Publications of PN 7

S. Beck et al., ChoreoVis: Planning and Assessing Formations in Dance Choreographies, vol. 43. The Eurographics Association and John Wiley & Sons Ltd., 2024. doi: 10.1111/cgf.15104.

J. Meißner, D. Göddeke, and M. Herschel, Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations. 2024.

J. Kneifl, J. Fehr, S. L. Brunton, and J. N. Kutz, “Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks.” 2024.

F. Zermiani, P. Dhar, E. Sood, F. Kögel, A. Bulling, and M. Wirzberger, “InteRead: An Eye Tracking Dataset of Interrupted Reading,” in Proc. 31st Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING), 2024, pp. 9154–9169. [Online]. Available: https://aclanthology.org/2024.lrec-main.802/

X. Yu, B. Lee, and M. Sedlmair, “Design Space of Visual Feedforward And Corrective Feedback in XR-Based Motion Guidance Systems,” in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI), ACM, 2024, pp. 1–7. doi: https://doi.org/10.1145/3613904.3642143.

C. Krauter, K. Angerbauer, A. Sousa Calepso, A. Achberger, S. Mayer, and M. Sedlmair, “Sitting Posture Recognition and Feedback: A Literature Review,” in CHI ’24: Proceedings of the CHI Conference on Human Factors in Computing Systems, in CHI. NY: ACM, 2024. doi: 10.1145/3613904.3642657.

X. Yu et al., “PerSiVal: On-Body AR Visualization of Biomechanical Arm Simulations,” IEEE Computer Graphics and Applications, pp. 1–14, 2024, doi: 10.1109/MCG.2024.3494598.

P. Rodegast, S. Maier, J. Kneifl, and J. Fehr, “On using machine learning algorithms for motorcycle collision detection,” Discover Applied Sciences, vol. 6, Art. no. 6, Jun. 2024, doi: 10.1007/s42452-024-06014-w.

M. Millard, N. Stutzig, J. Fehr, and T. Siebert, “A benchmark of muscle models to length changes great and small,” Journal of the Mechanical Behavior of Biomedical Materials, vol. 160, p. 106740, 2024, doi: 10.1016/j.jmbbm.2024.106740.

J. Kneifl, J. Fehr, S. L. Brunton, and J. N. Kutz, “Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks,” Computational Mechanics, Oct. 2024, doi: 10.1007/s00466-024-02553-6.

B. Flemisch et al., “Research Data Management in Simulation Science: Infrastructure, Tools, and Applications,” Datenbank-Spektrum, 2024, doi: https://doi.org/10.1007/s13222-024-00475-4.

T. Rau, M. Sedlmair, and A. Köhn, “chARpack: The Chemistry Augmented Reality Package,” J. Chem. Inf. Model., vol. 64, Art. no. 12, 2024, doi: 10.1021/acs.jcim.4c00462.

A. Strauß, J. Kneifl, A. Tkachuk, J. Fehr, and M. Bischoff, “Accelerated Non‐linear Stability Analysis Based on Predictions From Data‐Based Surrogate Models,” International Journal for Numerical Methods in Engineering, vol. 126, Art. no. 1, Dec. 2024, doi: 10.1002/nme.7649.

X. Yu, “DC Limb Motion Guidance in Extended Reality,” in 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Mar. 2023, pp. 967–968. doi: 10.1109/VRW58643.2023.00326.

E. Sood, L. Shi, M. Bortoletto, Y. Wang, P. Müller, and A. Bulling, “Improving Neural Saliency Prediction with a Cognitive Model of Human Visual Attention,” in Proc. the 45th Annual Meeting of the Cognitive Science Society (CogSci), Jul. 2023, pp. 3639–3646.

J. Haischt and M. Sedlmair, “What’s (Not) Tracking? Factors of Influence in Industrial Augmented Reality Tracking: A Use Case Study in an Automotive Environment,” in Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, in AutomotiveUI ’23. Ingolstadt, Germany: Association for Computing Machinery, Sep. 2023, pp. 42–51. doi: 10.1145/3580585.3607156.

P. Gebhardt et al., “Auxiliary Means to Improve Motion Guidance Memorability in Extended Reality,” in 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Mar. 2023, pp. 689–690. doi: 10.1109/VRW58643.2023.00187.

M. Wieland, M. Sedlmair, and T.-K. Machulla, “VR, Gaze, and Visual Impairment: An Exploratory Study of the Perception of Eye Contact across different Sensory Modalities for People with Visual Impairments in Virtual Reality,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in CHI EA ’23. Hamburg, Germany: Association for Computing Machinery, Apr. 2023, pp. 1–6. doi: 10.1145/3544549.3585726.

