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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.

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.

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.

The article discusses the relationship between sound frequencies, music, and human perception, focusing on the debate surrounding the tuning standard used in music. The paper begins by questioning the metaphysical and religious implications of sound as a creative force. It then delves into the physics of music and human hearing and continues emphasizing the role of frequencies in shaping tonality and timbre. The significance and effects of different tuning standards, particularly comparing 432Hz tuning to the more commonly used 440Hz standard, have also been reviewed. Additionally, the historical context and potential motives behind the adoption of the 440Hz tuning standard, suggesting military and commercial interests, have been investigated. By examining these aspects, the article aims to deepen our understanding of the effects of different musical tunings on human well-being and perception, calling for further research. Ultimately, the article serves as a call to action for further research in the field.

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.

This study aims to assess people’s perceptions regarding AI-generated images compared to those made by humans. This study used quantitative research in the form of a survey to find out how much respondents prefer AI-generated images. The findings indicated that most respondents favoured artificial intelligence (AI)- generated visuals. Age and time spent online were key determinants of this choice. These discoveries have important implications for organisations and people who produce and consume visual content in their jobs. It implies that as people become familiar to them and as technology advances, AI-generated images are likely to gain popularity in the future. It also emphasises how critical it is to be aware of audience preferences and adjust to the evolving state of technology and visual media. In conclusion, this study offers insightful information about the prospective effects of AI-generated images on the creative sectors and the larger society.

Recognizing the semantic categories of low-resolution (LR) aerial photos is an indispensable technique in geoscience and remote sensing. However, it is also a challenging task in practice. In this work, a semi-supervised perceptual feature selection (SPFS) pipeline is proposed for LR aerial photo categorization, focusing on selecting high quality perception-guided visual features. Specifically, by mimicking human vision system, a novel low-rank model is designed to decompose each LR aerial photo into multiple visually or semantically salient foreground regions coupled with the background non-salient regions. This model can: 1) produce the a gaze shifting path (GSP) simulating human gaze behavior; and 2) generate hierarchical deep representation for a GSP. Afterward, a semi-supervised feature selection (FS) is leveraged toward a succinct set of discriminative deep GSP features, wherein only labels of LR aerial photos are required. Based on the selected features, a classifier is trained for visual categorization. Comprehensive experimental results have validated our method’s advantage.

Urban greenery has been shown to impact the quality of life in our urbanizing societies. While greenery is traditionally mapped top-down, alternative computational approaches have emerged for mapping greenery from the street level to mimic human sight. Despite the variety of these novel mapping approaches, it has remained unclear how well they reflect human perception in reality. We compared a range of both novel and traditional mapping methods with the self-reported perception of urban greenery at randomly selected study sites across Helsinki, the capital of Finland. The mapping methods included both image segmentation and point cloud-based methods to capture human perspective as well as traditional approaches taking the top-down perspective, i.e., land cover and remote sensing-based mapping methods. The results suggest that all the methods tested are strongly associated with the human perception of greenery at the street-level. However, mapped greenery values were consistently lower than the perceived values. Our results support the use of semantic image segmentation methods over color segmentation methods for greenery extraction to be closer to human perception. Point cloud-based approaches and top-down methods can be used as alternatives to image segmentation in case data coverage for the latter is limited. The results highlight a further research need for a comprehensive evaluation on how human perspective should be mimicked in different temporal and spatial conditions.

This study investigates listeners’ perceptual responses in audio-visual interactions concerning binaural spatial audio. Audio stimuli are coupled with or without visual cues to the listeners. The subjective test participants are tasked to indicate the direction of the incoming sound while listening to the audio stimulus via loudspeakers or headphones with the head-related transfer function (HRTF) plugin. First, the methodology assumptions and the experimental setup are described to the participants. Then, the results are presented and analysed using statistical methods. The results indicate that the headphone trials showed much higher perceptual ambiguity for the listeners than when the sound is delivered via loudspeakers. The influence of the visual modality dominates the audio-visual evaluation when loudspeaker playback is employed. Moreover, when the visual stimulus is present, the headphone playback pattern of behavior is not always in response to the loudspeaker playback.

