05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access VisRecall++: analysing and predicting visualisation recallability from gaze behaviour(2024) Wang, Yao; Jiang, Yue; Hu, Zhiming; Ruhdorfer, Constantin; Bâce, Mihai; Bulling, AndreasQuestion answering has recently been proposed as a promising means to assess the recallability of information visualisations. However, prior works are yet to study the link between visually encoding a visualisation in memory and recall performance. To fill this gap, we propose VisRecall++ - a novel 40-participant recallability dataset that contains gaze data on 200 visualisations and five question types, such as identifying the title, and finding extreme values.We measured recallability by asking participants questions after they observed the visualisation for 10 seconds.Our analyses reveal several insights, such as saccade amplitude, number of fixations, and fixation duration significantly differ between high and low recallability groups.Finally, we propose GazeRecallNet - a novel computational method to predict recallability from gaze behaviour that outperforms several baselines on this task.Taken together, our results shed light on assessing recallability from gaze behaviour and inform future work on recallability-based visualisation optimisation.Item Open Access A muscle model for injury simulation(2023) Millard, Matthew; Kempter, Fabian; Fehr, Jörg; Stutzig, Norman; Siebert, TobiasCar accidents frequently cause neck injuries that are painful, expensive, and difficult to simulate. The movements that lead to neck injury include phases in which the neck muscles are actively lengthened. Actively lengthened muscle can develop large forces that greatly exceed the maximum isometric force. Although Hill-type models are often used to simulate human movement, this model has no mechanism to develop large tensions during active lengthening. When used to simulate neck injury, a Hill model will underestimate the risk of injury to the muscles but may overestimate the risk of injury to the structures that the muscles protect. We have developed a musculotendon model that includes the viscoelasticity of attached crossbridges and has an active titin element. In this work we evaluate the proposed model to a Hill model by simulating the experiments of Leonard et al. [1] that feature extreme active lengthening.Item Open Access SalChartQA: question-driven saliency on information visualisations(2024) Wang, Yao; Wang, Weitian; Abdelhafez, Abdullah; Elfares, Mayar; Hu, Zhiming; Bâce, Mihai; Bulling, AndreasUnderstanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA - a novel crowd-sourced dataset that uses the BubbleView interface as a proxy for human gaze and a question-answering (QA) paradigm to induce different information needs in users. SalChartQA contains 74,340 answers to 6,000 questions on 3,000 visualisations. Informed by our analyses demonstrating the tight correlation between the question and visual saliency, we propose the first computational method to predict question-driven saliency on information visualisations. Our method outperforms state-of-the-art saliency models, improving several metrics, such as the correlation coefficient and the Kullback-Leibler divergence. These results show the importance of information needs for shaping attention behaviour and paving the way for new applications, such as task-driven optimisation of visualisations or explainable AI in chart question-answering.Item Open Access Usable and fast interactive mental face reconstruction(2023) Strohm, Florian; Bâce, Mihai; Bulling, AndreasWe introduce an end-to-end interactive system for mental face reconstruction - the challenging task of visually reconstructing a face image a person only has in their mind. In contrast to existing methods that suffer from low usability and high mental load, our approach only requires the user to rank images over multiple iterations according to the perceived similarity with their mental image. Based on these rankings, our mental face reconstruction system extracts image features in each iteration, combines them into a joint feature vector, and then uses a generative model to visually reconstruct the mental image. To avoid the need for collecting large amounts of human training data, we further propose a computational user model that can simulate human ranking behaviour using data from an online crowd-sourcing study (N=215). Results from a 12-participant user study show that our method can reconstruct mental images that are visually similar to existing approaches but has significantly higher usability, lower perceived workload, and is faster. In addition, results from a third 22-participant lineup study in which we validated our reconstructions on a face ranking task show a identification rate of , which is in line with prior work. These results represent an important step towards new interactive intelligent systems that can robustly and effortlessly reconstruct a user’s mental image.Item Open Access SUPREYES: SUPer resolution for EYES using implicit neural representation learning(2023) Jiao, Chuhan; Hu, Zhiming; Bâce, Mihai; Bulling, AndreasWe introduce SUPREYES - a novel self-supervised method to increase the spatio-temporal resolution of gaze data recorded using low(er)-resolution eye trackers. Despite continuing advances in eye tracking technology, the vast majority of current eye trackers - particularly mobile ones and those integrated into mobile devices - suffer from low-resolution gaze data, thus fundamentally limiting their practical usefulness. SUPREYES learns a continuous implicit neural representation from low-resolution gaze data to up-sample the gaze data to arbitrary resolutions. We compare our method with commonly used interpolation methods on arbitrary scale super-resolution and demonstrate that SUPREYES outperforms these baselines by a significant margin. We also test on the sample downstream task of gaze-based user identification and show that our method improves the performance of original low-resolution gaze data and outperforms other baselines. These results are promising as they open up a new direction for increasing eye tracking fidelity as well as enabling new gaze-based applications without the need for new eye tracking equipment.Item Open Access Improving the accuracy of musculotendon models for the simulation of active