• The Mathematical Education
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• v.62 no.3
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• pp.381-400
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• 2023

As the era of solving various and complex problems in the real world using artificial intelligence and big data appears, problem-solving competencies that can solve realistic problems through a mathematical approach are required. In fact, the 2015 revised mathematics curriculum and the 2022 revised mathematics curriculum emphasize mathematical modeling as an activity and competency to solve real-world problems. However, the real-world problems presented in domestic and international textbooks have a high proportion of artificial problems that rarely occur in real-world. Accordingly, domestic and international countries are paying attention to the reality of mathematical modeling tasks and suggesting the need for authentic tasks that reflect students' daily lives. However, not only did previous studies focus on theoretical proposals for reality, but studies analyzing teachers' perceptions of reality and their competency to reflect reality in the task are insufficient. Accordingly, this study aims to analyze in-service mathematics teachers' perception of reality among the characteristics of tasks for mathematical modeling and the in-service mathematics teachers' competency for designing the mathematical modeling tasks. First of all, five criteria for satisfying the reality were established by analyzing literatures. Afterward, teacher training was conducted under the theme of mathematical modeling. Pre- and post-surveys for 41 in-service mathematics teachers who participated in the teacher training was conducted to confirm changes in perception of reality. The pre- and post- surveys provided a task that did not reflect reality, and in-service mathematics teachers determined whether the task given in surveys reflected reality and selected one reason for the judgment among five criteria for reality. Afterwards, frequency analysis was conducted by coding the results of the survey answered by in-service mathematics teachers in the pre- and post- survey, and frequencies were compared to confirm in-service mathematics teachers' perception changes on reality. In addition, the mathematical modeling tasks designed by in-service teachers were evaluated with the criteria for reality to confirm the teachers' competency for designing mathematical modeling tasks reflecting the reality. As a result, it was shown that in-service mathematics teachers changed from insufficient perception that only considers fragmentary criterion for reality to perceptions that consider all the five criteria of reality. In particular, as a result of analyzing the basis for judgment among in-service mathematics teachers whose judgment on reality was reversed in the pre- and post-survey, changes in the perception of in-service mathematics teachers was confirmed, who did not consider certain criteria as a criterion for reality in the pre-survey, but considered them as a criterion for reality in the post-survey. In addition, as a result of evaluating the tasks designed by in-service mathematics teachers for mathematical modeling, in-service mathematics teachers showed the competency to reflect reality in their tasks. However, among the five criteria for reality, the criterion for "situations that can occur in students' daily lives," "need to solve the task," and "require conclusions in a real-world situation" were relatively less reflected. In addition, it was found that the proportion of teachers with low task development competencies was higher in the teacher group who could not make the right judgment than in the teacher group who could make the right judgment on the reality of the task. Based on the results of these studies, this study provides implications for teacher education to enable mathematics teachers to apply mathematical modeling lesson in their classes.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Korean Security Journal
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• no.36
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• pp.387-416
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• 2013

This study's purpose is to present the improvement of effectiveness of security activity for international conference which can be held hereafter. On the basis of security activity problems originating in G20 summit meeding that had been held in Seoul in 2010. I made up questions three times to on the members of the police, military, fire figher and national intelligence service who had experienced in Seoul G20 summit meeding and recognition of possible problem and possibility of improvement on each item of questions was analyzed by Delphi Method. Also interviews with 4 security experts selected from each security agency were conducted to present improvement in each part of problem. The results obtained from the face to face interview with four experts of security-enforcement agency about the role of event site activity stage for international conference are as followings; First, 'security protocol section' protocol and security are needed mutual win-win enough to be compared with adaptative relationship, thereby being demanded the closer cooperation and information exchange. Second, 'situation management section' there is a need of reinforcing the cooperative system between situation rooms of each agency in order to possibly operate all of the security manpower integrally, which are dispersed by function and by event site, in addition to the swift and organic information exchange between wide-area local government and all the security agencies focusing on a preparation planning group. Third, 'security manpower resource management section' there is a need of encouragement and interest in the leadership in order to devise system that all of the security manpower can concentrate on event and to be possibly satisfied the given conditions. Fourth, 'local government cooperative support section' the wide-area local government of a hosting city as international city operates several kinds of the facilities for international conference, supports operation of conference, achieves a ripple effect of event such as tourism, maximizes service of accomodations, and performs the primary responsibility for the maintenance of the traffic facilities, thereby needing to execute special inspection under the responsibility of Si-Do governors.

• Clinical and Experimental Pediatrics
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• v.48 no.10
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• pp.1132-1138
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• 2005

Purpose : The mucopolysaccharidoses (MPSs) are a heterogeneous group of lysosomal storage disorders. They are caused by a deficiency of the enzymes involved in the degradation of glycosaminoglycans. Early recognition is important because recombinant enzyme replacement therapy is now available for MPS. We studied the clinical characteristics of 80 MPS children with the object of determining the epidemiological, clinical and radiological features in Korean MPS children. Methods : Diagnosis of MPS was confirmed by skin fibroblast enzyme analysis in 80 patients between February 1995 and December 2004. Charts were retrospectively reviewed for clinical and radiological findings, as well as for intelligence and speech evaluations. Results : Hunter syndrome (MPS type II) was the most prevalent type, appearing in 51/80 cases (64 %), followed by Sanfilippo syndrome (MPS III-18%), Hurler syndrome (MPS I-15%), and Morquio syndrome (MPS IV-4%). The average age at diagnosis was 5.5 years (range 1 to 20), and the male-to-female ratio was 4.7 : 1. Typical radiographic changes were observed in 45/54 cases (83%). Mitral regurgitation was the most common cardiac defect. Moderate to profound mental retardation and hearing loss were present in 14/35 cases (56%) and 33/38 cases (82%), respectively. Four MPS II patients had bone marrow transplantation, with mixed outcomes. Five MPS I patients are currently on enzyme replacement therapy. Conclusion : Our study showed a high proportion of MPS II cases (64%), which may represent population variability. By studying the clinical features of these patients, we hope to alert pediatricians of the warning signs of MPS.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.19-47
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• 2015