The price of a house is affected by both the subjective and objective factors of the street environment in a neighborhood. However, the relationships between these factors and housing prices are not fully understood. Street view imagery (SVI) has recently emerged as a new data source for housing price studies. The SVI contains both objective and subjective information and can be used to extract objective measurements describing the physical environment and subjective measurements depicting human perceptions. Compared to conventional methods, there is consistency between subjective and objective information extracted from SVIs, and the two types of information are acquired from the perspective of the human visual perceptual system. Therefore, using both objective and subjective information extracted from street view images to study their relationship with housing prices has several advantages. In this study, focusing on the city of Suzhou, China, we extracted subjective perception and objective view indices from SVIs and systematically assessed their effects on housing prices. The global ordinary least squares (OLS) regression model and the local geographically weighted regression (GWR) model were used to model the correlations between these measures and housing prices. The OLS reveals that overall objective measures have stronger explanatory power, and built environment factors have a greater impact on housing prices. GWR shows that subjective factors can explain more variance in housing prices on the local scale and that home buyers care more about the subjective perceptions of the neighborhood’s surroundings. The map of the GWR local coefficients demonstrates that the perception indicators have both positive and negative effects on housing prices in different places. In addition, a Monte Carlo test was performed to verify the spatially varying relationships between these measures. Our findings provide important references for urban designers and guide various applications, such as safe neighborhood design and sustainable city planning.

The human spatial perception of urban streets has a high complexity and traditional research methods often focus on access surveys of human perception. Urban streets serve as both a direct conduit for pedestrians’ impressions of a city and a reflection of the spatial quality of that city. Street-view images can provide a large amount of primary data for the image semantic segmentation technique. Deep learning techniques were used in this study to collect the boring, beautiful, depressing, lively, safe, and wealthy perception scores of street spaces based on these images. Then, the spatial pattern of urban street-space quality perception was analyzed by global Moran’s I and GIS hotspot analyses. The findings demonstrate that various urban facilities affect street quality perception in different ways and that the strength of an influencing factor’s influence varies depending on its geographical location. The results of the influencing factors reveal the difference in the degree of influence of positive and negative influencing factors on various perceptions of the visual dimension of pedestrians. The primary contribution of this study is that it reduces the potential bias of a single data source by using multi-dimensional impact analysis to explain the relationship between urban street perception and urban facilities and visual elements. The study’s findings offer direction for high-quality urban development as well as advice for urban planning and enhanced design.

Robotic ankle exoskeletons have the potential to extend human ability, and actuation timing serves as one of the critical parameters in its controller design. While many experiments have investigated the optimal actuation timing values to achieve different objective functions (e.g. minimizing metabolic cost), studies on users’ perception of control parameters are gaining interest as it gives information on people’s comfort, coordination, and trust in using devices, as well as providing foundations on how the sensorimotor system detects the exoskeleton behavior changes. The purpose of this study was to evaluate people’s sensitivity to changes in exoskeleton actuation timing and its associated exoskeleton ankle angle changes during walking. Participants (n =15) with little or no prior experience with ankle exoskeletons were recruited and performed a psychophysical experiment to characterize their just-noticeable difference (JND) thresholds for actuation timing. Participants wore a bilateral active ankle exoskeleton and compared pairs of torque profiles with different actuation timings and low peak torque (0.225 Nm/kg) while walking on the treadmill. The mean timing JND across participants was 2.8±0.6% stride period. Individuals exhibited different sensitivity towards actuation timing, and their associated exoskeleton ankle angle changes also varied. The variance in ankle angle changes might be explained by their differences in ankle stiffness and different ankle torques provided during walking. The results provide insights into how people perceive the changes in exoskeleton control parameters and show individual differences in exoskeleton usage. The actuation timing JND found in this study can also help determine the necessary controller precision.

Many religions use figurative language to convey their teachings on the spiritual journey. Identifying their similarities and differences might deepen the recognition of each core faith and create mutual appreciation. This article compared Serat Jatimurti, a Javanese indigenous spirituality text with the Exodus Homily of Origen, a text from antiquity. This article is a qualitative study to compare their teachings on the stages of the spiritual journey and the obstacles. The finding showed that both Serat Jatimurti and the Exodus Homily of Origen teach that there are several stages of the spiritual journey. It also found out that the main obstacle of the spiritual journey lies in the human inner tendency to rely on self-centred perception that prevents them from recognizing reality and the will of God for them. Both texts differ significantly in their views concerning the human capability to start a spiritual journey. Contribution: This study offers clues on the path to having mutual understanding between people who embrace different spiritual journey concepts and their obstacles. It also gives the voice of indigenous spirituality proper appreciation. Such an understanding might serve as a starting point for a deeper appreciation of each other and to development of further dialogues while deepening one’s recognition of the core of their faith.

