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"pages": "1--10","note": "spotlight","abstract": "Lifelogging is traditionally used for memory augmentation. However, recent research shows that users\u2019 trust in the completeness and accuracy of lifelogs might skew their memories. Privacy-protection alterations such as body blurring and content deletion are commonly applied to photos to circumvent capturing sensitive information. However, their impact on how users remember memories remain unclear. To this end, we conduct a white-hat memory attack and report on an iterative experiment (N=21) to compare the impact of viewing 1) unaltered lifelogs, 2) blurred lifelogs, and 3) a subset of the lifelogs after deleting private ones, on confidently remembering memories. Findings indicate that all the privacy methods impact memories\u2019 quality similarly and that users tend to change their answers in recognition more than recall scenarios. Results also show that users have high confidence in their remembered content across all privacy methods. Our work raises awareness about the mindful designing of technological interventions.",
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"pages": "9154--9169","abstract": "Eye movements during reading offer a window into cognitive processes and language comprehension, but the scarcity of reading data with interruptions -- which learners frequently encounter in their everyday learning environments -- hampers advances in the development of intelligent learning technologies. We introduce InteRead -- a novel 50-participant dataset of gaze data recorded during self-paced reading of real-world text. InteRead further offers fine-grained annotations of interruptions interspersed throughout the text as well as resumption lags incurred by these interruptions. Interruptions were triggered automatically once readers reached predefined target words. We validate our dataset by reporting interdisciplinary analyses on different measures of gaze behavior. In line with prior research, our analyses show that the interruptions as well as word length and word frequency effects significantly impact eye movements during reading. We also explore individual differences within our dataset, shedding light on the potential for tailored educational solutions. InteRead is accessible from our datasets web-page: https://www.ife.uni-stuttgart.de/en/llis/research/datasets/.",
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{"first" : "Satyananda", "last" : "Kashyap"},
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{"first" : "Leo Anthony", "last" : "Celi"},
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"volume": "210","pages": "165--183","abstract": "We propose the first method in which human and AI collaborate to iteratively reconstruct the human\u2019s mental image of another person\u2019s face only from their eye gaze. Current tools for generating digital human faces involve a tedious and time-consuming manual design process. While gaze-based mental image reconstruction represents a promising alternative, previous methods still assumed prior knowledge about the target face, thereby severely limiting their practical usefulness. The key novelty of our method is a collaborative, it- erative query engine: Based on the user\u2019s gaze behaviour in each iteration, our method predicts which images to show to the user in the next iteration. Results from two human studies (N=12 and N=22) show that our method can visually reconstruct digital faces that are more similar to the mental image, and is more usable compared to other methods. As such, our findings point at the significant potential of human-AI collaboration for recon- structing mental images, potentially also beyond faces, and of human gaze as a rich source of information and a powerful mediator in said collaboration.",
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"author": [
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{"first" : "Fabian", "last" : "Kögel"},
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"pages": "27--43","note": "spotlight","abstract": "We present VQA-MHUG - a novel 49-participant dataset of multimodal human gaze on both images and questions during visual question answering (VQA) collected using a high-speed eye tracker. We use our dataset to analyze the similarity between human and neural attentive strategies learned by five state-of-the-art VQA models: Modulated Co-Attention Network (MCAN) with either grid or region features, Pythia, Bilinear Attention Network (BAN), and the Multimodal Factorized Bilinear Pooling Network (MFB). While prior work has focused on studying the image modality, our analyses show - for the first time - that for all models, higher correlation with human attention on text is a significant predictor of VQA performance. This finding points at a potential for improving VQA performance and, at the same time, calls for further research on neural text attention mechanisms and their integration into architectures for vision and language tasks, including but potentially also beyond VQA.",
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"author": [
