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"abstract": "Body-centric locomotion allows users to control both movement speed and direction with body parts (e.g., head tilt, arm swing or torso lean) to navigate in virtual reality (VR). However, there is little research to systematically investigate the effects of body parts for speed and direction control on virtual locomotion by taking in account different transfer functions(L: linear function, P: power function, and CL: piecewise function with constant and linear function). Therefore, we conducted an experiment to evaluate the combinational effects of the three factors (body parts for direction control, body parts for speed control, and transfer functions) on virtual locomotion. Results showed that (1) the head outperformed the torso for movement direction control in task completion time and environmental collisions; (2) Arm-based speed control led to shorter traveled distances than both head and knee. Head-based speed control had fewer environmental collisions than knee; (3) Body-centric locomotion with CL function was faster but less accurate than both L and P functions. Task time significantly decreased from P, L to CL functions, while traveled distance and overshoot significantly increased from P, L to CL functions. L function was rated with the highest score of USE-S, -pragmatic and -hedonic; (4) Transfer function had a significant main effect on motion sickness: the participants felt more headache and nausea when performing locomotion with CL function. Our results provide implications for body-centric locomotion design in VR applications.",
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"givenName": "Boyu",
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"affiliation": "College of Information Science and Technology/Cyber Security, Jinan University, 47885 Guangzhou, guangdong, China, 510632",
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"affiliation": "Department of Computer Science and Information Technology, La Trobe University, 2080 Melbourne, Victoria, Australia",
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"abstract": "Appropriate gestures can enhance message delivery and audience engagement in both daily communication and public presentations. In this paper, we contribute a visual analytic approach that assists professional public speaking coaches in improving their practice of gesture training through analyzing presentation videos. Manually checking and exploring gesture usage in the presentation videos is often tedious and time-consuming. There lacks an efficient method to help users conduct gesture exploration, which is challenging due to the intrinsically temporal evolution of gestures and their complex correlation to speech content. In this paper, we propose GestureLens, a visual analytics system to facilitate gesture-based and content-based exploration of gesture usage in presentation videos. Specifically, the exploration view enables users to obtain a quick overview of the spatial and temporal distributions of gestures. The dynamic hand movements are firstly aggregated through a heatmap in the gesture space for uncovering spatial patterns, and then decomposed into two mutually perpendicular timelines for revealing temporal patterns. The relation view allows users to explicitly explore the correlation between speech content and gestures by enabling linked analysis and intuitive glyph designs. The video view and dynamic view show the context and overall dynamic movement of the selected gestures, respectively. Two usage scenarios and expert interviews with professional presentation coaches demonstrate the effectiveness and usefulness of GestureLens in facilitating gesture exploration and analysis of presentation videos.",
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"abstract": "Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis). In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. To stay focused, we primarily consider works that handle scalar and vector field data but exclude mesh data. We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. The paper concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community. This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.",
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"abstract": "The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have implemented various measures and policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the spread of the epidemic. These countermeasures seek to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policymakers have expressed the urgency to effectively evaluate the outcome of human restriction policies with the aid of big data and information technology. Thus, based on big human mobility data and city POI data, an interactive visual analytics system called Epidemic Mobility (EpiMob) was designed in this study. The system interactively simulates the changes in human mobility and infection status in response to the implementation of a certain restriction policy or a combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies. Then, the results reflecting the infection situation under different policies are dynamically displayed and can be flexibly compared and analyzed in depth. Multiple case studies consisting of interviews with domain experts were conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area) to demonstrate that the system can provide insight into the effects of different human mobility restriction policies for epidemic control, through measurements and comparisons.",
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"abstract": "A seated user watching his avatar walking in Virtual Reality (VR) may have an impression of walking. In this paper, we show that such an impression can be extended to other postures and other locomotion exercises. We present two user studies in which participants wore a VR headset and observed a first-person avatar performing virtual exercises. In the first experiment, the avatar walked and the participants (n=36) tested the simulation in 3 different postures (standing, sitting and Fowler's posture). In the second experiment, other participants (n=18) were sitting and observed the avatar walking, jogging or stepping over virtual obstacles. We evaluated the impression of locomotion by measuring the impression of walking (respectively jogging or stepping) and embodiment in both experiments. The results show that participants had the impression of locomotion in either sitting, standing and Fowler's posture. However, Fowler's posture significantly decreased both the level of embodiment and the impression of locomotion. The sitting posture seems to decrease the sense of agency compared to standing posture. Results also show that the majority of the participants experienced an impression of locomotion during the virtual walking, jogging, and stepping exercises. The embodiment was not influenced by the type of virtual exercise. Overall, our results suggest that an impression of locomotion can be elicited in different users' postures and during different virtual locomotion exercises. They provide valuable insight for numerous VR applications in which the user observes a self-avatar moving, such as video games, gait rehabilitation, training, etc.",