F. Grioui and T. Blascheck, “Heart Rate Visualizations on a Virtual Smartwatch to Monitor Physical Activity Intensity,” in Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, SCITEPRESS - Science and Technology Publications, 2023. doi: 10.5220/0011665500003417.

N. Hube, M. Reinelt, K. Vidackovic, and M. Sedlmair, “Work vs. Leisure – Differences in Avatar Characteristics Depending on Social Situations,” in Proceedings of the 16th International Symposium on Visual Information Communication and Interaction (VINCI ’23), Association for Computing Machinery, 2023. doi: https://doi.org/10.1145/3615522.3615537.

J. Kässinger, H. Trötsch, F. Dürr, and J. Edinger, “SimEdge: Towards Accelerated Real-Time Augmented Reality Simulations Using Adaptive Smart Edge Computing,” in Proceedings of the Int’l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems, Association for Computing Machinery, 2023, pp. 181–190. doi: 10.1145/3616388.3617526.

P. Elagroudy et al., “Impact of Privacy Protection Methods of Lifelogs on Remembered Memories,” in Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), 2023, pp. 1–10. doi: 10.1145/3544548.3581565.

F. Strohm, E. Sood, D. Thomas, M. Bâce, and A. Bulling, “Facial Composite Generation with Iterative Human Feedback,” in Proc. The 1st Gaze Meets ML workshop, PMLR, I. Lourentzou, J. Wu, S. Kashyap, A. Karargyris, L. A. Celi, B. Kawas, and S. Talathi, Eds., in Proceedings of Machine Learning Research, vol. 210. PMLR, 2023, pp. 165–183. [Online]. Available: https://proceedings.mlr.press/v210/strohm23a.html

S. Rigling, X. Yu, and M. Sedlmair, ““In Your Face!”: Visualizing Fitness Tracker Data in Augmented Reality,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in CHI EA ’23. Hamburg, Germany: Association for Computing Machinery, Apr. 2023, pp. 1–7. doi: 10.1145/3544549.3585912.

A. Schmitz-Hübsch, R. Becker, and M. Wirzberger, “Personality Traits in the Emotion-Performance-Relationship in Intelligent Tutoring Systems,” in Adaptive Instructional Systems. HCII 2023. Lecture Notes in Computer Science, Springer, 2023, pp. 60–75. doi: 10.1007/978-3-031-34735-1_5.

P. Rodegast, S. Maier, J. Kneifl, and J. Fehr, “Simulation Data from Motorcycle Sensors in Operational and Crash Scenarios,” 2023, doi: 10.18419/DARUS-3301.

J. Kneifl and J. Fehr, “Crash Simulations of a Racing Kart’s Structural Frame Colliding against a Rigid Wall,” 2023, doi: 10.18419/DARUS-3789.

H. Bonasch and B. V. Ehinger, “Decoding accuracies as well as ERP amplitudes do not show between-task correlations,” Conference on Cognitive Computational Neuroscience, 2023, doi: 10.32470/CCN.2023.1029-0.

R. S. Skukies and B. Ehinger, “The effect of estimation time window length on overlap correction in EEG data,” Conference on Cognitive Computational Neuroscience, 2023, doi: 10.32470/CCN.2023.1229-0.

J. Kässinger, D. Rosin, F. Dürr, B. Mehler, T. Hubatscheck, and K. Rothermel, “Persival: Using Delayed Remote Updates in a Distributed Mobile Simulation,” 2023.

L. R. Skreinig et al., “guitARhero: Interactive Augmented Reality Guitar Tutorials,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–10, 2023, doi: 10.1109/TVCG.2023.3320266.

J. Hay et al., “Application of data-driven surrogate models for active human model response prediction and restraint system optimization,” Frontiers in applied mathematics and statistics, vol. 9, pp. 1–16, 2023, doi: 10.3389/fams.2023.1156785.

R. Frömer, M. R. Nassar, B. V. Ehinger, and A. Shenhav, “Common neural choice signals emerge artifactually amidst multiple distinct value signals,” bioRxiv, 2023, doi: 10.1101/2022.08.02.502393.

J. Kneifl, D. Rosin, O. Avci, O. Röhrle, and J. Fehr, “Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction,” Archive of Applied Mechanics, vol. 93, pp. 3637–3663, 2023, doi: 10.1007/s00419-023-02458-5.

