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Every day, humans encounter complex social situations that need to be encoded effectively to allow interaction with others. Yet, principles for organizing the perception of social features from the external world remain poorly characterized. In this large-scale study, we investigated the principles of social perception in dynamic scenes. In the primary data set, we presented 234 movie clips (41 min) containing various social situations to 1,140 participants and asked them to evaluate the presence of 138 social features in each clip. Analyses of the social feature ratings revealed that some features are perceived categorically (present or absent) and others continuously (intensity) and simple social features requiring immediate response are perceived most consistently across participants. To establish the low-dimensional perceptual organization for social features based on movies, we used principal coordinate analysis and consensus clustering for the feature ratings. These dimension reduction analyses revealed that the social perceptual structure can be modeled with eight main dimensions and that behaviorally relevant perceptual categories emerge from these main dimensions. This social perceptual structure generalized from the perception of unrelated Hollywood movie clips to the perception of a full Finnish movie (70 min) and to the perception of static images (n = 468) and across three independent sets of participants (n = 2,254). Based on the results, we propose eight basic dimensions of social perception as a model for rapid social perception where social situations are perceived along eight orthogonal perceptual dimensions (most importantly emotional valence, empathy vs. dominance, and cognitive vs. physical behavior). [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as a pivotal force in modern business transformation, garnering widespread attention from both practitioners and academics. With a notable exponential increase in AI‐related studies, we provide a research framework aiming to synthesize the existing literature on AI in the business field. We conduct a comprehensive review of AI research spanning from 2010 to 2023 in 25 leading business journals according to this review framework. Specifically, we review the literature from three research perspectives: (i) AI applications, (ii) human perceptions of AI, and (iii) AI behavior. We also identify five principal research questions and offer suggestions for future research directions. [ABSTRACT FROM AUTHOR]

In Human–Robot Interaction, researchers typically utilize in-person studies to collect subjective perceptions of a robot. In addition, videos of interactions and interactive simulations (where participants control an avatar that interacts with a robot in a virtual world) have been used to quickly collect human feedback at scale. How would human perceptions of robots compare between these methodologies? To investigate this question, we conducted a 2 \({\times}\) 2 between-subjects study (N \({=}\) 160), which evaluated the effect of the interaction environment (Real vs. Simulated environment) and participants' interactivity during human-robot encounters (Interactive participation vs. Video observations) on perceptions about a robot (competence, discomfort, social presentation, and social information processing) for the task of navigating in concert with people. We also studied participants' workload across the experimental conditions. Our results revealed a significant difference in the perceptions of the robot between the real environment and the simulated environment. Furthermore, our results showed differences in human perceptions when people watched a video of an encounter versus taking part in the encounter. Finally, we found that simulated interactions and videos of the simulated encounter resulted in a higher workload than real-world encounters and videos thereof. Our results suggest that findings from video and simulation methodologies may not always translate to real-world human–robot interactions. In order to allow practitioners to leverage learnings from this study and future researchers to expand our knowledge in this area, we provide guidelines for weighing the tradeoffs between different methodologies. [ABSTRACT FROM AUTHOR]

The emotional expression of body movement, which is an aspect of emotional communication between humans, has not been considered enough in the field of human–robot interactions (HRIs). This paper explores human perceptions of the emotional expressions of humanoid robot body movements to study the emotional design of the bodily expressions of robots and the characteristics of the human perception of these emotional body movements. Six categories of emotional behaviors, including happiness, anger, sadness, surprise, fear, and disgust, were designed by imitating human emotional body movements, and they were implemented on a Yanshee robot. A total of 135 participants were recruited for questionnaires and eye-tracking measurements. Statistical methods, including K-means clustering, repeated analysis of variance (ANOVA), Friedman's ANOVA, and Spearman's correlation test, were used to analyze the data. According to the statistical results of emotional categories, intensities, and arousals perceived by humans, a guide to grading the designed robot's bodily expressions of emotion is created. By combining this guide with certain objective analyses, such as fixation and trajectory of eye movements, the characteristics of human perception, including the perceived differences between happiness and negative emotions and the trends of eye movements for different emotional categories, are described. This study not only illustrates subjective and objective evidence that humans can perceive robot bodily expressions of emotions through only vision but also provides helpful guidance for designing appropriate emotional bodily expressions in HRIs. [ABSTRACT FROM AUTHOR]

