Search Results (32)

Understanding how people perceive urban streetscapes is essential for enhancing the visual quality of the urban environment and optimizing street space design. While perceptions are shaped by the interplay of multiple visual elements, existing studies often isolate single semantic features, overlooking their combinations. This study proposes a Landscape Element Combination Extraction Method (SLECEM), which integrates the UniSal saliency detection model and semantic segmentation to identify landscape combinations that play a dominant role in human perceptions of streetscapes. Using street view images (SVIs) from the central area of Futian District, Shenzhen, China, we further construct a multi-dimensional feature–perception coupling analysis framework. The key findings are as follows: 1. Both low-level visual features (e.g., color, contrast, fractal dimension) and high-level semantic features (e.g., tree, sky, and building proportions) significantly influence streetscape perceptions, with strong nonlinear effects from the latter. 2. K-Means clustering of salient landscape element combinations reveals six distinct streetscape types and perception patterns. 3. Combinations of landscape features better reflect holistic human perception than single variables. 4. Tailored urban design strategies are proposed for different streetscape perception goals (e.g., beauty, safety, and liveliness). Overall, this study deepens the understanding of streetscape perception mechanisms and proposes a highly operational quantitative framework, offering systematic theoretical guidance and methodological tools to enhance the responsiveness and sustainability of urban streetscapes. Full article

Urban noise pollution significantly degrades people’s health and well-being and, furthermore, traditional noise reduction strategies often overlook individual perception differences. This study proposed to explore the role of eco-acoustic indices in capturing the interplay between biophony, geophony, and anthrophony, and their relationship with classical acoustic metrics and the perceived soundscapes within a University Campus (University of “Mila-no-Bicocca”, Italy). The study area is divided in to eight different sites in “Piazza della Scienza” square. Sound measurements and surveys conducted in June 2023 across four paved sites and adjacent courtyards involved 398 participants (51.7% female, 45.6% male, 2.7% other). The main noise sources included road traffic, technical installations, and human activity, where traffic noise was more prominent at street-level sites (Sites 1–4) and technical installations dominated underground courtyards (6–8). Human activity was most noticeable at Sites 4–8, especially at Site 5, which showed the highest activity levels. A circumplex model revealed that street-level sites were less pleasant and eventful than courtyards. Pairwise comparisons of noise variability showed significant differences among sites, with underground locations offering quieter environments. Eco-acoustic analysis identified two site groups: one linked to noisiness and spectral features, the other to intensity distribution metrics. Technical installations, people, and traffic noises showed distinct correlations with acoustic indices, influencing emotional responses like stimulation and liveliness. These findings emphasize the need to integrate subjective perceptions with objective noise metrics in soundscape descriptions. Full article

Human–robot interaction (HRI) is a key technology in the field of humanoid robotics, and motion imitation is one of the most direct ways to achieve efficient HRI. However, due to significant differences in structure, range of motion, and joint torques between the human body and robots, motion imitation remains a challenging task. Traditional retargeting algorithms, while effective in mapping human motion to robots, typically either ensure similarity in arm configuration (joint space-based) or focus solely on tracking the end-effector position (Cartesian space-based). This creates a conflict between the liveliness and accuracy of robot motion. To address this issue, this paper proposes an improved retargeting algorithm that ensures both the similarity of the robot’s arm configuration to that of the human body and accurate end-effector position tracking. Additionally, a multi-person pose estimation algorithm is introduced, enabling real-time capture of multiple imitators’ movements using a single RGB-D camera. The captured motion data are used as input to the improved retargeting algorithm, enabling multi-robot collaboration tasks. Experimental results demonstrate that the proposed algorithm effectively ensures consistency in arm configuration and precise end-effector position tracking. Furthermore, the collaborative experiments validate the generalizability of the improved retargeting algorithm and the superior real-time performance of the multi-person pose estimation algorithm. Full article

