• Information Systems Review
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• v.18 no.1
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• pp.57-78
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• 2016

Hacking has raised many critical issues in the modern world, particularly because the size and cost of the damages caused by this disruptive activity have steadily increased. Accordingly, many significant studies have been conducted by behavioral scientists to understand hackers and their practices. Nonetheless, only qualitative methods, such as interviews, meta-studies, and media studies, have been employed in such studies because of hacker sampling limitations. Existing studies have determined that intrinsic motivation was the dominant factor influencing hackers, and that their techniques were mainly acquired from online hacking communities. However, such results have yet to be causally proven. This study attempted to identify the causal factors influencing the motivational and environmental factors encouraging hackers to learn hacking skills. To this end, hacker community members using the theory of planned behavior were observed to identify the causal factors of their learning of hacking skills. We selected a group of students who were developing their hacking skills. The survey was conducted over a two-week period in May 2015 with a total of 227 students as respondents. After list-wise deletion, 215 of the responses were deemed usable (94.7 percent). In summary, the hackers were aware that hacking skills are considered socially unethical, and their attitudes toward the learning of hacking skills were affected by both intrinsic and extrinsic motivations. In addition, the characteristics of the online hacking community affected their perceived behavioral control. This study introduced new concepts in the process of conducting a causal relationship analysis on a hacker sample. Moreover, this research expanded the discussion on the causal direction of subjective norms in unethical research, and empirically confirmed that both intrinsic and extrinsic motivations affect the learning of hacking skills. This study also made a practical contribution by raising the educational and policy response issues for ethical hackers and demonstrating the necessity to intensify the punishment for hacking.

• Korean Journal of Biological Psychiatry
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• v.10 no.1
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• pp.62-69
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• 2003

Corticotropin-releasing factor(CRF) and neuropeptide Y(NPY) are known to play important roles in mediating stress responses and stress-related behavior. To elucidate the role of neuropeptides in response to the condition that had paired with traumatic event, we observed the changes of CRF and NPY by immunohistochemistry using a conditioned footshock paradigm. Male Sprague-Dawley rats were placed in a shuttle box and exposed to 20 pairings of a tone(< 70dB, 5sec) followed by a footshock(FS, 0.8mA, 1sec) over 60min. A second group was exposed to the tone-footshock pairings, returned to the homecage for 2days, and then reexposed to the test chamber and 20tones alone for 60min, prior to sacrifice. Control groups were : a) sacrificed without exposure to FS ; b) exposed to the tone-footshock pairings and then sacrificed two days later ; or c) exposed to the chamber and tones alone, returned to the homecage for 2days and then reexposed to the chamber and 20tones over 60min prior to sacrifice. CRF was increased in animals exposed to FS or the aversive condition(context and tone) that had paired to FS in bed nucleus of the stria terminalis (BNST) compared to the control. NPY was increased by FS in amygdala and PVN, but the condition previously associated with FS results in slight increase only in amygdala area. These results suggest that the BNST appears to be the mostly involved neural circuit in response to explicit cues previously paired with footshock. Moreover, this study raise the possibility that increased CRF peptide in the BNST in response to re-exposure to the aversive condition may underlie, in part, the experience of conditioned fear-related anxiety behavior.

• 대한공업교육학회지
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• v.42 no.2
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• pp.20-46
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• 2017

The purposes of this study are to derive supportive measures for the effective operation of vocational education and training high schools participating in the apprenticeship system (apprenticeship schools) and make policy suggestions by analyzing the need analysis and investigating the importance and the difficulty of teachers regarding the operation of those schools. To achieve these purposes, the study developed a questionnaire by deriving the areas and items for the operation of apprenticeship schools, and analyzed 121 completed questionnaires of head and senior teachers who manage the apprenticeship programs across the nation after conducting a survey. The findings of this study can be summarized as follows: First, the teachers of apprenticeship schools found all the operation areas of the schools are important but difficult. Out of the operation areas, teachers had relatively high needs for 'promotion, selection and management of enterprises', 'student management', 'development of apprenticeship programs, formation of the curriculum, and establishment of operation plans of the curriculum'. Second, the teachers found all the detailed items of the operation areas of apprenticeship schools are important but difficult generally. According to the results, it is required to relax the criteria for forming apprenticeship organizations; operate the local government-oriented apprenticeship system; establish information systems between schools and enterprises; improve the support methods of relevant agencies; and increase incentives of teachers who are in charge of apprenticeship programs. It is also necessary to operate exclusive agencies supporting for OJT; operate apprenticeship education centers of local small and medium-sized business associations; provide exclusive supervision of students; cultivate teachers who support industry-academia cooperation; and legislate on the NCS-based qualifications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.480-490
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• 2018