In the context of rapid advancements in AI, the accuracies and speeds among various AI models and methods are often compared. However, a basic question is rarely asked: is AI better than humans, and if so, under what conditions? This paper investigates human ability to detect distant landmark targets under cluttered surroundings such as buildings, trees, and clouds in NIR and LWIR images, aiming to facilitate AI object detection performance analysis. Our investigation employs perception tests and a human performance model to analyze object detection capabilities. The results reveal distinctive differences in NIR and LWIR detectability, showing that although LWIR performs less effectively at range, it offers superior robustness across various environmental conditions. Our findings suggest that AI could be particularly advantageous for object detection in LWIR as it outperform humans in terms of detection accuracy at a long range. [ABSTRACT FROM AUTHOR]

Interior contour, an essential component of the built environment, has drawn wide attention from home and abroad. From the perspective of neuroaesthetics, preference for architectural interior contour is closely tied to brain activity. Therefore, revealing the underlying neural basis of aesthetic preferences for architectural interior contour in terms of neuroimaging is of great importance. We recruited 30 healthy participants for behavioural assessment and functional magnetic resonance imaging (fMRI) scans. Using percent amplitude of fluctuation (PerAF), the current study measured participants' neuroaesthetic responses to curvilinear and rectilinear architectural interior contours in the three frequency bands. When participants viewed architectural images with curvilinear contours, the PerAF was significantly reduced in multiple brain regions, in comparison to rectilinear architectural interior contours. Moreover, significant negative correlations were observed between ratings of aesthetic judgments of curvilinear space and increased PerAF values in specific regions including the left Rolandic operculum. Aesthetic judgments were significantly correlated with PerAF values in specific brain regions, including the left middle frontal gyrus. Our study reveals that participants have a higher preference for curvilinear contours over rectilinear contours. Neural response varied across different architectural contours, highlighting the complex interplay of neural mechanisms underlying aesthetic perception. [ABSTRACT FROM AUTHOR]

Discovering the Representative places (RPs) of a city will benefit the understanding of local culture and help to improve life experiences. Previous studies have been limited in regard to the large-scale spatial identification of RPs due to the vagueness of boundaries and the lack of appropriate data sources and efficient tools. Furthermore, human perception of these places remains unclear. To address this gap, this research adopts a novel approach to identify and evaluate the RPs of a city from the perspective of human perception. Our methodology involves the utilization of deep learning systems, text semantic analysis, and other techniques to integrate multi-source data, including points of interest (POIs), street view images, and social media data. Taking Nanjing, China, as a case, we identified 192 RPs and their perceptual ranges (PRRPs). The results show the following: (1) Comparing RPs to non-RPs, RPs show higher average scores across four perceptual dimensions (positive indicators): Beautiful (7.11% higher), Lively (34.23% higher), Safety (28.42% higher), and Wealthy (28.26% higher). Conversely, RPs exhibit lower average scores in two perceptual dimensions (negative indicators): Boring (79.04% lower) and Depressing (20.35% lower). (2) Across various perceptual dimensions, RPs have utilized 15.13% of the land area to effectively cover approximately 50% of human perceptual hotspots and cold spots. (3) The RPs exhibit significant variations across different types, levels, and human preferences. These results demonstrate the positive perceived effects that RPs have, providing valuable insights to support urban management, the transformation of the built environment, and the promotion of sustainable urban development, and provide guidance for urban planners and designers to make improvements in urban design and planning to make these sites more attractive. [ABSTRACT FROM AUTHOR]

