TY - INPR A1 - Fatania, Ketan A1 - Chau, Kwai Y. A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Golbabaee, Mohammad T1 - Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI UR - https://doi.org/10.48550/arXiv.2211.12786 KW - Quantitative MRI KW - Magnetic Resonance Fingerprinting KW - Compressed Sensing KW - Inverse Problems KW - Self-Supervised Deep Learning KW - Equivariant Imaging Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2211.12786 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Gajek, Carola A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang ED - Amini, Massih-Reza ED - Canu, Stéphane ED - Fischer, Asja ED - Guns, Tias ED - Kralj Novak, Petra ED - Tsoumakas, Grigorios T1 - A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks T2 - Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Proceedings, Part I UR - https://doi.org/10.1007/978-3-031-26387-3_28 KW - Graph Machine Learning KW - Recommendation KW - Computer-aided Design KW - AI-aided Design Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-26387-3_28 SN - 978-3-031-26387-3 SN - 978-3-031-26386-6 SP - 457 EP - 473 PB - Springer CY - Cham ER - TY - CHAP A1 - Lodes, Lukas A1 - Schiendorfer, Alexander ED - Do, Phuc ED - Michau, Gabriel ED - Ezhilarasu, Cordelia T1 - Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks T2 - Proceedings of the European Conference of the PHM Society 2022 N2 - Machine Learning (ML), in particular classification with deep neural nets, can be applied to a variety of industrial tasks. It can augment established methods for controlling manufacturing processes such as statistical process control (SPC) to detect non-obvious patterns in high-dimensional input data. However, due to the widespread issue of model miscalibration in neural networks, there is a need for estimating the predictive uncertainty of these models. Many established approaches for uncertainty estimation output scores that are difficult to put into actionable insight. We therefore introduce the concept of certainty groups which distinguish the predictions of a neural network into the normal group and the certainty group. The certainty group contains only predictions with a very high accuracy that can be set up to 100%. We present an approach to compute these certainty groups and demonstrate our approach on two datasets from a PHM setting. UR - https://doi.org/10.36001/phme.2022.v7i1.3331 KW - machine Learning KW - classification KW - uncertainty KW - estimaton KW - neural network Y1 - 2022 UR - https://doi.org/10.36001/phme.2022.v7i1.3331 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31307 SN - 978-1-936263-36-3 SP - 294 EP - 305 PB - PHM Society CY - State College ER - TY - CHAP A1 - Kampa, Thomas A1 - El-Ankah, Amer A1 - Großmann, Daniel ED - Dörksen, Helene ED - Scanzio, Stefano ED - Jasperneite, Jürgen ED - Wisniewski, Lukasz ED - Man, Kim Fung ED - Sauter, Thilo ED - Seno, Lucia ED - Trsek, Henning ED - Vyatkin, Valeriy T1 - High Availability for virtualized Programmable Logic Controllers with Hard Real-Time Requirements on Cloud Infrastructures T2 - 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) UR - https://doi.org/10.1109/INDIN51400.2023.10218014 KW - RDMA KW - vPLC KW - state synchronization Y1 - 2023 UR - https://doi.org/10.1109/INDIN51400.2023.10218014 SN - 978-1-6654-9313-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Glocker, Kevin A1 - Herygers, Aaricia A1 - Georges, Munir T1 - Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes T2 - INTERSPEECH 2023 UR - https://doi.org/10.21437/Interspeech.2023-772 KW - speech recognition KW - cross-lingual KW - zero-shot KW - phoneme recognition Y1 - 2023 UR - https://doi.org/10.21437/Interspeech.2023-772 SP - 2258 EP - 2262 PB - ISCA CY - Grenoble ER - TY - CHAP A1 - Radtke, Henrik A1 - Bey, Henrik A1 - Sackmann, Moritz A1 - Schön, Torsten T1 - Predicting Driver Behavior on the Highway with Multi-Agent