This paper clarifies that the main factors of Kansei, which are the fundamental factors of human sensitivity, work well for accessing different kinds of media data having similar impression . First, the possibility of the existence of the common Kansei factors for pictures, sound clips, and video clips is shown . Five factors are exper-i mentally obtained for pictures and video clips. These factors are considered to be corresponding to one another . F our factors are obtained for sound clips. These are considered to be included in those for pictures and video clips . B y using these common Kansei factors, it is considered to be possible to adapt a kind of media data to another kind of media data based on impression. The prototype system based on this fact is constructed . This system compares the factor score of the key media data with those of other kinds of media data . For pictures and video clips, the comparison of factor scores is straitforward. For sound clips and the other types of media data , the factor scores of th e potency for sound clips are compared with those of the activity and of the potency for the other types of media data. This system is evaluated through the evaluation technique of the information retrieval . The evaluation results show that the prototype system can retrieve a kind of media data suitable to another kind of media data .

Abstract:In order to eliminate mismatches between the questioners and respondents at Question and Answer sites, this paper proposes a method of selecting respondents who can appropriately answer a question with the consideration of categories of answer statements. The proposed method uses the number of appearance of respondents and the score based on the distance between the factor scores of a question and an answer already posted. It has experimentally been evaluated by comparing it with the methods based on average scores and distances through precision and recall. There is room for improvement in obtaining precision by considering categories of answer statements. Therefore, finding respondents who can properly answer a newly posted question of the same category is investigated. As a result, considering categories of answer statements leads to the improvement of precision.

Summary The cyber security threats from phishing emails have been growing buoyed by the capacity of their distributors to fine‐tune their trickery and defeat previously known filtering techniques. The detection of novel phishing emails that had not appeared previously, also known as zero‐day phishing emails, remains a particular challenge. This paper proposes a multilayer hybrid strategy (MHS) for zero‐day filtering of phishing emails that appear during a separate time span by using training data collected previously during another time span. This strategy creates a large ensemble of classifiers and then applies a novel method for pruning the ensemble. The majority of known pruning algorithms belong to the following three categories: ranking based, clustering based, and optimization‐based pruning. This paper introduces and investigates a multilayer hybrid pruning. Its application in MHS combines all three approaches in one scheme: ranking, clustering, and optimization. Furthermore, we carry out thorough empirical study of the performance of the MHS for the filtering of phishing emails. Our empirical study compares the performance of MHS strategy with other machine learning classifiers. The results of our empirical study demonstrate that MHS achieved the best outcomes and multilayer hybrid pruning performed better than other pruning techniques. Copyright © 2016 John Wiley & Sons, Ltd.

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This paper tries to find the possibility of searching respondents capable of giving appropriate answers to a newly posted question in order to eliminate mismatches between the intentions of questioners and respondents of Question and Answer sites. The impressions of sixty questions and their responses posted at Yahoo! Chiebukuro have been experimentally evaluated. By applying factor analysis to the scores of the experiment, the impression of the statements could be captured by nine factors. Factor scores can be estimated by using the features including syntactic information, word imageability, closing sentence expressions, word familiarity, and notation validity. The good overall estimation accuracy has been obtained. The distances between the factor score of a question statement and those of answer statements already posted are calculated. It is shown that the distance and the number of appearance may help us select users who can give appropriate answers to a question. With the consideration of categories of answer statements, users who specialize in certain categories could be regarded to give more appropriate answers.