This study seeks to address the gap in knowledge regarding the varying effects of vegetation on human perception and preference, and to comprehend how green spaces can better serve community needs. The research assessed the impact of different vegetation structures on physiological responses during two types of on-site perceptions: sitting and walking, in both winter and summer. The green spaces included single-layer grassland, single-layer woodland, tree-shrub-grass composite woodlands, and tree-grass composite woodlands, and a non-vegetated square. The findings indicated the following. (1) The physiological recovery effect of walking in green spaces is relatively greater than that of sitting; walking in green spaces with different vegetation types was found to enhance participants' pNN50 values (p = 0). (2) During the summer, sitting and observing provided a better physiological recovery effect (p < 0.05), whereas in the winter, walking was more beneficial (p < 0.05). (3) Green spaces with vegetation were more beneficial for physiological recovery than the non-vegetated square, which could not sustain recovery effects for more than 1 min. Single-layer grassland and tree-shrub-grass composite woodlands had the most significant physiological recovery effects on health (p < 0.01). (4) Based on these conclusions, it is suggested that a combination of sitting and walking can lead to improved recovery outcomes. Therefore, when visiting parks during extreme weather conditions, individuals should adjust the duration of their sitting and walking experiences to enhance their overall experience. [ABSTRACT FROM AUTHOR]

High-quality street spaces have a positive impact on residents’ lives. Revealing the differences in human perception and walkability across street spaces can highlight priority areas for street quality enhancement and provide valuable insights that help urban managers make informed decisions. This study first utilised machine learning models (SegNet) to extract visual elements from street view images and collected volunteers’ ratings of six perceptual dimensions of street scenes. Subsequently, it employed random forest algorithms and spatial autocorrelation models to predict and analyse overall street perception. Furthermore, principal component analysis was used to consolidate the six perceptual scores, coupled with an analysis of street walkability, to identify streets that most required quality enhancement. Finally, correlation and regression analyses were conducted between human perception ratings and street visual elements. The results indicated that buildings positively influence perceptions of wealth, safety, liveliness, and beauty and are the most significant positive perception elements. Conversely, walls were the most significant negative perception elements. Additionally, trees positively impacted perceptions of liveliness and beauty. Lastly, streets categorised as “high walkable-low perception” exhibited a radial dispersion from the city centre. In conclusion, the findings of this study provide valuable insights for enhancing street-space quality in Chengdu and other cities.

Abstract In this paper, we undertake a novel two-pronged investigation into the human recognition of deepfake speech, addressing critical gaps in existing research. First, we pioneer an evaluation of the impact of prior information on deepfake recognition, setting our work apart by simulating real-world attack scenarios where individuals are not informed in advance of deepfake exposure. This approach simulates the unpredictability of real-world deepfake attacks, providing unprecedented insights into human vulnerability under realistic conditions. Second, we introduce a novel metric to evaluate the quality of deepfake audio. This metric facilitates a deeper exploration into how the quality of deepfake speech influences human detection accuracy. By examining both the effect of prior knowledge about deepfakes and the role of deepfake speech quality, our research reveals the importance of these factors, contributes to understanding human vulnerability to deepfakes, and suggests measures to enhance human detection skills.

Material texture significantly influences how people perceive built environments, yet empirical evidence supporting its impact on spaciousness perception is limited. This study aimed to explore how people perceive building materials through sense in the absence of visual attention and how texture meanings affect indoor spaciousness perception. Conducted with 160 participants (90 males, 70 females, mean age ±SD: 22 ± 5 years), the study comprised three phases: baseline tactile assessment, blindfolded navigation in a lab and exposure to realworld environments. Findings revealed the skin's ability to discern textural quality without visual cues, with slightly rough wall surfaces consistently perceived as more spacious. Moreover, the association between wall texture and perceived space varied, with subjective aspects influencing spaciousness in relatively spacious rooms, while objective factors played a larger role in less spacious settings. This study provides insights into sensory referral processes, warranting further investigation into the potential psychological impacts of textures. [ABSTRACT FROM AUTHOR]