In this era of knowledge economy, a variety of virtual communities are proliferating for the purpose of knowledge creation and utilization. Since the voluntary contributions of members are the essential source of knowledge, member turnover can have significant implications on the survival and success of virtual communities. However, there is a dearth of research on the effect of membership turnover and even the method of measurement for membership turnover is left unclear in virtual communities. In a traditional context, membership turnover is calculated as the ratio of the number of departing members to the average number of members for a given time period. In virtual communities, while the influx of newcomers can be clearly measured, the magnitude of departure is elusive since explicit withdrawals are seldom executed. In addition, there doesn't exist a common way to determine the average number of community members who return and contribute intermittently at will. This study initially examines the limitations in applying the concept of traditional turnover to virtual communities, and proposes five membership fluidity measures based on a preliminary analysis of editing behaviors of 2,978 featured articles in English Wikipedia. Subsequently, this work investigates the relationships between three selected membership fluidity measures and group collaboration performance, reflecting a moderating effect dependent on work characteristic. We obtained the following results: First, membership turnover relates to collaboration efficiency in a right-shortened U-shaped manner, with a moderating effect from work characteristic; given the same turnover rate, the promotion likelihood for a more professional task is lower than that for a less professional task, and the likelihood difference diminishes as the turnover rate increases. Second, contribution period relates to collaboration efficiency in a left-shortened U-shaped manner, with a moderating effect from work characteristic; the marginal performance change per unit change of contribution period is greater for a less professional task. Third, the number of new participants per month relates to collaboration efficiency in a left-shortened reversed U-shaped manner, for which the moderating effect from work characteristic appears to be insignificant.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• 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.

• The Korean Journal of Nuclear Medicine
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• v.38 no.3
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• pp.233-240
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• 2004

Purpose: We developed a computer-aided classifier using artificial neural network (ANN) to discriminate the cerebral metabolic pattern of medial and lateral temporal lobe epilepsy (TLE). Materials and Methods: We studied brain F-18-FDG PET images of 113 epilepsy patients sugically and pathologically proven as medial TLE (left 41, right 42) or lateral TLE (left 14, right 16). PET images were spatially transformed onto a standard template and normalized to the mean counts of cortical regions. Asymmetry indices for predefined 17 mirrored regions to hemispheric midline and those for medial and lateral temporal lobes were used as input features for ANN. ANN classifier was composed of 3 independent multi-layered perceptrons (1 for left/right lateralization and 2 for medial/lateral discrimination) and trained to interpret metabolic patterns and produce one of 4 diagnoses (L/R medial TLE or L/R lateral TLE). Randomly selected 8 images from each group were used to train the ANN classifier and remaining 51 images were used as test sets. The accuracy of the diagnosis with ANN was estimated by averaging the agreement rates of independent 50 trials and compared to that of nuclear medicine experts. Results: The accuracy in lateralization was 89% by the human experts and 90% by the ANN classifier Overall accuracy in localization of epileptogenic zones by the ANN classifier was 69%, which was comparable to that by the human experts (72%). Conclusion: We conclude that ANN classifier performed as well as human experts and could be potentially useful supporting tool for the differential diagnosis of TLE.

• Korean Security Journal
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• no.14
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• pp.367-388
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• 2007

Some of individuals working at various fields of security guard are given the legal right to use a gun to cope with expected risks and crimes. Not only in Korea but also throughout the world, a pistol is being used as a device for the workers of security guard such as the army, the police and intelligence agencies to keep the law and social order. A pistol, which is regarded as the last safety measure of security guard, is easy to carry but, because of its short barrel for high portability, its hit ratio and long-range shooting performance are much lower than ordinary guns. Despite such weak points, a pistol can be a great tool because of its high portability and if the users make steady efforts to improve their skills and, for this reason, it has been being used widely until now. National agency workers' pistol shooting skills depend on several important factors, but for the fast and accurate shooting, the mot important factor is how to aim at the target. Because it takes a relatively longer time to align the sight on the target, the change of the sight alignment method is expected to improve pistol shooting skills a lot. In Korea, however, most of national agency training centers teach the basic aiming method that crosses the foresight and the backsight and aligns the crossing to the target. That is, they teach the sight alignment method for fixed targets. Some agencies in Korea and foreign countries that have used guns more frequently and longer than us are teaching different sight alignment methods as well. Representative ones of them are aiming only with the foresight and pointing shooting. These aiming methods are expected to produce good results against moving targets. While the basic sight alignment method has to cross the foresight and the backsight and then to align the crossing to the target, so takes a longer time than other aiming methods, the other two aiming methods are expected to be effective in fields where both swiftness and accuracy are require. Because domestic agencies are teaching mainly the basic sight alignment method, the present study purposed to examine the effects of different aiming methods on shooting records and ultimately to contribute to national agency workers' operation. For this purpose, we selected 20 policemen from local police commandos under the National Police Agency who were accustomed to pistol shooting and measured their pistol shooting. According to the results, the pointed shooting method showed the best record in 5m short.range shooting. As to hit ratio in 15m shooting, which is relatively long.range for pistol shooting, the basic aiming method showed the best record, but the results of statistical analysis showed that pointed shooting is most superior. Of course, the results were obtained from a group who were used to and skillful in pistol shooting, but they are considered meaningful in that all the police and national agency workers should have such high shooting skills.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.1-31
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• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.