[{"author":[{"id":"248865","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-4112-802X/work/231505184","full_name":"Sanaullah, Sanaullah","first_name":"Sanaullah","orcid":"0000-0003-4112-802X","last_name":"Sanaullah"},{"last_name":"Byung-Kil","first_name":"Han","full_name":"Byung-Kil, Han"},{"id":"242294","full_name":"Jungeblut, Thorsten","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0001-7425-8766/work/231505185","last_name":"Jungeblut","orcid":"0000-0001-7425-8766","first_name":"Thorsten"},{"full_name":"Dmitrienko, Alexandra","first_name":"Alexandra","last_name":"Dmitrienko"},{"last_name":"Il Park","first_name":"Dong","full_name":"Il Park, Dong"}],"intvolume":"       709","_id":"7251","user_id":"242294","language":[{"iso":"eng"}],"publication_identifier":{"issn":["09252312"]},"date_updated":"2026-10-28T11:58:29Z","publication":"Neurocomputing","status":"public","publication_status":"published","date_created":"2026-10-28T11:56:13Z","project":[{"name":"Institut für Systemdynamik und Mechatronik","_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b"}],"title":"MindGuard: Embedded agentic VLM for real-time multimodal threat detection in physical AI","volume":709,"doi":"10.1016/j.neucom.2026.134624","type":"journal_article","publisher":"Elsevier BV","year":"2027","article_number":"134624"},{"year":"2026","publisher":"JMIR Publications Inc.","volume":5,"type":"journal_article","doi":"10.2196/94589","project":[{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"},{"name":"CareTech OWL - Zentrum für Gesundheit, Soziales und Technologie","_id":"72dfeb62-b436-11ed-9513-f39505d26204"}],"title":"Consumer Preferences for AI-Based Smart Home Medical Emergency Detection Among German Adults: Choice-Based Conjoint Analysis","oa":"1","publication_status":"published","date_created":"2026-09-15T22:05:30Z","page":"e94589-e94589","date_updated":"2026-10-28T11:50:44Z","publication":"JMIR AI","status":"public","language":[{"iso":"eng"}],"abstract":[{"text":"Background: Smart home technology powered by AI can detect anomalies and make emergency calls, enabling residents to live safely and independently. However, the adoption of such technologies for medical emergency detection remains limited.\r\n\r\nObjective: This study aimed to explore consumer preferences for AI-based smart home technology for medical emergency detection and identify predictors such as sociodemographic variables, AI literacy, and technology affinity.\r\n\r\nMethod: A sample of 300 participants (172/300, 57.33% female; 128/300, 42.67% male, aged 18‐69 years) completed a choice-based conjoint analysis (CBCA). Participants evaluated 15 choice sets describing smart home variants based on cost, location, emergency detection rate, type of sensor, and data processing.\r\n\r\nResults: Cost was the most important attribute (relative importance [RI]=41%), followed by emergency detection rate (RI=19%), data processing (RI=14%), and location (RI=14%). The type of sensor was the least important attribute (RI=9%). The preferred configuration combined an annual subscription of €70 (US $82), a 95% detection rate, wearable sensors, personalized AI, and installation in both intimate and shared rooms. Notably, 68.3% (205/300) of participants showed a positive none utility, indicating that even the optimal configuration did not overcome general reluctance to adopt such systems. While most expected correlations between sociodemographic variables and attribute importances were not observed, a significant correlation between self-reported health status and emergency detection rate was found (r=.16, P=.007). Interestingly, 61% (167/270) of participants preferred AI over human involvement in data processing, but logistic regression revealed that a significant correlation between self-reported health status and emergency detection rate was found (OR=0.58, P=.04).\r\n\r\nConclusions: These findings highlight the need to align smart home development with user preferences, emphasizing cost-effectiveness. Additionally, AI literacy plays an important role in technology adoption in the context of AI-based smart home technology. Further research is needed to understand and address the reluctance to adopt AI for medical emergency detection.","lang":"eng"}],"publication_identifier":{"eissn":["2817-1705"]},"user_id":"242294","_id":"7159","intvolume":"         5","main_file_link":[{"open_access":"1"}],"author":[{"id":"252878","first_name":"Inga","last_name":"Jagemann","orcid":"0000-0002-3468-3423","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-3468-3423/work/226873432","full_name":"Jagemann, Inga"},{"id":"247397","last_name":"Baudisch","orcid":"0000-0001-5565-0228","first_name":"Justin","full_name":"Baudisch, Justin","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0001-5565-0228/work/226873433"},{"id":"242294","first_name":"Thorsten","orcid":"0000-0001-7425-8766","last_name":"Jungeblut","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0001-7425-8766/work/226873434","full_name":"Jungeblut, Thorsten"},{"last_name":"Maier","first_name":"Günter