In this paper, we undertake a novel two-pronged investigation into the human recognition of deepfake speech, addressing critical gaps in existing research. First, we pioneer an evaluation of the impact of prior information on deepfake recognition, setting our work apart by simulating real-world attack scenarios where individuals are not informed in advance of deepfake exposure. This approach simulates the unpredictability of real-world deepfake attacks, providing unprecedented insights into human vulnerability under realistic conditions. Second, we introduce a novel metric to evaluate the quality of deepfake audio. This metric facilitates a deeper exploration into how the quality of deepfake speech influences human detection accuracy. By examining both the effect of prior knowledge about deepfakes and the role of deepfake speech quality, our research reveals the importance of these factors, contributes to understanding human vulnerability to deepfakes, and suggests measures to enhance human detection skills. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) plays an increasingly integral role in decision-making processes. In order to foster trust in AI predictions, many approaches towards explainable AI (XAI) have been developed and evaluated. Surprisingly, one factor that is essential for trust has been underrepresented in XAI research so far: uncertainty, both with respect to how it is modeled in Machine Learning (ML) and XAI as well as how it is perceived by humans relying on AI assistance. This review paper provides an in-depth analysis of both aspects. We review established and recent methods to account for uncertainty in ML models and XAI approaches and we discuss empirical evidence on how model uncertainty is perceived by human users of XAI systems. We summarize the methodological advancements and limitations of methods and human perception. Finally, we discuss the implications of the current state of the art in model development and research on human perception. We believe highlighting the role of uncertainty in XAI will be helpful to both practitioners and researchers and could ultimately support more responsible use of AI in practical applications.

With the world’s rapid urban development, it has been observed that space for sociability has decreased. To overcome the impact of the same, streets should develop as an urban space along transit corridors. These spaces encourage meaning and identity creation by offering visual quality to everyone. This paper focuses on assessing parameters through which an urban streetscape along transit corridor can be analyzed to enhance visual quality. The authors suggest nine essential parameters based on literature review and expert interviews.

Artificial Intelligence (AI) plays an increasingly integral role in decision-making processes. In order to foster trust in AI predictions, many approaches towards explainable AI (XAI) have been developed and evaluated. Surprisingly, one factor that is essential for trust has been underrepresented in XAI research so far: uncertainty, both with respect to how it is modeled in Machine Learning (ML) and XAI as well as how it is perceived by humans relying on AI assistance. This review paper provides an in-depth analysis of both aspects. We review established and recent methods to account for uncertainty in ML models and XAI approaches and we discuss empirical evidence on how model uncertainty is perceived by human users of XAI systems. We summarize the methodological advancements and limitations of methods and human perception. Finally, we discuss the implications of the current state of the art in model development and research on human perception. We believe highlighting the role of uncertainty in XAI will be helpful to both practitioners and researchers and could ultimately support more responsible use of AI in practical applications. [ABSTRACT FROM AUTHOR]

People spend more than 90% of their time indoors, and, as such, improving indoor lighting quality can enhance their quality of life by positively influencing both physiological and psychological aspects. Numerous studies suggest that perceptions of a space vary depending on a number of lighting attributes present. Significant effort has been made across various fields to identify the spatial lighting conditions and attributes that impact human perception, although we lack studies that explore the impact of these conditions in tandem. This paper investigates how interior lighting conditions influence human impressions of room ambiance. The study examines 16 different interior scenes, considering factors such as sky condition (sunny vs. overcast), shading blinds position (open vs. closed), presence or absence of electric light (on vs. off), and Correlated Color Temperature (CCT) (2700 K, 4000 K, and 6500 K). The evaluation is conducted within an office environment at Carnegie Mellon University, using a combination of objective lighting metrics and subjective assessments. In total, 26 participants, consisting of 11 females, 14 males, and one undisclosed, aged between 18 and 50, evaluated the office ambiance under various lighting conditions using semantic differential scales. The analysis showed that the variation of blinds and CCT levels significantly influenced the participants' impression of light. The study also identified statistically significant interactions between "blinds and CCT" and "blinds and sky" conditions, highlighting the combined influence of these variables on shaping indoor light impressions. This research offers valuable insights into the complex interplay of different lighting factors in shaping human perceptions, and underscores the importance of optimizing indoor lighting conditions for creating healthy and sustainable indoor environments. [ABSTRACT FROM AUTHOR]

With the world's rapid urban development, it has been observed that space for sociability has decreased. To overcome the impact of the same, streets should develop as an urban space along transit corridors. These spaces encourage meaning and identity creation by offering visual quality to everyone. This paper focuses on assessing parameters through which an urban streetscape along transit corridor can be analyzed to enhance visual quality. The authors suggest nine essential parameters based on literature review and expert interviews. [ABSTRACT FROM AUTHOR]