Human perception of urban landscape, which signifies to what extent urban landscape is appreciated by local dwellers, informs human-oriented policies that reinforce public participation. Yet, conventional studies on human perception of urban landscape are largely dependent on individual experience, which may restrict the co-production of knowledge that can be operationalized across spatial scales and sectors. In this study, we mapped human perception of urban landscape in Shanghai by leveraging an advanced deep-learning approach and street-view images. Specifically, the ResNet50 model was employed to map four critical perceptions, i.e., security, depression, vitality, and aesthetic, at parcel level. We further explored the relationship between human perception and land-use types. Our results show that highly urbanized area (Puxi district encompassed by the Inner Ring Road) is perceived as more secure and vital, but more depressing. Besides, human perception varies substantially across different land-use types, among which administrative and service land is favored with regard to all the four perception types. This study advances our understanding of urban landscape through the lens of human perception, and provides nuanced insights into steering human settlement towards sustainability by strategically promoting mixed land use.

Comparative experiments have greatly advanced the field of biolinguistics in the 21st century, but so far very little research has focused on human perception of non-human animal vocalizations. Studies with zebra finch (Taeniopygia guttata) songs found that humans cannot perceive the full range of acoustic cues that zebra finches hear in their songs, although it remained unclear how much individual information is lost. Individual heterospecific discrimination by humans has only been shown with rhesus monkey (Macaca mulatta) voices. The present study examined whether human adults could discriminate two individual zebra finches by their songs, using a forced- choice Same-Different Paradigm. Results showed that adults can discriminate two individual zebra finches with high accuracy and without prior training. Discrimination mostly relied on differences in pitch contour, but discrimination was still possible with lower accuracy when pitch contour was removed. Future studies should expand these findings with more diverse non-human animal vocalizations.

The main task of architecture has been and will be to create spaces for the user. Our experiment is based on an interdisciplinary study, combining knowledge from architec-ture, neuroscience, psychology and artificial intelligence. We think that these disciplines can better interpret the users’ perception to architects and designers, because how do we know what users want and how they react to the created matter? This fundamental question underlines the research, designates its direction and goals. The futuristic view and exploring the user is one aspect, complemented by the application of research results to today's reality. Nowadays, in Slovakia (and in the world), urban development is funded by the private sector and is significantly limited by the financial aspect of architecture and development, it is necessary to look for a concept that reduces the importance of finance at the expense of quality for users.

The paper presents a study on the human learning process during the classification of stimuli, defined by motion and color visual cues and their combination. Because the classification dimension and the features that define each category are uncertain, we model the learning curves using Bayesian inference and more precisely the Normalized Conjunctive Consensus rule, and also on the base of the more efficient probabilistic Proportional Conflict Redistribution rule No 5 (pPCR5) defined within Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning. Our goal is to study how these rules succeed to model consistently both: human individual and group behaviour during the learning of the associations between the stimuli and the responses in categorization tasks varying by the amount of relevant stimulus information. The effect of age on this process is also evaluated.

During the process of document recognition in a video stream using a mobile device camera, the image quality of the document varies greatly from frame to frame. Sometimes recognition system is required not only to recognize all the specified attributes of the document, but also to select final document image of the best quality. This is necessary, for example, for archiving or providing various services; in some countries it can be required by law. In this case, recognition system needs to assess the quality of frames in the video stream and choose the “best” frame. In this paper we considered the solution to such a problem where the “best” frame means the presence of all specified attributes in a readable form in the document image. The method was set up on a private dataset, and then tested on documents from the open MIDV-2019 dataset. A practically applicable result was obtained for use in recognition systems.

One of the cities in Indonesia that still performs the city arrangement is Kota Kupang in Nusa Tenggara Timur province. Several of criminal incidents and other crime allegedly occurred in areas that were still dark and there were no public street lighting. Therefore, a study has been carried out on the benefits of public street lighting in supporting population activities in Kota Kupang for six months, starting from January to June 2018. Determination sampling of respondents using purposive sampling technique. Based on this technique, then respondents in this study were 51 respondents scattered in each village at least 1 person. The data used in the research are primary and secondary data. The data obtained were then analyzed descriptively. The results of the research indicate that the implementation of public street lighting programs until 2017 has not given the Kota Kupang Government partisanship to the needs of population regarding public street lighting for daily activities.

