How do people internalize visualizations: as images or information? In this study, we investigate the nature of internalization for visualizations (i.e., how the mind encodes visualizations in memory) and how memory e...
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How do people internalize visualizations: as images or information? In this study, we investigate the nature of internalization for visualizations (i.e., how the mind encodes visualizations in memory) and how memory encoding affects its retrieval. This exploratory work examines the influence of various design elements on a user's perception of a chart. Specifically, which design elements lead to perceptions of visualization as an image (aims to provide visual references, evoke emotions, express creativity, and inspire philosophic thought) or as information (aims to present complex data, information, or ideas concisely and promote analytical thinking)? Understanding how design elements contribute to viewers perceiving a visualization more as an image or information will help designers decide which elements to include to achieve their communication goals. For this study, we annotated 500 visualizations and analyzed the responses of 250 online participants, who rated the visualizations on a bilinear scale as 'image' or 'information.' We then conducted an in-person study (n = 101) using a free recall task to examine how the image/information ratings and design elements impacted memory. The results revealed several interesting findings: Image-rated visualizations were perceived as more aesthetically 'appealing,' 'enjoyable,' and 'pleasing.' Information-rated visualizations were perceived as less 'difficult to understand' and more aesthetically 'likable' and 'nice,' though participants expressed higher 'positive' sentiment when viewing image-rated visualizations and felt less 'guided to a conclusion.' The presence of axes and text annotations heavily influenced the likelihood of participants rating the visualization as 'information.' We also found different patterns among participants that were older. Importantly, we show that visualizations internalized as 'images' are less effective in conveying trends and messages, though they elicit a more positive emotional judgment,
Neuron tracing, alternately referred to as neuron reconstruction, is the procedure for extracting the digital representation of the three-dimensional neuronal morphology from stacks of microscopic images. Achieving ac...
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Neuron tracing, alternately referred to as neuron reconstruction, is the procedure for extracting the digital representation of the three-dimensional neuronal morphology from stacks of microscopic images. Achieving accurate neuron tracing is critical for profiling the neuroanatomical structure at single-cell level and analyzing the neuronal circuits and projections at whole-brain scale. However, the process often demands substantial human involvement and represents a nontrivial task. Conventional solutions towards neuron tracing often contend with challenges such as non-intuitive user interactions, suboptimal data generation throughput, and ambiguous visualization. In this paper, we introduce a novel method that leverages the power of extended reality (XR) for intuitive and progressive semi-automatic neuron tracing in real time. In our method, we have defined a set of interactors for controllable and efficient interactions for neuron tracing in an immersive environment. We have also developed a GPU-accelerated automatic tracing algorithm that can generate updated neuron reconstruction in real time. In addition, we have built a visualizer for fast and improved visual experience, particularly when working with both volumetric images and 3D objects. Our method has been successfully implemented with one virtual reality (VR) headset and one augmented reality (AR) headset with satisfying results achieved. We also conducted two user studies and proved the effectiveness of the interactors and the efficiency of our method in comparison with other approaches for neuron tracing.
JIMING Zhejiang University, Symmetric key generation based on biometrics has emerged as a promising solution for wearables pairing. Among various biometrics, heartbeats offer significant potential owing to their inher...
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JIMING Zhejiang University, Symmetric key generation based on biometrics has emerged as a promising solution for wearables pairing. Among various biometrics, heartbeats offer significant potential owing to their inherent randomness and spontaneity. Ballistocardiography (BCG), in particular, stands out for its accessibility and inclusivity, as it measures the body's recoil forces in response to cardiac blood ejection into the vasculature. However, traditional approaches to BCG suffer from challenges in sensing on wearables and limited key generation rates. To this end, this paper presents MagKey, a system that enables wearables with BCG-based key generation. MagKey overcomes the difficulties in effective BCG sensing by translating skin vibration caused by recoil forces into magnetic field vibration (MFV). Moreover, MagKey demonstrates that the peak-to-peak trend (PPT) of MFV signals can reliably extract keys, and thus improve the key generation rate. To mitigate the impact of noise and motion artifacts on key generation, MagKey employs analog filters and a peak screening method for signal processing. We implement MagKey on a one-layer flexible printed circuit (FPC) and a two-layer printed circuit board (PCB). Extensive experiments show the usability and effectiveness of MagKey. Furthermore, our security analyses illustrate the scheme's resilience against potential attacks.
human factors such as fatigue and distraction often impair drivers' ability to gauge traffic dynamics, leading to collisions, especially at unsignalized intersections. Augmented reality (AR) technology, particular...
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human factors such as fatigue and distraction often impair drivers' ability to gauge traffic dynamics, leading to collisions, especially at unsignalized intersections. Augmented reality (AR) technology, particularly through advanced 3D projections and wearable head-mounted displays (HMDs), offers a promising enhancement by integrating comprehensive environmental awareness directly into the driver's field of view. This paper presents "ARive," an innovative AR driver-assistance system designed to improve road safety by projecting dynamic risk zones beneath other traffic participants, thus providing real-time kinematic information to promote safer driving distances and informed decision-making. The research involved developing two distinct AR designs and testing them using a fixed-base driving simulator with integrated real-time data communication. A user study with 17 participants revealed that while AR projections significantly improve distance maintenance, particularly in abrupt braking scenarios, they do not markedly affect brake response times or enhance safety during critical events. These findings suggest the need for further optimization of AR design elements to maximize effectiveness, highlighting the potential of AR in enhancing driver awareness and safety.
