• 대한공업교육학회지
• /
• v.41 no.1
• /
• pp.68-87
• /
• 2016

This research conducted a survey for total 166 teachers of schools so as to analyze validity and satisfaction for performance objectives of SMB administration subsidy the HRDP and the CEP in Specialized High School. The results of research are as follows. First, teachers recognize that purpose of HRDP is to expand employment of specialized high school and provide human resource of SMB. And, they recognize that HRDP is important to improve school outcomes and makes a positive effect on the improvement of school outcomes. Second, teachers recognize that objectives of HRDP are improvement of student's understanding for SMB, improvement of teacher's understanding for SMB, improvement of SMB's understanding of school, cultivation of student's occupational view, systematization of career guiding program based on employment process, strengthening of industry-academia cooperation education, improvement of the level of student's skill, fulfillment of workplace experience and practice focusing workplace learning, training of customized human resource for SMB, improvement of student's adaptation to the workplace, improvement of employment rate for SMB, expansion of job opportunities for students with SMB, preparation of the base of connection between school and SMB, publicity of school, expansion of opportunities to cooperate between SMB and school, establishment of cooperative system among industrial association and school, introduction and operation of the employment connective model for joint education and employment, strengthening of field professionalism of teachers. However, satisfaction for the achievement of objectives of HRDP except for strengthening of industry-academia cooperation education and improvement of employment rate for SMB is relatively lower than the validity. Third, teachers in charge of human resource training business of middle and small sized company's specialized high school recognize that objectives of CEP are expansion of job opportunities for students with SMB, excavation of good-quality SMB, expansion of opportunities to cooperate between SMB and school, fulfillment of workplace learning, improvement of student's major foundation and in-depth skill, improvement of literacy, math, teamwork and communication abilities for students' job performance, improvement of student's working attitude and student's proper career exploration decision. However, satisfaction for achievement of objectives of CEP is relatively lower than the validity.

• Journal of Intelligence and Information Systems
• /
• v.26 no.3
• /
• pp.37-50
• /
• 2020

Information security has become an important issue in the world. Various information and communication technologies, such as the Internet of Things, big data, cloud, and artificial intelligence, are developing, and the need for information security is increasing. Although the necessity of information security is expanding according to the development of information and communication technology, interest in information security investment is insufficient. In general, measuring the effect of information security investment is difficult, so appropriate investment is not being practice, and organizations are decreasing their information security investment. In addition, since the types and specification of information security measures are diverse, it is difficult to compare and evaluate the information security countermeasures objectively, and there is a lack of decision-making methods about information security investment. To develop the organization, policies and decisions related to information security are essential, and measuring the effect of information security investment is necessary. Therefore, this study proposes a method of constructing an investment portfolio for information security measures using game theory and derives an optimal defence probability. Using the two-person game model, the information security manager and the attacker are assumed to be the game players, and the information security countermeasures and information security threats are assumed as the strategy of the players, respectively. A zero-sum game that the sum of the players' payoffs is zero is assumed, and we derive a solution of a mixed strategy game in which a strategy is selected according to probability distribution among strategies. In the real world, there are various types of information security threats exist, so multiple information security measures should be considered to maintain the appropriate information security level of information systems. We assume that the defence ratio of the information security countermeasures is known, and we derive the optimal solution of the mixed strategy game using linear programming. The contributions of this study are as follows. First, we conduct analysis using real performance data of information security measures. Information security managers of organizations can use the methodology suggested in this study to make practical decisions when establishing investment portfolio for information security countermeasures. Second, the investment weight of information security countermeasures is derived. Since we derive the weight of each information security measure, not just whether or not information security measures have been invested, it is easy to construct an information security investment portfolio in a situation where investment decisions need to be made in consideration of a number of information security countermeasures. Finally, it is possible to find the optimal defence probability after constructing an investment portfolio of information security countermeasures. The information security managers of organizations can measure the specific investment effect by drawing out information security countermeasures that fit the organization's information security investment budget. Also, numerical examples are presented and computational results are analyzed. Based on the performance of various information security countermeasures: Firewall, IPS, and Antivirus, data related to information security measures are collected to construct a portfolio of information security countermeasures. The defence ratio of the information security countermeasures is created using a uniform distribution, and a coverage of performance is derived based on the report of each information security countermeasure. According to numerical examples that considered Firewall, IPS, and Antivirus as information security countermeasures, the investment weights of Firewall, IPS, and Antivirus are optimized to 60.74%, 39.26%, and 0%, respectively. The result shows that the defence probability of the organization is maximized to 83.87%. When the methodology and examples of this study are used in practice, information security managers can consider various types of information security measures, and the appropriate investment level of each measure can be reflected in the organization's budget.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.38 no.5
• /
• pp.1-11
• /
• 2010

