This paper proposes a new evaluation method and how to visualize its result in the field of furniture development for indoor public spaces through the application of a quantitative method. To achieve improved user satisfaction, we set up evaluation items for the furniture and conducted an evaluation experiment. First, we extracted information of possible importance for evaluating furniture design through judges' comments in design awards and edited them as evaluation items. Next, users evaluated the degrees of significance and satisfaction in relation to each evaluation item while actually using the furniture. We converted the evaluation result by calculating deviations and graphically represented them to highlight items that need improvement. The experiment involved two user groups: those who provide services and those who receive them. The results of both groups will be compared so that specific guidelines for improving furniture can be developed taking both groups into consideration.

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The mood in a conversation is estimated by examining the speakers' affective states. Developing the automatic mood recognition system is one of the bigest issues in smooth communication research of human-human and human-computer/robot interactions. In this research, the affective rating data in UUDB (Utsunomiya University Spoken Dialogue Database) is used as the speakers' affective states. Twenty participants heard speech data and they were asked to rate the mood for each five utterances of the dialogue in the UUDB. The head-counts of participant, who considered the mood as bad/negative for the utterance blocks, are modeled by the Poisson regression modeling technique. The results demonstrate that the model is able to estimate the mood in a conversation by utilizing the speakers' affective states before the target utterance block. Thus the current study indicates that it is possible for a computer/robot to infer mood and it is possible to have effective human-to-computer/robot communications.

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This paper proposes a color combination support system using image words clustering. In order to design color combination effectively, analyses of the relationship between various colors and the image humans have is important. We have collected such data produced by many color designers and analyzed them to find the relationships between hue, tone and the combinations. At the same time, several color combination techniques are also utilized to develop a software algorithm to implement a color combination support system. The proposed system has been examined in respect of user-friendliness, ease of use, and degree of coincidence between the output of the proposed system and human's image. According to the evaluation experiments, the proposed system obtained fairly good results.

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