Selected Paper Awards & Personal Awards

For more awards, please browse our news section.

All Publications

  1. 2024

    1. Y. Wang et al., “SalChartQA: Question-driven Saliency on Information Visualisations,” in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI), in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI). ACM, May 2024, pp. 1--14. doi: 10.1145/3613904.3642942.
  2. 2023

    1. C. Beck and M. Köllner, “GHisBERT -- Training BERT from scratch for lexical semantic investigations across historical German language stages,” in Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change, N. Tahmasebi, S. Montariol, H. Dubossarsky, A. Kutuzov, S. Hengchen, D. Alfter, F. Periti, and P. Cassotti, Eds., in Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change. Singapore: Association for Computational Linguistics, Dec. 2023, pp. 33--45. [Online]. Available: https://aclanthology.org/2023.lchange-1.4
    2. F. Heyen, Q. Q. Ngo, and M. Sedlmair, “Visual Overviews for Sheet Music Structure,” in Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023, in Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023. Nov. 2023, pp. 692--699. doi: doi.org/10.48550/arXiv.2308.06140.
    3. J. Schmalfuss, L. Mehl, and A. Bruhn, “Distracting Downpour: Adversarial Weather Attacks for Motion Estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Oct. 2023, pp. 10106–10116. [Online]. Available: https://openaccess.thecvf.com/content/ICCV2023/html/Schmalfuss_Distracting_Downpour_Adversarial_Weather_Attacks_for_Motion_Estimation_ICCV_2023_paper.html
    4. 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 Proceedings of the 45th Annual Meeting of the Cognitive Science Society (CogSci), in Proceedings of the 45th Annual Meeting of the Cognitive Science Society (CogSci). Jul. 2023, pp. 3639–3646. [Online]. Available: https://escholarship.org/uc/item/5968p71m
    5. L. Mehl, J. Schmalfuss, A. Jahedi, Y. Nalivayko, and A. Bruhn, “Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Jun. 2023, pp. 4981–4991. [Online]. Available: https://openaccess.thecvf.com/content/CVPR2023/html/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.html
    6. M. Kern, S. Jaeger-Honz, F. Schreiber, and B. Sommer, “APL@voro—interactive visualization and analysis of cell membrane simulations,” Bioinformatics, vol. 39, no. 2, Art. no. 2, Feb. 2023, doi: 10.1093/bioinformatics/btad083.
    7. Y. Wang, M. Bâce, and A. Bulling, “Scanpath Prediction on Information Visualisations,” IEEE Transactions on Visualization and Computer Graphics, pp. 1--15, Feb. 2023, doi: 10.1109/TVCG.2023.3242293.
    8. L. Mehl, A. Jahedi, J. Schmalfuss, and A. Bruhn, “M-FUSE: Multi-frame Fusion for Scene Flow Estimation,” in Proc. Winter Conference on Applications of Computer Vision (WACV), in Proc. Winter Conference on Applications of Computer Vision (WACV). Jan. 2023. doi: 10.48550/arXiv.2207.05704.
    9. T. Kosch, J. Karolus, J. Zagermann, H. Reiterer, A. Schmidt, and P. W. Woźniak, “A Survey on Measuring Cognitive Workload in Human-Computer Interaction,” ACM Comput. Surv., Jan. 2023, doi: 10.1145/3582272.
    10. N. Rodrigues, C. Schulz, S. Doring, D. Baumgartner, T. Krake, and D. Weiskopf, “Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, Art. no. 1, Jan. 2023, doi: 10.1109/TVCG.2022.3209429.
    11. C. Morariu, A. Bibal, R. Cutura, B. Frénay, and M. Sedlmair, “Predicting User Preferences of Dimensionality Reduction Embedding Quality,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1109/TVCG.2022.3209449.
    12. M. Koch, K. Kurzhals, M. Burch, and D. Weiskopf, “Visualization Psychology for Eye Tracking Evaluation,” in Visualization Psychology, D. Albers Szafir, R. Borgo, M. Chen, D. J. Edwards, B. Fisher, and L. Padilla, Eds., in Visualization Psychology. , Cham: Springer International Publishing, 2023, pp. 243--260. doi: 10.1007/978-3-031-34738-2_10.
    13. J. Schmalfuss, E. Scheurer, H. Zhao, N. Karantzas, A. Bruhn, and D. Labate, “Blind image inpainting with sparse directional filter dictionaries for lightweight CNNs,” Journal of Mathematical Imaging and Vision (JMIV), vol. 65, pp. 323--339, 2023, doi: 10.1007/s10851-022-01119-6.
    14. P. Paetzold, R. Kehlbeck, H. Strobelt, Y. Xue, S. Storandt, and O. Deussen, “RectEuler: Visualizing Intersecting Sets using Rectangles,” Computer Graphics Forum, vol. 42, no. 3, Art. no. 3, 2023, doi: https://doi.org/10.1111/cgf.14814.
    15. K.-T. Chen et al., “Reading Strategies for Graph Visualizations That Wrap Around in Torus Topology,” in Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, in Proceedings of the 2023 Symposium on Eye Tracking Research and Applications. Tubingen, Germany: Association for Computing Machinery, 2023. doi: 10.1145/3588015.3589841.
    16. R. Bauer et al., “Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment,” Transport in Porous Media, 2023, doi: https://doi.org/10.1007/s11242-023-02019-y.
    17. M. Jenadeleh, J. Zagermann, H. Reiterer, U.-D. Reips, R. Hamzaoui, and D. Saupe, “Relaxed forced choice improves performance of visual quality assessment methods,” in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX). 2023, pp. 37–42. doi: 10.1109/QoMEX58391.2023.10178467.
    18. T. Ge et al., “Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–13, 2023, doi: 10.1109/TVCG.2023.3284499.
    19. F. L. Dennig, M. Miller, D. A. Keim, and M. El-Assady, “FS/DS: A Theoretical Framework for the Dual Analysis of Feature Space and Data Space,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–17, 2023, doi: 10.1109/TVCG.2023.3288356.
    20. A. V. Reinschluessel and J. Zagermann, “Exploring Hybrid User Interfaces for Surgery Planning,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). 2023, pp. 208–210. doi: 10.1109/ISMAR-Adjunct60411.2023.00048.
    21. M. Butt, L. Carnesale, and T. Ahmed, “Experiencers vs. agents in Urdu/Hindi nominalized verbs of perception,” in Proceedings of the Lexical Functional Grammar Conference, in Proceedings of the Lexical Functional Grammar Conference, vol. 28. 2023, pp. 90--113. [Online]. Available: https://lfg-proceedings.org/lfg/index.php/main/article/view/46
    22. M. Xue et al., “Taurus: Towards a Unified Force Representation and Universal Solver for Graph Layout,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1109/TVCG.2022.3209371.
    23. D. Hägele, T. Krake, and D. Weiskopf, “Uncertainty-Aware Multidimensional Scaling,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1109/TVCG.2022.3209420.
    24. N. Doerr, K. Angerbauer, M. Reinelt, and M. Sedlmair, “Bees, Birds and Butterflies: Investigating the Influence of Distractors on Visual Attention Guidance Techniques,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. Hamburg, Germany: Association for Computing Machinery, 2023. doi: 10.1145/3544549.3585816.
    25. S. Hubenschmid, J. Zagermann, D. Leicht, H. Reiterer, and T. Feuchtner, “ARound the Smartphone: Investigating the Effects of Virtually-Extended Display Size on Spatial Memory,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). New York, NY, USA: ACM, 2023. doi: https://doi.org/10.1145/3544548.3581438.
    26. S. Hubenschmid, D. I. Fink, J. Zagermann, J. Wieland, H. Reiterer, and T. Feuchtner, “Colibri: A Toolkit for Rapid Prototyping of Networking Across Realities,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). 2023, pp. 9–13. doi: 10.1109/ISMAR-Adjunct60411.2023.00010.
    27. C. Schneegass, M. L. Wilson, H. A. Maior, F. Chiossi, A. L. Cox, and J. Wiese, “The Future of Cognitive Personal Informatics,” in Proceedings of the 25th International Conference on Mobile Human-Computer Interaction, in Proceedings of the 25th International Conference on Mobile Human-Computer Interaction. Athens, Greece: Association for Computing Machinery, 2023. doi: 10.1145/3565066.3609790.
  3. 2022

    1. D. I. Fink, J. Zagermann, H. Reiterer, and H.-C. Jetter, “Re-Locations: Augmenting Personal and Shared Workspaces to Support Remote Collaboration in Incongruent Spaces,” Proc. ACM Hum.-Comput. Interact., vol. 6, no. ISS, Art. no. ISS, Nov. 2022, doi: 10.1145/3567709.
    2. K. Angerbauer and M. Sedlmair, “Toward Inclusion and Accessibility in Visualization Research: Speculations on Challenges, Solution Strategies, and Calls for Action (Position Paper),” in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV). Oct. 2022, pp. 20–27. doi: 10.1109/BELIV57783.2022.00007.
    3. C. Müller, M. Heinemann, D. Weiskopf, and T. Ertl, “Power Overwhelming: Quantifying the Energy Cost of Visualisation,” in Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), in Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond - Methodological Approaches for Visualization (BELIV). Oct. 2022, pp. 38–46. doi: 10.1109/BELIV57783.2022.00009.
    4. J. Schmalfuss, P. Scholze, and A. Bruhn, “A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow,” Proceedings of the European Conference on Computer Vision (ECCV), Oct. 2022.
    5. A. Jahedi, L. Mehl, M. Rivinius, and A. Bruhn, “Multi-Scale RAFT: combining hierarchical concepts for learning-based optical flow estimation,” Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 1236–1240, Oct. 2022, doi: 10.1109/ICIP46576.2022.9898048.
    6. H. Lin, H. Men, Y. Yan, J. Ren, and D. Saupe, “Crowdsourced Quality Assessment of Enhanced Underwater Images - a Pilot Study,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). IEEE, Sep. 2022, pp. 1--4. doi: 10.1109/QoMEX55416.2022.9900904.
    7. P. Schäfer, N. Rodrigues, D. Weiskopf, and S. Storandt, “Group Diagrams for Simplified Representation of Scanpaths,” in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI), in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI). ACM, Aug. 2022. doi: 10.1145/3554944.3554971.
    8. S. Dosdall, K. Angerbauer, L. Merino, M. Sedlmair, and D. Weiskopf, “Toward In-Situ Authoring of Situated Visualization with Chorded Keyboards,” in VINCI ’22: Proceedings of the 15th International Symposium on Visual Information Communication and Interaction, M. Burch, G. Wallner, and D. Limberger, Eds., in VINCI ’22: Proceedings of the 15th International Symposium on Visual Information Communication and Interaction. ACM, Aug. 2022, pp. 1–5. doi: 10.1145/3554944.3554970.
    9. M. Zameshina et al., “Fairness in generative modeling: do it unsupervised!,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, in Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, Jul. 2022, pp. 320--323. doi: 10.1145/3520304.3528992.
    10. Y. Wang, M. Koch, M. Bâce, D. Weiskopf, and A. Bulling, “Impact of Gaze Uncertainty on AOIs in Information Visualisations,” in 2022 Symposium on Eye Tracking Research and Applications, in 2022 Symposium on Eye Tracking Research and Applications. ACM, Jun. 2022, pp. 1–6. doi: 10.1145/3517031.3531166.
    11. P. Balestrucci, D. Wiebusch, and M. O. Ernst, “ReActLab: A Custom Framework for Sensorimotor Experiments ‘in-the-wild,’” Frontiers in Psychology, vol. 13, Jun. 2022, doi: 10.3389/fpsyg.2022.906643.
    12. M. Koch, D. Weiskopf, and K. Kurzhals, “A Spiral into the Mind: Gaze Spiral Visualization for Mobile Eye Tracking,” Proceedings of the ACM on Computer Graphics and Interactive Techniques, vol. 5, no. 2, Art. no. 2, May 2022, doi: 10.1145/3530795.
    13. M. Philipp, N. Bacher, S. Sauer, F. Mathis-Ullrich, and A. Bruhn, “From Chairs To Brains: Customizing Optical Flow For Surgical Activity Localization,” in Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), in Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, Mar. 2022, pp. 1–5. doi: 10.1109/ISBI52829.2022.9761704.
    14. F. Petersen, B. Goldluecke, O. Deussen, and H. Kuehne, “Style Agnostic 3D Reconstruction via Adversarial Style Transfer,” in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, Jan. 2022, pp. 2273–2282. doi: 10.1109/WACV51458.2022.00233.
    15. D. Weiskopf, “Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization,” Frontiers in Bioinformatics, vol. 2, 2022, doi: 10.3389/fbinf.2022.793819.
    16. C. Schneegass, V. Füseschi, V. Konevych, and F. Draxler, “Investigating the Use of Task Resumption Cues to Support Learning in Interruption-Prone Environments,” Multimodal Technologies and Interaction, vol. 6, no. 1, Art. no. 1, 2022, doi: 10.3390/mti6010002.
    17. F. Chiossi et al., “Adapting visualizations and interfaces to the user,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: 10.1515/itit-2022-0035.
    18. Y. Wang, C. Jiao, M. Bâce, and A. Bulling, “VisRecall: Quantifying Information Visualisation Recallability Via Question Answering,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 12, Art. no. 12, 2022, doi: 10.1109/TVCG.2022.3198163.
    19. H. Lin et al., “Large-Scale Crowdsourced Subjective Assessment of Picturewise Just Noticeable Difference,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 9, Art. no. 9, 2022, doi: 10.1109/TCSVT.2022.3163860.
    20. L. Joos, S. Jaeger-Honz, F. Schreiber, D. A. Keim, and K. Klein, “Visual Comparison of Networks in VR,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 11, Art. no. 11, 2022, doi: 10.1109/TVCG.2022.3203001.
    21. M. Abdelaal, N. D. Schiele, K. Angerbauer, K. Kurzhals, M. Sedlmair, and D. Weiskopf, “Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2022, doi: 10.1109/TVCG.2022.3209427.
    22. F. Petersen, B. Goldluecke, C. Borgelt, and O. Deussen, “GenDR: A Generalized Differentiable Renderer,” in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR). 2022, pp. 3992–4001. doi: 10.1109/CVPR52688.2022.00397.
    23. F. Chiossi, R. Welsch, S. Villa, L. Chuang, and S. Mayer, “Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience,” Big Data and Cognitive Computing, vol. 6, no. 2, Art. no. 2, 2022, doi: 10.3390/bdcc6020055.
    24. A. Niarakis et al., “Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology,” Briefings in bioinformatics, vol. 23, no. 4, Art. no. 4, 2022, doi: 10.1093/bib/bbac212.
    25. F. Schreiber and D. Weiskopf, “Quantitative Visual Computing,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0048.
    26. Y. Zhang, K. Klein, O. Deussen, T. Gutschlag, and S. Storandt, “Robust Visualization of Trajectory Data,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0036.
    27. Q. Q. Ngo, F. L. Dennig, D. A. Keim, and M. Sedlmair, “Machine Learning Meets Visualization – Experiences and Lessons Learned,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0034.
    28. T. Krake, D. Klötzl, B. Eberhardt, and D. Weiskopf, “Constrained Dynamic Mode Decomposition,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2022, doi: 10.1109/TVCG.2022.3209437.
    29. S. Su et al., “Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model,” CoRR, 2022, doi: 10.48550/ARXIV.2207.04904.
    30. T. Krake, A. Bruhn, B. Eberhardt, and D. Weiskopf, “Efficient and Robust Background Modeling with Dynamic Mode Decomposition,” Journal of Mathematical Imaging and Vision (2022), 2022, doi: 10.1007/s10851-022-01068-0.
    31. G. Richer, A. Pister, M. Abdelaal, J.-D. Fekete, M. Sedlmair, and D. Weiskopf, “Scalability in Visualization,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–15, 2022, doi: 10.1109/TVCG.2022.3231230.
    32. D. Bienroth et al., “Spatially resolved transcriptomics in immersive environments,” Visual Computing for Industry, Biomedicine, and Art, vol. 5, no. 1, Art. no. 1, 2022, doi: 10.1186/s42492-021-00098-6.
    33. K. Klein, M. Sedlmair, and F. Schreiber, “Immersive Analytics: An Overview,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0037.
    34. J. Görtler et al., “Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. New Orleans, LA, USA: Association for Computing Machinery, 2022, pp. 1–13. doi: 10.1145/3491102.3501823.
    35. P. Fleck, A. Sousa Calepso, S. Hubenschmid, M. Sedlmair, and D. Schmalstieg, “RagRug: A Toolkit for Situated Analytics,” IEEE Transactions on Visualization and Computer Graphics, 2022, doi: 10.1109/TVCG.2022.3157058.
    36. J. Schmalfuss, L. Mehl, and A. Bruhn, “Attacking Motion Estimation with Adversarial Snow,” in Proc. ECCV Workshop on Adversarial Robustness in the Real World (AROW), in Proc. ECCV Workshop on Adversarial Robustness in the Real World (AROW). 2022. doi: 10.48550/arXiv.2210.11242.
    37. N. Rodrigues, L. Shao, J. J. Yan, T. Schreck, and D. Weiskopf, “Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection,” in 2022 Symposium on Eye Tracking Research and Applications, in 2022 Symposium on Eye Tracking Research and Applications. Seattle, WA, USA: Association for Computing Machinery, 2022, pp. 59:1-59:7. doi: 10.1145/3517031.3531165.
    38. M. Becher et al., “Situated Visual Analysis and Live Monitoring for Manufacturing,” IEEE Computer Graphics and Applications, pp. 1–1, 2022, doi: 10.1109/MCG.2022.3157961.
    39. T. Krake, M. von Scheven, J. Gade, M. Abdelaal, D. Weiskopf, and M. Bischoff, “Efficient Update of Redundancy Matrices for Truss and Frame Structures,” Journal of Theoretical, Computational and Applied Mechanics, 2022, doi: 10.46298/jtcam.9615.
    40. D. Dietz et al., “Walk This Beam: Impact of Different Balance Assistance Strategies and Height Exposure on Performance and Physiological Arousal in VR,” in 28th ACM Symposium on Virtual Reality Software and Technology, in 28th ACM Symposium on Virtual Reality Software and Technology. 2022, pp. 1--12. doi: 10.1145/3562939.3567818.
    41. T. Kosch, R. Welsch, L. Chuang, and A. Schmidt, “The Placebo Effect of Artificial Intelligence in Human-Computer Interaction,” ACM Transactions on Computer-Human Interaction, 2022, doi: 10.1145/3529225.
    42. R. Kehlbeck, J. Görtler, Y. Wang, and O. Deussen, “SPEULER: Semantics-preserving Euler Diagrams,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, Art. no. 1, 2022, doi: 10.1109/TVCG.2021.3114834.
    43. F. Götz-Hahn, V. Hosu, and D. Saupe, “Critical Analysis on the Reproducibility of Visual Quality Assessment Using Deep Features,” PLoS ONE, vol. 17, no. 8, Art. no. 8, 2022, doi: 10.1371/journal.pone.0269715.
    44. J. Lou, H. Lin, D. Marshall, D. Saupe, and H. Liu, “TranSalNet: Towards perceptually relevant visual saliency prediction,” Neurocomputing, vol. 494, pp. 455–467, 2022, doi: https://doi.org/10.1016/j.neucom.2022.04.080.
    45. K. Angerbauer et al., “Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. New Orleans, LA, USA: Association for Computing Machinery, 2022. doi: 10.1145/3491102.3502133.
    46. D. Hägele et al., “Uncertainty Visualization: Fundamentals and Recent Developments,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: 10.1515/itit-2022-0033.
    47. D. Garkov, C. Müller, M. Braun, D. Weiskopf, and F. Schreiber, “Research Data Curation in Visualization: Position Paper,” in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), M. Sedlmair, Ed., in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV). 2022, pp. 56–65. doi: 10.1109/BELIV57783.2022.00011.
    48. J. Zagermann et al., “Complementary Interfaces for Visual Computing,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0031.
    49. S. Hubenschmid et al., “ReLive: Bridging In-Situ and Ex-Situ Visual Analytics for Analyzing Mixed Reality User Studies,” in CHI Conference on Human Factors in Computing Systems (CHI ’22), in CHI Conference on Human Factors in Computing Systems (CHI ’22). New York, NY: ACM, 2022, pp. 1–20. doi: 10.1145/3491102.3517550.
    50. V. Bruder, M. Larsen, T. Ertl, H. Childs, and S. Frey, “A Hybrid In Situ Approach for Cost Efficient Image Database Generation,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–1, 2022, doi: 10.1109/TVCG.2022.3169590.
    51. S. Frey et al., “Parameter Adaptation In Situ: Design Impacts and Trade-Offs,” in In Situ Visualization for Computational Science, H. Childs, J. C. Bennett, and C. Garth, Eds., in In Situ Visualization for Computational Science. Cham: Springer International Publishing, 2022, pp. 159--182. doi: 10.1007/978-3-030-81627-8_8.
    52. H. Tarner, V. Bruder, T. Ertl, S. Frey, and F. Beck, “Visually Comparing Rendering Performance from Multiple Perspectives,” in Vision, Modeling, and Visualization, J. Bender, M. Botsch, and D. Keim, Eds., in Vision, Modeling, and Visualization. The Eurographics Association, 2022. doi: 10.2312/vmv.20221211.
    53. A. Huang, P. Knierim, F. Chiossi, L. L. Chuang, and R. Welsch, “Proxemics for Human-Agent Interaction in Augmented Reality,” in CHI Conference on Human Factors in Computing Systems, in CHI Conference on Human Factors in Computing Systems. 2022, pp. 1--13. doi: 10.1145/3491102.3517593.
  4. 2021

