• The Korean Journal of Air & Space Law and Policy
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• v.26 no.2
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• pp.125-147
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• 2011

Considering a trend of logistics and transport industry in these days, it can be said that international express courier service is one of the most familiar transport type to the general public. Especially in Korea, due to development of electronic commercial transaction and the popularity of television home shopping, it can easily anticipated that express courier business will continuously grown in the future. However, the legal basis for international express courier is not properly set up so far. The only clause about this can be found on Korean Aviation Law said as 'commercial documents delivery business'. The origin of the commercial documents delivery business in Aviation Law is to make exception from public postal services which has been exclusive status as monopoly based on the Korean Postal Law. Basically, according to this regulation, all the private postal delivery is prohibited except some sort of commercial documents such as consignment notes, packing list, invoice etc. Thus, those documents could be delivered not only by public postal services but also by private courier company according to the Korean Postal Law. This waiver has probably come from under developing condition of Korean postal circumstances, however it should be revised according to the modernized business practice. Reflecting these revisions, the articles of Korean Postal Law adopted 'international express courier document' as the exception of postal service. Therefore, Korean Aviation Law also needs to be revised as Postal Law in due course. In addition to revision of Korean Aviation Law, some sort of new legislation is required to govern the private legal aspects such as legal liabilities, duties and rights of each parties on international express courier. This should be governed by 'law' not by 'terms and conditions' provided by business operators. Furthermore, to support and develop the current domestic logistics companies as international express courier company, it is required to regulate with the separate express courier law.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• International Commerce and Information Review
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• v.13 no.4
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• pp.51-79
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• 2011

Korea has accomplished the establishment of the National Single Window for Paperless Trade. Since 1991, it has developed Trade Automation Service System based on EDI technology. In 2003, Korean government and private sectors jointly began to set up National Paperless Trade Service( e-Trade Service) as one of the e-government projects. In 2008, they commenced the uTradeHub Service which was equipped with Internet based e-B/L and e-Nego service systems for the first time in the world To facilitate the service Korea amended its e-Trade facilitation Act and Law by 2007. At the end of 2011, Korea historically recorded its trade volume of 1 trillion US dollars and joined '$1 trillion trade club' as the 9the member country since the country had started international trade less than five decades ago. A rolling out of the e-B/L and e-Nego service will 'ally reduce the transaction costs of trading businesses and accelerate the activation e-trade services. The purposes of the study are to examine 'e-B/L Korea' service and its facilitation strategies as well as identify obstacles to utilize the 'e-B/L Korea' service. The paper reviewed and analyzed Korea's Paperless trade system and distinctive characteristics of the 'e-B/L Korea Service. Parts of the fOWld distinctive characteristics of the Korea's e-B/L service are as follows; It is well equiped with IT and legal system. It also has more that 30,000 potential users who are already uTradeHub service users. The paper indicated several weaknesses of the current system such as global KPI issues, circulation of the electronic documents not only in the domestic market but also among economies, development of the electronic Bill of Exchange. As resolution measures, the paper recommended the introduction of mutual recognition system of PKI among trade partner counties, setting up e-trade solution for small and medium companies, and special attention to raise users' awareness of the e-B/L service.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.31 no.6
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• pp.515-525
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• 2005

