An Approach to Security for Unstructured Big Data

Security of Big Data is a huge concern. In a broad sense, Big Data contains two types of data: structured and unstructured. Providing security to unstructured data is more difficult than providing security to structured data. In this paper, we have developed an approach to provide adequate security to unstructured data by considering types of data and their sensitivity levels. We have reviewed the different analytics methods of Big Data to build nodes of different types of data. Each type of data has been classified to provide adequate security and enhance the overhead of the security system. To provide security to a data node, and a security suite has been designed by incorporating different security algorithms. Those security algorithms collectively form a security suite which has been interfaced with the data node. Information on data sensitivity has been collected through a survey. We have shown through several experiments on multiple computer systems with varied configurations that data classification with respect to sensitivity levels enhances the performance of the system. The experimental results show how and in what amount the designed security suite reduces overhead and increases security simultaneously.     

中关村翠湖科技园：高端产业加速器

在中关村翠湖科技园已建成的28.29万平方米科技企业加速器里,200家企业贡献了115亿元收入,平均产出强度为4亿元/万平方米。在原动力空间二期的某些企业独栋,平均产出强度达到6.7亿元/万平方米。中关村翠湖科技园创新不止,奇迹不断。     

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.     

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.     

Simulation Analysis of a Dynamic Reallocation-based Routing Functionality for SAGE Application

A large amount of scientific data is generated through computation and analysis. Also, today’s scientific research necessitates an aggregation of diverse expertise from experts with different backgrounds. Therefore, visualizing and sharing these data among scientists in an intuitive and easy manner is of more importance in scientific collaborative research than ever. We have focused on SAGE, a middleware that realizes a large-scale remote visualization leveraging network streaming technique as a promising building block technology. However, no advanced network control function for efficient use of the network is equipped with SAGE although SAGE relies on a network streaming techniques. Our previous research integrated a functionality that detected network failures avoided such failure by rerouting network flows of SAGE into the functionality. Nonetheless, a network congestion avoidance functionality to adapt a collection of network flows generated by SAGE to the underlying network has not been developed yet. In this paper we explore the feasibility of a reallocation-based dynamic routing functionality for SAGE, with which all network flows on the network are rearranged. For feasibility research, we perform simulation experiments to observe the behavior of the functionality. The evaluation confirms that our proposed functionality works correctly and brings better throughput for each flow.     

Sales Strategy Mining Support with Visualization of Moving History

Strategic usage in data mining of sales data has come under increased focus as a result of the growth of data mining studies using electrical data, such as Point of Sale (POS) data. However, many of these mining systems only output results such as frequently appearing patterns, while, instead, they should be aimed at people who mine data. In this paper, a framework for leading and supporting new ideas from data is introduced. Based on this framework, a system that supports strategic usage in the data mining of sales data is proposed. The system deals not only with POS data, but also with the moving history of each customer. The system visualizes POS and moving history data, so that an analyzer inputs conditions to narrow down customers and acquires customer features. In addition to the ordinal outputs of a mining system, the analyzer interprets the meaning of his/her input and output to create a new strategy based on the framework. According to the experimental results, new strategies derived from the moving history were created.     

实创股份：金秋送爽“大丰收”

2011年以来．实创股份所属园区新签约入驻企业70多家。其中7月份就有25家企业入驻签约。新落户企业预计达产后将实现总收入240亿元以上,有助于快速提升北部研发服务和高新技术产业聚集区经济规模体量,有助于加快形成战略性新兴产业领域的高端集群。     

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 Role of Cross-Border Networks of Skilled Labor in Offshore Outsourcing: Empirical Evidence Based on the Network Theory

This study investigates the determinant role of the cross-border movement of skilled labor in the expansion of service trade between the US, and both developed and developing countries. For this purpose, we employ the key concepts of network theory as an analytical framework and conduct panel data analysis and graphical modeling analysis for 31 countries from 1999 to 2008. In this decade, offshore outsourcing in the service trade took off worldwide. We use data for each country’s service exports to the US, number of H-1B visas issued, GNI per-capita, network readiness index, and an English dummy for the official language. We illustrate the trajectory and interactions between these factors. These analyses yield three observations. First, service trade with the US is more intensive among higher income countries. Second, the number of H-1B visas issued has a positive effect on service exports to the US. Third, individuals in lower income countries tend to desire H-1B visas and create intensive skilled labor networks with the US, the path through which developing countries such as India expanded their service exports to the US.     