    1. C. Schulz et al., “Multi-Class Inverted Stippling,” ACM Trans. Graph., vol. 40, no. 6, Art. no. 6, Dec. 2021, doi: 10.1145/3478513.3480534.
    2. B. Roziere et al., “EvolGAN: Evolutionary Generative Adversarial Networks,” in Computer Vision -- ACCV 2020, in Computer Vision -- ACCV 2020. Cham: Springer International Publishing, Nov. 2021, pp. 679--694. doi: 10.1007/978-3-030-69538-5_41.
    3. K. Klein, D. Garkov, S. Rütschlin, T. Böttcher, and F. Schreiber, “QSDB—a graphical Quorum Sensing Database,” Database, vol. 2021, no. 2021, Art. no. 2021, Nov. 2021, doi: 10.1093/database/baab058.
    4. R. Sevastjanova, A.-L. Kalouli, C. Beck, H. Schäfer, and M. El-Assady, “Explaining Contextualization in Language Models using Visual Analytics,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Online: Association for Computational Linguistics, Aug. 2021, pp. 464--476. doi: 10.18653/v1/2021.acl-long.39.
    5. M. Aichem et al., “Visual exploration of large metabolic models,” Bioinformatics, vol. 37, no. 23, Art. no. 23, May 2021, doi: 10.1093/bioinformatics/btab335.
    6. P. Balestrucci, V. Maffei, F. Lacquaniti, and A. Moscatelli, “The Effects of Visual Parabolic Motion on the Subjective Vertical and on Interception,” Neuroscience, vol. 453, pp. 124–137, Jan. 2021, doi: 10.1016/j.neuroscience.2020.09.052.
    7. K. C. Kwan and H. Fu, “Automatic Image Checkpoint Selection for Guider-Follower Pedestrian Navigation,” Computer Graphics Forum, vol. 40, no. 1, Art. no. 1, 2021, doi: https://doi.org/10.1111/cgf.14192.
    8. S. Giebenhain and B. Goldlücke, “AIR-Nets: An Attention-Based Framework for Locally Conditioned Implicit Representations,” in 2021 International Conference on 3D Vision (3DV), in 2021 International Conference on 3D Vision (3DV). 2021, pp. 1054–1064. doi: 10.1109/3DV53792.2021.00113.
    9. F. Draxler, C. Schneegass, J. Safranek, and H. Hussmann, “Why Did You Stop? - Investigating Origins and Effects of Interruptions during Mobile Language Learning,” in Mensch Und Computer 2021, in Mensch Und Computer 2021. Ingolstadt, Germany: Association for Computing Machinery, 2021, pp. 21–33. doi: 10.1145/3473856.3473881.
    10. K. Gadhave et al., “Predicting intent behind selections in scatterplot visualizations,” Information Visualization, vol. 20, no. 4, Art. no. 4, 2021, doi: 10.1177/14738716211038604.
    11. K. Lu et al., “Palettailor: Discriminable Colorization for Categorical Data,” IEEE Transactions on Visualization & Computer Graphics, vol. 27, no. 02, Art. no. 02, 2021, doi: 10.1109/TVCG.2020.3030406.
    12. L. Mehl, C. Beschle, A. Barth, and A. Bruhn, “An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation,” in Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), in Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Springer, 2021, pp. 140--152. doi: 10.1007/978-3-030-75549-2_12.
    13. C. Krauter, J. Vogelsang, A. S. Calepso, K. Angerbauer, and M. Sedlmair, “Don’t Catch It: An Interactive Virtual-Reality Environment to Learn About COVID-19 Measures Using Gamification Elements,” in Mensch und Computer, in Mensch und Computer. ACM, 2021, pp. 593--596. doi: 10.1145/3473856.3474031.
    14. C. Morariu, A. Bibal, R. Cutura, B. Frenay, and M. Sedlmair, “DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality,” arXiv preprint, Technical Report arXiv:2105.09275, 2021. [Online]. Available: https://arxiv.org/abs/2105.09275
    15. G. J. Rijken et al., “Illegible Semantics: Exploring the Design Space of Metal Logos,” in IEEE VIS alt.VIS Workshop, in IEEE VIS alt.VIS Workshop. 2021. [Online]. Available: https://arxiv.org/abs/2109.01688
    16. S. Su, V. Hosu, H. Lin, Y. Zhang, and D. Saupe, “KonIQ++: Boosting No-Reference Image Quality Assessment in the Wild by Jointly Predicting Image Quality and Defects,” in 32nd British Machine Vision Conference, in 32nd British Machine Vision Conference. 2021, pp. 1–12. [Online]. Available: https://www.bmvc2021-virtualconference.com/assets/papers/0868.pdf
    17. S. Hubenschmid, J. Zagermann, S. Butscher, and H. Reiterer, “STREAM: Exploring the Combination of Spatially-Aware Tablets with Augmented Reality Head-Mounted Displays for Immersive Analytics,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. , New York, NY, USA: Association for Computing Machinery, 2021. doi: 10.1145/3411764.3445298.
    18. B. Roziere et al., “Tarsier: Evolving Noise Injection in Super-Resolution GANs,” in 2020 25th International Conference on Pattern Recognition (ICPR), in 2020 25th International Conference on Pattern Recognition (ICPR). 2021, pp. 7028–7035. doi: 10.1109/ICPR48806.2021.9413318.
    19. K. Klein et al., “Visual analytics of sensor movement data for cheetah behaviour analysis,” Journal of Visualization, 2021, doi: 10.1007/s12650-021-00742-6.
    20. H. Booth and C. Beck, “Verb-second and Verb-first in the History of Icelandic,” Journal of Historical Syntax, vol. 5, no. 27, Art. no. 27, 2021, doi: 10.18148/hs/2021.v5i28.112.
    21. F. Götz-Hahn, V. Hosu, H. Lin, and D. Saupe, “KonVid-150k : A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild,” IEEE Access, vol. 9, pp. 72139--72160, 2021, doi: 10.1109/ACCESS.2021.3077642.
    22. H. Men, H. Lin, M. Jenadeleh, and D. Saupe, “Subjective Image Quality Assessment with Boosted Triplet Comparisons,” IEEE Access, vol. 9, pp. 138939–138975, 2021, doi: 10.1109/ACCESS.2021.3118295.
    23. H. Lin, G. Chen, and F. W. Siebert, “Positional Encoding: Improving Class-Imbalanced Motorcycle Helmet use Classification,” in 2021 IEEE International Conference on Image Processing (ICIP), in 2021 IEEE International Conference on Image Processing (ICIP). 2021, pp. 1194–1198. doi: 10.1109/ICIP42928.2021.9506178.
    24. K. Schatz et al., “2019 IEEE Scientific Visualization Contest Winner: Visual Analysis of Structure Formation in Cosmic Evolution,” IEEE Computer Graphics and Applications, vol. 41, no. 6, Art. no. 6, 2021, doi: 10.1109/MCG.2020.3004613.
    25. N. Grossmann, J. Bernard, M. Sedlmair, and M. Waldner, “Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation,” in IEEE Visualization Conference  (VIS, Short Paper), in IEEE Visualization Conference  (VIS, Short Paper). 2021, pp. 61--65. doi: 10.1109/VIS49827.2021.9623326.
    26. D. Bethge et al., “VEmotion: Using Driving Context for Indirect Emotion Prediction in Real-Time,” in The 34th Annual ACM Symposium on User Interface Software and Technology, in The 34th Annual ACM Symposium on User Interface Software and Technology. , New York, NY, USA: Association for Computing Machinery, 2021, pp. 638–651. doi: 10.1145/3472749.3474775.
    27. T. Müller, C. Schulz, and D. Weiskopf, “Adaptive Polygon Rendering for Interactive Visualization in the Schwarzschild Spacetime,” European Journal of Physics, vol. 43, no. 1, Art. no. 1, 2021, doi: 10.1088/1361-6404/ac2b36.
    28. K. Klein, M. Aichem, Y. Zhang, S. Erk, B. Sommer, and F. Schreiber, “TEAMwISE : synchronised immersive environments for exploration and analysis of animal behaviour,” Journal of Visualization, 2021, doi: 10.1007/s12650-021-00746-2.
    29. J. Wieland, J. Zagermann, J. Müller, and H. Reiterer, “Separation, Composition, or Hybrid? : Comparing Collaborative 3D Object Manipulation Techniques for Handheld Augmented Reality,” in 2021 IEEE International Symposium on Mixed and Augmented Reality, in 2021 IEEE International Symposium on Mixed and Augmented Reality. Piscataway, NJ: IEEE, 2021, pp. 403--412. doi: 10.1109/ISMAR52148.2021.00057.
    30. L. Zhou, C. R. Johnson, and D. Weiskopf, “Data-Driven Space-Filling Curves,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030473.
    31. J. Bernard, M. Hutter, M. Sedlmair, M. Zeppelzauer, and T. Munzner, “A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling,” ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 11, no. 3–4, Art. no. 3–4, 2021, doi: 10.1145/3439333.
    32. Y. Chen, K. C. Kwan, L.-Y. Wei, and H. Fu, “Autocomplete Repetitive Stroking with Image Guidance,” in SIGGRAPH Asia 2021 Technical Communications, in SIGGRAPH Asia 2021 Technical Communications. Tokyo, Japan: Association for Computing Machinery, 2021. doi: 10.1145/3478512.3488595.
    33. T. Krake, S. Reinhardt, M. Hlawatsch, B. Eberhardt, and D. Weiskopf, “Visualization and Selection of Dynamic Mode Decomposition Components for Unsteady Flow,” Visual Informatics, vol. 5, no. 3, Art. no. 3, 2021, doi: 10.1016/j.visinf.2021.06.003.
    34. F. Frieß, M. Braun, V. Bruder, S. Frey, G. Reina, and T. Ertl, “Foveated Encoding for Large High-Resolution Displays,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030445.
    35. R. Cutura, C. Morariu, Z. Cheng, Y. Wang, D. Weiskopf, and M. Sedlmair, “Hagrid — Gridify Scatterplots with Hilbert and Gosper Curves,” in The 14th International Symposium on Visual Information Communication and Interaction, in The 14th International Symposium on Visual Information Communication and Interaction. Potsdam, Germany: Association for Computing Machinery, 2021, p. 1:1—1:8. doi: 10.1145/3481549.3481569.
    36. S. Hubenschmid, J. Zagermann, D. Fink, J. Wieland, T. Feuchtner, and H. Reiterer, “Towards Asynchronous Hybrid User Interfaces for Cross-Reality Interaction,” in ISS’21 Workshop Proceedings: “Transitional Interfaces in Mixed and Cross-Reality: A new frontier?,” H.-C. Jetter, J.-H. Schröder, J. Gugenheimer, M. Billinghurst, C. Anthes, M. Khamis, and T. Feuchtner, Eds., in ISS’21 Workshop Proceedings: “Transitional Interfaces in Mixed and Cross-Reality: A new frontier?” 2021. doi: 10.18148/kops/352-2-84mm0sggczq02.
    37. J. Bernard, M. Hutter, M. Zeppelzauer, M. Sedlmair, and T. Munzner, “ProSeCo: Visual analysis of class separation measures and dataset characteristics,” Computers & Graphics, vol. 96, pp. 48–60, 2021, doi: https://doi.org/10.1016/j.cag.2021.03.004.
    38. R. Bian et al., “Implicit Multidimensional Projection of Local Subspaces,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030368.
    39. M. Kraus et al., “Immersive Analytics with Abstract 3D Visualizations: A Survey,” Computer Graphics Forum, 2021, doi: https://doi.org/10.1111/cgf.14430.
    40. H. Ben Lahmar and M. Herschel, “Collaborative filtering over evolution provenance data for interactive visual data exploration,” Information Systems, vol. 95, p. 101620, 2021, doi: 10.1016/j.is.2020.101620.
    41. K. Vock, S. Hubenschmid, J. Zagermann, S. Butscher, and H. Reiterer, “IDIAR : Augmented Reality Dashboards to Supervise Mobile Intervention Studies,” in Mensch und Computer 2021 (MuC ’21), in Mensch und Computer 2021 (MuC ’21). New York, NY: ACM, 2021. doi: 10.1145/3473856.3473876.
    42. M. Burch, W. Huang, M. Wakefield, H. C. Purchase, D. Weiskopf, and J. Hua, “The State of the Art in Empirical User Evaluation of Graph Visualizations,” IEEE Access, vol. 9, pp. 4173–4198, 2021, doi: 10.1109/ACCESS.2020.3047616.
    43. M. Kraus, K. Klein, J. Fuchs, D. A. Keim, F. Schreiber, and M. Sedlmair, “The Value of Immersive Visualization,” IEEE Computer Graphics and Applications (CG&A), vol. 41, no. 4, Art. no. 4, 2021, doi: 10.1109/MCG.2021.3075258.
    44. R. Cutura, K. Angerbauer, F. Heyen, N. Hube, and M. Sedlmair, “DaRt: Generative Art using Dimensionality Reduction Algorithms,” in 2021 IEEE VIS Arts Program (VISAP), in 2021 IEEE VIS Arts Program (VISAP). IEEE, 2021, pp. 59--72. doi: 10.1109/VISAP52981.2021.00013.
    45. C. Bu et al., “SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030404.
    46. F. Frieß, M. Becher, G. Reina, and T. Ertl, “Amortised Encoding for Large High-Resolution Displays,” in 2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV), in 2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV). 2021, pp. 53–62. doi: 10.1109/LDAV53230.2021.00013.
    47. F. L. Dennig, M. T. Fischer, M. Blumenschein, J. Fuchs, D. A. Keim, and E. Dimara, “ParSetgnostics: Quality Metrics for Parallel Sets,” Computer Graphics Forum, vol. 40, no. 3, Art. no. 3, 2021, doi: https://doi.org/10.1111/cgf.14314.
  5. 2020