This paper explores the entanglement between photography and aerial military operations during the World Wars, showing how, in warfare, the camera became a technology of power serving a dual purpose: 1) It was a weapon used to map the territory as well as to detect and bomb specific targets, and 2) it constituted a powerful propagandistic medium employed to circulate persuading and aesthetically innovative aerial vistas among civilians. The technological and industrial sophistication that was adapted to the modern aerial battlefield required optical and photo-developments. These technical improvements challenged military activity while also reshaping civilians’ perception and conception of the landscape as well as determining new aesthetic canons. At the core of this article there is the notion of training of the eye – understood as the process, which involved both experts and the general public, of assimilating new photographic vistas from the sky. Using mostly the North American and German frames of reference, and interweaving military technology, visual culture, and landscape studies, this paper analyzes production and reception of “the view from above” mainly through mass-market illustrated magazines, such as the American Life and the German Berliner Illustrirte Zeitung. Developed within the military context, the peculiarity of aerial photography became embroiled with the idea of a cold, hunting, distanced and simultaneously penetrating gaze. However, recent scholarship understands the aerial view differently, due to the latest use of aerial photography for environmental science, and with the purpose of raising public awareness on the devastating ecological impact of industrialization and militarization. The contemporary progression from aerial photography to satellite imagery can in fact be interpreted along two directions: the God's-eye view of surveillance and/or the bird’s-eye view of environmental care.

Remote work can have many benefits when set up properly. Nevertheless, the preferences of home workers’ comfort havenot been profoundly studied yet. Therefore, this study aims to understand their accepted Indoor Environmental Quality values in winter based on self-reported comfort. In this regard, 26 households in Catalonia’s Mediterranean region were monitored and surveyed for separated periods of 15 days during from 22 November 2021 to 6 March 2022. Measurements including temperature, temperature, CO2 and Relative Humidity levels were data logged in their most used workspace. Results show that most people work between 18 °C to 20 °C and find those temperatures acceptable. Common spaces used as homeoffices are on average 0.8 °C warmer and there are 470 ppm lower CO2 concentrations. Families with children or teenagers and especially women tend to have a warmer mean operative temperature. The comparison between theoretical thermal comfort models (Fanger and Adaptive) to direct vote perception from a sample of spaces and conditions shows low correlation with real perception having the Adaptive model a better prediction of votes. In general, people report feeling more comfortable at the office, although a high number of participants feel indifferent or reckon that depends on other factors.

This paper investigates and compares people’s subjective impression of an office with a biophilic design and blue lighting. Existing studies have examined their influence on perception separately, but how they compare is unclear. Additionally, only a few studies have used an office setting as a case study. To address this research gap, this study collected people’s ratings and rankings of four simulated interior scenes of a private office using an online survey. The scenes include blue lighting, a biophilic design with daylight and view, a biophilic design with indoor plants, and a non-biophilic baseline with conventional white lighting. A total of 284 complete responses were collected and analyzed using a mixed-effect model. It was found that the two biophilic designs improved people’s perception of the office compared to the base case. The biophilic design with access to daylight and view outperformed the space with indoor plants in all the examined perceptual categories, specifically how the office space was perceived by participants as brighter, more comfortable, and spacious. On the contrary, the space with blue lighting decreased people’s ratings in most perceptual attributes in comparison to the baseline. The negative influence was notably significant in how lively, comfortable, bright, and appealing the space was perceived as being by participants. Subjects’ preference rankings of the four simulated office spaces showed a similar pattern.

It is widely accepted that houses in better-designed neighborhoods are found to enjoy a price premium. Prior studies have mainly examined the impact of macro-level neighborhood attributes (e.g., park accessibility using land use data) on housing prices. More recently, research has investigated the micro-level features using street view imagery (SVI) data, though scholars limited the scope to objective indicators such as the green view index and sky view index. The role of subjectively measured street qualities is less discussed due to the lack of large-scale perception data. To provide better explanations of whether and how the micro-level neighborhood environment affects housing prices, this article introduces a framework to collect designers’ perceptions on five subjective urban design perceptions from pairwise SVI rankings in Shanghai with an online visual survey and further predicted through machine learning (ML) algorithms. We also extracted ten important objective features from the scenes. The predictive power of micro-level neighborhood street perceptions (subjective perceptions and objective features) on housing prices was investigated using the hedonic price model (HPM) through ordinary least squares (OLS) and spatial regression, which considers spatial dependence. The findings prove the significance of the value of perceived qualities of the neighborhoods. It reveals that both objective perceived features and subjective perceptions significantly contribute to housing prices; while the objective features show more collective strengths, individual subjective perceptions have more explanatory power, and we argue that these two measures can complement each other. This study provides an important reference for decision makers when selecting street quality indicators to inform city planning, urban design, and community and housing development plans.