Coloration in wildlife serves numerous biological purposes, including sexual selection signaling, thermoregulation, and camouflage. However, the physical appearance of wildlife also influences the ways in which humans interact with them. Wildlife conservation has largely revolved around humans' propensity to favor charismatic megafauna, but human perceptions of wildlife species extend beyond conservation measures into our everyday interactions with individual wildlife. Our aesthetic appreciation for different species interplays with culture, lore, and the economic interest they carry. As such, one characteristic that may underpin and interact with social drivers of perception is the coloration of a particular individual. We provide case studies illustrating the dynamism in interactions people have with conspicuously colored wildlife – i.e., individuals that vary from their species-typical coloration. We focus on melanism, leucism, and albinism across four species commonly thought of as pests in the United States: coyotes (Canis latrans), eastern gray squirrels (Sciurus carolinensis), white-tailed deer (Odocoileus virginianus), and black-tailed deer (O. hemionus). [ABSTRACT FROM AUTHOR]

We identify a list of characteristics of a text document that we use as the basis for a study on human perception of what the structure of a document should look like. Our study reveals that the native language dimension has a significant impact on the human perception of some such characteristics, including the average number of paragraphs starting with a verb, as well as other characteristics describing average paragraph proportions. The study results also show what the mean values are, for the different document characteristics, and their distribution across the native language dimension, therefore providing some idea of what a normalised structure of a document should look like. The results of the study have a direct application in a new method for embedding secret messages in text documents that has been recently proposed by the authors and which uses manipulations in the paragraph layout of a document. [ABSTRACT FROM AUTHOR]

IntroductionUrban green space (GS) exposure is recognized as a nature-based strategy for addressing urban challenges. However, the stress relieving effects and mechanisms of GS exposure are yet to be fully explored. The development of machine learning and street view images offers a method for large-scale measurement and precise empirical analysis.MethodsThis study focuses on the central area of Shanghai, examining the complex effects of GS exposure on psychological stress perception. By constructing a multidimensional psychological stress perception scale and integrating machine learning algorithms with extensive street view images data, we successfully developed a framework for measuring urban stress perception. Using the scores from the psychological stress perception scale provided by volunteers as labeled data, we predicted the psychological stress perception in Shanghai's central urban area through the Support Vector Machine (SVM) algorithm. Additionally, this study employed the interpretable machine learning model eXtreme Gradient Boosting (XGBoost) algorithm to reveal the nonlinear relationship between GS exposure and residents' psychological stress.ResultsResults indicate that the GS exposure in central Shanghai is generally low, with significant spatial heterogeneity. GS exposure has a positive impact on reducing residents' psychological stress. However, this effect has a threshold; when GS exposure exceeds 0.35, its impact on stress perception gradually diminishes.DiscussionWe recommend combining the threshold of stress perception with GS exposure to identify urban spaces, thereby guiding precise strategies for enhancing GS. This research not only demonstrates the complex mitigating effect of GS exposure on psychological stress perception but also emphasizes the importance of considering the “dose-effect” of it in urban planning and construction. Based on open-source data, the framework and methods developed in this study have the potential to be applied in different urban environments, thus providing more comprehensive support for future urban planning.

Material texture significantly influences how people perceive built environments, yet empirical evidence supporting its impact on spaciousness perception is limited. This study aimed to explore how people perceive building materials through sense in the absence of visual attention and how texture meanings affect indoor spaciousness perception. Conducted with 160 participants (90 males, 70 females, mean age ±SD: 22 ± 5 years), the study comprised three phases: baseline tactile assessment, blindfolded navigation in a lab and exposure to real-world environments. Findings revealed the skin’s ability to discern textural quality without visual cues, with slightly rough wall surfaces consistently perceived as more spacious. Moreover, the association between wall texture and perceived space varied, with subjective aspects influencing spaciousness in relatively spacious rooms, while objective factors played a larger role in less spacious settings. This study provides insights into sensory referral processes, warranting further investigation into the potential psychological impacts of textures.

