In this paper, first, analyses of many maxims are carried out to obtain knowledge for heart-touching phrases. Next, a creation system for heart-touching encouraging sentences is proposed using these findings. As for analyses, we collected 1,830 maxims because heart-touching phrases have a large effect on healing and encouragement. As a result, some interesting findings could be obtained such that positive and sentimental words are often used in heart-touching sentences. Contrast and emphatic sentences also tend to contribute to impressive expressions. The proposed system utilizes these findings to create heart-touching encouraging sentences. We performed evaluation experiments. We compared the sentences created by the proposed system with sentences created by humans. Many output sentences received evaluation values close to those created by humans.
In this paper, we describe a case study of a service and application design using good memories. Purpose of this case study is to create a new service and a new application that offers an excellent user experience. We introduce good memories in the design process in order to increase the quality of the user experience with the service and application. The good memories are used in the idea creation step and in the design step of the service. The created service and application is a gift service that offers experiential gifts. The experiential gift is selected from the good memories of the receiver. We developed a dialog interface with 5W1H words to recall the good memories for the gift service. High acceptability of the created gift service is obtained through evaluations. Therefore, the design process utilizing good memories is effective in creating new services offering an excellent user experience.
When consumers purchase the interior building materials, they sometimes complain about the difference between the catalog goods and the real ones. This problem seems to be because of that when they decide to purchase the interior building materials, they rely only on the visual, not on the touch. In this study, we investigated physical parameters which have an influence on visual and haptic perception by sensory testing using various woody floor material. In addition, we analyzed the influence on the impression by physical parameters which influence the visual and haptic perception. The physical parameters from visual perception have “colors”, “surface gloss” and the physical parameter from haptic perception has “surface roughness”. As a result, it was found that human feelings are affected by the difference from visual and haptic perception and by kinds of physical parameters.
In this paper, we show that the power set of attributes of the furniture for indoor public spaces constitutes a fuzzy measure. From the results, by using fuzzy integrals, we propose a fundamental methodology for obtaining an overall rating of the furniture. Also, we have shown practical examples in order to show the effectiveness of the method proposed here. An experiment that researched how much an evaluator puts importance on each property was conducted for specialists of furniture manufacturers and a user group in their twenties. We clarified the difference of their viewpoint between users and manufacturers by comparing the results. We found out what were fundamentally desired by the evaluators by not only numerically measuring the values of importance that the evaluators comprehensively put on the combinations of individual attributes, but also hearing about the newly created values that the evaluators saw in the combinations of values.
In order to realize the use of ear biometrics to support criminal investigations, it is essential to study the methods for adjusting the difference in the shooting angles between the security camera and suspect database images. To make up the difference between the shooting angles, previous works have attempted to estimate the feature values of different shooting angles by using multivariate analysis and machine learning techniques. In this study, we propose a method to obtain the feature values using a modified Gabor filter; moreover, we conduct comparative experiments between our proposed method and the conventional method using only using multivariate analysis and machine learning techniques. The experimental results demonstrate that our proposed method achieves an improvement of approximately two percent in terms of equal error rate.