Keitaro Naruse

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The objective of this paper is to understand an aspect of human social interaction in public bulletin board systems (BBSs). We try to answer the question of why and how a long and hot chain of articles often emerges in BBSs. This paper presents the following three contributions. (1) empirical results: we measured and analyzed actual BBS logs, and found that(More)
The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative(More)
The objective of this research is to develop a wearable power assist device which helps a person to lift up a heavy object. The device is designed to support him by holding his upper body weight and reducing his inner force. It turns to the reduction of a compression force of his lower back discs, which is a major factor of a lower back injury. In a(More)
This paper proposes a novel search system for speech and song segments. The amount of accumulated video data in the World Wide Web is expanding and its content is varied. Video content includes natural voices and singing voices, and these differ in their phoneme lengths. Our system uses frame-wise phoneme recognition and continuous dynamic programming(More)
Spotting recognition is the simultaneous realization of both recognition and segmentation. It is able to extract suitable information from an input dataset satisfying a query, and has developed into a research topic known as word spotting that uses dynamic programming or hidden Markov models. Continuous dynamic programming (CDP) is a promising method for(More)
To extract temporal variations in the relation between two or more words in a large time-series script, we propose three procedures for adoption by the existing Associated Keyword Space system, as follows. First, we begin the calculations from a previous state. Second, we add a random seed if a new object was present in the previous state. Thrid, we forget(More)
We propose a novel method for mining knowledge from linked Web pages. Unlike most conventional methods for extracting knowledge from linked data, which are based on graph theory, the proposed method is based on our associated keyword space (ASKS), which is a nonlinear version of linear multidimensional scaling (MDS), such as quantification method type IV(More)