• Journal of KIISE:Databases
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• v.33 no.1
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• pp.102-116
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• 2006

Electronic catalogs (or e-catalogs) hold information about the goods and services offered or requested by the participants, and consequently, form the basis of an e-commerce transaction. Catalog management is complicated by a number of factors and product classification is at the core of these issues. Classification hierarchy is used for spend analysis, custom3 regulation, and product identification. Classification is the foundation on which product databases are designed, and plays a central role in almost all aspects of management and use of product information. However, product classification has received little formal treatment in terms of underlying model, operations, and semantics. We believe that the lack of a logical model for classification Introduces a number of problems not only for the classification itself but also for the product database in general. It needs to meet diverse user views to support efficient and convenient use of product information. It needs to be changed and evolved very often without breaking consistency in the cases of introduction of new products, extinction of existing products, class reorganization, and class specialization. It also needs to be merged and mapped with other classification schemes without information loss when B2B transactions occur. For these requirements, a classification scheme should be so dynamic that it takes in them within right time and cost. The existing classification schemes widely used today such as UNSPSC and eClass, however, have a lot of limitations to meet these requirements for dynamic features of classification. In this paper, we try to understand what it means to classify products and present how best to represent classification schemes so as to capture the semantics behind the classifications and facilitate mappings between them. Product information implies a plenty of semantics such as class attributes like material, time, place, etc., and integrity constraints. In this paper, we analyze the dynamic features of product databases and the limitation of existing code based classification schemes. And describe the semantic classification model, which satisfies the requirements for dynamic features oi product databases. It provides a means to explicitly and formally express more semantics for product classes and organizes class relationships into a graph. We believe the model proposed in this paper satisfies the requirements and challenges that have been raised by previous works.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.561-568
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• 2006

WPS(Wearable Personal Station) for next generation PC can define as a core terminal of 'Ubiquitous Computing' that include information processing and network function and overcome spatial limitation in acquisition of new information. As a way to acquire significant dynamic gesture data of user from haptic devices, traditional gesture recognizer based on desktop-PC using wire communication module has several restrictions such as conditionality on space, complexity between transmission mediums(cable elements), limitation of motion and incommodiousness on use. Accordingly, in this paper, in order to overcome these problems, we implement hand gesture recognition system using fuzzy algorithm and neural network for Post PC(the embedded-ubiquitous environment using blue-tooth module and WPS). Also, we propose most efficient and reasonable hand gesture recognition interface for Post PC through evaluation and analysis of performance about each gesture recognition system. The proposed gesture recognition system consists of three modules: 1) gesture input module that processes motion of dynamic hand to input data 2) Relational Database Management System(hereafter, RDBMS) module to segment significant gestures from input data and 3) 2 each different recognition modulo: fuzzy max-min and neural network recognition module to recognize significant gesture of continuous / dynamic gestures. Experimental result shows the average recognition rate of 98.8% in fuzzy min-nin module and 96.7% in neural network recognition module about significantly dynamic gestures.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.223-234
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• 2008

Recently the web service area provides the efficient integrated environment of the internal and external of corporation and enterprise that wants the introduction of it is increasing. Also the web service develops and the new business model appears, the domestic enterprise environment and e-business environment are changing caused by web service. The web service which provides the similar function increases, most the method which searches the suitable service in demand of the user is more considered seriously. When it needs to choose one among the similar web services, service consumer generally needs quality information of web service. The problem, however, is that the advertised QoS information of a web service is not always trustworthy. A service provider may publish inaccurate QoS information to attract more customers, or the published QoS information may be out of date. Allowing current customers to rate the QoS they receive from a web service, and making these ratings public, can provide new customers with valuable information on how to rank services. This paper suggests the agent-based quality broker architecture which helps to find a service providing the optimum quality that the consumer needs in a position of service consumer. It is able to solve problem which modify quality requirements of the consumer from providing the architecture it selects a web service to consumer dynamically. Namely, the consumer is able to search the service which provides the optimal quality criteria through UDDI browser which is connected in quality broker server. To quality criteria value decision of each service the user intervention is excluded the maximum. In the existing selection architecture, the objective evaluation was difficult in subjective class of service selecting of the consumer. But the proposal architecture is able to secure an objectivity with the quality criteria value decision where the agent monitors binding information in consumer location. Namely, it solves QoS information of service which provider does not provide with QoS information sharing which is caused by with feedback of consumer side agents.

