Earticle

Recently, the problem of plagiarism has emerged as a big social issue because not only literature but also thesis become the target of plagiarism. Even the government requires conformation for plagiarism of high-ranking official’s thesis as a standard of their ethical morality. Plagiarism is not just direct copy but also paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. We propose a plagiarism detection scheme called a bit signature in which each unique word of document is represented by 0 or 1. The bit signature scheme can find the similar documents by comparing their absolute and relative bit signatures. Experiments show that a bit signature scheme produces better performance for document copy detection than existing similar schemes.

Due to development of smart and network technologies, social network service (SNS) has become popular in daily life. With increasing use of SNS, the scope of communication has been widened. Lots of activities can be easily shared daily on SNS. Differing with opinions about more activated communication on SNSs, some people have viewpoints that, in some cases, communications may be decreased among SNS users. This research is to investigate whether there may be the differences among generations in the social network age. Major concerns of study are as follows. First, there may be differences in age related to the decrease of communication activities. On the other hand, there may be differences among generations related to the increase of communication. Two research steps have been adopted that the FGI (focus group interview) was to understand. Defining actual communication status was the first step, and an empirical approach, a survey to verify hypotheses, was performed as the second step. Key findings of the study were that older generations perceived communication has been decreased. Research results can provide implications regarding characteristics of SNS communication.

For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.

Business intelligence has been attracting much attention these days. Despite such popularity of BI systems, it is widely known that about a half of BI system projects have failed. To grasp why many BI projects end in failure and what factors would make BI projects less failure-prone, a number of BI studies were made to produce a variety of CSFs. However, there is a paucity of information on whether these CSFs are distinctive from those of typical information systems. By identifying how BI CSFs differ from CSFs of typical information systems, we would be able to explain why most BI projects are more likely to be failure. It is believed that a corrective measure about CSFs will lead to more success in future BI projects. In addition, though there have been a number of similar types of BI systems such as decision support systems and executive information systems in existence, there was no study to determine whether there is ever a discrimination between CSFs of BI systems and the similarly-titled systems. This study is to answer these questions using a literature review analysis. The findings of our study are expected to be helpful in a successful implementation of BI systems.

This study is a subsequent research results of Lee and Hwang (2015) and presents results of the case in which an information system is developed and applied for the performance management scheme proposed by the early study. This study aims to provide the organizations that want to implement and apply an effective IS operational performance management scheme with the practical alternatives that they may consider. This study first summarize the major contents of the performance management scheme proposed by the early study. Then, the processes and detailed contents in which an information system is developed in order to operate and improve the performance management scheme. The specific contents provided by the study include the system development environments and methods, system architecture, and application structure. Finally, this study summarizes the major results of the study and suggests future research directions.

In Korea, ｢Act on the Development of Cloud Computing and Protection of its Users｣ has been enforced since September 28, 2015. Many countries implemented ‘Cloud First’ policies and global companies such as Amazon, Microsoft, IBM started cloud services in Korea. Under these circumstance, the Act was established for developing the cloud computing industry. The Act includes clauses for encouraging the use of private cloud computing by public organizations, supporting small- and medium-size cloud service providers, and utilizing secure cloud computing services by users. However, some terms appear to be similar but have different meanings from ｢Act on Promotion of Information and Communications Network Utilization and Information Protection, etc.｣ and ｢Personal Information Protection Act｣. This generated some confusion and conflicts in relation to providing user information to a 3rd party and notifying the intrusion in the Cloud Computing Act. This paper discusses these issues and suggestions for revision of the Cloud Computing Act.

