• The Journal of Bigdata
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• v.6 no.2
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• pp.99-108
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• 2021

Recently, unexpected and more advanced cyber medical treat attacks are on the rise. However, in responding to various patterns of cyber medical threat attack, rule-based security methodologies such as physical blocking and replacement of medical devices have the limitations such as lack of the man-power and high cost. As a way to solve the problems, the medical community is also paying attention to artificial intelligence technology that enables security threat detection and prediction by self-learning the past abnormal behaviors. In this study, there has collecting and learning the medical information data from integrated Medical-Information-Systems of the medical center and introduce the research methodology which is to develop the AI-based Net-Working Behavior Adaptive Information data. By doing this study, we will introduce all technological matters of rule-based security programs and discuss strategies to activate artificial intelligence technology in the medical information business with the various restrictions.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.185-191
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• 2020

This article proposes the unified an arm's length price of transfer pricing for thin capitalization since the scope of permanent establishment has been enlarged under Digital Economy and the implementation of Blackchain system to resolve the drawback of finding an arm's length price. The rule of current thin capitalization runs against the non-discrimination of taxation of the tax treaties and the national treatment which deals fairly with goods, sercice and capital money within the country under the treaty of commerce and navigator. In addition, the information of comparable uncontrolled debt are not available of current system to prove the debt which is not subject to the rule of thin capitalization. The united an arm's length price of transfer pricing for thin capitalization can apply to foreign investment as well as domestic corporations, thereby resolving the problem of the non-discrimination of taxation of the tax treaties and the treaty of commerce and navigation. The availability of transaction level data through Blockchain platform to decide whether the debt can be subject to thin capitalization can resolve the issue of comparable uncontrolled debt transaction which can't be found in current business transactions. This article should shed light on the proposing of the unified an arm's length price of transfer pricing for thin capitalization and Blockchain system to prevent the income shifting. This propose provide implication for policymakers on current system of thin capitalization and arm's length principles.

• International Commerce and Information Review
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• v.14 no.1
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• pp.171-195
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• 2012

This paper is aimed to prepare some policy-measures which is helpful for China Money FDI in Korea by analysing FDI-related data and political strength and weakness between the two countries and studying fundamental preconditions required for Korea's China Money FDI strategies. As the result of research, key preconditions found out can be summarized as follows; First, because China-Korea economic relationship is largely insufficient in a complementary view as well as in a cooperative state level, Korea should remove the threats in advance that could lead two countries to unlimited competition, and then expand to a relationship of trust between China and Korea. Second, Korea, at least from the perspective of China, may not be an attractive investment destination. Therefore, it is necessary to take advantage of Korea's FTA-expansion-strategy opportunities such as Korea-US FTA which has entered into force recently. Third, because China always has a lot of alternative investment opportunities among world instead of Korea, so Korea should not overlook the fact that China has the bargaining power in large part related on the investment conditions in Korea, such as investment field, investment size, how to invest China Money to Korea, etc. Fourth, if Korea's FDI policy is trapped in the existing rules of the political frame, and moreover Korea can not have the role of rule breaker, it will be difficult to expect Korea's China Money FDI results compared to those efforts. Fifth, if Korea will execute China Money FDI strategies in the context of overestimating the China Power or China Money, it should be noted that Korea may have unexpected losses lead to a national by reason of outward and quantitative investment or bad investment.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.

• Review of Korean Society for Internet Information
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• v.14 no.3
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• pp.78-85
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• 2013

The revelations made possible by Edward Snowden, a contractor of the US intelligence service NSA, are a sobering reminder that the Internet is not an 'anonymous' means of communication. In fact, the Internet has never been conceived with anonymity in mind. If anything, the Internet and networking technologies provide far more detailed and traceable information about where, when, with whom we communicate. The content of the communication can also be made available to third parties who obtain encryption keys or have the means of exploiting vulnerabilities (either by design or by oversight) of encryption software. Irrebuttable evidence has emerged that the US and the UK intelligence services have had an indiscriminate access to the meta-data of communications and, in some cases, the content of the communications in the name of security and protection of the public. The conventional means of judicial scrutiny of such an access turned out to be ineffectual. The most alarming attitude of the public and some politicians is "If you have nothing to hide, you need not be concerned." Where individuals have nothing to hide, intelligence services have no business in the first place to have a peek. If the public espouses the groundless assumption that State organs are benevolent "( they will have a look only to find out whether there are probable grounds to form a reasonable suspicion"), then the achievements of several hundred years of struggle to have the constitutional guarantees against invasion into privacy and liberty will quickly evaporate. This is an opportune moment to review some of the basic points about the protection of privacy and freedom of individuals. First, if one should hold a view that security can override liberty, one is most likely to lose both liberty and security. Civilized societies have developed the rule of law as the least damaging and most practicable arrangement to strike a balance between security and liberty. Whether we wish to give up the rule of law in the name of security requires a thorough scrutiny and an informed decision of the body politic. It is not a decision which can secretly be made in a closed chamber. Second, protection of privacy has always depended on human being's compliance with the rules rather than technical guarantees or robustness of technical means. It is easy to tear apart an envelope and have a look inside. It was, and still is, the normative prohibition (and our compliance) which provided us with protection of privacy. The same applies to electronic communications. With sufficient resources, surreptitiously undermining technical means of protecting privacy (such as encryption) is certainly 'possible'. But that does not mean that it is permissible. Third, although the Internet is clearly not an 'anonymous' means of communication, many users have a 'false sense of anonymity' which make them more vulnerable to prying eyes. More effort should be made to educate the general public about the technical nature of the Internet and encourage them to adopt user behaviour which is mindful of the possibilities of unwanted surveillance. Fourth, the US and the UK intelligence services have demonstrated that an international cooperation is possible and worked well in running the mechanism of massive surveillance and infiltration into data which travels globally. If that is possible, it should equally be possible to put in place a global mechanism of judicial scrutiny over a global attempt at surveillance.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.195-211
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• 2010