Hundreds of satellites orbiting at various altitudes capture an extensive array of aerial photographs daily. High-altitude satellites typically acquire low-resolution (LR) images that cover vast areas, whereas their low-altitude counterparts obtain high-resolution (HR) images detailing much smaller regions. The accurate interpretation of LR aerial imagery is crucial in the field of computer vision, yet it presents significant challenges, including the complexity of emulating human hierarchical visual perception and the daunting task of annotating enough data for effective training. To address these challenges, we introduce a cross-resolution perceptual knowledge propagation (CPKP) framework, which aims to leverage the visual perceptual insights gained from HR aerial imagery to enhance the categorization of LR images. This approach involves a novel low-rank model that segments each LR aerial photo into distinct visually and semantically significant foreground regions, alongside less pertinent background areas. This model is capable of generating a gaze shifting path (GSP) that reflects human gaze patterns and formulating a deep feature for each GSP. Subsequently, a kernel-induced feature selection algorithm is deployed to extract a concise yet powerful set of deep GSP features that are effective across both LR and HR aerial images. Utilizing these features, a linear classifier is collaboratively trained using labels from both LR and HR images, facilitating the categorization of LR images. Notably, the CPKP framework enhances the efficiency of training the linear classifier, given that HR photo labels are more readily available. Our comprehensive visualizations and comparative analysis underscore the effectiveness and superiority of this innovative approach.

Face privacy concerns revolve around the ethical, social, and technological implications of collecting, storing, and using facial data. With the advancement of deep learning techniques, realistic face privacy involves techniques that obscure or alter facial features effectively without compromising the usability or quality of the visual content. Modern face privacy techniques suffer from three main problems: 1) lack of human perception, 2) indistinguishability, and 3) loss of facial attributes. Modern face privacy techniques generate random, realistic faces to conceal the identifiable features of the original faces but lack the application of human perception to face de-identification. Indistinguishability arises with the highly realistic nature of fake faces used in face privacy, making it difficult to distinguish whether a face has been manipulated. Most face-privacy methods also fails to retain the facial attributes of the de-identified faces. Our face de-identification method is designed to address all three issues mentioned. We propose a novel face de-identification method that considers both human perception and face recognition models when de-identifying a face. We explore the tradeoff between a user misidentifying the original identity with a well-known celebrity and a facial recognition model that tries to identify the original identity. We generate caricature faces of the de-identified faces to ensure our manipulated faces can be distinguished effortlessly. The face caricatures are the exaggeration of the eyes and mouth region, and we provide different exaggeration scales depending on preference and application. We perform an attribute preservation optimization process to retrieve all the facial attributes. We demonstrate our method through a series of both qualitative and quantitative experiments with numerous user studies.

The emotional expression of body movement, which is an aspect of emotional communication between humans, has not been considered enough in the field of human–robot interactions (HRIs). This paper explores human perceptions of the emotional expressions of humanoid robot body movements to study the emotional design of the bodily expressions of robots and the characteristics of the human perception of these emotional body movements. Six categories of emotional behaviors, including happiness, anger, sadness, surprise, fear, and disgust, were designed by imitating human emotional body movements, and they were implemented on a Yanshee robot. A total of 135 participants were recruited for questionnaires and eye-tracking measurements. Statistical methods, including K-means clustering, repeated analysis of variance (ANOVA), Friedman’s ANOVA, and Spearman’s correlation test, were used to analyze the data. According to the statistical results of emotional categories, intensities, and arousals perceived by humans, a guide to grading the designed robot’s bodily expressions of emotion is created. By combining this guide with certain objective analyses, such as fixation and trajectory of eye movements, the characteristics of human perception, including the perceived differences between happiness and negative emotions and the trends of eye movements for different emotional categories, are described. This study not only illustrates subjective and objective evidence that humans can perceive robot bodily expressions of emotions through only vision but also provides helpful guidance for designing appropriate emotional bodily expressions in HRIs.