    1. C. Beck, “DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings,” in Proceedings of the Fourteenth Workshop on Semantic Evaluation, in Proceedings of the Fourteenth Workshop on Semantic Evaluation. Barcelona (online): International Committee for Computational Linguistics, Dec. 2020, pp. 50--58. [Online]. Available: https://www.aclweb.org/anthology/2020.semeval-1.4
    2. C. Beck, H. Booth, M. El-Assady, and M. Butt, “Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias,” in Proceedings of the 14th Linguistic Annotation Workshop, in Proceedings of the 14th Linguistic Annotation Workshop. Barcelona, Spain: Association for Computational Linguistics, Dec. 2020, pp. 60--73. [Online]. Available: https://www.aclweb.org/anthology/2020.law-1.6
    3. M. Blumenschein, “Pattern-Driven Design of Visualizations for High-Dimensional Data,” Universität Konstanz, Konstanz, 2020. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-18wp9dhmhapww8
    4. V. Bruder, C. Müller, S. Frey, and T. Ertl, “On Evaluating Runtime Performance of Interactive Visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, pp. 2848–2862, Sep. 2020, doi: 10.1109/TVCG.2019.2898435.
    5. M. Dias, D. Orellana, S. Vidal, L. Merino, and A. Bergel, “Evaluating a Visual Approach for Understanding JavaScript Source Code,” in Proceedings of the 28th International Conference on Program Comprehension, in Proceedings of the 28th International Conference on Program Comprehension. ACM, Jul. 2020, pp. 128–138. doi: https://doi.org/10.1145/3387904.3389275.
    6. L. Zhou, M. Rivinius, C. R. Johnson, and D. Weiskopf, “Photographic High-Dynamic-Range Scalar Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 6, Art. no. 6, 2020, doi: 10.1109/TVCG.2020.2970522.
    7. A. Streichert, K. Angerbauer, M. Schwarzl, and M. Sedlmair, “Comparing Input Modalities for Shape Drawing Tasks,” in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Papers (ETRA-SP), in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Papers (ETRA-SP). ACM, 2020, pp. 1–5. doi: 10.1145/3379156.3391830.
    8. O. Wiedemann, V. Hosu, H. Lin, and D. Saupe, “Foveated Video Coding for Real-Time Streaming Applications,” in 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), in 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX). 2020, pp. 1–6. doi: 10.1109/QoMEX48832.2020.9123080.
    9. S. Cornelsen et al., “Drawing Shortest Paths in Geodetic Graphs,” in Graph Drawing and Network Visualization, D. Auber and P. Valtr, Eds., in Graph Drawing and Network Visualization. Cham: Springer International Publishing, 2020, pp. 333--340. doi: 10.1007/978-3-030-68766-3_26.
    10. H. Lin, M. Jenadeleh, G. Chen, U. Reips, R. Hamzaoui, and D. Saupe, “Subjective Assessment of Global Picture-Wise Just Noticeable Difference,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME). 2020, pp. 1–6. doi: 10.1109/ICMEW46912.2020.9106058.
    11. J. Zagermann, U. Pfeil, P. von Bauer, D. Fink, and H. Reiterer, “‘It’s in my other hand!’ : Studying the Interplay of Interaction Techniques and Multi-Tablet Activities,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, in Proceedings of the CHI Conference on Human Factors in Computing Systems. 2020, pp. 1–13. [Online]. Available: https://kops.uni-konstanz.de/bitstream/handle/123456789/48393/CHI_2020_Camera_Ready%20%281%29.pdf?sequence=1&isAllowed=y
    12. R. Garcia and D. Weiskopf, “Inner-Process Visualization of Hidden States in Recurrent Neural Networks,” in Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, in Proceedings of the 13th International Symposium on Visual Information Communication and Interaction. Eindhoven, Netherlands: ACM, 2020, pp. 20:1-20:5. doi: 10.1145/3430036.3430047.
    13. D. Schubring, M. Kraus, C. Stolz, N. Weiler, D. A. Keim, and H. Schupp, “Virtual Reality Potentiates Emotion and Task Effects of Alpha/Beta Brain Oscillations,” Brain Sciences, vol. 10, no. 8, Art. no. 8, 2020, doi: 10.3390/brainsci10080537.
    14. N. Brich et al., “Visual Analysis of Multivariate Intensive Care Surveillance Data,” in Eurographics Workshop on Visual Computing for Biology and Medicine, B. Kozlíková, M. Krone, N. Smit, K. Nieselt, and R. G. Raidou, Eds., in Eurographics Workshop on Visual Computing for Biology and Medicine. The Eurographics Association, 2020. doi: 10.2312/vcbm.20201174.
    15. C. Schätzle and M. Butt, “Visual Analytics for Historical Linguistics: Opportunities and Challenges,” Journal of Data Mining and Digital Humanities, 2020, doi: 10.46298/jdmdh.6707.
    16. T. Guha et al., “ATQAM/MAST’20: Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends,” in Proceedings of the 28th ACM International Conference on Multimedia, in Proceedings of the 28th ACM International Conference on Multimedia. Seattle, WA, USA: Association for Computing Machinery, 2020, pp. 4758–4760. doi: 10.1145/3394171.3421895.
    17. A. Kumar, D. Mohanty, K. Kurzhals, F. Beck, D. Weiskopf, and K. Mueller, “Demo of the EyeSAC System for Visual Synchronization, Cleaning, and Annotation of Eye Movement Data,” in ACM Symposium on Eye Tracking Research and Applications, in ACM Symposium on Eye Tracking Research and Applications. Stuttgart, Germany: Association for Computing Machinery, 2020. doi: 10.1145/3379157.3391988.
    18. B. Roziere et al., “Evolutionary Super-Resolution,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Cancún, Mexico: Association for Computing Machinery, 2020, pp. 151–152. doi: 10.1145/3377929.3389959.
    19. N. Chotisarn et al., “A Systematic Literature Review of Modern Software Visualization,” Journal of Visualization, vol. 23, no. 4, Art. no. 4, 2020, doi: 10.1007/s12650-020-00647-w.
    20. H. Lin et al., “SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning,” Quality and User Experience, vol. 5, no. 1, Art. no. 1, 2020, doi: 10.1007/s41233-020-00034-1.
    21. M. Borowski, J. Zagermann, C. N. Klokmose, H. Reiterer, and R. Rädle, “Exploring the Benefits and Barriers of Using Computational Notebooks for Collaborative Programming Assignments,” in Proceedings of the ACM Technical Symposium on Computer Science Education (SIGCSE), in Proceedings of the ACM Technical Symposium on Computer Science Education (SIGCSE). 2020, pp. 468–474. doi: 10.1145/3328778.3366887.
    22. M. Kraus et al., “Assessing 2D and 3D Heatmaps for Comparative Analysis: An Empirical Study,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, in Proceedings of the CHI Conference on Human Factors in Computing Systems. 2020, pp. 546:1–546:14. doi: 10.1145/3313831.3376675.
    23. D. Weiskopf, “Vis4Vis: Visualization for (Empirical) Visualization Research,” in Foundations of Data Visualization, M. Chen, H. Hauser, P. Rheingans, and G. Scheuermann, Eds., in Foundations of Data Visualization. , Springer International Publishing, 2020, pp. 209--224. doi: 10.1007/978-3-030-34444-3_10.
    24. X. Zhao, H. Lin, P. Guo, D. Saupe, and H. Liu, “Deep Learning VS. Traditional Algorithms for Saliency Prediction of Distorted Images,” in 2020 IEEE International Conference on Image Processing (ICIP), in 2020 IEEE International Conference on Image Processing (ICIP). 2020, pp. 156–160. doi: 10.1109/ICIP40778.2020.9191203.
    25. L. Merino, M. Schwarzl, M. Kraus, M. Sedlmair, D. Schmalstieg, and D. Weiskopf, “Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009 -- 2019),” in IEEE International Symposium on Mixed and Augmented Reality (ISMAR), in IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 2020. doi: doi: 10.1109/ISMAR50242.2020.00069.
    26. M. Jenadeleh, M. Pedersen, and D. Saupe, “Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition,” Sensors, vol. 20, no. 5, Art. no. 5, 2020, doi: 10.3390/s20051308.
    27. P. Angelini, S. Chaplick, S. Cornelsen, and G. Da Lozzo, “Planar L-Drawings of Bimodal Graphs,” in Graph Drawing and Network Visualization, D. Auber and P. Valtr, Eds., in Graph Drawing and Network Visualization. Cham: Springer International Publishing, 2020, pp. 205–219. doi: 10.1007/978-3-030-68766-3_17.
    28. P. Balestrucci et al., “Pipelines Bent, Pipelines Broken: Interdisciplinary Self-Reflection on the Impact of COVID-19 on Current and Future Research (Position Paper),” in 2020 IEEE Workshop on Evaluation and Beyond-Methodological Approaches to Visualization (BELIV), in 2020 IEEE Workshop on Evaluation and Beyond-Methodological Approaches to Visualization (BELIV). IEEE, 2020, pp. 11--18. doi: 10.1109/BELIV51497.2020.00009.
    29. O. Wiedemann and D. Saupe, “Gaze Data for Quality Assessment of Foveated Video,” in ACM Symposium on Eye Tracking Research and Applications, in ACM Symposium on Eye Tracking Research and Applications. Stuttgart, Germany: Association for Computing Machinery, 2020. doi: 10.1145/3379157.3391656.
    30. A. Kumar, P. Howlader, R. Garcia, D. Weiskopf, and K. Mueller, “Challenges in Interpretability of Neural Networks for Eye Movement Data,” in ACM Symposium on Eye Tracking Research and Applications, in ACM Symposium on Eye Tracking Research and Applications. Stuttgart, Germany: Association for Computing Machinery, 2020. doi: 10.1145/3379156.3391361.
    31. T. Kosch, A. Schmidt, S. Thanheiser, and L. L. Chuang, “One Does Not Simply RSVP: Mental Workload to Select Speed Reading Parameters Using Electroencephalography,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2020, pp. 637:1-637:13. doi: 10.1145/3313831.3376766.
    32. M. Blumenschein, L. J. Debbeler, N. C. Lages, B. Renner, D. A. Keim, and M. El-Assady, “v-plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions,” Computer Graphics Forum, vol. 39, no. 3, Art. no. 3, 2020, doi: 10.1111/cgf.14002.
    33. S. Öney et al., “Evaluation of Gaze Depth Estimation from Eye Tracking in Augmented Reality,” in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Paper (ETRA-SP), in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Paper (ETRA-SP). ACM, 2020, pp. 49:1-49:5. doi: 10.1145/3379156.3391835.
    34. F. Heyen et al., “ClaVis: An Interactive Visual Comparison System for Classifiers,” in Proceedings of the International Conference on Advanced Visual Interfaces, in Proceedings of the International Conference on Advanced Visual Interfaces. ACM, 2020, pp. 9:1-9:9. doi: 10.1145/3399715.3399814.
    35. V. Hosu, H. Lin, T. Sziranyi, and D. Saupe, “KonIQ-10k : An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment,” IEEE Transactions on Image Processing, vol. 29, pp. 4041--4056, 2020, doi: 10.1109/TIP.2020.2967829.
    36. T. Stankov and S. Storandt, “Maximum Gap Minimization in Polylines,” in Web and Wireless Geographical Information Systems - 18th International Symposium, W2GIS 2020, Wuhan, China, November 13-14, 2020, Proceedings, in Web and Wireless Geographical Information Systems - 18th International Symposium, W2GIS 2020, Wuhan, China, November 13-14, 2020, Proceedings. 2020, pp. 181--196. doi: 10.1007/978-3-030-60952-8\_19.
    37. D. R. Wahl et al., “Why We Eat What We Eat: Assessing Dispositional and In-the-Moment Eating Motives by Using Ecological Momentary Assessment,” JMIR mHealth and uHealth., vol. 8, no. 1, Art. no. 1, 2020, doi: doi:10.2196/13191.
    38. M. Blumenschein, X. Zhang, D. Pomerenke, D. A. Keim, and J. Fuchs, “Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates,” Computer Graphics Forum, vol. 39, no. 3, Art. no. 3, 2020, doi: 10.1111/cgf.14000.
    39. F. Draxler, A. Labrie, A. Schmidt, and L. L. Chuang, “Augmented Reality to Enable Users in Learning Case Grammar from Their Real-World Interactions,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2020, pp. 410:1-410:12. doi: 10.1145/3313831.3376537.
    40. N. Rodrigues, C. Schulz, A. Lhuillier, and D. Weiskopf, “Cluster-Flow Parallel Coordinates: Tracing Clusters Across Subspaces,” in Proceedings of Graphics Interface 2020, in Proceedings of Graphics Interface 2020. Canadian Human-Computer Communications Society / Société canadienne du dialogue humain-machine, 2020, pp. 382–392. doi: 10.20380/GI2020.38.
    41. H. Bast, P. Brosi, and S. Storandt, “Metro Maps on Octilinear Grid Graphs,” in Computer Graphics Forum, in Computer Graphics Forum. Hoboken, New Jersey: Wiley-Blackwell - STM, 2020, pp. 357--367. doi: 10.1111/cgf.13986.
    42. V. Hosu et al., “From Technical to Aesthetics Quality Assessment and Beyond: Challenges and Potential,” in Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends, in Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends. Seattle, WA, USA: Association for Computing Machinery, 2020, pp. 19–20. doi: 10.1145/3423268.3423589.
    43. N. Patkar, L. Merino, and O. Nierstrasz, “Towards Requirements Engineering with Immersive Augmented Reality,” in Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming, in Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming. Porto, Portugal: ACM, 2020, pp. 55–60. doi: 10.1145/3397537.3398472.
    44. N. Pathmanathan et al., “Eye vs. Head: Comparing Gaze Methods for Interaction in Augmented Reality,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA). Stuttgart, Germany: ACM, 2020, pp. 50:1-50:5. doi: 10.1145/3379156.3391829.
    45. F. Frieß, C. Müller, and T. Ertl, “Real-Time High-Resolution Visualisation,” in Proceedings of the Eurographics Symposium on Vision, Modeling, and Visualization (VMV), J. Krüger, M. Niessner, and J. Stückler, Eds., in Proceedings of the Eurographics Symposium on Vision, Modeling, and Visualization (VMV). The Eurographics Association, 2020, pp. 127–135. doi: 10.2312/vmv.20201195.
    46. M. Beck and S. Storandt, “Puzzling Grid Embeddings,” in Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, in Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020. 2020, pp. 94--105. doi: 10.1137/1.9781611976007.8.
    47. J. Bernard, M. Hutter, M. Zeppelzauer, M. Sedlmair, and T. Munzner, “SepEx: Visual Analysis of Class Separation Measures,” in Proceedings of the International Workshop on Visual Analytics (EuroVA), C. Turkay and K. Vrotsou, Eds., in Proceedings of the International Workshop on Visual Analytics (EuroVA). The Eurographics Association, 2020, pp. 1–5. doi: 10.2312/eurova.20201079.
    48. H. Men, V. Hosu, H. Lin, A. Bruhn, and D. Saupe, “Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). 2020, pp. 1–6. doi: 10.1109/QoMEX48832.2020.9123096.
    49. D. Okanovic et al., “Can a Chatbot Support Software Engineers with Load Testing? Approach and Experiences,” in Proceedings of the ACM/SPEC International Conference on Performance Engineering (ICPE), in Proceedings of the ACM/SPEC International Conference on Performance Engineering (ICPE). 2020, pp. 120–129. doi: 10.1145/3358960.3375792.
    50. K. Kurzhals, F. Göbel, K. Angerbauer, M. Sedlmair, and M. Raubal, “A View on the Viewer: Gaze-Adaptive Captions for Videos,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, in Proceedings of the CHI Conference on Human Factors in Computing Systems. 2020, pp. 139:1–139:12. doi: 10.1145/3313831.3376266.
    51. M. Sondag, W. Meulemans, C. Schulz, K. Verbeek, D. Weiskopf, and B. Speckmann, “Uncertainty Treemaps,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis). 2020, pp. 111–120. doi: 10.1109/PacificVis48177.2020.7614.
    52. L. Merino et al., “Toward Agile Situated Visualization: An Exploratory User Study,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA). 2020, p. LBW087:1–LBW087:7. doi: 10.1145/3334480.3383017.
    53. F. Bishop, J. Zagermann, U. Pfeil, G. Sanderson, H. Reiterer, and U. Hinrichs, “Construct-A-Vis: Exploring the Free-Form Visualization Processes of Children,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, Art. no. 1, 2020, doi: 10.1109/TVCG.2019.2934804.
    54. L. Merino, M. Lungu, and C. Seidl, “Unleashing the Potentials of Immersive Augmented Reality for Software Engineering,” in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER), in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER). 2020, pp. 517–521. doi: 10.1109/SANER48275.2020.9054812.
    55. M. Lan Ha, V. Hosu, and V. Blanz, “Color Composition Similarity and Its Application in Fine-grained Similarity,” in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway, NJ: IEEE, 2020, pp. 2548--2557. doi: 10.1109/WACV45572.2020.9093522.
    56. H. Lin, J. D. Deng, D. Albers, and F. W. Siebert, “Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning,” IEEE Access, vol. 8, pp. 162073–162084, 2020, doi: 10.1109/ACCESS.2020.3021357.
    57. H. Men, V. Hosu, H. Lin, A. Bruhn, and D. Saupe, “Subjective annotation for a frame interpolation benchmark using artefact amplification,” Quality and User Experience, vol. 5, no. 1, Art. no. 1, 2020, doi: 10.1007/s41233-020-00037-y.
    58. U. Ju, L. L. Chuang, and C. Wallraven, “Acoustic Cues Increase Situational Awareness in Accident Situations: A VR Car-Driving Study,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–11, 2020, doi: 10.1109/TITS.2020.3035374.
    59. J. Spoerhase, S. Storandt, and J. Zink, “Simplification of Polyline Bundles,” in 17th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2020, June 22-24, 2020, Tórshavn, Faroe Islands, in 17th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2020, June 22-24, 2020, Tórshavn, Faroe Islands. 2020, pp. 35:1--35:20. doi: 10.4230/LIPIcs.SWAT.2020.35.
    60. K. Kurzhals, M. Burch, and D. Weiskopf, “What We See and What We Get from Visualization: Eye Tracking Beyond Gaze Distributions and Scanpaths,” CoRR, vol. abs/2009.14515, 2020, [Online]. Available: https://arxiv.org/abs/2009.14515
    61. K. Kurzhals et al., “Visual Analytics and Annotation of Pervasive Eye Tracking Video,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA). Stuttgart, Germany: ACM, 2020, pp. 16:1-16:9. doi: 10.1145/3379155.3391326.
    62. M. Kraus et al., “A Comparative Study of Orientation Support Tools in Virtual Reality Environments with Virtual Teleportation,” in 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), in 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 2020, pp. 227–238. doi: 10.1109/ISMAR50242.2020.00046.
  6. 2019

