The Contents-Based Website Classification for the Internet Advertising Planning: An Empirical Application of the Natural Language Analysis

This study proposes a model for website classification using website content, and discusses applications for the Internet advertising (ad) strategies. Internet ad agencies have a vast amount of ad-spaces embedded in websites and have to choose which advertisements are feasible for place. Therefore, ad agencies have to know the properties and topics of each website to optimize advertising submission strategy. However, since website content is in natural languages, they have to convert these qualitative sentences into quantitative data if they want to classify websites using statistical models. To address this issue, this study applies statistical analysis to website information written in natural languages. We apply a dictionary of neologisms to decompose website sentences into words and create a data set of indicator matrices to classify the websites. From the data set, we estimate the topics of each website using latent Dirichlet allocation, which is fast and robust method for sparse matrices. Finally, we discuss how to apply the results obtained to optimize ad strategies.     

Bookmarks Recommendation in Bibsonomy using Community Detection

Several academic social networks have emerged to help researchers who need to search for documents relevant to their interests. The recommendation has been adopted in many websites to suggest relevant documents to users according to their profiles. However, many academic social networks and digital libraries still lack recommendations. In this paper, we propose a new document recommendation approach for the academic social bookmarking website: Bibsonomy. In our method, we use a community detection technique to identify related users. Then, for each target user, the recommended documents are selected from their learning communities. Experimental results show that the proposed method performs better than state-of-the-art recommendation methods.     

A Pricing Strategy with Consumption Externality when the Reference Group is Large

This paper provides an analysis of firms’ optimal prices when individual utility exhibits both material and other consumer effects. We construct a model and conduct an analysis in the following four steps: (1) By using a conjoint analysis of data, we set up a hierarchical Bayesian model and estimate its parameters, which are composed of individual utility functions. (2) We calculate the number of sales in each time period and derive the total sales throughout the given period. (3) We estimate the demand functions in the presence and absence of consumption externalities. (4) Finally, we calculate the profit functions and derive the optimal prices taking into account various product attributes. By applying our analysis to the book market of Japan, we find that the optimal price is lower when an externality is present than when absent. The intuition behind this result is that pricing low and selling a large number of books from an early stage increases the externality effect, yielding a higher profit for firms.     

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.     

The Substitution Effect of Mobile Games on Console Games: An Empirical Analysis of the Japanese Video Game Industry

In this paper, we discuss the substitution effects between mobile games and console games. We estimate such effects by a fixed-effect regression with instrumental variables using panel data of about 100,000 observations. The results showed that the substitution effects of playing smart devices on 3DS, Wii, and PSP were recognizable, but did not have significant effects on PS3, and the substitution effects on PSP were very small. Therefore, mobile games had a substitution effect on casual console games, or on console games in which the play situation resembled mobile games. In addition, the substitution effects were at most about 0.1. The substitution effects were small. Our results indicate that mobile games represent the pioneers in the new market of gaming at least during our observation period. But new games and traditional games will coexist for a while.     

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.     

Research Note: A High Resolution Graph Viewer for Multi-monitor Visualization Environment

Bigdata analysis is a hot topic in all fields of science. Particularly in scientific fields investigating data relations, observation and analysis of relation among diverse data sets has begun to play a role of great importance. A graph is a representative expression for the purpose and many graph viewers and tools prevail today. However, most of these graph viewers do not offer the capability for displaying graphs on a multi-monitor visualization environment, even though multi-monitor visualization environment facilitates scientists in understanding data in a more detailed and intuitive manner. In this paper, our first exploration towards a high resolution graph viewer for a multi-monitor visualization environment is explored and reported.     

An Advertising Strategy Using Consumption Externality and Forgetting in the Case of Japanese Electronic Books

We construct a two-period model in which a consumer recognizes the existence of goods after advertised by firms, and total sales of the first period affect the utility of each consumer’s purchase in the second period, indicating a consumption externality. Some consumers see advertisements in the first period and remember the product, whereas some forget the product in the second period. We show that the advertising volume changes given the differences in the forgetting rate. In particular, we apply our method to the data on Japan’s electronic books obtained through a conjoint analysis survey to clarify that a better strategy is to sell a product to a small number of people at a low price or to lower the price to a certain level during the early period, and then to sell the product to a specific consumer segment at a higher price after reflecting the externality.     

The Future of Socionetwork Strategies

In the first stage of creating technological improvements for a democratic society, the leaders of the Society of Socionetwork Strategies have conducted statistical research on the effect of information and communication technology on the global economy. In the second stage, younger professors, based on their Positive Studies, have leapt into a multi-agent simulation of Grid Computing. In the third stage, a terra-bite scale of social data-collecting and the mining of large-scale data in our real world will be next. The next generation of scientists will share knowledge and intellectual inquiry in the fields of statistics, mathematics, and the social sciences with the help of computers connecting such scholars from all over the world.     

港府“吃喝账单”三年仅600万的启示

曾荫权四年外出访问花费不足百万港元,公示账单记载了其每项活动日期、地点、行程项目、机票费用及其他开支情况。     

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.     

Data Mining Approach for Direct Marketing of Banking Products with Profit/Cost Analysis

Nowadays, many businesses, such as banks, use direct marketing methods to reach customers to minimize the campaigning cost and maximize the return rate. To achieve this, huge customer data should be analyzed to determine the most appropriate product offer for each customer and the most effective channel to reach her/him. However, since only a very small amount of responses collected from the customers are positive to the offers, the dataset is very imbalanced. This decreases sensitivity ratio of prediction results and makes it difficult to make a successful product and channel selection for the offer. In this paper, we propose a hybrid system, which first classifies the customers to decide if s/he is interested in the offered product, and then clusters them for product and channel suggestions. Experiments with real life banking data show very promising accuracy results for predicting the proper product and channel for the customers. Moreover, cost-profit analysis is also added to this problem. Our experiment results show that the proposed method decreases a fraction of the total profit, but since the decrease in the total cost is very large, there is a huge increase in the overall profit/cost ratio.     

