, Christian Meske, Celine-Chiara Hey, and Ayşegül Doğangün. “User Involvement in Training Smart Home Agents.” In <i>International Conference on Human-Agent Interaction</i>. ACM, 2022. <a href=\"https://doi.org/10.1145/3527188.3561914\">https://doi.org/10.1145/3527188.3561914</a>."},"oa":"1","date_updated":"2024-05-30T18:04:45Z","author":[{"id":"93402","full_name":"Sieger, Leonie Nora","last_name":"Sieger","first_name":"Leonie Nora"},{"full_name":"Hermann, Julia","last_name":"Hermann","first_name":"Julia"},{"first_name":"Astrid","last_name":"Schomäcker","full_name":"Schomäcker, Astrid"},{"id":"11871","full_name":"Heindorf, Stefan","last_name":"Heindorf","orcid":"0000-0002-4525-6865","first_name":"Stefan"},{"last_name":"Meske","full_name":"Meske, Christian","first_name":"Christian"},{"first_name":"Celine-Chiara","last_name":"Hey","full_name":"Hey, Celine-Chiara"},{"full_name":"Doğangün, Ayşegül","last_name":"Doğangün","first_name":"Ayşegül"}],"main_file_link":[{"open_access":"1","url":"https://papers.dice-research.org/2022/HAI_SmartHome/User_Involvement_in_Training_Smart_Home_Agents_public.pdf"}],"doi":"10.1145/3527188.3561914","conference":{"end_date":"2022-12-08","location":"Christchurch, New Zealand","name":"HAI '22: International Conference on Human-Agent Interaction","start_date":"2022-12-05"},"type":"conference","status":"public","project":[{"name":"TRR 318 - B1: TRR 318 - Subproject B1","_id":"121","grant_number":"438445824"}],"_id":"34674","user_id":"11871","department":[{"_id":"574"},{"_id":"760"}],"alternative_title":["Increasing Perceived Control and Understanding"],"file_date_updated":"2024-05-30T18:04:31Z"},{"year":"2022","page":"266-286","citation":{"ama":"Janicki N. Entwicklung und Evaluation eines Fortbildungskonzepts im Kontext der technischen Bildung an Grundschulen am Beispiel von Lernrobotern. In: Binder M, Wiesmüller C, Finkbeiner T, eds. <i>Technik: Verstehen wir, was wir nutzen!? - Tagungsband zur 23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung in Mannheim vom 24.-25. September 2021. </i>. ; 2022:266-286.","chicago":"Janicki, Nicole. “Entwicklung und Evaluation eines Fortbildungskonzepts im Kontext der technischen Bildung an Grundschulen am Beispiel von Lernrobotern.” In <i>Technik: Verstehen wir, was wir nutzen!? - Tagungsband zur 23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung in Mannheim vom 24.-25. September 2021. </i>, edited by Martin Binder, Christian Wiesmüller, and Timo Finkbeiner, 266–86, 2022.","ieee":"N. Janicki, “Entwicklung und Evaluation eines Fortbildungskonzepts im Kontext der technischen Bildung an Grundschulen am Beispiel von Lernrobotern,” in <i>Technik: Verstehen wir, was wir nutzen!? - Tagungsband zur 23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung in Mannheim vom 24.-25. September 2021. </i>, M. Binder, C. Wiesmüller, and T. Finkbeiner, Eds. 2022, pp. 266–286.","short":"N. Janicki, in: M. Binder, C. Wiesmüller, T. Finkbeiner (Eds.), Technik: Verstehen wir, was wir nutzen!? - Tagungsband zur 23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung in Mannheim vom 24.-25. September 2021. , 2022, pp. 266–286.","mla":"Janicki, Nicole. “Entwicklung und Evaluation eines Fortbildungskonzepts im Kontext der technischen Bildung an Grundschulen am Beispiel von Lernrobotern.” <i>Technik: Verstehen wir, was wir nutzen!? - Tagungsband zur 23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung in Mannheim vom 24.-25. September 2021. </i>, edited by Martin Binder et al., 2022, pp. 266–86.","bibtex":"@inbook{Janicki_2022, title={Entwicklung und Evaluation eines Fortbildungskonzepts im Kontext der technischen Bildung an Grundschulen am Beispiel von Lernrobotern}, booktitle={Technik: Verstehen wir, was wir nutzen!? - Tagungsband zur 23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung in Mannheim vom 24.-25. September 2021. }, author={Janicki, Nicole}, editor={Binder, Martin and Wiesmüller, Christian and Finkbeiner, Timo}, year={2022}, pages={266–286} }","apa":"Janicki, N. (2022). Entwicklung und Evaluation eines Fortbildungskonzepts im Kontext der technischen Bildung an Grundschulen am Beispiel von Lernrobotern. In M. Binder, C. Wiesmüller, &#38; T. Finkbeiner (Eds.), <i>Technik: Verstehen wir, was wir nutzen!? - Tagungsband zur 23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung in Mannheim vom 24.-25. September 2021. </i> (pp. 266–286)."},"publication_identifier":{"unknown":["978-3-947868-03-2"]},"publication_status":"published","title":"Entwicklung und Evaluation eines Fortbildungskonzepts im Kontext der technischen Bildung an Grundschulen am Beispiel von Lernrobotern","conference":{"name":"23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung","start_date":"24.09.2021","end_date":"25.09.2021","location":"Mannheim"},"date_updated":"2023-01-25T11:50:51Z","author":[{"last_name":"Janicki","full_name":"Janicki, Nicole","id":"50915","first_name":"Nicole"}],"date_created":"2023-01-22T14:09:52Z","editor":[{"full_name":"Binder, Martin","last_name":"Binder","first_name":"Martin"},{"first_name":"Christian","last_name":"Wiesmüller","full_name":"Wiesmüller, Christian"},{"first_name":"Timo","full_name":"Finkbeiner, Timo","last_name":"Finkbeiner"}],"status":"public","publication":"Technik: Verstehen wir, was wir nutzen!? - Tagungsband zur 23. Jahrestagung der Deutschen Gesellschaft für Technische Bildung in Mannheim vom 24.-25. September 2021. ","type":"book_chapter","keyword":["technology education","teacher professionalisation","Computational Thinking","digitalization","learning robots"],"language":[{"iso":"ger"}],"_id":"37902","department":[{"_id":"588"}],"user_id":"50915"},{"page":"19-24","intvolume":"        55","citation":{"apa":"Schön, O., Götte, R.-S., &#38; Timmermann, J. (2022). Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, <i>55</i>(12), 19–24. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>","bibtex":"@inproceedings{Schön_Götte_Timmermann_2022, title={Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}, volume={55}, DOI={<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>}, number={12}, booktitle={14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}, author={Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}, year={2022}, pages={19–24} }","mla":"Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.” <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, vol. 55, no. 12, 2022, pp. 19–24, doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>.","short":"O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.","ama":"Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>. Vol 55. ; 2022:19-24. doi:<a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>","ieee":"O. Schön, R.-S. Götte, and J. Timmermann, “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems,” in <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, Casablanca, Morocco, 2022, vol. 55, no. 12, pp. 19–24, doi: <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>.","chicago":"Schön, Oliver, Ricarda-Samantha Götte, and Julia Timmermann. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.” In <i>14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)</i>, 55:19–24, 2022. <a href=\"https://doi.org/10.1016/j.ifacol.2022.07.282\">https://doi.org/10.1016/j.ifacol.2022.07.282</a>."