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External Awards3rd Annual Congress of the Japanese Society for Quality and Safety in Healthcare, Best Trial AwardWinners: Manabu Motegi†1, Narimune Matsumura†1, Tomohiro Yamada†1, Shin-yo Muto†1, Masanobu Abe†1, Yumiko Ookubo†2, Yoshiko Morita†2, Keiko Kasai†2, Tomoko Yamamoto†2, and Chikayuki Ochiai†2 Date: November 23, 2008 Organization: Japanese Society for Quality and Safety in Healthcare For “Standing-up movement patterns of patients to prevent bed-related falls” (in Japanese). Falls from bed are one of the most common problems faced by elderly people in hospitals and nursing homes. They often occur when patients try to get out of bed without assistance. We observed the activities of elderly people in hospital beds during the night by using an infrared camera. Our analysis of the captured video showed that the getting-up motions could be classified into five patterns. These results should enable a monitoring system to alert nurses to patients at risk of falling and thus lead to better patient care in the future. WebDB Forum 2008 Enterprise Paper AwardWinners: Shigeru Fujimura, Hiroyuki Toda, Yumiko Matsuura, and Ryoji Kataoka, NTT Cyber Solutions Laboratories Date: December 2, 2008 Organization: Information Processing Society of Japan For “Analysis of the Blog Network Considering Local Structure” (in Japanese). In this paper, assuming that the characteristics of a weblog are well represented by the role of the local structure, which consists of its neighbors, we analyzed them a weblog’s characteristics by using its feature vector considering the local structure. We found that spam messages had a distinctive local structure and we also found some common points in the feature vectors of famous bloggers. WebDB Forum 2008 Enterprise Paper AwardWinners: Yoshihiko Suhara, Yukio Uematsu, Takafumi Inoue, and Ryoji Kataoka, NTT Cyber Solutions Laboratories Date: December 2, 2008 Organization: Information Processing Society of Japan For “Spammer Detection based on Tagging Behavior in Social Bookmarking Systems”. Social annotation services such as social bookmarking services let users easily add contents or attach tags. Therefore, they unintentionally also let spammers bookmark or link to spam sites readily. Spamming behavior in social bookmarking services can be divided into two types: attaching attractive tags to lure users to spam contents and attaching improper tags to regular contents. It is difficult to detect spammers of the latter type by content analysis or from user information about who attached the tag. We consider that spammers have different tagging intervals and different time changes of attached tags from non-spammers. In this research, given a dataset that includes labeled spammer and non-spammer data, we tried to generate a classifier for detecting the spammers by means of user-behavior-based features. The evaluation results show that incorporating these features into a conventional method can reduce the misclassification of non-spammers. Through an examination of social bookmarking data, we show that feature thresholds calculated from training data can classify spammers with very high precision. 16th IPSJ Workshop on Multimedia Communication and Distributed Processing––Best Presentation AwardWinners: Tamio Kihara, NTT Cyber Solutions Laboratories Date: December 12, 2008 Organization: Information Processing Society of Japan For “A Composition Method of Situation Conformity Digital-signage Interface Using Human Position and Movement” (in Japanese). Most Cited IEEE Software Articles (in the top 10)Winners: Kenji Takahashi, NTT Information Sharing Platform Laboratories Date: January 2009 Organization: IEEE Software For “Inquiry-Based Requirements Analysis”. This approach emphasizes pinpointing where and when information needs occur; at its core is the inquiry cycle model: a structure for describing and supporting discussions about system requirements. We use a case study to describe the model’s conversation metaphor, which follows analysis activities from requirements elicitation and documentation through refinement. |