Perceptual constancy refers to the fact that the perceived geometrical and physical characteristics of objects remain constant despite transformations of the objects such as rigid motion. Perceptual constancy is essential in everything we do, like recognition of familiar objects and scenes, planning and executing visual navigation, visuomotor coordination, and many more. Perceptual constancy would not exist without the geometrical and physical permanence of objects: their shape, size, and weight. Formally, perceptual constancy and permanence of objects are invariants, also known in mathematics and physics as symmetries. Symmetries of the Laws of Physics received a central status due to mathematical theorems of Emmy Noether formulated and proved over 100 years ago. These theorems connected symmetries of the physical laws to conservation laws through the least-action principle. We show how Noether's theorem is applied to mirror-symmetrical objects and establishes mental shape representation (perceptual conservation) through the application of a simplicity (least-action) principle. This way, the formalism of Noether's theorem provides a computational explanation of the relation between the physical world and its mental representation.

For two decades, the formalism of quantum mechanics has been successfully used to describe human decision processes, situations of heuristic reasoning, and the contextuality of concepts and their combinations. The phenomenon of ‘categorical perception’ has put us on track to find a possible deeper cause of the presence of this quantum structure in human cognition. Thus, we show that in an archetype of human perception consisting of the reconciliation of a bottom up stimulus with a top down cognitive expectation pattern, there arises the typical warping of categorical perception, where groups of stimuli clump together to form quanta, which move away from each other and lead to a discretization of a dimension. The individual concepts, which are these quanta, can be modeled by a quantum prototype theory with the square of the absolute value of a corresponding Schrödinger wave function as the fuzzy prototype structure, and the superposition of two such wave functions accounts for the interference pattern that occurs when these concepts are combined. Using a simple quantum measurement model, we analyze this archetype of human perception, provide an overview of the experimental evidence base for categorical perception with the phenomenon of warping leading to quantization, and illustrate our analyses with two examples worked out in detail.

In his solution to the problem of sensible knowledge, Leibniz seeks to avoid the extreme positions held by the Cartesians and Hobbes. The conceptual distinctions that Leibniz introduces allow him to overcome difficulties raised by his own earlier definitions of sensus. He is consequently able to reconcile the continuity he introduces between animal and human perceptions with the normative and, at the same time, propositional aspects of perceptual judgments. In this paper I will begin by describing his late conception of both human and animal perception, and I will compare this notion with his early notion of perception and the difficulties it gave rise to; finally, I will show the advantages that his new conception can offer for solving the epistemic problem of perception. La solution leibnizienne au problème de la connaissance sensible cherche à éviter les positions extrêmes que les cartésiens et Hobbes ont proposées. Les distinctions conceptuelles que Leibniz introduit lui permettent de surmonter des difficultés suscitées par ses premières définitions du sensus, et, en conséquence, de concilier les continuités entre les perceptions animale et humaine avec les conditions normatives et, pourtant, propositionnelles du jugement perceptuel. Dans cette étude, je commencerai par la description de cette conception tardive de la perception tant humaine qu’animale, et comparerai cette notion avec la première notion de la perception et les difficultés qu’elle suscite ; finalement, je présenterai les avantages que la nouvelle conception peut offrir pour le problème épistémique de la perception.

An attempt has been made to evaluate seismic hazard in the Kathmandu Valley and its adjoining region. A segment of the Main Himalayan Thrust, which produced the 2015 Gorkha Earthquake is used as an earthquake source. Two rupture models of the source are utilized assigning unequal weight to estimate seismic hazard. The first model considers entire north-south rupture of the source that is similar to the rupture of the 1934 Bihar-Nepal Earthquake (Mw 8.2). The second model considers the northern-half rupture of the source, which is similar to that of the 2015 Gorkha Earthquake (Mw 7.8) and possibly the 1833 Nepal Earthquake (Mw 7.6).Seismic hazard maps are prepared for 500 and 200 year return periods at engineering rock site condition. A comparison is made between the peak ground acceleration (PGA) of Kathmandu and Nijgadh estimated for 200 year return period with the recorded PGA of the 2015 Gorkha Earthquake in Kathmandu (Kirtipur: a rock site) and empirically estimated PGA of that earthquake in Nijgadh respectively. Kathmandu and Nijgadh are selected for the comparison because Kathmandu falls within the rupture area of the 2015 Gorkha Earthquake and Nijgadh is in the Indogangetic Plain and away from the rupture area of that earthquake. The result shows that the PGA (0.23 g) estimated for Kathmandu for 200 year return period closely agrees with the instrumentally recorded PGA of the 2015 Gorkha Earthquake in Kathmandu (0.25 g). Instrumentally recorded PGA is not available in Nijgadh, which is at about 55 km in the south from Kathmandu. A seismic intensity survey was carried out in Nijgadh and its adjoining region to estimate the intensity of the 2015 Gorkha Earthquake in Nijgadh. The probabilistically estimated PGA (0.1 g) for Nijgadh for 200 year return period is in reasonable agreement with that of the 2015 Gorkha Earthquake (0.07 g) estimated from the seismic intensity (VI on MMI).

