• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.157-173
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• 2011

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. This means the fatal damage can be caused by these intrusions in the government agency, public office, and company operating various systems. For such reasons, there are growing interests and demand about the intrusion detection systems (IDS)-the security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. The intrusion detection models that have been applied in conventional IDS are generally designed by modeling the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. These kinds of intrusion detection models perform well under the normal situations. However, they show poor performance when they meet a new or unknown pattern of the network attacks. For this reason, several recent studies try to adopt various artificial intelligence techniques, which can proactively respond to the unknown threats. Especially, artificial neural networks (ANNs) have popularly been applied in the prior studies because of its superior prediction accuracy. However, ANNs have some intrinsic limitations such as the risk of overfitting, the requirement of the large sample size, and the lack of understanding the prediction process (i.e. black box theory). As a result, the most recent studies on IDS have started to adopt support vector machine (SVM), the classification technique that is more stable and powerful compared to ANNs. SVM is known as a relatively high predictive power and generalization capability. Under this background, this study proposes a novel intelligent intrusion detection model that uses SVM as the classification model in order to improve the predictive ability of IDS. Also, our model is designed to consider the asymmetric error cost by optimizing the classification threshold. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, when considering total cost of misclassification in IDS, it is more reasonable to assign heavier weights on FNE rather than FPE. Therefore, we designed our proposed intrusion detection model to optimize the classification threshold in order to minimize the total misclassification cost. In this case, conventional SVM cannot be applied because it is designed to generate discrete output (i.e. a class). To resolve this problem, we used the revised SVM technique proposed by Platt(2000), which is able to generate the probability estimate. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 1,000 samples from them by using random sampling method. In addition, the SVM model was compared with the logistic regression (LOGIT), decision trees (DT), and ANN to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell 4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on SVM outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that our model reduced the total misclassification cost compared to the ANN-based intrusion detection model. As a result, it is expected that the intrusion detection model proposed in this paper would not only enhance the performance of IDS, but also lead to better management of FNE.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.249-259
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• 2018

This research had intended to find out regarding the present influences of the Smart Work on the intention to use continuously with the staff members working in the small- and medium-sized enterprises as the subject. And, finally, it had intended to find out about the Smart Work environments of the IT corporations and the non-IT corporations. For this research, the questionnaire survey data were collected from the staff members working at the small- and medium-sized enterprises. Through the questionnaire survey data that were collected, an empirical analysis was carried out. And, through the reliability analysis, the feasibility analysis, the discriminatory feasibility analysis, and the inspection of the degree of suitableness of the structural equation model, finally, the research model was verified and, finally, a difference analysis of the IT corporations and the non-IT corporations was carried out. Regarding the results of the analysis of the research, it appeared that the factors of the job efficiency and the job autonomy of the special characteristics of the job had the positive influences on the usefulness and the job satisfaction, which were the parameters and which were perceived. And it appeared that the time flexibility of the job form could not have any influences on the usefulness and the job satisfaction, which were the parameters and which were perceived. And it appeared that the spatial flexibility had the influences on the job satisfaction only. The perceived usefulness, which was a parameter, had the positive influences on the job satisfaction and the intention to use continuously. And, finally, the job satisfaction had the positive influences on the intention to use continuously. And it appeared that there were the differences, too, between the IT corporations and the non-IT corporations. It is thought that, through the results of this research and through the Smart Work environment, the positive influences on the workers and the organizations could be induced and that a better working environment than previously can be provided to the workers to fit the special characteristics of the corporations.

• Korean journal of applied entomology
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• v.48 no.2
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• pp.237-243
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• 2009

This study was conducted to develop economic injury level (ElL) and economic threshold (ET) of Cabbage armyworm, Mamestra brassicae L. on cabbage (Brassica oleracea L. var). The changes of cabbage biomass and M. brassicae density were investigated after introduction of larval M. brassicae (2nd instar) at different densities: 0, 1, 2, 4, 8, and 16 larvae per plant at 40 d after planting for an open field experiment, and 0, 2, 5, 8 and 12 larvae per plant at 25 d after planting for a glass house experiment. In the field experiment, the yield loss of cabbage was not significantly different among treated-plots at 30 d after the larval introduction, showing an over-compensatory response of cabbage plants to M. brassicae attack. In the glasshouse experiment, however, the biomass of cabbage at 15 d after the larval introduction significantly decreased with increasing the initial introduced number of M. brassicae, resulting in 38.3, 36.7, 21.7, 23.3 and 16.7g in above treated-plots, respectively. The relationship between cumulative insect days (CID) and yield loss (%) of cabbage was well described by a nonlinear logistic equation. Using the estimated equation, ElL of M. brassicae on cabbage was estimated at 44 CID per plant based on the yield loss 14%, which take into account of an empirical gain threshold 5% and marketable rate 91% of cabbage. Also, ET was calculated at 80% of the EIL: 35 CID per plant. Until a more elaborate EIL-model is developed, the present result may be useful for M. brassicae management at early growth stage of cabbage.

