The illegal parking of bicycles is a serious urban problem in Tokyo. The purpose of this study was to sustainably build Linked Open Data (LOD) to assist in solving the problem of illegally parked bicycles (IPBs) by raising social awareness, in cooperation with the Office for Youth Affairs and Public Safety of the Tokyo Metropolitan Government (Tokyo Bureau). We first extracted information on the problem factors and designed LOD schema for IPBs. Then we collected pieces of data from the Social Networking Service (SNS) and the websites of municipalities to build the illegally parked bicycle LOD (IPBLOD) with more than 200,000 triples. We then estimated the temporal missing data in the LOD based on the causal relations from the problem factors and estimated spatial missing data based on geospatial features. As a result, the number of IPBs can be inferred with about 70% accuracy, and places where bicycles might be illegally parked are estimated with about 31% accuracy. Then we published the complemented LOD and a Web application to visualize the distribution of IPBs in the city. Finally, we applied IPBLOD to large social activity in order to raise social awareness of the IPB issues and to remove IPBs, in cooperation with the Tokyo Bureau.

We propose a programming language, Flage, for building software systems which dynamically adapt to changing local situations. In our language, we construct applications by agents; concurrent mobile objects with the metalevel architecture. Metalevel programming facilities realize a self-control of an agent's actions and an autonomous adaptation to changes. We also introduce another kind of program element called field. A field represents a local situation around agents. For example, one field represents a virtual place to get local information in a network environment and another represents a virtual place where agents do cooperative works. If an agent enters a field, it gets programs and shared information in the field. By moving field to field, an agent can change its program composition by itself and it adapts to changing local situations. In this paper, we describe the language specification of Flage, the implementation of the platform for Flage programming and show some program examples.

In many movies, social conditions and awareness of the issues of the times are depicted in any form. Even if fantasy and science fiction are works far from reality, the character relationship does mirror the real world. Therefore, we try to understand social conditions of the real world by analyzing the movie. As a way to analyze the movies, we propose a method of estimating interpersonal relationships of the characters, using a machine learning technique called Markov Logic Network (MLN) from movie script databases on the Web. The MLN is a probabilistic logic network that can describe the relationships between characters, which are not necessarily satisfied on every line. In experiments, we confirmed that our proposed method can estimate favors between the characters in a movie with F-measure of 58.7%. Finally, by comparing the relationships with social indicators, we discussed the relevance of the movies to the real world.

This paper describes the architecture to implement an application in network environments, which adapts to unexpected change in the development phase. In this architecture, an application is expressed as an agent which consists of two layers: base level and meta level. The base level program is an application program and the meta level program is the program that controls the execution of the base level and changes the base level program. Virtual places are also provided in the network. They are used for the release of programs and information which agents retrieve to change their own base level program. An application (or an agent), when a change is required, moves from places to places for the retrieval of programs to adapt to the change. A program search strategy is introduced to adapt to changes by using distributed thesauri of released programs, which realizes an agent's program retrieval method in network environments.

Interoperability between different systems is becoming a more important issue for open distributed systems. In this paper, we investigate what kind of framework we need for constructing open distributed systems. Firstly, we enumerate the features and functions which the framework should have. We then evaluate a proposed multi-agent framework, Bee-gent, by using a typical example of open distributed systems. Lastly, we show clearly what is required for such a framework.

The objective of this paper is to recognize and classify the poses of idols in still images on the web. The poses found in Japanese idol photos are often complicated and their classification is highly challenging. Although advances in computer vision research have made huge contributions to image recognition, it is not enough to estimate human poses accurately. We thus propose a method that refines result of human pose estimation by Pose Guide Ontology (PGO) and a set of energy functions. PGO, which we introduce in this paper, contains useful background knowledge, such as semantic hierarchies and constraints related to the positional relationship between body parts. Energy functions compute the right positions of body parts based on knowledge of the human body. Through experiments, we also refine PGO iteratively for further improvement of classification accuracy. We demonstrate pose classification into 8 classes on a dataset containing 400 idol images on the web. Result of experiments shows the efficiency of PGO and the energy functions; the F-measure of classification is 15% higher than the non-refined results. In addition to this, we confirm the validity of the energy functions.

Systems comprised of multiple interacting mobile agents provide an alternate network computing paradigm that integrates remote data access, message exchange and migration; which up until now have largely been considered independently. On the surface distributed systems design could be helped by a complete specification of the different interaction patterns, however the number of possible designs in any large scale system undergoes a combinatorial explosion. As a consequence this paper focuses on basic one-to-one agent interactions, or paradigms, which can be used as building blocks; allowing larger system characteristics and performance to be understood in terms of their combination. This paper defines three basic agent paradigms and presents associated performance models. The paradigms are evaluated quantitatively in terms of network traffic, overall processing time and size of memory used, in the context of a distributed DB system developed using the Bee-gent Agent Framework. Comparison of the results and models illustrates the performance trade-off for each paradigm, which are not represented in the models, and some implementation issues of agent frameworks. The paper ends with a case study of how to select an appropriate paradigm.