W","full_name":"Maier, Günter W"},{"orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-2143-4564/work/226873436","full_name":"Hirschfeld, Gerrit","first_name":"Gerrit","last_name":"Hirschfeld","orcid":"0000-0003-2143-4564","id":"234690"}]},{"department":[{"_id":"103"}],"author":[{"orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-1837-2254/work/227695870","full_name":"Ahmed, Qazi Arbab","first_name":"Qazi Arbab","last_name":"Ahmed","orcid":"0000-0002-1837-2254","id":"257333"},{"last_name":"Karki","first_name":"Suraj","full_name":"Karki, Suraj"},{"id":"242294","full_name":"Jungeblut, Thorsten","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0001-7425-8766/work/227695871","orcid":"0000-0001-7425-8766","last_name":"Jungeblut","first_name":"Thorsten"}],"_id":"7184","citation":{"apa":"Ahmed, Q. A., Karki, S., &#38; Jungeblut, T. (2026). FINN-Tro: Exploiting Verification Gaps in Dataflow Inference Accelerators. arXiv. <a href=\"https://doi.org/10.48550/ARXIV.2609.26824\">https://doi.org/10.48550/ARXIV.2609.26824</a>","chicago":"Ahmed, Qazi Arbab, Suraj Karki, and Thorsten Jungeblut. “FINN-Tro: Exploiting Verification Gaps in Dataflow Inference Accelerators.” arXiv, 2026. <a href=\"https://doi.org/10.48550/ARXIV.2609.26824\">https://doi.org/10.48550/ARXIV.2609.26824</a>.","ama":"Ahmed QA, Karki S, Jungeblut T. FINN-Tro: Exploiting Verification Gaps in Dataflow Inference Accelerators. In: arXiv; 2026. doi:<a href=\"https://doi.org/10.48550/ARXIV.2609.26824\">10.48550/ARXIV.2609.26824</a>","bibtex":"@inproceedings{Ahmed_Karki_Jungeblut_2026, title={FINN-Tro: Exploiting Verification Gaps in Dataflow Inference Accelerators}, DOI={<a href=\"https://doi.org/10.48550/ARXIV.2609.26824\">10.48550/ARXIV.2609.26824</a>}, publisher={arXiv}, author={Ahmed, Qazi Arbab and Karki, Suraj and Jungeblut, Thorsten}, year={2026} }","short":"Q.A. Ahmed, S. Karki, T. Jungeblut, in: arXiv, 2026.","mla":"Ahmed, Qazi Arbab, et al. <i>FINN-Tro: Exploiting Verification Gaps in Dataflow Inference Accelerators</i>. arXiv, 2026, doi:<a href=\"https://doi.org/10.48550/ARXIV.2609.26824\">10.48550/ARXIV.2609.26824</a>.","ieee":"Q. A. Ahmed, S. Karki, and T. Jungeblut, “FINN-Tro: Exploiting Verification Gaps in Dataflow Inference Accelerators,” 2026.","alphadin":"<span style=\"font-variant:small-caps;\">Ahmed, Qazi Arbab</span> ; <span style=\"font-variant:small-caps;\">Karki, Suraj</span> ; <span style=\"font-variant:small-caps;\">Jungeblut, Thorsten</span>: FINN-Tro: Exploiting Verification Gaps in Dataflow Inference Accelerators. In:  : arXiv, 2026"},"user_id":"257333","language":[{"iso":"eng"}],"status":"public","date_updated":"2026-09-24T12:32:15Z","date_created":"2026-09-24T12:31:20Z","publication_status":"published","research_group":[{"_id":"f025f82c-c221-11ec-90a2-d82e18d06cb8","name":"CareTech OWL - Zentrum für Gesundheit, Soziales und Technologie"}],"project":[{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"}],"quality_controlled":"1","title":"FINN-Tro: Exploiting Verification Gaps in Dataflow Inference Accelerators","doi":"10.48550/ARXIV.2609.26824","type":"conference","publisher":"arXiv","year":"2026"},{"author":[{"full_name":"Karki, Suraj","first_name":"Suraj","last_name":"Karki"},{"id":"257333","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-1837-2254/work/227259862","full_name":"Ahmed, Qazi Arbab","first_name":"Qazi Arbab","orcid":"0000-0002-1837-2254","last_name":"Ahmed"},{"last_name":"Jungeblut","orcid":"0000-0001-7425-8766","first_name":"Thorsten","full_name":"Jungeblut, Thorsten","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0001-7425-8766/work/227259884","id":"242294"}],"department":[{"_id":"103"}],"_id":"7171","citation":{"apa":"Karki, S., Ahmed, Q. A., &#38; Jungeblut, T. (2026). <i>No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA</i>. <a href=\"https://doi.org/10.48550/arXiv.2607.13106\">https://doi.org/10.48550/arXiv.2607.13106</a>","chicago":"Karki, Suraj, Qazi Arbab Ahmed, and Thorsten Jungeblut. <i>No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA</i>, 2026. <a href=\"https://doi.org/10.48550/arXiv.2607.13106\">https://doi.org/10.48550/arXiv.2607.13106</a>.","ama":"Karki S, Ahmed QA, Jungeblut T. <i>No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA</i>.; 2026. doi:<a href=\"https://doi.org/10.48550/arXiv.2607.13106\">10.48550/arXiv.2607.13106</a>","bibtex":"@book{Karki_Ahmed_Jungeblut_2026, title={No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA}, DOI={<a href=\"https://doi.org/10.48550/arXiv.2607.13106\">10.48550/arXiv.2607.13106</a>}, author={Karki, Suraj and Ahmed, Qazi Arbab and Jungeblut, Thorsten}, year={2026} }","mla":"Karki, Suraj, et al. <i>No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA</i>. 