An Exploratory Look at Supply Chains in Japan from Multiscale Network Perspectives

In social network analysis, advances in social networking and computing techniques have increasingly become a popular approach for extracting features and rules of real-world networks. The network language—\(G=\{V, E \}\) provides a common representation of various networks, where G, V, and E denote the system, components, and interactions, respectively. In this study, we employ this emerging technique to discuss supply chains in Japan. We construct the supply network (i.e., system) based on the firms (i.e., components) and their transactional relationships (i.e., interactions). In comparison with the traditional approaches of industrial sectors and regional clusters, this study represents an exploratory look at supply networks, and investigates different scales of supply networks from three perspectives. (1) In the macro-scale perspective, we evaluate the “small-world” separation of supply networks using average path length. (2) In the meso-scale perspective, we detect communities of the supply networks, which can be marked for cross-location and cross-industry features. (3) In the micro-scale perspective, we investigate the “scale-free” nature of supply networks and each community using node degree-prior connections, which can find “hub” firms and simultaneously estimate the robustness of supply networks using a sequential elimination choice strategy of these hubs.     

Implementation issues in a grid-based multi-agent simulation system used for increasing labor supply

This paper describes issues of implementation for a Grid-based multi-agent simulation used to support decision-makers in making their decisions. Recently, social scientists including sociologists, economists, and social psychologists, have become interested in large- scale multi-agent simulation. However, users who are not familiar with operating large-scale computing resources may have a difficult time implementing these simulations. Therefore, the authors have developed a computing environment with a task-control support framework for a day care center allocation problem. In addition, we show how to implement the Grid-based multi-agent simulation system. In this paper, we examine the effectiveness of the proposed system using experiments with human subjects. By using the proposed prototype system, decision-makers were able to find near optimal solutions without examining all possible tasks.     

Social Network Rebuilder: A Tool to Estimate a Social Network of Financial Crisis Propagation

The objective of this paper is to find a new method to estimate real social networks based on observed data collected by questionnaire surveys. Studies on social networks have been increasing in order to analyze social phenomena from a micro viewpoint. Most social phenomena can be explained by micro-level interactions among people. Spread of rumor and pandemics are typical example of micro interaction? However, there has not been much work on an analysis of real social networks based on observed data. This study tries to establish a methodology that exploits a genetic algorithm to rebuild a social network based on the data observed indirectly from real social networks. This paper introduces our proposed method, which allows us to rebuild a social network to some extent from degree distributions of a target real social network.     

An Interactive Independent Topic Analysis for a Mass Document Review Service

In this paper, we propose an interactive constrained independent topic analysis in text data mining. Independent topic analysis (ITA) is a method for extracting independent topics from document data using independent component analysis. In this independent topic analysis, the most independent topics between each topic are extracted. By extracting the independent topic, managing documents with a large number of text data is easy with document access support systems and document management systems. However, the topics extracted by ITA are often different from the topics a user requests. For the system to be of service to users, an interactive system that reflects the user’s requests is necessary. Thus, we propose an interactive ITA that works for the user. For example, if there are three topics, i.e., topic A, topic B, and topic C, and a user choose the content from topics A and B, a user can merge those topics into one topic D. In addition, if a user wants to analyze topic A in more detail, a user could separate topic A into topics E and topic F. To that end, we define Merge Link constraints and Separate Link constraints as user requests. The Merge Link constraint is a constraint that merges two topics into one topic. The Separate Link constraint is a constraint that separates two topics from one topic. In this paper, we propose a method for extracting a highly independent topic that meets these constraints. We conducted evaluation experiments on our proposed methods, and obtained results to show the effectiveness of our approach.     