    1. T. Munz, L. L. Chuang, S. Pannasch, and D. Weiskopf, “VisME: Visual microsaccades explorer,” Journal of Eye Movement Research, vol. 12, no. 6, Art. no. 6, Dec. 2019, doi: 10.16910/jemr.12.6.5.
    2. P. Balestrucci and M. Ernst, “Visuo-motor adaptation during interaction with a user-adaptive system,” Journal of Vision, vol. 19, p. 187a, Sep. 2019, doi: 10.1167/19.10.187a.
    3. N. Silva et al., “Eye Tracking Support for Visual Analytics Systems: Foundations, Current Applications, and Research Challenges,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), K. Krejtz and B. Sharif, Eds., in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA). ACM, 2019, pp. 11:1-11:9. doi: 10.1145/3314111.3319919.
    4. T. Castermans, M. van Garderen, W. Meulemans, M. Nöllenburg, and X. Yuan, “Short Plane Supports for Spatial Hypergraphs,” in Graph Drawing and Network Visualization. GD 2018. Lecture Notes in Computer Science, vol. 11282, T. Biedl and A. Kerren, Eds., in Graph Drawing and Network Visualization. GD 2018. Lecture Notes in Computer Science, vol. 11282. , Springer International Publishing, 2019, pp. 53–66. doi: 10.1007/978-3-030-04414-5_4.
    5. C. Fan et al., “SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2019, pp. 1–6. doi: 10.1109/QoMEX.2019.8743204.
    6. V. Hosu, B. Goldlücke, and D. Saupe, “Effective Aesthetics Prediction with Multi-level Spatially Pooled Features,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9367–9375, 2019, doi: 10.1109/CVPR.2019.00960.
    7. Y. Wang, Z. Wang, C.-W. Fu, H. Schmauder, O. Deussen, and D. Weiskopf, “Image-Based Aspect Ratio Selection.,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, Art. no. 1, 2019, doi: 10.1109/TVCG.2018.2865266.
    8. B. Sommer et al., “Tiled Stereoscopic 3D Display Wall - Concept, Applications and Evaluation,” Electronic Imaging, vol. 2019, no. 3, Art. no. 3, 2019, doi: 10.2352/ISSN.2470-1173.2019.3.SDA-641.
    9. C. Schätzle, F. L. Dennig, M. Blumenschein, D. A. Keim, and M. Butt, “Visualizing Linguistic Change as Dimension Interactions,” in Proceedings of the International Workshop on Computational Approaches to Historical Language Change, in Proceedings of the International Workshop on Computational Approaches to Historical Language Change. 2019, pp. 272–278. doi: 10.18653/v1/W19-4734.
    10. T. M. Benz, B. Riedl, and L. L. Chuang, “Projection Displays Induce Less Simulator Sickness than Head-Mounted Displays in a Real Vehicle Driving Simulator,” in Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI), C. P. Janssen, S. F. Donker, L. L. Chuang, and W. Ju, Eds., in Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI). ACM, 2019, pp. 379–387. doi: 10.1145/3342197.3344515.
    11. K. Klein, M. Aichem, B. Sommer, S. Erk, Y. Zhang, and F. Schreiber, “TEAMwISE: Synchronised Immersive Environments for Exploration and Analysis of Movement Data,” in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI), in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI). ACM, 2019, pp. 9:1-9:5. doi: 10.1145/3356422.3356450.
    12. H. Lin, V. Hosu, and D. Saupe, “KADID-10k: A Large-scale Artificially Distorted IQA Database,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2019, pp. 1–3. doi: 10.1109/QoMEX.2019.8743252.
    13. H. Booth and C. Schätzle, “The Syntactic Encoding of Information Structure in the History of Icelandic,” in Proceedings of the LFG’19 Conference, M. Butt, T. H. King, and I. Toivonen, Eds., in Proceedings of the LFG’19 Conference. CSLI Publications, 2019, pp. 69–89. [Online]. Available: http://web.stanford.edu/group/cslipublications/cslipublications/LFG/LFG-2019/lfg2019-booth-schaetzle.pdf
    14. F. L. Dennig, T. Polk, Z. Lin, T. Schreck, H. Pfister, and M. Behrisch, “FDive: Learning Relevance Models using Pattern-based Similarity Measures,” Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), 2019, doi: 10.1109/VAST47406.2019.8986940.
    15. H. Men, H. Lin, V. Hosu, D. Maurer, A. Bruhn, and D. Saupe, “Visual Quality Assessment for Motion Compensated Frame Interpolation,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2019, pp. 1–6. doi: 10.1109/QoMEX.2019.8743221.
    16. V. Bruder, K. Kurzhals, S. Frey, D. Weiskopf, and T. Ertl, “Space-Time Volume Visualization of Gaze and Stimulus,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), K. Krejtz and B. Sharif, Eds., in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA). ACM, 2019, pp. 12:1-12:9. doi: 10.1145/3314111.3319812.
    17. S. Jaeger et al., “Challenges for Brain Data Analysis in VR Environments,” in 2019 IEEE Pacific Visualization Symposium (PacificVis), in 2019 IEEE Pacific Visualization Symposium (PacificVis). 2019, pp. 42–46. doi: 10.1109/PacificVis.2019.00013.
    18. Y. Wang et al., “Improving the Robustness of Scagnostics,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, Art. no. 1, 2019, doi: 10.1109/TVCG.2019.2934796.
    19. J. Müller, J. Zagermann, J. Wieland, U. Pfeil, and H. Reiterer, “A Qualitative Comparison Between Augmented and Virtual Reality Collaboration with Handheld Devices,” in Mensch und Computer 2019 – Tagungsband (MuC), F. Alt, A. Bulling, and T. Döring, Eds., in Mensch und Computer 2019 – Tagungsband (MuC). GI, ACM, 2019, pp. 399–410. doi: 10.1145/3340764.3340773.
    20. M. Miller, X. Zhang, J. Fuchs, and M. Blumenschein, “Evaluating Ordering Strategies of Star Glyph Axes,” in Proceedings of the IEEE Visualization Conference (VIS), in Proceedings of the IEEE Visualization Conference (VIS). IEEE, 2019, pp. 91–95. doi: 10.1109/VISUAL.2019.8933656.
    21. D. Pomerenke, F. L. Dennig, D. A. Keim, J. Fuchs, and M. Blumenschein, “Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters,” in Proceedings of the IEEE Visualization Conference (VIS), in Proceedings of the IEEE Visualization Conference (VIS). IEEE, 2019, pp. 86–90. doi: 10.1109/VISUAL.2019.8933706.
    22. R. Netzel, N. Rodrigues, A. Haug, and D. Weiskopf, “Compensation of Simultaneous Orientation Contrast in Superimposed Textures,” in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), A. Kerren, C. Hurter, and J. Braz, Eds., in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), vol. 3: IVAPP. SciTePress, 2019, pp. 48–57. doi: 10.5220/0007356800480057.
    23. K. Klein et al., “Fly with the flock : immersive solutions for animal movement visualization and analytics,” Journal of the Royal Society Interface, vol. 16, no. 153, Art. no. 153, 2019, doi: 10.1098/rsif.2018.0794.
    24. J. Görtler, M. Spicker, C. Schulz, D. Weiskopf, and O. Deussen, “Stippling of 2D Scalar Fields,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 6, Art. no. 6, 2019, doi: 10.1109/TVCG.2019.2903945.
    25. K. Schatz et al., “Visual Analysis of Structure Formation in Cosmic Evolution,” in Proceedings of the IEEE Scientific Visualization Conference (SciVis), in Proceedings of the IEEE Scientific Visualization Conference (SciVis). 2019, pp. 33–41. doi: 10.1109/scivis47405.2019.8968855.
    26. V. Bruder, C. Schulz, R. Bauer, S. Frey, D. Weiskopf, and T. Ertl, “Voronoi-Based Foveated Volume Rendering,” in Proceedings of the Eurographics Conference on Visualization - Short Papers (EuroVis), J. Johansson, F. Sadlo, and G. E. Marai, Eds., in Proceedings of the Eurographics Conference on Visualization - Short Papers (EuroVis). Eurographics Association, 2019, pp. 67–71. doi: 10.2312/evs.20191172.
    27. M. Aupetit, M. Sedlmair, M. M. Abbas, A. Baggag, and H. Bensmail, “Toward Perception-based Evaluation of Clustering Techniques for Visual Analytics,” in Proceedings of the IEEE Visualization Conference (VIS), in Proceedings of the IEEE Visualization Conference (VIS). IEEE, 2019, pp. 141–145. doi: 10.1109/VISUAL.2019.8933620.
    28. L. Zhou, R. Netzel, D. Weiskopf, and C. R. Johnson, “Spectral Visualization Sharpening.,” in Proceedings of the ACM Symposium on Applied Perception (SAP), S. Neyret, E. Kokkinara, M. González-Franco, L. Hoyet, D. W. Cunningham, and J. Swidrak, Eds., in Proceedings of the ACM Symposium on Applied Perception (SAP). ACM, 2019, pp. 18:1-18:9. doi: 10.1145/3343036.3343133.
    29. H. Zhang, S. Frey, H. Steeb, D. Uribe, T. Ertl, and W. Wang, “Visualization of Bubble Formation in Porous Media,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, Art. no. 1, 2019, doi: 10.1109/TVCG.2018.2864506.
    30. V. Bruder et al., “Volume-Based Large Dynamic Graph Analysis Supported by Evolution Provenance,” Multimedia Tools and Applications, vol. 78, no. 23, Art. no. 23, 2019, doi: 10.1007/s11042-019-07878-6.
    31. C. Schätzle and H. Booth, “DiaHClust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change,” in Proceedings of the International Workshop on Computational Approaches to Historical Language Change, in Proceedings of the International Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics, 2019, pp. 126–135. doi: 10.18653/v1/W19-4716.
    32. K. Klein et al., “Visual Analytics for Cheetah Behaviour Analysis.,” in VINCI, in VINCI. ACM, 2019, pp. 16:1-16:8. [Online]. Available: http://dblp.uni-trier.de/db/conf/vinci/vinci2019.html#0001JMWHBS19
    33. C. Müller, M. Braun, and T. Ertl, “Optimised Molecular Graphics on the HoloLens,” in IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019, Osaka, Japan, March 23-27, 2019, in IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019, Osaka, Japan, March 23-27, 2019. IEEE, 2019, pp. 97–102. doi: 10.1109/VR.2019.8798111.
  7. 2018

