• KIISE Transactions on Computing Practices
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• v.20 no.9
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• pp.492-497
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• 2014

In this paper, we propose a method of retrieving opinioned tweets in Twitter, which is the one of the popular Social Network Services and shares diverse opinions among various users. In typical opinion retrieval systems, they may consider the presence of sentiment phrases (subjectivity) as the important factor even if the subjective phrases are not related to a given query or speaker. To alleviate these problems, we utilized the syntactic structure of a sentence to identify the relationships between 1) subjectivity-query and 2) subjectivity-speaker and 3) the syntactic role of subjectivity. Besides, our learning-to-rank approach is trained to retrieve opinioned tweets based on query-relevance, textual features, user information, and Twitter-specific features. Experimental results on real world data show that our proposed method can achieve better performance than several baseline methods in terms of precision and nDCG.

• Korean Journal of Child Studies
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• v.33 no.3
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• pp.83-97
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• 2012

The purpose of this study is to investigate the effects of sand play therapy on the parenting stress and saliva cortisol levels of parents undergoing child counseling programs. The study was conducted with 13 parents who were undergoing child counseling programs. The sessions were held every week for 45 minutes from July, 2011 to September, 2011. To evaluate the effects of sand play therapy, pre test and post test were conducted and the results were then analyzed. The therapy sessions consist of parents making sand boxes, and this was done without consideration of any particular theme. The instrument used was Abidin (1990)'s Parenting Stress Instrument (PSI). Saliva cortisol levels were obtained at the pre-post stage of the sand play therapy. The data were analyzed by means of frequency and the Wilcoxon Rank Sum Test was conducted by the SPSS. The major findings were as follows; There were significant differences in terms of the decreases in the areas of perception of all the parenting stresses examined, including daily life stress, child temperament stress, child relationship stress, and learning-expectation stress, as well as in saliva cortisol levels. Finally, this study suggests that parenting stress and saliva cortisol levels have a positive relationship, and the effects of sand play therapy were significant with decreases in parenting stress and saliva cortisol levels. There is a clear need for parents undergoing child counseling programs to engage in sand play therapy to decrease parenting stress and saliva cortisol levels.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.983-993
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• 2021

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

• Journal of the Korean Society of Physical Medicine
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• v.12 no.1
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• pp.121-129
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• 2017

PURPOSE: This study was conducted to determine the effect of dual-task training (based on the International Classification of Functioning, Disability, and Health; ICF) on walking ability and self-efficacy in individuals with chronic stroke. METHODS: 22 chronic stroke patients participated in this study. Participants were randomly allocated into either the single-task group (n=11) or the dual-task group (n=11). Both groups had physical training three a week for 4 weeks, and at a three-week follow-up. Outcome measures included the 10m walking test (10MWT), figure of 8 walk test (F8WT), dynamic gait index (DGI), and Self-efficacy scale. All data were analyzed using SPSS 18.0 for Windows. Between-group and within-group comparison were analyzed by using the Mann-Whitney U test and Wilcoxon singed-rank test respectively. RESULTS: In the dual-task group, the 10MWT, time and steps of F8WT, DGI, and self-efficacy showed significant differences between pre- and post-test (p<.05). The Changes between the pre- and post-test values of 10MWT (p<.05), DGI (p<.05), and self-efficacy scale (p<.05) showed significant differences between the dual-task group and single-task group. CONCLUSION: Participants reported improved walking ability and self-efficacy, suggesting that dual-task training holds promise in the rehabilitation of walking in chronic stroke patients. This study showed that ICF-based on a dual-task protocol contiributes to motor learning after chronic stroke.

• Korean Journal of Adult Nursing
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• v.28 no.2
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• pp.169-179
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• 2016

Purpose: The purpose of this study was to investigate the effects of simulation-based training (SBT) for basic life support (BLS) utilizing video-assisted debriefing (VAD) about non-technical skills (NTSs) and technical skills (TSs). The goal of the proposed study is the evaluation of a teaching method about the correct application of cardiopulmonary resuscitation (CPR). Methods: The study design was a control group pre- and post-test non-synchronized experimental design. The sample included twelve teams of 36 nursing students. Both the experimental and the control groups received the SBT for BLS. Only the experimental groups received VAD where as the control groups had a verbal debriefing. Raters who used checklists for TSs and NTSs evaluated both groups. Data were analyzed by the SPSS 20.0 using Cronbach's ${\alpha}$, Intraclass Correlation Coefficient (ICC), Mann-Whitney U test and Willcoxon signed rank test. Results: The experimental groups scored higher than the control groups in both TSs (p=.004) and the NTSs (p=.008). Conclusion: The findings of this study suggest that NTSs are an important factor that lead CPR successfully, so VAD can be used as an efficient teaching-learning strategy in the SBT for BLS for nursing students and nurses.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.171-180
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• 2012