This article provides an introduction to experimental research on top-down human attention in complex scenes, written for cognitive scientists in general. We emphasize the major effects of goals and intention on mental function, measured with behavioral experiments. We describe top-down attention as an open category of mental actions that initiates particular task sets, which are assembled from a wide range of mental processes. We call this attention-setting. Experiments on visual search, task switching, and temporal attention are described and extended to the important human time scale of seconds.

This paper describes the development of three main functionalities for a domestic mobile service robot and an automatic benchmarking system used for the systematic performance evaluation of the robot’s functionalities. Three main robot functionalities are addressed: (1) People Perception, (2) People Following and (3) Pick and Placing, where the hardware and software systems developed for each functionality are described and demonstrated on an actual mobile service robot, with the goal of providing assistance to an elderly person inside the house. Furthermore, a set of innovative benchmarks and an automatic performance evaluation system are proposed and used to evaluate the performance of the developed functionalities. These benchmarks are now made publicly available and is part of the European Robotics League (ERL)-Consumer to systematically evaluate the performance of service robot solutions at different testbeds around Europe.

In environments shared with humans, Autonomous Mobile Robots (AMRs) should be designed with human-aware motion-planning skills. Even when AMRs can effectively avoid humans, only a handful of studies have evaluated the human perception of mobile robots. To establish appropriate non-verbal communication, robot movement should be legible and should consider the human element. In this paper, a study that evaluates humans’ perceptions of different AMR courtesy behaviors at industrial facilities, particularly at crossing areas, is presented. To evaluate the proposed kinesic courtesy cues, we proposed five tests (four proposed cues—stop, deceleration, retreating, and retreating and moving aside—and one control test) with a set of participants taken two by two. We assessed three different metrics, namely, the participants’ self-reported trust in AMR behavior, the legibility of the courtesy cues in the participants’ opinions, and the behavioral analysis of the participants related to each courtesy cue tested. The retreating courtesy cue, regarding the legibility of the AMR behavior, and the decelerate courtesy cue, regarding the behavioral analysis of the participants’ signs of hesitation, are better perceived from the forward view. The results obtained regarding the participants’ self-reported trust showed no significant differences in the two participant perspectives.

Human sound evaluations not only depend on the characteristics of the sound but are also driven by factors related to the listener and the situation. Our research aimed to investigate crucial factors influencing the perception of low-level sounds as they—in addition to the often-researched loud-level sounds—might be decisive to people’s quality of life and health. We conducted an online study in which 1,301 participants reported on up to three everyday situations in which they perceived low-level sounds, resulting in a total of 2,800 listening situations. Participants rated the sounds’ perceived loudness, timbre, and tonality. Additionally, they described the listening situations employing situational eight dimensions and reported their affective states. All sounds were then assigned to the categories natural, human, and technical. Linear models suggest a significant difference of annoyance ratings across sound categories for binary loudness levels. The ability to mentally fade-out sound was the most crucial situational variable after valence, arousal, and the situation dimensions positivity and negativity. We ultimately selected the most important factors from a large number of independent variables by applying the percentile least absolute shrinkage and selection operator (Lasso) regularization method. The resulting linear regression showed that this novel machine-learning variable-selection technique is applicable in hypothesis testing of noise effects and soundscape research. The typical problems of overfitting and multicollinearity that occur when many situational and personal variables are involved were overcome. This study provides an extensive database of evaluated everyday sounds and listening situations, offering an enormous test power. Our machine learning approach, whose application leads to comprehensive models for the prediction of sound perception, is available for future study designs aiming to model sound perception and evaluation.