    1. C. Schätzle, “Dative Subjects: Historical Change Visualized,” PhD diss., Universität Konstanz, Konstanz, 2018. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1d917i4avuz1a2
    2. D. Maurer and A. Bruhn, “ProFlow: Learning to Predict Optical Flow,” in Proceedings of the British Machine Vision Conference (BMVC), in Proceedings of the British Machine Vision Conference (BMVC), vol. 86:1-86:13. BMVA Press, 2018. doi: arXiv:1806.00800.
    3. H. Ben Lahmar, M. Herschel, M. Blumenschein, and D. A. Keim, “Provenance-based Visual Data Exploration with EVLIN,” in Proceedings of the Conference on Extending Database Technology (EDBT), in Proceedings of the Conference on Extending Database Technology (EDBT). 2018, pp. 686–689. doi: 10.5441/002/edbt.2018.85.
    4. V. Bruder, M. Hlawatsch, S. Frey, M. Burch, D. Weiskopf, and T. Ertl, “Volume-Based Large Dynamic Graph Analytics,” in Proceedings of the International Conference Information Visualisation (IV), E. Banissi, R. Francese, M. W. McK. Bannatyne, T. G. Wyeld, M. Sarfraz, J. M. Pires, A. Ursyn, F. Bouali, N. Datia, G. Venturini, G. Polese, V. Deufemia, T. D. Mascio, M. Temperini, F. Sciarrone, D. Malandrino, R. Zaccagnino, P. Díaz, F. Papadopoulo, A. F. Anta, A. Cuzzocrea, M. Risi, U. Erra, and V. Rossano, Eds., in Proceedings of the International Conference Information Visualisation (IV). IEEE, 2018, pp. 210–219. doi: 10.1109/iV.2018.00045.
    5. V. Hosu, H. Lin, and D. Saupe, “Expertise Screening in Crowdsourcing Image Quality,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2018, pp. 276–281. doi: https://dx.doi.org/10.1109/QoMEX.2018.8463427.
    6. V. Schwind, K. Leicht, S. Jäger, K. Wolf, and N. Henze, “Is there an Uncanny Valley of Virtual Animals? A Quantitative and Qualitative Investigation,” International Journal of Human-Computer Studies, vol. 111, pp. 49–61, 2018, doi: 10.1016/j.ijhcs.2017.11.003.
    7. L. J. Debbeler, M. Gamp, M. Blumenschein, D. A. Keim, and B. Renner, “Polarized But Illusory Beliefs About Tap and Bottled Water: A Product- and Consumer-Oriented Survey and Blind Tasting Experiment,” Science of the Total Environment, vol. 643, pp. 1400–1410, 2018, doi: 10.1016/j.scitotenv.2018.06.190.
    8. L. L. Chuang and U. Pfeil, “Transparency and Openness Promotion Guidelines for HCI,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA). ACM, 2018, p. SIG04:1-SIG04:4. doi: 10.1145/3170427.3185377.
    9. S. Hubenschmid, J. Zagermann, S. Butscher, and H. Reiterer, “Employing Tangible Visualisations in Augmented Reality with Mobile Devices,” in Proceedings of the Working Conference on Advanced Visual Interfaces (AVI), in Proceedings of the Working Conference on Advanced Visual Interfaces (AVI). 2018, pp. 1–4. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1iooenfo4fofm8
    10. N. Rodrigues and D. Weiskopf, “Nonlinear Dot Plots,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, Art. no. 1, 2018, doi: 10.1109/TVCG.2017.2744018.
    11. V. Yoghourdjian, T. Dwyer, K. Klein, K. Marriott, and M. Wybrow, “Graph Thumbnails: Identifying and Comparing Multiple Graphs at a Glance,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 12, Art. no. 12, 2018, doi: 10.1109/TVCG.2018.2790961.
    12. D. Laupheimer, P. Tutzauer, N. Haala, and M. Spicker, “Neural Networks for the Classification of Building Use from Street-view Imagery,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. IV–2, pp. 177–184, 2018, doi: 10.5194/isprs-annals-IV-2-177-2018.
    13. A. Nesti, G. Rognini, B. Herbelin, H. H. Bülthoff, L. L. Chuang, and O. Blanke, “Modulation of Vection Latencies in the Full-Body Illusion,” PLoS ONE, vol. 13, no. 12, Art. no. 12, 2018, doi: 10.1371/journal.pone.0209189.
    14. P. Knierim, V. Schwind, A. M. Feit, F. Nieuwenhuizen, and N. Henze, “Physical Keyboards in Virtual Reality: Analysis of Typing Performance and Effects of Avatar Hands,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2018, pp. 345:1–345:9. doi: 10.1145/3173574.3173919.
    15. C. Schulz, K. Schatz, M. Krone, M. Braun, T. Ertl, and D. Weiskopf, “Uncertainty Visualization for Secondary Structures of Proteins,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2018, pp. 96–105. doi: 10.1109/PacificVis.2018.00020.
    16. Y. Wang et al., “A Perception-driven Approach to Supervised Dimensionality Reduction for Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 5, Art. no. 5, 2018, doi: 10.1109/TVCG.2017.2701829.
    17. T. Torsney-Weir, S. Afroozeh, M. Sedlmair, and T. Möller, “Risk Fixers and Sweet Spotters: a Study of the Different Approaches to Using Visual Sensitivity Analysis in an Investment Scenario,” in Proceedings of the Eurographics Conference on Visualization (EuroVis), J. Johansson, F. Sadlo, and T. Schreck, Eds., in Proceedings of the Eurographics Conference on Visualization (EuroVis). Eurographics Association, 2018, pp. 119–123. doi: 10.2312/eurovisshort.20181089.
    18. M. Jenadeleh, M. Pedersen, and D. Saupe, “Realtime Quality Assessment of Iris Biometrics Under Visible Light,” in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPRW), CVPR Workshops, in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPRW), CVPR Workshops. IEEE, 2018, pp. 443–452. doi: 10.1109/CVPRW.2018.00085.
    19. D. Varga, D. Saupe, and T. Szirányi, “DeepRN: A Content Preserving Deep Architecture for Blind Image Quality Assessment,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2018, pp. 1–6. doi: 10.1109/ICME.2018.8486528.
    20. C. Glatz, S. S. Krupenia, H. H. Bülthoff, and L. L. Chuang, “Use the Right Sound for the Right Job: Verbal Commands and Auditory Icons for a Task-Management System Favor Different Information Processes in the Brain,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2018, pp. 472:1-472:13. doi: 10.1145/3173574.3174046.
    21. M. Klapperstueck et al., “Contextuwall: Multi-site Collaboration Using Display Walls,” Journal of Visual Languages & Computing, vol. 46, pp. 35–42, 2018, doi: 10.1016/j.jvlc.2017.10.002.
    22. F. Frieß, M. Landwehr, V. Bruder, S. Frey, and T. Ertl, “Adaptive Encoder Settings for Interactive Remote Visualisation on High-Resolution Displays,” in Proceedings of the IEEE Symposium on Large Data Analysis and Visualization - Short Papers (LDAV), in Proceedings of the IEEE Symposium on Large Data Analysis and Visualization - Short Papers (LDAV). IEEE, 2018, pp. 87–91. doi: 10.1109/LDAV.2018.8739215.
    23. D. Maurer, Y. C. Ju, M. Breuß, and A. Bruhn, “Combining Shape from Shading and Stereo: A Joint Variational Method for Estimating Depth, Illumination and Albedo,” International Journal of Computer Vision, vol. 126, no. 12, Art. no. 12, 2018, doi: 10.1007/s11263-018-1079-1.
    24. D. Maurer, N. Marniok, B. Goldluecke, and A. Bruhn, “Structure-from-motion-aware PatchMatch for Adaptive Optical Flow Estimation,” in Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol. 11212, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds., in Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol. 11212. , Springer International Publishing, 2018, pp. 575–592. doi: 10.1007/978-3-030-01237-3_35.
    25. S. Oppold and M. Herschel, “Provenance for Entity Resolution,” in Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science, vol. 11017, K. Belhajjame, A. Gehani, and P. Alper, Eds., in Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science, vol. 11017. , Springer International Publishing, 2018, pp. 226–230. doi: 10.1007/978-3-319-98379-0_25.
    26. M. Blumenschein et al., “SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach,” in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), R. Chang, H. Qu, and T. Schreck, Eds., in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2018, pp. 36–47. doi: 10.1109/VAST.2018.8802486.
    27. M. Behrisch et al., “Quality Metrics for Information Visualization,” Computer Graphics Forum, vol. 37, no. 3, Art. no. 3, 2018, doi: https://doi.org/10.1111/cgf.13446.
    28. N. Marniok and B. Goldluecke, “Real-time Variational Range Image Fusion and Visualization for Large-scale Scenes using GPU Hash Tables,” in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). 2018, pp. 912–920. doi: 10.1109/WACV.2018.00105.
    29. H. Bast, P. Brosi, and S. Storandt, “Efficient Generation of Geographically Accurate Transit Maps,” in Proceedings of the ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL), F. B. Kashani, E. G. Hoel, R. H. Güting, R. Tamassia, and L. Xiong, Eds., in Proceedings of the ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL). ACM, 2018, pp. 13–22. doi: 10.1145/3274895.3274955.
    30. K. Hänsel, R. Poguntke, H. Haddadi, A. Alomainy, and A. Schmidt, “What to Put on the User: Sensing Technologies for Studies and Physiology Aware Systems,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2018, pp. 145:1-145:14. doi: 10.1145/3173574.3173719.
    31. K. Marriott et al., Immersive Analytics, vol. 11190. in Lecture Notes in Computer Science (LNCS), vol. 11190. Springer International Publishing, 2018. doi: 10.1007/978-3-030-01388-2.
    32. Y. Zhu et al., “Genome-scale Metabolic Modeling of Responses to Polymyxins in Pseudomonas Aeruginosa,” GigaScience, vol. 7, no. 4, Art. no. 4, 2018, doi: 10.1093/gigascience/giy021.
    33. C. Glatz and L. L. Chuang, “The Time Course of Auditory Looming Cues in Redirecting Visuo-Spatial Attention,” Nature - Scientific Reports, vol. 9, pp. 743:1-743:10, 2018, doi: 10.1038/s41598-018-36033-8.
    34. M. Scheer, H. H. Bülthoff, and L. L. Chuang, “Auditory Task Irrelevance: A Basis for Inattentional Deafness,” Human Factors, vol. 60, no. 3, Art. no. 3, 2018, doi: 10.1177/0018720818760919.
    35. J. Karolus, H. Schuff, T. Kosch, P. W. Wozniak, and A. Schmidt, “EMGuitar: Assisting Guitar Playing with Electromyography,” in Proceedings of the Designing Interactive Systems Conference (DIS), I. Koskinen, Y.-K. Lim, T. C. Pargman, K. K. N. Chow, and W. Odom, Eds., in Proceedings of the Designing Interactive Systems Conference (DIS). ACM, 2018, pp. 651–655. doi: 10.1145/3196709.3196803.
    36. T. Dingler, R. Rzayev, A. S. Shirazi, and N. Henze, “Designing Consistent Gestures Across Device Types: Eliciting RSVP Controls for Phone, Watch, and Glasses,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2018, pp. 419:1–419:12. doi: 10.1145/3173574.3173993.
    37. T. Spinner, J. Körner, J. Görtler, and O. Deussen, “Towards an Interpretable Latent Space: An Intuitive Comparison of Autoencoders with Variational Autoencoders,” in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI), in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI). IEEE VIS, 2018. [Online]. Available: https://thilospinner.com/towards-an-interpretable-latent-space/
    38. J. Görtler, C. Schulz, O. Deussen, and D. Weiskopf, “Bubble Treemaps for Uncertainty Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, Art. no. 1, 2018, doi: 10.1109/TVCG.2017.2743959.
    39. D. Sacha et al., “SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, Art. no. 1, 2018, doi: 10.1109/TVCG.2017.2744805.
    40. M. Ghaffar et al., “3D Modelling and Visualisation of Heterogeneous Cell Membranes in Blender,” in Proceedings of the 11th International Symposium on Visual Information Communication and Interaction, in Proceedings of the 11th International Symposium on Visual Information Communication and Interaction. Växjö, Sweden: Association for Computing Machinery, 2018, pp. 64–71. doi: 10.1145/3231622.3231639.
    41. D. Maurer, M. Stoll, and A. Bruhn, “Directional Priors for Multi-Frame Optical Flow,” in Proceedings of the British Machine Vision Conference (BMVC), in Proceedings of the British Machine Vision Conference (BMVC). BMVA Press, 2018, pp. 106:1-106:13. [Online]. Available: http://bmvc2018.org/contents/papers/0377.pdf
    42. S. Frey, “Spatio-Temporal Contours from Deep Volume Raycasting,” Computer Graphics Forum, vol. 37, no. 3, Art. no. 3, 2018, doi: 10.1111/cgf.13438.
    43. C. Schulz, A. Zeyfang, M. van Garderen, H. Ben Lahmar, M. Herschel, and D. Weiskopf, “Simultaneous Visual Analysis of Multiple Software Hierarchies,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT). IEEE, 2018, pp. 87–95. doi: 10.1109/VISSOFT.2018.00017.
    44. N. Rodrigues, R. Netzel, J. Spalink, and D. Weiskopf, “Multiscale Scanpath Visualization and Filtering,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), L. L. Chuang, M. Burch, and K. Kurzhals, Eds., in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS). ACM, 2018, pp. 2:1-2:5. doi: 10.1145/3205929.3205931.
    45. A. C. Valdez, M. Ziefle, and M. Sedlmair, “Priming and Anchoring Effects in Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, Art. no. 1, 2018, doi: 10.1109/TVCG.2017.2744138.
    46. H. Men, H. Lin, and D. Saupe, “Spatiotemporal Feature Combination Model for No-Reference Video Quality Assessment,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2018, pp. 1–3. doi: 10.1109/QoMEX.2018.8463426.
    47. J. Zagermann, U. Pfeil, and H. Reiterer, “Studying Eye Movements as a Basis for Measuring Cognitive Load,” Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), p. LBW095:1-LBW095:6, 2018, doi: 10.1145/3170427.3188628.
    48. M. de Ridder, K. Klein, and J. Kim, “A Review and Outlook on Visual Analytics for Uncertainties in Functional Magnetic Resonance Imaging,” Brain Informatics, vol. 5, no. 2, Art. no. 2, 2018, doi: 10.1186/s40708-018-0083-0.
    49. A. Hautli-Janisz, C. Rohrdantz, C. Schätzle, A. Stoffel, M. Butt, and D. A. Keim, “Visual Analytics in Diachronic Linguistic Investigations,” Linguistic Visualizations, 2018.
    50. S. S. Borojeni, S. C. J. Boll, W. Heuten, H. H. Bülthoff, and L. L. Chuang, “Feel the Movement: Real Motion Influences Responses to Take-Over Requests in Highly Automated Vehicles,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2018, pp. 246:1-246:13. doi: 10.1145/3173574.3173820.
    51. T. Kosch, M. Funk, A. Schmidt, and L. L. Chuang, “Identifying Cognitive Assistance with Mobile Electroencephalography: A Case Study with In-Situ Projections for Manual Assembly.,” Proceedings of the ACM on Human-Computer Interaction (ACMHCI), vol. 2, pp. 11:1-11:20, 2018, doi: 10.1145/3229093.
    52. J. Görtler, R. Kehlbeck, and O. Deussen, “A Visual Exploration of Gaussian Processes,” in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI), in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI). 2018. doi: 10.23915/distill.00017.
  8. 2017