},"date_updated":"2024-11-13T08:43:16Z","volume":55,"author":[{"last_name":"Schön","full_name":"Schön, Oliver","first_name":"Oliver"},{"full_name":"Götte, Ricarda-Samantha","id":"43992","last_name":"Götte","first_name":"Ricarda-Samantha"},{"first_name":"Julia","last_name":"Timmermann","full_name":"Timmermann, Julia","id":"15402"}],"conference":{"end_date":"2022-07-01","location":"Casablanca, Morocco","name":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)","start_date":"2022-06-29"},"doi":"https://doi.org/10.1016/j.ifacol.2022.07.282","type":"conference","status":"public","_id":"31066","department":[{"_id":"153"},{"_id":"880"}],"user_id":"43992","quality_controlled":"1","issue":"12","year":"2022","date_created":"2022-05-05T06:22:55Z","title":"Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems","publication":"14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)","abstract":[{"lang":"eng","text":"While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. "}],"keyword":["neural networks","physics-guided","data-driven","multi-objective optimization","system identification","machine learning","dynamical systems"],"language":[{"iso":"eng"}]},{"quality_controlled":"1","year":"2022","date_created":"2022-08-02T11:56:03Z","publisher":"Springer International Publishing","title":"HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs","publication":"The Semantic Web -- ISWC 2022","file":[{"creator":"uqudus","date_created":"2022-12-22T15:45:29Z","date_updated":"2022-12-22T15:45:29Z","file_id":"34853","access_level":"closed","file_name":"hybrid_fact_check_iswc2022.pdf","file_size":296218,"content_type":"application/pdf","relation":"main_file","success":1}],"abstract":[{"text":" We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, of\r\nwhich each is subject to partially overlapping limitations. In particular, current text-based approaches are limited by manual feature engineering. Path-based and rule-based approaches are limited by their exclusive use of knowledge graphs as background knowledge, and embedding-based approaches suffer from low accuracy scores on current fact-checking tasks. We propose a hybrid approach—dubbed HybridFC—that exploits the diversity of existing categories of fact-checking approaches within an ensemble learning setting to achieve a significantly better prediction performance. In particular, our approach outperforms the state of the art by 0.14 to 0.27 in terms of Area Under the Receiver Operating Characteristic curve on the FactBench dataset. Our code is open-source and can be found at https://github.com/dice-group/HybridFC.","lang":"eng"}],"language":[{"iso":"eng"}],"keyword":["fact checking · ensemble learning · knowledge graph veracit"],"ddc":["000"],"publication_identifier":{"isbn":["978-3-031-19433-7"]},"has_accepted_license":"1","publication_status":"accepted","page":"462--480","citation":{"ama":"Qudus U, Röder M, Saleem M, Ngonga Ngomo A-C. HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs. In: Sattler U, Hogan A, Keet M, Presutti V, eds. <i>The Semantic Web -- ISWC 2022</i>. Springer International Publishing; :462--480. doi:<a href=\"https://doi.org/10.1007/978-3-031-19433-7_27\">10.1007/978-3-031-19433-7_27</a>","chicago":"Qudus, Umair, Michael Röder, Muhammad Saleem, and Axel-Cyrille Ngonga Ngomo. “HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs.” In <i>The Semantic Web -- ISWC 2022</i>, edited by Ulrike Sattler, Aidan Hogan, Maria Keet, and Valentina Presutti, 462--480. Cham: Springer International Publishing, n.d. <a href=\"https://doi.org/10.1007/978-3-031-19433-7_27\">https://doi.org/10.1007/978-3-031-19433-7_27</a>.","ieee":"U. Qudus, M. Röder, M. Saleem, and A.-C. Ngonga Ngomo, “HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs,” in <i>The Semantic Web -- ISWC 2022</i>, Hanghzou, China, pp. 462--480, doi: <a href=\"https://doi.org/10.1007/978-3-031-19433-7_27\">10.1007/978-3-031-19433-7_27</a>.","short":"U. Qudus, M. Röder, M. Saleem, A.-C. Ngonga Ngomo, in: U. Sattler, A. Hogan, M. Keet, V. Presutti (Eds.), The Semantic Web -- ISWC 2022, Springer International Publishing, Cham, n.d., pp. 462--480.","mla":"Qudus, Umair, et al. “HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs.” <i>The Semantic Web -- ISWC 2022</i>, edited by Ulrike Sattler et al., Springer International Publishing, pp. 462--480, doi:<a href=\"https://doi.org/10.1007/978-3-031-19433-7_27\">10.1007/978-3-031-19433-7_27</a>.","bibtex":"@inproceedings{Qudus_Röder_Saleem_Ngonga Ngomo, place={Cham}, title={HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-19433-7_27\">10.1007/978-3-031-19433-7_27</a>}, booktitle={The Semantic Web -- ISWC 2022}, publisher={Springer International Publishing}, author={Qudus, Umair and Röder, Michael and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}, editor={Sattler, Ulrike and Hogan, Aidan and Keet, Maria and Presutti, Valentina}, pages={462--480} }","apa":"Qudus, U., Röder, M., Saleem, M., &#38; Ngonga Ngomo, A.-C. (n.d.). HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs. In U. Sattler, A. Hogan, M. Keet, &#38; V. Presutti (Eds.), <i>The Semantic Web -- ISWC 2022</i> (pp. 462--480). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-19433-7_27\">https://doi.org/10.1007/978-3-031-19433-7_27</a>"},"place":"Cham","author":[{"first_name":"Umair","orcid":"0000-0001-6714-8729","last_name":"Qudus","full_name":"Qudus, Umair","id":"83392"},{"full_name":"Röder, Michael","id":"67199","orcid":"https://orcid.org/0000-0002-8609-8277","last_name":"Röder","first_name":"Michael"},{"last_name":"Saleem","full_name":"Saleem, Muhammad","first_name":"Muhammad"},{"last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716","first_name":"Axel-Cyrille"}],"date_updated":"2025-09-11T09:37:16Z","doi":"10.1007/978-3-031-19433-7_27","conference":{"start_date":"2022-10-23","name":"International Semantic Web Conference (ISWC)","location":"Hanghzou, China","end_date":"2022-10-27"},"type":"conference","popular_science":"1","status":"public","editor":[{"last_name":"Sattler","full_name":"Sattler, Ulrike","first_name":"Ulrike"},{"first_name":"Aidan","last_name":"Hogan","full_name":"Hogan, Aidan"},{"full_name":"Keet, Maria","last_name":"Keet","first_name":"Maria"},{"first_name":"Valentina","full_name":"Presutti, Valentina","last_name":"Presutti"}],"department":[{"_id":"34"}],"user_id":"83392","_id":"32509","project":[{"name":"KnowGraphs: KnowGraphs: Knowledge Graphs at Scale","_id":"410"}],"file_date_updated":"2022-12-22T15:45:29Z"},{"abstract":[{"lang":"eng","text":"Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources -- increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources."}],"file":[{"success":1,"relation":"main_file","content_type":"application/pdf","file_size":534737,"file_name":"Preprint___Reinforcement_Learning_for_Dynamic_Resource_Allocation_in_Wireless_Networks.pdf","access_level":"closed","file_id":"25279","date_updated":"2021-10-04T10:43:19Z","date_created":"2021-10-04T10:43:19Z","creator":"hafifi"}],"status":"public","type":"conference","publication":"2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS'21)","ddc":["000"],"keyword":["reinforcement learning","admission control","wireless sensor networks"],"file_date_updated":"2021-10-04T10:43:19Z","language":[{"iso":"eng"}],"project":[{"_id":"27","name":"Akustische Sensornetzwerke - Teilprojekt \"Verteilte akustische Signalverarbeitung über funkbasierte Sensornetzwerke"}],"_id":"25278","user_id":"65718","place":"Hyderabad, India","year":"2021","citation":{"chicago":"Afifi, Haitham, Fabian Jakob Sauer, and Holger Karl. “Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding.” In <i>2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>. Hyderabad, India, 2021.","ieee":"H. Afifi, F. J. Sauer, and H. Karl, “Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding,” 2021.","ama":"Afifi H, Sauer FJ, Karl H. Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding. In: <i>2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>. ; 2021.","apa":"Afifi, H., Sauer, F. J., &#38; Karl, H. (2021). Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding. <i>2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>.","mla":"Afifi, Haitham, et al. “Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding.” <i>2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>, 2021.","short":"H. Afifi, F.J. Sauer, H. Karl, in: 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21), Hyderabad, India, 2021.","bibtex":"@inproceedings{Afifi_Sauer_Karl_2021, place={Hyderabad, India}, title={Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding}, booktitle={2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)}, author={Afifi, Haitham and Sauer, Fabian Jakob and Karl, Holger}, year={2021} }"},"has_accepted_license":"1","title":"Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding","date_updated":"2022-01-06T06:56:58Z","date_created":"2021-10-04T10:42:20Z","author":[{"first_name":"Haitham","last_name":"Afifi","id":"65718","full_name":"Afifi, Haitham"},{"first_name":"Fabian Jakob","full_name":"Sauer, Fabian Jakob","last_name":"Sauer"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}]},{"status":"public","file":[{"date_updated":"2021-10-04T10:58:07Z","creator":"hafifi","date_created":"2021-10-04T10:58:07Z","file_size":283616,"access_level":"closed","file_name":"ITG_2021_paper_26 (3).pdf","file_id":"25282","content_type":"application/pdf","success":1,"relation":"main_file"}],"abstract":[{"text":"Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal processing applications. Due to the spatial diversity of the microphone and their relative position to the acoustic source, not all microphones are equally useful for subsequent audio signal processing tasks, nor do they all have the same wireless data transmission rates. Hence, a central task in WASNs is to balance a microphone’s estimated acoustic utility against its transmission delay, selecting a best-possible subset of microphones to record audio signals.