• Journal of Legislation Research
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• no.44
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• pp.487-538
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• 2013

It is the most critical issue in recent international intellectual property licence disputes to decide the applicable laws to the license contracts. As Korea and the European Union(EU) reached free trade agreement(FTA), and the EU-Korea FTA entered into force on July 1, 2011, the FTA has boosted social, economic, cultural exchanges between the two. As a result of the increased transactions in those sectors, legal disputes are also expected to grow. This situation calls for extensive research and understanding of the choice of law principles applicable to international intellectual property license contracts in the EU. To decide the laws applicable to issues arising from international intellectual property license contracts disputes, the characterization of those issues is necessary for the purpose of applying private international law principles to them. In terms of characterization, intellectual property license contracts fall within contractual matters. In the EU, the primary rule of choice of law principles in contractual obligations is the Rome I Regulation. Because the choice of law rules, such as private international law principles, the Rome Convention(1980), and the Rome I Regulation, differ in the time of application, it is essential to clarify the time factor of related contracts. For example, the Rome I Regulation applies to contracts which were concluded as from December 17, 2009. Although party autonomy in international contracts disputes is generally allowed, if there is no choice of law agreement between the parties to the contracts, the objective test rule of private international law doctrine could be the best option. Following this doctrine, the Rome I Regulation Article 4, Paragraph 1 provides the governing law rules based on the types of contracts, but there is no room for intellectual property license contracts. After all, as the rule for governing law of those contracts, the Rome I Regulation Article 4, Paragraph 2 should be applied and if there are countries which are more closely connected to the contracts under the Rome I Regulation Article 4, Paragraph 3, the laws of those countries become the governing laws of the contracts. Nevertheless, if it is not possible to decide the applicable laws to the license contracts, the Rome I Regulation Article 4, Paragraph 4 should be applied in the last resort and the laws of the countries which are the most closely connected to the contracts govern the license contracts. Therefore, this research on the laws applicable to intellectual property license contracts under the Rome I Regulation suggests more systematic and effective solutions for future disputes in which Korea and the EU countries play the significant role as the connecting factors in the conflict of laws rules. Moreover, it helps to establish comprehensive and theoretical understanding of applying the Korean Private International Law to multifarious choice-of-law cases.

• Journal of Legislation Research
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• no.44
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• pp.343-386
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• 2013

"Financial ADR" system in South Korea can be represented by so-called "Financial Dispute Resolution System", in which Financial Supervisory Service (FSS) and Financial Dispute Resolution Committee are the principal actors in operation of the system, and this is discussed as an "Administrative Financial ADR System". The system has over 10-year history since it was introduced in around 1999. Nonetheless, it was not until when financial consumer protection began to be highlighted after the 2008 financial crisis that Financial ADR system actually started to draw attention in Korea. This was because interest has been rising in "Alternative Dispute Resolution (ADR)" as an institutional measure to protect financial consumers damaged via financial transactions. However, the current discussion on the domestic Financial ADR system shows an aspect that it is confined to who is to be a principal actor for the operation of Financial ADR institution with main regards to reorganization of supervisory system. This article aims to embody these facts in an institutional model by recognizing them as a problem and analyzing the features of the Financial ADR system, thereby clarifying problems of the system and presenting the direction of improvement. The Korean Financial ADR system can be judged as "administrative model integrated model consensual model quasi-judicial model non-prepositive Internal Dispute Resolution (IDR) model". However, at the same time, it is confronted with a task to overcome the two problems; the system is not equipped with institutional basis for securing its validity in spite of the adopted quasi-judicial effect model; and a burden of operating an integrated ADR system is considerable. From this perspective, the article suggests improvement plans for security of validity in the current system and for expansion of industry-control ADR system, in particular, a system of prepositive IDR model. Amongst them, it suggests further plans for securing the validity of the system as follows; promotion to expand the number of internal persons and to differentiate mediation procedures and effect; a plan to keep a financial institution from filing a lawsuit before an agreement recommendation or a mediation proposal is advised; and a plan to grant suspension of extinctive prescription as well as that of procedures of the lawsuit.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• 한국노년학
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• v.38 no.3
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• pp.569-592
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• 2018

