• Journal of Engineering Education Research
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• v.17 no.5
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• pp.28-32
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• 2014

Engineering Ethics is a fundamental and essential subject and the understanding of ethics is of great importance for students in engineering courses and professional engineers. Most courses would consist of ethical tests, decision making opportunities, case studies, case methods, and group discussion. It is important to consider each case carefully, so we offer a number of hypothetical short stories to students as case methods that they cover in detail. We check the behavior decisions of students as they read the hypothetical short stories. In this study, the short story was about 200 words in length. This paper shows how, with the addition of minor changes to the text, some students changed their behavioral decisions. For example, with the addition of "if you take financial liability for the losses," some thought that they would not want to carry the debt. Other cases showed how some students disliked the majority rule. The paper shows that this arranged hypothetical short story method can often guide student's decision-making process, and can result in decreased undesirable decisions.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.99-104
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of Veterinary Clinics
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• v.36 no.5
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• pp.245-247
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• 2019

Taylorella equigenitalis (T. equigenitalis), Klebsiella pneumoniae (K. pneumoniae), and Pseudomonas aeruginosa (P. aeruginosa) are sexually transmittable bacteria known to cause venereal diseases (VD) in horses. T. equigenitalis causes contagious equine metritis (CEM), which is a considerable concern for equine breeding industry. K. pneumoniae and P. aeruginosa may cause endometritis and infertility in susceptible mares. The purpose of this study was to investigate the prevalence of these bacteria among breeding Thoroughbreds in South Korea. External genital swabs were collected from 178 breeding Thoroughbreds, including 11 stallions and 167 mares. The samples were tested using a commercial multiplex real-time PCR kit. T. equigenitalis, P. aeruginosa, and K. pneumoniae were present in 5.6%, 7.3%, and 5.6% of tested Thoroughbreds, respectively. The results highlight the need for regular testing of South Korean Thoroughbreds, particularly those used for breeding, for these bacteria. The regular pre-breeding test for these bacteria will prevent health complications for the horse and financial losses for the owner as a result of VD.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.75-88
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• 2021

Automobile credit business has developed rapidly in recent years, and corresponding default phenomena occur frequently. Credit default will bring great losses to automobile financial institutions. Therefore, the successful prediction of automobile credit default is of great significance. Firstly, the missing values are deleted, then the random forest is used for feature selection, and then the sample data are randomly grouped. Finally, six prediction models of support vector machine (SVM), random forest and k-nearest neighbor (KNN), logistic, decision tree, and artificial neural network (ANN) are constructed. The results show that these six machine learning models can be used to predict the default of automobile credit. Among these six models, the accuracy of decision tree is 0.79, which is the highest, but the comprehensive performance of SVM is the best. And random grouping can improve the efficiency of model operation to a certain extent, especially SVM.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.280-287
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• 2022

Recently, the number of mobile ransomware types has increased. Moreover, the number of cases of damage caused by mobile ransomware is increasing. Representative damage cases include encrypting files on the victim's smart device or making them unusable, causing financial losses to the victim. This study classifies ransomware apps by analyzing several representative ransomware apps to identify trends in the malicious behavior of ransomware. We present a technique for recovering from the damage, from a digital forensic perspective, using reverse engineering ransomware apps to analyze vulnerabilities in malicious functions applied with various cryptographic technologies. Our study found that ransomware applications are largely divided into three types: locker, crypto, and hybrid. In addition, we presented a method for recovering the damage caused by each type of ransomware app using an actual case. This study is expected to help minimize the damage caused by ransomware apps and respond to new ransomware apps.