Gazing toward humans is fundamental in dog–human communication. This study aimed to characterize companion dogs based on their gazing behavior and to explore the potential links between gazing, personality (liveliness, confidence, aggressiveness, and attachment), emotional comfort provided to the owners, and breed. We observed 171 dogs in an ambiguous situation involving a moving stimulus, a remote-controlled toy car and gathered additional data with questionnaires. Our behavior analysis, based on gaze frequency directed at the owner and experimenter, identified four distinct clusters of dogs: low gazers, experimenter-focused gazers, owner-focused gazers, and frequent gazers. We found that experimenter-focused gazers exhibited lower levels of liveliness than frequent gazers and were reported to be less aggressive than low gazers and frequent gazers. Owner-focused gazers provided more emotional comfort to their owners than low gazers. Regarding breeds, 56.5% of German shepherd dogs were low gazers, compared to only 5.3% of golden retrievers. Age, sex, neutered status, and approaching the stimulus were not associated with cluster membership. These results highlight the complexity of dog–human communication, suggesting that personality, emotional factors, and breed shape how dogs seek guidance and support from humans or express anxiety. Full article

This article seeks to introduce a novel perspective on understanding Laozi’s Dao as the “Energy God” by engaging with existing philosophical and religious frameworks. Conceptualizing Laozi’s Dao within a philosophical framework fails to capture the fluidity, simplicity, liveliness, and directness inherent in the Daodejing. Conversely, the personification and deification of Laozi within the religious framework do not sufficiently address the formlessness and namelessness of the Dao. Retaining its original characteristics, Laozi’s Dao transcends both philosophical conceptualization and religious deification. Inspired by the term “Energy God”, two fundamental attributes of Laozi’s Dao emerge. First, Laozi’s Dao reflects a specific value judgment that consistently regards the lowly and the simple as virtuous, while perceiving the high and complex as detrimental. Second, as the Energy God, Laozi’s Dao possesses a profound power that animates and propels all things, yet resists being reduced to a mere tool. In its role as the Energy God, the Dao eludes conceptualization and objectification; it is fundamentally omnipresent throughout the universe. Full article

As the decline of public transportation in rural areas becomes a growing concern, initiatives to introduce attractive next-generation transportation systems to promote public transportation usage are being considered across various regions. In Toyama City, Toyama Prefecture, where the next-generation light rail transit (LRT) system has been introduced, the number of users has significantly increased compared to before its introduction, with some users riding the LRT for the sake of the experience itself. On the other hand, there is a demand for a more micro-level and quantitative evaluation of the impact that the LRT has on the liveliness of areas along its route. Therefore, this study uses inverse reinforcement learning (IRL), a type of machine learning, to build a model that estimates route-choice behavior along the LRT lines based on behavioral trajectories generated from smartphone location data. The model is capable of evaluating the characteristics of location data with high accuracy. The findings indicate that routes along the LRT lines tend to be selected, suggesting that both the appeal of the LRT itself and the attractiveness of the spaces along its route contribute to this tendency. Full article

This paper investigates the relationship between public spaces and quality of life (QoL) for older adults, aiming to identify knowledge gaps within the context of population ageing and urbanisation. Recognising the growing importance of sustainable urban development, the research explores how cities can foster active ageing and improve QoL through accessible and inclusive public spaces. A scoping literature review identifies five key QoL domains for older adults: autonomy, meaningful activities, positive social relationships, leisure opportunities, and health. To identify knowledge gaps, a review of the literature was conducted for each of the highlighted themes. The review highlights areas requiring further investigation, including the interplay between environmental design and social connections, intergenerational perspectives on public space liveliness, the influence of context on QoL and the built environment, and the value of qualitative research in this field. By contributing to the understanding of QoL in relation to public spaces through the lens of person–environment fit theory, this study aims to inform urban design, landscape architecture, and policymaking in the creation of age-friendly communities with inclusive public spaces. Full article

Haptic feedback holds the potential to enhance the engagement and expressivity of future digital and electric musical instruments. This study investigates the impact of artificial vibration on the perceived quality of a silent electric cello. We developed a haptic cello prototype capable of rendering vibration signals of varying degree of congruence with the produced sound. Experienced cellists participated in an experiment comparing setups with and without vibrotactile feedback, rating them on preference, perceived power, liveliness, and feel. Results show nuanced effects, with added vibrations moderately enhancing feel and liveliness, and significantly increasing perceived power when using vibrations obtained from the pickup at the cello’s bridge. High uncertainty in our statistical model parameters underscores substantial individual differences in the participants responses, as commonly found in qualitative assessments, and highlights the importance of consistent feedback in the vibrotactile and auditory channels. Our findings contribute valuable insights to the intersection of haptics and music technology, paving the way for creating richer and more engaging experiences with future musical instruments. Full article

