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Item DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation(The Eurographics Association, 2022) Kwon, Bum Chul; Lee, Jungsoo; Chung, Chaeyeon; Lee, Nyoungwoo; Choi, Ho-Jin; Choo, Jaegul; Agus, Marco; Aigner, Wolfgang; Hoellt, ThomasImage classification models often learn to predict a class based on irrelevant co-occurrences between input features and an output class in training data. We call the unwanted correlations ''data biases,'' and the visual features causing data biases ''bias factors.'' It is challenging to identify and mitigate biases automatically without human intervention. Therefore, we conducted a design study to find a human-in-the-loop solution. First, we identified user tasks that capture the bias mitigation process for image classification models with three experts. Then, to support the tasks, we developed a visual analytics system called DASH that allows users to visually identify bias factors, to iteratively generate synthetic images using a state-of-the-art image-toimage translation model, and to supervise the model training process for improving the classification accuracy. Our quantitative evaluation and qualitative study with ten participants demonstrate the usefulness of DASH and provide lessons for future work.Item Prototype to Control a Mid-air CG Character Using Motion Capture Data of a Plush Toy(The Eurographics Association, 2022) Fukuoka, Miyu; Ando, Shohei; Koizumi, Naoya; Theophilus Teo; Ryota KondoWe propose an integrated operation system that combines puppet motion capture and human body movements to easily control various movements of a mid-air CG character. The proposed method addresses the problems in controlling CG characters via the body movements of a human operator and puppet. This method can also be used to control spatial movements and multiple parts of a character simultaneously. In addition, our method enables an operator to easily move the character in the depth direction, which is a key characteristic of a mid-air image.Item Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude(The Eurographics Association and John Wiley & Sons Ltd., 2020) Liem, Johannes; Perin, Charles; Wood, Jo; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, TatianaIn the visualization community, it is often assumed that visual data storytelling increases memorability and engagement, making it more effective at communicating information. However, many assumptions about the efficacy of storytelling in visualization lack empirical evaluation. Contributing to an emerging body of work, we study whether selected techniques commonly used in visual data storytelling influence people's attitudes towards immigration. We compare (a) personal visual narratives designed to generate empathy; (b) structured visual narratives of aggregates of people; and (c) an exploratory visualization without narrative acting as a control condition. We conducted two crowdsourced between-subject studies comparing the three conditions, each with 300 participants. To assess the differences in attitudes between conditions, we adopted established scales from the social sciences used in the European Social Survey (ESS). Although we found some differences between conditions, the effects on people's attitudes are smaller than we expected. Our findings suggest that we need to be more careful when it comes to our expectations about the effects visual data storytelling can have on attitudes.Item Sketching Vocabulary for Crowd Motion(The Eurographics Association and John Wiley & Sons Ltd., 2022) Mathew, C. D. Tharindu; Benes, Bedrich; Aliaga, Daniel; Dominik L. Michels; Soeren PirkThis paper proposes and evaluates a sketching language to author crowd motion. It focuses on the path, speed, thickness, and density parameters of crowd motion. A sketch-based vocabulary is proposed for each parameter and evaluated in a user study against complex crowd scenes. A sketch recognition pipeline converts the sketches into a crowd simulation. The user study results show that 1) participants at various skill levels and can draw accurate crowd motion through sketching, 2) certain sketch styles lead to a more accurate representation of crowd parameters, and 3) sketching allows to produce complex crowd motions in a few seconds. The results show that some styles although accurate actually are less preferred over less accurate ones.Item PerSleep: A Visual Analytics Approach for Performance Assessment of Sleep Staging Models(The Eurographics Association, 2021) Garcia Caballero, Humberto S.; Corvò, Alberto; Meulen, Fokke van; Fonseca, Pedro; Overeem, Sebasitaan; Wijk, Jarke J. van; Westenberg, Michel A.; Oeltze-Jafra, Steffen and Smit, Noeska N. and Sommer, Björn and Nieselt, Kay and Schultz, ThomasMachine learning is becoming increasingly popular in the medical domain. In the near future, clinicians expect predictive models to support daily tasks such as diagnosis and prognostic analysis. For this reason, it is utterly important to evaluate and compare the performance of such models so that clinicians can safely rely on them. In this paper, we focus on sleep staging wherein machine learning models can be used to automate or support sleep scoring. Evaluation of these models is complex because sleep is a natural process, which varies among patients. For adoption in clinical routine, it is important to understand how the models perform for different groups of patients. Moreover, models can be trained to recognize different characteristics in the data, and model developers need to understand why and how performance of the different models varies. To address these challenges, we present a visual analytics approach to evaluate the performance of predictive models on sleep staging and to help experts better understand these models with respect to patient data (e.g., conditions, medication, etc.). We illustrate the effectiveness of our approach by comparing multiple models trained on real-world sleep staging data with experts.Item Evaluating Bloom's Taxonomy-based Learning Modules for Parallel Coordinates Literacy(The Eurographics Association, 2022) Peng, Ilena; Firat, Elif E.; Laramee, Robert