lengthening(2023) Millard, Matthew; Kempter, Fabian; Stutzig, Norman; Siebert, Tobias; Fehr, JörgVehicle accidents can cause neck injuries which are costly for individuals and society. Safety systems could be designed to reduce the risk of neck injury if it were possible to accurately simulate the tissue-level injuries that later lead to chronic pain. During a crash, reflexes cause the muscles of the neck to be actively lengthened. Although the muscles of the neck are often only mildly injured, the forces developed by the neck’s musculature affect the tissues that are more severely injured. In this work, we compare the forces developed by MAT_156, LS-DYNA’s Hill-type model, and the newly proposed VEXAT muscle model during active lengthening. The results show that Hill-type muscle models underestimate forces developed during active lengthening, while the VEXAT model can more faithfully reproduce experimental measurements.Item Open Access Impact of gaze uncertainty on AOIs in information visualisations(2022) Wang, Yao; Koch, Maurice; Bâce, Mihai; Weiskopf, Daniel; Bulling, AndreasGaze-based analysis of areas of interest (AOIs) is widely used in information visualisation research to understand how people explore visualisations or assess the quality of visualisations concerning key characteristics such as memorability. However, nearby AOIs in visualisations amplify the uncertainty caused by the gaze estimation error, which strongly influences the mapping between gaze samples or fixations and different AOIs. We contribute a novel investigation into gaze uncertainty and quantify its impact on AOI-based analysis on visualisations using two novel metrics: the Flipping Candidate Rate (FCR) and Hit Any AOI Rate (HAAR). Our analysis of 40 real-world visualisations, including human gaze and AOI annotations, shows that gaze uncertainty frequently and significantly impacts the analysis conducted in AOI-based studies. Moreover, we analysed four visualisation types and found that bar and scatter plots are usually designed in a way that causes more uncertainty than line and pie plots in gaze-based analysis.Item Open Access Mouse2Vec: learning reusable semantic representations of mouse behaviour(2024) Zhang, Guanhua; Hu, Zhiming; Bâce, Mihai; Bulling, AndreasThe mouse is a pervasive input device used for a wide range of interactive applications. However, computational modelling of mouse behaviour typically requires time-consuming design and extraction of handcrafted features, or approaches that are application-specific. We instead propose Mouse2Vec - a novel self-supervised method designed to learn semantic representations of mouse behaviour that are reusable across users and applications. Mouse2Vec uses a Transformer-based encoder-decoder architecture, which is specifically geared for mouse data: During pretraining, the encoder learns an embedding of input mouse trajectories while the decoder reconstructs the input and simultaneously detects mouse click events. We show that the representations learned by our method can identify interpretable mouse behaviour clusters and retrieve similar mouse trajectories. We also demonstrate on three sample downstream tasks that the representations can be practically used to augment mouse data for training supervised methods and serve as an effective feature extractor.Item Open Access Cervical muscle reflexes during lateral accelerations(2023) Millard, Matthew; Hunger, Susanne; Broß, Lisa; Fehr, Jörg; Holzapfel, Christian; Stutzig, Norman; Siebert, TobiasAutonomous vehicles will allow a variety of seating orientations that may change the risk of neck injury during an accident. Having a rotated head at the time of a rear-end collision in a conventional vehicle is associated with a higher risk of acute and chronic whiplash. The change in posture affects both the movement of the head and the response of the muscles. We are studying the reflexes of the muscles of the neck so that we can validate the responses of digital human body models that are used in crash simulations. The neck movements and muscle activity of 21 participants (11 female) were recorded at the Stuttgart FKFS mechanical driving simulator. During the maneuver we recorded the acceleration of the seat and electromyographic (EMG) signals from the sternocleidomastoid (STR) muscles using a Biopac MP 160 system (USA). As intuition would suggest, the reflexes of the muscles of the neck are sensitive to posture and the direction of the acceleration.Item Open Access The Grant Negotiation and Authorization Protocol : attacking, fixing, and verifying an emerging standard(2023) Helmschmidt, Florian; Hosseyni, Pedram; Küsters, Ralf; Pruiksma, Klaas; Waldmann, Clara; Würtele, TimThe Grant Negotiation and Authorization Protocol (GNAP) is an emerging authorization and authentication protocol which aims to consolidate and unify several use-cases of OAuth 2.0 and many of its common extensions while providing a higher degree of security. OAuth 2.0 is an essential cornerstone of the security of authorization and authentication for the Web, IoT, and beyond, and is used, among others, by many global players, like Google, Facebook, and Microsoft. Historical limitations of OAuth 2.0 and its extensions have led prominent members of the OAuth community to create GNAP, a newly designed protocol for authorization and authentication. Given GNAP's advantages over OAuth 2.0 and its support within the OAuth community, GNAP is expected to become at least as important as OAuth 2.0. In this work, we present the first formal security analysis of GNAP. We build a detailed formal model of GNAP, based on the Web Infrastructure Model (WIM) of Fett, Küsters, and Schmitz, and provide formal statements of the key security properties of GNAP, namely authorization, authentication, and session integrity. We discovered several attacks on GNAP in the process of trying to prove these properties. We present these attacks, as well as changes to the protocol that prevent them. These modifications have been incorporated into the GNAP specification after discussion with the GNAP working group. We give the first formal security guarantees for GNAP, by proving that GNAP, with our modifications applied, satisfies the mentioned security properties. GNAP was still an early draft when we began our analysis, but is now on track to be adopted as an IETF standard. Hence, our analysis is just in time to help ensure the security of this important emerging standard.