\r\n\r\nIn this work, we use reinforcement learning to decide if a microphone should be used or switched off to maximize the acoustic quality at low transmission delays, while minimizing switching frequency. In experiments with moving sources in a simulated acoustic environment, our method outperforms naive baseline comparisons","lang":"eng"}],"publication":"14. ITG Conference on Speech Communication (ITG 2021)","type":"conference","language":[{"iso":"eng"}],"file_date_updated":"2021-10-04T10:58:07Z","keyword":["microphone utility","microphone selection","wireless acoustic sensor network","network delay","reinforcement learning"],"ddc":["620"],"user_id":"65718","_id":"25281","project":[{"_id":"27","name":"Akustische Sensornetzwerke - Teilprojekt \"Verteilte akustische Signalverarbeitung über funkbasierte Sensornetzwerke"}],"citation":{"apa":"Afifi, H., Guenther, M., Brendel, A., Karl, H., &#38; Kellermann, W. (2021). Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities. <i>14. ITG Conference on Speech Communication (ITG 2021)</i>.","short":"H. Afifi, M. Guenther, A. Brendel, H. Karl, W. Kellermann, in: 14. ITG Conference on Speech Communication (ITG 2021), 2021.","mla":"Afifi, Haitham, et al. “Reinforcement Learning-Based Microphone Selection in Wireless Acoustic Sensor Networks Considering Network and Acoustic Utilities.” <i>14. ITG Conference on Speech Communication (ITG 2021)</i>, 2021.","bibtex":"@inproceedings{Afifi_Guenther_Brendel_Karl_Kellermann_2021, title={Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities}, booktitle={14. ITG Conference on Speech Communication (ITG 2021)}, author={Afifi, Haitham and Guenther, Michael and Brendel, Andreas and Karl, Holger and Kellermann, Walter}, year={2021} }","ieee":"H. Afifi, M. Guenther, A. Brendel, H. Karl, and W. Kellermann, “Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities,” 2021.","chicago":"Afifi, Haitham, Michael Guenther, Andreas Brendel, Holger Karl, and Walter Kellermann. “Reinforcement Learning-Based Microphone Selection in Wireless Acoustic Sensor Networks Considering Network and Acoustic Utilities.” In <i>14. ITG Conference on Speech Communication (ITG 2021)</i>, 2021.","ama":"Afifi H, Guenther M, Brendel A, Karl H, Kellermann W. Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities. In: <i>14. ITG Conference on Speech Communication (ITG 2021)</i>. ; 2021."},"year":"2021","has_accepted_license":"1","title":"Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities","date_created":"2021-10-04T10:59:50Z","author":[{"first_name":"Haitham","last_name":"Afifi","full_name":"Afifi, Haitham","id":"65718"},{"first_name":"Michael","last_name":"Guenther","full_name":"Guenther, Michael"},{"full_name":"Brendel, Andreas","last_name":"Brendel","first_name":"Andreas"},{"first_name":"Holger","last_name":"Karl","id":"126","full_name":"Karl, Holger"},{"full_name":"Kellermann, Walter","last_name":"Kellermann","first_name":"Walter"}],"date_updated":"2022-01-06T06:56:59Z"},{"publication":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","type":"conference","abstract":[{"text":"Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.","lang":"eng"}],"status":"public","_id":"20125","project":[{"_id":"4","name":"SFB 901 - Project Area C"},{"name":"SFB 901 - Subproject C4","_id":"16"},{"_id":"1","name":"SFB 901"}],"department":[{"_id":"75"}],"user_id":"63288","keyword":["Flow scheduling","Deadlines","Reinforcement learning"],"ddc":["000"],"language":[{"iso":"eng"}],"publication_status":"accepted","year":"2021","citation":{"apa":"Hasnain, A., &#38; Karl, H. (n.d.). Learning Flow Scheduling. In <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>. Las Vegas, USA: IEEE Computer Society. <a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>","bibtex":"@inproceedings{Hasnain_Karl, title={Learning Flow Scheduling}, DOI={<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>}, booktitle={2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)}, publisher={IEEE Computer Society}, author={Hasnain, Asif and Karl, Holger} }","mla":"Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>, IEEE Computer Society, doi:<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>.","short":"A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC), IEEE Computer Society, n.d.","ama":"Hasnain A, Karl H. Learning Flow Scheduling. In: <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>. IEEE Computer Society. doi:<a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>","chicago":"Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” In <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>. IEEE Computer Society, n.d. <a href=\"https://doi.org/10.1109/CCNC49032.2021.9369514\">https://doi.org/10.1109/CCNC49032.2021.9369514</a>.","ieee":"A. Hasnain and H. Karl, “Learning Flow Scheduling,” in <i>2021 IEEE 18th Annual Consumer Communications &#38; Networking Conference (CCNC)</i>, Las Vegas, USA."},"date_updated":"2022-01-06T06:54:20Z","publisher":"IEEE Computer Society","date_created":"2020-10-19T14:27:17Z","author":[{"first_name":"Asif","id":"63288","full_name":"Hasnain, Asif","last_name":"Hasnain"},{"first_name":"Holger","id":"126","full_name":"Karl, Holger","last_name":"Karl"}],"title":"Learning Flow Scheduling","conference":{"start_date":"2021-01-09","name":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","location":"Las Vegas, USA","end_date":"2021-01-12"},"doi":"https://doi.org/10.1109/CCNC49032.2021.9369514","main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9369514"}]},{"issue":"5","year":"2021","date_created":"2021-12-07T10:32:28Z","title":"Digitale Räume geben und nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie","publication":"Informationen Deutsch als Fremdsprache","abstract":[{"lang":"ger","text":"Das Auftreten der COVID-19-Pandemie stellt Fremdsprachenkurse vielerorts vor Herausforderungen. Unter Zuhilfenahme diverser digitaler Tools werden nicht nur Lernmaterialien online geteilt, sondern auch die Interaktion zwischen Lehrenden und Lernenden sowie der Lernenden untereinander in den virtuellen Raum verlagert. Qualitative Interviews mit den Beteiligten erfassen, wie diese mit den Herausforderungen videogestützten Sprachunterrichts umgehen und welche Strategien sie wählen, um Sprachenlernen zu ermöglichen. Die Ergebnisse zeigen auf, wo seitens der Kursorganisation und -durchführung Handlungsbedarf besteht.\r\n-----\r\nThe rise of the COVID-19 pandemic challenges the teaching and learning of foreign languages at many institutions. The implementation of various digital tools aids not only the online sharing of learning materials, but also shifts teacher-learner and learner-learner interaction to the virtual space. Via qualitative interviews, this study examines how both teachers and learners handle the challenges of language instruction based on videoconferences, and what strategies they employ to enable language learning. The results highlight areas in need of improvement in terms of course organization and facilitation."}],"keyword":["German language courses at university","interaction","digital space","language learning/teaching via videoconference"],"language":[{"iso":"ger"}],"publication_identifier":{"issn":["2511-0853","0724-9616"]},"publication_status":"published","page":"496-515","intvolume":"        48","citation":{"ama":"Drumm S, Müller M, Stenzel N. Digitale Räume geben und nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie. <i>Informationen Deutsch als Fremdsprache</i>. 