The composition has a strong influence on the overall of the work in the quality of arrangement and the impression. While the type of composition makes an impression, the beginners may struggle to place the human subject in the initial stage of sketches. Therefore, we propose a method for the improvements to the placement of the human object in an illustration at the initial sketch stage in this paper. We implemented a system to generate the output image in two basic compositions by using human objects in based on the object detection method by using the deep learning. We conduct quantitative experiments to show the effectiveness of our method.

Evaluations of “neatness,” “naturally accustomed to,” and “preference” were conducted using the stimuli of real clothes and images. Stimuli were the combination of a simple top and a straight/wide black pants with various lengths and silhouettes of the top. Four groups of women observers (students from Fashion, Human Life and Science, and Engineering departments, and a middle to senior group), participated. Regarding the “neatness” and “preference,” a tucked-in blouse style obtained high ratings, while a loose-tunic received low ratings. As for the “naturally accustomed to,” a tucked-in blouse with a wide-pants showed high scores, but a loose-tunic indicated moderate ratings. Based on the evaluations, formulas for the three terms were proposed using variables such as the ratio of the vertical lengths of the top and pants. A prototype of recommendation system using a small fashion-image database with user’s inputs of “neatness” and “naturally accustomed to” was developed and well rated.

There is a folk tale that describes how one can hear the sound of the ocean by placing a seashell close to one’s ear. This phenomenon is believed to occur owing to the acoustic effects of the surrounding environmental sounds and the cavity of the shell. However, this phenomenon has not been clarified. In this study, we recorded in-ear sounds in instances in which a resonant tube was placed close to real or artificial ears in a noisy environment (reproduced in a soundproof room) and investigated the spectral changes of these sounds. As a result, it has been revealed that not only the previously known resonance but also antiresonance occurs near the resonant frequency of the resonance tube in noisy environments, but did not occur in quiet environments. Based on the listening experiments, the impression that the antiresonance sounds are muffled is obtained.

In recent years, the usefulness of rough set theory has been gaining attention in Kansei engineering. The authors defined “core” as combination of attribute values that appear frequently based on decision rules and developed a notation method that comprehensively consolidates decision rules containing core. However, there were challenges such as the difficulty in interpreting the importance of attributes other than core and the impact of each core on the results. Therefore, this study aimed to develop a notation system for decision rules based on rough sets with TF-IDF applied to solve these challenges, and a system to visualize the structure of decision rules containing core using formal concept analysis. Furthermore, this system was applied to analyze attribute data of individual investors and its usefulness was verified. As a result, significant attribute values could be discovered, and cores could be classified into three types based on their impact on the results.

To extract features of the listing data for purchasing motivation of potential buyers, we focused not only on textual information but also on product images. The target of this study was shoes and we performed a predictive analysis using XGBoost with 1,000 items of past listing data. The dependent variable was whether the item was sold or not, and the explanatory variables were 17 features extracted from the basic information of the item, the characteristics of the item description, and the characteristics of the item image. The results were showed that it was important for preparing item images to have a large number of item images, to use a single background color and to photograph shoes at a 45-degree angle. The results of a questionnaire survey with 240 people revealed that the listing information reflecting the extracted characteristics enhance the purchasing motivation of infrequent users of flea market apps.

Our goal has been to quantify the natural language categories of financial institutions. It is difficult to create formulas in natural languages because the units are different and some have no units. Our company has devised a unique method that makes this difficult task possible, and has built it as a method for analyzing the information we have. This technology is based on a mathematical worldview of financial institution information. This has made it possible to numerically identify potential customer groups from existing customers of financial institutions in a commercial area. The quantification of existing customers in the area market field is a new way of using it, making it possible not only to analyze and analyze customers, but also to analyze customer diversity in more detail. The key to diversity in companies is to seek out the needs and diversity of wealthy customers by structuring various categories such as “age, health, educational background, lifestyle, and values”of customers who bring profits.

The aim of this study was to determine the effects of packaging design elements on the product choice behavior for a reciprocal gift. The participants were 64 female university students who, after reading a scenario about receiving help, chose a reciprocal gift. The results of the experiment showed that in situations where state gratitude was weak, experimental participants with high trait gratitude chose a package with a lower colorimetric lightness of the figure of the package, i.e. with a heavier visual weight, when the burden on the helper was heavier. By contrast, this effect was also confirmed in the high state gratitude scenario among participants with low state gratitude. The study suggests that the match between the psychological weight of gratitude and the visual weight of the packaging may influence product choice, and that trait/state gratitude may be involved in this psychological process.

We conducted laboratory and field experiments to evaluate the road rage prevention effectiveness of using a “You are being recorded” sticker on a private passenger vehicle. For our first laboratory experiment, 30 college students viewed photographs of three types and rated the depicted vehicle’s likelihood of being a victim of road rage. Results indicated that cars with a “You are being recorded” sticker were regarded as less likely to be victims of road rage than cars with no sticker or cars with an “After you” sticker. Then for a field experiment, we asked 10 college students to use the stickers on their own private vehicles to assess their effectiveness at preventing road rage. Results suggest that cars with a “You are being recorded” sticker, compared to cars without stickers, have some effect on the distance of following vehicles and on feelings of safety while driving.