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"abstract": "We present the results of a scientometric analysis of 30 years of IEEE VIS publications between 1990-2020, in which we conducted a multifaceted analysis of interdisciplinary collaboration and gender composition among authors. To this end, we curated BiblioVIS, a bibliometric dataset that contains rich metadata about IEEE VIS publications, including 3032 papers and 6113 authors. One of the main factors differentiating BiblioVIS from similar datasets is the authors' gender and discipline data, which we inferred through iterative rounds of computational and manual processes. Our analysis shows that, by and large, inter-institutional and interdisciplinary collaboration has been steadily growing over the past 30 years. However, interdisciplinary research was mainly between a few fields, including Computer Science, Engineering and Technology, and Medicine and Health disciplines. Our analysis of gender shows steady growth in women's authorship. Despite this growth, the gender distribution is still highly skewed, with men dominating (~75%) of this space. Our predictive analysis of gender balance shows that if the current trends continue, gender parity in the visualization field will not be reached before the third quarter of the century (~2070). Our primary goal in this work is to call the visualization community's attention to the critical topics of collaboration, diversity, and gender. Our research offers critical insights through the lens of diversity and gender to help accelerate progress towards a more diverse and representative research community.",
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"content": "The development of digitized humanity information provides a new perspective on data-oriented studies of history. Many previous studies have ignored uncertainty in the exploration of historical figures and events, which has limited the capability of researchers to capture complex processes associated with historical phenomena. We propose a visual reasoning system to support visual reasoning of uncertainty associated with spatio-temporal events of historical figures based on data from the China Biographical Database Project. We build a knowledge graph of entities extracted from a historical database to capture uncertainty generated by missing data and error. The proposed system uses an overview of chronology, a map view, and an interpersonal relation matrix to describe and analyse heterogeneous information of events. The system also includes uncertainty visualization to identify uncertain events with missing or imprecise spatio-temporal information. Results from case studies and expert evaluations suggest that the visual reasoning system is able to quantify and reduce uncertainty generated by the data.",
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"title": "Visual Reasoning for Uncertainty in Spatio-temporal Events of Historical Figures",
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"givenName": "Wei",
"surname": "Zhang",
"fullName": "Wei Zhang",
"affiliation": "Computer Scienece, Zhejiang University, 12377 Hangzhou, Zhejiang, China, 310058 (e-mail: [email protected])",
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"givenName": "Siwei",
"surname": "Tan",
"fullName": "Siwei Tan",
"affiliation": "Computer Science, Zhejiang University, 12377 Hangzhou, Zhejiang, China, 310058 (e-mail: [email protected])",
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"surname": "Chen",
"fullName": "Siming Chen",
"affiliation": "School of Data Science, Fudan University, 12478 Shanghai, Shanghai, China, 200433 (e-mail: [email protected])",
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"givenName": "Linhao",
"surname": "Meng",
"fullName": "Linhao Meng",
"affiliation": "Department of Mathematics and Computer Science, University of Technology Eindhoven Department of Mathematics and Computer Science, 215692 Eindhoven, North Brabant, Netherlands, (e-mail: [email protected])",
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"fullName": "Wei Chen",
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"content": "Coloring line art images based on the colors of reference images is an important stage in animation production, which is time-consuming and tedious. In this paper, we propose a deep architecture to automatically color line art videos with the same color style as the given reference images. Our framework consists of a color transform network and a temporal refinement network based on 3U-net. The color transform network takes the target line art images as well as the line art and color images of the reference images as input, and generates corresponding target color images. To cope with the large differences between each target line art image and the reference color images, we propose a distance attention layer that utilizes non-local similarity matching to determine the region correspondences between the target image and the reference images and transforms the local color information from the references to the target. To ensure global color style consistency, we further incorporate Adaptive Instance Normalization (AdaIN) with the transformation parameters obtained from a multiple-layer AdaIN that describes the global color style of the references, extracted by an embedder network. The temporal refinement network learns spatiotemporal features through 3D convolutions to ensure the temporal color consistency of the results. Our model can achieve even better coloring results by fine-tuning the parameters with only a small number of samples when dealing with an animation of a new style. To evaluate our method, we build a line art coloring dataset.",
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"content": "Data workers use various scripting languages for data transformation, such as SAS, R, and Python. However, understanding intricate code pieces requires advanced programming skills, which hinders data workers from grasping the idea of data transformation at ease. Program visualization is beneficial for debugging and education and has the potential to illustrate transformations intuitively and interactively. In this paper, we explore visualization design for demonstrating the semantics of code pieces in the context of data transformation. First, to depict individual data transformations, we structure a design space by two primary dimensions, i.e., key parameters to encode and possible visual channels to be mapped. Then, we derive a collection of 23 glyphs that visualize the semantics of transformations. Next, we design a pipeline, named Somnus, that provides an overview of the creation and evolution of data tables using a provenance graph. At the same time, it allows detailed investigation of individual transformations. User feedback on Somnus is positive. Our study participants achieved better accuracy with less time using Somnus, and preferred it over carefully-crafted textual description. Further, we provide two example applications to demonstrate the utility and versatility of Somnus.",
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