This paper recounts the author’s reluctant journey of translating Matthew Fuller and Eyal Weizman’s Investigative Aesthetics: Conflicts and Commons in the Politics of Truth into Japanese, a process that turned out to be a mix of philosophical tightrope walking and comedic pratfalls. Along the way, we meet Baumgarten, the original translator who coined the aesthetica nomenclature, Kant, who insists that there can be no such thing as a science of sensibility, and a parade of Japanese translators who took great artistic (...) liberties in rendering an alien term into a complicated language formed by three layers of different writing systems. The author reflects on his coining of a new translation for “aesthetics” in Japanese—Kansei-Jutsu (“Sensibility-Art”)—a term that baffled publishers, thrilled a few cultural studies scholars, and may have earned a side-eye from beauty salons already using “estetikusu” for facials. The translation saga spirals into debates about what “aesthetics” even means, culminating in a bittersweet realisation: translation is less about getting it right and more about sparking delightful, sometimes ridiculous, new ways of thinking. By the end, aesthetics re-emerges as a celebration of difference, proving that even conflicts can create a strange and wonderful commons when approached with an openness to diverse sensibilities. (shrink)

人間の愛は「不合理」なもの?自由だと勝手に信じている人間が実際には「不自由」なのではないか?なぜ人間は生まれて死ななければならないという「不条理」に遭遇しているのか?そもそも、人間とは何か...?―「 行為」「意志」「存在」の限界をテーマに、行動経済学者や認知科学者、進化論者、実存主義者など多様な分野の学者からカント主義者や急進的フェミニスト、会社員、運動選手、大学生も加わり、楽しく深い広い議論を繰 り広げる。そこから見えてくる人間の姿とは―。.

This paper describes a system to support transmission of human focusing skill by visualized their gaze behavior in Kansei interaction. In order to share personal Kansei information with other people, we need to transform it into cleared information. If this information is expressed by visualized style and transmitted to human from human, Kansei interaction is more creative than ever before. Our research group focuses on human gaze behavior that naturally reflects human actions, intentions and knowledge. Generally, it (...) is difficult for us to feel other people's gaze behavior. Therefore, we constructed a VR-space, called ``Mirror Agent System'', where some users can work together and their gaze are visualized coincidentally. By using this system, a user can become aware of not only a gaze history of himself, but also other user's them, while looking at the scenes in the VR-space. When he can feel other's gaze behavior, he can guess their actions, intentions and knowledge. In this way, we expected to promote human-human Kansei interaction, and to improve user's focusing skill. However, our privious system lacked to visualize only useful parts from a gaze history, because following two problems. First problem is the quantity problem. If the quantity of a gaze history increases in the VR-space gradually, the background scenes are covered with the visualized history and a user feels interfereing in his work. Second problem is the quality problem. It is very difficult for a user to interpret other user's gaze history. (shrink)

Product recommendation system is realized by applying business rules acquired by data maining techniques. Business rules such as demographical patterns of purchase, are able to cover the groups of users that have a tendency to purchase products, but it is difficult to recommend products adaptive to various personal preferences only by utilizing them. In addition to that, it is very costly to gather the large volume of high quality survey data, which is necessary for good recommendation based on personal preference (...) model. A method collecting kansei information automatically without questionnaire survey is required. The constructing personal preference model from less favor data is also necessary, since it is costly for the user to input favor data. In this paper, we propose product recommendation system based on kansei information extracted by text mining and user's preference model constructed by Category-guided Adaptive Modeling, CAM for short. CAM is a feature construction method that can generate new features constructing the space where same labeled examples are close and different labeled examples are far away from some labeled examples. It is possible to construct personal preference model by CAM despite less information of likes and dislikes categories. In the system, retrieval agent gathers the products' specification and user agent manages preference model, user's likes and dislikes. Kansei information of the products is gained by applying text mining technique to the reputation documents about the products on the web site. We carry out some experimental studies to make sure that prefrence model obtained by our method performs effectively. (shrink)