Masahiro Hamasaki

POLYPHONET: an advanced social network extraction system from the web

Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLYPHONET, which employs several advanced techniques to extract relations of persons, detect groups of persons, and obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents.Several studies have used search engines to extract social networks from the Web, but our research advances the following points: First, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social networking mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Super Social Network Mining is proposed; it utilizes simple modules using Google and is characterized by scalability and Relate-Identify processes: Identification of each entity and extraction of relations are repeated to obtain a more precise social network.

POLYPHONET: An advanced social network extraction system from the Web

Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLYPHONET, which employs several advanced techniques to extract relations of persons, to detect groups of persons, and to obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents. Several studies have used search engines to extract social networks from the Web, but our research advances the following points: first, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social network mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Iterative Social Network Mining is proposed. It utilizes simple modules using Google and is characterized by scalability and relate-identify processes: identification of each entity and extraction of relations are repeated to obtain a more precise social network.

TrBagg: A Simple Transfer Learning Method and its Application to Personalization in Collaborative Tagging

The aim of transfer learning is to improve prediction accuracy on a target task by exploiting the training examples for tasks that are related to the target one. Transfer learning has received more attention in recent years, because this technique is considered to be helpful in reducing the cost of labeling. In this paper, we propose a very simple approach to transfer learning: TrBagg, which is the extension of bagging. TrBagg is composed of two stages: Many weak classifiers are first generated as in standard bagging, and these classifiers are then filtered based on their usefulness for the target task. This simplicity makes it easy to work reasonably well without severe tuning of learning parameters. Further, our algorithm equips an algorithmic scheme to avoid negative transfer. We applied TrBagg to personalized tag prediction tasks for social bookmarks Our approach has several convenient characteristics for this task such as adaptation to multiple tasks with low computational cost.

Familial collaborations in a museum

Studies of interactive systems in museums have raised important design considerations, but so far have failed to address sufficiently the particularities of family interaction and co-operation. This paper introduces qualitative video-based observations of Japanese families using an interactive portable guide system in a museum. Results show how unexpected usage can occur through particularities of interaction between family members. The paper highlights the necessity to more fully consider familial relationships in HCI.

Songrium: a music browsing assistance service based on visualization of massive open collaboration within music content creation community

This paper describes a music browsing assistance service, Songrium (http://songrium.jp), that helps a user enjoy songs while seeing visualization of open collaboration. Songrium focuses on open collaboration for music content creation on the most popular Japanese video-sharing service. Since this open collaboration generates more than half a million video clips with a rich variety of music content, we call it massive open collaboration. To develop a shared understanding of this collaboration we have analyzed, we developed Songrium that visualizes relations among both original songs and derivative works generated from the collaboration. Songrium also features a social annotation framework to verbalize and share various relations among songs, and a flexible ranking mechanism to find interesting songs. After we launched Songrium in August 2012, more than 7,000 users have used our service in which over 98,000 songs and 520,000 derivative works have automatically been registered. We hope Songrium will not only encourage creators to create more derivative works, but also attract consumers to participate in the collaboration as creators.

Doing community: co-construction of meaning and use with interactive information kiosks

One of the challenges for ubiquitous computing is to design systems that can be both understood by their users and at the same time understand the users themselves. As information and its meaning becomes more associated with the communities that provide and use it, how will it be possible to build effective systems for these users? We have been examining these issues via ethnographic analysis of the information and community supporting system that we have developed and employed at conference events. This paper presents initial analysis and suggests greater focus on the interaction between members of micro-communities of users in future ubicomp research.

Discovering Relationships Among Catalogs

When we have a large amount of information, we usually use categories with a hierarchy, in which all information is assigned. The Yahoo! Internet directory is one such example. This paper proposes a new method of integrating two catalogs with hierarchical categories. The proposed method uses not only the contents of information but also the structures of both hierarchical categories. In order to evaluate the proposed method, we conducted experiments using two actual Internet directories, Yahoo! and Google. The results show improved performance compared with the previous approaches.

Community focused social network extraction

A social networking service can become the basis for the information infrastructure of the future For that purpose, it is important to extract social networks that reflect actual social networks which users have already had Providing a simple means for users to register their social relations is also important We propose a method that combines various approaches to extract social networks Especially, three kinds of networks are extracted: user-registered Know-link networks; Web-mined Web-link networks; and face-to-face Touch-link networks This paper describes the combination of social network extraction for an event-participant community Analyses on the extracted social networks are also presented.

Proposal of Handover System for Care Workers Using Community Intelligence

The experience and intuition gathered over many years of employment are extremely important for providing high-quality service in the fields of nursing and care. However, these experience and intuition are subjective, making it difficult to pass on experience and related know-how to a novice. To realize such field community intelligence, we have taken the approach that presenting records taken by other workers and their procedures will foster increased communication among workers and that, in doing so, knowledge and know-how will flow naturally among them. Consequently, by structuring the recorded information according to the work context, systematically organized knowledge can be exchanged and circulated. This chapter describes the handover system prototypes that can realize community intelligence by changing the handover workflow.

A New Model of Parallel Distributed Genetic Algorithms for Cluster Systems: Dual Individual DGAs

A new model of parallel distributed genetic algorithm, Dual Individual Distributed Genetic Algorithm (DuDGA), is proposed. This algorithm frees the user from having to set some parameters because each island of Distributed Genetic Algorithm (DGA) has only two individuals. DuDGA can automatically determine crossover rate, migration rate, and island number. Moreover, compared to simple GA and DGA methods, DuDGA can find better solutions with fewer analyses. Capability and effectiveness of the DuDGA method are discussed using four typical numerical test functions.