• Steel and Composite Structures
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• v.48 no.5
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• pp.499-505
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• 2023

Geological hazards in landslide is one of the most extensive and destructive phenomena are among natural disasters. According to the topography high mountains, tectonic activity, high seismicity, diverse conditions Geology and climate, basically China to create a wide spectrum of landslides have natural conditions and these landslides are annual. They cause a lot of financial losses to the country. It is very difficult to predict the time of the landslide, hence the identification landslide sensitive areas and zoning of these areas based on the potential risk is very important. Therefore, it should be susceptible areas landslides should be identified in order to reduce damages caused by landslides find. the main purpose of landslide sensitivity analysis is identification high-risk areas and as a result, reducing damages caused by landslides It is the way of appropriate actions.

• Journal of Korea Trade
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• v.27 no.5
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• pp.41-62
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• 2023

Purpose - This paper examines whether the imposition of countervailing duties by the United States on undervalued foreign currency is legally consistent with the WTO's SCM Agreement. Design/methodology - The study uses a methodology that involves analyzing relevant WTO agreements, prior panel reports, Appellate Body decisions, and other legal documents. Findings - The findings suggest that to impose countervailing duties, certain legal requirements must be met, including financial contribution, benefit, and specificity. The paper also notes that when calculating the benefits of undervalued foreign currency, losses from import activities due to currency undervaluation must be considered. Additionally, classifying all exports to the US under specific industries or business groups is likely to be inconsistent with the SCM Agreement. Originality/value - Even the US countervailing measures on exchange rate subsidies may not comply with WTO regulations due to incorrect calculation of benefits and a lack of specificity, however, it suggests that when intervening in the foreign exchange market, the measures should aim to achieve only minimum policy goals.

• KDI Journal of Economic Policy
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• v.28 no.2
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• pp.97-129
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• 2006

This study investigates whether dynamic loan loss provisioning is necessary in Korean banking environments. Under the dynamic provisioning rule banks are required to accumulate additional reserves to general and specific provisionings in preparation for expected loan losses until maturity. This provisioning is most effective in the case that banks tend to recognize less loan losses in the business upturns and/or in the periods of increasing profits. The empirical study, however, shows that banks support procyclicality of loan loss privisioning and earning smoothing behavior over profit fluctuations. These findings suggest that Korea would not seriously need the introduction of dynamic loan loss provisioning. But this policy implication does not seem robust in view that the recent experience shows the countercyclicality of loan loss provisioning practices and negative correlation between earnings and provisioning after financial restructuring was completed. This result is partly attributable to vigorous shareholder activism because of high foreign ownership of most commercial banks. Once it is true that bank management is more interested in short-term performances, current loan loss provisioning would have attributes of impairing capital adequacy, hence strengthening loan loss provisiong requirements.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.29-43
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• 2012

Financial data such as stock index returns and exchange rates have the properties of heavy tail and asymmetry compared to normal distribution. When we estimate VaR using the GARCH model (with the conditional return distribution of normal) it shows the tendency of the lower estimation and clustering in the losses over the estimated VaR. In this paper, we argue that this problem can be resolved through the adaptation of the unbounded Johnson distribution as that of the condition return. We also compare this model with the GARCH with the conditional return distribution of normal and student-t. Using the losses exceed the ex-ante VaR, estimates, we check the validity of the GARCH models through the failure proportion test and the clustering test. We nd that the GARCH model with conditional return distribution of unbounded Johnson provides an appropriate estimation of the VaR and does not occur the clustering of violations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.341-346
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• 2022

Due to global warming and abnormal climate, the frequency and damage of floods are increasing, and the number of people exposed to flood-prone areas has increased by 25% compared to 2000. Floods cause huge financial and human losses, and in order to reduce the losses caused by floods, it is necessary to predict the flood in advance and decide to evacuate quickly. This paper proposes a flood risk determination model using a CNN-based classification model so that timely evacuation decisions can be made using rainfall and water level data, which are key data for flood prediction. By comparing the results of the CNN-based classification model proposed in this paper and the DNN-based classification model, it was confirmed that it showed better performance. Through this, it is considered that it can be used as an initial study to determine the risk of flooding, determine whether to evacuate, and make an evacuation decision at the optimal time.