In this study, we implement an Ukiyo-e recommender system based on a restricted Boltzmann machine (RBM) for recommending Ukiyo-e prints to Ukiyo-e novices. This system can solve the problem of searching Ukiyo-e prints using keywords that are known only to Ukiyo-e experts. This system thus enables even an Ukiyo-e novice to obtain appropriate Ukiyo-e prints based on his interests. The user selects his interests amongst the few prints that are displayed initially. Then, with the use of an RBM, the system can find prints that match the user's interests. It can reconstruct the content information before making any recommendations.

Previously, we proposed a playlist recommendation method that recommends a music sequence that has smooth transitions of the acoustic features. Our previous method uses the last two songs in a playlist to make recommendations for following songs that have a smooth transition of acoustic features from the current songs. However, in this previous method, if two or more songs are not given, it is not possible to recommend a suitable next song. Also, there is insufficient correspondence to songs which changing characteristics, such as a tune that gradually rises with a quiet melody.

This paper proposes a music similarity retrieval method considering the shift of the pitch and the difference of tempos of music pieces. These are not considered in the current various services yet. The proposed method can retrieve music pieces arranged from the original ones as similar ones. The proposed method uses the pitch information and the length of the sound. The pitch and the tempo are normalized in calculating the similarity of music pieces. The effectiveness of the proposed method is experimentally shown.

As the advance of embodied conversational agent (ECA) technologies, there are more and more real-world deployed applications of ECAâ??s. The guides in museums or exhibitions are typical examples. However, in these situations, the agent systems usually need to engage groups of visitors rather than individual ones. In such a multi-user situation, which is much more complex than single user one, specialized additional features are required. One of them is the ability for the agent to smoothly intervene user-user conversation. In order to realize this, at first, a Wizard-of-Oz (WOZ) experiment was conducted for collecting human interaction data. By analyzing the collected data corpus, four kinds of timings that potentially allow the agent to do intervention were found. The collected corpus was then annotated with these defined timings by recruited evaluators with a dedicated and intuitive tool. Finally, as the trial of the possibility of automatic detection on these timings, the use of non-verbal low level features were able to achieve a moderate accuracy.

With the rapid performance improvement and popularization of sensor devices, a large amount of human action data can be captured in databases. Classification, recognition, searching, and mining of such human actions are promising applications. Although many of these applications have been developed, searching the large quantity of data, especially given the high dimensionality of the captured temporal data sequence is time-consuming. To reduce this time cost, we use a novel method for approximating a multi-dimensional time-series, named multi-dimensional time-series Approximation with use of Local features at Thinned-out Keypoints (A-LTK). With A-LTK applications for two human motion types, sign language and dancing, we found that the categorization of human action data and the search for the most similar human action became more accurate and reduced the time cost.

In this paper, a Smart and Quick Unexpected-Event Detector, SQUED, is proposed to detect and localize unexpected events, such as traffic accidents, using crowd sourcing with smartphone devices. When users find an event, they point their smartphones in the direction of the event. SQUED can determine the location and the time of the event using global positioning system (GPS) and geomagnetic sensor data transferred from the built-in devices. In SQUED, a user doesn't need to swipe or tap; he simply points the device. We developed SQUED on Android smartphones and evaluated our event-detection method in real-world settings. It was shown that SQUED can stably detect an event with high precision, even with noisy and fluctuating data for observed locations and directions.

Despite the rapid progress in the development of sensor technologies, as well as of information management using database technology, no appropriate database system exists for comprehensively archiving and searching human behavior processes. In this paper, we propose a human behavior database prototype system which can archive and support surgery activities. The database system can be used to find human processes similar to a given process and distinguish the differences between them. The significance of this prototype system is that it can implement a comprehensive archive and an efficient search of complex human activities. The system can be used to support medical skill training by presenting users with the proper surgery processes, and the differences that need to be noticed. In this paper, we first describe the basic concepts and structure design of the prototype system. We also discuss how our proposed system is suitable for human behavior process management.

A tropical cyclone is one of the most threatening natural phenomena and can result in great human and economic loss. To reduce the damage and protect people's lives, it is becoming increasingly important to predict the movement or track of a typhoon. Although there are several methods of predicting a typhoon track, the results are not sufficiently accurate to utilize when a typhoon is threatening a country or area. To reduce the prediction error, in this paper a multi-dimensional time series similarity method called Modified A-LTK, Approximation with use of Local features at Thinned-out Keypoints, is applied to the prediction. Our preliminary evaluation indicates that the error between the original data and the predicted data was reduced using Modified A-LTK compared with other existing methods such as DTW and AMSS.