Office well-being aims to explore and support a healthy, balanced and active work style in office environments. Recent work on tangible user interfaces has started to explore the role of physical, tangible interfaces ...
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Office well-being aims to explore and support a healthy, balanced and active work style in office environments. Recent work on tangible user interfaces has started to explore the role of physical, tangible interfaces as active interventions to explore how to tackle problems such as inactive work and lifestyles, and increasingly sedentary behaviours. We identify a fragmented research landscape on tangible Office well-being interventions, missing the relationship between interventions, data, design strategies, and outcomes, and behaviour change techniques. Based on the analysis of 40 papers, we identify 7 classifications in tangible Office well-being interventions and analyse the intervention based on their role and foundation in behaviour change. Based on the analysis, we present design considerations for the development of future tangible Office well-being design interventions and present an overview of the current field and future research into tangible Office well-being interventions to design for a healthier and active office environment.
Stylized avatars are common virtual representations used in VR to support interaction and communication between remote collaborators. However, explicit expressions are notoriously difficult to create, mainly because m...
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Stylized avatars are common virtual representations used in VR to support interaction and communication between remote collaborators. However, explicit expressions are notoriously difficult to create, mainly because most current methods rely on geometric markers and features modeled for human faces, not stylized avatar faces. To cope with the challenge of emotional and expressive generating talking avatars, we build the Emotional Talking Avatar Dataset which is a talking-face video corpus featuring 6 different stylized characters talking with 7 different emotions. Together with the dataset, we also release an emotional talking avatar generation method which enables the manipulation of emotion. We validated the effectiveness of our dataset and our method in generating audio based puppetry examples, including comparisons to state-of-the-art techniques and a user study. Finally, various applications of this method are discussed in the context of animating avatars in VR.
As deep reinforcement learning (RL)'s capabilities surpass traditional reinforcement learning, the community is working to make these black boxes less opaque. Explanations about algorithms' choices and strateg...
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As deep reinforcement learning (RL)'s capabilities surpass traditional reinforcement learning, the community is working to make these black boxes less opaque. Explanations about algorithms' choices and strategies serve this purpose. However, information about RL algorithms' operations is not easily accessible. Our research aimed to extract such information, use it to build explanations, and test those explanations with users. For our user study, eight RL agents were trained using OpenAI baselines. Then, the HIGHLIGHTS-VIS algorithm was created by altering previous HIGHLIGHTS algorithms to collect data about the agents' interactions with the environment. The data were used to create explanations that were compared to previous works' video-based summaries in a user study. The between-subjects user study had participants answer questions about different agents. Participants were measured using both self-reported trust and performance on downstream tasks. Downstream tasks are tasks that a participant is more likely to do correctly with the information contained in the explanation. Collecting data about both trust and the utility of explanations allowed comparison and analysis of the explanations' effectiveness. Results showed that the alternative explanations built from the collected data led to more correct answers about the agents and their strategies. Additionally, explanations' utility depended on the context. Finally, users' reported trust in an explanation did not directly correlate to performance. These results suggest trust and effectiveness may need to be measured and calibrated separately in future examinations of explanations.
The group-in-a-box (GIB) layout is an efficient graph-drawing method designed to visualize the group structure of graphs. The layout communicates group sizes and both within-group and between-group network structures ...
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The group-in-a-box (GIB) layout is an efficient graph-drawing method designed to visualize the group structure of graphs. The layout communicates group sizes and both within-group and between-group network structures simultaneously. The layout is characterized by its composition of multiple elements, including nodes, edges, and boxes. However, there is limited empirical guidance on how these elements should be combined. In this paper, we measured participants' task performance and eye movements while identifying the group with the largest number of internal edges. We investigated the effect of visualization elements on task performance while controlling the density of internal edges and the box size. The results revealed that the box size in a GIB layout significantly affects the task accuracy either positively or negatively while eye-tracking data suggest that participants focused on internal edges, not the box size. These findings contribute empirical guidance for GIB layout design and lay the groundwork for future research as GIB layout becomes more widely used.
Emotional labor refers to the process in which workers are required to express emotions regardless of their actual feelings by the organization. In workplaces where such display rules exist, workers experience an emot...
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Emotional labor refers to the process in which workers are required to express emotions regardless of their actual feelings by the organization. In workplaces where such display rules exist, workers experience an emotional workload. Continued exposure to emotional workload can lead to severe mental and psychological issues. Nevertheless, research on assessing emotional workload remains understudied. In this study, we propose a machine learning model to automatically evaluate workers' emotional workload in emotional labor situations through multimodal sensing. The data collection study was designed based on a call center scenario. Within the study, we manipulated customer behaviors as confederates and assessed the worker's emotional workload. As a result, this study provides a benchmark using well-known features and standard machine learning methods. We achieved an accuracy of up to 87% for binary and three-class classification cases. Finally, we discuss the significance of assessing emotional workload and considerations for its practical application in the workplace.
Local disasters such as the Ahr Valley flood in Germany, the international backdrop of the Russo-Ukrainian War, or the global impact of the COVID-19 pandemic place high demands on the people and organisations that are...
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