2021;48(5):496-515. doi:<a href=\"https://doi.org/10.1515/infodaf-2021-0069\">10.1515/infodaf-2021-0069</a>","ieee":"S. Drumm, M. Müller, and N. Stenzel, “Digitale Räume geben und nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie,” <i>Informationen Deutsch als Fremdsprache</i>, vol. 48, no. 5, pp. 496–515, 2021, doi: <a href=\"https://doi.org/10.1515/infodaf-2021-0069\">10.1515/infodaf-2021-0069</a>.","chicago":"Drumm, Sandra, Mareike Müller, and Nadja Stenzel. “Digitale Räume geben und nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie.” <i>Informationen Deutsch als Fremdsprache</i> 48, no. 5 (2021): 496–515. <a href=\"https://doi.org/10.1515/infodaf-2021-0069\">https://doi.org/10.1515/infodaf-2021-0069</a>.","bibtex":"@article{Drumm_Müller_Stenzel_2021, title={Digitale Räume geben und nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie}, volume={48}, DOI={<a href=\"https://doi.org/10.1515/infodaf-2021-0069\">10.1515/infodaf-2021-0069</a>}, number={5}, journal={Informationen Deutsch als Fremdsprache}, author={Drumm, Sandra and Müller, Mareike and Stenzel, Nadja}, year={2021}, pages={496–515} }","mla":"Drumm, Sandra, et al. “Digitale Räume geben und nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie.” <i>Informationen Deutsch als Fremdsprache</i>, vol. 48, no. 5, 2021, pp. 496–515, doi:<a href=\"https://doi.org/10.1515/infodaf-2021-0069\">10.1515/infodaf-2021-0069</a>.","short":"S. Drumm, M. Müller, N. Stenzel, Informationen Deutsch als Fremdsprache 48 (2021) 496–515.","apa":"Drumm, S., Müller, M., &#38; Stenzel, N. (2021). Digitale Räume geben und nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie. <i>Informationen Deutsch als Fremdsprache</i>, <i>48</i>(5), 496–515. <a href=\"https://doi.org/10.1515/infodaf-2021-0069\">https://doi.org/10.1515/infodaf-2021-0069</a>"},"date_updated":"2022-01-06T06:58:02Z","volume":48,"author":[{"first_name":"Sandra","full_name":"Drumm, Sandra","last_name":"Drumm"},{"last_name":"Müller","id":"71540","full_name":"Müller, Mareike","first_name":"Mareike"},{"last_name":"Stenzel","full_name":"Stenzel, Nadja","first_name":"Nadja"}],"doi":"10.1515/infodaf-2021-0069","type":"journal_article","status":"public","_id":"28349","department":[{"_id":"468"}],"user_id":"71540","article_type":"original"},{"file_date_updated":"2021-10-15T15:54:41Z","language":[{"iso":"eng"}],"ddc":["000"],"keyword":["Software Requirements","Natural Language Processing","Transfer Learning","On-The-Fly Computing"],"user_id":"58701","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B1","_id":"9"}],"_id":"26049","file":[{"file_size":411667,"file_name":"Bäumer et al. (2021), Baeumer2021.pdf","access_level":"closed","file_id":"26282","date_updated":"2021-10-15T15:54:41Z","date_created":"2021-10-15T15:54:41Z","creator":"jkers","success":1,"relation":"main_file","content_type":"application/pdf"}],"status":"public","abstract":[{"text":"Content is the new oil. Users consume billions of terabytes a day while surfing on news sites or blogs, posting on social media sites, and sending chat messages around the globe. While content is heterogeneous, the dominant form of web content is text. There are situations where more diversity needs to be introduced into text content, for example, to reuse it on websites or to allow a chatbot to base its models on the information conveyed rather than of the language used. In order to achieve this, paraphrasing techniques have been developed: One example is Text spinning, a technique that automatically paraphrases text while leaving the intent intact. This makes it easier to reuse content, or to change the language generated by the bot more human. One method for modifying texts is a combination of translation and back-translation. This paper presents NATTS, a naive approach that uses transformer-based translation models to create diversified text, combining translation steps in one model. An advantage of this approach is that it can be fine-tuned and handle technical language.","lang":"eng"}],"type":"conference","publication":"PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021","conference":{"name":"18th International Conference on Applied Computing","start_date":"13.10.2021","end_date":"15.10.2021","location":"Lisbon, Portugal"},"title":"IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING","author":[{"full_name":"Bäumer, Frederik Simon","last_name":"Bäumer","first_name":"Frederik Simon"},{"full_name":"Kersting, Joschka","id":"58701","last_name":"Kersting","first_name":"Joschka"},{"full_name":"Denisov, Sergej","last_name":"Denisov","first_name":"Sergej"},{"first_name":"Michaela","orcid":"0000-0002-8180-5606","last_name":"Geierhos","id":"42496","full_name":"Geierhos, Michaela"}],"date_created":"2021-10-11T15:26:58Z","publisher":"IADIS","date_updated":"2022-01-06T06:57:16Z","citation":{"ieee":"F. S. Bäumer, J. Kersting, S. Denisov, and M. Geierhos, “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING,” in <i>PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021</i>, Lisbon, Portugal, 2021, pp. 221--225.","chicago":"Bäumer, Frederik Simon, Joschka Kersting, Sergej Denisov, and Michaela Geierhos. “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING.” In <i>PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021</i>, 221--225. IADIS, 2021.","ama":"Bäumer FS, Kersting J, Denisov S, Geierhos M. IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING. In: <i>PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021</i>. IADIS; 2021:221--225.","mla":"Bäumer, Frederik Simon, et al. “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING.” <i>PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021</i>, IADIS, 2021, pp. 221--225.","short":"F.S. Bäumer, J. Kersting, S. Denisov, M. Geierhos, in: PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, IADIS, 2021, pp. 221--225.","bibtex":"@inproceedings{Bäumer_Kersting_Denisov_Geierhos_2021, title={IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING}, booktitle={PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021}, publisher={IADIS}, author={Bäumer, Frederik Simon and Kersting, Joschka and Denisov, Sergej and Geierhos, Michaela}, year={2021}, pages={221--225} }","apa":"Bäumer, F. S., Kersting, J., Denisov, S., &#38; Geierhos, M. (2021). IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING. <i>PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021</i>, 221--225."},"page":"221--225","year":"2021","has_accepted_license":"1"},{"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","type":"journal_article","status":"public","abstract":[{"lang":"eng","text":"Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers."}],"department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"user_id":"5786","_id":"21004","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"language":[{"iso":"eng"}],"keyword":["Automated Machine Learning","Multi Label Classification","Hierarchical Planning","Bayesian Optimization"],"publication_identifier":{"issn":["0162-8828","2160-9292","1939-3539"]},"publication_status":"published","page":"1-1","citation":{"apa":"Wever, M. D., Tornede, A., Mohr, F., &#38; Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>","mla":"Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, pp. 1–1, doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","bibtex":"@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}, year={2021}, pages={1–1} }","short":"M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1.","ama":"Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. Published online 2021:1-1. doi:<a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>","ieee":"M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, pp. 1–1, 2021, doi: <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">10.1109/tpami.2021.3051276</a>.","chicago":"Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 2021, 1–1. <a href=\"https://doi.org/10.1109/tpami.2021.3051276\">https://doi.org/10.1109/tpami.2021.3051276</a>."