With the aim of realizing a serendipity-oriented music recommendation, we analyzed the correlation between music similarity and serendipity. A user may be familiar with the musical piece if its metadata, such as the artist's names, and the title, is similar to the music he/she has ever listened to. In addition, a user may prefer the music if it is acoustically similar to the music he/she prefers. Based on these notions, we set up the following hypotheses: Hypothesis I: the user is familiar with the music is if the metadata-based similarity between it and the music he/she prefers is high. Hypothesis II: the music is preferred by the user if the acoustic-based distance between it and the music he/she prefers is low. Hypothesis III: the music is serendipitous (unexpected and useful) if the music has both a low metadata-based similarity and low acoustic-based distance with his/her preferred music. This paper presents our examination of the above hypotheses using data from 1,000 real musical recording.

In this paper, a novel method for subsequence matching of human behaviors is proposed. Since the human behavior data taken from many sensors monitoring human motions is a multi-dimensional time series, we extend an existing time-series approximation method, A-LTK (Approximation with use of Local features at Thinned-out Keypoints), to improve its performance as well as its accuracy in subsequence matching. Since A-LTK can change its approximation level using a parameter, the approach introduced in this paper uses two types of A-LTK levels, coarse followed by fine. The A-LTK-based Coarse-to-Fine subsequence matching method, called A-LTK 2.0, is discussed. We also evaluate the method, by comparing it with existing matching methods, DTW, AMSS, and the original A-LTK. The evaluations showed that A-LTK 2.0 is superior to the others in subsequence matching for long human-behavior sequences.

Social network sites such as Twitter and Facebook are used for sharing knowledge and information among users. As social networks grow larger, it becomes difficult for a user to find frequently followed group of social entities. Recently, the frequent following pattern (FFP) mining concept and method were proposed to extract patterns of the relationship between a set of following entities and their most frequently followed entities. In this paper, we propose two similarity definitions: FFP similarity and FFP-based Entity (FbE) similarity. These similarities can be used to recommend a new appropriate social entity to a “read-only-user”. In other words, these similarities can be defined only with followed-and-following (F-F) relationships and without additional information such as entity characteristics or entity access logs. To the best of our knowledge, this is the first attempt to define these similarity definitions for social entity recommendations. Some examples show the effectiveness of our similarity definitions by checking their satisfaction of established requirement.

Tourism recommender systems suggest suitable tourist spots by matching the characteristics of the tourist spots with those of the user. In this paper, we focus on an essential source of these characteristics—geotagged tweets. To solve the problem of associating geotagged tweets to tourist spots, we propose a mapping method that infers the region of a target spot on the basis of two geotagged items. The first is a geotagged tweet, which demonstrates that the tweeter was indeed at the target spot at the time the tweet was posted. We call this a “now-tweet.” The second item is a geotagged photo of the target spot, which we call a “spot-photo.” We regard these now-tweets and spot-photos as training data, and then determine the region of the tourist spot by inferring the geographical distribution of the training data. Next, we map geotagged tweets from the extracted region to the target spot. To improve the accuracy with which the tourist spot is inferred, we apply a clustering algorithm to the training data. Experimental results indicate that photo-based mapping with sophisticated training data produces the most improved performance over baseline methods. When applied to 4,559,643 geotagged tweets, our method maps them to tourist spots with an average granularity of 144.85 m.

Music is used by people for music therapy and provision of mental support. Recently, in the field of music information science, studies on music classification have been conducted. However, most of these studies have classified music on the basis of melody and tempo. No classification method that is based on lyrics has been proposed for music therapy and the provision of mental support. Lyrics are an important element of music in addition to melody and tempo. Therefore we propose a music classification method based on lyrics. In this study, we conducted the emotion weighting of non-emotional words by using similarity in order to improve the previously proposed method that uses only emotional words. We realized emotion extraction from the entire lyrics by using non-emotional words in the proposed method. Further, we showed that a relatively high classification precision is obtained when compared with the conventional method.

