Hisaaki Yamaba

On a serendipity-oriented recommender system based on folksonomy

This paper proposes a recommendation method that focuses on not only predictive accuracy but also serendipity. On many of the conventional recommendation methods, items are categorized according to their attributes (a genre, an authors, etc.) by the recommender in advance, and recommendation is made using the results of the categorization. In this study, impressions of users to items are adopted as a feature of the items, and each item is categorized according to the feature. Impressions used in such categorization are prepared using folksonomy, which classifies items using tags given by users. Next, the idea of “concepts” was introduced to avoid synonym and polysemy problems of tags. “Concepts” are impressions of users on items inferred from attached tags of folksonomy. The inferring method was also devised. A recommender system based on the method was developed in java language, and the effectiveness of the proposed method was verified through recommender experiments.

An Authentication Method Independent of Tap Operation on the Touchscreen of a Mobile Device

At the present time, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, are detected over the skin surface. Muscle movement can be differentiated by analyzing the s-EMG. In this paper, a series of experiments was carried out to investigate the prospect of an authentication method using s-EMGs. Specifically, several gestures of the wrist were introduced, and the s-EMGs generated for each motion pattern were measured. We compared the s-EMG patterns generated by each subject with the patterns generated by other subjects. As a result, it was found that each subject has similar patterns that are different from those of other subjects. Thus, sEMGs can be used to confirm one’s identification for authenticating passwords on touchscreen devices.

On a Serendipity-oriented Recommender System based on Folksonomy and its Evaluation☆

Abstract The present paper proposes a recommendation method that focuses not only on predictive accuracy but also serendipity. In many of the conventional recommendation methods, items are categorized according to their attributes (genre, author, etc.) by the recommender in advance, and recommendations are made using the categorization. In the present study, the impression of users regarding an item is adopted as its feature, and items are categorized according to this feature. Such impressions are derived using folksonomy. A recommender system based on the proposed method was developed in the Java language, and the effectiveness of the proposed method was verified through recommender experiments.

Evaluation of feature values of surface electromyograms for user authentication on mobile devices

At the present time, mobile devices, such as tablet-type PCs and smart phones, have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that use surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, are detected over the skin surface. Muscle movement can be differentiated by analyzing the s-EMG. In this paper, a method that uses a list of gestures as a password is proposed. And also, results of experiments are presented that was carried out to investigate the performance of the method extracting feature values from s-EMG signals (using the Fourier transform) adopted in this research.

Acquisition of AGV Control Rules Using Profit Sharing Method and Evaluation of the Rules

Myo^{TM}

Proposal of a Visualizing Method of Data Transitions to Support Debugging for Java Programs

MyoTM, which is the candidate of s-EMG measurement device used in a prototype system for future substantiative experiments, was used in the experiment together with the s-EMG measuring device used in the previous research to investigate its performance.

Proposal of a Supporting Method for Debugging to Reproduce Java Multi-threaded Programs by Petri-net

At present, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are detected over the skin surface, are generated by the electrical activity of muscle fibers during contraction. Muscle movement can be differentiated by analyzing the s-EMG. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In this paper, we introduced support vector machines (SVM) for improvement of the method of identifying gestures. A series of experiments was carried out to evaluate the performance of the SVM based method as a gesture classifier and we also discussed its security.

Proposal of a Supporting Method to Generate a Decision Table from the Formal Specification

When a simulation-based approach is adopted in order to design production systems under uncertain conditions, a design support environment that generates simulators of various configurations automatically is indispensable. Also, such environment is required to devise suitable operating rules of each design candidate at the same time.