This study purported to investigate the current state of human rights of older adults residing in rural areas of Korea. The study utilized, as an analytic framework, 4 priority directions (1. "older persons and development", 2. "rural area development", 3. "advancing health and well-being into old age", and 4. "ensuring enabling and supportive environments") with 13 task actions recommended by Madrid International Plan of Action on Ageing (MIPAA). Furthermore, the study examined gender differences in all items included in the analytic framework. Data was collected by the face-to-face survey on 800 subjects aged 65 and over. Statistical analyses were conducted using STATA 13.0 program. The main results were summarized in order of 4 priority directions as follows. First, average working hours per day were 6.2, and men reportedly participated in economic activities and needed job training more than women, while women participated in lifelong education programs more than men. Awareness of fire and disaster prevention facilities was low in both genders. Second, accessibility to the support center for the elderly living alone as well as protective services for the vulnerable elderly was found to be low. IT-based services and networking were used more by men than women, and specifically, IT-based financial transactions and welfare services were least used. Third, medical check-ups and vaccinations were well received, while consistent treatments for chronic illnesses and long-term care services were relatively less given. In addition, accessibility to mental health service centers was considerably low. Fourth, although old house structures and the lack of convenience facilities were found to be circumstantial risk factors for these elders, experiences of receiving housing support services were scarce. The elderly were found to rely more on informal care, and concerns for their care were higher in women than men. Plus, accessibility to elderly abuse services was markedly low. Based on these results, discussed were implications for implementing policies and practical interventions to raise the levels of the human rights for this population.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Korean Journal of Poultry Science
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• v.46 no.2
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• pp.55-63
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• 2019

This study was conducted to evaluate animal welfare approved farms in three housing systems (open, windowless, and free-range). The survey was conducted in 25 animal welfare approved farms, and 10 farms were surveyed for distribution status. The main breed in all animal welfare approved farms of laying hens was Hy-Line Brown variety. In the case of open house, laying hens were bred in traditional and panel houses simultaneously; however, the ratio of panel house was 58.3%, which was higher than that of the traditional house. All the windowless houses were made of panels and more than 15,000 laying hens were housed in a single windowless house. In the case of free-range house, it was maintained on a small scale of less than 12,000 birds. Fifty-six percent of the surveyed farms were breeding at $7{\sim}8birds/m^2$. In terms of male and female ratios, most farms maintained 1 male:15 females, but there was a farmhouse that switched 17 or 20 females to 1 male. The daily dietary allowance was 110~170 g, and 32% of the surveyed farms provided feed of more than 150 g/day, which showed that forage feed was important. The age of at the first egg was 123 days, 122 days, and 120 days, and the peak percent was 91.8%, 94.9%, and 86.5% in open, windowless and free-range houses, respectively. The average egg production rate was 74.0%, 84.6%, and 72.7% in open, windowless, and free-range houses respectively, thus, there was no correlation between feed intake and hen-housed eggs. Distribution of welfare certified eggs was mainly a direct deal with the consumer or through contract production. The ratio of direct transactions between large-scale marts and eco-friendly specialty stores of welfare approved eggs was higher than that of conventional eggs. The rate of contract sales of eggs in both the barn and free-range systems was high, and the percentage of courier sales farms was also high. Excluding courier services, price of eggs in the barn system rose to more than 30 won/egg in the second half of 2017 (after AI). Price of eggs in the free-range system rose to more than 50 won/egg in the second half of 2017 (after AI). In the case of courier sales, the same price of 500 won was maintained before and after AI. In conclusion, the results of this study can be used as basic data for improving the animal welfare certification system for laying hens in Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.