With the continuous acceleration of urbanization, urban planning and design require more in-depth research and development. Street view images can express rich urban features and guide residents’ emotions toward a city, thereby providing the most intuitive reflection of their perception of the city’s spatial quality. However, current researchers mainly conduct research on urban spatial quality through subjective experiential judgment, which includes problems such as a high cost and a low judgment accuracy. In response to these problems, this study proposes a multi-task learning urban spatial attribute perception model that integrates an attention mechanism. Via this model, the existing attributes of urban street scenes are analyzed. Then, the model is improved by introducing semantic segmentation and instance segmentation to identify and match the qualities of the urban space. The experimental results show that the multi-task learning urban spatial attribute perception model with an integrated attention mechanism has prediction accuracies of 79.54%, 78.62%, 79.68%, 77.42%, 78.45%, and 76.98% for the urban spatial attributes of beauty, boredom, depression, liveliness, safety, and richness, respectively. The accuracy of the multi-task learning urban spatial scene feature image segmentation model with an integrated attention mechanism is 95.4, 94.8, 96.2, 92.1, and 96.7 for roads, walls, sky, vehicles, and buildings, respectively. The multi-task learning urban spatial scene feature image segmentation model with an integrated attention mechanism has a higher recognition accuracy for urban spatial buildings than other models. These research results indicate the model’s effectiveness in matching urban spatial quality with public perception. Full article

The unity of the Persons of the Trinity is the source and highest exemplar of Christian unity which all ecumenical initiatives seek. During his earthly ministry, our Lord Jesus Christ prayed for the unity of Christians (John 17:21). This prayer of Jesus furnishes us with the fundamental inspiration for the ecumenical movement in all its dimensions of expression. Right from the beginning, the Christian church has experienced rifts in both the West and the East. The modern ecumenical movement is an attempt to restore Christian unity against the background of 16th century divisions attendant upon the Protestant Reformation. It is also conducted in hindsight of the Protestant Reformation that the 19th and 20th century missionary activities of Christian churches from Europe in Nigeria took place. Several historical factors have shaped the successes and failures of significant missionary endeavors. This article highlights a few of those historical factors in a bid to identify the roots of discord and footprints of ecumenism at the beginning of the establishment of Christian churches in Nigeria. The goal intended to be realized through this historical excursus is to engender a livelier ecumenical hope for Christian unity in Nigeria while maintaining the already-achieved progress made so far, as well as countering further divisions. Full article

We present a synthetic augmentation approach towards improving monocular face presentation–attack–detection (PAD) robustness to real-world noise additions. Face PAD algorithms secure authentication systems against spoofing attacks, such as pictures, videos, and 2D-inspired masks. Best-in-class PAD methods typically use 3D imagery, but these can be expensive. To reduce application cost, there is a growing field investigating monocular algorithms that detect facial artifacts. These approaches work well in laboratory conditions, but can be sensitive to the imaging environment (e.g., sensor noise, dynamic lighting, etc.). The ideal solution for noise robustness is training under all expected conditions; however, this is time consuming and expensive. Instead, we propose that physics-informed noise-augmentations can pragmatically achieve robustness. Our toolbox contains twelve sensor and lighting effect generators. We demonstrate that our toolbox generates more robust PAD features than popular augmentation methods in noisy test-evaluations. We also observe that the toolbox improves accuracy on clean test data, suggesting that it inherently helps discern spoof artifacts from imaging artifacts. We validate this hypothesis through an ablation study, where we remove liveliness pairs (e.g., live or spoof imagery only for participants) to identify how much real data can be replaced with synthetic augmentations. We demonstrate that using these noise augmentations allows us to achieve better test accuracy while only requiring 30% of participants to be fully imaged under all conditions. These findings indicate that synthetic noise augmentations are a great way to improve PAD, addressing noise robustness while simplifying data collection. Full article