The outdoor space of an elementary school is the most familiar and most educational area for children. A paradigm shift in education has demanded a new role and direction for these outdoor spaces. The construction of children-friendly spaces, however, lags behind. The child-participatory design process is very meaningful at a time when many outdoor spaces have difficulties in reflecting the varied and specific demands of children. This study realized the necessity for a design that includes a child-participatory design process in construction the outdoor spaces of elementary schools. Through reference study and a theoretical approach of related laws, this study established a child-participatory design process model and applied it to Seoul Don-Am Elementary School. The design process included playing games and providing interesting tools to increase the participation of children in suggesting and presenting their opinions more freely. The design process of this study is described in five steps(eliciting interest in and recognition of the target space, Understanding children's expectations and the expressing thereof, Establishing factors for planning, Visualizing and arranging spaces, and Decision-making and building a final design plan). This process was applied to the planning and design of an outdoor space for Seoul Don-Am Elementary School. In this study, it is clear that the design of the participators and experts have a different purpose. Thus, the process of the design has more meaning than the final product. In addition, it is expected that an improvement in both tangible and intangible designs will be seen. Using a participatory design process, this study successfully improved the facilities and arrangement planning of an outdoor space. At the same time, it also enhanced the interest and participation of children in the process of creating the kind of school they desire. The significance of this study is that it has suggested an effective model to reflect the demands of children, the true users of the outdoor space, and the results were actually applied to elementary school outdoor planning and designing. This study enhanced the awareness of school members in the process of building the school's outdoor space.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.29-45
• /
• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.117-129
• /
• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.79-99
• /
• 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 the Korean Institute of Landscape Architecture
• /
• v.45 no.2
• /
• pp.40-50
• /
• 2017

Compared to the traditional format, the revised IPA is an effective method for selecting a management strategy as compared to the traditional IPA. Comparison between the traditional IPA and revised IPA with a management strategy has been, however, limited. Therefore, the difference between the traditional IPA and revised IPA was compared to select an effective management strategy in the Suncheon Bay Wetland Reserve. First of all, related papers were reviewed to select an appropriate revised IPA. It was found that Deng (2007)'s revised IPA was appropriate for quantifying service quality and a management strategy that affects the measurable satisfaction of visitors in the space. Second, the results of the traditional IPA were compared with the revised IPA in the Suncheon Bay Wetland Reserve and the management strategy of the revised IPA and the changes of management factors were discussed. It was found that some management factors deviated from the order of the quadrant "low priority for managers", "Concentrate management here", "Keep up the good work" were moved to the order of the quadrants "Concentrate management here", "low priority for managers" and "Possible overkill" in the revised IPA grid. The complexity as a management factor resulted in higher demand management than the traditional IPA, which moved from "low priority for managers" to "Concentrate management here". Management factors resulted in lower demand management than the traditional IPA moved from "Concentrate management here" to "low priority for managers"; these consisted of shade trees, exhibition exteriors, programs, and a guided tour. Also, management factors moved from "Keep up the good work" to "Possible overkill" consisted of relaxation facilities, glow of the setting sun, a hedge, and an exhibition interior. Over all, the revised IPA responded properly to changes in the measurable satisfaction of visitors to the Suncheon Bay Wetland Reserve. Therefore, a revised IPA should be provided for accurate and reliable guidelines when decision makers establish management strategies.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.33-56
• /
• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Korean Journal of Social Welfare Studies
• /
• v.48 no.3
• /
• pp.171-208
• /
• 2017