Personal Authentication Using a Kinect Sensor

This article proposes a new approach to personal authentication by exploring the features of a person’s face and voice. Microsoft’s Kinect sensor is used for facial and voice recognition. Parts of the face including the eyes, nose, and mouth, etc., are analyzed as position vectors. For voice recognition, a Kinect microphone array is adopted to record personal voices. Mel-frequency cepstrum coefficients, logarithmic power, and related values involved in the analysis of personal voice are also estimated from the voices. Neural networks,support vector machines and principal components analysis are employed and compared for personal authentication. To achieve accurate results, 20 examinees were selected for face and voice data used for training the authentication models. The experimental results show that the best accuracy is achieved when the model is trained by a support vector machine using both facial and voice features.     

Facebook Content Strategies and Citizens’ Online Engagement: The Case of Greek Local Governments

This paper analyzes the communication strategies used by Greek local governments through the utilization of Web 2.0 technologies, specifically Facebook, and the effectiveness of these strategies in relation to citizens’ online engagement. More specifically, it examines Facebook communication strategies and levels of citizens’ engagement. For this purpose, we conducted a content analysis on the active and official Facebook pages of local municipalities in Greece from January 2017 until the end of September 2017. Our results suggest a rise in the percentage of active Facebook pages maintained by local governments in comparison to our 2014 study. Our results also show that local governments in Greece are using Facebook in a predominantly top-down manner to promote events organized by the municipality and to push one-way information to citizens about their services and actions. Local authorities have, however, made significant progress in relation to posts that support transparency and accountability and that enhance or mobilize citizens’ participation. Our evaluation of local government Facebook strategies indicates that marketing the municipality to external public, such as tourists, and providing information about services are effective strategies that drive citizens’ online attitude expression (liking), engagement (commenting), and advocacy behavior (sharing). According to our analysis, local governments in Greece prefer the strategies that we found to be the least engaging. In addition, our study provides interesting details of how specific characteristics and modes of Facebook messages (photos, videos, URLs, hashtags, and mentions) impact on citizens’ engagement. Finally, our results provide valuable insights for social media managers in local government who aim to increase the impact of their municipal Facebook pages.     

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.     

An Emergency Evacuation Planning Model Using the Universally Quickest Flow

In recent years, catastrophic disasters by massive earthquakes have been increasing in the world, and disaster management is required more than ever. In the case of disasters such as tsunamis, a slight delay in evacuation may deprive evacuees of life. In this article, we formalize the emergency evacuation planning model for evacuation from tsunamis and other disasters based on the idea of the universally quickest flow. We show that there does not always exist a universally quickest flow when the capacity constraint of refuges is taken into account. Therefore, we propose an alternative criterion that approximates a universally quickest flow, and presents an algorithm for finding an optimal flow for this criterion. Numerical experiments are carried out for the evacuation of a local city in Japan where tsunami damages are assumed to occur when a large earthquake occurs in the ocean nearby.     

Who Works at the Interface in Knowledge Spillover Across Organizational Boundaries?

To investigate the characteristics of engineers who act as an interface in transferring knowledge across national boundaries, I conducted an empirical analysis regarding the mobility of engineers in electrical manufacturing firms from Japan to South Korea. Statistical findings from negative binomial regressions show that the experience of working with foreign engineers facilitates knowledge spillover. In addition, the results show that interfirm knowledge transfer is more likely to occur when the engineers of the hiring firm work together with mobile engineers who (1) were focal members at their previous firm, (2) had a good connection with focal members, and (3) are young. This study suggests that engineers from external organizations are effective in knowledge transfer, especially if they are young, mobile engineers who were focal members at their previous firm and had a good connection with a focal member—and can act as an interface in transferring tacit knowledge.     

绿伞：技术先锋造的责任洗衣液

同样达到A级去污标准,绿伞洗衣液使用表面活性剂的浓度仅为12%,而同行至少需要达到25%,这意味着绿伞在能源消耗方面比同行至少节约50%。而先进的抗再沉积技术,使得洗衣过程只需漂水一次就很干净,这又意味着如果北京市每个家庭都使用绿伞洗衣液,那么北京就不用南水北调了!     

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.     

The Effect of Changes in Nuptiality Behavior after the 1980s on the Poverty Rate for the Elderly in Japan -Analysis Using a Dynamic Microsimulation Model-

The social security system in Japan was developed under the premise that postwar families represented the most common type of family. A “postwar famil” refers to a family in which: (1) men and women are married; (2) husbands work as regular employees and wives are dependent homemakers; and (3) husbands and wives seldom get divorced. Therefore, the social security system is particularly generous towards dependent wives and widows. However, these premises are no longer valid because Japanese nuptiality behavior has completely changed since the 1980s. Marriage rates have decreased and divorce rates have significantly increased. Nevertheless, society still suffers from a wage inequality between men and women. As a result, the number of never-married or divorced elderly women will increase, and these women might face a serious poverty risk in the future. In this study, the author makes simulations of the living arrangements and poverty rates for the elderly in Japan and evaluates the effect of changes in nuptiality behavior on these poverty rates using a dynamic microsimulation model. The simulation results indicate that changes in nuptiality behavior will affect the poverty rate for elderly women, but not for elderly men.