S.; Joshi, Alark; Bourdin, Jean-Jacques; Paquette, EricIn this paper, we present the results of an intervention designed to introduce parallel coordinates to students. The intervention contains six new modules inspired by Bloom's taxonomy that featured a combination of videos, tests, and tasks. We studied the impact of our modules with a corrective feedback mechanism inspired by Mastery Learning. Based on analyzing the data of our students, we found that students in the Corrective Immediate Feedback (CIF) group performed better on average on all the modules as compared to the students in the No Feedback (NF) group. In the tasks where students were required to construct parallel coordinates plots, students in the Corrective Immediate Feedback group produced plots with appropriate use of color, labels, legends, etc. Overall, students in both groups grew more confident in their ability to recognize parallel coordinates plots and expressed high confidence in their ability to interpret, create, and use parallel coordinates plots for data exploration and presentation in the future.Item TopoLines: Topological Smoothing for Line Charts(The Eurographics Association, 2020) Rosen, Paul; Suh, Ashley; Salgado, Christopher; Hajij, Mustafa; Kerren, Andreas and Garth, Christoph and Marai, G. ElisabetaLine charts are commonly used to visualize a series of data values. When the data are noisy, smoothing is applied to make the signal more apparent. Conventional methods used to smooth line charts, e.g., using subsampling or filters, such as median, Gaussian, or low-pass, each optimize for different properties of the data. The properties generally do not include retaining peaks (i.e., local minima and maxima) in the data, which is an important feature for certain visual analytics tasks. We present TopoLines, a method for smoothing line charts using techniques from Topological Data Analysis. The design goal of TopoLines is to maintain prominent peaks in the data while minimizing any residual error. We evaluate TopoLines for 2 visual analytics tasks by comparing to 5 popular line smoothing methods with data from 4 application domains.Item EMCA: Explorer of Monte Carlo based Algorithms(The Eurographics Association, 2021) Ruppert, Lukas; Kreisl, Christoph; Blank, Nils; Herholz, Sebastian; Lensch, Hendrik P. A.; Andres, Bjoern and Campen, Marcel and Sedlmair, MichaelDebugging or analyzing the performance of global illumination algorithms is a challenging task due to the complex path-scene interaction and numerous places where errors and programming bugs can occur. We present a novel, lightweight visualization tool to aid in the understanding of global illumination and the debugging of rendering frameworks. The tool provides detailed information about intersections and light transport paths. Users can add arbitrary data of their choosing to each intersection, based on their specific demands. Aggregate plots allow users to quickly discover and select outliers for further inspection across the globally linked visualization views. That information is further coupled with 3D visualization of the scene where additional aggregated information on the surfaces can be inspected in false colors. These include 3D heat maps such as the density of intersections as well as more advanced colorings such as a diffuse transport approximation computed from local irradiance samples and diffuse material approximations. The necessary data for the 3D coloring is collected as a side-product of quickly rendering the image at low sample counts without significantly slowing down the rendering process. It requires almost no precomputation and very little storage compared to point cloud-based approaches. We present several use cases of how novices and advanced rendering researchers can leverage the presented tool to speed up their research.Item KidCAD: An Interactive Cohort Analysis Dashboard of Patients with Chronic Kidney Diseases(The Eurographics Association, 2023) Höhn, Markus; Schwindt, Sarah; Hahn, Sara; Patyna, Sammy; Büttner, Stefan; Kohlhammer, Jörn; Angelini, Marco; El-Assady, MennatallahChronic Kidney Diseases (CKD) are a prominent health problem. With an ongoing process, CKD leads to impaired kindey function with decreased ability to filter the patients' blood, concluding in multiple complications, like heart disease and finally death. We developed a prototype to support nephrologists to gain an overview of their CKD patients. The prototype visualizes the patients in cohorts according to their pairwise similarity. The user can interactively modify the similarity by changing the underlying weights of the included features. The prototype was developed in response to the needs of physicians due to a context of use analysis. A qualitative user study shows the need and suitability of our new approach.Item Saliency Clouds: Visual Analysis of Point Cloud-oriented Deep Neural Networks in DeepRL for Particle Physics(The Eurographics Association, 2022) Mulawade, Raju Ningappa; Garth, Christoph; Wiebel, Alexander; Archambault, Daniel; Nabney, Ian; Peltonen, JaakkoWe develop and describe saliency clouds, that is, visualization methods employing explainable AI methods to analyze and interpret deep reinforcement learning (DeepRL) agents working on point cloud-based data. The agent in our application case is tasked to track particles in high energy physics and is still under development. The point clouds contain properties of particle hits on layers of a detector as the input to reconstruct the trajectories of the particles. Through visualization of the influence of different points, their possible connections in an implicit graph, and other features on the decisions of the policy network of the DeepRL agent, we aim to explain the decision making of the agent in tracking particles and thus support its development. In particular, we adapt gradient-based saliency mapping methods to work on these point clouds. We show how the properties of the methods, which were developed for image data, translate to the structurally different point cloud data. Finally, we present visual representations of saliency clouds supporting visual analysis and interpretation of the RL agent's policy network.