2026, doi:<a href=\"https://doi.org/10.48550/arXiv.2607.13106\">10.48550/arXiv.2607.13106</a>.","ieee":"S. Karki, Q. A. Ahmed, and T. Jungeblut, <i>No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA</i>. 2026.","alphadin":"<span style=\"font-variant:small-caps;\">Karki, Suraj</span> ; <span style=\"font-variant:small-caps;\">Ahmed, Qazi Arbab</span> ; <span style=\"font-variant:small-caps;\">Jungeblut, Thorsten</span>: <i>No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA</i>, 2026","short":"S. Karki, Q.A. Ahmed, T. Jungeblut, No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA, 2026."},"main_file_link":[{"open_access":"1"}],"language":[{"iso":"eng"}],"abstract":[{"text":"Edge-based Artificial Intelligence (AI) acceleration has recently improved progress in real-time object detection. Object detection on edge devices requires a balance between accuracy, speed, and power efficiency. This paper proposes a customized Deep Learning Processor Unit (DPU)-aware architecture for attention-based YOLO variants deployed on AMD FPGAs. Specifically, we evaluate and benchmark YOLOv26 and YOLOv11, two modern attention-based YOLO variants, on the Xilinx ZCU104 across both standard and oriented object detection tasks. We replace unsupported activation functions, substitute split operations with 1x1 convolutions, and approximate the spatial attention mechanism in a DPU-compatible way. All models are then trained and evaluated across six benchmark datasets such as COCO, Pascal VOC, KITTI, DOTA, DIOR-R, and an in-house human presence dataset, and benchmarked across all eight DPU configurations (B512 to B4096) in terms of mAP, FPS, latency, power, and resource utilization. Notably, YOLOv26n and YOLOv26n-OBB deliver the highest end-to-end throughput at 34.05 and 29.55 FPS for standard and oriented detection, respectively, with an average of 5% absolute reduction in accuracy due to quantization while achieving up to approximately 3x lower power consumption compared with the state of the art.","lang":"eng"}],"user_id":"220548","date_created":"2026-09-20T15:30:48Z","date_updated":"2026-09-24T12:29:24Z","status":"public","title":"No Attention, No Problem: DPU-Aware Attention Approximation in Modern YOLO on FPGA","project":[{"name":"Institut für Systemdynamik und Mechatronik","_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b"}],"oa":"1","type":"working_paper","doi":"10.48550/arXiv.2607.13106","year":"2026"},{"oa":"1","project":[{"name":"Institut für Systemdynamik und Mechatronik","_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b"},{"_id":"72dfeb62-b436-11ed-9513-f39505d26204","name":"CareTech OWL - Zentrum für Gesundheit, Soziales und Technologie"}],"title":"Advancements In Biosignal Analysis For Models of Musculotendon Actuators. Ultrasound B-Mode Imaging for Tendon Stiffness Estimation and sEMG Forward Modelling for In Vivo Innervation Zone Tracking","supervisor":[{"last_name":"Rückert","first_name":"Ulrich","full_name":"Rückert, Ulrich"},{"full_name":"Schneider, Axel","first_name":"Axel","orcid":"0000-0002-6632-3473","last_name":"Schneider","id":"213480"}],"year":"2026","doi":"10.4119/UNIBI/3016397","type":"dissertation","publisher":"Universität Bielefeld","extern":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.4119/unibi/3016397"}],"_id":"6914","citation":{"alphadin":"<span style=\"font-variant:small-caps;\">Mechtenberg, Malte</span>: <i>Advancements In Biosignal Analysis For Models of Musculotendon Actuators. Ultrasound B-Mode Imaging for Tendon Stiffness Estimation and sEMG Forward Modelling for In Vivo Innervation Zone Tracking</i> : Universität Bielefeld, 2026","ieee":"M. Mechtenberg, <i>Advancements In Biosignal Analysis For Models of Musculotendon Actuators. Ultrasound B-Mode Imaging for Tendon Stiffness Estimation and sEMG Forward Modelling for In Vivo Innervation Zone Tracking</i>. Universität Bielefeld, 2026.","mla":"Mechtenberg, Malte. <i>Advancements In Biosignal Analysis For Models of Musculotendon Actuators. Ultrasound B-Mode Imaging for Tendon Stiffness Estimation and SEMG Forward Modelling for In Vivo Innervation Zone Tracking</i>. Universität Bielefeld, 2026, doi:<a href=\"https://doi.org/10.4119/UNIBI/3016397\">10.4119/UNIBI/3016397</a>.","short":"M. Mechtenberg, Advancements In Biosignal Analysis For Models of Musculotendon Actuators. Ultrasound B-Mode Imaging for Tendon Stiffness Estimation and SEMG Forward Modelling for In Vivo