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.     

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.

     

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.     

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.     

万宇怦“小天王”

北京市昌平区中关村生命科学园，一栋6层高的企业独栋办公楼里，万字平每天穿梭在办公室与实验室之间，未见他有停下来的时候。     

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.     

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.     

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.     

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.     

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。     

中文在线：用数字出版传承中华文明

科技与文化的融合，在诸多产业掀起了无数朵美丽的浪花，如果有一朵能第一眼就吸引你的眼球。那一定是浪高最高的数字出版业这一朵。最新数据显示，2011年中国数字出版产业总产值达到1400亿元，占新闻出版业总收入10％左右。根据新闻出版业“十二五”发展规划，“十二五”期末我国数字出版业营收将占国民收入的12％左右，成为当之无愧的支柱产业。     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.