• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.19
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• pp.191-223
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• 2003

Electronic commerce and the Internet offer unprecedented opportunities. The explosive expansion of the use of the Internet makes it possible for businesses to expand their markets and render services. Global transaction costs are easy to cut off using Internet and transaction speed is faster than before. Where cyberspace is not free from claims, Offline transaction can lead to problems and disputes the same is for cyberspace transactions. However ADR is not meet for the online transaction for speed, cost and open network system, ODR methods to resolve electronic commerce conflicts is crucial for building confidence and permitting access to justice in an online business environment. The use of the Internet and the network in dispute resolution has an impact on the types of communication implied in the relevant processes such as automated negotiation, online mediation and online arbitration and involves new technological issues such as the integrity and confidentiality of data and communication used to transmit and store data. Among the ODR systems Online Arbitration is currently binding both parties disputed and can achieve the aim of dispute award the same as the traditional arbitration. Arbitration is based on the New York Convention 1958, Arbitration Model law 1985 and national Arbitration Act that are founded on territorial area and rested on arbitration agreement, constitution of the arbitral tribunal, due process, final and binding award and enforcement of the arbitration award. To compare with this issues Online arbitration has unnecessarily legal unstability and risk. ODR is the burgeoning field and has created a new issues. All such issues which have been debated in the ADR are composed with ODR. But these are not limited Some of issues are further complicated by the nature of the online environment such as confidentiality and principle of parties. It is true that online arbitration should comply with legal provisions, but which is impossible to adhere of the law. Flexible translation and functional equivalence of legal provisions are needed for acceptance of electronic commerce disputes. Finally electronic commerce now takes place on the Internet, it is inevitable that the commercial world wants access to dispute resolution process that best suits the new commercial environment. ODR methods are processing for development and legal issues are considered by both national and international authorities. Introduction of new Conventions or amend Convention and Model law of ODR comes near.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.60-73
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• 2010

This paper aims to describe KorLexClas 1.5 which provides us with a very large list of Korean numeral classifiers, and with the co-occurring noun categories that select each numeral classifier. Differently from KorLex of other POS, of which the structure depends largely on their reference model (Princeton WordNet), KorLexClas 1.0 and its extended version 1.5 adopt a direct building method. They demand a considerable time and expert knowledge to establish the hierarchies of numeral classifiers and the relationships between lexical items. For the efficiency of construction as well as the reliability of KorLexClas 1.5, we use following processes: (1) to use various language resources while their cross-checking for the selection of classifier candidates; (2) to extend the list of numeral classifiers by using a shallow parsing techniques; (3) to set up the hierarchies of the numeral classifiers based on the previous linguistic studies; and (4) to determine LUB(Least Upper Bound) of the numeral classifiers in KorLexNoun 1.5. The last process provides the open list of the co-occurring nouns for KorLexClas 1.5 with the extensibility. KorLexClas 1.5 is expected to be used in a variety of NLP applications, including MT.

• Journal of the Korean Society for information Management
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• v.37 no.1
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• pp.153-177
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• 2020

In the context of open science, data sharing and reuse are becoming important researchers' activities. Among the discussions about data sharing and reuse, data journals and data papers shows visible results. Data journals are published in many academic fields, and the number of papers is increasing. Unlike the data itself, data papers contain activities that cite and receive citations, thus creating their own intellectual structures. This study analyzed 14 data journals indexed by Web of Science, 6,086 data papers and 84,908 cited references to examine the intellectual structure of data journals and data papers in academic community. Along with the author's details, the co-citation analysis and bibliographic coupling analysis were visualized in network to identify the detailed subject areas. The results of the analysis show that the frequent authors, affiliated institutions, and countries are different from that of traditional journal papers. These results can be interpreted as mainly because the authors who can easily produce data publish data papers. In both co-citation and bibliographic analysis, analytical tools, databases, and genome composition were the main subtopic areas. The co-citation analysis resulted in nine clusters, with specific subject areas being water quality and climate. The bibliographic analysis consisted of a total of 27 components, and detailed subject areas such as ocean and atmosphere were identified in addition to water quality and climate. Notably, the subject areas of the social sciences have also emerged.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.192-207
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• 2020

We acquire seismic data with regularly or irregularly missing traces, due to economic, environmental, and mechanical problems. Since these missing data adversely affect the results of seismic data processing and analysis, we need to reconstruct the missing data before subsequent processing. However, there are economic and temporal burdens to conducting further exploration and reconstructing missing parts. Many researchers have been studying interpolation methods to accurately reconstruct missing data. Recently, various machine learning technologies such as support vector regression, autoencoder, U-Net, ResNet, and generative adversarial network (GAN) have been applied in seismic data interpolation. In this study, by reviewing these studies, we found that not only neural network models, but also support vector regression models that have relatively simple structures can interpolate missing parts of seismic data effectively. We expect that future research can improve the interpolation performance of these machine learning models by using open-source field data, data augmentation, transfer learning, and regularization based on conventional interpolation technologies.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.97-105
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• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• International Commerce and Information Review
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• v.19 no.4
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• pp.29-50
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• 2017