In this paper, we introduce the concept of altruistic regulation through active inference. Our approach involves incorporating models of others and minimizing the combined expected free energy of oneself and others. We apply this concept to agent passing interactions to investigate its impact on fluency. The results demonstrate that this approach enables the estimation of others’ purposes and the generation of altruistic behavior. Furthermore, we show that the proportion of summation regulates the balance between altruism and selfishness. Simulation results suggest that moderate altruism contributes to minimizing the time required to reach a goal and indicate that the appropriate degree of altruism varies depending on the disparities in abilities and environmental factors among agents. These findings highlight that the degree of altruistic behavior can be adjusted through the weighting of summed expected free energy. Moreover, this adjustment impacts the fluency of agent passing interactions.

The aesthetic appeal of a shape is a critical factor in product evaluation. Previous research has suggested that aesthetic evaluations of shapes improve when individuals recognize additional regularity. In this study, we hypothesized that this enhancement in aesthetic evaluation is attributed to the interest sparked by the transition in the recognition process. We conceptualized this hypothesis using the free energy principle, grounded in dual-process theory. Furthermore, to address the motivation behind considering additional regularity, we formulated concepts of both the surprise elicited by the additional regularity and the cognitive effort required to recognize this regularity. To test the validity of these constructs, we conducted experiments with various combinations of primitive shapes. Our results revealed that both our aesthetic indicator and the formulated surprise construct were significantly positively correlated with subjective evaluations upon recognizing additional regularity. Leveraging the findings of this study could enable the design of shapes incorporating additional regularity, thus enhancing aesthetic appeal or creating captivating surprises.

Within the context of urban design and planning in the 21st century, the increasing number of studies explores positive psychophysiological effects from urban rooftop gardens for enhancing human health and wellbeing, while such psychophysiological studies often require impression evaluation scales for urban rooftop gardens. In this context, this study examined if impressions of two different urban rooftop gardens having flat vs. stepped spatial configurations can be evaluated by UGS (Urban Green Space Scale), which is an impression evaluation scale for recent urban green space projects in Tokyo. Based on the results of the online questionnaire (N=670), confirmatory factor analyses indicated that urban rooftop gardens have poor or unacceptable fit to UGS, while exploratory factor analyses yielded factor structures different from UGS. Our findings suggest that impression evaluations of urban rooftop gardens require new scales.

The Policy Outline for a Beautiful Country formulated in 2003 calls for many specific measures, including the development of systems for evaluating qualities of urban green spaces. The UGS (Urban Green Space Scale) was developed for evaluating impressions of urban green space projects from recent public offering processes in Tokyo. This study aims to validate the UGS’s application rage by using the UGS’s original construct alone, and by using the UGS, the Positive Affect and Negative Affect scales, and questions asking visit intentions as the Stimulus-Organism-Response model, based on the results of online survey (N=600) showing images of a shrine and temple precinct forest, an ecological conservation project, a Japanese garden, and an urban green space from recent public offering processes in Tokyo. We found that the UGS can be used for evaluating impressions of all types of urban green spaces used in this study except for Japanese gardens.

To quantify tacit architect expertise using floor plans, we proposed a method to quantify four knowledge of expertise; ease of housework, abundance of storage, ease of work, and ease of enjoying hobbies. We surveyed 10 architects to clarify the characteristics of floor plans for each knowledge. Based on the importance of the extracted features of floor plans, we calculated the degree for each knowledge by performing character recognition and image processing for floor plan images. The evaluation was performed with 15 architects, and we confirmed that the rankings for the values calculated by the proposed method and the rankings for the evaluation values were matched for all knowledge.

Texture is considered to be one of the factors that determines the perception of palatability. In this study, samples of milk chocolate with different particle size were used to evaluate the subjective well-being perceived from the quality of the chocolate, and EEG and autonomic nervous system were measured as physiological indices during and after consumption of the samples. The results showed that subjective well-being increased with consumption of both samples, which was higher for the finer particle size sample. In addition, differences in postprandial EEG and autonomic indices were also observed depending on the particle size of the milk chocolate. These results suggest that the smooth texture of milk chocolate may have influenced the difference in subjective well-being, which could also be captured by physiological indicators.

With anxiety disorders on the rise in recent years, early detection and assessment of anxiety states is extremely important. Conventional approaches have mainly used self-reported questionnaires, but they have reliability problems. Therefore, this study aims to generate facial images that include minute changes in facial expression using deep learning techniques to more accurately and simply assess subjects’ anxiety states. This method quantitatively evaluates the degree of anxiety state by analyzing the subject’s facial expression recognition of the generated images. The validity of this method was verified through correlation analysis with the results of the State-Trait Anxiety Inventory (STAI), an existing anxiety assessment tool. The results showed a significant correlation between the facial expression recognition score and the STAI score, indicating that this method is effective in assessing anxiety states. This study aims to innovate the evaluation method of anxiety state by integrating deep learning and psychology.

In this study, we developed a prototype of a blink-based communication device called the “Blinkcommunicator” to verify the feasibility of creating a simple and practical system for selecting picture card messages focusing on blinking. Users of this device blink to select a target from among eight rapidly flashing options (picture cards). The blink-related changes in eye potentials are detected in real-time using a pattern recognition method (referred to as the “blink switch”). Assuming this system as a cognitive task, we investigated whether participants, consisting of 14 healthy individuals, could accurately select the target specified by the blinking switch (target decoding accuracy based on pattern recognition). As a result, the patterns of blinks associated with task performance (particularly peak amplitude and latency) were consistent within individuals, leading to a remarkably high decoding accuracy averaging over 95%. This further enhances expectations for the practical application of this system.