Comments’ Extraction by User ID,Keywords, and Anchor Texts from the Thread of a Bulletin Board System

Bulletin Board Systems (BBSs) on the Web are used by many users posting comments on threads. Each thread carries a subject of discussion. Most users post comments related to a subject. However, unrelated comments are also posted. The unrelated comments distract users and diminish their understanding of the entire story of a thread. Comments unrelated to a subject have to be filtered automatically. This paper proposes a method that extracts comments related to a thread subject from a thread of BBS. The method extracts two types of comments: comments related to a thread subject (main comments), and comments related to main comments (sub-comments). The main comments are extracted by a user ID and keywords in the comments. The sub-comments are extracted by explicit and implicit anchor texts in the comments. We experimented with the proposed method and verified that comments extracted by the proposed method support participants in understanding the entire story of a thread.     

Agent-based simulation approach for disaster rescue using active RFID

After the 2004 earthquake in Japan’sNiigata Prefecture, there were many difficulties finding the victims. The earthquake caused a wired network disconnection. In addition, fixed-phone and cell-phone service systems crashed due to the rapid increase in the volume of communications. The systems’ collapse was due to the large number of users who were worried about the safety of their relatives. The same situation happened during Hurricane Katrina in the USA. Reflecting on these situations, the Japanese Ministry of Internal Affairs and Communications is trying to adopt several methods to correct these communications problems. Although these methods have not been disclosed precisely yet, we think that a predominant goal of one of the methods is most likely to find victims using RFIDs (Radio Frequency Identification) with computer sensors. To do this, the RFID method would have to be incorporated in the following ways: 1) by scattering sensor devices from helicopters when a large-scale disaster such as an earthquake occurs, 2) by making RFIDs self-organized into a network, and 3) by making the self-organized network pinpoint the position of victims. A certain amount of field-testing may be necessary to confirm the effects of this plan. However, such testing requires a great amount of time and high costs. Therefore, we adopt an agent-based simulation for such a test. In this paper, we pay specific attention to a simple and assured algorithm for discovering victims using RFIDs. First, we explain our method to confirm the location of victims. Second, we explain our method for pinpointing the position of victims using trilateration. Finally, we present our simulation results.     

An Algorithm of Recommending Apposite ID Photos

This article proposes an algorithm to recommend apposite ID photos for users by judging the photo of which the facial expression is apposite or not as the ID photo. Microsoft’s Kinect sensor is used for taking photos. Parts of the face, such as eyes, nose, and mouth, are analyzed as explanatory variables for judging face expression. Some body coordinate information such as head and shoulders is used to trim the photos. Neural networks and support vector machines are employed and compared to our proposed method. To achieve accurate results, ten examinees including specialized staff are selected for taking ID photo used for training models. A series of experiments are conducted to examine the validity. As a result, the accuracy of neural networks is better than that of the support vector machine. Furthermore, we analyze and discuss the difference between system results and specialized staffs’ opinions.

     

A Conjoint Analysis of Demand for the Japanese Mobile Phone Market

The Japanese mobile phone market is 55.8% of the total market in the telecommunications industry. This paper is a conjoint analysis on Japanese consumer preferences for Japanese mobile phones. The analysis in this paper is presented after a questionnaire survey was given to 3,412 Japanese mobile phone users over 15 years old. The results of this analysis show that a decline in the price of mobile phone sets, an increase in transmission speed, the application of family discounts, and the continuation of contracts with the same telecommunication companies, all raise demand for mobile networks. In addition, the author found that user groups of NTT DOCOMO, au (KDDI), Softbank, and MVNO have different utility functions. According to the estimation of Mean Willingness to Pay (MWTP) in this paper, suppliers of mobile phone networks may be able to optimize and maximize demand by adjusting attributions and their levels.     

对学术造假者仅撤销奖励还不够

治理学术不端行为,不仅需要勇气和魄力,更需要制度的完善和法律的跟进。当前,最紧迫的是要建立学术腐败专门处理机构,公开接受社会举报,将学术腐败置于公众监督之下。     

Mining History of Gamification Towards Finding Expertise in Question and Answering Communities: Experience and Practice with Stack Exchange

Recently, an online Q&A tool has become an essential part of individual communities and organizations of experts on specific topics. Using the answers to questions about specific topics will help such communities work more efficiently in their fields. Currently, Q&A online communities are adopting gamification to engage users by granting awards to successful users. In this paper, we investigate how to mine award achievement histories to find expertise. We propose the use of sequence analysis and clustering techniques. Specifically, we study the history of Stack Exchange, a large Q&A community that employs gamification. To the best of our knowledge, this is the first study of using award achievement history to find expertise in Q&A communities.     

