We used a social robot as a teaching assistant in a class for children's collaborative learning. In the class, a group of 6th graders learned together using Lego Mindstorms. The class consisted of seven lessons with Robovie, a social robot, followed by one lesson to test their individual achievement. Robovie managed the class and explained how to use Lego Mindstorms. In addition to such basic management behaviors for the class, we prepared social behaviors for building relationships with the children and encouraging them. The result shows that the social behavior encouraged children to work more in the first two lessons, but did not affect them in later lessons. On the other hand, social behavior contributed to building relationships and attaining better social acceptance.

BACKGROUND: Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored. RESULTS: We propose a method of predicting interaction sites in proteins with unknown function by using both of PPI networks and protein structures. For a protein with unknown function as a target, several clusters are extracted from the neighboring proteins based on their structural similarity. Then, interaction sites are predicted by extracting similar sites from the group of a protein cluster and the target protein. Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process. CONCLUSIONS: The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed. The challenge will now be to apply the method to large-scale datasets.

Biological creatures, some of them are very simple, seem to perform efficient search strategy. Recent researches show that noise in their internal mechanism may have an important role to manage this behavior. This paper focuses on realizing a simple, noise utilizing, mathematical framework that enables a mobile robot to perform random search adaptively and efficiently under changing target density. Our approach is to model and implement bacterial movement based on a recent perspective of noise utilizing mechanism in living beings: biological fluctuation. As a result, the robot will adaptively switch its random search pattern between Levy walk and Brownian walk to increase the search efficiency in a patchy environment where the target density naturally alternates.

Many biologically inspired approaches have been investigated in relation with researches on mobile robot(s) that can effectively locate targets that induce gradient information. Here, we concentrate on realizing an adaptive searching behavior in mobile robot that is simple, yet effective with and without sensing the gradient information. We are interested in two searching behaviors found in biological creatures: bacterial chemotaxis, probably the simplest yet effective gradient sources searching behavior found in living creatures; and Levy walk, specialized random walks with fractal movement trajectories that optimize random search for sparsely and randomly distributed target(s). Our approach is to implement and combine the two searching behaviors based on ¿yuragi¿, a Japanese word for biological fluctuation.

Living organisms have various kinds of flexibility and robustness which are realized by ¿yuragi,¿ i.e. biological fluctuations or noises. Bacterial motion is a form of noise-based motion, since bacteria can move towards a higher concentration of some chemical which they prefer even though they have only a limited 1-DOF of flagella for mobility. Bacteria also have only a limited sensory device which cannot detect the spatial gradient of the chemical at a time. The simple strategies that bacteria take to realize chemotaxis are (1) to tumble (or turn) to change orientation randomly with unbundled flagella in various directions being hit by surrounding water molecules, and (2) to change the frequency of tumbling according as the time change of chemical concentration. In this paper, we describe a small and simple, 1-DOF swimming robot developed by mimicking the bacterial motion generation mechanism. The robot only has a single motor and a single sensor (a photo detector), however, by changing its orientation in response to various noises which exist in the environment, and by changing the frequency of turning, the robot can approach its goal. Experimental results indicate that the robot statistically approaches the goal (a light source) in two dimensional space with its 1-DOF actuator, which would be impossible for the robot to achieve without utilizing noises in the environment.

This paper reports a method that uses humanoid robots as a communication medium. There are many interactive robots under development, but due to their limited perception, their interactivity is still far poorer than that of humans. Our approach in this paper is to limit robots' purpose to a non-interactive medium and to look for a way to attract people's interest in the information that robots convey. We propose using robots as a passive-social medium, in which multiple robots converse with each other. We conducted a field experiment at a train station for eight days to investigate the effects of a passive-social medium.

This paper describes a group attention control (GAC) system that enables a communication robot to simultaneously interact with many people. GAC is based on controlling social situations and indicating explicit control to unify all purposes of attention. We implemented a semi-autonomous GAC system into a communication robot that guides visitors to exhibits in a science museum and engages in free-play interactions with them. The GAC system's effectiveness was demonstrated in a two-week experiment in the museum. We believe these results will allow us to develope interactive humanoid robots that can interact effectively with groups of people.

This paper introduces a "teleoperated communication robot" whose unique point is that its language component is performed by an operator to avoid the automatic recognition difficulty of spoken language. On the other hand, conceptually, its nonverbal behavior will be autonomously controlled. One important point is to identify whether ordinary people will accept this type of communication robot. Therefore, we conducted a preliminary field trial at a shopping center, which revealed positive perspectives as well as technical problems to be solved

Many studies have been conducted on supporting communication in home and office spaces, but relatively few studies have explored supporting communication in large-scale public spaces, despite the importance of such environments in our daily lives. We propose a transcendent means of communication as an emerging style in this pervasive computing era: a system that allows administrative staff to effectively help visitors in large-scale public spaces. The visitors' context is used to provide a bird's-eye view of a simulated public space for the staff to grasp the situation and point at a particular location within the view to indicate the visitors they intend to address. The results of an experiment showed synergic effects between the bird's-eye view and the first-person one in determining the spatial movements of people. In indoor and outdoor large-scale public spaces, a central railway station and a park, we installed our prototypes and learned the implications of its use.

The goal of this research is to develop systems that support human activities through environmental intelligence. These systems may include agents that interact with people in a sensor-rich setting. As a first step, we have developed a distributed vision system with 12 omnidirectional cameras to recognize what is happening at a subway station. Conventional recognition systems require intermediate models of human behavior, such as geometrical models. However, it is hard to calibrate many cameras with enough accuracy to use this framework. Therefore, we propose a memory-based method that does not require any calibration or a priori models. The developed system recognizes "people are walking toward a ticket gate," "people are going down the stairs," and so on. In addition, we have developed a method to select what information is necessary to make these discriminations. This method, which is analogous to an aspect of attention, reduces the computation involved in learning support vector machines to about 0.5% of its original value and increases classification accuracy by 2 to 23%.