    1. O. Deussen, M. Spicker, and Q. Zheng, “Weighted Linde-Buzo-Gray Stippling,” ACM Transactions on Graphics, vol. 36, no. 6, Art. no. 6, Nov. 2017, doi: 10.1145/3130800.3130819.
    2. S. Frey, “Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks,” in Informatics, in Informatics, vol. 4. Multidisciplinary Digital Publishing Institute (MDPI), 2017, p. 27. doi: 10.3390/informatics4030027.
    3. D. Maurer, M. Stoll, S. Volz, P. Gairing, and A. Bruhn, “A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow,” in Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science, vol. 10302, F. Lauze, Y. Dong, and A. B. Dahl, Eds., in Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science, vol. 10302. , Springer International Publishing, 2017, pp. 537–549. doi: 10.1007/978-3-319-58771-4_43.
    4. D. Bahrdt et al., “Growing Balls in ℝd,” in Proceedings of the Meeting on Algorithm Engineering and Experiments (ALENEX), S. P. Fekete and V. Ramachandran, Eds., in Proceedings of the Meeting on Algorithm Engineering and Experiments (ALENEX). SIAM, 2017, pp. 247–258. doi: 10.1137/1.9781611974768.20.
    5. P. Gralka, C. Schulz, G. Reina, D. Weiskopf, and T. Ertl, “Visual Exploration of Memory Traces and Call Stacks,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT). IEEE, 2017, pp. 54–63. doi: 10.1109/VISSOFT.2017.15.
    6. D. Maurer, A. Bruhn, and M. Stoll, “Order-adaptive and Illumination-aware Variational Optical Flow Refinement,” in Proceedings of the British Machine Vision Conference (BMVC), in Proceedings of the British Machine Vision Conference (BMVC). BMVA Press, 2017, pp. 150:1-150:13. doi: 10.5244/C.31.150.
    7. C. Schulz, A. Nocaj, J. Goertler, O. Deussen, U. Brandes, and D. Weiskopf, “Probabilistic Graph Layout for Uncertain Network Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598919.
    8. R. Diestelkämper, M. Herschel, and P. Jadhav, “Provenance in DISC Systems: Reducing Space Overhead at Runtime,” in Proceedings of the USENIX Conference on Theory and Practice of Provenance (TAPP), in Proceedings of the USENIX Conference on Theory and Practice of Provenance (TAPP). 2017, pp. 1–13. [Online]. Available: https://dl.acm.org/doi/abs/10.5555/3183865.3183883
    9. M. Heinemann, V. Bruder, S. Frey, and T. Ertl, “Power Efficiency of Volume Raycasting on Mobile Devices,” in Proceedings of the Eurographics Conference on Visualization (EuroVis) - Poster Track, in Proceedings of the Eurographics Conference on Visualization (EuroVis) - Poster Track. 2017. doi: 10.2312/eurp.20171166.
    10. V. Hosu et al., “The Konstanz natural video database (KoNViD-1k).,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2017, pp. 1–6. doi: 10.1109/QoMEX.2017.7965673.
    11. M. Spicker, F. Hahn, T. Lindemeier, D. Saupe, and O. Deussen, “Quantifying Visual Abstraction Quality for Stipple Drawings,” in Proceedings of the Symposium on Non-Photorealistic Animation and Rendering (NPAR), ACM, Ed., in Proceedings of the Symposium on Non-Photorealistic Animation and Rendering (NPAR). Association for Computing Machinery, 2017, pp. 8:1-8:10. [Online]. Available: https://doi.org/http://dx.doi.org/10.1145/3092919.3092923
    12. H. Sattar, A. Bulling, and M. Fritz, “Predicting the Category and Attributes of Visual Search Targets Using Deep Gaze Pooling,” in Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), in Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW). 2017, pp. 2740–2748. doi: 10.1109/ICCVW.2017.322.
    13. V. Schwind, P. Knierim, C. Tasci, P. Franczak, N. Haas, and N. Henze, “‘These are not my hands!’: Effect of Gender on the Perception of Avatar Hands in Virtual Reality,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, G. Mark, S. R. Fussell, C. Lampe, m. c. schraefel, J. P. Hourcade, C. Appert, and D. Wigdor, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2017, pp. 1577–1582. doi: 10.1145/3025453.3025602.
    14. V. Schwind, P. Knierim, L. L. Chuang, and N. Henze, “‘Where’s Pinky?’: The Effects of a Reduced Number of Fingers in Virtual Reality,” in Proceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY), B. A. M. Schouten, P. Markopoulos, Z. O. Toups, P. A. Cairns, and T. Bekker, Eds., in Proceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY). ACM, 2017, pp. 507–515. doi: 10.1145/3116595.3116596.
    15. D. Jäckle, M. Hund, M. Behrisch, D. A. Keim, and T. Schreck, “Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces,” in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), B. Fisher, S. Liu, and T. Schreck, Eds., in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2017, pp. 1–12. doi: 10.1109/VAST.2017.8585613.
    16. R. Netzel, M. Hlawatsch, M. Burch, S. Balakrishnan, H. Schmauder, and D. Weiskopf, “An Evaluation of Visual Search Support in Maps,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598898.
    17. S. Funke, T. Mendel, A. Miller, S. Storandt, and M. Wiebe, “Map Simplification with Topology Constraints: Exactly and in Practice,” in Proceedings of the Meeting on Algorithm Engineering and Experiments (ALENEX), S. P. Fekete and V. Ramachandran, Eds., in Proceedings of the Meeting on Algorithm Engineering and Experiments (ALENEX). SIAM, 2017, pp. 185–196. doi: 10.1137/1.9781611974768.15.
    18. M. Burch, M. Hlawatsch, and D. Weiskopf, “Visualizing a Sequence of a Thousand Graphs (or Even More),” Computer Graphics Forum, vol. 36, no. 3, Art. no. 3, 2017, doi: 10.1111/cgf.13185.
    19. N. Marniok, O. Johannsen, and B. Goldluecke, “An Efficient Octree Design for Local Variational Range Image Fusion,” in Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science, vol. 10496, V. Roth and T. Vetter, Eds., in Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science, vol. 10496. , Springer International Publishing, 2017, pp. 401–412. doi: 10.1007/978-3-319-66709-6_32.
    20. A. Barth, B. Harrach, N. Hyvönen, and L. Mustonen, “Detecting Stochastic Inclusions in Electrical Impedance Tomography,” Inverse Problems, vol. 33, no. 11, Art. no. 11, 2017, doi: 10.1088/1361-6420/aa8f5c.
    21. Y. Abdelrahman, P. Knierim, P. W. Wozniak, N. Henze, and A. Schmidt, “See Through the Fire: Evaluating the Augmentation of Visual Perception of Firefighters Using Depth and Thermal Cameras,” in Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Symposium on Wearable Computers (UbiComp/ISWC), S. C. Lee, L. Takayama, and K. N. Truong, Eds., in Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Symposium on Wearable Computers (UbiComp/ISWC). ACM, 2017, pp. 693–696. doi: 10.1145/3123024.3129269.
    22. L. L. Chuang, C. Glatz, and S. S. Krupenia, “Using EEG to Understand why Behavior to Auditory In-vehicle Notifications Differs Across Test Environments,” in Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI), S. Boll, B. Pfleging, B. Donmez, I. Politis, and D. R. Large, Eds., in Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI). ACM, 2017, pp. 123–133. doi: 10.1145/3122986.3123017.
    23. M. Krone et al., “Molecular Surface Maps,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, Art. no. 1, 2017, [Online]. Available: http://dx.doi.org/10.1109/TVCG.2016.2598824
    24. M. Correll and J. Heer, “Surprise! Bayesian Weighting for De-Biasing Thematic Maps.,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, Art. no. 1, 2017, [Online]. Available: http://dblp.uni-trier.de/db/journals/tvcg/tvcg23.html#CorrellH17
    25. K. Kurzhals, E. Çetinkaya, Y. Hu, W. Wang, and D. Weiskopf, “Close to the Action: Eye-tracking Evaluation of Speaker-following Subtitles,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, ACM, Ed., in Proceedings of the CHI Conference on Human Factors in Computing Systems. 2017, pp. 6559–6568. doi: https://doi.org/10.1145/3025453.3025772.
    26. N. Rodrigues et al., “Visualization of Time Series Data with Spatial Context: Communicating the Energy Production of Power Plants,” in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI), in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI). 2017, pp. 37–44. doi: https://doi.org/10.1145/3105971.3105982.
    27. C. Schätzle, “Genitiv als Stilmittel in der Novelle,” Scalable Reading. Zeitschrift für Literaturwissenschaft und Linguistik (LiLi), vol. 47, pp. 125–140, 2017, doi: 10.1007/s41244-017-0043-9.
    28. M. de Ridder, K. Klein, and J. Kim, “Temporaltracks: Visual Analytics for Exploration of 4D fMRI Time-series Coactivation,” in Proceedings of the Computer Graphics International Conference (CGI), X. Mao, D. Thalmann, and M. L. Gavrilova, Eds., in Proceedings of the Computer Graphics International Conference (CGI). ACM, 2017, pp. 13:1-13:6. doi: 10.1145/3095140.3095153.
    29. J. Karolus, P. W. Wozniak, L. L. Chuang, and A. Schmidt, “Robust Gaze Features for Enabling Language Proficiency Awareness,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, G. Mark, S. R. Fussell, C. Lampe, m. c. schraefel, J. P. Hourcade, C. Appert, and D. Wigdor, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2017, pp. 2998–3010. doi: 10.1145/3025453.3025601.
    30. H. V. Le, V. Schwind, P. Göttlich, and N. Henze, “PredicTouch: A System to Reduce Touchscreen Latency using Neural Networks and Inertial Measurement Units,” in Proceedings of the ACM International Conference on Interactive Surfaces and Spaces (ISS), S. Subramanian, J. Steimle, R. Dachselt, D. M. Plasencia, and T. Grossman, Eds., in Proceedings of the ACM International Conference on Interactive Surfaces and Spaces (ISS). ACM, 2017, pp. 230–239. doi: 10.1145/3132272.3134138.
    31. C. Schulz, M. Burch, F. Beck, and D. Weiskopf, “Visual Data Cleansing of Low-Level Eye Tracking Data,” in Eye Tracking and Visualization: Foundations, Techniques, and Applications. ETVIS 2015, M. Burch, L. Chuang, B. Fisher, A. Schmidt, and D. Weiskopf, Eds., in Eye Tracking and Visualization: Foundations, Techniques, and Applications. ETVIS 2015. , Springer International Publishing, 2017, pp. 199–216. doi: 10.1007/978-3-319-47024-5_12.
    32. H. Ben Lahmar and M. Herschel, “Provenance-based Recommendations for Visual Data Exploration,” in Proceedings of the USENIX Conference on Theory and Practice of Provenance (TAPP), in Proceedings of the USENIX Conference on Theory and Practice of Provenance (TAPP). 2017, pp. 1–7.
    33. M. Herschel, R. Diestelkämper, and H. Ben Lahmar, “A Survey on Provenance - What for? What form? What from?,” The VLDB Journal, vol. 26, pp. 881–906, 2017, doi: 10.1007/s00778-017-0486-1.
    34. M. van Garderen, B. Pampel, A. Nocaj, and U. Brandes, “Minimum-Displacement Overlap Removal for Geo-referenced Data Visualization,” Computer Graphics Forum, vol. 36, no. 3, Art. no. 3, 2017, doi: 10.1111/cgf.13199.
    35. M. Tonsen, J. Steil, Y. Sugano, and A. Bulling, “InvisibleEye: Mobile Eye Tracking Using Multiple Low-Resolution Cameras and Learning-Based Gaze Estimation,” in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol. 1. 2017, pp. 106:1-106:21. doi: https://doi.org/10.1145/3130971.
    36. X. Zhang, Y. Sugano, and A. Bulling, “Everyday Eye Contact Detection Using Unsupervised Gaze Target Discovery,” in Proceedings of the ACM Symposium on User Interface Software and Technology (UIST), in Proceedings of the ACM Symposium on User Interface Software and Technology (UIST). 2017, pp. 193–203. doi: 10.1145/3126594.3126614.
    37. K. Kurzhals, M. Hlawatsch, C. Seeger, and D. Weiskopf, “Visual Analytics for Mobile Eye Tracking,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598695.
    38. O. Johannsen et al., “A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshops, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshops. IEEE, 2017, pp. 1795–1812. doi: 10.1109/CVPRW.2017.226.
    39. T. Machulla, L. Chuang, F. Kiss, M. O. Ernst, and A. Schmidt, “Sensory Amplification Through Crossmodal Stimulation,” in Proceedings of the CHI Workshop on Amplification and Augmentation of Human Perception, in Proceedings of the CHI Workshop on Amplification and Augmentation of Human Perception. 2017.
    40. J. Zagermann, U. Pfeil, C. Acevedo, and H. Reiterer, “Studying the Benefits and Challenges of Spatial Distribution and Physical Affordances in a Multi-device Workspace,” in Proceedings of the International Conference on Mobile and Ubiquitous Multimedia (MUM), in Proceedings of the International Conference on Mobile and Ubiquitous Multimedia (MUM). 2017, pp. 249–259. doi: https://doi.org/10.1145/3152832.3152855.
    41. J. Zagermann, U. Pfeil, D. Fink, P. von Bauer, and H. Reiterer, “Memory in Motion: The Influence of Gesture- and Touch-based Input Modalities on Spatial Memory,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, G. Mark, S. R. Fussell, C. Lampe, m. c. schraefel, J. P. Hourcade, C. Appert, and D. Wigdor, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2017, pp. 1899–1910. doi: 10.1145/3025453.3026001.
    42. C. Schätzle, M. Hund, F. L. Dennig, M. Butt, and D. A. Keim, “HistoBankVis: Detecting Language Change via Data Visualization,” in Proceedings of the NoDaLiDa 2017 Workshop Processing Historical Language, G. Bouma and Y. Adesam, Eds., in Proceedings of the NoDaLiDa 2017 Workshop Processing Historical Language. Linköping University Electronic Press, 2017, pp. 32–39. [Online]. Available: https://www.aclweb.org/anthology/W17-0507
    43. D. Fritsch and M. Klein, “3D and 4D Modeling for AR and VR App Developments,” in Proceedings of the International Conference on Virtual System & Multimedia (VSMM), in Proceedings of the International Conference on Virtual System & Multimedia (VSMM). 2017, pp. 1–8. doi: 10.1109/VSMM.2017.8346270.
    44. D. Fritsch, “Photogrammetrische Auswertung digitaler Bilder – Neue Methoden der Kamerakalibration, dichten Bildzuordnung und Interpretation von Punktwolken,” in Photogrammetrie und Fernerkundung, C. Heipke, Ed., in Photogrammetrie und Fernerkundung. , Springer Spektrum, 2017, pp. 157–196. doi: 10.1007/978-3-662-47094-7_41.
    45. P. Tutzauer and N. Haala, “Processing of Crawled Urban Imagery for Building Use Classification,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-1/W1, pp. 143–149, 2017, doi: 10.5194/isprs-archives-XLII-1-W1-143-2017.
    46. P. Tutzauer, S. Becker, and N. Haala, “Perceptual Rules for Building Enhancements in 3d Virtual Worlds,” i-com, vol. 16, no. 3, Art. no. 3, 2017, doi: 10.1515/icom-2017-0022.
    47. M. Behrisch et al., “Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598467.
    48. D. Maurer, M. Stoll, and A. Bruhn, “Order-adaptive Regularisation for Variational Optical Flow: Global, Local and in Between.,” in Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science, F. Lauze, Y. Dong, and A. B. Dahl, Eds., in Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science, vol. 10302. Springer International Publishing, 2017, pp. 550–562. doi: 10.1007/978-3-319-58771-4_44.
    49. D. Sacha et al., “Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598495.
    50. S. Frey and T. Ertl, “Flow-Based Temporal Selection for Interactive Volume Visualization,” Computer Graphics Forum, vol. 36, no. 8, Art. no. 8, 2017, doi: 10.1111/cgf.13070.
    51. S. Frey and T. Ertl, “Progressive Direct Volume-to-Volume Transformation,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2599042.
    52. X. Zhang, Y. Sugano, M. Fritz, and A. Bulling, “MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 1, Art. no. 1, 2017, doi: 10.1109/TPAMI.2017.2778103.
    53. K. Kurzhals, M. Stoll, A. Bruhn, and D. Weiskopf, “FlowBrush: Optical Flow Art,” in Symposium on Computational Aesthetics, Sketch-Based Interfaces and Modeling, and Non-Photorealistic Animation and Rendering (EXPRESSIVE, co-located with SIGGRAPH)., in Symposium on Computational Aesthetics, Sketch-Based Interfaces and Modeling, and Non-Photorealistic Animation and Rendering (EXPRESSIVE, co-located with SIGGRAPH). 2017, pp. 1:1-1:9. doi: 10.1145/3092912.3092914.
    54. T. Dingler, A. Schmidt, and T. Machulla, “Building Cognition-Aware Systems: A Mobile Toolkit for Extracting Time-of-Day Fluctuations of Cognitive Performance,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol. 1, no. 3, Art. no. 3, 2017, doi: 10.1145/3132025.
    55. H. T. Nim et al., “Design Considerations for Immersive Analytics of Bird Movements Obtained by Miniaturised GPS Sensors,” in Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), in Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM). Eurographics Association, 2017. doi: 10.2312/vcbm.20171234.
    56. K. de Winkel, A. Nesti, H. Ayaz, and H. Bülthoff, “Neural Correlates of Decision Making on Whole Body Yaw Rotation: an fNIRS Study,” Neuroscience Letters, vol. 654, pp. 56–62, 2017, doi: 10.1016/j.neulet.2017.04.053.
    57. A. Nesti, K. de Winkel, and H. Bülthoff, “Accumulation of Inertial Sensory Information in the Perception of Whole Body Yaw Rotation,” PloS ONE, vol. 12, no. 1, Art. no. 1, 2017, doi: 10.1371/journal.pone.0170497.
    58. V. Schwind, K. Wolf, and N. Henze, “FaceMaker - A Procedural Face Generator to Foster Character Design Research,” vol. Game Dynamics: Best Practices in Procedural and Dynamic Game Content Generation, O. Korn and N. Lee, Eds., Springer International Publishing, 2017, pp. 95–113. doi: 10.1007/978-3-319-53088-8_6.
    59. P. Knierim et al., “Tactile Drones - Providing Immersive Tactile Feedback in Virtual Reality through Quadcopters,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), G. Mark, S. R. Fussell, C. Lampe, m. c. schraefel, J. P. Hourcade, C. Appert, and D. Wigdor, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA). ACM, 2017, pp. 433–436. doi: https://doi.org/10.1145/3027063.3050426.
    60. S. Egger-Lampl et al., “Crowdsourcing Quality of Experience Experiments,” in Information Systems and Applications, incl. Internet/Web, and HCI, vol. Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments. Dagstuhl Seminar 15481, Dagstuhl Castle, Germany, November 22 – 27, 2015, Revised Contributions, no. LNCS 10264, D. Archambault, H. Purchase, and T. Hossfeld, Eds., in Information Systems and Applications, incl. Internet/Web, and HCI, vol. Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments. Dagstuhl Seminar 15481, Dagstuhl Castle, Germany, November 22 – 27, 2015, Revised Contributions. , Springer International Publishing, 2017, pp. 154–190. doi: 10.1007/978-3-319-66435-4_7.
    61. U. Gadiraju et al., “Crowdsourcing Versus the Laboratory: Towards Human-centered Experiments Using the Crowd,” in Information Systems and Applications, incl. Internet/Web, and HCI, vol. Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments. Dagstuhl Seminar 15481, Dagstuhl Castle, Germany, November 22 – 27, 2015, Revised Contributions, no. LNCS 10264, D. Archambault, H. Purchase, and T. Hossfeld, Eds., in Information Systems and Applications, incl. Internet/Web, and HCI, vol. Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments. Dagstuhl Seminar 15481, Dagstuhl Castle, Germany, November 22 – 27, 2015, Revised Contributions. , Springer International Publishing, 2017, pp. 6–26. doi: 10.1007/978-3-319-66435-4_2.
    62. N. Rodrigues, M. Burch, L. Di Silvestro, and D. Weiskopf, “A Visual Analytics Approach for Word Relevances in Multiple Texts,” in Proceedings of the International Conference on Information Visualisation (IV), in Proceedings of the International Conference on Information Visualisation (IV). IEEE, 2017, pp. 1–7. doi: 10.1109/iV.2017.62.
    63. C. Schulz, N. Rodrigues, K. Damarla, A. Henicke, and D. Weiskopf, “Visual Exploration of Mainframe Workloads,” in Proceedings of the SIGGRAPH Asia Symposium on Visualization, in Proceedings of the SIGGRAPH Asia Symposium on Visualization. ACM, 2017, pp. 4:1-4:7. [Online]. Available: http://dx.doi.org/10.1145/3139295.3139312
    64. M. Stoll, D. Maurer, and A. Bruhn, “Variational Large Displacement Optical Flow Without Feature Matches.,” in Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science, M. Pelillo and E. R. Hancock, Eds., in Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science, vol. 10746. Springer International Publishing, 2017, pp. 79–92. doi: 10.1007/978-3-319-78199-0_6.
    65. M. Stoll, D. Maurer, S. Volz, and A. Bruhn, “Illumination-aware Large Displacement Optical Flow,” in Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science, vol. 10746, M. Pelillo and E. R. Hancock, Eds., in Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science, vol. 10746. , Springer International Publishing, 2017, pp. 139–154. doi: 10.1007/978-3-319-78199-0_10.
    66. K. Srulijes et al., “Visualization of Eye-Head Coordination While Walking in Healthy Subjects and Patients with Neurodegenerative Diseases,” Poster (reviewed) presented on Symposium of the International Society of Posture and Gait Research (ISPGR), 2017.
    67. J. Kratt, F. Eisenkeil, M. Spicker, Y. Wang, D. Weiskopf, and O. Deussen, “Structure-aware Stylization of Mountainous Terrains,” in Vision, Modeling & Visualization, M. Hullin, R. Klein, T. Schultz, and A. Yao, Eds., in Vision, Modeling & Visualization. , The Eurographics Association, 2017. doi: 10.2312/vmv.20171255.
    68. L. Merino et al., “On the Impact of the Medium in the Effectiveness of 3D Software Visualizations,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT). IEEE, 2017, pp. 11–21. doi: 10.1109/VISSOFT.2017.17.
    69. M. A. Baazizi, H. Ben Lahmar, D. Colazzo, G. Ghelli, and C. Sartiani, “Schema Inference for Massive JSON Datasets,” in Proceedings of the Conference on Extending Database Technology (EDBT), in Proceedings of the Conference on Extending Database Technology (EDBT). 2017, pp. 222–233. doi: 10.5441/002/edbt.2017.21.
    70. G. Tkachev, S. Frey, C. Müller, V. Bruder, and T. Ertl, “Prediction of Distributed Volume Visualization Performance to Support Render Hardware Acquisition,” in Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), in Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (EGPGV). Eurographics Association, 2017, pp. 11–20. doi: 10.2312/pgv.20171089.
    71. D. Sacha et al., “What You See Is What You Can Change: Human-Centered Machine Learning by Interactive Visualization,” Neurocomputing, vol. 268, pp. 164–175, 2017, doi: 10.1016/j.neucom.2017.01.105.
    72. R. Netzel, J. Vuong, U. Engelke, S. I. O’Donoghue, D. Weiskopf, and J. Heinrich, “Comparative Eye-tracking Evaluation of Scatterplots and Parallel Coordinates,” Visual Informatics, vol. 1, no. 2, Art. no. 2, 2017, doi: 10.1016/j.visinf.2017.11.001.
    73. V. Bruder, S. Frey, and T. Ertl, “Prediction-Based Load Balancing and Resolution Tuning for Interactive Volume Raycasting,” Visual Informatics, vol. 1, no. 2, Art. no. 2, 2017, doi: 10.1016/j.visinf.2017.09.001.
    74. S. Funke, N. Schnelle, and S. Storandt, “URAN: A Unified Data Structure for Rendering and Navigation,” in Web and Wireless Geographical Information Systems. W2GIS 2017. Lecture Notes in Computer Science, vol. 10181, D. Brosset, C. Claramunt, X. Li, and T. Wang, Eds., in Web and Wireless Geographical Information Systems. W2GIS 2017. Lecture Notes in Computer Science, vol. 10181. , 2017, pp. 66–82. doi: 10.1007/978-3-319-55998-8_5.
    75. J. Iseringhausen et al., “4D Imaging through Spray-on Optics,” ACM Transactions on Graphics, vol. 36, no. 4, Art. no. 4, 2017, doi: 10.1145/3072959.3073589.
    76. D. Jäckle, F. Stoffel, S. Mittelstädt, D. A. Keim, and H. Reiterer, “Interpretation of Dimensionally-Reduced Crime Data: A Study with Untrained Domain Experts,” in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), vol. 3. 2017, pp. 164–175. doi: http://dx.doi.org/10.5220/0006265101640175.
    77. H. Booth, C. Schätzle, K. Börjars, and M. Butt, “Dative Subjects and the Rise of Positional Licensing in Icelandic,” in Proceedings of the LFG’17 Conference, in Proceedings of the LFG’17 Conference. 2017, pp. 104–124. [Online]. Available: http://web.stanford.edu/group/cslipublications/cslipublications/LFG/LFG-2017/lfg2017-bsbb.pdf
    78. J. Allsop, R. Gray, H. Bülthoff, and L. Chuang, “Eye Movement Planning on Single-Sensor-Single-Indicator Displays is Vulnerable to User Anxiety and Cognitive Load,” Journal of Eye Movement Research, vol. 10, no. 5, Art. no. 5, 2017, doi: 10.16910/jemr.10.5.8.
    79. M. Stein et al., “Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis,” in IEEE Transactions on Visualization and Computer Graphics, in IEEE Transactions on Visualization and Computer Graphics, vol. 24. 2017, pp. 13–22. doi: 10.1109/TVCG.2017.2745181.
  9. 2016