Innervation Zone Tracking, Universität Bielefeld, 2026.","bibtex":"@book{Mechtenberg_2026, title={Advancements In Biosignal Analysis For Models of Musculotendon Actuators. Ultrasound B-Mode Imaging for Tendon Stiffness Estimation and sEMG Forward Modelling for In Vivo Innervation Zone Tracking}, DOI={<a href=\"https://doi.org/10.4119/UNIBI/3016397\">10.4119/UNIBI/3016397</a>}, publisher={Universität Bielefeld}, author={Mechtenberg, Malte}, year={2026} }","ama":"Mechtenberg M. <i>Advancements In Biosignal Analysis For Models of Musculotendon Actuators. Ultrasound B-Mode Imaging for Tendon Stiffness Estimation and SEMG Forward Modelling for In Vivo Innervation Zone Tracking</i>. Universität Bielefeld; 2026. doi:<a href=\"https://doi.org/10.4119/UNIBI/3016397\">10.4119/UNIBI/3016397</a>","chicago":"Mechtenberg, Malte. <i>Advancements In Biosignal Analysis For Models of Musculotendon Actuators. Ultrasound B-Mode Imaging for Tendon Stiffness Estimation and SEMG Forward Modelling for In Vivo Innervation Zone Tracking</i>. Universität Bielefeld, 2026. <a href=\"https://doi.org/10.4119/UNIBI/3016397\">https://doi.org/10.4119/UNIBI/3016397</a>.","apa":"Mechtenberg, M. (2026). <i>Advancements In Biosignal Analysis For Models of Musculotendon Actuators. Ultrasound B-Mode Imaging for Tendon Stiffness Estimation and sEMG Forward Modelling for In Vivo Innervation Zone Tracking</i>. Universität Bielefeld. <a href=\"https://doi.org/10.4119/UNIBI/3016397\">https://doi.org/10.4119/UNIBI/3016397</a>"},"author":[{"full_name":"Mechtenberg, Malte","first_name":"Malte","last_name":"Mechtenberg","orcid":"0000-0002-8958-0931","id":"218573"}],"status":"public","date_updated":"2026-06-30T20:45:45Z","date_created":"2026-05-20T17:29:01Z","publication_status":"published","user_id":"218573","language":[{"iso":"eng"}]},{"conference":{"end_date":"2025-09-19","location":"Porto, Portugal","name":"ECML PKDD 2025","start_date":"2025-09-15"},"title":"Low Query Budget Active Learning for Classification and Regression","project":[{"_id":"f432a2ee-bceb-11ed-a251-a83585c5074d","name":"Institute for Data Science Solutions"},{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"}],"oa":"1","year":"2026","place":"Cham","series_title":"Communications in Computer and Information Science","publisher":"Springer Nature Switzerland","alternative_id":["6899"],"doi":"10.1007/978-3-032-19105-2_1","type":"conference","citation":{"chicago":"Jaster, Bjarne, Alaa Tharwat, Eiram Mahera Sheikh, Martin Kohlhase, and Wolfram Schenck. “Low Query Budget Active Learning for Classification and Regression.” In <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>, edited by Irena Koprinska, João Mendes-Moreira, and Paula Branco, 5–21. Communications in Computer and Information Science. Cham: Springer Nature Switzerland, 2026. <a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">https://doi.org/10.1007/978-3-032-19105-2_1</a>.","apa":"Jaster, B., Tharwat, A., Sheikh, E. M., Kohlhase, M., &#38; Schenck, W. (2026). Low Query Budget Active Learning for Classification and Regression. In I. Koprinska, J. Mendes-Moreira, &#38; P. Branco (Eds.), <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i> (pp. 5–21). Cham: Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">https://doi.org/10.1007/978-3-032-19105-2_1</a>","bibtex":"@inproceedings{Jaster_Tharwat_Sheikh_Kohlhase_Schenck_2026, place={Cham}, series={Communications in Computer and Information Science}, title={Low Query Budget Active Learning for Classification and Regression}, DOI={<a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">10.1007/978-3-032-19105-2_1</a>}, booktitle={Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV}, publisher={Springer Nature Switzerland}, author={Jaster, Bjarne and Tharwat, Alaa and Sheikh, Eiram Mahera and Kohlhase, Martin and Schenck, Wolfram}, editor={Koprinska, Irena and Mendes-Moreira, João and Branco, PaulaEditors}, year={2026}, pages={5–21}, collection={Communications in Computer and Information Science} }","ama":"Jaster B, Tharwat A, Sheikh EM, Kohlhase M, Schenck W. Low Query Budget Active Learning for Classification and Regression. In: Koprinska I, Mendes-Moreira J, Branco P, eds. <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>. Communications in Computer and Information Science. Cham: Springer Nature Switzerland; 2026:5-21. doi:<a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">10.1007/978-3-032-19105-2_1</a>","alphadin":"<span style=\"font-variant:small-caps;\">Jaster, Bjarne</span> ; <span style=\"font-variant:small-caps;\">Tharwat, Alaa</span> ; <span style=\"font-variant:small-caps;\">Sheikh, Eiram Mahera</span> ; <span style=\"font-variant:small-caps;\">Kohlhase, Martin</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span>: Low Query Budget Active Learning for Classification and Regression. In: <span style=\"font-variant:small-caps;\">Koprinska, I.