In emerging technologies, innovation processes are dynamic in that the government needs to regularly review its policies to resonate with rapid technological advancements, changing public needs, and evolving global trends. In the 1990s, the Internet grew at an explosive rate, but many applications were constrained due to security concerns. Public Key Infrastructure (PKI) seemed to be the fundamental technology to address these concerns by providing security functions. As of 2017, PKI is still one of the best technologies for electronic authentication in an open network, but it is used only in limited areas: for user authentications in closed networks and for server authentications within network security infrastructure like SSL/TLS. The difference between expectation and reality of PKI usage is due to the evolution of the Internet along with the global adoption of new authentication policies under the Internet governance in the early 2000s. The new Internet governance based on the cooperation between multi-stakeholders is changing the way in which a government should act with regard to its technological policies. This paper analyzes different PKI policy approaches in the United States and Korea from the perspective of path-dependence theory. Their different policy results show evidence of the rise of the Internet governance, and may have important implications for policy-makers in the current global Internet society.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.565-574
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• 2018

The purpose of this study is to investigate the features of science classes of Science Core Schools (SCSs) perceived by students. 654 students from 14 SCSs were surveyed with two open-ended questions on the features of science classes. The students' responses were analyzed with NetMiner 4.5, in terms of the centrality (of betweenness and of degree) analysis and the community analysis. The results of the research are as follows: (1) the science classes of SCSs were perceived by students to be of the environment of free questioning, active participation and communication, caring teacher, more science experiments and advanced contents, and knowledge sharing; (2) science classes in SCSs were perceived to be different from those of ordinary high schools because SCSs provide more opportunities for science-related special courses (like project work, advanced science subjects), extra-curricular activities, inquiry and research activities, school supports, hard-working classroom environment, longer studying hours, R&E and club activities. The students' perceptions of SCS science classes appear to be in line with the characteristics of 'good' science lessons from previous studies. The SCS project itself and the features of SCS science classes would help us to see how we introduce educational innovations into actual schools.

• Journal of Korea Game Society
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• v.15 no.5
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• pp.57-78
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• 2015

This Study is the effect of the inclusive institution on a nation's economic development. Therefore, we focused on the gaming industry as an index that can drive the economic growth in the future. The reason to compare the game institution in South Korea and Germany is that both countries began to develop the game by the State, but the game institution in South Korea and Germany at the present time are sharply opposed, because the institutions can focus on the main points that are experiencing this difference. The results of this study, first, open/closed network in institutionalized aspect affects the social status of the game. This second, game workers in the legal institution has been classified as artists in Germany, as addicts in South Korea. And, Germany also has incentives to creators protected profits reinvested in the gaming industry, Korea leads to punitive exploitation is being transferred to the group for addiction treatment that revenue. Third, this exclusive and inclusive institutional system could affect the stable growth of the game market. As a result, South Korea's state institutions will notice that you get a result away from opportunities for economic development due to the loss of inclusiveness.

• Journal of the Korea Society for Simulation
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• v.16 no.2
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• pp.27-35
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• 2007

When sensor networks are deployed in open environments, all the sensor nodes are vulnerable to physical threat. An attacker can physically capture a sensor node and obtain the security information including the keys used for data authentication. An attacker can easily inject false reports into the sensor network through the compromised node. False report can lead to not only false alarms but also the depletion of limited energy resource in battery powered sensor networks. To overcome this threat, Fan Ye et al. proposed that statistical on-route filtering scheme(SEF) can do verify the false report during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power and energy, where security threshold value is the number of message authentication code for verification of false report. In this paper, we propose a fuzzy rule-based system for security threshold determination that can conserve energy, while it provides sufficient detection power in the SEF based sensor networks. The fuzzy logic determines a security threshold by considering the probability of a node having non-compromised keys, the number of compromised partitions, and the remaining energy of nodes. The fuzzy based threshold value can conserve energy, while it provides sufficient detection power.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.431-440
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• 2018

In this study, authors predicted probability of resignation of newly employed nurses using TensorFlow, an open source software library for numerical computation and machine learning developed by Google, and suggested strategic human resources management plan. Data of 1,018 nurses who resigned between 2010 and 2017 in single university hospital were collected. After the order of data were randomly shuffled, 80% of total data were used for machine leaning and the remaining data were used for testing purpose. We utilized multiple neural network with one input layer, one output layer and 3 hidden layers. The machine-learning algorithm correctly predicted for 88.7% of resignation of nursing staff with in one year of employment and 79.8% of that within 3 years of employment. Most of resigned nurses were in their late 20s and 30s. Leading causes of resignation were marriage, childbirth, childcare and personal affairs. However, the most common cause of resignation of nursing staff with in one year of employment were maladaptation to the work and problems in interpersonal relationship.