With the emergence of inexpensive network components and high-speed network services, a variety of network-capable electronic devices have become available. However, as no self-defence mechanism is equipped in a usual network device, the device can be attacked by other devices or by attackers, which causes it in a vulnerable state. In this paper, we propose a system for keeping an Intra-LAN in a secure state with isolation of such a network information device. The system the communications between devices in the LAN by introducing the safety-and-performance ratio control policies. The control policies are controlled dynamically. The isolation of a devices is performed using an intrusion detection system. A secure LAN can be maintained by the dynamic controlled policies.We have confirmed that the LAN can keep secure with actual LAN environment experiments of our system.

As the advance of embodied conversational agent (ECA) technologies, there are more and more real-world deployed applications of ECA's like the guides in museums or exhibitions. However, in these applications, the agent systems are usually used by groups of visitors rather than individuals. In such multi-user situation, which is more complex sophisticated than single user one, specific features are required. There can be difference in how and when to intervene in the conversation of others due to the variety of personality. In order to realize a more implement the human-like and more helpful guide agent, this work tries to explore the relationship between personality and the willing to intervene in users' conversation as the role of a guide. In this paper, the analysis results of the intervention action and personality traits are reported.

Music is used for people practicing sports, for elderly individuals, and to help train the mind. Recently in music information science, studies have been conducted on music therapy and on music classification from a therapeutic point of view. However, most of these studies have classified music based on melody and tempo. No classification method that is based on lyrics has been performed for music therapy support. In this paper, we propose a music classification method using emotional words included in lyrics toward music therapy. Music is classified by using a clustering method. In addition, this paper shows that a higher classification precision is obtained when compared with the conventional

In this paper, a novel graphical user identifier scheme for web-based mobile services. Despite a rapid progress in biological authentication technologies for user identification, the traditional text-based UserID-and-password scheme has still widely been used even for mobile web-services. In the mobile usage, a virtual keyboard is difficult to use for input texts, which causes much time to input a text. To decrease the difficulty, GUIDES (Graphical User IDEntifier using Sketching) is proposed in this paper. With GUIDES, a user can input its user identifier, called GUID (Graphical User ID), by drawing a sketch on a mobile touchscreen device. From our experiments, it is concluded that GUIDES can enable a user input his/her User-ID more efficiently and remember it more, compared with the text-based user-id scheme.

Although much research on Music Information Retrieval (MIR) has been done in the last decade, the input of the current MIR to specify a user query for finding a similar piece of music is still either by the existing old-fashioned keywords or by music contents. We aim to realize a new type of MIR equipped with brain-computer interfaces using electroencephalogram (EEG) signals. Toward the new MIR, we propose an architecture of MIR driven by EEG signals in this paper. While the architecture contains many issues to be solved, the point of the architecture is to construct user's music query in multi-layered aggregation of EEG signals. We describe in this paper the preliminary experiments conducted for selecting some appropriate low-level features for our multi-layered query construction and matching. It is obtained that the mental states of users while listening to music can be classified with high accuracy by using EEG signal aggregated features. We are starting development of detailed design of the architecture using the results described in the paper.

In this paper, we propose a new graphical password using object-based image ranking, called OBIR, which enables appropriate images to be presented to users during authentication. Research on graphical password is being conducted and receiving more and more public attention due to its potential of being an alternative for textual password. However, the main problem of graphical password is its vulnerability to shoulder surfing attacks, especially on mobile devices where the login password is easily visible in public. In order to overcome this issue, we propose a novel graphical password using image ranking method based on the objects in the image itself. The higher the ranking of an image is, the more appropriate it is to be user's selection without exposing too much information of the password to the shoulder-surfer. Upon our experiments, it is obtained that our image ranking method is effective in filtering appropriate pass-image that even if it is selected in public, the password is safe and therefore resistant to shoulder surfing.

There have been some innovative research studies for detecting the Most Popular Route (MRP) using GPS devices in order to support tourists who travel in an unfamiliar area. The MPR is a route on which many moving objects move the most among the entire possible routes. Current MRP detection methods do not take into account the time of trajectory measurement, however, road conditions vary depending on a time zone. Therefore, the detected MRP may not be an appropriate route that was defined outside of the certain time zone. The aim of this study is to propose a new method to detect the MRP which is capable of considering a time zone of trajectory measurement. In addition to the new method, "Popularity Measure" is proposed in order to verify the suitability of the detected MRP. The detected MRP using the existing and proposed method are evaluated by compared from a viewpoint of this popularity measures.