A. R. Nikolaev, B. V. Ehinger, R. N. Meghanathan, and C. van Leeuwen, “Planning to revisit: Neural activity in refixation precursors,” Journal of Vision, vol. 23, Art. no. 7, Jul. 2023, doi: 10.1167/jov.23.7.2.

C. Yan, B. Ehinger, A. Pérez-Bellido, M. V. Peelen, and F. P. d. Lange, “Humans predict the forest, not the trees : statistical learning of spatiotemporal structure in visual scenes,” Cerebral cortex, vol. 33, Art. no. 13, 2023, doi: 10.1093/cercor/bhad115.

P. Gebhardt, X. Yu, A. Köhn, and M. Sedlmair, MolecuSense: Using Force-Feedback Gloves for Creating and Interacting with Ball-and-Stick Molecules in VR. in VINCI ’22. New York, NY, USA: Association for Computing Machinery, 2022, pp. 1–5. doi: 10.1145/3554944.3554956.

A. Abdou, E. Sood, P. Müller, and A. Bulling, “Gaze-enhanced Crossmodal Embeddings for Emotion Recognition,” in Proc. International Symposium on Eye Tracking Research and Applications (ETRA), 2022, pp. 1–18. doi: 10.1145/3530879.

A. Sousa Calepso, N. Hube, N. Berenguel Senn, V. Brandt, and M. Sedlmair, “cARdLearner: Using Expressive Virtual Agents when Learning Vocabulary in Augmented Reality,” in ACM Conference on Human Factors in Computing Systems Extended Abstracts (CHI-EA)), New Orleans, LA, USA, 2022. doi: 10.1145/3491101.3519631.

L. R. Skreinig et al., “AR Hero: Generating Interactive Augmented Reality Guitar Tutorials,” in 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Mar. 2022, pp. 395–401. doi: 10.1109/VRW55335.2022.00086.

J. Kässinger, D. Rosin, F. Dürr, N. Hornischer, K. Rothermel, and O. Röhrle, “Persival: Simulating Complex 3D Meshes on Resource-Constrained Mobile AR Devices Using Interpolation,” in Proceedings of the 2022 IEEE International Conference on Distributed Computing Systems (ICDCS), 2022, pp. 961–971. doi: 10.1109/ICDCS54860.2022.00097.

N. Hube, K. Vidackovic, and M. Sedlmair, “Using Expressive Avatars to Increase Emotion Recognition: A Pilot Study,” in Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, in CHI EA ’22. New Orleans, LA, USA: Association for Computing Machinery, Apr. 2022, pp. 1–7. doi: 10.1145/3491101.3519822.

S. Oppold and M. Herschel, “Provenance-based explanations: are they useful?,” in International Workshop on the Theory and Practice of Provenance (TAPP), 2022, pp. 2:1––2:4. doi: 10.1145/3530800.3534529.

A. Abdessaied, E. Sood, and A. Bulling, “Video Language Co-Attention with Multimodal Fast-Learning Feature Fusion for VideoQA,” in Proceedings of the 7th Workshop on Representation Learning for NLP, Association for Computational Linguistics, 2022, pp. 143–155. doi: 10.18653/v1/2022.repl4nlp-1.15.

Q. Zhou, J. Fehr, D. Bestle, and X. Rui, “Simulation of generally shaped 3D elastic body dynamics with large motion using transfer matrix method incorporating model order reduction,” Multibody System Dynamics, vol. 59, Art. no. 3, 2022, doi: 10.1007/s11044-022-09869-2.

J. Nicodemus, J. Kneifl, J. Fehr, and B. Unger, “Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators,” IFAC-PapersOnLine, vol. 55, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.117.

J. Eirich, M. Münch, D. Jäckle, M. Sedlmair, J. Bonart, and T. Schreck, “RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines,” Computer Graphics Forum, vol. 41, Art. no. 6, Mar. 2022, doi: 10.1111/cgf.14452.

S. Hermann and J. Fehr, “Documenting research software in engineering science,” Scientific Reports, vol. 12, Art. no. 1, Apr. 2022, doi: 10.1038/s41598-022-10376-9.

J. Kneifl, J. Hay, and J. Fehr, “Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme,” IFAC-PapersOnLine, vol. 55, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.109.

A. L. Gert, B. V. Ehinger, S. Timm, T. C. Kietzmann, and P. König, “WildLab: A naturalistic free viewing experiment reveals previously unknown electroencephalography signatures of face processing,” European Journal of Neuroscience, vol. 56, Art. no. 11, Nov. 2022, doi: 10.1111/ejn.15824.

F. Grioui and T. Blascheck, Study of Heart Rate Visualizations on a Virtual Smartwatch. ACM, 2021. doi: https://doi.org/10.1145/3489849.3489913.