Purpose of study: Lingual nerve damage can be caused by surgery or trauma such as physical irriatation, radiation, chemotherapy, infection and viral infection. Once nerve damage occurred, patients sometimes complain taste change and loss of taste along with serious disturbance of tongue. The purpose of this study was to evaluate the effects of unilateral lingual nerve transection on taste as well as on the maintenance of taste buds. Materials & Methods: Male Sprague-Dawley rats weighing 220-250g received unilateral transection of lingual nerve, subjected to the preference test for various taste solutions (0.1M NaCl, 0.1M sucrose, 0.01M QHCl, or 0.01M HCl) with two bottle test paradigm at 2, 4, 6, or 8 weeks after the operation. Tongue was fixed with 8% paraformaldehyde. After fixation, they were observed with scanning electron microscope(JSM-$840A^{(R)}$, JEOL, JAPAN) and counted the number of the dorsal surface of the fungiform papilla for changes of fungiform papilla. And, Fungiform papilla were obtained from coronal sections of the anterior tongue(cryosection). After cryosection, immunostaining with $G{\alpha}gust$(I-20)(Santa Cruz Biotechnology, USA), $PLC{\beta}2$(Q-15)(Santa Cruz Biotechnology, USA), and $T_1R_1$(Alpha Diagnostic International, USA) were done. Immunofluorescence of labeled taste bud cells was examined by confocal microscopy(F92-$300^{(R)}$, Olympus, JAPAN). Results: The preference score for salty and sweet tended to be higher in the operated rats with statistical significance, compared to the sham rats. Fungiform papilla counting were decreased after lingual nerve transaction. In 2 weeks, maximum differences occurred. Gustducin and $T_1R_1$ expressions of taste receptor in 2 and 4 weeks were decreased. $PLC{\beta}2$ were not expressed in both experimental and control group. Conclusion: This study demonstrated that the taste recognition for sweet and salty taste changed by week 2 and 4 after unilateral lingual nerve transection. However, regeneration related taste was occurred in the presence of preserving mesoneurial tissue and the time was 6 weeks. Our results demonstrated that unilateral lingual nerve damage caused morphological and numerical change of fungiform papilla. It should be noted in our study that lingual nerve transection resulted in not only morphological and numerical change but also functional change of fungiform papillae.

• Korean journal of communication and information
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• v.22
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• pp.131-166
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• 2003

The opening of advertising market and introduction of the free competition doctrine make the competition harsher among advertising agencies. Advertising agencies do their best to execute their ad more efficiently and scientifically. But, it is the reality that broadcasting advertising industry in korea did not construct enough infrastructure to execute the systematic activities compared with that of advanced countries. So, we need to grasp the present conditions and draw a time-table to construct primarily necessary infrastructures. In case of hardware infrastructure in advertising industry, digitalization of broadcasting and convergence of broadcasting with telecommunication make it hurry to construct that. But as the ad agencies was in the situation to compete each other, they have a difficulty to construct common hardware infrastructure enthusiastically. Thus, it is necessary to build hardware infrastructure in advertising industry for policy. And the construction of that should be executed systematically not for the short term effects but for the long term objectives. Also, it is the most important to construct reliable Software infrastructure in advertising industry from all of ad agencies. In these days, ad agencies have a tendency not to believe the important information, like the data of ratings and advertising transaction information, in relation to the advertising activities. And they do not share and communicate about the information of the advertising industry trends, research trends, advertisement related information. So, it is also hurry to build the on-line and off-line database system. Finally, for the development of brainware infrastructure in advertising industry, it is the most necessary to activate the cooperation relation between university and advertising agencies. Universities need to invite experts in the advertising to teach the students practical knowledge and ad agencies to recruit students who want to develop their carrier in the advertising industries. In conclusion, advertising industry in korea to solve these tasks for the development of advertising industry infrastructure in the way of cooperation and harmony of each other rationally and efficiently.

• The Journal of Society for e-Business Studies
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• v.19 no.4
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• pp.231-249
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• 2014