In this paper, we propose a novel method to detect music genre borders for the music genre classification. The music genre classification is getting more important because music is influenced by an increasing amount of different musical styles. A general approach to classify music genres is a single genre labeling that usually gives the meaning of inherent stylistic elements to a musical piece. However this gives ambiguity in case of a music piece having multiple genres. To solve the problem, we consider separating the multi-label classification task into the single-label genre classification task. We propose a novel method to detect music genre borders for multi-label genre classification. The proposed method can find borderlines of different genres in music. Moreover, it is strongly expected to realize the multi-label genre classification to apply the single-label genre classification to each detected music segment.

With the emergence of inexpensive network components and high-speed network services, a variety of network-capable electronic devices have become available. Typical examples of such devices include printers, network access storage (NAS), and video recorders. Because software on these devices is not always kept up-to-date, the devices are susceptible to intra-local- area-network (LAN) attacks. In this paper, a novel network system architecture is proposed to protect network devices from intra-LAN attacks by dynamically isolating infected devices with OpenFlow. Preliminary evaluation results demonstrate that the architecture is effective in actual LAN environments.

In this paper, we propose a novel recommendation method for photo-taking points from a large amount of social community photo collections. There are many research activities on photo-related recommendations from a lot of photos stored and managed by photo sharing web services, such as Flickr, Picas a and Panoramio, Although some methods, such as landmark recommendation, tag recommendation and photo recommendation have already been proposed, no photo-taking point recommendation methods have been realized yet for social photo collections. In order to realize photo-taking point recommendation, we introduce a novel point and photo selection method based on nested clustering. From our experiments, it is shown that better recommendation accuracy with our proposed method can be attained.

An emergency system of sharing and exchanging user's personal information is demanded in medical treatment and disaster situations. In such a system, personal information access control depending on user situations is greatly necessary. However, it is complicated to manage personal information access control directly, because the existing access control methods only support regular access control, not for an emergency case. In this paper, we propose a new access control model, called STRAC, which enables access control of user personal information with consideration of context changes. In our proposed model, a concept of situations is introduced. Moreover, the proposed model is based on a concept of TMAC, which is an extension of a conventional RBAC model.

In this paper, we propose a music retrieval method based on the distributions of features in the music. In common music retrieval methods, if several features are similar between the query and the retrieval target, the retrieval systems return that the query is similar to the retrieval target. However, a problem is that several features in the music are ignored. If the other features in the query and the retrieval target are quite different, the query and the retrieval target should be treated as different types of music. Therefore, we calculate the importance of each feature in the music. Then, we compare the importance of features between the query and the retrieval target, and we can retrieve the music without ignoring the importance of several features. In our experimental evaluation, we can confirm that our proposed system has better accuracy than the baseline method.

Time-series data, such as stock exchange rates and weather data, has widely been used in many fields. Similarity search of time-series data is important because it is useful for predicting data changes and searching for common sources. In this paper, we propose a new similarity search method of time-series data using both a discrete Fourier transform (DFT) and wavelet transform (WT). A method of reducing time-series indexing size, using a correlation coefficient, is also presented.

Describes a database query language needed to produce a visual database interface, an important element in improving current database interfaces. The interface is based on a term input technique, which is sequence independent, i.e. in which a user can input any predefined terms in any order to specify a query. The more he uses this interface, the better the results he may be expected to obtain. The author presents an interpretation technique, based on a conceptual graph, which is used to interpret a database query represented in this database query language. The language can be applied to the production of a visually-oriented interface, one in which terms are visualized.>

This paper describes a modification for the unified dynamic hashing method presented by J. K. Mullin.The main advantage of this modified dynamic hashing method is that it provides a single file access to a record, while the unified dynamic hashing method may require several accesses for records in buckets that oYerfl.owed.This method is spatially efficient because it does not use indexes or tables commonly used by other dynamic hashing methods.

An infological database design system (AIDS), which provides a systematic approach for,designing a mixed data model system, is proposed. Two conceptual level languages are used to model application areas. An information definition language CIDL) is used to specify information structure and information semantics related to each application process. According to a schema model specification, AIDS generates a prototype of source schema through the IDL internal model (Basic Object Model). The user interactively designs the logical and physical schema until a complete schema is obtained. An application process definition language (APDL) is used to specify process behaviors. AIDS also provides a data dictionary facility.