F. Strohm, E. Sood, S. Mayer, P. Müller, M. Bâce, and A. Bulling, “Neural Photofit : Gaze-based Mental Image Reconstruction,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, 2021, pp. 245–254. doi: 10.1109/ICCV48922.2021.00031.

R. Diestelkämper, S. Lee, M. Herschel, and B. Glavic, “To not miss the forest for the trees - A holistic approach for explaining missing answers over nested data,” in In Proceedings of the ACM SIG Conference on the Management of Data (SIGMOD), 2021.

J. Kühnert, D. Göddeke, and M. Herschel, “Provenance-integrated parameter selection and optimization in numerical simulations,” in International Workshop on the Theory and Practice of Provenance (TAPP), USENIX Association, 2021.

E. Sood, F. Kögel, F. Strohm, P. Dhar, and A. Bulling, “VQA-MHUG: A gaze dataset to study multimodal neural attention in VQA,” in Proc. ACL SIGNLL Conference on Computational Natural Language Learning (CoNLL), Association for Computational Linguistics, Nov. 2021, pp. 27–43. doi: 10.18653/v1/2021.conll-1.3.

A. Czeszumski et al., “Coordinating With a Robot Partner Affects Neural Processing Related to Action Monitoring,” Frontiers in Neurorobotics, vol. 15, Aug. 2021, doi: 10.3389/fnbot.2021.686010.

J. Kneifl, D. Grunert, and J. Fehr, “A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning,” International Journal for Numerical Methods in Engineering, Apr. 2021, doi: 10.1002/nme.6712.

J. Fehr, C. Himpe, S. Rave, and J. Saak, “Sustainable Research Software Hand-Over,” Journal of Open Research Software, vol. 9, Art. no. 5, 2021, doi: 10.5334/jors.307.

P. Müller, E. Sood, and A. Bulling, “Anticipating Averted Gaze in Dyadic Interactions,” in ACM Symposium on Eye Tracking Research and Applications, in ETRA ’20 Full Papers. Stuttgart, Germany: Association for Computing Machinery, Jun. 2020, pp. 1–10. doi: 10.1145/3379155.3391332.

E. Sood, S. Tannert, D. Frassinelli, A. Bulling, and N. T. Vu, “Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension,” in Proceedings of the 24th Conference on Computational Natural Language Learning, Online: Association for Computational Linguistics, Nov. 2020, pp. 12–25. doi: 10.18653/v1/2020.conll-1.2.

X. Yu, K. Angerbauer, P. Mohr, D. Kalkofen, and M. Sedlmair, “Perspective Matters: Design Implications for Motion Guidance in Mixed Reality,” in Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2020. doi: 10.1109/ISMAR50242.2020.00085.

R. Diestelkämper and M. Herschel, “Tracing nested data with structural provenance for big data analytics,” in Proceedings of the International Conference on Extending Database Technology (EDBT), 2020, pp. 253–264. doi: 10.5441/002/edbt.2020.23.

E. Sood, S. Tannert, P. Mueller, and A. Bulling, “Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention,” in Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., Curran Associates, Inc., 2020, pp. 6327–6341. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/460191c72f67e90150a093b4585e7eb4-Paper.pdf

R. Diestelkämper and M. Herschel, “Distributed Tree-Pattern Matching in Big Data Analytics Systems,” in In Proceedings of the Conference on Advances in Databases and Information Systems (ADBIS), in Lecture Notes in Computer Science. Springer, 2020, pp. 171–186. doi: https://doi.org/10.1007/978-3-030-54832-2_14.

F. Kempter, L. Lantella, N. Stutzig, J. C. Fehr, and T. Siebert, “Neck Reflex Behavior in Driving Simulator Experiments - Academic-Scale Simulator at ITM.” 2023. doi: 10.18419/darus-3000.

A. Baier and D. Frank, “deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning.” 2023. doi: 10.18419/darus-3455.

J. Kneifl, D. Rosin, O. Avci, O. Röhrle, and J. C. Fehr, “Continuum-mechanical Forward Simulation Results of a Human Upper-limb Model Under Varying Muscle Activations.” 2023. doi: 10.18419/darus-3302.

R. S. Skukies, “2023 CCN Time Window Project Code.” 2023. doi: 10.18419/darus-3635.

J. Kneifl, J. Hay, and J. Fehr, “Human Occupant Motion in Pre-Crash Scenario.” 2022. doi: 10.18419/darus-2471.

J. Rettberg et al., “Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2023. doi: 10.18419/darus-3248.