An Internet Access Solution: MANET Routing and a Gateway Selection Approach for Disaster Scenarios

After natural and man-made disasters, the telecommunication infrastructure is usually severely damaged, thus hampering communication and rescue services. It is impossible for disaster victims to make use of communication devices such as cellular phones, iPads, or their laptops to make a connection with the outside world (Internet). With infrastructure less and decentralized features, the mobile ad hoc network (MANET) can play an important role in improving communication in post-disaster affected areas. Therefore, the important functionalities of a MANET that allow users to create dynamically configurable wireless networks without fixed infrastructure using common devices such as mobile phones is necessary. This paper reports on the development of new techniques for routing selection and gateway load balancing in MANETs. Network fairness, throughput, and packet delays are measured empirically. The proposed mechanisms can reduce network congestion and consequently improve communication in affected areas.     

Broadband Demand Analysis for Network Building

An increase of broadband demand is forecasted by transitional methods that consider the effect of this increase through many factors, such as customer requirement diversification, and new service introduction and deployment under competition. Broadband demand forecasting has become important for closing the digital divide, promoting regional developments, and constructing networks economically; therefore, a demand forecast model that considers the mechanisms of market structure is necessary. In this paper, a demand analysis method for broadband access combining macro- and micro-data mining is proposed, and the service choice behaviour of customers is introduced as a customer model not only to express the macro trend of market structure, but also to consider area marketing. The proposed method can estimate the potential demand, determine the point at which broadband demand growth peaks in a specified area, and support a decision for ultra high-speed broadband access facility installation.     

Fuzzy Similarity Matching Method for Interior Design Drawing Recommendation

A cosine similarity matching method for a binary measurement scale was proposed and applied to a recommender system in our previous study for retrieving interior design drawings. However, for mixed types of intervals, nominal, ordinal, or ratio scales, this matching method fails, as the cosine similarity measure function is not defined for ordinal and nominal values. Compared to our previous study, this paper proposes a new fuzzy similarity matching method for mixed measurement scales and applies the matching method to a recommender system. A numerical case study was carried out to demonstrate the effectiveness and capabilities of the proposed similarity matching method for handling interior design drawing recommendation problems.     

Evaluation of a Multi-robot Cafe Based on Service Quality Dimensions

While AI applications are popular in many domains, they should work harmoniously with domain exerts and end users. Furthermore, to develop such harmonious AI applications, we need agile AI platforms for not only developers, but also domain experts. Currently, we have developed PRactical INTElligent aPplicationS (PRINTEPS), which is a user-centric platform to develop integrated intelligent applications. This paper reports on a multi-robot cafe as a practical application of PRINTEPS and evaluates its service quality at a university festival. It is not clear if robotic services are perceived as attractive and/or valuable, and how the concept of robotic services could lead to customer satisfaction. Therefore, the evaluation of the quality of such services is necessary to identify the key factors that could contribute to improving customer satisfaction. The purpose of this research is to identify key factors in improving customer satisfaction in robotic services by evaluating the service quality of the multi-robot cafe. We designed questionnaire items based on SERVQUAL which is one of the service quality evaluation measurement methods and conducted a questionnaire survey at a multi-robot cafe held at a university festival. From the collected data, we modeled and evaluated the relationship between service quality and customer satisfaction to identify key factors in robotic services using a Bayesian network. In addition, the experiment confirms the usefulness of PRINTEPS.     

