In this study, we investigate syntactic and prosodic features of the speaker's speech at points where turn-taking and backchannels occur, on the basis of our analysis of Japanese spontaneous dialogs. Specifically, we focus on features such as part of speech, duration, F0 contour pattern, relative height of the peak F0, energy trajectory pattern, and relative height of the peak energy at the final part of speech segments. We examine, first, the relationship between turn-taking/backchannels and each feature of speech segments independently, showing that the features examined in this study are all related to turn-taking or backchannels and that the way they correlate is fairly consistent with previous studies. Next, we explore the inter-relationship among the features with respect to turn-taking and backchannels. We show that in both turn-taking and backchannels, (1) some instances of syntactic features make extremely strong contributions, and (2) in general, syntax has a stronger contribution than any individual prosodic feature, although the whole prosody contributes as strongly as, or even more strongly than, syntax. We also discuss some implications of our results, comparing them with previous models that have mentioned roles of syntax and prosody in turn-taking and backchannels.

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In this study, we developed a compact wireless Laplacian electrode module for electromyograms (EMGs). One of the advantages of the Laplacian electrode configuration is that EMGs obtained with it are expected to be sensitive to the firing of the muscle directly beneath the measurement site. The performance of the developed electrode module was investigated in two human interface applications: character-input interface and detection of finger movement during finger Braille typing. In the former application, the electrode module was combined with an EMG-mouse click converter circuit. In the latter, four electrode modules were used for detection of finger movements during finger Braille typing. Investigation on the character-input interface indicated that characters could be input stably by contraction of (a) the masseter, (b) trapezius, (c) anterior tibialis and (d) flexor carpi ulnaris muscles. This wide applicability is desirable when the interface is applied to persons with physical disabilities because the disability differs one to another. The investigation also demonstrated that the electrode module can work properly without any skin preparation. Finger movement detection experiments showed that each finger movement was more clearly detectable when comparing to EMGs recorded with conventional electrodes, suggesting that the Laplacian electrode module is more suitable for detecting the timing of finger movement during typing. This could be because the Laplacian configuration enables us to record EMGs just beneath the electrode. These results demonstrate the advantages of the Laplacian electrode module.

Collaborative filtering (CF) is one of the most popular recommender system technologies. It tries to identify users that have relevant interests and preferences by calculating similarities among user profiles. The idea behind this method is that, it may be of benefit to one's search for information to consult the preferences of other users who share the same or relevant interests and whose opinion can be trusted. However, the applicability of CF is limited due to the sparsity and coldstart problems. The sparsity problem occurs when available data are insufficient for identifying similar users (neighbors) and it is a major issue that limits the quality of recommendations and the applicability of CF in general. Additionally, the cold-start problem occurs when dealing with new users and new or updated items in web environments. Therefore, we propose an efficient iterative prediction technique to convert user-item sparse matrix to dense one and overcome the cold-start problem. Our experiments with MovieLens and book-crossing data sets indicate substantial and consistent improvements in recommendations accuracy compared with item-based collaborative filtering, singular value decomposition (SVD)-based collaborative filtering and semi explicit rating collaborative filtering.