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"volume": "6","pages": "1--18","abstract": "Emotional expressions are inherently multimodal -- integrating facial behavior, speech, and gaze -- but their automatic recognition is often limited to a single modality, e.g. speech during a phone call. While previous work proposed crossmodal emotion embeddings to improve monomodal recognition performance, despite its importance, a representation of gaze was not included. We propose a new approach to emotion recognition that incorporates an explicit representation of gaze in a crossmodal emotion embedding framework. We show that our method outperforms the previous state of the art for both audio-only and video-only emotion classification on the popular One-Minute Gradual Emotion Recognition dataset. Furthermore, we report extensive ablation experiments and provide insights into the performance of different state-of-the-art gaze representations and integration strategies. Our results not only underline the importance of gaze for emotion recognition but also demonstrate a practical and highly effective approach to leveraging gaze information for this task.",
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"author": [
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{"first" : "Fabian", "last" : "Kögel"},
{"first" : "Philipp", "last" : "Müller"},
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"pages": "1--11","note": "arxiv:2109.13139","abstract": "Human-like attention as a supervisory signal to guide neural attention has shown significant promise but is currently limited to uni-modal integration \u2013 even for inherently multi-modal tasks such as visual question answering (VQA). We present the Multimodal Human-like Attention Network (MULAN) \u2013 the first method for multimodal integration of human-like attention on image and text during training of VQA models. MULAN integrates attention predictions from two state-of-the-art text and image saliency models into neural self-attention layers of a recent transformer-based VQA model. Through evaluations on the challenging VQAv2 dataset, we show that MULAN achieves a new state-of-the-art performance of 73.98% accuracy on test-std and 73.72% on test-dev and, at the same time, has approximately 80% fewer trainable parameters than prior work. Overall, our work underlines the potential of integrating multimodal human-like and neural attention for VQA.",
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{"first" : "Florian", "last" : "Strohm"},
{"first" : "Ekta", "last" : "Sood"},
{"first" : "Sven", "last" : "Mayer"},
{"first" : "Philipp", "last" : "Müller"},
{"first" : "Mihai", "last" : "Bâce"},
{"first" : "Andreas", "last" : "Bulling"}
],
"pages": "245-254",
"venue" : "Online",
"isbn" : "978-1-6654-2812-5 and 978-1-6654-2813-2",
"research-areas" : "Computer Science",
"language" : "eng",
"eventdate" : "2021-10-10/2021-10-17",
"eventtitle" : "2021 IEEE/CVF International Conference on Computer Vision (ICCV)",
"affiliation" : "Strohm, F (Corresponding Author), Univ Stuttgart, Stuttgart, Germany.\n Strohm, Florian; Sood, Ekta; Bace, Mihai; Bulling, Andreas, Univ Stuttgart, Stuttgart, Germany.\n Mayer, Sven, Ludwig Maximilians Univ Munchen, Munich, Germany.\n Mueller, Philipp, German Res Ctr Artificial Intelligence DFKI, Saarbrucken, Germany.",
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"doi" : "10.1109/ICCV48922.2021.00031",
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"label" : "Improving Neural Saliency Prediction with a Cognitive Model of Human Visual Attention",
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"author": [
"Ekta Sood","Lei Shi","Matteo Bortoletto","Yao Wang","Philipp Müller","Andreas Bulling"
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{"first" : "Ekta", "last" : "Sood"},
{"first" : "Lei", "last" : "Shi"},
{"first" : "Matteo", "last" : "Bortoletto"},
{"first" : "Yao", "last" : "Wang"},
{"first" : "Philipp", "last" : "Müller"},
{"first" : "Andreas", "last" : "Bulling"}
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"pages": "3639--3646","note": "spotlight","abstract": "We present a novel method for saliency prediction that leverages a cognitive model of visual attention as an inductive bias. This approach is in stark contrast to recent purely data-driven saliency models that achieve performance improvements mainly by increased capacity, resulting in high computational costs and the need for large-scale training datasets. We demonstrate that by using a cognitive model, our method achieves competitive performance to the state of the art across several natural image datasets while only requiring a fraction of the parameters. Furthermore, we set the new state of the art for saliency prediction on information visualizations, demonstrating the effectiveness of our approach for cross-domain generalization. We further provide augmented versions of the full MSCOCO dataset with synthetic gaze data using the cognitive model, which we used to pre-train our method. Our results are highly promising and underline the significant potential of bridging between cognitive and data-driven models, potentially also beyond attention.",
"code" : "https://git.hcics.simtech.uni-stuttgart.de/public-projects/neural-saliency-prediction-with-a-cognitive-model/",
"supp" : "Yes",
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"label" : "Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction",
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"journal": "Archive of Applied Mechanics",
"year": "2023",
"url": "https://doi.org/10.1007/s00419-023-02458-5",
"author": [
"Jonas Kneifl","David Rosin","Okan Avci","Oliver Röhrle","Jörg Fehr"
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{"first" : "Jonas", "last" : "Kneifl"},
{"first" : "David", "last" : "Rosin"},
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{"first" : "Jörg", "last" : "Fehr"}