This paper explores in parallel the underlying mechanisms in human perception of biological motion and the best approaches for automatic classification of gait. The experiments tested three different learning paradigms, namely, biological, biomimetic, and non-biomimetic models for gender identification from human gait. Psychophysical experiments with twenty-one observers were conducted along with computational experiments without applying any gender specific modifications to the models or the stimuli. Results demonstrate the utilization of a generic memory based learning system in humans for gait perception, thus reducing ambiguity between two opposing learning systems proposed for biological motion perception. Results also support the biomimetic nature of memory based artificial neural networks (ANN) in their ability to emulate biological neural networks, as opposed to non-biomimetic models. In addition, the comparison between biological and computational learning approaches establishes a memory based biomimetic model as the best candidate for a generic artificial gait classifier (83% accuracy, p < 0.001), compared to human observers (66%, p < 0.005) or non-biomimetic models (83%, p < 0.001) while adhering to human-like sensitivity to gender identification, promising potential for application of the model in any given non-gender based gait perception objective with superhuman performance.

Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and the visual characteristics of the streetscape. The image-segmentation model DeepLabv3+ automatically extracts each pixel’s semantic information and classifies visual elements from 120,012 collected panoramic street view images of Zhengzhou, China, using the entropy weighting method and weighted superposition to calculate the street perception summary score. This deep learning approach can successfully describe the semantics of streets and the connection between population density and street perception. The study provides a new quantitative method for urban planning and the development of high-density cities.

In this conceptual article we propose a framework for describing the experience of delightful daylighting in Nordic homes and a method to correlate it with an existing quantitative daylighting assessment. In contrast to earlier research on daylighting, the present work gives priority to developing the methodology for researching the experience of the inhabitant in a real situation and relying the quantitative assessment on an existing method. In this way, we shift the focus of daylight studies from quantitative evaluations towards qualitative descriptions of the human experience of daylight. The framework enables future research that can broaden the way the experience of daylighting is described and to see if the quantitative assessment according to the standard EN 17037:2018 Daylighting in buildings correlates with these descriptions. Firstly, the current state of research on subjective daylight preferences and daylight assessment is reviewed. Secondly, a novel method, the long-term spatial interview, is introduced. The aim of the method is to describe a long-term experience of a spatial phenomenon, in this case, delightful daylighting of Nordic homes, through a phenomenological perspective and enable localisation of the qualitative research results. Finally, the use of the existing EN-standard as a tool to quantitatively describe the daylight situation of spaces is explained and a correlation analysis of the quantitative and qualitative results is explicated. Future research based on the framework can provide useful information for designers aiming at creating delightful daylighting experiences in Nordic homes.

Human thoughts, in addition to the type of education and the factors influencing the material environment, are influenced by the transcendentalism, which affects the spiritual existence of man, and among which these elements are inspirational and inculcated, which, in the language of the verses and traditions of inspiration and indoctrination, From angel and devil to man. Mutahh from the devil that makes man astray, and the thoughts of the angel, which leads man to righteous deeds. The Quran reads the concepts that are instilled into the heart; and what Satan calls into the hearts of men is the word, promise, command, temptation, revelation, and promise. This descriptive and analytical study questions the relation between the influence of Satan and human perceptions on the interpretations of the Quranic verses and, by referring to the interpretive books, discussed the subject and the discussion to answer this question. Many verses in the Holy Quran expressly acknowledge Satan's hostility, or in the sense of the causal agent of evil, and the realm of Satan's seduction and subjugation, human perception, and tools of his work are the emotions, emotions, instincts, desires and human senses that mankind The Quranic verses are mentioned.

The infra-sound spectra recorded inside homes located even several kilometres far from wind turbine installations is characterized by large pressure fluctuation in the low frequency range. There is a significant body of literature suggesting inaudible sounds at low frequency are sensed by humans and affect the wellbeing through different mechanisms. These mechanisms include amplitude modulation of heard sounds, stimulating subconscious pathways, causing endolymphatic hydrops, and possibly potentiating noise-induced hearing loss. We suggest the study of infra-sound active cancellation and mitigation to address the low frequency noise issues. Loudspeakers generate pressure wave components of same amplitude and frequency but opposite phase of the recorded infra sound. They also produce pressure wave components within the audible range reducing the perception of the infra-sound to minimize the sensing of the residual infra sound.

In the current era of a strongly competitive business environment, it is more difficult for companies to attract customers. Consumer neuroscience has growing potential here, as it reveals internal consumer preferences by using innovative methods and tools, which can effectively examine consumer behavior and attract new customers. In particular, smell has a great ability to subconsciously influence customers and, thus, support profitability. This paper examines the importance of consumer neuroscience and its modern technologies used for exploring human perceptions to influence customers and benefit from the aromatization of business spaces. We focused our analysis on various service sectors. Despite the potential of the examined issue, there are a limited number of studies in the field of service providers that use neuroscience tools to examine the effect of aromas on human emotions. Most studies took place in laboratory conditions, and the used methodological procedures varied widely. Our analysis showed that, in spite of the positive impact of aromatization in the majority of aromatized spaces, service companies still do not use the potential of consumer neuroscience and aroma marketing to a sufficient degree. Innovative methods and tools, in particular, are still very underused.