• Journal of the Korea Concrete Institute
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• v.26 no.5
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• pp.651-660
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• 2014

Structural members using ultra high strength concrete which usually used with steel fiber is designed with guidelines based on several investigation of SF-RPC(steel fiber reinforced reactive powder concrete). However, there are not clear design method yet. Especially, SF-RPC member should be casted with steam(90 degree delicious) and members with SF-RPC usually used with precast members. Although the most important design parameter is development method between SF-RPC and steel reinforcement(rebar), there are no clear design method in the SF-RPC member design guidelines. There are many controversial problems on safety and economy. Therefore, in order to make design more optimum safe design, in this study, we investigated bond stress between steel rebar and SF-RPC according to test. Test results were compared with previously suggested analysis method. Test was carried out with direct pull out test using variables of compressive strength of concrete, concrete cover and inclusion ratio of steel fiber. According to test results, bond stress between steel rebar and SF-RPC increased with increase of compressive strength of concrete and concrete cover. Increasing rate of bond stress were decrease with increase of compressive strength of SF-RPC and concrete cover significantly. 1% volume fraction inclusion of steel fiber increase the bond stress between steel rebar and SF-RPC with two times but 2% volume fraction cannot affect the bond stress significantly. There are no exact or empirical equations for evaluation of SF-RPC bond stress. In order to make safe bond design of SF-RPC precast members, previously suggested analysis method for bond stress by Tepfers were evaluated. This method have shown good agreement with test results, especially for steel fiber reinforced RPC.

• Journal of the Korean earth science society
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• v.21 no.2
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• pp.137-158
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• 2000

The correlation and Empirical Orthogonal Function (EOF) analyses over the globe have been applied to intercompare lower-stratospheric (${\sim}$70hPa) temperature obtained from satellite data and two model reanalyses. The data is the19 years (1980-98) Microwave Sounding Unit (MSU) channel 4 (Ch4) brightness temperature, and the reanalyses are GCM (NCEP, 1980-97; GEOS, 1981-94) outputs. In MSU monthly climatological anomaly, the temperature substantially decreases by ${\sim}$21k in winter over southern polar regions, and its annual cycle over tropics is weak. In October the temperature and total ozone over the area south of Australia remarkably increase together. High correlations (r${\ge}$0.95) between MSU and reanalyses occur in most global areas, but they are lower (r${\sim}$O.75) over the 20-3ON latitudes, northern America and southern Andes mountains. The first mode of MSU and reanalyses for monthly-mean Ch4 temperature shows annual cycle, and the lower-stratospheric warming due to volcanic eruptions. The analyses near the Korean peninsula show that lower-stratospheric temperature, out of phase with that for troposphere, increases in winter and decreases in summer. In the first mode for anomaly over the tropical Pacific, MSU and reanalyses indicate lower-stratospheric warming due to volcanic eruptions. In the second mode MSU and GEOS present Quasi-Biennial Oscillation (QBO) while NCEP, El Ni${\tilde{n}}$o. Volcanic eruption and QBO have more impact on lower-stratospheric thermal state than El Ni${\tilde{n}}$o. The EOF over the tropical Atlantic is similar to that over the Pacific, except a negligible effect of El Ni${\tilde{n}}$o. This study suggests that intercomparison of satellite data with model reanalyses may estimate relative accuracy of both data.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.1-15
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• 2015