The purpose of this study is to classify the underlying and parsimonious types of child maltreatment and examine whether the effects of risk factors on child maltreatment recurrence differ by type of maltreatment. We utilized the multiyear national administrative data from the National Child Maltreatment Information System collected by Child Protection Agency in Korea. Of 26,921 child maltreatment victims reported and substantiated on or after January 1, 2012, 1,447 children who had recurrence of child maltreatment until December 31, 2015 were selected as maltreatment recurrence group and 4,580 children who had not experienced maltreatment since first substantiation were assigned as maltreatment non-recurrence group. Latent class analysis(LCA) and latent transition analysis(LTA) were used to group children with similar maltreatment subtypes into discrete classes of child maltreatment recurrence. Logistic regression is employed to examine the association between the child maltreatment predominant types and risk factors for recurrence. Results of LCA and LTA showed four latent classes representing predominant type of child maltreatment: 'physical abuse predominant type', 'emotional abuse predominant type', 'sexual abuse predominant type', and 'neglect type'. Significant differences in the effect of risk factors among latent classes were found in child's age and gender, perpetrator's gender, family poverty, biological parent as the perpetrator, domestic violence toward partner, perpetrator's alcoholic problem, insufficient parenting skills, and out-of-home care service, Based on these findings, results suggested how the typology can be used to guide decision about who to target in prevention and intervention programs, and which features of risk factors to target. Practice and policy implications as well as further research tasks were discussed in the lights of searching for useful and important strategies to prevent recurrence of child maltreatment.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.237-237
• /
• 2015

The district of Marlborough has had more than its share of river management projects over the past 150 years, each one uniquely affecting the geomorphology and flood hazard of the Wairau Plains. A major early project was to block the Opawa distributary channel at Conders Bend. The Opawa distributary channel took a third and more of Wairau River floodwaters and was a major increasing threat to Blenheim. The blocking of the Opawa required the Wairau and Lower Wairau rivers to carry greater flood flows more often. Consequently the Lower Wairau River was breaking out of its stopbanks approximately every seven years. The idea of diverting flood waters at Tuamarina by providing a direct diversion to the sea through the beach ridges was conceptualised back around the 1920s however, limits on resources and machinery meant the mission of excavating this diversion didn't become feasible until the 1960s. In 1964 a 10 m wide pilot channel was cut from the sea to Tuamarina with an initial capacity of $700m^3/s$. It was expected that floods would eventually scour this 'Wairau Diversion' to its design channel width of 150 m. This did take many more years than initially thought but after approximately 50 years with a little mechanical assistance the Wairau Diversion reached an adequate capacity. Using the power of the river to erode the channel out to its design width and depth was a brilliant idea that saved many thousands of dollars in construction costs and it is somewhat ironic that it is that very same concept that is now being used to deal with the aggradation problem that the Wairau Diversion has caused. The introduction of the Wairau Diversion did provide some flood relief to the lower reaches of the river but unfortunately as the Diversion channel was eroding and enlarging the Lower Wairau River was aggrading and reducing in capacity due to its inability to pass its sediment load with reduced flood flows. It is estimated that approximately $2,000,000m^3$ of sediment was deposited on the bed of the Lower Wairau River in the time between the Diversion's introduction in 1964 and 2010, raising the Lower Wairau's bed upwards of 1.5m in some locations. A numerical morphological model (MIKE-11 ST) was used to assess a number of options which led to the decision and resource consent to construct an erodible (fuse plug) bank at the head of the Wairau Diversion to divert more frequent scouring-flows ($+400m^3/s$)down the Lower Wairau River. Full control gates were ruled out on the grounds of expense. The initial construction of the erodible bank followed in late 2009 with the bank's level at the fuse location set to overtop and begin washing out at a combined Wairau flow of $1,400m^3/s$ which avoids berm flooding in the Lower Wairau. In the three years since the erodible bank was first constructed the Wairau River has sustained 14 events with recorded flows at Tuamarina above $1,000m^3/s$ and three of events in excess of $2,500m^3/s$. These freshes and floods have resulted in washout and rebuild of the erodible bank eight times with a combined rebuild expenditure of $80,000. Marlborough District Council's Rivers & Drainage Department maintains a regular monitoring program for the bed of the Lower Wairau River, which consists of recurrently surveying a series of standard cross sections and estimating the mean bed level (MBL) at each section as well as an overall MBL change over time. A survey was carried out just prior to the installation of the erodible bank and another survey was carried out earlier this year. The results from this latest survey show for the first time since construction of the Wairau Diversion the Lower Wairau River is enlarging. It is estimated that the entire bed of the Lower Wairau has eroded down by an overall average of 60 mm since the introduction of the erodible bank which equates to a total volume of $260,000m^3$. At a cost of $$0.30/m^3$ this represents excellent value compared to mechanical dredging which would likely be in excess of $$10/m^3$. This confirms that the idea of using the river to enlarge the channel is again working for the Wairau River system and that in time nature's "excavator" will provide a channel capacity that will continue to meet design requirements.