Twitter is a famous social networking service and has received attention recently. Twitter user have increased rapidly, and many users exchange information. When 2011 Tohoku earthquake and tsunami happened, people were able to obtain information from social networking service. Though Twitter played the important role, one of the problem of Twitter, a false rumor diffusion, was pointed out. In this research, we focus on a false rumor diffusion. We propose a information diffusion model based on SIR model, classify the way of diffusion in four categories, and reapper the real diffussion by using this new model.

サービス現場において集積されている大規模なID 付POS データ(購買履歴)を用いて消費者を理解し，需要を予測することで消費者にとっての価値の向上や経費の削減をはかることが望まれている．本研究では，確率的潜在意味解析(PLSA)を用いて顧客を潜在クラスに分類した後，その潜在クラスを説明する構造化モデルをベイジアンネットとして構築する潜在クラス構造化モデルをID 付POS データから構築する．

Models that estimate latent classes for movie recommendation based on PLSA and decision trees are proposed. Proposed model can explain the reason why such recommendation results are given. Using proposed model for so-called cold start problems in recommendation, we can handle the users who don't have enough records. Instead of conventional PLSA for recommendation, we use decision tree models consist of some questions. So, instead of using user's records, we can recommend suitable movies using user's answers as input of decision trees. In an experiment of questionnaire survey, improvement of the satisfaction of the proposal is 45% in comparison with the previous method by showing the recommendation reason. Another experiment is implemented where the users who have less than 9 movie viewing are recommended more appropriate movies after answering 5 questionnaires.

This paper analyzes the vulunerability of firms transaction networks empirically. First we try to reproduce the product and money flows on the network using firms' attributes and input-output table. Then we apply the flows to large real transaction dataset. Finally, we identify the most critical firms and industries for the network vulunerability.

大規模な地震災害に対するアプローチとして，RoboCup Rescue 等のさまざまな取り組みが現在進行中であり，災害状況における経路探索は重要な問題となっている．このような災害状況における経路探索では，環境（道路情報）に関する事前知識が正確でなくなっていることを想定しなければならない．一方で，平常時での周辺環境調査等により災害時における環境変化についてある程度の予想が可能であると仮定することは妥当であると考えられる．つまり，災害時における移動計画においては，これらの不確実性と予想可能性を考慮して経路選択をすることが望ましい．そこで本研究では，確率的な障害が発生する環境下で，効率の良い経路発見を行う手法について検討，評価を行う．

We have a project for the Smart City Hakodate. The goal is to realize the public transportation called the Smart Access Vehicle System, which provides traffic vehicles on demand from users. In this paper, we focus on the reports recording individual traffic behaviors for a traffic flow simulator in Hakodate.

We developed O2, an e-participation web platform, that facilitates public involvement by utilizing background information behind regional social issues gathered fromWeb. The platform is developed on the basis of a Linked Open Dataset called SOCIA, which contains web news articles, tweets, and meeting minutes related to geographic regions. Since Japanese regional communities face complicated and ongoing social issues, there is an urgent need to develop technology for sharing background information and facilitating public debate. This paper presents a roadmap to practical application of our platform in regional communities and discusses remaining requirements.