In order to outline the kinematics and movement history of a new Quaternary fault, Jingwan Fault in Dangjin, West Korea, we analyzed the geometry of the fault zone composed of a few gouge zones, and made ESR dating for fault gouge materials. The $N55^{\circ}E$ striking Jingwan Fault is a normal fault and exhibits a gradual change in dip (gentle in the lower part, steep in the upper part), indicating a listric fault. As for the fault gouge zone, its thickness varies and reaches 2~3 cm in the lower part or between basement rocks, and 20~30 cm in the middle-upper part or between the basement and Quaternary deposit. It is observed in the latter case that more than three gouge zones develop with different colors, and branch out and re-merge, or they are partly superimposed, indicating different movement episodes. The cumulative displacement is estimated to be about 10 m using the geological cross-sections, from which it is inferred that the total length of fault may be about 2.5 km on the basis of the empirical relation between cumulative displacement and fault length. Therefore, a more study would be needed to verify the entire fault length. The results of ESR dating for three gouge samples at different spots along the fault yields ages of $651{\pm}47$, $649{\pm}96$, and $436{\pm}66ka$, indicating at least two movement episodes. Slickenlines observed on the fault planes indicate a pure dip slip (normal faulting), which suggests that the ENE-WSW trending Jingwan Fault was presumably moved under a NNW-SSE extensional environment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.167-184
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• 2020

This study analyzed the effects of information orientation and technology commercialization ability on technological performance of corporate workers. Information Orientation consisted of information technology capability, information management ability, information behavior and value, and technology commercialization capability consisted of productization capability, production capability, and marketing capability as sub-variables, and technology accumulation capacity was used as a coordinating variable. An empirical analysis was performed on 349 online and offline questionnaires collected from corporate employees. Analysis results using SPSS v22.0 and Process macro v3.4 First, information orientation and technical performance were found to have a significant effect.In addition, information orientation had a significant effect on technology commercialization capability. The magnitude of the influence on the productive capacity and the productive capacity in the variable of competency was in the order of information technology ability, information management ability, information behavior and value, but the influence on marketing capability was different from the previous results. Information management ability and information technology ability were in order. Second, the product commercialization capability, production capability, and marketing ability of technology commercialization ability had a significant effect on technology performance independently of information orientation. Third, the information technology ability and information management ability had a significant influence on the technical performance, but the indirect effect through the commercialization ability and marketing ability in information behavior and value was significant, the indirect effect of transit was not significant. Fourth, only the interaction terms of production capacity and technology accumulation capacity were significant among the sub-variables of technology commercialization capacity, and technology accumulation capacity, commercialization capacity, and marketing ability were not significant. Therefore, the relationship between productive capacity and technological performance can be interpreted as lower in firms with high technology accumulating ability than in lower firms, subsequent studies will require the introduction of other independent variables, models through the introduction of parameters and control variables.

• Asia pacific journal of information systems
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• v.19 no.1
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• pp.33-62
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• 2009