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"volume": "93","number": "9","pages": "3637--3663","abstract": "Over the last decades, computer modeling has evolved from a supporting tool for engineering prototype design to an ubiquitous instrument in non-traditional fields such as medical rehabilitation. This area comes with unique challenges, e.g. the complex modeling of soft tissue or the analysis of musculoskeletal systems. Conventional modeling approaches like the finite element (FE) method are computationally costly when dealing with such models, limiting their usability for real-time simulation or deployment on low-end hardware, if the model at hand cannot be simplified without losing its expressiveness. Non-traditional approaches such as surrogate modeling using data-driven model order reduction are used to make complex high-fidelity models more widely available regardless. They often involve a dimensionality reduction step, in which the high-dimensional system state is transformed onto a low-dimensional subspace or manifold, and a regression approach to capture the reduced system behavior. While most publications focus on one dimensionality reduction, such as principal component analysis (PCA) (linear) or autoencoder (nonlinear), we consider and compare PCA, kernel PCA, autoencoders, as well as variational autoencoders for the approximation of a continuum-mechanical system. In detail, we demonstrate the benefits of the surrogate modeling approach on a complex musculoskeletal system of a human upper-arm with severe nonlinearities and physiological geometry. We consider both, the model\u2019s deformation and the internal stress as the two main quantities of interest in a FE context. By doing so we are able to create computationally low-cost surrogate models which capture the system behavior with high approximation quality and fast evaluations.",
"issn" : "1432-0681",
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"journal": "International Journal for Numerical Methods in Engineering","publisher":"Wiley",
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"Anika Strauß","Jonas Kneifl","Anton Tkachuk","Jörg Fehr","Manfred Bischoff"
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{"first" : "Anika", "last" : "Strauß"},
{"first" : "Jonas", "last" : "Kneifl"},
{"first" : "Anton", "last" : "Tkachuk"},
{"first" : "Jörg", "last" : "Fehr"},
{"first" : "Manfred", "last" : "Bischoff"}
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"label" : "PerSiVal: On-Body AR Visualization of Biomechanical Arm Simulations",
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"journal": "IEEE Computer Graphics and Applications",
"year": "2024",
"url": "https://doi.org/10.1109/MCG.2024.3494598",
"author": [
"Xingyao Yu","David Rosin","Johannes Kässinger","Benjamin Lee","Frank Dürr","Christian Becker","Oliver Röhrle","Michael Sedlmair"
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{"first" : "Xingyao", "last" : "Yu"},
{"first" : "David", "last" : "Rosin"},
{"first" : "Johannes", "last" : "Kässinger"},
{"first" : "Benjamin", "last" : "Lee"},
{"first" : "Frank", "last" : "Dürr"},
{"first" : "Christian", "last" : "Becker"},
{"first" : "Oliver", "last" : "Röhrle"},
{"first" : "Michael", "last" : "Sedlmair"}
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"pages": "1-14","abstract": "In this work, we explore different combinations of techniques for an interactive, on-body visualization in augmented reality (AR) of an upper arm muscle simulation model. In terms of data, we focus on a continuum-mechanical simulation model involving five different muscles of the human upper arm, with physiologically realistic geometry. In terms of use cases, we focus on the immersive illustration, education, and dissemination of such simulation models. We describe the process of developing six on-body visualization prototypes over the period of five years. For each prototype, we employed different types of motion capture, AR display technologies, and visual encoding approaches, and gathered feedback throughout outreach activities. We reflect on the development of the individual prototypes and summarize lessons learned of our exploration process into the design space of situated on-body visualization.",
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"url": "https://doi.org/10.1007/s00466-024-02553-6",
"author": [
"Jonas Kneifl","Jörg Fehr","Steven L. Brunton","J. Nathan Kutz"
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{"first" : "Jonas", "last" : "Kneifl"},
{"first" : "Jörg", "last" : "Fehr"},
{"first" : "Steven L.", "last" : "Brunton"},
{"first" : "J. Nathan", "last" : "Kutz"}
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"abstract": "Highly nonlinear dynamic finite element simulations using explicit time integration are particularly valuable tools for structural analysis in fields like automotive, aerospace, and civil engineering, or in the study of injury biomechanics. However, such state-of-the-art simulation models demand significant computational resources. Conventional data-driven surrogate modeling approaches address this by evolving the dynamics on low-dimensional embeddings, yet the majority of them operate directly on high-resolution data obtained from numerical discretizations, making them costly and unsuitable for adaptive resolutions or for handling information flow over large spatial distances. We therefore propose a multi-hierarchical framework for the structured creation of a series of surrogate models at different resolutions. Macroscale features are captured on coarse surrogates, while microscale effects are resolved on finer ones, while leveraging transfer learning to pass information between scales. The objective of this study is to develop efficient surrogates for a kart frame model in a frontal impact scenario. To achieve this, its mesh is simplified to obtain multi-resolution representations of the kart. Subsequently, a graph-convolutional neural network-based surrogate learns parameter-dependent low-dimensional latent dynamics on the coarsest representation. Following surrogates are trained on residuals using finer resolutions, allowing for multiple surrogates with varying hardware requirements and increasing accuracy.",
