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.

     

Personal Identification Through Pedestrians’ Behavior

This article focuses on a new approach for personal identification by exploring the features of pedestrian behavior. The recent progress of a motion capture sensor system enables personal identification using human behavioral data observed from the sensor. Kinect is a motion sensing input device developed by Microsoft for Xbox 360 and Xbox One. Personal identification using the Microsoft Kinect sensor (hereafter referred to as Kinect) is presented in this study. Kinect is used to estimate body sizes and the walking behaviors of pedestrians. Body sizes such as height and width, and walking behavior such as joint angles and stride lengths, for example, are used as explanatory variables for personal identification. An algorithm for the personal identification of pedestrians is defined by a traditional neural network and by a support vector machine. In the numerical experiments, pictures of body sizes and the walking behaviors are captured from fifteen examinees through Kinect. The walking direction of pedestrians was specified as 0°, 90°, 180°, and 225°, and then the accuracies were compared. The results indicate that identification accuracy was best when the walking direction was 180°. In addition, the accuracy of the vector machine was better than that of the neural network.     

Prediction Model for Bollywood Movie Success: A Comparative Analysis of Performance of Supervised Machine Learning Algorithms

The main purpose of this paper is to do a comparative analysis of prediction models using various machine learning techniques. The models will be used to predict whether a movie will be a hit or flop before it is actually released. The techniques used for comparisons are decision tree, random forest (RF), support vector machine, logistics regression, adaptive tree boosting, and artificial neural network algorithms. The major predictors used in the models are the ratings of the lead actor, IMDb ranking of a movie, music rank of the movie, and total number of screens planned for the release of a movie. The results of most models indicated a reasonable accuracy, ranging from 80 to 90%. However, models based on two techniques, RF and logistic regression, achieved an accuracy of 92%. From the results, the most important predictors of a movie’s success are music rating, followed by its IMDb rating and total screens used for release.

     

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.     

The Perceptual Gaps Between Companies’ and University Students’ Views on Fundamental Competencies

This study examines the gap between the competencies that companies expect of university students and those that university students believe companies are seeking. We find that a gap exists between companies’ and students’ evaluations of the competencies required in the workplace. Surprisingly, companies’ and students’ priorities related to competencies are different. Our results show that both companies and students highly value versatile personal competencies as essential skills for employees. Teamwork skills are not a top priority for companies although students value them highest of the various attributes. Gaps also exist between the two groups regarding their evaluation of personality traits. These findings contribute to the development of appropriate curricula for universities and career education.     

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.     

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.     

类“番茄花园案”要复制到何时？

面对反垄断的官司,微软在美国、欧盟、韩国没有一次胜诉,都得老老实实缴纳巨额罚款了事。微软的继续高价垄断,普通消费者就会继续保持对盗版的巨大需求,＂番茄花园案＂再次发生的土壤就依然存在。     

A Conjoint Analysis of a Next Generation Network (NGN) in Japan

A conjoint analysis on a Web survey in 2010 with 160 participants and 8 questions shows that a Marginal Willingness To Pay (MWTP) on a Next Generation Network (NGN) of Flet’s Hikari Next is lower than its actual price as provided by NTT East. Conjoint analysis is one application of conditional logit. MWTP is WTP when one unit of property is increased, and MWTP is used to verify how much each person evaluates each property. Higher transmission speed, digital TV availability, and higher Internet security affect the MWTP positively, and the amount of the monthly payment of NGN affects the MWTP negatively. Therefore, it is assumed that the NGN of Flet’s Hikari Next by NTT East will be accepted by potential customers and diffused as a major communications service of NTT East and NTT West. NGN providers may be able to maximize their benefits by adjusting the NGN of Flet’s Hikari Next property to meet customer demands, especially in digital TV availability, higher transmission speed, and communications security.     

A Practical Teacher–Robot Collaboration Lesson Application Based on PRINTEPS

To support elementary school teachers in teaching by encouraging active learning while maintaining the interest of pupils, this study focuses on supporting teaching, learning, and monitoring the progress of students through a Teacher–Robot collaboration lesson application using not only laptops and tablets, but also robots and sensors. Since developing a lesson application is time consuming for teachers, we have developed an integrated intelligent application development platform named PRactical INTElligent aPplicationS (PRINTEPS) to aid Teacher–Robot collaboration. However, several functions and interfaces for education are missing. Therefore, in this study, we extend several functions for education to PRINTEPS. In addition, since it is necessary in learning and monitoring the progress of students to present learning content suitable to each pupil’s level of understanding, we also have provided support through the use of a tablet quiz system based on ontologies and rule bases. In the case study, we developed a Teacher–Robot collaboration lesson application and conducted lessons for sixth-grade pupils at an elementary school. From the case study, we have confirmed the effectiveness of our platform and the application.     

