This paper presents a tele-control system comprised of a robot hand/arm and an operator. In our system, the angle of the finger of the robot hand is controlled according to the angle of the operator's finger, and the position of the robot arm is controlled according to the position of the operator's arm. Simultaneously, the operator feels the environmental force as detected by the touch sensor attached to the robot hand, resulting in so-called bilateral master/slave control. To date, there have been a few studies of bilateral master/slave systems that use a multi-fingered humanoid robot hand in communication networks with delay caused by physical distance. The purpose of our study was to achieve tele-operation with bilateral master/slave control between the operator's hand/arm and the multi-fingered humanoid robot hand/arm using the Internet with delay caused by distance. For our experiment, the operator was in the USA, and the multi-fingered humanoid robot hand/arm and object were in Japan. Using our system, the operator could successfully grasp and move an object while he felt the reaction force from the object. This technology can applicable to tele-operation such as feeling the hardness and weight of the stone which exist in the moon, on the ground etc.

A method for creating a constrained clustering ensemble by learning the priorities of pair wise constraints is proposed in this paper. This method integrates multiple clusters produced by using a simple constrained K-means algorithm that we modify to utilize the constraints priorities. The cluster ensemble is executed according to a boosting framework, which adaptively learns the constraints priorities and provides them for the modified constrained K-means to create diverse clusters that finally improve the clustering performance. The experimental results show that our proposed method outperforms the original constrained K-means and is comparable to several state-of-the-art constrained clustering methods.

This paper presents a tele-control system constructed by a robot hand/arm and operator. The angle of the robot hand is controlled by the angle of the operator's finger, and the operator feels the environmental force, as detected by the touch sensor of the robot hand, constituting so-called bilateral master/slave control. The position of the robot arm is controlled by the position of the operator's arm.

Query expansion is an information retrieval technique in which new query terms are selected to improve search performance. Although useful terms can be extracted from documents whose relevance is already known, it is difficult to get enough of such feedback from a user in actual use. We propose a query expansion method that performs well even if a user makes practically minimum effort, that is, chooses only a single relevant document. To improve searches in these conditions, we made two refinements to a well-known query expansion method. One uses transductive learning to obtain pseudorelevant documents, thereby increasing the total number of source documents from which expansion terms can be extracted. The other is a modified parameter estimation method that aggregates the predictions of multiple learning trials to sort candidate terms for expansion by importance. Experimental results show that our method outperforms traditional methods and is comparable to a state-of-the-art method.