Adversarial Inverse Reinforcement Learning T2 - IEEE IV 2023 IEEE Intelligent Vehicles Symposium: Proceedings UR - https://doi.org/10.1109/IV55152.2023.10186547 KW - AIRL KW - PPO KW - Reinforcement Learning KW - Behavior Prediction KW - Multi-Agent Y1 - 2023 UR - https://doi.org/10.1109/IV55152.2023.10186547 SN - 979-8-3503-4691-6 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Freisinger, Steffen A1 - Schneider, Fabian A1 - Herygers, Aaricia A1 - Georges, Munir A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian T1 - Unsupervised Multilingual Topic Segmentation of Video Lectures: What can Hierarchical Labels tell us about the Performance? T2 - Proceedings 9th Workshop on Speech and Language Technology in Education (SLaTE) UR - https://doi.org/10.21437/SLaTE.2023-27 KW - topic segmentation KW - video lectures KW - hierarchical topics KW - multilingual Y1 - 2023 UR - https://doi.org/10.21437/SLaTE.2023-27 SP - 141 EP - 145 PB - International Speech Communication Association (ISCA) CY - Baixas ER - TY - CHAP A1 - Wu, Di A1 - Georges, Munir T1 - A Pipeline for Automatic Construction and Applications of College Curriculum Knowledge Graph T2 - TSD 2024 Paper Abstracts Y1 - 2024 UR - https://www.tsdconference.org/tsd2024/abstracts.html#I1270 ER - TY - CHAP A1 - Hohenadl, Tom A1 - Axmann, Bernhard A1 - Stummeyer, Christian ED - Di Ciccio, Claudio ED - Fdhila, Walid ED - Agostinelli, Simone ED - Amyot, Daniel ED - Leopold, Henrik ED - Krčál, Michal ED - Malinova Mandelburger, Monika ED - Polančič, Gregor ED - Tomičić-Pupek, Katarina ED - Gdowska, Katarzyna ED - Grisold, Thomas ED - Sliż, Piotr ED - Beerepoot, Iris ED - Gabryelczyk, Renata ED - Plattfaut, Ralf T1 - Decision-Making in Robotic Process Automation Programming and its Influence on Robotic Process Mining T2 - Business Process Management: Blockchain, Robotic Process Automation, Central and Eastern European, Educators and Industry Forum, BPM 2024 Blockchain, RPA, CEE, Educators and Industry Forum Krakow, Poland, September 1–6, 2024, Proceedings UR - https://doi.org/10.1007/978-3-031-70445-1_11 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-70445-1_11 SN - 978-3-031-70445-1 SN - 978-3-031-70444-4 SP - 169 EP - 184 PB - Springer CY - Cham ER - TY - CHAP A1 - Ruttmann, Julia A1 - Schiendorfer, Alexander ED - Hotho, Andreas ED - Rudolph, Sebastian T1 - SocialCOP: Reusable Building Blocks for Collective Constraint Optimization T2 - KI 2024: Advances in Artificial Intelligence, 47th German Conference on AI Würzburg, Germany, September 25–27, 2024 Proceedings UR - https://doi.org/10.1007/978-3-031-70893-0_15 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-70893-0_15 SN - 978-3-031-70893-0 SN - 978-3-031-70892-3 SP - 204 EP - 217 PB - Springer CY - Cham ER - TY - CHAP A1 - Hartung, Kai A1 - Mallick, Sambit A1 - Gröttrup, Sören A1 - Georges, Munir ED - Nöth, Elmar ED - Horák, Aleš ED - Sojka, Petr T1 - Evaluation Metrics in LLM Code Generation T2 - Text, Speech, and Dialogue: 27th International Conference, TSD 2024 Brno, Czech Republic, September 9–13, 2024 Proceedings, Part I UR - https://doi.org/10.1007/978-3-031-70563-2_17 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-70563-2_17 SN - 978-3-031-70563-2 SN - 978-3-031-70562-5 SP - 214 EP - 226 PB - Springer CY - Cham ER - TY - CHAP A1 - Kumar, Ankit A1 - Georges, Munir ED - Nöth, Elmar ED - Horák, Aleš ED - Sojka, Petr T1 - Joint-Average Mean and Variance Feature Matching (JAMVFM) Semi-supervised GAN with Additional-Objective Training Function for Intent Detection T2 - Text, Speech, and Dialogue: 27th International Conference, TSD 2024 Brno, Czech Republic, September 9–13, 2024 Proceedings, Part II UR - https://doi.org/10.1007/978-3-031-70566-3_24 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-70566-3_24 SN - 978-3-031-70566-3 SN - 978-3-031-70565-6 SP - 275 EP - 287 PB - Springer CY - Cham ER - TY - CHAP A1 - Srinivasagan, Gokul A1 - Georges, Munir ED - Nöth, Elmar ED - Horák, Aleš ED - Sojka, Petr T1 - Retrieval Augmented Spoken Language Generation for Transport Domain T2 - Text, Speech, and Dialogue: 27th International Conference, TSD 2024 Brno, Czech Republic, September 9–13, 2024 Proceedings, Part II UR - https://doi.org/10.1007/978-3-031-70566-3_1 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-70566-3_1 SN - 978-3-031-70566-3 SN - 978-3-031-70565-6 SP - 3 EP - 12 PB - Springer CY - Cham ER - TY - CHAP A1 - Ranzenberger, Thomas A1 - Freier, Carolin A1 - Reinold, Luca A1 - Riedhammer, Korbinian A1 - Schneider, Fabian A1 - Simic, Christopher A1 - Simon, Claudia A1 - Freisinger, Steffen A1 - Georges, Munir A1 - Bocklet, Tobias ED - Schulz, Sandra ED - Kiesler, Natalie T1 - A Multidisciplinary Approach to AI-based self-motivated Learning and Teaching with Large Language Models T2 - DELFI 2024, Die 22. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. N2 - We present a learning experience platform that uses machine learning methods to support students and lecturers in self-motivated online learning and teaching processes. The platform is being developed as an agile open-source collaborative project supported by multiple universities and partners. The development is guided didactically, reviewed, and scientifically evaluated in several cycles. Transparency, data protection and the copyright compliant use of the system is a central part of the project. The system further employs large language models (LLMs). Due to privacy concerns, we utilize locally hosted LLM instances and explicitly do not rely on available cloud products. Students and lecturers can interact with an LLM-based chatbot in the current prototype. The AI-generated outputs contain cross-references to the current educational video’s context, indicating if sections are based on the lectures context or world knowledge. We present the prototype and results of our qualitative evaluation from the perspective of lecturers and students. UR - https://doi.org/10.18420/delfi2024_11 Y1 - 2024 UR - https://doi.org/10.18420/delfi2024_11 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-51089 SN - 978-3-88579-255-0 SP - 133 EP - 140 PB - Gesellschaft für Informatik CY - Bonn ER - TY - JOUR A1 - Rößle, Dominik A1 - Prey, Lukas A1 - Ramgraber, Ludwig A1 - Hanemann, Anja A1 - Cremers, Daniel A1 - Noack, Patrick Ole A1 - Schön, Torsten T1 - Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset JF - Plant Phenomics N2 - Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red–green–blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen’s kappa, κ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the Fw1 score by 0.14, the accuracy by 0.11, κ by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year’s data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at https://github.com/cvims/FHB_classification. UR - https://doi.org/10.34133/plantphenomics.0068 Y1 - 2023 UR - https://doi.org/10.34133/plantphenomics.0068 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40853 SN - 2643-6515 VL - 5 PB - American Association for the Advancement of Science (AAAS) CY - Washington ER - TY - JOUR A1 - Bilal, Mühenad A1 - Podishetti, Ranadheer A1 - Koval, Leonid A1 - Gaafar, Mahmoud A. A1 - Großmann, Daniel A1 - Bregulla, Markus T1 - The Effect of Annotation Quality on Wear Semantic Segmentation by CNN JF - Sensors N2 - In this work, we investigate the impact of annotation quality and domain expertise on the performance of Convolutional Neural Networks (CNNs) for semantic segmentation of wear on titanium nitride (TiN) and titanium carbonitride (TiCN) coated end mills. Using an innovative measurement system and customized CNN architecture, we found that domain expertise significantly affects model performance. Annotator 1 achieved maximum mIoU scores of 0.8153 for abnormal wear and 0.7120 for normal wear on TiN