    1. M. Aupetit and M. Sedlmair, “SepMe: 2002 New Visual Separation Measures.,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), C. Hansen, I. Viola, and X. Yuan, Eds., in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2016, pp. 1–8. doi: 10.1109/PACIFICVIS.2016.7465244.
    2. T. Dingler, R. Rzayev, V. Schwind, and N. Henze, “RSVP on the go - Implicit Reading Support on Smart Watches Through Eye Tracking,” in Proceedings of the ACM International Symposium on Wearable Computers (ISWC), ACM, Ed., in Proceedings of the ACM International Symposium on Wearable Computers (ISWC). 2016, pp. 116–119. doi: 10.1145/2971763.2971794.
    3. V. Bruder, S. Frey, and T. Ertl, “Real-Time Performance Prediction and Tuning for Interactive Volume Raycasting,” in Proceedings of the SIGGRAPH Asia Symposium on Visualization, ACM, Ed., in Proceedings of the SIGGRAPH Asia Symposium on Visualization. ACM, 2016, pp. 1–8. doi: 10.1145/3002151.3002156.
    4. L. Lischke, S. Mayer, K. Wolf, N. Henze, H. Reiterer, and A. Schmidt, “Screen Arrangements and Interaction Areas for Large Display Work Places.,” in Proceedings of the ACM International Symposium on Pervasive Displays (PerDis), T. Ojala, V. Kostakos, J. Müller, and N. Memarovic, Eds., in Proceedings of the ACM International Symposium on Pervasive Displays (PerDis). ACM, 2016, pp. 228–234. doi: 10.1145/2914920.2915027.
    5. S. Cheng and K. Mueller, “The Data Context Map: Fusing Data and Attributes into a Unified Display.,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, Art. no. 1, 2016, [Online]. Available: http://dblp.uni-trier.de/db/journals/tvcg/tvcg22.html#ChengM16
    6. A. Barth, R. Bürger, I. Kröker, and C. Rohde, “Computational Uncertainty Quantification for a Clarifier-thickener Model with Several Random Perturbations: A Hybrid Stochastic Galerkin Approach,” Computers & Chemical Engineering, vol. 89, pp. 11–26, 2016, doi: 10.1016/j.compchemeng.2016.02.016.
    7. T. Waltemate et al., “The Impact of Latency on Perceptual Judgments and Motor Performance in Closed-loop Interaction in Virtual Reality,” in Proceedings of the ACM Conference on Virtual Reality Software and Technology (VRST), D. Kranzlmüller and G. Klinker, Eds., in Proceedings of the ACM Conference on Virtual Reality Software and Technology (VRST). ACM, 2016, pp. 27–35. doi: 10.1145/2993369.2993381.
    8. J. Zagermann, U. Pfeil, and H. Reiterer, “Measuring Cognitive Load using Eye Tracking Technology in Visual Computing,” in Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV), M. Sedlmair, P. Isenberg, T. Isenberg, N. Mahyar, and H. Lam, Eds., in Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV). ACM, 2016, pp. 78–85. doi: 10.1145/2993901.2993908.
    9. B. Pfleging, D. K. Fekety, A. Schmidt, and A. L. Kun, “A Model Relating Pupil Diameter to Mental Workload and Lighting Conditions,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, J. Kaye, A. Druin, C. Lampe, D. Morris, and J. P. Hourcade, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2016, pp. 5776–5788. doi: 10.1145/2858036.2858117.
    10. M. Greis, P. El.Agroudy, H. Schuff, T. Machulla, and A. Schmidt, “Decision-Making under Uncertainty: How the Amount of Presented Uncertainty Influences User Behavior,” in Proceedings of the 9th Nordic Conference on Human-Computer Interaction (NordiCHI), ACM, Ed., in Proceedings of the 9th Nordic Conference on Human-Computer Interaction (NordiCHI), vol. 2016. 2016. doi: 10.1145/2971485.2971535.
    11. C. Schätzle and D. Sacha, “Visualizing Language Change: Dative Subjects in Icelandic,” in Proceedings of the LREC 2016 Workshop VisLRII: Visualization as Added Value in the Development, Use and Evaluation of Language Resources, in Proceedings of the LREC 2016 Workshop VisLRII: Visualization as Added Value in the Development, Use and Evaluation of Language Resources. 2016, pp. 8–15. [Online]. Available: http://www.lrec-conf.org/proceedings/lrec2016/workshops/LREC2016Workshop-VisLR%20II_Proceedings.pdf
    12. P. Xu, Y. Sugano, and A. Bulling, “Spatio-Temporal Modeling and Prediction of Visual Attention in Graphical User Interfaces,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, in Proceedings of the CHI Conference on Human Factors in Computing Systems. 2016, pp. 3299–3310. doi: 10.1145/2858036.2858479.
    13. D. Weiskopf, M. Burch, L. L. Chuang, B. Fischer, and A. Schmidt, Eye Tracking and Visualization: Foundations, Techniques, and Applications. Berlin, Heidelberg: Springer, 2016. doi: 10.1007/978-3-319-47024-5_7.
    14. K. Kurzhals, M. Hlawatsch, F. Heimerl, M. Burch, T. Ertl, and D. Weiskopf, “Gaze Stripes: Image-based Visualization of Eye Tracking Data,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, Art. no. 1, 2016, doi: 10.1109/TVCG.2015.2468091.
    15. M. Hund et al., “Visual Analytics for Concept Exploration in Subspaces of Patient Groups,” Brain Informatics, vol. 3, no. 4, Art. no. 4, 2016, doi: 10.1007/s40708-016-0043-5.
    16. S. Funke, F. Krumpe, and S. Storandt, “Crushing Disks Efficiently,” in Combinatorial Algorithms. IWOCA 2016. Lecture Notes in Computer Science, vol. 9843, V. Mäkinen, S. J. Puglisi, and L. Salmela, Eds., in Combinatorial Algorithms. IWOCA 2016. Lecture Notes in Computer Science, vol. 9843. , Springer International Publishing, 2016, pp. 43–54. doi: 10.1007/978-3-319-44543-4_4.
    17. A. Barth and F. G. Fuchs, “Uncertainty Quantification for Hyperbolic Conservation Laws with Flux Coefficients Given by Spatiotemporal Random Fields,” SIAM Journal on Scientific Computing, vol. 38, no. 4, Art. no. 4, 2016, doi: 10.1137/15M1027723.
    18. R. Netzel, M. Burch, and D. Weiskopf, “Interactive Scanpath-oriented Annotation of Fixations,” Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), pp. 183–187, 2016, doi: 10.1145/2857491.2857498.
    19. M. Burch, R. Woods, R. Netzel, and D. Weiskopf, “The Challenges of Designing Metro Maps,” in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), vol. 2: IVAPP. SciTePress, 2016. doi: 10.5220/0005679601950202.
    20. K. Kurzhals, B. Fisher, M. Burch, and D. Weiskopf, “Eye Tracking Evaluation of Visual Analytics,” Information Visualization, vol. 15, no. 4, Art. no. 4, 2016, doi: 10.1177/1473871615609787.
    21. A. Barth and A. Stein, “Approximation and simulation of infinite-dimensional Lévy processes,” Stochastics and Partial Differential Equations: Analysis and Computations, vol. 6, no. 2, Art. no. 2, 2016, doi: 10.1007/s40072-017-0109-2.
    22. J. Zagermann, U. Pfeil, R. Rädle, H.-C. Jetter, C. N. Klokmose, and H. Reiterer, “When Tablets meet Tabletops: The Effect of Tabletop Size on Around-the-Table Collaboration with Personal Tablets,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, J. Kaye, A. Druin, C. Lampe, D. Morris, and J. P. Hourcade, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2016, pp. 5470–5481. doi: 10.1145/2858036.2858224.
    23. M. Scheer, H. H. Bülthoff, and L. L. Chuang, “Steering Demands Diminish the Early-P3, Late-P3 and RON Components of the Event-Related Potential of Task-Irrelevant Environmental Sounds,” in Frontiers in Human Neuroscience, F. in Human Neuroscience, Ed., in Frontiers in Human Neuroscience, vol. 10. 2016, pp. 73:1-73:15. doi: 10.3389/fnhum.2016.00073.
    24. N. Flad, J. C. Ditz, A. Schmidt, H. H. Bülthoff, and L. L. Chuang, “Data-Driven Approaches to Unrestricted Gaze-Tracking Benefit from Saccade Filtering,” in Proceedings of the Second Workshop on Eye Tracking and Visualization (ETVIS), M. Burch, L. L. Chuang, and A. T. Duchowski, Eds., in Proceedings of the Second Workshop on Eye Tracking and Visualization (ETVIS). IEEE, 2016, pp. 1–5. doi: 10.1109/ETVIS.2016.7851156.
    25. P. Tutzauer, S. Becker, D. Fritsch, T. Niese, and O. Deussen, “A Study of the Human Comprehension of Building Categories Based on Different 3D Building Representations,” Photogrammetrie - Fernerkundung - Geoinformation, vol. 2016, no. 5–6, Art. no. 5–6, 2016, doi: 10.1127/pfg/2016/0302.
    26. P. Tutzauer, S. Becker, T. Niese, O. Deussen, and D. Fritsch, “Understanding Human Perception of Building Categories in Virtual 3d Cities - a User Study,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS), vol. XLI-B2, pp. 683–687, 2016, doi: http://dx.doi.org/10.5194/isprs-archives-XLI-B2-683-2016.
    27. A. Voit, T. Machulla, D. Weber, V. Schwind, S. Schneegaß, and N. Henze, “Exploring Notifications in Smart Home Environments,” in Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (MobileHCI), ACM, Ed., in Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (MobileHCI). 2016, pp. 942–947. doi: 10.1145/2957265.2962661.
    28. S. Funke, A. Nusser, and S. Storandt, “On k-Path Covers and their Applications.,” VLDB Journal, vol. 25, no. 1, Art. no. 1, 2016, doi: 10.1007/s00778-015-0392-3.
    29. R. Netzel and D. Weiskopf, “Hilbert Attention Maps for Visualizing Spatiotemporal Gaze Data,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), E. 2016, Ed., in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS). 2016, pp. 21–25. doi: 10.1109/ETVIS.2016.7851160.
    30. A. Kumar, R. Netzel, M. Burch, D. Weiskopf, and K. Mueller, “Multi-Similarity Matrices of Eye Movement Data,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), E. 2016, Ed., in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS). 2016, pp. 26–30. doi: 10.1109/ETVIS.2016.7851161.
    31. X. Zhang, Y. Sugano, M. Fritz, and A. Bulling, “It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016, pp. 2299–2308. doi: 10.1109/CVPRW.2017.284.
    32. J. Hildenbrand, A. Nocaj, and U. Brandes, “Flexible Level-of-Detail Rendering for Large Graphs,” vol. Graph Drawing and Network Visualization. GD 2016. Lecture Notes in Computer Science, no. 9801, Y. Hu and M. Nöllenburg, Eds., 2016. doi: 10.1007/978-3-319-50106-2.
    33. A. Nocaj, M. Ortmann, and U. Brandes, “Adaptive Disentanglement Based on Local Clustering in Small-World Network Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 6, Art. no. 6, 2016, doi: 10.1109/TVCG.2016.2534559.
    34. O. Johannsen, A. Sulc, N. Marniok, and B. Goldluecke, “Layered Scene Reconstruction from Multiple Light Field Camera Views,” in Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science, vol. 10113, S.-H. Lai, V. Lepetit, K. Nishino, and Y. Sato, Eds., in Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science, vol. 10113. , Springer International Publishing, 2016, pp. 3–18. doi: 10.1007/978-3-319-54187-7_1.
    35. J. Karolus, P. W. Woźniak, and L. L. Chuang, “Towards Using Gaze Properties to Detect Language Proficiency,” in Proceedings of the 9th Nordic Conference on Human-Computer Interaction (NordiCHI), in Proceedings of the 9th Nordic Conference on Human-Computer Interaction (NordiCHI). New York, NY, USA: ACM, 2016, pp. 118:1-118:6. doi: 10.1145/2971485.2996753.
    36. L. Lischke, V. Schwind, K. Friedrich, A. Schmidt, and N. Henze, “MAGIC-Pointing on Large High-Resolution Displays,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), J. Kaye, A. Druin, C. Lampe, D. Morris, and J. P. Hourcade, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA). ACM, 2016, pp. 1706–1712. doi: 10.1145/2851581.2892479.
    37. K. Kurzhals, M. Hlawatsch, M. Burch, and D. Weiskopf, “Fixation-Image Charts,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), ACM, Ed., in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), vol. 1. ACM, 2016, pp. 11–18. [Online]. Available: http://dx.doi.org/10.1145/2857491.2857507
    38. C. Schulz et al., “Generative Data Models for Validation and Evaluation of Visualization Techniques,” in Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV), in Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV). ACM, 2016, pp. 112–124. doi: 10.1145/2993901.2993907.
    39. M. Hund et al., “Visual Quality Assessment of Subspace Clusterings,” in Proceedings of the KDD Workshop on Interactive Data Exploration and Analytics (IDEA), I. KDD 2016, Ed., in Proceedings of the KDD Workshop on Interactive Data Exploration and Analytics (IDEA). 2016, pp. 53–62.
    40. I. Zingman, D. Saupe, O. A. B. Penatti, and K. Lambers, “Detection of Fragmented Rectangular Enclosures in Very High Resolution Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, Art. no. 8, 2016, doi: 10.1109/TGRS.2016.2545919.
    41. D. Saupe, F. Hahn, V. Hosu, I. Zingman, M. Rana, and S. Li, “Crowd Workers Proven Useful: A Comparative Study of Subjective Video Quality Assessment,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX). 2016, pp. 1–2. [Online]. Available: https://www.uni-konstanz.de/mmsp/pubsys/publishedFiles/SaHaHo16.pdf
    42. E. Wood, T. Baltrusaitis, L.-P. Morency, P. Robinson, and A. Bulling, “A 3D Morphable Eye Region Model for Gaze Estimation,” in Proceedings of the European Conference on Computer Vision (ECCV), in Proceedings of the European Conference on Computer Vision (ECCV). 2016, pp. 297–313. doi: 10.1007/978-3-319-46448-0_18.
    43. E. Wood, T. Baltrusaitis, L.-P. Morency, P. Robinson, and A. Bulling, “Learning an Appearance-Based Gaze Estimator from One Million Synthesised Images,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA). 2016, pp. 131–138. doi: 10.1145/2857491.2857492.
    44. S. Frey and T. Ertl, “Auto-Tuning Intermediate Representations for In Situ Visualization,” in Proceedings of the New York Scientific Data Summit (NYSDS), in Proceedings of the New York Scientific Data Summit (NYSDS). IEEE, 2016, pp. 1–10. doi: 10.1109/NYSDS.2016.7747807.
    45. T. Blascheck, F. Beck, S. Baltes, T. Ertl, and D. Weiskopf, “Visual Analysis and Coding of Data-rich User Behavior,” in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), G. L. Andrienko, S. Liu, and J. T. Stasko, Eds., in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2016, pp. 141–150. doi: 10.1109/VAST.2016.7883520.
    46. M. Herschel and M. Hlawatsch, “Provenance: On and Behind the Screens,” in Proceedings of the ACM International Conference on the Management of Data (SIGMOD), F. Özcan, G. Koutrika, and S. Madden, Eds., in Proceedings of the ACM International Conference on the Management of Data (SIGMOD). ACM, 2016, pp. 2213–2217. doi: 10.1145/2882903.2912568.
    47. R. Netzel, M. Burch, and D. Weiskopf, “User Performance and Reading Strategies for Metro Maps: An Eye Tracking Study,” Spatial Cognition and Computation, Special Issue: Eye Tracking for Spatial Research, 2016, doi: http://dx.doi.org/10.1080/13875868.2016.1226839.
    48. D. Sacha et al., “Human-Centered Machine Learning Through Interactive Visualization: Review and Open Challenges.,” in Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), in Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2016. [Online]. Available: http://dblp.uni-trier.de/db/conf/esann/esann2016.html#SachaSZLWNK16
    49. V. Hosu, F. Hahn, I. Zingman, and D. Saupe, “Reported Attention as a Promising Alternative to Gaze in IQA Tasks,” in Proceedings of  the 5th ISCA/DEGA Workshop on Perceptual Quality of Systems (PQS 2016), in Proceedings of  the 5th ISCA/DEGA Workshop on Perceptual Quality of Systems (PQS 2016). 2016, pp. 117–121. doi: 10.21437/PQS.2016-25.
    50. V. Hosu, F. Hahn, O. Wiedemann, S.-H. Jung, and D. Saupe, “Saliency-driven Image Coding Improves Overall Perceived JPEG Quality,” in Proceedings of the Picture Coding Symposium (PCS), in Proceedings of the Picture Coding Symposium (PCS). IEEE, 2016, pp. 1–5. doi: 10.1109/PCS.2016.7906397.
    51. J. Müller, R. Rädle, and H. Reiterer, “Virtual Objects as Spatial Cues in Collaborative Mixed Reality Environments: How They Shape Communication Behavior and User Task Load,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, J. Kaye, A. Druin, C. Lampe, D. Morris, and J. P. Hourcade, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems. ACM, 2016, pp. 1245–1249. doi: 10.1145/2858036.2858043.
    52. S. Butscher and H. Reiterer, “Applying Guidelines for the Design of Distortions on Focus+Context Interfaces,” in Proceedings of the Working Conference on Advanced Visual Interfaces (AVI), P. Buono, R. Lanzilotti, M. Matera, and M. F. Costabile, Eds., in Proceedings of the Working Conference on Advanced Visual Interfaces (AVI). ACM, 2016, pp. 244–247. doi: 10.1145/2909132.2909284.
    53. M. Correll and J. Heer, “Black Hat Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, Art. no. 1, 2016, [Online]. Available: http://idl.cs.washington.edu/files/2017-BlackHatVis-DECISIVe.pdf
  10. 2015