},"year":"2021","author":[{"last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","id":"33176","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik"},{"first_name":"Alexander","last_name":"Tornede","full_name":"Tornede, Alexander","id":"38209"},{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"date_created":"2021-01-16T14:48:13Z","date_updated":"2022-01-06T06:54:42Z","doi":"10.1109/tpami.2021.3051276","title":"AutoML for Multi-Label Classification: Overview and Empirical Evaluation"},{"publisher":"IEEE Communications Society","date_created":"2021-01-16T18:24:19Z","title":"Learning Coflow Admissions","year":"2021","keyword":["Coflow scheduling","Reinforcement learning","Deadlines"],"ddc":["000"],"language":[{"iso":"eng"}],"publication":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","abstract":[{"text":"Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering.  LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines.","lang":"eng"}],"date_updated":"2022-01-06T06:54:42Z","author":[{"id":"63288","full_name":"Hasnain, Asif","last_name":"Hasnain","first_name":"Asif"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"doi":"10.1109/INFOCOMWKSHPS51825.2021.9484599","conference":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","start_date":"2021-05-10","end_date":"2021-05-13","location":"Vancouver BC Canada"},"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9484599"}],"publication_status":"accepted","related_material":{"link":[{"url":"https://ieeexplore.ieee.org/document/9484599","relation":"confirmation"}]},"citation":{"ama":"Hasnain A, Karl H. Learning Coflow Admissions. In: <i>IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>. IEEE Communications Society. doi:<a href=\"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599\">10.1109/INFOCOMWKSHPS51825.2021.9484599</a>","ieee":"A. Hasnain and H. Karl, “Learning Coflow Admissions,” in <i>IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>, Vancouver BC Canada.","chicago":"Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” In <i>IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>. IEEE Communications Society, n.d. <a href=\"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599\">https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599</a>.","apa":"Hasnain, A., &#38; Karl, H. (n.d.). Learning Coflow Admissions. In <i>IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>. Vancouver BC Canada: IEEE Communications Society. <a href=\"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599\">https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599</a>","bibtex":"@inproceedings{Hasnain_Karl, title={Learning Coflow Admissions}, DOI={<a href=\"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599\">10.1109/INFOCOMWKSHPS51825.2021.9484599</a>}, booktitle={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}, publisher={IEEE Communications Society}, author={Hasnain, Asif and Karl, Holger} }","short":"A. Hasnain, H. Karl, in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, n.d.","mla":"Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” <i>IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)</i>, IEEE Communications Society, doi:<a href=\"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599\">10.1109/INFOCOMWKSHPS51825.2021.9484599</a>."},"_id":"21005","project":[{"name":"SFB 901 - Subproject C4","_id":"16"},{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901","_id":"1"}],"department":[{"_id":"75"}],"user_id":"63288","type":"conference","status":"public"},{"title":"A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks","date_updated":"2022-01-06T06:55:00Z","author":[{"id":"65718","full_name":"Afifi, Haitham","last_name":"Afifi","first_name":"Haitham"},{"full_name":"Ramaswamy, Arunselvan","id":"66937","orcid":"https://orcid.org/ 0000-0001-7547-8111","last_name":"Ramaswamy","first_name":"Arunselvan"},{"first_name":"Holger","last_name":"Karl","id":"126","full_name":"Karl, Holger"}],"date_created":"2021-03-12T16:03:53Z","year":"2021","citation":{"apa":"Afifi, H., Ramaswamy, A., &#38; Karl, H. (2021). A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks. In <i>2021 IEEE 18th Annual Consumer Communications \\&#38; Networking Conference (CCNC) (CCNC 2021)</i>.","mla":"Afifi, Haitham, et al. “A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks.” <i>2021 IEEE 18th Annual Consumer Communications \\&#38; Networking Conference (CCNC) (CCNC 2021)</i>, 2021.","short":"H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications \\&#38; Networking Conference (CCNC) (CCNC 2021), 2021.","bibtex":"@inproceedings{Afifi_Ramaswamy_Karl_2021, title={A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks}, booktitle={2021 IEEE 18th Annual Consumer Communications \\&#38; Networking Conference (CCNC) (CCNC 2021)}, author={Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}, year={2021} }","chicago":"Afifi, Haitham, Arunselvan Ramaswamy, and Holger Karl. “A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks.” In <i>2021 IEEE 18th Annual Consumer Communications \\&#38; Networking Conference (CCNC) (CCNC 2021)</i>, 2021.","ieee":"H. Afifi, A. Ramaswamy, and H. Karl, “A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks,” in <i>2021 IEEE 18th Annual Consumer Communications \\&#38; Networking Conference (CCNC) (CCNC 2021)</i>, 2021.","ama":"Afifi H, Ramaswamy A, Karl H. A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks. In: <i>2021 IEEE 18th Annual Consumer Communications \\&#38; Networking Conference (CCNC) (CCNC 2021)</i>. ; 2021."},"keyword":["reinforcement learning","wireless sensor networks","resource allocation","acoustic sensor networks"],"language":[{"iso":"eng"}],"project":[{"_id":"27","name":"Akustische Sensornetzwerke - Teilprojekt \"Verteilte akustische Signalverarbeitung über funkbasierte Sensornetzwerke"}],"_id":"21479","user_id":"65718","abstract":[{"lang":"eng","text":"Two of the most important metrics when developing Wireless Sensor Networks (WSNs) applications are the Quality of Information (QoI) and Quality of Service (QoS). The former is used to specify the quality of the collected data by the sensors (e.g., measurements error or signal's intensity), while the latter defines the network's performance and availability (e.g., packet losses and latency). In this paper, we consider an example of wireless acoustic sensor networks, where we select a subset of microphones for two different objectives. First, we maximize the recording quality under QoS constraints. Second, we apply a trade-off between QoI and QoS. We formulate the problem as a constrained Markov Decision Problem (MDP) and solve it using reinforcement learning (RL). We compare the RL solution to a baseline model and show that in case of QoS-guarantee objective, the RL solution has an optimality gap up to 1\\%. Meanwhile, the RL solution is better than the baseline with improvements up to 23\\%, when using the trade-off objective."}],"status":"public","type":"conference","publication":"2021 IEEE 18th Annual Consumer Communications \\& Networking Conference (CCNC) (CCNC 2021)"},{"citation":{"ieee":"S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination Using Deep Reinforcement Learning,” in <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>, Washington, DC, USA, 2021.","chicago":"Schneider, Stefan Balthasar, Haydar Qarawlus, and Holger Karl. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” In <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>. IEEE, 2021.","ama":"Schneider SB, Qarawlus H, Karl H. Distributed Online Service Coordination Using Deep Reinforcement Learning. In: <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>. IEEE; 2021.","bibtex":"@inproceedings{Schneider_Qarawlus_Karl_2021, title={Distributed Online Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Distributed Computing Systems (ICDCS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}, year={2021} }","short":"S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE, 2021.","mla":"Schneider, Stefan Balthasar, et al. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>, IEEE, 2021.","apa":"Schneider, S. B., Qarawlus, H., &#38; Karl, H. (2021). Distributed Online Service Coordination Using Deep Reinforcement Learning. In <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>. Washington, DC, USA: IEEE."},"related_material":{"link":[{"relation":"software","url":"https://github.com/ RealVNF/distributed-drl-coordination"}]},"has_accepted_license":"1","conference":{"name":"IEEE International Conference on Distributed Computing Systems (ICDCS)","location":"Washington, DC, USA"},"author":[{"first_name":"Stefan Balthasar","id":"35343","full_name":"Schneider, Stefan Balthasar","orcid":"0000-0001-8210-4011","last_name":"Schneider"},{"full_name":"Qarawlus, Haydar","last_name":"Qarawlus","first_name":"Haydar"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"date_updated":"2022-01-06T06:55:04Z","oa":"1","status":"public","type":"conference","file_date_updated":"2021-03-18T17:12:56Z","user_id":"35343","department":[{"_id":"75"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"16","name":"SFB 901 - Subproject C4"}],"_id":"21543","year":"2021","title":"Distributed Online Service Coordination Using Deep Reinforcement Learning","date_created":"2021-03-18T17:15:47Z","publisher":"IEEE","file":[{"content_type":"application/pdf","relation":"main_file","date_updated":"2021-03-18T17:12:56Z","creator":"stschn","date_created":"2021-03-18T17:12:56Z","title":"Distributed Online Service Coordination Using Deep Reinforcement Learning","file_size":606321,"file_name":"public_author_version.pdf","file_id":"21544","access_level":"open_access"}],"abstract":[{"text":"Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks.\r\n\r\nTo address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).","lang":"eng"}],"publication":"IEEE International Conference on Distributed Computing Systems (ICDCS)","language":[{"iso":"eng"}],"ddc":["000"],"keyword":["network management","service management","coordination","reinforcement learning","distributed"]},{"_id":"22481","department":[{"_id":"59"},{"_id":"485"}],"user_id":"38240","keyword":["Image Processing","Defect Detection","wooden surfaces","Machine Learning","Neural Networks"],"language":[{"iso":"eng"}],"publication":"22nd IEEE International Conference on Industrial Technology (ICIT)","type":"conference","abstract":[{"lang":"eng","text":"During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters."}],"status":"public","date_updated":"2022-01-06T06:55:33Z","publisher":"IEEE","author":[{"last_name":"Sander","full_name":"Sander, Tom","first_name":"Tom"},{"first_name":"Sven","id":"38240","full_name":"Lange, Sven","last_name":"Lange"},{"first_name":"Ulrich","last_name":"Hilleringmann","full_name":"Hilleringmann, Ulrich"},{"first_name":"Volker","full_name":"Geneis, Volker","last_name":"Geneis"},{"full_name":"Hedayat, Christian","last_name":"Hedayat","first_name":"Christian"},{"first_name":"Harald","last_name":"Kuhn","full_name":"Kuhn, Harald"},{"full_name":"Gockel, Franz-Barthold","last_name":"Gockel","first_name":"Franz-Barthold"}],"date_created":"2021-06-20T23:32:11Z","title":"Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction","doi":"10.1109/icit46573.2021.9453646","conference":{"end_date":"2021-03-12","location":"Valencia, Spain ","name":"22nd IEEE International Conference on Industrial Technology (ICIT)","start_date":"2021-03-10"},"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/9453646"}],"publication_identifier":{"isbn":["9781728157306"]},"publication_status":"published","place":"Valencia, Spain ","year":"2021","citation":{"ama":"Sander T, Lange S, Hilleringmann U, et al. Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction. In: <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>. Valencia, Spain : IEEE; 2021. doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">10.1109/icit46573.2021.9453646</a>","ieee":"T. Sander <i>et al.</i>, “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction,” in <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, Valencia, Spain , 2021.","chicago":"Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneis, Christian Hedayat, Harald Kuhn, and Franz-Barthold Gockel. “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction.” In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>. Valencia, Spain : IEEE, 2021. <a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">https://doi.org/10.1109/icit46573.2021.9453646</a>.","bibtex":"@inproceedings{Sander_Lange_Hilleringmann_Geneis_Hedayat_Kuhn_Gockel_2021, place={Valencia, Spain }, title={Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction}, DOI={<a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">10.1109/icit46573.2021.9453646</a>}, booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)}, publisher={IEEE}, author={Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneis, Volker and Hedayat, Christian and Kuhn, Harald and Gockel, Franz-Barthold}, year={2021} }","mla":"Sander, Tom, et al. “Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction.” <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">10.1109/icit46573.2021.9453646</a>.","short":"T. Sander, S. Lange, U. Hilleringmann, V. Geneis, C. Hedayat, H. Kuhn, F.-B. Gockel, in: 22nd IEEE International Conference on Industrial Technology (ICIT), IEEE, Valencia, Spain , 2021.","apa":"Sander, T., Lange, S., Hilleringmann, U., Geneis, V., Hedayat, C., Kuhn, H., &#38; Gockel, F.-B. (2021). Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction. In <i>22nd IEEE International Conference on Industrial Technology (ICIT)</i>. Valencia, Spain : IEEE. <a href=\"https://doi.org/10.1109/icit46573.2021.9453646\">https://doi.org/10.1109/icit46573.2021.9453646</a>"}},{"citation":{"apa":"Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., &#38; Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. <i>Transactions on Network and Service Management</i>. <a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">https://doi.org/10.1109/TNSM.2021.3076503</a>","short":"S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, A. Hecker, Transactions on Network and Service Management (2021).","mla":"Schneider, Stefan Balthasar, et al. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network and Service Management</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>.","bibtex":"@article{Schneider_Khalili_Manzoor_Qarawlus_Schellenberg_Karl_Hecker_2021, title={Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}, DOI={<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>}, journal={Transactions on Network and Service Management}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}, year={2021} }","ama":"Schneider SB, Khalili R, Manzoor A, et al. Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. <i>Transactions on Network and Service Management</i>. 2021. doi:<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>","ieee":"S. B. Schneider <i>et al.</i>, “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning,” <i>Transactions on Network and Service Management</i>, 2021.","chicago":"Schneider, Stefan Balthasar, Ramin Khalili, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, and Artur Hecker. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network and Service Management</i>, 2021. <a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">https://doi.org/10.1109/TNSM.2021.3076503</a>."},"year":"2021","has_accepted_license":"1","doi":"10.1109/TNSM.2021.3076503","title":"Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning","author":[{"last_name":"Schneider","orcid":"0000-0001-8210-4011","full_name":"Schneider, Stefan Balthasar","id":"35343","first_name":"Stefan Balthasar"},{"first_name":"Ramin","full_name":"Khalili, Ramin","last_name":"Khalili"},{"full_name":"Manzoor, Adnan","last_name":"Manzoor","first_name":"Adnan"},{"first_name":"Haydar","full_name":"Qarawlus, Haydar","last_name":"Qarawlus"},{"first_name":"Rafael","last_name":"Schellenberg","full_name":"Schellenberg, Rafael"},{"id":"126","full_name":"Karl, Holger","last_name":"Karl","first_name":"Holger"},{"first_name":"Artur","full_name":"Hecker, Artur","last_name":"Hecker"}],"date_created":"2021-04-27T08:04:16Z","date_updated":"2022-01-06T06:55:15Z","oa":"1","publisher":"IEEE","status":"public","file":[{"description":"Author version of the accepted paper","file_size":4172270,"file_id":"21809","file_name":"ris-accepted-version.pdf","access_level":"open_access","date_updated":"2021-04-27T08:01:26Z","date_created":"2021-04-27T08:01:26Z","creator":"stschn","relation":"main_file","content_type":"application/pdf"}],"abstract":[{"text":"Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge).\r\n\r\nWe propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.","lang":"eng"}],"publication":"Transactions on Network and Service Management","type":"journal_article","file_date_updated":"2021-04-27T08:01:26Z","language":[{"iso":"eng"}],"keyword":["network management","service management","coordination","reinforcement learning","self-learning","self-adaptation","multi-objective"],"article_type":"original","ddc":["000"],"department":[{"_id":"75"}],"user_id":"35343","_id":"21808","project":[{"_id":"1","name":"SFB 901"},{"_id":"4","name":"SFB 901 - Project Area C"},{"name":"SFB 901 - Subproject C4","_id":"16"}]},{"abstract":[{"lang":"eng","text":"Graph neural networks (GNNs) have been successfully applied in many structured data domains, with applications ranging from molecular property prediction to the analysis of social networks. Motivated by the broad applicability of GNNs, we propose the family of so-called RankGNNs, a combination of neural Learning to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences between graphs, suggesting that one of them is preferred over the other. One practical application of this problem is drug screening, where an expert wants to find the most promising molecules in a large collection of drug candidates. We empirically demonstrate that our proposed pair-wise RankGNN approach either significantly outperforms or at least matches the ranking performance of the naive point-wise baseline approach, in which the LtR problem is solved via GNN-based graph regression."}],"publication":"Proceedings of The 24th International Conference on Discovery Science (DS 2021)","keyword":["Graph-structured data","Graph neural networks","Preference learning","Learning to rank"],"language":[{"iso":"eng"}],"external_id":{"arxiv":["2104.08869"]},"year":"2021","quality_controlled":"1","title":"Ranking Structured Objects with Graph Neural Networks","publisher":"Springer","date_created":"2021-11-11T14:15:18Z","editor":[{"full_name":"Soares, Carlos","last_name":"Soares","first_name":"Carlos"},{"full_name":"Torgo, Luis","last_name":"Torgo","first_name":"Luis"}],"status":"public","type":"conference","_id":"27381","user_id":"48192","series_title":"Lecture Notes in Computer Science","department":[{"_id":"355"}],"citation":{"ama":"Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks. In: Soares C, Torgo L, eds. <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>. Vol 12986. Lecture Notes in Computer Science. Springer; 2021:166-180. doi:<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>","ieee":"C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural Networks,” in <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>.","chicago":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” In <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, 12986:166–80. Lecture Notes in Computer Science. Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">https://doi.org/10.1007/978-3-030-88942-5</a>.","apa":"Damke, C., &#38; Hüllermeier, E. (2021). Ranking Structured Objects with Graph Neural Networks. In C. Soares &#38; L. Torgo (Eds.), <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i> (Vol. 12986, pp. 166–180). Springer. <a href=\"https://doi.org/10.1007/978-3-030-88942-5\">https://doi.org/10.1007/978-3-030-88942-5</a>","mla":"Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph Neural Networks.” <i>Proceedings of The 24th International Conference on Discovery Science (DS 2021)</i>, edited by Carlos Soares and Luis Torgo, vol. 12986, Springer, 2021, pp. 166–80, doi:<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>.","bibtex":"@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-88942-5\">10.1007/978-3-030-88942-5</a>}, booktitle={Proceedings of The 24th International Conference on Discovery Science (DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke}, editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture Notes in Computer Science} }","short":"C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of The 24th International Conference on Discovery Science (DS 2021), Springer, 2021, pp. 166–180."},"intvolume":"     12986","page":"166-180","publication_status":"published","publication_identifier":{"isbn":["9783030889418","9783030889425"],"issn":["0302-9743","1611-3349"]},"conference":{"location":"Halifax, Canada","end_date":"2021-10-13","start_date":"2021-10-11","name":"24th International Conference on Discovery Science"},"doi":"10.1007/978-3-030-88942-5","date_updated":"2022-04-11T22:08:12Z","author":[{"first_name":"Clemens","full_name":"Damke, Clemens","id":"48192","last_name":"Damke","orcid":"0000-0002-0455-0048"},{"first_name":"Eyke","last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke"}],"volume":12986},{"language":[{"iso":"eng"}],"keyword":["Ideational impact","citation classification","academic recommender systems","natural language processing","deep learning","cumulative tradition"],"ddc":["000"],"publication":"Decision Support Systems","file":[{"relation":"main_file","content_type":"application/pdf","file_name":"DECSUP-D-20-00312 - PREPUBLICATION.pdf","access_level":"open_access","file_id":"20213","file_size":440903,"date_created":"2020-10-27T13:31:01Z","creator":"hsiemes","date_updated":"2020-10-27T13:31:01Z"}],"abstract":[{"text":"Ideational impact refers to the uptake of a paper's ideas and concepts by subsequent research. It is defined in stark contrast to total citation impact, a measure predominantly used in research evaluation that assumes that all citations are equal. Understanding ideational impact is critical for evaluating research impact and understanding how scientific disciplines build a cumulative tradition. Research has only recently developed automated citation classification techniques to distinguish between different types of citations and generally does not emphasize the conceptual content of the citations and its ideational impact. To address this problem, we develop Deep Content-enriched Ideational Impact Classification (Deep-CENIC) as the first automated approach for ideational impact classification to support researchers' literature search practices. We evaluate Deep-CENIC on 1,256 papers citing 24 information systems review articles from the IT business value domain. We show that Deep-CENIC significantly outperforms state-of-the-art benchmark models. We contribute to information systems research by operationalizing the concept of ideational impact, designing a recommender system for academic papers based on deep learning techniques, and empirically exploring the ideational impact of the IT business value domain.\r\n","lang":"eng"}],"date_created":"2020-10-27T13:28:21Z","title":"Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach","issue":"January","year":"2021","department":[{"_id":"277"}],"user_id":"72850","_id":"20212","file_date_updated":"2020-10-27T13:31:01Z","article_number":"113432","type":"journal_article","status":"public","volume":140,"author":[{"last_name":"Prester","full_name":"Prester, Julian","first_name":"Julian"},{"first_name":"Gerit","full_name":"Wagner, Gerit","last_name":"Wagner"},{"first_name":"Guido","id":"72850","full_name":"Schryen, Guido","last_name":"Schryen"},{"first_name":"Nik Rushdi","full_name":"Hassan, Nik Rushdi","last_name":"Hassan"}],"date_updated":"2022-06-10T06:55:32Z","oa":"1","has_accepted_license":"1","intvolume":"       140","citation":{"apa":"Prester, J., Wagner, G., Schryen, G., &#38; Hassan, N. R. (2021). Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach. <i>Decision Support Systems</i>, <i>140</i>(January), Article 113432.","bibtex":"@article{Prester_Wagner_Schryen_Hassan_2021, title={Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach}, volume={140}, number={January113432}, journal={Decision Support Systems}, author={Prester, Julian and Wagner, Gerit and Schryen, Guido and Hassan, Nik Rushdi}, year={2021} }","short":"J. Prester, G. Wagner, G. Schryen, N.R. Hassan, Decision Support Systems 140 (2021).","mla":"Prester, Julian, et al. “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.” <i>Decision Support Systems</i>, vol. 140, no. January, 113432, 2021.","ama":"Prester J, Wagner G, Schryen G, Hassan NR. Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach. <i>Decision Support Systems</i>. 2021;140(January).","ieee":"J. Prester, G. Wagner, G. Schryen, and N. R. Hassan, “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach,” <i>Decision Support Systems</i>, vol. 140, no. January, Art. no. 113432, 2021.","chicago":"Prester, Julian, Gerit Wagner, Guido Schryen, and Nik Rushdi Hassan. “Classifying the Ideational Impact of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.” <i>Decision Support Systems</i> 140, no. January (2021)."}},{"citation":{"ama":"Schneider SB, Karl H, Khalili R, Hecker A. <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>.; 2021.","ieee":"S. B. Schneider, H. Karl, R. Khalili, and A. Hecker, <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>. 2021.","chicago":"Schneider, Stefan Balthasar, Holger Karl, Ramin Khalili, and Artur Hecker. <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>, 2021.","mla":"Schneider, Stefan Balthasar, et al. <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>. 2021.","short":"S.B. Schneider, H. Karl, R. Khalili, A. Hecker, DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning, 2021.","bibtex":"@book{Schneider_Karl_Khalili_Hecker_2021, title={DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning}, author={Schneider, Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2021} }","apa":"Schneider, S. B., Karl, H., Khalili, R., &#38; Hecker, A. (2021). <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>."},"year":"2021","has_accepted_license":"1","title":"DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning","author":[{"last_name":"Schneider","orcid":"0000-0001-8210-4011","full_name":"Schneider, Stefan Balthasar","id":"35343","first_name":"Stefan Balthasar"},{"first_name":"Holger","full_name":"Karl, Holger","id":"126","last_name":"Karl"},{"last_name":"Khalili","full_name":"Khalili, Ramin","first_name":"Ramin"},{"last_name":"Hecker","full_name":"Hecker, Artur","first_name":"Artur"}],"date_created":"2022-10-20T16:44:19Z","oa":"1","date_updated":"2022-11-18T09:59:27Z","file":[{"date_updated":"2022-10-20T16:41:10Z","date_created":"2022-10-20T16:41:10Z","creator":"stschn","file_size":2521656,"file_name":"preprint.pdf","file_id":"33855","access_level":"open_access","content_type":"application/pdf","relation":"main_file"}],"status":"public","abstract":[{"lang":"eng","text":"Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice.\r\n\r\nInstead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches."}],"type":"working_paper","language":[{"iso":"eng"}],"file_date_updated":"2022-10-20T16:41:10Z","ddc":["004"],"keyword":["mobility management","coordinated multipoint","CoMP","cell selection","resource management","reinforcement learning","multi agent","MARL","self-learning","self-adaptation","QoE"],"user_id":"477","department":[{"_id":"75"}],"project":[{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"},{"_id":"1","name":"SFB 901: SFB 901"}],"_id":"33854"},{"year":"2021","publisher":"Springer","date_created":"2021-12-22T17:20:50Z","title":"Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis","publication":"Edition Fachdidaktiken","abstract":[{"lang":"ger","text":"Das Studium der Wirtschaftspädagogik bereitet Studierende auf das didaktische Handeln in beruflichen Lehr-Lernkontexten (u. a. berufliche Schulen, Ausbildung in Betrieben) vor. Theorie-Praxis-Verzahnung ist somit aus zwei Perspektiven zu modellieren: Einerseits geht es um den Aufbau eines fachwissenschaftlichen Verständnisses, welches von den Handlungszusammenhängen in einer beruflichen Domäne mit kaufmännisch-verwaltenden Bezügen ausgeht und weniger auf einer rein fachwissenschaftlichen Bildung beruht. Die zukünftige Berufspraxis der Schülerinnen und Schüler muss in den Blick genommen werden. Andererseits geht es um die Professionalisierung als pädagogisches Personal, welches berufsbezogene Lernprozesse fachdidaktisch gestalten kann. Die zukünftige Lehrpraxis in beruflichen Lehr-Lernkontexten ist in den Blick zu nehmen. Zielstellung des Beitrages ist es, diese doppelte Theorie-Praxis-Verzahnung als Konstitutionsmerkmal der Wirtschaftspädagogik aufzuzeigen (Abschn. 2), um darauf basierend anhand von Theorien des Lernens am Arbeitsplatz Potenziale und Grenzen des Lernortes Praxis als Beitrag zur Professionalisierung angehender Wirtschaftspädagog*innen im Studium herauszuarbeiten (Abschn. 3). Am Beispiel des Konzeptes von Universitätsschulen soll eine Umsetzungsvariante zur Theorie-Praxis-Verzahnung unter Herausarbeitung der Potenziale der jeweiligen Lernorte Schule und Universität aufgezeigt werden (Abschn. 4)."}],"keyword":["Berufliche Lehrerbildung","Professional Learning","Theorie-Praxis-Verzahnung","Wirtschaftspädagogik","Universitätsschulen"],"language":[{"iso":"ger"}],"publication_status":"published","publication_identifier":{"issn":["2524-8677","2524-8685"],"isbn":["9783658325671","9783658325688"]},"related_material":{"link":[{"url":"https://link.springer.com/chapter/10.1007%2F978-3-658-32568-8_22","relation":"confirmation"}]},"place":"Wiesbaden","citation":{"apa":"Gerholz, K.-H., &#38; Goller, M. (2021). Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis. In C. Caruso, C. Harteis, &#38; A. Gröschner (Eds.), <i>Edition Fachdidaktiken</i> (pp. 393–419). Springer. <a href=\"https://doi.org/10.1007/978-3-658-32568-8_22\">https://doi.org/10.1007/978-3-658-32568-8_22</a>","bibtex":"@inbook{Gerholz_Goller_2021, place={Wiesbaden}, title={Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis}, DOI={<a href=\"https://doi.org/10.1007/978-3-658-32568-8_22\">10.1007/978-3-658-32568-8_22</a>}, booktitle={Edition Fachdidaktiken}, publisher={Springer}, author={Gerholz, Karl-Heinz and Goller, Michael}, editor={Caruso, Carina and Harteis, Christian and Gröschner, Alexander}, year={2021}, pages={393–419} }","mla":"Gerholz, Karl-Heinz, and Michael Goller. “Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis.” <i>Edition Fachdidaktiken</i>, edited by Carina Caruso et al., Springer, 2021, pp. 393–419, doi:<a href=\"https://doi.org/10.1007/978-3-658-32568-8_22\">10.1007/978-3-658-32568-8_22</a>.","short":"K.-H. Gerholz, M. Goller, in: C. Caruso, C. Harteis, A. Gröschner (Eds.), Edition Fachdidaktiken, Springer, Wiesbaden, 2021, pp. 393–419.","ama":"Gerholz K-H, Goller M. Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis. In: Caruso C, Harteis C, Gröschner A, eds. <i>Edition Fachdidaktiken</i>. Springer; 2021:393-419. doi:<a href=\"https://doi.org/10.1007/978-3-658-32568-8_22\">10.1007/978-3-658-32568-8_22</a>","ieee":"K.-H. Gerholz and M. Goller, “Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis,” in <i>Edition Fachdidaktiken</i>, C. Caruso, C. Harteis, and A. Gröschner, Eds. Wiesbaden: Springer, 2021, pp. 393–419.","chicago":"Gerholz, Karl-Heinz, and Michael Goller. “Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis.” In <i>Edition Fachdidaktiken</i>, edited by Carina Caruso, Christian Harteis, and Alexander Gröschner, 393–419. Wiesbaden: Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-658-32568-8_22\">https://doi.org/10.1007/978-3-658-32568-8_22</a>."},"page":"393-419","date_updated":"2022-02-03T13:26:19Z","author":[{"first_name":"Karl-Heinz","last_name":"Gerholz","full_name":"Gerholz, Karl-Heinz"},{"first_name":"Michael","id":"30984","full_name":"Goller, Michael","orcid":"0000-0002-2820-9178","last_name":"Goller"}],"doi":"10.1007/978-3-658-32568-8_22","type":"book_chapter","editor":[{"first_name":"Carina","full_name":"Caruso, Carina","last_name":"Caruso"},{"first_name":"Christian","full_name":"Harteis, Christian","last_name":"Harteis"},{"first_name":"Alexander","full_name":"Gröschner, Alexander","last_name":"Gröschner"}],"status":"public","_id":"29102","user_id":"79910","department":[{"_id":"452"}]}]