</span> ; <span style=\"font-variant:small-caps;\">Mendes-Moreira, J.</span> ; <span style=\"font-variant:small-caps;\">Branco, P.</span> (Hrsg.): <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>, <i>Communications in Computer and Information Science</i>. Cham : Springer Nature Switzerland, 2026, S. 5–21","mla":"Jaster, Bjarne, et al. “Low Query Budget Active Learning for Classification and Regression.” <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>, edited by Irena Koprinska et al., Springer Nature Switzerland, 2026, pp. 5–21, doi:<a href=\"https://doi.org/10.1007/978-3-032-19105-2_1\">10.1007/978-3-032-19105-2_1</a>.","ieee":"B. Jaster, A. Tharwat, E. M. Sheikh, M. Kohlhase, and W. Schenck, “Low Query Budget Active Learning for Classification and Regression,” in <i>Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV</i>, Porto, Portugal, 2026, pp. 5–21.","short":"B. Jaster, A. Tharwat, E.M. Sheikh, M. Kohlhase, W. Schenck, in: I. Koprinska, J. Mendes-Moreira, P. Branco (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV, Springer Nature Switzerland, Cham, 2026, pp. 5–21."},"_id":"6898","main_file_link":[{"open_access":"1","url":"https://rdcu.be/fh03T"}],"editor":[{"full_name":"Koprinska, Irena","last_name":"Koprinska","first_name":"Irena"},{"full_name":"Mendes-Moreira, João","first_name":"João","last_name":"Mendes-Moreira"},{"full_name":"Branco, Paula","last_name":"Branco","first_name":"Paula"}],"author":[{"orcid":"0000-0002-8362-5369","last_name":"Jaster","first_name":"Bjarne","full_name":"Jaster, Bjarne","id":"252434"},{"id":"238549","last_name":"Tharwat","first_name":"Alaa","full_name":"Tharwat, Alaa"},{"last_name":"Sheikh","first_name":"Eiram Mahera","full_name":"Sheikh, Eiram Mahera"},{"id":"226669","full_name":"Kohlhase, Martin","first_name":"Martin","orcid":"0009-0002-9374-0720","last_name":"Kohlhase"},{"full_name":"Schenck, Wolfram","orcid":"0000-0003-3300-2048","last_name":"Schenck","first_name":"Wolfram","id":"224375"}],"publication_status":"published","date_created":"2026-05-10T07:55:29Z","page":"5-21","publication":"Machine Learning and Principles and Practice of Knowledge Discovery in Databases. International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part IV","date_updated":"2026-06-01T15:18:27Z","status":"public","abstract":[{"lang":"eng","text":"The labeling process for supervised learning is costly and time-consuming, and is often impractical to scale due to real-world constraints. Active learning (AL) addresses this challenge by strategically selecting representative and informative data points to reduce labeling efforts. This paper focuses on an AL scenario in which only a very limited number of labels can be acquired. We propose an algorithm operating in two phases: (1) an exploration phase that prioritizes representative and diverse data points using density-driven criteria, and (2) an exploitation phase that combines predictive uncertainty with density weighting to select informative samples from densely populated regions. This enhances both representativeness and informativeness. Our results demonstrate significant improvements in model quality compared to other algorithms typically employed for this scenario, across various scenarios involving imbalanced data in classification tasks and skewness in regression tasks. Through this work, we aim to provide a new algorithm for this scenario and investigate general principles for AL. While most AL studies focus on either classification or regression, our work applies the algorithms to both. Therefore, we can analyze the differences between classification and regression problems and their effects on AL strategies. Furthermore, we explore different categories of AL criteria and their effectiveness in the low-budget regime. These results also provide insight into the cold-start problem, which involves selecting an initial labeled set and is faced by many model-based AL methods."