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"label" : "Promoting sustainable behavior: addressing user clusters through targeted incentives",
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"url": "http://dx.doi.org/10.1057/s41599-024-03581-6",
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{"first" : "Laura", "last" : "Höpfl"},
{"first" : "Maximilian", "last" : "Grimlitza"},
{"first" : "Isabella", "last" : "Lang"},
{"first" : "Maria", "last" : "Wirzberger"}
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"abstract": "AbstractGiven the urgency of climate change action and the significant climate impact of household emissions, understanding the drivers of individuals\u2019 sustainable behavior patterns is more important than ever. Consequently, we investigate whether different clusters of individual users can be distinguished regarding sustainability-related values, attitudes, and intentions. If these diverse clusters exist, we can explore tailored approaches to promote sustainable behavior patterns among them based on their unique needs and targets. For this purpose, we employ a mixed-method approach combining qualitative interviews with a quantitative survey. The obtained insights help us identify core factors that drive sustainable behavior, develop representations of different user groups, and suggest individualized interventions for supporting sustainable behavior patterns. The qualitative part comprised interviews with ten participants, resulting in the development of qualitative personas. Emerging differences could subsequently be used to select validated psychological scales for the quantitative part to confirm the differences. Applying data-driven clustering, we identify five intention-based clusters that vary regarding factors such as belief in climate change, collaboration, or skepticism concerning sustainability. Building on both qualitative and quantitative insights, five validated personas are created for research and practical use. These personas include Socially Sustainable, Responsible Savers, Unconcerned Spenders, Comfort-Oriented, and Skeptical Consumers. Individuals corresponding to the selected persona may, for example, respond positively to sustainability benefits, while others may be more receptive to hedonistic benefits. Addressing related varying motivational factors raises the demand for individualized interventions. These could be achieved by incorporating the personas\u2019 needs with more individualized products and services to promote sustainable behavior.",
"issn" : "2662-9992",
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"label" : "ChoreoVis: Planning and Assessing Formations in Dance Choreographies",
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"url": "https://diglib.eg.org/handle/10.1111/cgf15104",
"author": [
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"authors": [
{"first" : "Samuel", "last" : "Beck"},
{"first" : "Nina", "last" : "Doerr"},
{"first" : "Kuno", "last" : "Kurzhals"},
{"first" : "Alexander", "last" : "Riedlinger"},
{"first" : "Fabian", "last" : "Schmierer"},
{"first" : "Michael", "last" : "Sedlmair"},
{"first" : "Steffen", "last" : "Koch"}
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"volume": "43","number": "3","abstract": "Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real-world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.",
"issn" : "1467-8659",
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"label" : "Sitting Posture Recognition and Feedback: A Literature Review",
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"booktitle": "CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems","series": "CHI","publisher":"ACM","address":"NY",
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"url": "https://github.com/ChristianKrauter/sitting-posture-review-material",
"author": [
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"authors": [
{"first" : "Christian", "last" : "Krauter"},
{"first" : "Katrin", "last" : "Angerbauer"},
{"first" : "Aimée", "last" : "Sousa Calepso"},
{"first" : "Alexander", "last" : "Achberger"},
{"first" : "Sven", "last" : "Mayer"},
{"first" : "Michael", "last" : "Sedlmair"}
],
"affiliation" : "Krauter, Christian, University of Stuttgart. Angerbauer, Katrin, Visualisierungsinstitut der Universität Stuttgart. Sousa Calepso, Aimée, Visualisierungsinstitut der Universität Stuttgart. Achberger, Alexander, Visualisierungsinstitut der Universität Stuttgart. Sedlmair, Michael, Visualisierungsinstitut der Universität Stuttgart",
"orcid-numbers" : "Krauter, Christian/0000-0002-9787-0816, Angerbauer, Katrin/0000-0002-1126-5288, Sedlmair, Michael/0000-0001-7048-9292",
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"label" : "Persival: Simulating Complex 3D Meshes on Resource-Constrained Mobile AR Devices Using Interpolation",
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"booktitle": "Proceedings of the 2022 IEEE International Conference on Distributed Computing Systems (ICDCS)",
"year": "2022",
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"author": [
"Johannes Kässinger","David Rosin","Frank Dürr","Niklas Hornischer","Kurt Rothermel","Oliver Röhrle"
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{"first" : "Frank", "last" : "Dürr"},
{"first" : "Niklas", "last" : "Hornischer"},
{"first" : "Kurt", "last" : "Rothermel"},
{"first" : "Oliver", "last" : "Röhrle"}
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