This article reviews historically significant phenomenological studies of visual mental imagery (VMI), starting with Fechner in 1860 and continuing to the present. This synthesis of diverse VMI phenomenological studies in healthy adults serves as a unique resource for investigators of individual differences, cognitive development and clinical and neurological conditions. The review focuses on two kinds of VMI, “memory imagery” and “eidetic imagery”. Ten primary studies are drawn from three periods of the scholarly literature: early (1860–1929), middle (1930–1999) and recent (2000–2023). It is concluded that memory and eidetic imagery are two forms of constructive imagery, varying along a continuum of intensity or vividness. Vividness is a combination of clarity, colourfulness and liveliness, where clarity is defined by brightness and sharpness, colourfulness by image saturation and liveliness by vivacity, animation, feeling, solidity, projection and metamorphosis. The findings are integrated in a template that specifies, as a tree-like structure, the 16 properties of VMI vividness in healthy adult humans. The template takes into account the weight of evidence drawn from the accounts and reveals an extraordinary degree of consistency in reported VMI characteristics, revealed by specialized studies of healthy adult humans across time, space and culture. Full article

During the development stage of a space vehicle, instrumented tests are carried out on the ground to prove the operational capacity of each liquid-propellant rocket engine, which is installed in this type of vehicle. The task of elaborating a Test Bench project for a propulsion unit with this application is complex and involves several steps, one of these steps being related to the analysis of this bench capacity to meet the algorithms for the liquid-propellant rocket-engine full run of tests, which is considered fundamental for this project’s operational success. Due to the high costs involved in this project’s elaboration and execution, it is strategic to use computational resources to evaluate, by simulation, the main operational functionalities that are previously established for this bench to perform. In this context, this work presents a model proposal through Petri Nets to evaluate, by computer simulation, an architecture capacity that was designed for the Test Bench to meet an algorithm dedicated to the liquid-propellant pipelines purge during the run of hot tests with the liquid-propellant rocket engine. The method used in this work to carry out the simulation shows the operational response of each module of this architecture, in accordance with the steps contained in the purge algorithm, which allows for analyzing, for each event of the process, the Petri Nets properties, mainly those related to the conservativeness, liveliness, deadlock-type, and confusion-type conflicts. The simulation carried out with the proposed model allows for the portrayal of the physical architecture and the operational states of the purge system according to the steps foreseen in the algorithm, showing that the conservation property is met because the number of marks remains constant, the vivacity property is also met since all positions have been reached, and there is no mortal-type conflict, as the simulation is not stopped; only confusion-type conflict is identified, which was solved with the strategic insertion of resources in the model in order to fix crashes related to the competition for tokens in the transition-enabled entries. The satisfactory results obtained in these simulations suggest that the modules provided for this architecture are sufficient and appropriate for carrying out all the steps contained in the purge algorithm, which will minimize or even eliminate the disorders that may be caused by the presence of foreign elements in the propellant supply lines during the tests with the rocket engine. Full article

This paper presents a novel material spectroscopy approach to facial presentation–attack–defense (PAD). Best-in-class PAD methods typically detect artifacts in the 3D space. This paper proposes similar features can be achieved in a monocular, single-frame approach by using controlled light. A mathematical model is produced to show how live faces and their spoof counterparts have unique reflectance patterns due to geometry and albedo. A rigorous dataset is collected to evaluate this proposal: 30 diverse adults and their spoofs (paper-mask, display-replay, spandex-mask and COVID mask) under varied pose, position, and lighting for 80,000 unique frames. A panel of 13 texture classifiers are then benchmarked to verify the hypothesis. The experimental results are excellent. The material spectroscopy process enables a conventional MobileNetV3 network to achieve 0.8% average-classification-error rate, outperforming the selected state-of-the-art algorithms. This demonstrates the proposed imaging methodology generates extremely robust features. Full article

Face-swap-attacks (FSAs) are a new threat to face recognition systems. FSAs are essentially imperceptible replay-attacks using an injection device and generative networks. By placing the device between the camera and computer device, attackers can present any face as desired. This is particularly potent as it also maintains liveliness features, as it is a sophisticated alternation of a real person, and as it can go undetected by traditional anti-spoofing methods. To address FSAs, this research proposes a noise-verification framework. Even the best generative networks today leave alteration traces in the photo-response noise profile; these are detected by doing a comparison of challenge images against the camera enrollment. This research also introduces compression and sub-zone analysis for efficiency. Benchmarking with open-source tampering-detection algorithms shows the proposed compressed-PRNU verification robustly verifies facial-image authenticity while being significantly faster. This demonstrates a novel efficiency for mitigating face-swap-attacks, including denial-of-service attacks. Full article