@inproceedings{BraunsteinGoldmannAlbrachtetal.2013, author = {Braunstein, Bjoern and Goldmann, Jan-Peter and Albracht, Kirsten and Sanno, Maximilian and Willwacher, Steffen and Heinrich, Kai and Herrmann, Volker and Br{\"u}ggemann, Gert-Peter}, title = {Joint specific contribution of mechanical power and work during acceleration and top speed in elite sprinters}, series = {31 International Conference on Biomechanics in Sports, Taipei, Taiwan, July 07 - July 22, 2013}, booktitle = {31 International Conference on Biomechanics in Sports, Taipei, Taiwan, July 07 - July 22, 2013}, issn = {1999-4168}, year = {2013}, language = {en} } @inproceedings{KolditzAlbrachtFasseetal.2015, author = {Kolditz, Melanie and Albracht, Kirsten and Fasse, Alessandro and Albin, Thivaharan and Br{\"u}ggemann, Gert-Peter and Abel, Dirk}, title = {Evaluation of an industrial robot as a leg press training device}, series = {XV International Symposium on Computer Simulation in Biomechanics July 9th - 11th 2015, Edinburgh, UK}, booktitle = {XV International Symposium on Computer Simulation in Biomechanics July 9th - 11th 2015, Edinburgh, UK}, pages = {41 -- 42}, year = {2015}, language = {en} } @inproceedings{KolditzAlbinFasseetal.2015, author = {Kolditz, Melanie and Albin, Thivaharan and Fasse, Alessandro and Br{\"u}ggemann, Gert-Peter and Abel, Dirk and Albracht, Kirsten}, title = {Simulative Analysis of Joint Loading During Leg Press Exercise for Control Applications}, series = {IFAC-PapersOnLine}, volume = {48}, booktitle = {IFAC-PapersOnLine}, number = {20}, doi = {10.1016/j.ifacol.2015.10.179}, pages = {435 -- 440}, year = {2015}, language = {en} } @inproceedings{BehbahaniRibleMoulinecetal.2015, author = {Behbahani, Mehdi and Rible, Sebastian and Moulinec, Charles and Fournier, Yvan and Nicolai, Mike and Crosetto, Paolo}, title = {Simulation of the FDA Centrifugal Blood Pump Using High Performance Computing}, series = {World Academy of Science, Engineering and Technology International Journal of Mechanical and Mechatronics Engineering}, volume = {9}, booktitle = {World Academy of Science, Engineering and Technology International Journal of Mechanical and Mechatronics Engineering}, number = {5}, year = {2015}, language = {en} } @inproceedings{BuesgenKloeserKohletal.2023, author = {B{\"u}sgen, Andr{\´e} and Kl{\"o}ser, Lars and Kohl, Philipp and Schmidts, Oliver and Kraft, Bodo and Z{\"u}ndorf, Albert}, title = {From cracked accounts to fake IDs: user profiling on German telegram black market channels}, series = {Data Management Technologies and Applications}, booktitle = {Data Management Technologies and Applications}, editor = {Cuzzocrea, Alfredo and Gusikhin, Oleg and Hammoudi, Slimane and Quix, Christoph}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-37889-8 (Print)}, doi = {10.1007/978-3-031-37890-4_9}, pages = {176 -- 202}, year = {2023}, abstract = {Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.}, language = {en} } @inproceedings{KohlFreyerKraemeretal.2023, author = {Kohl, Philipp and Freyer, Nils and Kr{\"a}mer, Yoka and Werth, Henri and Wolf, Steffen and Kraft, Bodo and Meinecke, Matthias and Z{\"u}ndorf, Albert}, title = {ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP}, series = {Deep Learning Theory and Applications}, booktitle = {Deep Learning Theory and Applications}, editor = {Conte, Donatello and Fred, Ana and Gusikhin, Oleg and Sansone, Carlo}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-39058-6 (Print)}, doi = {10.1007/978-3-031-39059-3_16}, pages = {235 -- 253}, year = {2023}, abstract = {Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.}, language = {en} } @inproceedings{KloeserBuesgenKohletal.2023, author = {Kl{\"o}ser, Lars and B{\"u}sgen, Andr{\´e} and Kohl, Philipp and Kraft, Bodo and Z{\"u}ndorf, Albert}, title = {Explaining relation classification models with semantic extents}, series = {Deep Learning Theory and Applications}, booktitle = {Deep Learning Theory and Applications}, editor = {Conte, Donatello and Fred, Ana and Gusikhin, Oleg and Sansone, Carlo}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-39058-6 (Print)}, doi = {10.1007/978-3-031-39059-3_13}, pages = {189 -- 208}, year = {2023}, abstract = {In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions. We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.