An exhibition is defined as market events for specific duration to present exhibitors' main product range to either business or private visitors, and it also plays a key role as effective marketing channels. Especially, as the effect of the opinions of the visitors after the exhibition impacts directly on sales or the image of companies, exhibition organizers must consider various needs of visitors. To meet needs of visitors, ubiquitous technologies have been applied in some exhibitions. However, despite of the development of the ubiquitous technologies, their services cannot always reflect visitors' preferences as they only generate information when visitors request. As a result, they have reached their limit to meet needs of visitors, which consequently might lead them to loss of marketing opportunity. Recommendation systems can be the right type to overcome these limitations. They can recommend the booths to coincide with visitors' preferences, so that they help visitors who are in difficulty for choices in exhibition environment. One of the most successful and widely used technologies for building recommender systems is called Collaborative Filtering. Traditional recommender systems, however, only use neighbors' evaluations or behaviors for a personalized prediction. Therefore, they can not reflect visitors' dynamic preference, and also lack of accuracy in exhibition environment. Although there is much useful information to infer visitors' preference in ubiquitous environment (e.g., visitors' current location, booth visit path, and so on), they use only limited information for recommendation. In this study, we propose a booth recommendation methodology using Sequential Association Rule which considers the sequence of visiting. Recent studies of Sequential Association Rule use the constraints to improve the performance. However, since traditional Sequential Association Rule considers the whole rules to recommendation, they have a scalability problem when they are adapted to a large exhibition scale. To solve this problem, our methodology composes the confidence database before recommendation process. To compose the confidence database, we first search preceding rules which have the frequency above threshold. Next, we compute the confidences of each preceding rules to each booth which is not contained in preceding rules. Therefore, the confidence database has two kinds of information which are preceding rules and their confidence to each booth. In recommendation process, we just generate preceding rules of the target visitors based on the records of the visits, and recommend booths according to the confidence database. Throughout these steps, we expect reduction of time spent on recommendation process. To evaluate proposed methodology, we use real booth visit records which are collected by RFID technology in IT exhibition. Booth visit records also contain the visit sequence of each visitor. We compare the performance of proposed methodology with traditional Collaborative Filtering system. As a result, our proposed methodology generally shows higher performance than traditional Collaborative Filtering. We can also see some features of it in experimental results. First, it shows the highest performance at one booth recommendation. It detects preceding rules with some portions of visitors. Therefore, if there is a visitor who moved with very a different pattern compared to the whole visitors, it cannot give a correct recommendation for him/her even though we increase the number of recommendation. Trained by the whole visitors, it cannot correctly give recommendation to visitors who have a unique path. Second, the performance of general recommendation systems increase as time expands. However, our methodology shows higher performance with limited information like one or two time periods. Therefore, not only can it recommend even if there is not much information of the target visitors' booth visit records, but also it uses only small amount of information in recommendation process. We expect that it can give real?time recommendations in exhibition environment. Overall, our methodology shows higher performance ability than traditional Collaborative Filtering systems, we expect it could be applied in booth recommendation system to satisfy visitors in exhibition environment.

• Korean Business Review
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• v.17
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• pp.111-136
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• 2004

The aim of this study is to review the improvement method of Korea capital gains tax according to the alienation of the real estate, and to suggest an improvement plan. The study has been carried out by reviewing the related literatures. Capital gains tax could be calculated either using the actual price of sale or the standard prices. Korea capital gains tax has been revised many times since 1975 when it was first enacted. Initially the actual price of sale was the default rule and the standard prices was allowed only exceptionally if the actual price of sale could not be detected. The actual price of sale rather than the standard prices should be used for determining the capital gains tax on the transfer. By doing so, the desired principles of taxation such as "principle of taxation on tax paying ability". In conclusion, the present capital gains tax of Korea should be improved in many aspects in order to promote income redistribution function and efficient allocation of resources.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.39-44
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• 2012

The objective of this paper is to provide the conceptual selection framework of SLA metrics to maximize the operation efficiency and satisfaction of IT outsourcing and how to select most efficient auto service call center and system. With these metrics, both customers and service providers can measure service performance of IT outsourcing service. Hence, it is expected to boost operation efficiency and customers' satisfaction. In that sense, this study gives the value to both outsourcing and outsourced companies through suggesting the proper SLA metrics selection framework which provides the standards of service performance measurement and the management of IT outsourcing service in accordance with their business strategy quantitatively and qualitatively. Also we perform a survey for two customers in real business to prove the logicality of this selection framework is working and to find out relationship between SLA practice and customers' satisfaction while they outsource their IT service.