• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1131-1141
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• 2015

In this paper, we proposed a new approach of machine learning based method for detecting game-bots from normal players in MMORPG by inspecting the player's action log data especially in-game money increasing/decreasing event log data. DBSCAN (Density Based Spatial Clustering of Applications with Noise), an one of density based clustering algorithms, is used to extract the attributes of spatial characteristics of each players such as a number of clusters, a ratio of core points, member points and noise points. Most of all, even game-bot developers know principles of this detection system, they cannot avoid the system because moving a wide area to hunt the monster is very inefficient and unproductive. As the result, game-bots show definite differences from normal players in spatial characteristics such as very low ratio, less than 5%, of noise points while normal player's ratio of noise points is high. In experiments on real action log data of MMORPG, our game-bot detection system shows a good performance with high game-bot detection accuracy.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Distribution Research
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• v.14 no.2
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• pp.97-125
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• 2009

The main purpose of this study is to provide a new approach to conceptualize and measure distrust based on existing researches concerning trust. One of the traditional approach has viewed that trust and distrust are opposed constructs placed on a continuous measure and that trust has positive impact and distrust has negative impact. But this study tries to test empirically that trust and distrust are independent constructs which can be distinguishable, and that both constructs are ambivalent. In addition, this study also tries to provide empirical test that distrust can have a positive effect on transaction relationship. We analyzed the possibility that both trust and distrust can be distinguishable and ambivalent with various antecedents and consequences of two constructs. We also analyzed the effect of distrust on cooperation and functional conflict in order to manifest the positive role of distrust as a relationship variable. The result for testing hypotheses is as follows: First, all hypotheses for antecedents and consequences of trust are significant, but some of hypotheses for antecedents and consequences of distrust are not significant. Second, both constructs can be distinguishable and ambivalent to some extent as the hypotheses for reputation are significant, which is one of the antecedents of distrust and considered jointly with other antecedents, transaction specific asset and environmental uncertainty. Lastly, the result showed the positive role of distrust that has a positive effect on functional conflict.

• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.47-63
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• 2018

This study suggested quantitative models to calculate a royalty rate as an important input factor of the relief from royalty method which has the characteristics of income approach method and market approach method that are generally used in the valuation of intangible assets. This study built a royalty rate regression model by referring to the patent infringement damages cases based on royalties, i.e., by using the royalty rates as a dependent variable and the patent indexes of the corresponding patent right as independent variables. Then, a logistic regression model was constructed by referring to inter-partes review cases of patent rights, i.e. by using not-unpatentable results as a dependent variable and the patent indexes of the corresponding patent right as independent variables. A final royalty rate was calculated by matching the royalty rate from the royalty rate regression model with a not-unpatentable probability from the logistic regression model. The suggested royalty rate was compared with the royalty rate obtained by the traditional methods to check its reliability.

• Economic Analysis
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• v.27 no.1
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• pp.67-90
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• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.

• Korean Journal of Construction Engineering and Management
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• v.17 no.3
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• pp.125-133
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• 2016

The Project Risk Management is intended to result in the effective management by identifying in advance and mitigating all significant risks including project risks and opportunities during the entire project life cycle - from project inception to completion of construction. It is impossible to predict an exact budget and construction duration before finishing a project. So, Washington Department of Transportation mandates that workshop-based risk management is conducted for projects over specific cost. However, the domestic construction sites have depended on numerical risk analysis without any workshop and efficient risk management have not made. Therefore, in this study, we propose the effective risk management using the RBES program which is very useful for workshop-based risk management and pre & post mitigation, by workshop-based risk management techniques. This proposed risk management approach is applied to a domestic 'A' river recovery project. It is concluded that we may expect the effect to mitigate the total cost overrun problem and the construction duration delay effect in the project by identifying significant risks and by preparing effective risk mitigation strategies.

