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Heuristics for evaluating narrative visualization: a validation study

JOURNAL OF VISUALIZATION 2025年 1-15页

作者： Errey, Nina Zowghi, Didar Leong, Tuck Wah Wu, Yadong Yuan, Xiaoru Huang, Weidong Liang, Christy Jie UTS Sch Comp Sci Sydney NSW Australia CSIRO Data61 Sydney Australia RMIT Sch Comp Technol Melbourne Australia Sichuan Univ Sci & Engn Comp Sci & Engn Yibin Peoples R China Peking Univ Sch Intelligence Sci & Technol Beijing Peoples R China UTS Transdisciplinary Sch Sydney Australia

Narrative visualization integrates data visualization and narrative techniques to convey a compelling story. Narrative visualization is notoriously difficult to evaluate. One solution is heuristic evaluation, using a domain-specific set of heuristics. This paper validates a set of heuristics proposed specifically for evaluating narrative visualization. We conducted studies with experienced narrative visualization practitioners in both summative and formative settings. We found that the set of heuristics showed promise in a summative setting, where similar responses evidenced that the set of heuristics could provide reliable evaluation metrics. Furthermore, in a formative setting, implementing the set of heuristics was reported to be useful in the design process;however, due to their limited focus, we recommend that it be implemented in conjunction with other evaluation guidelines.

关键词： human-centered computing Information visualization Visualization Visualization application domains Empirical studies in visualization

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Layout-Agnostic human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2025年第1期9卷 1-38页

作者： Thukral, Megha Dhekane, Sourish gunesh Hiremath, Shruthi k. Haresamudram, Harish Ploetz, Thomas Georgia Inst Technol Sch Interact Comp Atlanta GA 30332 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA USA

human activity recognition (HAR) using ambient sensors in smart homes has numerous applications for human healthcare and wellness. However, building general-purpose HAR models that can be deployed to new smart home environments requires a significant amount of annotated sensor data and training overhead. Most smart homes vary significantly in their layouts, i.e., floor plans and the specifics of sensors embedded, resulting in low generalizability of HAR models trained for specific homes. We address this limitation by introducing a novel, layout-agnostic modeling approach for HAR systems in smart homes that utilizes the transferrable representational capacity of natural language descriptions of raw sensor data. To this end, we generate Textual Descriptions Of Sensor Triggers (TDOST) that encapsulate the surrounding trigger conditions and provide cues for underlying activities to the activity recognition models. Leveraging textual embeddings, rather than raw sensor data, we create activity recognition systems that predict standard activities across homes without (re-)training or adaptation to target homes. Through an extensive evaluation, we demonstrate the effectiveness of TDOST-based models in unseen smart homes through experiments on benchmark Orange4Home and CASAS datasets. Furthermore, we conduct a detailed analysis of how the individual components of our approach affect downstream activity recognition performance.

关键词： human-centered computing Ubiquitous and mobile computing Empirical studies in ubiquitous and mobile computing computing methodologies Machine learning

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Efficient Decoding of Affective States from Video-elicited EEG Signals: An Empirical Investigation

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ACM TRANSACTIONS ON MULTIMEDIA computing COMMUNICATIONS AND APPLICATIONS 2024年第10期20卷 1-24页

作者： Latifzadeh, Kayhan Gozalpour, Nima Traver, V. Javier Ruotsalo, Tuukka Kawala-Sterniuk, Aleksandra Leiva, Luis A. Univ Luxembourg Esch Sur Alzette Luxembourg Univ Jaume 1 INIT Castellon de la Plana Spain Univ Copenhagen Copenhagen Denmark LUT Univ Lahti Finland Opole Univ Technol Opole Poland

Affect decoding through brain-computer interfacing (BCI) holds great potential to capture users' feelings and emotional responses via non-invasive electroencephalogram (EEG) sensing. Yet, little research has been conducted to understand efficient decoding when users are exposed to dynamic audiovisual contents. In this regard, we study EEG-based affect decoding from videos in arousal and valence classification tasks, considering the impact of signal length, window size for feature extraction, and frequency bands. We train both classic Machine Learning models (SVMs and k-NNs) and modern Deep Learning models (FCNNs and GTNs). Our results show that: (1) affect can be effectively decoded using less than 1 minute of EEG signal;(2) temporal windows of 6 and 10 seconds provide the best classification performance for classic Machine Learning models but Deep Learning models benefit from much shorter windows of 2 seconds;and (3) any model trained on the Beta band alone achieves similar (sometimes better) performance than when trained on all frequency bands. Taken together, our results indicate that affect decoding can work in more realistic conditions than currently assumed, thus becoming a viable technology for creating better interfaces and user models.