The field of wildlife habitat conservation research has attracted attention as integrated biodiversity management strategies. Considering the status of the species surveying data and the environmental variables in Korea, the GARP and Maxent models optimized for presence-only data could be one of the most suitable models in habitat modeling. For make sure applicability in the domestic environment we applied the machine learning species distribution model for analyzing habitats of the Korea water deer($Hydropotes$ $inermis$ $argyropus$) in the $Sapgyocheon$ watershed, $Chungcheong$ province. We used the $3^{rd}$ National Natural Environment Survey data and 10 environment variables by literature review for the modelling. Analysis results showed that habitats for the Korea water deer were predicted 16.3%(Maxent) and 27.1%(GARP), respectively. In terms of accuracy(training/test) the Maxent(0.85/0.69) was higher than the GARP(0.65/0.61), and the Spearman's rank correlation coefficient result of the Maxent(${\rho}$=0.71, p<0.01) was higher than the result of GARP(${\rho}$=0.55, p<0.05). However results could be depended on sites and target species, therefore selection of the appropriate model considering on the situation will be important to analyzing habitats.

• Journal of Family Relations
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• v.21 no.1
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• pp.77-98
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• 2016

Objectives: The aim of this study was to explore perceived meanings of dating and marriage among well-educated Korean couples who were in optimal marriageable ages. Particularly, an emphasis was placed on finding out where the traditional gender norms and post-modern contexts intersect on the couples' course of dating and marriage. Method: We undertook a qualitative analysis of 8 couples (age: 26-34) dating. Participants were limited to university graduates of upper-middle rank universities in Seoul, South Korea. The rationale for choosing such sample was based on the idea that characteristics of class is inherent in the act of dating and marriage, and that such characteristics lead to different contextual experiences in dating and marriage. This study was based on interviews conducted over a three-month time span. The interviews were first transcribed into research text and then subjects and key categories were drawn from the transcripts for analysis. Results: Participants sought meanings of joy, learning, and self-improvement in dating, and they were free from traditional gender norms in their romantic relationships. They viewed marriage as having a permanent companionship with their partner, becoming independent from their parents, and/or a social norm to be followed. Participants reported mixed perceptions about marriage in such fashion that they described their parents' relationship in terms of a gendered leader-supporter relationship, while viewing their own relationship as being genderless partners. In transition to parenthood, however, they regressed to traditional gender norms dichotomized as women being a homemaker and men being a breadwinner. In sum, participants displayed expectations that were inconsistent with regard to dating and marriage over the study period. That is, during the course of dating and early marriage, they did not hold separated gender norms; however, when transitioning from being a newly married couple to giving their first childbirth, expectations shifted to traditional gender norms and values. Conclusion: This suggests that it is not marriage, but the experience of childbirth and motherhood, which strengthen traditional gendered norms, engendering regeneration of the gender norms in families. The results indicate that there is a need to promote co-parenting behavior among the newly-married couples and to educate gender equality about parent roles or for parents in South Korea so that they can overcome traditional gendered norms in family.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• Journal of Korean Home Economics Education Association
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• v.31 no.4
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• pp.129-147
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• 2019

This study aimed at exploring the influence of 'Healthy couple relationship' education on the relationship formation competencies and marriage values of high school students. To achieve the research objective, the 'Healthy couple relationship' lesson plan developed by the author was executed in two high schools for eight weeks from September 1 to November 3, 2018 from which the effects were analyzed. The results are as follows. First, the 'healthy couple relationship' education for high school students has been effective in improving their relationship performance, which is a part of the home economics curriculum. In S high school, the paired t-test of pre-/post-test comparison results showed statistically significant differences in the areas of 'communication', 'conflict resolution' and 'relationship formation performance'. For Sejong City campus-type joint curriculum group, where Wilcoxson signed-rank test was applied due to small sample size, showed that the overall scores as well as all the subsections of 'relationship formation performance' (i.e., 'communication', 'self-understanding', 'conflict resolution', and 'empathy') have improved, although not statistically significant. Second, the 'Healthy couple relationship' education for high school students had positive effects on the marriage values of high school students. In S high school, students' perception of marriage values rendered a statistically significant positive change, while in campus-type joint curriculum in Sejong City, no statistical significance was detected. In conclusion, the 'Healthy couple relationship' education can help high school students build positive values by cultivating their 'relationship formation competence', which is a part of the competencies listed in home economics curriculum, and also broaden their understanding of marriage, by acquiring knowledge and skills to build healthy couple relationships, and learning to implement the knowledge and skills in their own lives.