Washoku, traditional Japanese food culture, was evaluated as a social custom for food that embodies the Japanese spirit of respect for nature and was registered as a UNESCO Intangible Cultural Heritage in 2013. However, an actual meal is limited to taste and visual information such as taste, ingredients, tableware, and arrangements; it is difficult to become thoroughly familiar with the cultural characteristics of Japanese cuisine. This study achieved a system that conveys the characteristics of Japanese cuisine, such as the importance of seasonality and ingredients, by displaying the cultural background related to food in text form. The natural environment is projected by a projector on a table, and seasons progress as the meal advances. The food was created in consultation with the chef to be suitable for the system. The users who participated in our survey and experienced the system were conveyed that Japanese cuisine is supported by the richness and seasons of nature and that it also affects traditional events.

Hearing aids have a great potential to facilitate better speech communication not only for hearing impaired users but also for people with normal hearing since the device will allow users to render sound with better speech intelligibility using signal processing techniques. This paper studies how a sound source separation technique would enable improving the intelligibility of a targeted speech when the technique is applied to the behind-the-ear hearing aids which has multiple microphones on each device. The sound source separation technique utilised in this study is based on the beamforming with post-filter framework and separates sound arriving from different direction. Experimental results using microphones attached to a head and torso simulator suggest the speech intelligibility can be improved by emphasising the target speech while suppressing sound from other angles,

Spatial centrality, whereby samples closer to the center of a dataset tend to be closer to all other samples, is regarded as one source of hubness. Hubness is well known to degrade k-nearest-neighbor (k-NN) classification. Spatial centrality can be removed by centering, i.e., shifting the origin to the global center of the dataset, in cases where inner product similarity is used. However, when Euclidean distance is used, centering has no effect on spatial centrality because the distance between the samples is the same before and after centering. As described in this paper, we propose a solution for the hubness problem when Euclidean distance is considered. We provide a theoretical explanation to demonstrate how the solution eliminates spatial centrality and reduces hubness. We then present some discussion of the reason the proposed solution works, from a viewpoint of density gradient, which is regarded as the origin of spatial centrality and hubness. We demonstrate that the solution corresponds to flattening the density gradient. Using real-world datasets, we demonstrate that the proposed method improves k-NN classification performance and outperforms an existing hub-reduction method.

Hubness has been recently identified as a problematic phenomenon occurring in high-dimensional space. In this paper, we address a different type of hubness that occurs when the number of samples is large. We investigate the difference between the hubness in high-dimensional data and the one in large-sample data. One finding is that centering, which is known to reduce the former, does not work for the latter. We then propose a new hub-reduction method, called localized centering. It is an extension of centering, yet works effectively for both types of hubness. Using real-world datasets consisting of a large number of documents, we demonstrate that the proposed method improves the accuracy of k-nearest neighbor classification.

It is known that memory-based collaborative filtering systems are vulnerable to shilling attacks. In this paper, we demonstrate that hubness, which occurs in high dimensional data, is exploited by the attacks. Hence we explore methods for reducing hubness in user-response data to make these systems robust against attacks. Using the MovieLens dataset, we empirically show that the two methods for reducing hubness by transforming a similarity matrix(i) centering and (ii) conversion to a commute time kernel-can thwart attacks without degrading the recommendation performance.

In previous studies, we conducted several experiments, including identification tests for young and elderly listeners using /shi/–/chi/ (CV) and /ishi/–/ichi/ (VCV) continua. For the CV stimuli, confusion of /shi/ as /chi/ increased when the frication had a long rise time, and /chi/ was confused with /shi/ when the frication had a short rise time. This was true for the group with the following auditory property degradation: 1) elevation of absolute threshold, 2) presence of loudness recruitment, and 3) deficit of auditory temporal resolution. When auditory property degradation was observed, the weighting of acoustic cues shifted to frication duration rather than the gradient of the amplitude of frication. The latter was calculated by dividing frication amplitude by rise time. In the VCV stimuli, confusion of /ichi/ as /ishi/ occurred for a long silent interval between the first V and C with auditory property degradation, and the weighting of acoustic cues shifted from the silent interval to frication duration. In the present study, we unified these findings into a single framework and found that degradation of auditory properties causes listeners to prefer duration of frication as a cue for identifying fricatives and affricates. Index Terms: elderly listeners, hearing impairments, aging, weighting shift of acoustic cues, trading relations, speech perception, fricatives/affricates

This paper presents kernel regularization information criterion (KRIC), which is a new criterion for tuning regularization parameters in kernel logistic regression (KLR) and support vector machines (SVMs). The main idea of the KRIC is based on the regularization information criterion (RIC). We derive an eigenvalue equation to calculate the KRIC and solve the problem. The computational cost for parameter tuning by the KRIC is reduced drastically by using the Nyström approximation. The test error rate of SVMs or KLR with the regularization parameter tuned by the KRIC is comparable with the one by the cross validation or evaluation of the evidence. The computational cost of the KRIC is significantly lower than the one of the other criteria.