We propose a novel wearable haptic device that can provide kinesthetic haptic feedback for stiffness rendering of virtual objects in augmented reality (AR). Rendering stiffness of objects using haptic feedback is crucial for realistic finger-based object manipulation, yet challenging particularly in AR due to the co-presence of a real hand, haptic device, and rendered AR objects in the scenes. By adopting passive actuation with a tendon-based transmission mechanism, the proposed haptic device can generate kinesthetic feedback strong enough for immersive manipulation and prevention of inter-penetration in a small-form-factor, while maximizing the wearability and minimizing the occlusion in AR usage. A selective locking module is adopted in the device to allow for the rendering of the elasticity of objects. We perform an experimental study of two-finger grasping to verify the efficacy of the proposed haptic device for finger-based manipulation in AR. We also quantitatively compare/articulate the effects of different types of feedbacks across haptic and visual sense (i.e., kinesthetic haptic feedback, vibrotactile haptic feedback, and visuo-haptic feedback) for stiffness rendering of virtual objects in AR for the first time.

Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities have emerged. However, human perception (e.g., imageability) have a subtle relationship to visual elements that cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain human behaviors more. However, the effectiveness of integrating subjective measures with SVI datasets has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected ratings from experts on sample SVIs regarding these four qualities, which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting scores. We found a strong correlation between the predicted complexity score and the density of urban amenities and services points of interest (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five urban cores that are renowned worldwide. Rather than predicting perceptual scores directly from generic image features using a convolution neural network, our approach follows what urban design theory has suggested and confirmed as various streetscape features affecting multi-dimensional human perceptions. Therefore, the results provide more interpretable and actionable implications for policymakers and city planners.

The design of colormaps can help tomography operators obtain accurate visual comprehension, thereby assisting safety-critical decisions. The research presented here is about deploying colormaps that promote the best affective responses for industrial microwave tomography (MWT). To answer the two research questions related to our study, we firstly conducted a quantitative analysis of 11 frequently-used colormaps on a segmentation task. Secondly, we presented the same colormaps within a crowdsourced study comprising two parts to verify the quantitative outcomes. The first part encoded affective responses from participants into a prevailing four-quadrant valence–arousal grid; the second part recorded participant ratings towards the accuracy of each colormap on MWT segmentation. We concluded that three colormaps are the best suited in the context of MWT tasks. We also found that the colormaps triggering emotions in the positive–exciting quadrant can facilitate more accurate visual comprehension than other affect-related quadrants. A synthetic colormap design guideline was consequently proposed.

The illustration of the fundamental, pre-scientific, presuppositions of natural scientific approaches to psychology, psychiatry and the related areas of research has been one of the most significant achievements of Heidegger's collaboration with the Swiss psychiatrist, Medard Boss. The central issue in this regard is the limitations of research based on the natural scientific orientation in understanding human experience. In this paper, different aspects of the Daseinsanalytic critique of this orientation would be discussed with reference to the four basic problems: the problem of natural scientific calculation, the problem of reality, the problem of human perception, and the problem of the conflation between the motive and the cause of human behavior. While thoughtlessness about these problems may result in scientific absolutism, questioning can free researchers from theoretical dogmas and the problematic ideas especially the idea of the full separation of psychology, as an exact science, similar to the natural modern science, from philosophy.

Production and perception experiments were conducted to examine whether focus prosody varies by phrase-initial tones in Seoul Korean. We also trained an automatic classifier to locate prosodic focus within a sentence. Overall, focus prosody in Seoul Korean was weak and confusing in production, and poorly identified in perception. However, Seoul Korean’s focus prosody differed between phrase-initial low and high tones. The low tone group induced a smaller pitch increase by focus than the high tone group. The low tone group was also subject to a greater degree of confusion, although both tone groups showed some degree of confusion spanning the entire phrase as a focus effect. The identification rate was, therefore, approximately half in the low tone group (23.5%) compared to the high tone group (40%). In machine classification, the high tone group was also more accurately identified (high: 86% vs. low: 68%) when trained separately, and the machine’s general performance when the two tone groups were trained together was much superior to the human’s (machine: 65% vs. human: 32%). Although the focus prosody in Seoul Korean was weak and confusing, the identification rate of focus was higher under certain circumstances, which avers that focus prosody can vary within a single language.