We define $21^{st}$ century as an amalgamation of globalization and localization, or Glocalization. Additionally, due to the increasing supply of smart phones and wide usage of social networking services, the ability to utilize such global and regional information has increased a coperation's competitiveness in its market, and even the business models have evolved from the conventional "production and distribution" to E-commerce, through which either a direct or a non-direct transaction is possible. My hypothesis is that the ability to adapt to this trend is possible through transfer of learning, and consequently, this will have an impact on company's performance. Thus, this thesis analyzes the mid- to the long-term impact of such ability and environmental factors on the performance and technology innovation of Korean companies in China. Ultimately, this study intends to engender a basic foundation for a corporation's management strategy in China. Finally this research focuses on those Korean companies in China only and on the proof of influential factors' impact on technological innovation and technological innovation's impact on those corporations' future performances. Section I is an abstract and section II, the case examines the uniqueness and current status of Korean companies in China identifies the concept and the definition of influential factors such as education-orientation, technological innovation, and performance, and then scrutinizes each factors through a closer look at their past researches. Section III explains the thesis model, the survey's method and target, the thesis, variable factors, the content, and the method of analysis. In section IV, the thesis is proved based on the outcome of the survey. The result in Section V highlights the high comprehension of technological innovation: both education-orientation and technological innovation prove to have a positive (+) correlation with the performance. The vision on education orientation proves to have a positive (+) influence on technological innovation. The vision on education-orientation and technological innovation prove to have a positive (+) influence individually on company's performance.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.45-55
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• 2013

Thereby using smartphone and mobile device be more popular the more people utilize mobile device in many area such as education, news, financial. In January, 2007 Apple release i-phone it touch off rapid increasing in user of smartphone and it create new market and these broaden its utilization area. Smartphone use WiFi or 3G mobile radio communication network and it has a feature that can access to internet whenever and anywhere. Also using smartphone application people can search arrival time of public transportation in real time and application is used in mobile banking and stock trading. Computer's function is replaced by smartphone so it involves important user's information such as financial and personal pictures, videos. Present smartphone security systems are not only too simple but the unlocking methods are spreading out covertly. I-phone is secured by using combination of number and character but USA's IT magazine Engadget reveal that it is easily unlocked by using combination with some part of number pad and buttons Android operation system is using pattern system and it is known as using 9 point dot so user can utilize various variable but according to Jonathan smith professor of University of Pennsylvania Android security system is easily unlocked by tracing fingerprint which remains on the smartphone screen. So both of Android and I-phone OS are vulnerable at security threat. Compared with problem of password and pattern finger recognition has advantage in security and possibility of loss. The reason why current using finger recognition smart phone, and device are not so popular is that there are many problem: not providing reasonable price, breaching human rights. In addition, finger recognition sensor is not providing reasonable price to customers but through continuous development of the smartphone and device, it will be more miniaturized and its price will fall. So once utilization of finger recognition is actively used in smartphone and if its utilization area broaden to financial transaction. Utilization of biometrics in smart device will be debated briskly. So in this thesis we will propose fingerprint numbering system which is combined fingerprint and password to fortify existing fingerprint recognition. Consisted by 4 number of password has this kind of problem so we will replace existing 4number password and pattern system and consolidate with fingerprint recognition and password reinforce security. In original fingerprint recognition system there is only 10 numbers of cases but if numbering to fingerprint we can consist of a password as a new method. Using proposed method user enter fingerprint as invested number to the finger. So attacker will have difficulty to collect all kind of fingerprint to forge and infer user's password. After fingerprint numbering, system can use the method of recognization of entering several fingerprint at the same time or enter fingerprint in regular sequence. In this thesis we adapt entering fingerprint in regular sequence and if in this system allow duplication when entering fingerprint. In case of allowing duplication a number of possible combinations is $\sum_{I=1}^{10}\;{_{10}P_i}$ and its total cases of number is 9,864,100. So by this method user retain security the other hand attacker will have a number of difficulties to conjecture and it is needed to obtain user's fingerprint thus this system will enhance user's security. This system is method not accept only one fingerprint but accept multiple finger in regular sequence. In this thesis we introduce the method in the environment of smartphone by using multiple numbered fingerprint enter to authorize user. Present smartphone authorization using pattern and password and fingerprint are exposed to high risk so if proposed system overcome delay time when user enter their finger to recognition device and relate to other biometric method it will have more concrete security. The problem should be solved after this research is reducing fingerprint's numbering time and hardware development should be preceded. If in the future using fingerprint public certification becomes popular. The fingerprint recognition in the smartphone will become important security issue so this thesis will utilize to fortify fingerprint recognition research.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.