Recently, The MRV (monitoring, reporting and verification) regulation, which measures, reports and verifies the emission gas of vessel to head for member countries of Europe Union (EU), is being implemented. As part this reason, we develop a system that estimates simple fuel consumption and recommends optimized stop-over ports of vessel, to calculate amount of carbon emission. To do this, we analyze fuel, distance and time consumption between port and the other port based on stored voyage data for over 10 years of real-ship, and implement a simple fuel consumption estimation module using analyzed result. Also, we design and implement the optimized route recommendation algorithm, existing navigation route display function including comparison with the optimized routes and user custom route plan function. Therefore, we expect the developed system is helpful when makes a navigation route and so on by reference indexes and we anticipate the system to have a sense for future research which learns and predicts for accuracy result.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Journal of Life Science
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• v.29 no.9
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• pp.996-1001
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• 2019

Edible insects are reported to be rich in protein, minerals and vitamins, and much attention has been paid to them as a future food source. In Korea, they were massively reared and sold. In order to enhance the market value of edible insects for industrialized mass production, it is important to develop the safe and nutritious feed sources for rearing them are needed. In this study, a chlorella-free control feed (Exp1) and six experimental feeds supplemented with 0.5~2.0% liquid or powder types of chlorella were formulated. Protaetia brevitarsis and Allomyrina dichotoma, registered as food ingredients in Korea, were fed with the designed feeds and parameters of growth including larval survivorship, larval body weight, and larval period were analyzed. When chlorella added, larval survivorship was increased 2~13%(p>0.05) and 9~22%(p<0.05) in each beetle compared to the control. Interestingly, the larval period of chlorella powder-added groups was shortened by 24 days (Exp3, p<0.05) in P. brevitarsis and 19 days (Exp4, p<0.01) in A. dichotoma. Meanwhile, some parameters, crude protein, crude fiber, copper, zinc, potassium, magnesium, and phosphorous, in chlorella-added groups of P. brevitarsis were also higher than the control group. Therefore, chlorella could promote the larval growth performance of these two beetles and be used as a feed additive in rearing them.

• Journal of the Korean Applied Science and Technology
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• v.40 no.1
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• pp.59-70
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• 2023

This study is a meta-analysis study to comprehensively investigate the effects of flipped learning teaching applied to nursing students in Korea through systematic review. Data collection was conducted by a team of two researchers from November 20 to December 20, 2022. A total of 129 papers were searched through the domestic database, and duplicate papers were removed and the final 9 studies were selected. Flipped learning improved critical thinking disposition of nursing students 0.91(Z=8.36, p<.001), learning self-efficacy 0.35 (Z=2.62, p=.009), self-directed learning ability 0.81(Z=6.53, p<.001), academic achievement 0.60(Z=5.18, p<.001), and self-efficacy 0.66(Z=4.79, p<.001). Based on the results of this study, it was confirmed that flipped learning is an effective teaching method applicable to the domestic nursing education field, and an objective basis was presented for the direction of flipped learning class design. In the future, we suggest repeated studies that comprehensively analyze the effects of various outcome variables that have a positive effect on flipped learning.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of Nutrition and Health
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• v.47 no.2
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• pp.145-156
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• 2014

Purpose: The purpose of this study was to establish accreditation systems of reliable educational materials for nutrition and dietary life which could be used in schools, workplace, and health promotion. Methods: The study was conducted from April 2011 to October 2011. Literature reviews, institutional visits, and telephone interviews were conducted. Expert meetings and advisory councils were held in order to receive feedback on development of the accreditation systems. A survey was conducted for the accreditation procedures on 143 professionals, including professors, researchers, health and medical experts, teachers, nutrition teachers, dietitians, and clinical nutritionists. Results: The final procedure of the developed accreditation system was finalized as follows: 1) receiving application twice per year 2) complete desk review (written evaluation) by three reviewers within two months, 3) board review (all board members) and decision, and 4) notification of results. The accreditation system is set for printed materials, web-site, and materials for activities. The certificate and accreditation mark is issued to the final certified educational materials. Expiration date is established only for the web-site form. The accreditation length lasts for two years, and can be extended by renewal application. Conclusion: The dietary and nutrition related materials, which are certificated by this accreditation system, could impart reliable information and knowledge to both learners and educators, and help them in effective selection of educational materials. Therefore, this accreditation system might be expected to increase satisfaction for teaching and learning about nutrition and healthy dietary life.

• 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.