Married Women’s Lifestyles in Japan: Disparities Based on the Number of Children

In order to explore the complex mechanisms of married women’s decisions in matters of childbirth, we studied empirically the relations between family size and aspects of lifestyle through a questionnaire survey administered in 2006 to married women living in the city of Suita, Japan, a suburb of Osaka. Lifestyle was taken as a complex of mutual relationships among individual (biographical, psychological) and social (socioeconomic, social support) factors, and our aim was to clarify differences in Japan in lifestyle among married women based on the number of their children. Analysis of 495 respondents showed mothers with only a single child were more self-reliant: they tend to give birth at a late age, enjoy cooperation with their husbands in family finances, be psychologically at ease, and not seek help from others in child rearing. Mothers with three or more children embody more of the traditional role for Japanese women: they tend to give birth at an early age, be reliant on their husbands financially, be less at ease psychologically, and seek others’ help in child rearing. These differences suggest that more focus on psychological, not just economic support in government policy to counteract declining fertility is an important issue for future consideration.     

A Statistical Analysis Between Consumer Behavior and a Social Network Service: A Case Study of Used-Car Demand Following the Great East Japan Earthquake and Tsunami of 2011

When a large-scale disaster hits a community, especially a water-related disaster, there is a scarcity of automobiles and a sudden increase in the demand for used cars in the damaged areas. This paper conducts a case study of a recent massive natural disaster, the Great East Japan Earthquake and Tsunami of 2011 to understand those car scarcities and demand in the aftermath of the catastrophe. We analyze the reasons for the increase in demand for used cars and how social media can predict people’s demand for used automobiles. In other words, this paper explores whether social media data can be used as a sensor of socio-economic recovery status in damaged areas during large-scale water-related disaster-recovery phases. For this purpose, we use social media communication as a proxy for estimating indicators of people’s activities in the real world. This study conducts both qualitative analysis and quantitative analysis. For the qualitative research, we carry out semi-structured interviews with used-car dealers in the tsunami-stricken area and unveil why people in the area demanded used cars. For the quantitative analysis, we collected Facebook page communication data and used-car market data before and after the Great East Japan Earthquake and Tsunami of 2011. By combining and analyzing these two types of data, we find that social media communication correlates with people’s activities in the real world. Furthermore, this study suggests that different types of communication on social media have different types of correlations with people’s activities. More precisely, we find that social media communication related to people’s activities for rebuilding and for emotional support is positively correlated with the demand for used cars after the Great East Japan Earthquake and Tsunami. On the other hand, communication about anxiety and information seeking correlates negatively with the demand for used cars.     

Investment Literacy and Individual Investor Biases: Survey Evidence in the Japanese Stock Market

In this paper, we analyzed the results of our survey on Japanese stock investor’s asset risk management and empirically examined the effect of their investment literacy on their decision-making biases. This paper revealed that the higher the investors’ investment literacy, the lower their overconfidence bias. This suggests that high investment literacy can prevent investors from being influenced by an overconfidence bias and consequently making biased decisions in investment. Therefore, efforts to improve investors’ investment literacy by enhancing social systems such as investment education could be beneficial in guiding investors to make unbiased investment decisions.     

Did Social Security Improve Labor Productivity?

This note statistically investigates the relationship between social security expenditure indexes and labor productivity during 1980- 2009 in Japan based on the Solow type of production function. In the case of real social security expenditure per labor and per total working hours, labor productivity decreases with respect to real social security. On the other hand, in the case of real social security expenditure per private capital stock, labor productivity increases with respect to real social security. However, the partial regression coefficients of these three indexes are not significant statistically. Therefore, we could not derive a clear relationship between the social security indexes and labor productivity.     

BLOCKS: Efficient and Stable Online Visualization of Dynamic Network Evolution

It is computationally and cognitively expensive to observe the evolution of community structure on dynamic networks in real-time, not only because the data sets tend to be complex, but also because the visual interfaces are often complicated. We introduce BLOCKS, a simple but efficient framework to abstract and visualize the evolution of community structure on dynamic networks. Instead of indicating detailed changes of nodes and links temporally, BLOCKS regards communities as visual entities and focuses on representing their behaviors and relation changes on a time series. Experiments detected a stable performance of BLOCKS compared with previous methods while detecting the community structure of networks. We also present a case study that shows an effective learning process of network evolution with BLOCKS.