In this paper, we propose a method to estimate sequences of functions to help to analyze folk tales. Functions are proposed by a Russian folklorist, V. Propp, to analyze folk tales. Estimating sequences of functions is a problem of assigning a function to each sentence in a tale, where a function represents an act of some characters in a sentence. We propose a method of supervised learning for estimating sequences of functions by using a Conditional Random Field (CRF). Observation sequences of a CRF are features of sentences that consist of nouns, verbs, and structures of sentences. We report some results to estimate sequences of functions from 50 folk tales.

In this paper we propose a method to estimate working activities from sensor data without user annotation. Estimation of working activities are much more difficult than that of simple activities such as 'walking' and 'still' in terms of their complexity and length. To address this issue, we assume that they are a probabilistic combination of various simple activities and propose a method to discover working activities by multistage estimation, in which firstly classify sensor data into some basic activities and then estimate them by using topic model.We focused on nursing service as one of professional working activities, in whicn visualization by estimation of working activities is critical, conducted the experiment to observe it in the hospital and verified our method.

We propose a method of estimating latent structures behind string data in order to apply them to knowledge discovery with giving problems. Our goal is to learn the structures and to represent them as models of data generating processes. For the models of generating processes, we introduce two concepts, components and assemblers with patterns and refinements of patterns, where we adopt patterns to represent templates of patterns and refinements to represent generation of them. A component is defined as a 3-tuple with a pattern, a set of substitutions, and a probability density function on the set. An assembler is a function to construct string data with sub-strings generated by components. In this paper, we propose three sub-problems and algorithms to solve one of them, and also present some simple experimental results towards our goal.

In this paper, authors present table-oriented agent simulation framework and indicate internal design of the framework. The table-oriented agent simulation is a concept of all data including agents ' properties considered as cells of tables, and rules stored other spaces drive table-shaped data or agents. The framework for table-oriented agent simulation applies conventional thinking to create codes and tables, because of convenience to regard a name of a variable or a rule as the name of a file. The framework also provides a scaffold when a user executes the function of scaffolding with a template of a simulation process and initial data types. Finally, this paper shows an example on the framework to easily create a simulation program of Schelling's segregation model utilizing the template of Sugarscape model.

With increase of an Internet usage rate, more people use online shops. All goods are managed in a distribution center, and many of work are the picking work that workers collect goods according to a check. This work is still performed by people's hand in many distribution centers, so it is needed that the worker picks goods efficiency by a brief model. In this research, we improve the algorithm that hot goods are collected naturally in storing in the time of delivery of goods.

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, and discuss how to prevent a false rumor diffusion.

The Great East Japan Earthquake struck on March 11th, 2011, and nuclear power plants in the Fukushima-Daiichi nuclear power station were damaged seriously. This accident was addressed many kinds of media. In this paper, we focused Twitter, which is one of social media, and TV reports, is one of main media, and analyzed how many kinds of information concerning the Fukushima-Daiichi nuclear power station accident propagated on these medias.