}, language = {en} } @inproceedings{KetelhutGoellBraunsteinetal.2019, author = {Ketelhut, Maike and G{\"o}ll, Fabian and Braunstein, Bjoern and Albracht, Kirsten and Abel, Dirk}, title = {Iterative learning control of an industrial robot for neuromuscular training}, series = {2019 IEEE Conference on Control Technology and Applications}, booktitle = {2019 IEEE Conference on Control Technology and Applications}, publisher = {IEEE}, address = {New York}, isbn = {978-1-7281-2767-5 (ePub)}, doi = {10.1109/CCTA.2019.8920659}, pages = {7 Seiten}, year = {2019}, abstract = {Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.}, language = {en} } @inproceedings{MaurerMiskiwAcostaetal.2023, author = {Maurer, Florian and Miskiw, Kim K. and Acosta, Rebeca Ramirez and Harder, Nick and Sander, Volker and Lehnhoff, Sebastian}, title = {Market abstraction of energy markets and policies - application in an agent-based modeling toolbox}, series = {EI.A 2023: Energy Informatics}, booktitle = {EI.A 2023: Energy Informatics}, editor = {Jorgensen, Bo Norregaard and Pereira da Silva, Luiz Carlos and Ma, Zheng}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-48651-7 (Print)}, doi = {10.1007/978-3-031-48652-4_10}, pages = {139 -- 157}, year = {2023}, abstract = {In light of emerging challenges in energy systems, markets are prone to changing dynamics and market design. Simulation models are commonly used to understand the changing dynamics of future electricity markets. However, existing market models were often created with specific use cases in mind, which limits their flexibility and usability. This can impose challenges for using a single model to compare different market designs. This paper introduces a new method of defining market designs for energy market simulations. The proposed concept makes it easy to incorporate different market designs into electricity market models by using relevant parameters derived from analyzing existing simulation tools, morphological categorization and ontologies. These parameters are then used to derive a market abstraction and integrate it into an agent-based simulation framework, allowing for a unified analysis of diverse market designs. Furthermore, we showcase the usability of integrating new types of long-term contracts and over-the-counter trading. To validate this approach, two case studies are demonstrated: a pay-as-clear market and a pay-as-bid long-term market. These examples demonstrate the capabilities of the proposed framework.}, language = {en} } @inproceedings{IomdinaKiselevaKotliaretal.2020, author = {Iomdina, Elena N. and Kiseleva, Anna A. and Kotliar, Konstantin and Luzhnov, Petr V.}, title = {Quantification of Choroidal Blood Flow Using the OCT-A System Based on Voxel Scan Processing}, series = {Proceedings of the International Conference on Biomedical Innovations and Applications- BIA 2020}, booktitle = {Proceedings of the International Conference on Biomedical Innovations and Applications- BIA 2020}, publisher = {IEEE}, address = {New York, NY}, isbn = {978-1-7281-7073-2}, doi = {10.1109/BIA50171.2020.9244511}, pages = {41 -- 44}, year = {2020}, abstract = {The paper presents a method for the quantitative assessment of choroidal blood flow using an OCT-A system. The developed technique for processing of OCT-A scans is divided into two stages. At the first stage, the identification of the boundaries in the selected portion was performed. At the second stage, each pixel mark on the selected layer was represented as a volume unit, a voxel, which characterizes the region of moving blood. Three geometric shapes were considered to represent the voxel. On the example of one OCT-A scan, this work presents a quantitative assessment of the blood flow index. A possible modification of two-stage algorithm based on voxel scan processing is presented.}, language = {en} }