No abstract

In order to achieve the intended level of communication with visitors in museums where large displays are installed, it is essential to understand how various display factors affect visitors. We explore the effects of the display angle on individual users. In our experiment, we set up three types of flat displays-vertical, horizontal, and tilted-and comprehensively tested users' cognitive, behavioral, and subjective aspects. The results showed that a significant difference could be discerned in regards to cognitive and subjective aspects. Test results for the cognitive aspect showed that the display angle on which the displayed content was easy to understand and remember differed depending on age. Test results for the subjective aspect showed that irrespective of age, users rated tilted displays as being quicker to attract attention and easier to peruse, to understand and remember the content, and to interact with, and such displays were the most preferred.

SUMMARYAs design work using computer spreads, usage environments are appearing which have operations involving aesthetic sensibilities via the GUIs in a variety of graphics software and have the representational capacity to represent three-dimensional images and images with the feel of oil paintings. However, their operation still relies on the users sensibilities and knowledge. In this paper, we propose a user interface for a user support system for creating artistic patterns. Its features are summarized by the following three points. First, the system has knowledge about designs. The result of design analysis provided the following knowledge.(1) The elements producing a design are the colors, shapes, and composition. (2) To turn an image into a pattern, the image is conceived of as a specific scene, then the pattern can be drawn by extracting the colors, shapes, and composition based on the scene by combining these elements. This knowledge is held in the system. Second, there is a user interface to easily transmit the image to the system. The media to convey kansei (affective information) are considered to be both linguistic and nonlinguistic. And an interface was designed to enable direct manipulation by pen or mouse to select from a list box of a three-level kansei language and to select from a pattern candidate group. Another user interface was designed which accounts for the inability of a person to grasp the kansei from the start to completion and allows repeating the kansei specification by the user and the presentation of patterns by the system. Third, there is a mechanism that automatically creates the pattern in response to the users kansei. We designed an algorithm to create a design by (1) using fuzzy logic to deduce the users image and (2) creating the pattern by using the parameters needed to draw the pattern which were extracted from the deduced image. Furthermore, we developed a support system for creating artistic patterns that has the above functions, and had multiple testers test its operation to verify the systems effectiveness in application tests on real problems.

Human daily activities are stored in various kinds of data representations using ICT devices nowadays, named lifelogs. It is highly requested to retrieve useful information from lifelogs because these raw data are hard to handle. Extracting human activities from these logs is promising to enrich our life. Context-awareness services can be provided depending on user activities extracted from these logs. Recently, a lot of people post a message called tweet within Twitter to show what they are doing, thinking, feeling, and so on. Tweets have potential to record human activities, because many people post tweets so frequently every day. This paper focused on the tweets to retrieve human behavior from them. The length of tweets are limited within short sentence, so this causes some difficulties. The users will use domain-specific terms and will post grammatically incorrect sentences to fit with the constraints. These make us hard to analyze tweets with grammatical manner or with dictionaries. To tackle them, we are applying character n-gram tokenization and naïve Bayes classifier to extract appropriate behavioral information from tweets. Using n-gram tokenizer, domain-specific words can be identified and incorrect grammar can be handled. Our approach is examined using real tweets in Japanese. The index of precision, recall and F-measure shows the promising results. Some experiments have been carried out to show the feasibility of our approach. At this point, our system applied to Japanese tweets but it is applicable to any other languages.

No abstract