This study aims at classifying and analyzing the business models of reverse overseas direct purchasing through online shopping-malls. After analyzing the current status of the reverse overseas direct purchasing online markets, this study identifies relevant critical business factors and synthesizes prior studies to construct and analyse the business models. This study proposes that the business models can be classified into five types according to the operation modes and three types according to the delivery methods. So theoretically 15 business models can be identified. For each business model this study analyzes the characteristics and the pros & cons. It also suggests deployment strategies for companies by considering cost reduction, brand establishment, customer management, customer aquisition, and easiness from the business perspective. From the customers perspective, cost reduction, reliability, royalty, ease to purchase, and accessibility can be considered according to the types of operation mode. The main contribution of this study is to provide the basic classifications and structures of reverse overseas direct purchasing business models systematically. As the result, our study evaluates the business models that which one is better than others in a situation in terms of company and customer. Lastly, we talk about limits and future prospects of the study.

Existing studies for recommender have focused on recommending an appropriate item based on the customer preference. However, it has not yet been studied actively to recommend purchase timing for the repurchase product despite of its importance. This study aims to propose MLP and RNN models based on the only simple purchase history data to predict the timing of customer repurchase and compare performances in the perspective of prediction accuracy and quality. As an experiment result, RNN model showed outstanding performance compared to MLP model. The proposed model can be used to develop CRM system which can offer SMS or app based promotion to the customer at the right time. This model also can be used to increase sales for repurchase product business by balancing the level of order as well as inducing repurchase of customer.

Cambodia retail industry starts to entry SNS market. The online market of Cambodia is very unique. Facebook users of Cambodia are purchasing products, without electronic payment and delivery system. Therefore, this study focused on the immature online environment, proved the influencing factors of purchase intention on SNS. And also this study proved that the influencing factors on purchase intention are different, depending on whether or not a purchase experience exists. As results of analyzing with full data, price reduction, convenience, and customer service had significant impacts on purchase intention. The experienced group has significant effects of price reduction and customer service on purchase intention, while the inexperienced group has significant effects of convenience and customer service on purchase intention. This study provides marketing and strategic implications for companies seeking to enter the online market of Cambodia.

As the number of people going aboard is growing and technology is developed rapidly, Chinese customers are also getting better understanding about overseas products, and they hope to get less expensive and better ones, which leads to the growth of the online overseas purchasing agent service. In this paper, we tried to analyze the factors that impact the usage of online overseas purchasing agent service using the survey data. We found that customers pursue not only the reasonable prices but also enjoyment of shopping in the online overseas purchasing agent service. In addition, product scarcity and the information literacy of a customer were positively related with the use of online overseas purchasing agent service.

This paper proposes a simple sound source localization (SSL) method based on signal energies comparison and partial cross correlation for TDOA computation. Many sound source localization methods include multiple TDOA computations in order to eliminate front-back confusion. Multiple TDOA computations however increase the methods’ computation times which need to be as minimal as possible for real-time applications. Our aim in this paper is to achieve the same results of localization using fewer computations. Using three microphones, we first compare signal energies to predict which quadrant the sound source is in, and then we use partial cross correlation to estimate the TDOA value before computing the azimuth value. Also, we apply a threshold value to reinforce our prediction method. Our experimental results show that the proposed method has less computation time; spending approximately 30% less time than previous three microphone methods.

This study proposes a novel recommendation algorithm that reflects the results from trust/distrust network analysis as a solution to enhance prediction accuracy of recommender systems. The recommendation algorithm of our study is based on memory-based collaborative filtering (CF), which is the most popular recommendation algorithm. But, unlike conventional CF, our proposed algorithm considers not only the correlation of the rating patterns between users, but also the results from trust/distrust relationship network analysis (e.g. who are the most trusted/distrusted users?, whom are the target user trust or distrust?) when calculating the similarity between users. To validate the performance of the proposed algorithm, we applied it to a real-world dataset that contained the trust/distrust relationships among users as well as their numeric ratings on movies. As a result, we found that the proposed algorithm outperformed the conventional CF with statistical significance. Also, we found that distrust relationship was more important than trust relationship in measuring similarities between users. This implies that we need to be more careful about negative relationship rather than positive one when tracking and managing social relationships among users.