Internet computing is a disruptive IT innovation. Semantic Web can be considered as an IT innovation because the Semantic Web technology possesses the potential to reduce information overload and enable semantic integration, using capabilities such as semantics and machine-processability. How should organizations adopt the Semantic Web? What factors affect the adoption and diffusion of Semantic Web innovation? Most studies on adoption and diffusion of innovation use empirical analysis as a quantitative research methodology in the post-implementation stage. There is criticism that the positivist requiring theoretical rigor can sacrifice relevance to practice. Rapid advances in technology require studies relevant to practice. In particular, it is realistically impossible to conduct quantitative approach for factors affecting adoption of the Semantic Web because the Semantic Web is in its infancy. However, in an early stage of introduction of the Semantic Web, it is necessary to give a model and some guidelines and for adoption and diffusion of the technology innovation to practitioners and researchers. Thus, the purpose of this study is to present a model of adoption and diffusion of the Semantic Web and to offer propositions as guidelines for successful adoption through a qualitative research method including multiple case studies and in-depth interviews. The researcher conducted interviews with 15 people based on face-to face and 2 interviews by telephone and e-mail to collect data to saturate the categories. Nine interviews including 2 telephone interviews were from nine user organizations adopting the technology innovation and the others were from three supply organizations. Semi-structured interviews were used to collect data. The interviews were recorded on digital voice recorder memory and subsequently transcribed verbatim. 196 pages of transcripts were obtained from about 12 hours interviews. Triangulation of evidence was achieved by examining each organization website and various documents, such as brochures and white papers. The researcher read the transcripts several times and underlined core words, phrases, or sentences. Then, data analysis used the procedure of open coding, in which the researcher forms initial categories of information about the phenomenon being studied by segmenting information. QSR NVivo version 8.0 was used to categorize sentences including similar concepts. 47 categories derived from interview data were grouped into 21 categories from which six factors were named. Five factors affecting adoption of the Semantic Web were identified. The first factor is demand pull including requirements for improving search and integration services of the existing systems and for creating new services. Second, environmental conduciveness, reference models, uncertainty, technology maturity, potential business value, government sponsorship programs, promising prospects for technology demand, complexity and trialability affect the adoption of the Semantic Web from the perspective of technology push. Third, absorptive capacity is an important role of the adoption. Fourth, suppler's competence includes communication with and training for users, and absorptive capacity of supply organization. Fifth, over-expectance which results in the gap between user's expectation level and perceived benefits has a negative impact on the adoption of the Semantic Web. Finally, the factor including critical mass of ontology, budget. visible effects is identified as a determinant affecting routinization and infusion. The researcher suggested a model of adoption and diffusion of the Semantic Web, representing relationships between six factors and adoption/diffusion as dependent variables. Six propositions are derived from the adoption/diffusion model to offer some guidelines to practitioners and a research model to further studies. Proposition 1 : Demand pull has an influence on the adoption of the Semantic Web. Proposition 1-1 : The stronger the degree of requirements for improving existing services, the more successfully the Semantic Web is adopted. Proposition 1-2 : The stronger the degree of requirements for new services, the more successfully the Semantic Web is adopted. Proposition 2 : Technology push has an influence on the adoption of the Semantic Web. Proposition 2-1 : From the perceptive of user organizations, the technology push forces such as environmental conduciveness, reference models, potential business value, and government sponsorship programs have a positive impact on the adoption of the Semantic Web while uncertainty and lower technology maturity have a negative impact on its adoption. Proposition 2-2 : From the perceptive of suppliers, the technology push forces such as environmental conduciveness, reference models, potential business value, government sponsorship programs, and promising prospects for technology demand have a positive impact on the adoption of the Semantic Web while uncertainty, lower technology maturity, complexity and lower trialability have a negative impact on its adoption. Proposition 3 : The absorptive capacities such as organizational formal support systems, officer's or manager's competency analyzing technology characteristics, their passion or willingness, and top management support are positively associated with successful adoption of the Semantic Web innovation from the perceptive of user organizations. Proposition 4 : Supplier's competence has a positive impact on the absorptive capacities of user organizations and technology push forces. Proposition 5 : The greater the gap of expectation between users and suppliers, the later the Semantic Web is adopted. Proposition 6 : The post-adoption activities such as budget allocation, reaching critical mass, and sharing ontology to offer sustainable services are positively associated with successful routinization and infusion of the Semantic Web innovation from the perceptive of user organizations.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.14 no.2
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• pp.139-149
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• 2003

Objectives：To review the history of the development and current status of Korean Child and Adolescent Psychiatry and to propose the future tasks at this 20th anniversary of Korean Academy of Child and Adolescent Psychiatry. Methods：Records and publications of Korean Academy, personal communications with senior members of the Academy, the author's recollections and memoirs. Results：Korean Child Psychiatry started with opening Child Psychiatry Clinic at Seoul National University Hospital in 1979 and establishing Division of Child and Adolescent Psychiatry at the same hospital in 1980. A Child Psychiatry Study Group was formed in 1983 and it transformed into Korean Academy of Child and Adolescent Psychiatry in 1986. In the past 20 years, Korean child and adolescent psychiatry made an impressive progress and contributed significantly to the development of Korean medicine as well as to the establishment of child mental health service. There are 77 board-certified and 48 board-eligible child psychiatrists, and 18 child psychiatry fellows are in training as of 2003. Main forces of this successful introduction of child psychiatry are 1) 7 child psychiatry training centers at major university hospitals, 2) publication of Korean Journal of Child and Adolescent Psychiatry in 1990, 3) the establishment child psychiatry board certification system in 1996. Korean child psychiatry has extended its role in the international child psychiatry and took leadership in establishing Asian Society of Child and Adolescent Psychiatry and Allied Professions(ASCAPAP). Conclusion：Korean child psychiatry has achieved a remarkable progress for the past 20 years and is currently producing 15 or more child psychiatry each year. However, there are needs of more active involvements in adolescent problems, more active consultation-liaison services at general hospitals as well as community mental health institutions. More school consultation and preventive educational services are desired. There is a urgent need of carrying out more empirical studies with strong scientific rigor. Korean Academy should pay attention and provide assistance to the development of child psychiatry service in other countries in Asia.