学术牛虻的重任不能指望饶毅们

试图通过个体反抗或内部改良的传统暴力不合作路径，来换取在体制内的思考自由与改革呼声，从来都是行不通的。     

A New Type of Computer Premium and Its Correlation to Individual Wages

This paper examines statistical correlations between workers?? income levels or wages, and Information and Communication Technology (ICT). The analyzed data set is micro data collected through the Web in 2008 and 2010 by the Research Institute for Socionetwork Strategies. This micro data estimates the rank correlation of income classes with respect to three types of ICT labor service. Next, multiple linear regression functions are estimated for the wage levels with respect to age, sex, years of continuous employment, and three types of ICT labor service over all industries and in the manufacturing industry or service industry. Finally, a multiple regression analysis is conducted for individual utility levels with respect to income, leisure, and three types of ICT labor service. The results show that, first, the coefficients of rank correlation for all pooled data are positive and statistically significant. Second, individual ICT labor services have positive effects on workers?? wage levels, and the partial regression coefficients are statistically significant. Third, the partial regression coefficient of personal computer operation time decreased but the partial regression coefficient of mobile computer operation time related to work increased drastically from 2008 to 2010. Fourth, the use of the personal and mobile computer shifts the utility function upward with respect to income and leisure.     

Research Note: Analysis of the Characteristics of Victims in Information Security Incident Damages: The Case of Japanese Internet Users

In this article, the authors investigate the attributes of victims in information security incident damages for the purpose of reducing the damages. The Information-Technology Promotion Agency, Japan, in 2010, conducted an Internet survey targeted to Japanese Internet home users entitled, “Survey of awareness toward information security incidents” that is used in this article. Using micro data collected from this survey, they employed multinomial logit regression analysis to show factors affecting the user’s experience of the incidents of damage through particular incidents. They concluded that the overconfidence regarding information security knowledge increased the probability for phishing and spoofing.     

江南古镇寻同里

同里气魄非凡,在上千年前就看到了天下一家,地球会成为一个村的未来,喊出了天下同里的真理。     

Defect Detection Using Unanimous Vote Among Mahalanobis Classifiers for Each Color Component

In manufacturing industries, product inspection is automated and the use of image data is increasingly being employed for defect detection. A manufacturing company in Japan produces an item and inspects the produced products using image data. Reducing the error rate is important in product inspection because poor inspection of products might lead to the delivery of defective products to consumers (consumer’s risk) and strict inspection increases production cost (producer’s risk). To reduce the error rate, we highlighted fault points using a two-dimensional moving range filter and discriminated defect production through a unanimous vote among Mahalanobis classifiers for each color component. For results, we achieved a lower error rate than the current system. This research is an empirical study of how to use image data in defect detection.     

The Boosting Effect of E-WOM on Macro-level Consumption: A Cross-Industry Empirical Analysis in Japan

In this study, we carry out an empirical analysis on how electronic word-of-mouth (hereinafter “e-WOM”) marketing on e-WOM websites and electronic-commerce websites on the Internet boosts consumption on a macro-level. In our analysis, we conduct a model analysis of consumer behavior using data composed of more than 30,000 questionnaire surveys and quantitatively find the elasticity coefficient of the boost to consumption by performing a two-step GMM (generalized method of moments), which uses instrumental variables. The results of the analysis show e-WOM significantly increased expenditures in six fields: computers, electrical appliances, etc.; music; hobbies; clothing, accessories, etc.; beauty products, etc.; and goods for everyday life, etc. Furthermore, there was no field that had a significantly negative value. These results showed that, in the majority of the target fields, e-WOM had not only the effect of winning customers from the competition, but also the effect of boosting consumption on a macro-level. In addition, even from people’s subjective evaluations, there were many in all the generational groups who said that e-WOM boosted expenditures.     

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 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.     

银发市场的金色机遇

中国＂人口红利＂时代即将终结。中国的劳动力人口日益老化并逐渐步入退休年龄。到2050年,60岁及以上人口将从2010年的约1.65亿激增至近4.4亿,将占中国人口总数的34%左右。     

Graph-Based Entropy for Detecting Explanatory Signs of Changes in Market

Graph-based entropy, an index of the diversity of events in their distribution to parts of a co-occurrence graph, is proposed for detecting signs of structural changes in the data that are informative in explaining latent dynamics of consumers’ behavior. For obtaining graph-based entropy, connected sub-graphs are first obtained from the graph of co-occurrences of items in the data. Then, the distribution of items occurring in events in the data to these sub-graphs is reflected on the value of graph-based entropy. For the data on the position of sale, a change in this value is regarded as a sign of the appearance, the separation, the disappearance, or the uniting of consumers’ interests. These phenomena are regarded as the signs of dynamic changes in consumers’ behavior that may be the effects of external events and information. Experiments show that graph-based entropy outperforms baseline methods that can be used for change detection, in explaining substantial changes and their signs in consumers’ preference of items in supermarket stores.