In the context of rapid advancements in AI, the accuracies and speeds among various AI models and methods are often compared. However, a basic question is rarely asked: is AI better than humans, and if so, under what conditions? This paper investigates human ability to detect distant landmark targets under cluttered surroundings such as buildings, trees, and clouds in NIR and LWIR images, aiming to facilitate AI object detection performance analysis. Our investigation employs perception tests and a human performance model to analyze object detection capabilities. The results reveal distinctive differences in NIR and LWIR detectability, showing that although LWIR performs less effectively at range, it offers superior robustness across various environmental conditions. Our findings suggest that AI could be particularly advantageous for object detection in LWIR as it outperform humans in terms of detection accuracy at a long range.

Anticipating a future scenario where the robot initiates its own actions and behaves voluntarily when collaborating with humans, our research focuses on human understanding and perception of unanticipated robot actions during physical human-robot interaction. While the current literature searches for key factors that make the human-robot collaboration successful, the question of how people experience the robot's unanticipated action as cooperative or uncooperative seems to remain open. We designed a game-based experiment (N = 35) where the participant played a "catch-falling-coins" game by moving a robotic arm. Our experiment introduced unanticipated robot actions in an "active session" where the robot targeted higher-valued coins without first informing the participants. Through semi-structured interviews and statistical analysis of questionnaires (Big Five Personality Test, SAM, NARS and CH33), we examined the participants' understanding of the robot's "intention" and their positive or negative perception of the robot as cooperative or uncooperative. Among the participants who understood that the robot's "intention" was to catch the higher-valued coins, the majority of them reported a positive perception of the robot (cooperative or helpful) while this was not the case among those who did not understand the robot's intention. We also observed relevant relationships between some personality traits and a person's understanding of the robot's intention. Qualitative analysis of the interviews allowed us to structure the process of perception change during the game into three phases: confusion, investigation, and adaptation. We believe that our research contributes to the study of human perception, and particularly to the relationship between a human's understanding of unanticipated robot actions and their positive or negative perception of the robot. [ABSTRACT FROM AUTHOR]

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Environmental noise knows no boundaries, affecting even protected areas. Noise pollution, originating from both external and internal sources, imposes costs on these areas. It is associated with adverse health effects, while natural sounds contribute to cognitive and emotional improvements as ecosystem services. When it comes to parks, individual visitors hold unique perceptions of soundscapes, which can be shaped by various factors such as their motivations for visiting, personal norms, attitudes towards specific sounds, and expectations. In this study, we utilized linear models and geospatial data to evaluate how visitors' personal norms and attitudes, the park's acoustic environment, visitor counts, and the acoustic environment of visitors' neighborhoods influenced their perception of soundscapes at Muir Woods National Monument. Our findings indicate that visitors' subjective experiences had a greater impact on their perception of the park's soundscape compared to purely acoustic factors like sound level of the park itself. Specifically, we found that motivations to hear natural sounds, interference caused by noise, sensitivity to noise, and the sound levels of visitors' home neighborhoods influenced visitors' perception of the park's soundscape. Understanding how personal factors shape visitors' soundscape perception can assist urban and non-urban park planners in effectively managing visitor experiences and expectations. [ABSTRACT FROM AUTHOR]

Environmental noise knows no boundaries, affecting even protected areas. Noise pollution, originating from both external and internal sources, imposes costs on these areas. It is associated with adverse health effects, while natural sounds contribute to cognitive and emotional improvements as ecosystem services. When it comes to parks, individual visitors hold unique perceptions of soundscapes, which can be shaped by various factors such as their motivations for visiting, personal norms, attitudes towards specific sounds, and expectations. In this study, we utilized linear models and geospatial data to evaluate how visitors’ personal norms and attitudes, the park’s acoustic environment, visitor counts, and the acoustic environment of visitors’ neighborhoods influenced their perception of soundscapes at Muir Woods National Monument. Our findings indicate that visitors’ subjective experiences had a greater impact on their perception of the park’s soundscape compared to purely acoustic factors like sound level of the park itself. Specifically, we found that motivations to hear natural sounds, interference caused by noise, sensitivity to noise, and the sound levels of visitors’ home neighborhoods influenced visitors’ perception of the park’s soundscape. Understanding how personal factors shape visitors’ soundscape perception can assist urban and non-urban park planners in effectively managing visitor experiences and expectations.