}],"language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1865-0937"],"issn":["1865-0929"],"eisbn":["978-3-032-19105-2"],"isbn":["978-3-032-19104-5"]},"user_id":"226669"},{"main_file_link":[{"url":"https://doi.org/10.1016/j.engappai.2026.114295","open_access":"1"}],"_id":"6655","intvolume":"       171","citation":{"short":"A. Tharwat, B. Jaster, W. Schenck, M. Kohlhase, Engineering Applications of Artificial Intelligence 171 (2026).","mla":"Tharwat, Alaa, et al. “Active Learning Evaluation Metrics for Classification and Regression Frameworks.” <i>Engineering Applications of Artificial Intelligence</i>, vol. 171, 114295, Elsevier BV, 2026, doi:<a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">10.1016/j.engappai.2026.114295</a>.","ieee":"A. Tharwat, B. Jaster, W. Schenck, and M. Kohlhase, “Active learning evaluation metrics for classification and regression frameworks,” <i>Engineering Applications of Artificial Intelligence</i>, vol. 171, 2026.","alphadin":"<span style=\"font-variant:small-caps;\">Tharwat, Alaa</span> ; <span style=\"font-variant:small-caps;\">Jaster, Bjarne</span> ; <span style=\"font-variant:small-caps;\">Schenck, Wolfram</span> ; <span style=\"font-variant:small-caps;\">Kohlhase, Martin</span>: Active learning evaluation metrics for classification and regression frameworks. In: <i>Engineering Applications of Artificial Intelligence</i> Bd. 171, Elsevier BV (2026)","ama":"Tharwat A, Jaster B, Schenck W, Kohlhase M. Active learning evaluation metrics for classification and regression frameworks. <i>Engineering Applications of Artificial Intelligence</i>. 2026;171. doi:<a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">10.1016/j.engappai.2026.114295</a>","bibtex":"@article{Tharwat_Jaster_Schenck_Kohlhase_2026, title={Active learning evaluation metrics for classification and regression frameworks}, volume={171}, DOI={<a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">10.1016/j.engappai.2026.114295</a>}, number={114295}, journal={Engineering Applications of Artificial Intelligence}, publisher={Elsevier BV}, author={Tharwat, Alaa and Jaster, Bjarne and Schenck, Wolfram and Kohlhase, Martin}, year={2026} }","chicago":"Tharwat, Alaa, Bjarne Jaster, Wolfram Schenck, and Martin Kohlhase. “Active Learning Evaluation Metrics for Classification and Regression Frameworks.” <i>Engineering Applications of Artificial Intelligence</i> 171 (2026). <a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">https://doi.org/10.1016/j.engappai.2026.114295</a>.","apa":"Tharwat, A., Jaster, B., Schenck, W., &#38; Kohlhase, M. (2026). Active learning evaluation metrics for classification and regression frameworks. <i>Engineering Applications of Artificial Intelligence</i>, <i>171</i>. <a href=\"https://doi.org/10.1016/j.engappai.2026.114295\">https://doi.org/10.1016/j.engappai.2026.114295</a>"},"author":[{"full_name":"Tharwat, Alaa","first_name":"Alaa","last_name":"Tharwat","id":"238549"},{"id":"252434","first_name":"Bjarne","last_name":"Jaster","orcid":"0000-0002-8362-5369","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-8362-5369/work/207108820","full_name":"Jaster, Bjarne"},{"id":"224375","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3300-2048/work/207108821","full_name":"Schenck, Wolfram","first_name":"Wolfram","orcid":"0000-0003-3300-2048","last_name":"Schenck"},{"id":"226669","first_name":"Martin","orcid":"0009-0002-9374-0720","last_name":"Kohlhase","orcid_put_code_url":"https://api.orcid.org/v2.0/0009-0002-9374-0720/work/207108822","full_name":"Kohlhase, Martin"}],"date_updated":"2026-06-01T15:11:58Z","publication":"Engineering Applications of Artificial Intelligence","status":"public","publication_status":"published","date_created":"2026-02-27T10:10:55Z","user_id":"226669","language":[{"iso":"eng"}],"publication_identifier":{"issn":["09521976"]},"oa":"1","title":"Active learning evaluation metrics for classification and regression frameworks","project":[{"_id":"f432a2ee-bceb-11ed-a251-a83585c5074d","name":"Institute for Data Science Solutions"},{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"}],"year":"2026","article_number":"114295","volume":171,"type":"journal_article","doi":"10.1016/j.engappai.2026.114295","publisher":"Elsevier BV"},{"date_updated":"2026-06-01T15:15:50Z","publication_identifier":{"eissn":["2504-2289"]},"author":[{"id":"232004","first_name":"Marvin","last_name":"Niederhaus","orcid":"0009-0008-8581-2708","orcid_put_code_url":"https://api.orcid.org/v2.0/0009-0008-8581-2708/work/194508828","full_name":"Niederhaus, Marvin"},{"orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-7223-1735/work/194483982","full_name":"Migenda, Nico","first_name":"Nico","last_name":"Migenda","orcid":"0000-0002-7223-1735","id":"218473"},{"full_name":"Weller, Julian","first_name":"Julian","last_name":"Weller"},{"first_name":"Martin","last_name":"Kohlhase","orcid":"0009-0002-9374-0720","orcid_put_code_url":"https://api.orcid.org/v2.0/0009-0002-9374-0720/work/194483983","full_name":"Kohlhase, Martin","id":"226669"},{"id":"224375","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3300-2048/work/194483984","full_name":"Schenck, Wolfram","first_name":"Wolfram","orcid":"0000-0003-3300-2048","last_name":"Schenck"}],"file":[{"file_name":"BDCC-09-00261.pdf","relation":"main_file","content_type":"application/pdf","file_id":"6245","file_size":13995224,"success":1,"date_updated":"2025-10-17T07:11:25Z","creator":"mniederhaus","date_created":"2025-10-17T07:11:25Z","access_level":"open_access"}],"volume":9,"type":"journal_article","doi":"10.3390/bdcc9100261","urn":"urn:nbn:de:hbz:bi10-62444","publication_status":"published","date_created":"2025-10-17T07:19:59Z","publication":"Big Data and Cognitive Computing","status":"public","language":[{"iso":"eng"}],"user_id":"226669","citation":{"ama":"Niederhaus M, Migenda N, Weller J, Kohlhase M, Schenck W. 