关键词： human-centered computing User models Ambient intelligence computing methodologies Machine learning approaches

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What I Don't Like about You?: A Systematic Review of Impeding Aspects for the Usage of Conversational Agents

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INTERACTING WITH COMPUTERS 2024年第5期36卷 293-312页

作者： Hennekeuser, Darius Vaziri, Daryoush Golchinfar, David Stevens, Gunnar Univ Appl Sci Bonn Rhein Sieg Dept Business Adm Grantham-Allee 20 D-53757 St Augustin Germany Univ Siegen Dept IT Secur Adolf Reichwein Str 2a D-57076 Siegen Germany

The application and use cases for conversational agents (CAs) are versatile. Smart speakers such as Alexa and Google Home are used in smart home environments, digital agents are integrated into car systems and chatbots are increasingly used in customer service processes. However, human-computer interaction researchers identify and investigate a wide-ranging variety of aspects impeding the usage of CAs by end-users. In general, impediments differ depending on use case contexts, user group characteristics and the CA's technological infrastructure. Hence, it is difficult and often ambiguous for designers and developers to generate an appropriate awareness about aspects impeding CA usage. We address this problem, by conducting a systematic review of 65 publications surveying impeding aspects of the usage of CAs.

关键词： human computer interaction (HCI) HCI design and evaluation methods interaction design human-centered computing natural language interfaces

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Image or Information? Examining the Nature and Impact of Visualization Perceptual Classification

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第1期30卷 1030-1040页

作者： Arunkumar, Anjana Padilla, Lace Bae, Gi-Yeul Bryan, Chris Arizona State Univ Tempe AZ 85281 USA Northeastern Univ Boston MA USA

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,

关键词： Data visualization Visualization Task analysis Economics Sports Meteorology Geology Information Visualization human-centered computing Perception & Cognition Takeaways

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Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第11期30卷 7299-7309页

作者： Chen, Jie Yuan, Zexin Xi, Jiaqi Gao, Ziqin Li, Ying Zhu, Xiaoqiang Shi, Yun Stone Guan, Frank Wang, Yimin Guangdong Inst Intelligent Sci & Technol Guangzhou Peoples R China Shanghai Univ Shanghai Peoples R China Singapore Inst Technol Singapore Singapore

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.

关键词： Neurons Image reconstruction Accuracy Manuals Visualization Extended reality Three-dimensional displays Extended Reality Neuron Tracing human-centered computing Eye Tracking

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MagKey: Empowering Wearables with Ballistocardiography-based Key Generation through Magnetic Field Vibration Sensing

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2025年第1期9卷 1-28页

作者： Huang, Junying Guo, Xiuzhen Gu, Chaojie Miao, Yuchen He, Shibo Shu, Yuanchao Chen, Jiming Zhejiang Univ Hangzhou Peoples R China

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.

关键词： human-centered computing Ubiquitous and mobile computing systems and tools Security and privacy Authentication Computer systems organization Embedded and cyber-physical systems

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ARive: Assisting Drivers with In-Car Augmented Reality for Risk Zone Detection

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2025年第1期9卷 1-27页

作者： Wang, Chao Chu, Derck Martens, Marieke Honda Res Insititue EU Offenbach Germany Eindhoven Univ Technol Eindhoven Netherlands

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.

关键词： human-centered computing Mixed / augmented reality

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Tangible interventions for office work well-being: approaches, classification, and design considerations

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BEHAVIOUR & INFORMATION TECHNOLOGY 2024年第10期43卷 2151-2175页

作者： Brombacher, Hans Houben, Steven Vos, Steven Eindhoven Univ Technol Dept Ind Design Eindhoven Netherlands Fontys Univ Appl Sci Eindhoven Netherlands

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.

关键词： human-centered computing human computer interaction (HCI) HCI design and evaluation methods > Office well-being tangible user interfaces work well-being Office environment

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Expressive Talking Avatars

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024年第5期30卷 2538-2548页

作者： Pan, Ye Tan, Shuai Cheng, Shengran Lin, Qunfen Zeng, Zijiao Mitchell, Kenny Shanghai Jiao Tong Univ Shanghai Peoples R China Tencent Games Shenzhen Peoples R China Edinburgh Napier Univ Edinburgh Scotland

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.

关键词： human computer interaction (HCI) HCI design and evaluation methods User studies Computer graphics Graphics systems and interfaces Virtual reality human-centered computing

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