    1. L. L. Chuang and H. H. Bülthoff, “Towards a Better Understanding of Gaze Behavior in the Automobile,” in Position papers of the workshops at AutomotiveUI’15, in Position papers of the workshops at AutomotiveUI’15. Sep. 2015. [Online]. Available: https://www.auto-ui.org/15/p/workshops/2/8_Towards%20a%20Better%20Understanding%20of%20Gaze%20Behavior%20in%20the%20Automobile_Chuang.pdf
    2. K. Kurzhals, M. Burch, T. Pfeiffer, and D. Weiskopf, “Eye Tracking in Computer-based Visualization,” Computing in Science & Engineering, vol. 17, no. 5, Art. no. 5, 2015, doi: 10.1109/MCSE.2015.93.
    3. S. Frey, F. Sadlo, and T. Ertl, “Balanced Sampling and Compression for Remote Visualization,” in Proceedings of the SIGGRAPH Asia Symposium on High Performance Computing, in Proceedings of the SIGGRAPH Asia Symposium on High Performance Computing. ACM, 2015, pp. 1–4. doi: 10.1145/2818517.2818529.
    4. L. Lischke et al., “Using Space: Effect of Display Size on Users’ Search Performance,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), B. Begole, J. Kim, K. Inkpen, and W. Woo, Eds., in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA). ACM, 2015, pp. 1845–1850. doi: 10.1145/2702613.2732845.
    5. V. Schwind and S. Jäger, “The Uncanny Valley and the Importance of Eye Contact,” in Mensch und Computer 2015 - Tagungsband, S. Diefenbach and N. H. & M. Pielot, Eds., in Mensch und Computer 2015 - Tagungsband. , De Gruyter Oldenbourg, 2015, pp. 153–162.
    6. C. Schulz, M. Burch, and D. Weiskopf, “Visual Data Cleansing of Eye Tracking Data,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS). 2015. [Online]. Available: http://etvis.visus.uni-stuttgart.de/etvis2015/papers/etvis15_schulz.pdf
    7. M. Spicker, J. Kratt, D. Arellano, and O. Deussen, “Depth-aware Coherent Line Drawings,” in Proceedings of the SIGGRAPH Asia Symposium on Computer Graphics and Interactive Techniques, Technical Briefs, in Proceedings of the SIGGRAPH Asia Symposium on Computer Graphics and Interactive Techniques, Technical Briefs. ACM, 2015, pp. 1:1-1:5. [Online]. Available: http://doi.acm.org/10.1145/2820903.2820909
    8. M. Sedlmair and M. Aupetit, “Data-driven Evaluation of Visual Quality Measures,” Computer Graphics Forum, vol. 34, no. 3, Art. no. 3, 2015, doi: 10.1111/cgf.12632.
    9. L. L. Chuang, “Error Visualization and Information-Seeking Behavior for Air-Vehicle Control,” in Foundations of Augmented Cognition. AC 2015. Lecture Notes in Computer Science, vol. 9183, D. Schmorrow and C. M. Fidopiastis, Eds., in Foundations of Augmented Cognition. AC 2015. Lecture Notes in Computer Science, vol. 9183. , Springer, 2015, pp. 3–11. doi: 10.1007/978-3-319-20816-9_1.
    10. L. Lischke, J. Grüninger, K. Klouche, A. Schmidt, P. Slusallek, and G. Jacucci, “Interaction Techniques for Wall-Sized Screens,” Proceedings of the International Conference on Interactive Tabletops & Surfaces (ITS), pp. 501–504, 2015, doi: 10.1145/2817721.2835071.
    11. L. Lischke, P. Knierim, and H. Klinke, “Mid-Air Gestures for Window Management on Large Displays,” in Mensch und Computer 2015 – Tagungsband (MuC), S. Diefenbach, N. Henze, and M. Pielot, Eds., in Mensch und Computer 2015 – Tagungsband (MuC). De Gruyter, 2015, pp. 439–442. doi: 20.500.12116/7858.
    12. M. Hund et al., “Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion,” in Similarity Search and Applications. International Conference on Similarity Search and Applications (SISAP). Lecture Notes in Computer Science, vol. 9371, G. Amato, R. Connor, F. Falchi, and C. Gennaro, Eds., in Similarity Search and Applications. International Conference on Similarity Search and Applications (SISAP). Lecture Notes in Computer Science, vol. 9371. , Springer, Cham, 2015, pp. 307–313. doi: 10.1007/978-3-319-25087-8_29.
    13. N. Flad, T. Fomina, H. H. Bülthoff, and L. L. Chuang, “Unsupervised Clustering of EOG as a Viable Substitute for Optical Eye Tracking,” in Eye Tracking and Visualization: Foundations, Techniques, and Applications, M. Burch, L. L. Chuang, B. D. Fisher, A. Schmidt, and D. Weiskopf, Eds., in Eye Tracking and Visualization: Foundations, Techniques, and Applications. , Springer International Publishing, 2015, pp. 151–167. doi: 10.1007/978-3-319-47024-5_9.
    14. T. Chandler et al., “Immersive Analytics,” in Proceedings of the IEEE Symposium on Big Data Visual Analytics (BDVA), in Proceedings of the IEEE Symposium on Big Data Visual Analytics (BDVA). IEEE, 2015, pp. 73–80. doi: 10.1109/BDVA.2015.7314296.

Project Group A

Models and Measures

 

Completed

 

Project Group B

Adaptive Algorithms

 

Completed

 

Project Group C

Interaction

 

Completed

 

Project Group D

Applications

 

Completed