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Schenck, Big Data and Cognitive Computing 9 (2025).","chicago":"Niederhaus, Marvin, Nico Migenda, Julian Weller, Martin Kohlhase, and Wolfram Schenck. “Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems.” <i>Big Data and Cognitive Computing</i> 9, no. 10 (2025). <a href=\"https://doi.org/10.3390/bdcc9100261\">https://doi.org/10.3390/bdcc9100261</a>.","apa":"Niederhaus, M., Migenda, N., Weller, J., Kohlhase, M., &#38; Schenck, W. (2025). Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. <i>Big Data and Cognitive Computing</i>, <i>9</i>(10). <a href=\"https://doi.org/10.3390/bdcc9100261\">https://doi.org/10.3390/bdcc9100261</a>"},"_id":"6244","intvolume":"         9","main_file_link":[{"open_access":"1","url":"https://www.mdpi.com/2504-2289/9/10/261/pdf"}],"department":[{"_id":"103"}],"year":"2025","article_number":"261","article_type":"original","publisher":"MDPI AG","funded_apc":"1","issue":"10","has_accepted_license":"1","quality_controlled":"1","title":"Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems","project":[{"name":"Institut für Systemdynamik und Mechatronik","_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b"},{"_id":"f432a2ee-bceb-11ed-a251-a83585c5074d","name":"Institute for Data Science Solutions"}],"keyword":["prescriptive analytics","prescriptive platforms","advanced data analytics","retrieval-augmented generation","graph-based retrieval-augmented generation","large language models","generative AI","genAI","recommender system"],"file_date_updated":"2025-10-17T07:11:25Z","oa":"1"},{"project":[{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"}],"title":"Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers","year":"2025","article_number":"3669","publisher":"MDPI AG","volume":13,"doi":"10.3390/math13223669","issue":"22","type":"journal_article","citation":{"apa":"Suleimenov, K., Kapsalyamov, A., Abdikenov, B., Ozhikenova, A., Igembay, Y., &#38; Ozhikenov, K. (2025). Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers. <i>Mathematics</i>, <i>13</i>(22). <a href=\"https://doi.org/10.3390/math13223669\">https://doi.org/10.3390/math13223669</a>","chicago":"Suleimenov, Kanat, Akim Kapsalyamov, Beibit Abdikenov, Aiman Ozhikenova, Yerbolat Igembay, and Kassymbek Ozhikenov. “Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers.” <i>Mathematics</i> 13, no. 22 (2025). <a href=\"https://doi.org/10.3390/math13223669\">https://doi.org/10.3390/math13223669</a>.","mla":"Suleimenov, Kanat, et al. “Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers.” <i>Mathematics</i>, vol. 13, no. 22, 3669, MDPI AG, 2025, doi:<a href=\"https://doi.org/10.3390/math13223669\">10.3390/math13223669</a>.","ieee":"K. Suleimenov, A. Kapsalyamov, B. Abdikenov, A. Ozhikenova, Y. Igembay, and K. Ozhikenov, “Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers,” <i>Mathematics</i>, vol. 13, no. 22, 2025.","alphadin":"<span style=\"font-variant:small-caps;\">Suleimenov, Kanat</span> ; <span style=\"font-variant:small-caps;\">Kapsalyamov, Akim</span> ; <span style=\"font-variant:small-caps;\">Abdikenov, Beibit</span> ; <span style=\"font-variant:small-caps;\">Ozhikenova, Aiman</span> ; <span style=\"font-variant:small-caps;\">Igembay, Yerbolat</span> ; <span style=\"font-variant:small-caps;\">Ozhikenov, Kassymbek</span>: Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers. In: <i>Mathematics</i> Bd. 13, MDPI AG (2025), Nr. 22","short":"K. Suleimenov, A. Kapsalyamov, B. Abdikenov, A. Ozhikenova, Y. Igembay, K. Ozhikenov, Mathematics 13 (2025).","ama":"Suleimenov K, Kapsalyamov A, Abdikenov B, Ozhikenova A, Igembay Y, Ozhikenov K. Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers. <i>Mathematics</i>. 