• Human Ecology Research
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• v.53 no.4
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• pp.405-412
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• 2015

This study investigated the moderating effect of negative emotionality on the association between teacher-child intimacy and peer interaction based on a differential susceptibility model. The participants were 252 three-year-old children recruited from a day care center and preschool located in Incheon and Gyeonggi province. The teacher-child relationship was measured on a Student-Teacher Relationship Scale. This measure is a type of teacher's report with ratings based on a teacher's daily observations. This scale is composed of closeness items on the degree of warmth and open communication in teacher-child relationships. Peer interactions were measured with a Penn Interactive Peer Play Scale. This measure is composed of play interaction items, play disruption items and play disconnection. Negative emotionality was measured with Child Behavior Questionnaire. Teachers measured teacher-child intimacy and peer interaction scales. Parents reported children's negative emotionality. The research showed that negative emotionality moderated the association of teacher-child intimacy, play interaction, play isolation and play disruption. The magnitude of association between teacher-child intimacy and play disconnection as well as play interaction was greater for high levels of negative emotionality. Teacher-child intimacy was significantly associated with play disruption only for high levels of negative emotionality. The findings of this study support a differential susceptibility model.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.9-14
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• 2018

This study aims to investigate an importance of Author keyword with analysis the position of author keyword. An analysis was carried out on the position of author keyword. we examined an importance of Author keyword by using degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In the next stage, we performed analysis on correlation between network centrality measures and the position of keyword. As a result, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality both has a high value in 4th author keyword order. eigenvector centrality was the comparatively effective method to separate of author keyword order method than other 3 centrality. Correlation analysis result shows that the network analysis value are increasing in order. This study has significance in that it was able to examine the author keyword behavior. Future research is needed to identify and supplement future situational factors, behavior, and psychology.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.76-81
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• 1999

The degree of relative similarity between 2D patterns is obtained using Open-Ball Scheme. Open-Ball Scheme employs a method of transforming the geometrical information on 3D objects or 2D patterns into the features to measure the relative similarity for object(patten) recognition, with invariance on scale, rotation, and translation. The feature of an object is used to obtain the relative similarity and mapped into [0, 1] the interval of real line. For decades, Moment-Invariant Method has been used as one of the excellent methods for pattern classification and object recognition. Open-Ball Scheme uses the geometrical structure of patterns while Moment Invariant Method uses the statistical characteristics. Open-Ball Scheme is compared to Moment Invariant Method with respect to the way that it interprets two-dimensional patten classification, especially the paradigms are compared by the degree of closeness to human's intuitive understanding. Finally the effectiveness of the proposed Open-Ball Scheme is illustrated through simulations.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• Korean Journal of Child Studies
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• v.13 no.1
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• pp.16-37
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• 1992

The purpose of this study was the development of a scale to assess maternal anxiety after mother-child separation. Subjects consisted of 384 mothers who had children from 6 to .36 months of age. A questionnaire consisting of eighty Likert-type items and Spielberger's(1970) State Anxiety Scale were administered to mothers and data were analyzed using item analysis. factor analysis. multiple regression. Cronbach's ${\alpha}$. Pearson's correlation and F-test. Sixty of the eighty items were significant and deemed acceptible through item discrimination method with indices ranging from. 32 to .95. Factor analytic procedures have selected 54 items of the 60-item scale and supported a 5-factor solution. The subscales labeled 'Maternal Separation Anxiety'. 'Perception of Separation Effects on the Child', 'Desire for Physical Cuddling and Closeness'. 'Attitudes toward the Value or Importance of Exclusive Maternal Care' and 'Employment-related Separation Concerns,' Finally. a multiple regression analysis conducted to reduce the length of the scale yielded a 39-item form for the Maternal Separation Anxiety Scale(MSAS). Internal consistency of the MSAS was high(Cronbach's ${\alpha}$ =.85). The correlation of the MSAS with the Spielberger's State Anxiety measure yielded a coefficient of .36, revealing a moderate and positive relationship.

• Journal of the Korean Institute of Gas
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• v.3 no.1
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• pp.27-32
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• 1999

Chemical processes are highly nonlinear, multivariable systems and have complex structures. However, the controllability evaluation procedures are complicated, and the required information is very often unknown at the early design stage. Therefore, it is necessary to develop a procedure to evaluate and enhance controllability while designing processes and plants. To evaluate controllability in the design stage, it is most efficient to analyze process structure. Relative order can be used as a measure of 'physical closeness' between input and output variable. Structural controllability analysis using relative order is shown to be effective in a case study of heat exchanger network synthesis.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.577-585
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• 2004

Storing and estimating the high order probability distribution of classifiers and class labels is exponentially complex and unmanageable without an assumption or an approximation, so we rely on an approximation scheme using the dependency. In this paper, as an extended study of the second-order dependency-based approximation, the probability distribution is optimally approximated by the third-order dependency. The proposed third-order dependency-based approximation is applied to the combination of multiple classifiers recognizing handwritten numerals from Concordia University and the University of California, Irvine and its usefulness is demonstrated through the experiments.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.99-114
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• 2011

Collaborative Filtering (CF) suffers from two major problems:sparsity and cold-start recommendation. This paper focuses on the cold-start problem for new customers with no purchase records and the sparsity problem for the customers with very few purchase records. For the purpose, we propose a method for the new customer recommendation by using a combined measure based on three well-used centrality measures to identify the customers who are most likely to become neighbors of the new customer. To alleviate the sparsity problem, we also propose a hybrid approach that applies our method to customers with very few purchase records and CF to the other customers with sufficient purchases. To evaluate the effectiveness of our method, we have conducted several experiments using a data set from a department store in Korea. The experiment results show that the combination of two measures makes better recommendations than not only a single measure but also the best-seller-based method and that the performance is improved when applying the hybrid approach.

• Korean Journal of Childcare and Education
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• v.10 no.5
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• pp.25-45
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• 2014

This study examined the moderating role of teacher-child relationship on the relation between children's shyness and peer victimization. Participants were 200 children(97 boys, 103 girls; recruited from classes with 5-6 year olds) and their kindergarten teachers. The teachers completed rating scales to measure the children's peer victimization, shyness and teacher-child relationship. The collected data were analyzed using descriptive statistics, t-tests, correlations, and hierarchical multiple regressions. Boys and girls were analyzed separately. Results showed that children's shyness had a positive relation to their peer victimization. Teacher-child relationship significantly related to children's peer victimization. Hierarchical regression analysis indicated that the interaction of boys' shyness and teacher-child closeness predicted boys' peer victimization. Boys' shyness, whose teachers demonstrated the lowest level of teacher-child closeness, was significantly associated with their peer victimization. Boys' shyness had a significant relation to their peer victimization, especially for the highest level of teacher-child conflictual relationship. Results are discussed in terms of the role of teachers to shy boys' peer victimization.