datasets, whereas Annotator 3 with the lowest expertise achieved significantly lower scores. Sensitivity to annotation inconsistencies and model hyperparameters were examined, revealing that models for TiCN datasets showed a higher coefficient of variation (CV) of 16.32% compared to 8.6% for TiN due to the subtle wear characteristics, highlighting the need for optimized annotation policies and high-quality images to improve wear segmentation. UR - https://doi.org/10.3390/s24154777 Y1 - 2024 UR - https://doi.org/10.3390/s24154777 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49551 SN - 1424-8220 VL - 24 IS - 15 PB - MDPI CY - Basel ER - TY - JOUR A1 - Phan, Thomy A1 - Sommer, Felix A1 - Ritz, Fabian A1 - Altmann, Philipp A1 - Nüßlein, Jonas A1 - Kölle, Michael A1 - Belzner, Lenz A1 - Linnhoff-Popien, Claudia T1 - Emergent cooperation from mutual acknowledgment exchange in multi-agent reinforcement learning JF - Autonomous Agents and Multi-Agent Systems N2 - Peer incentivization (PI) is a recent approach where all agents learn to reward or penalize each other in a distributed fashion, which often leads to emergent cooperation. Current PI mechanisms implicitly assume a flawless communication channel in order to exchange rewards. These rewards are directly incorporated into the learning process without any chance to respond with feedback. Furthermore, most PI approaches rely on global information, which limits scalability and applicability to real-world scenarios where only local information is accessible. In this paper, we propose Mutual Acknowledgment Token Exchange (MATE), a PI approach defined by a two-phase communication protocol to exchange acknowledgment tokens as incentives to shape individual rewards mutually. All agents condition their token transmissions on the locally estimated quality of their own situations based on environmental rewards and received tokens. MATE is completely decentralized and only requires local communication and information. We evaluate MATE in three social dilemma domains. Our results show that MATE is able to achieve and maintain significantly higher levels of cooperation than previous PI approaches. In addition, we evaluate the robustness of MATE in more realistic scenarios, where agents can deviate from the protocol and communication failures can occur. We also evaluate the sensitivity of MATE w.r.t. the choice of token values. UR - https://doi.org/10.1007/s10458-024-09666-5 Y1 - 2024 UR - https://doi.org/10.1007/s10458-024-09666-5 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49287 SN - 1573-7454 SN - 1387-2532 VL - 38 IS - 2 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Lisca, Gheorghe A1 - Prodaniuc, Cristian A1 - Grauschopf, Thomas A1 - Axenie, Cristian T1 - Less Is More: Learning Insights From a Single Motion Sensor for Accurate and Explainable Soccer Goalkeeper Kinematics JF - IEEE Sensors Journal UR - https://doi.org/10.1109/JSEN.2021.3094929 Y1 - 2021 UR - https://doi.org/10.1109/JSEN.2021.3094929 SN - 1530-437X VL - 21 IS - 18 SP - 20375 EP - 20387 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Glocker, Kevin A1 - Georges, Munir ED - Abbas, Mourad T1 - Hierarchical Multi-task Learning with Articulatory Attributes for Cross-Lingual Phoneme Recognition T2 - Practical Solutions for Diverse Real-World NLP Applications UR - https://doi.org/10.1007/978-3-031-44260-5_4 Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-44260-5_4 SN - 978-3-031-44259-9 SN - 978-3-031-44260-5 SP - 59 EP - 75 PB - Springer CY - Cham ER - TY - JOUR A1 - Ulrichshofer, Anna A1 - Walzl, Markus T1 - A Labor Market for Persuaders: Theory and Evidence from Financial Advice JF - Journal of Institutional and Theoretical Economics UR - https://doi.org/10.1628/jite-2023-0036 Y1 - 2023 UR - https://doi.org/10.1628/jite-2023-0036 SN - 1614-0559 SN - 0932-4569 VL - 180 IS - 1 SP - 187 EP - 209 PB - Mohr Siebeck CY - Tübingen ER -