2025;13(22). doi:<a href=\"https://doi.org/10.3390/math13223669\">10.3390/math13223669</a>","bibtex":"@article{Suleimenov_Kapsalyamov_Abdikenov_Ozhikenova_Igembay_Ozhikenov_2025, title={Comparative Analysis of Model-Based and Data-Driven Control for Tendon-Driven Robotic Fingers}, volume={13}, DOI={<a href=\"https://doi.org/10.3390/math13223669\">10.3390/math13223669</a>}, number={223669}, journal={Mathematics}, publisher={MDPI AG}, author={Suleimenov, Kanat and Kapsalyamov, Akim and Abdikenov, Beibit and Ozhikenova, Aiman and Igembay, Yerbolat and Ozhikenov, Kassymbek}, year={2025} }"},"_id":"6315","intvolume":"        13","author":[{"full_name":"Suleimenov, Kanat","first_name":"Kanat","last_name":"Suleimenov"},{"id":"257451","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3700-6214/work/197758764","full_name":"Kapsalyamov, Akim","first_name":"Akim","orcid":"0000-0003-3700-6214","last_name":"Kapsalyamov"},{"full_name":"Abdikenov, Beibit","last_name":"Abdikenov","first_name":"Beibit"},{"last_name":"Ozhikenova","first_name":"Aiman","full_name":"Ozhikenova, Aiman"},{"full_name":"Igembay, Yerbolat","first_name":"Yerbolat","last_name":"Igembay"},{"first_name":"Kassymbek","last_name":"Ozhikenov","full_name":"Ozhikenov, Kassymbek"}],"publication_status":"published","date_created":"2025-11-24T10:25:13Z","date_updated":"2026-03-17T15:29:29Z","publication":"Mathematics","status":"public","language":[{"iso":"eng"}],"publication_identifier":{"eissn":["2227-7390"]},"user_id":"257451"},{"year":"2025","type":"preprint","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publisher":"MDPI AG","title":"AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms","project":[{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"}],"status":"public","date_updated":"2026-03-17T15:29:29Z","date_created":"2025-11-24T10:25:15Z","publication_status":"published","user_id":"257451","language":[{"iso":"eng"}],"citation":{"apa":"Chegedekova, I., Kapsalyamov, A., Jamwal, P. K., Kystaubayeva, Z., Parthimos, D., &#38; Jasani, B. (2025). AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms. MDPI AG.","chicago":"Chegedekova, Ingkar, Akim Kapsalyamov, Prashant Kumar Jamwal, Zaidagul Kystaubayeva, Dimitris Parthimos, and Bharat Jasani. “AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms.” MDPI AG, 2025.","ama":"Chegedekova I, Kapsalyamov A, Jamwal PK, Kystaubayeva Z, Parthimos D, Jasani B. AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms. 2025.","bibtex":"@article{Chegedekova_Kapsalyamov_Jamwal_Kystaubayeva_Parthimos_Jasani_2025, title={AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms}, publisher={MDPI AG}, author={Chegedekova, Ingkar and Kapsalyamov, Akim and Jamwal, Prashant Kumar and Kystaubayeva, Zaidagul and Parthimos, Dimitris and Jasani, Bharat}, year={2025} }","short":"I. Chegedekova, A. Kapsalyamov, P.K. Jamwal, Z. Kystaubayeva, D. Parthimos, B. Jasani, (2025).","ieee":"I. Chegedekova, A. Kapsalyamov, P. K. Jamwal, Z. Kystaubayeva, D. Parthimos, and B. Jasani, “AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms.” MDPI AG, 2025.","mla":"Chegedekova, Ingkar, et al. <i>AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms</i>. MDPI AG, 2025.","alphadin":"<span style=\"font-variant:small-caps;\">Chegedekova, Ingkar</span> ; <span style=\"font-variant:small-caps;\">Kapsalyamov, Akim</span> ; <span style=\"font-variant:small-caps;\">Jamwal, Prashant Kumar</span> ; <span style=\"font-variant:small-caps;\">Kystaubayeva, Zaidagul</span> ; <span style=\"font-variant:small-caps;\">Parthimos, Dimitris</span> ; <span style=\"font-variant:small-caps;\">Jasani, Bharat</span>: AI-Based Computational Pathology for Precision Lung Cancer Management: A Systematic Review and Meta-Analysis of Diagnostics and Prognostic Algorithms, MDPI AG (2025)"},"_id":"6316","author":[{"first_name":"Ingkar","last_name":"Chegedekova","full_name":"Chegedekova, Ingkar"},{"full_name":"Kapsalyamov, Akim","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3700-6214/work/197758964","last_name":"Kapsalyamov","orcid":"0000-0003-3700-6214","first_name":"Akim","id":"257451"},{"last_name":"Jamwal","first_name":"Prashant Kumar","full_name":"Jamwal, Prashant Kumar"},{"last_name":"Kystaubayeva","first_name":"Zaidagul","full_name":"Kystaubayeva, Zaidagul"},{"full_name":"Parthimos, Dimitris","last_name":"Parthimos","first_name":"Dimitris"},{"full_name":"Jasani, Bharat","last_name":"Jasani","first_name":"Bharat"}]},{"date_created":"2025-11-24T10:25:16Z","publication_status":"published","status":"public","publication":"Sensors","date_updated":"2026-03-17T15:29:29Z","publication_identifier":{"eissn":["1424-8220"]},"language":[{"iso":"eng"}],"user_id":"257451","citation":{"chicago":"Abdikenov, Beibit, Darkhan Zholtayev, Kanat Suleimenov, Nazgul Assan, Kassymbek Ozhikenov, Aiman Ozhikenova, Nurbek Nadirov, and Akim Kapsalyamov. “Emerging Frontiers in Robotic Upper-Limb Prostheses: Mechanisms, Materials, Tactile Sensors and Machine Learning-Based EMG Control: A Comprehensive Review.” <i>Sensors</i> 25, no. 13 (2025). <a href=\"https://doi.org/10.3390/s25133892\">https://doi.org/10.3390/s25133892</a>.","apa":"Abdikenov, B., Zholtayev, D., Suleimenov, K., Assan, N., Ozhikenov, K., Ozhikenova, A., … Kapsalyamov, A. 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