• Management & Information Systems Review
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• v.29 no.4
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• pp.169-186
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• 2010

This study aims to reveal the ways to sharpen the edges of Korean companies through the relativity analysis between knowledge management and innovational cluster in environmental changes in resent Busan. That is, according to the knowledge management approach, the methods and directions of strengthening industrial competition were established, while the strategy of innovational clusters was suggested as a way of expanding and encouraging knowledge management. The key words of innovational cluster are in this research are the framework of Cluster theory, the importance of innovational cluster, and the change of managerial strategy paradigm. This study provide the several implication for the practice of knowledge management and the researchers. Based on these theories of knowledge management and industrial clusters, their close relationships were analyzed. As a result, industrial clusters were found to be effectively utilized to enlarge and deepen knowledge management. In addition, this suggests the efficient operation guideline of knowledge management. this study indicates both knowledge and innovational cluster should be operated and handled together in the managerial strategy. but this research has limitations in generaling the study result because it collects data from local firms only in Busan.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.15-26
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• 2014

This study focus on a economic value of the Big Data technologies by real options model using big data technology company's stock price to determine the price of the economic value of incremental assessed value. For estimating stochastic process of company's stock price by big data technology to extract the incremental shares, Generalized Moments Method (GMM) are used. Option value for Black-Scholes partial differential equation was derived, in which finite difference numerical methods to obtain the Big Data technology was introduced to estimate the economic value. As a result, a option value of big data technology investment is 38.5 billion under assumption which investment cost is 50 million won and time value is a about 1 million, respectively. Thus, introduction of big data technology to create a substantial effect on corporate profits, is valuable and there are an effects on the additional time value. Sensitivity analysis of lower underlying asset value appear decreased options value and the lower investment cost showed increased options value. A volatility are not sensitive on the option value due to the big data technological characteristics which are low stock volatility and introduction periods.

• Korean Journal of Heritage: History & Science
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• v.42 no.4
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• pp.50-73
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• 2009

Recently old stone walls were designated as registered cultural properties that meant an extension of categories about cultural properties from a spot area to whole area. Moreover given the changing situation of residential pattern, which is due to rapid social change, this designation can be seen as a significant measure to keep as intact as possible traditional landscapes in agricultural and fishing villages. In this paper, I analyze the symbol system and meaning of old stone walls and attempt to pick out the cultural elements which are related to them. These days we have made efforts to various aspects for which make traditional cultural resources into cultural contents. But many studies had done before emphasized aspects for beauty only. Especially existing studies about an old stone wall was mainly focused on architectural interpretation and tourist route. So we need to build a plot around oral research and need a creative approach for sharing with tourists. Cultural contents combine the original form, potential and capabilities with media by detecting original form of culture and finding out the worth and meaning. In this paper examined the probability of using by investigating a stone wall in Sang-hak Village that is related with recovering of places to live in contemporary society and finding cultural contents. I suggest more creative ways to make cultural properties into tourist resources by considering the possibilities of place marketing using storytelling, based on an analysis of data gathered.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.107-125
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• 2023

The pace of sustainability transition within the maritime industry has been accelerating. This shift primarily necessitates changes in the industry's heavy reliance on fossil fuel-driven ecosystems. Additionally, numerous sustainability laws and regulations, such as the EU's CBAM and IMO's EEXI, have been implemented. This transition is poised to amplify the competitive edge of firms equipped with greater resources, as it introduces substantial operational burdens due to expensive eco-friendly fuel adoption and regulatory compliance. To diverge from the traditional competitive landscape, this paper aims to explore innovative maritime finance models enabling domestic firms to gain competitive advantages on a global scale. Employing analogical reasoning and modeling as a research method, this paper demonstrates that maritime firms can leverage the sustainability transition by aligning sustainable maritime operations with ETS (Emission Trading Schemes). Expanding on this novel approach, the paper delves into potential connections between CCM (Compliance Carbon Market), VCM (Voluntary Carbon Market), and digital asset exchanges. This newly proposed digital/net-zero maritime ecosystem holds the potential to significantly impact the shipping, shipbuilding, and ship finance industries, positioning Busan as a sustainable maritime finance hub. This study holds significance as pioneering research that may stimulate subsequent case-based studies and offer strategic guidance to market participants and policymakers as the maritime industry moves towards a net-zero transition