• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4625-4647
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• 2020

The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of "generative" and "adversarial", researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.111-117
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• 2022

Many studies have been conducted on software fault prediction models for decades, and the models using machine learning techniques showed the best performance. Deep learning techniques have become the most popular in the field of machine learning, but few studies have used them as classifiers for fault prediction models. Some studies have used deep learning to obtain semantic information from the model input source code or syntactic data. In this paper, we produced several models by changing the model structure and hyperparameters using MLP with three or more hidden layers. As a result of the model evaluation experiment, the MLP-based deep learning models showed similar performance to the existing models in terms of Accuracy, but significantly better in AUC. It also outperformed another deep learning model, the CNN model.

• Korean Management Science Review
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• v.17 no.3
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• pp.181-201
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• 2000

This paper performs a case study on an Internet business networking company, oneQ.com. We define the functions of Internet business networker and discuss its characteristics such as network effect, lock-in effect, and increasing returns etc. Through reviewing the business models, strategies, and implemented technologies of the oneQ.com, we investigate the applicability and effectiveness of the Internet business networker as well as its research implications.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.43-46
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• 1998

Internet is now changing the paradigms of traditional commercial activities. As more and more companies are actively involved in electronic commerce using Internet, it is becoming more imperative to understand the customers' perception of electronic commerce to foster healthy growth of Internet electronic commerce. However, research on customer satisfaction in electronic commerce has been scarce. This research investigates the factors affecting customer perception of electronic commerce and suggests the models and the results of the empirical study.

• Journal of Korea Technology Innovation Society
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• v.8 no.3
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• pp.1139-1156
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• 2005

The explosive growth in wireless networks and Internet services has created considerable demand for mobile Internet services based on the mobile phone. Mobile Internet has become the new business model in telecommunication market, therefore the open network policy for mobile Internet has been formulated and implemented by the government in Korea. In spite of the open network policy for mobile Internet, there has been no systematic analysis of the various interconnection issues, including pricing, in mobile Internet network. This paper aims to suggest the interconnection pricing methods for mobile Internet network by reviewing the current pricing models for various communications services, and adapting them to mobile Internet communications circumstances. Results of this paper can be used as a guideline for government policy directions and management decision making after the introduction of the open network policy for mobile Internet.

• Structural Engineering and Mechanics
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• v.37 no.1
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• pp.17-37
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• 2011

This paper presents a new Internet-based computational framework for the realistic simulation of multi-scale response of structural systems. Two levels of parallel processing are involved in this frame work: multiple local distributed computing environments connected by the Internet to form a cluster-to-cluster distributed computing environment. To utilize such a computing environment for a realistic simulation, the simulation task of a structural system has been separated into a simulation of a simplified global model in association with several detailed component models using various scales. These related multi-scale simulation tasks are distributed amongst clusters and connected to form a multi-level hierarchy. The Internet is used to coordinate geographically distributed simulation tasks. This paper also presents the development of a software framework that can support the multi-level hierarchical simulation approach, in a cluster-to-cluster distributed computing environment. The architectural design of the program also allows the integration of several multi-scale models to be clients and servers under a single platform. Such integration can combine geographically distributed computing resources to produce realistic simulations of structural systems.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1053-1061
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• 2013

In this paper, we propose the time series forecasting models for internet traffic with long memory and heteroscedasticity. To control and forecast traffic volume, we first introduce the traffic forecasting models which are determined by the volatility and heteroscedasticity of the traffic. We then analyze and predict the heteroscedasticity and the long memory properties for forecasting traffic volume. Depending on the characteristics of the traffic, Fractional ARIMA model, Fractional ARIMA-GARCH model are applied and compared with the MAPE(Mean Absolute Percentage Error) Criterion.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.41-56
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• 2006

The real estate portal has grown into a successful e-Business model that is combined on and off line. Although IT technologies have shown rapid growth, the real estate portals have failed to satisfy the expectations of the Internet users. Based on Michael Porter's competitive forces framework, this study proposes five competitive strategies for continuing growth of the real estate portals. First, to strengthen bargaining power against supplier, buyer and potential new entrants, the real estate portals need to construct a basic network that is cost efficient and maintains real estate goods and makes profits by collaborative deals. Second, strengthen brand value and endeavor to escape from dependency on the Internet portals. Third, develop services to consider changed circumstances and give a lot of sources to make profit to real estate agencies. Fourth, concentrate on marketing to draw in the Internet users and adapt strategies that have been successful in other fields. Finally, real estate fields can seek out ideas for developing new business models from other successful e-Business models and should benchmark them to reduce expenses to a minimum and increase benefits to a maximum.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4275-4291
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• 2021

With the unprecedented growth of textual information on the Internet, an efficient automatic summarization system has become an urgent need. Recently, the neural network models based on the encoder-decoder with an attention mechanism have demonstrated powerful capabilities in the sentence summarization task. However, for paragraphs or longer document summarization, these models fail to mine the core information in the input text, which leads to information loss and repetitions. In this paper, we propose an abstractive document summarization method by applying guidance signals of key sentences to the encoder based on the hierarchical encoder-decoder architecture, denoted as KI-HABS. Specifically, we first train an extractor to extract key sentences in the input document by the hierarchical bidirectional GRU. Then, we encode the key sentences to the key information representation in the sentence level. Finally, we adopt key information representation guided selective encoding strategies to filter source information, which establishes a connection between the key sentences and the document. We use the CNN/Daily Mail and Gigaword datasets to evaluate our model. The experimental results demonstrate that our method generates more informative and concise summaries, achieving better performance than the competitive models.

• ETRI Journal
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• v.25 no.6
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• pp.423-436
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• 2003

This paper describes our design of a contents distribution framework that supports transparent distribution of digital contents on the Internet as well as copyright protection of participants in the contents distribution value chain. Copyright protection must ensure that participants in the distribution channel get the royalties due to them and that purchasers use the contents according to usage rules. It must also prevent illegal draining of digital contents. To design a contents distribution framework satisfying the above requirements, we first present four digital contents distribution models. On the basis of the suggested distribution models, we designed a contract system for distribution of royalties among participants in the contents distribution channel, a license mechanism for enforcement of contents usage to purchasers, and both a packaging mechanism and a secure client system for prevention of illegal draining of digital contents.