Human pose estimation is a fundamental and appealing task in computer vision. Although traditional cameras are commonly applied, their reliability decreases in scenarios under high dynamic range or heavy motion blur, where event cameras offer a robust solution. Predominant event-based methods accumulate events into frames, ignoring the asynchronous and high temporal resolution that is crucial for distinguishing distinct actions. To address this issue and to unlock the 3D potential of event information, we introduce two 3D event representations: the Rasterized Event Point Cloud (RasEPC) and the Decoupled Event Voxel (DEV). The RasEPC aggregates events within concise temporal slices at identical positions, preserving their 3D attributes along with statistical information, thereby significantly reducing memory and computational demands. Meanwhile, the DEV representation discretizes events into voxels and projects them across three orthogonal planes, utilizing decoupled event attention to retrieve 3D cues from the 2D planes. Furthermore, we develop and release EV-3DPW, a synthetic event-based dataset crafted to facilitate training and quantitative analysis in outdoor scenes. Our methods are tested on the DHP19 public dataset, MMHPSD dataset, and our EV-3DPW dataset, with further qualitative validation via a derived driving scene dataset EV-JAAD and an outdoor collection vehicle. Our code and dataset have been made publicly available at https://github.com/MasterHow/EventPointPose. • Explore event-based HPE using 3D event representation instead of 2D event frames. • Demonstrate feasibility of event point clouds with LiDAR backbones for HPE. • An integrated 2D CNN with Decoupled Event Attention Module for HPE is proposed. • A synthetic outdoor EV-3DPW dataset is generated as a new challenging benchmark. • Both proposed representations show strong generalization in unseen scenarios. [ABSTRACT FROM AUTHOR]

Machine vision-based food quality evaluation systems have achieved great attention in industry and academia in recent years because of their high-throughput and non-invasive properties. In practice, the environmental illumination conditions variations would cause visual evaluation bias for both human and machine perceptions. Compared to other existing studies which take sample images and human visual grading under fixed illumination conditions, this study first investigated the environmental illumination effects on the performance of visual-based food grading for both humans and machines. Taking lettuce samples as an example, an image dataset that considered the environmental illumination variations was first established. This dataset encompasses human visual grading scores obtained from sensory panels as well. In contrast to current studies that utilize a single grader or L*a*b* color features to assess sample freshness, our study reveals a substantial impact of environmental illumination on both human perception (p < 0. 0001) and L*a*b* color features (p < 0. 0001). In order to enhance the performance of machine vision-based freshness prediction, multitask learning protocols were incorporated into the proposed new network architectures. This allowed the simultaneous prediction of both sample freshness and illumination conditions. In comparison to commonly used generic convolutional neural network models and vision transformer models, the newly proposed model exhibited superior freshness prediction performance. It minimized the prediction error by 20.36%, outperforming the generic ResNet model. This research represents the first quantitative study addressing human and machine perceptions under varied illumination conditions for food quality evaluation. The findings are anticipated to play a pivotal role in expediting the integration of machine vision applications into food engineering practices. [Display omitted] • Built the first food quality evaluation dataset for illumination robustness studies. • Firstly evaluate human and machine perception robustness to different illumination. • Firstly benchmark off-the-shelf vision models for food freshness predictions. • Introduce auxiliary illumination predictions to improve the model performances. [ABSTRACT FROM AUTHOR]

Virtual Humans (VHs) emerged over 50 years ago and have since experienced notable advancements. Initially, developing and animating VHs posed significant challenges. However, modern technology, both commercially available and freely accessible, has democratized the creation and animation processes, making them more accessible to users, programmers, and designers. These advancements have led to the replication of authentic traits and behaviors of real actors in VHs, resulting in visually convincing and behaviorally lifelike characters. As a consequence, many research areas arise as functional VH technologies. This paper explored the evolution of four areas and emerging trends related to VHs while examining some of the implications and challenges posed by highly realistic characters within these domains. [Display omitted] • We present a detailed foundation and history of Virtual Humans. • We conducted a literature review on four relevant areas for Virtual Humans • Areas are: Perception; Facial Transferring; Conversational Agents; and Crowd Simulation. • We discuss the main challenges still present in the four mentioned areas. [ABSTRACT FROM AUTHOR]