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.     

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.     

中关村昌平园留创园：新星闪耀

“我们希望吸引更多留学人员来昌平留创园创业．这是当下最要紧的工作。”海归们回国创办企业实现”中国梦”，他们愿意成为帮助他们成功的“圆梦人”。     

Dynamic Microsimulation Modeling for Policy Support: An Application to Belgium and possibilities for Japan

This paper provides an overview of dynamic microsimulation modeling as well as its recent developments. It then discusses in detail a recent trend in microsimulation research, which uses alignment methodologies to simulate microsimulation models in conjunction with macro-economic models. The goal of this ??top down-integrated approach?? is to jointly assess the budgetary costs of demographic ageing and pension reform, as well as their consequences on the adequacy of pensions. This paper then discusses, also in detail, how such an integrated approach using shared demographic and macroeconomic assumptions has been developed in Belgium. It describes the dynamic microsimulation model MIDAS, highlighting how it aligns to the simulation results of the semi-aggregate model MALTESE. Finally, this paper suggests that the joint assessment using the joint application of two similar models might be useful to assess pension reform in the Japanese context.     

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.     

Designing an Interactive Visualization to Explore Eye-movement Data

For many user applications large data sets may be collected passively and unobtrusively in the flow of their activity, and on scales ranging from the individual to increasingly larger communities. Large data sets, however, bring a concomitant need for tools to help understand what the data indicates. With the emergence of smart eyewear and the availability of sophisticated but affordable eye-tracking devices, eye movement data becomes a source of detailed information about a user’s focus and indirectly about their cognition and attention. Visualizing this usefully in terms meaningful for diagnosis however, remains a challenge. In this paper we report a new data representation from significant data sets generated by a gaze-controlled digital reading application for second language speakers. Current tools provide data sets aimed primarily towards statistical analysis of patterns: our focus is on end-user exploration of data sets in domain terms, so that practical implications can be readily identified. The visualization of horizontal eye movement data allows rapid diagnosis of problem areas in texts, informing educators immediately of individual or wider issues. The general applicability of this visualization to other applications is discussed.     

The Effectiveness of Social Media for Business Activities in Japanese Firms

A large number of firms use social media for various business activities. This paper clarifies how social media has become effective to sales in Japanese firms. To this purpose, the following are addressed: (i) whether social media is useful for company activities, (ii) what kinds of business activities are affected using social media, and (iii) how social media improves the final outcomes of sales for firms. This study uses data of business activities and social media conducted in 2014. Probit analysis and structural equation modeling (SEM) are employed. Probit analysis clarifies the relationships between the effects of social media and its use in sales. The SEM model clarifies the relationships among the effects of social media use in business. In the SEM, a model is examined by taking sales caused by use of social media as an explained variable, while the explanatory variables are the first and intermediate effects of social media. The results indicate that social media is effective for business activities such as promoting sales. This study may help to promote social media in Japanese firms.     

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.     

一种可借鉴的经济预测法——读《我在高盛的经济预测法》

凡事预则立,不预则废。无论你承认不承认,你都已经被卷入了经济的漩涡之中,如何在这个漩涡之中“立”而不“废”呢,这就需要对经济走势或日经济周期的把握了。《我在高盛的经济预测法》,就是一本谈论预测经济走势的书。作者约瑟夫·H·埃利斯,是高盛集团的合伙人,一位有着35年行业分析背景并连续18年被著名杂志《机构投资者》评为华尔街第一零售业分析师的天才。     

不妨给院士候选人评选引入社会监督机制

我国工程院院士在人数上已经足以和英国皇家学会媲美了,但在科研成果上我们还停留在第三世界水平。