• 한국체육학회지인문사회과학편
• /
• v.57 no.3
• /
• pp.117-133
• /
• 2018

The purpose of this study was to explore integrated approaches to physical education in general education by examining methodology of physical education aiming for convergence education. This case study was conducted, using a qualitative approach during March, 2017 to November, 2017. Data were collected through non-participant observation, in-depth interviews, field-notes, students' journal, syllabus and lecture materials. The key findings are as follows: First, "Emotion Coaching through Movement" is a course of 'understanding of body' approaching integrated humanities science and natural science. Second, it is a convergence education, conducting 'text to daily practice' by approaching positive psychology and neurophysiology. Third, it is a physical education with 'integrated theory and practice' in higher education. These results indicate that students can understand their own body, observe their daily and fixed movement or reaction pattern, and enhance the ability of understanding others through a physical education in general education.

• Korean Journal of Culture and Social Issue
• /
• v.19 no.2
• /
• pp.133-158
• /
• 2013

The present research aimed at investigating the impacts of volunteer activities on self-development and sociability-development. In study 1, the scale comprised of 15 items was developed to measure the meaningfulness of volunteer activities. An exploratory factor analysis was conducted on the data obtained from 428 undergraduates (193 males and 235 females). A confirmatory factor analysis was then conducted on the data obtained from 280 undergraduates (124 males and 156 females). In study 2, the impacts of volunteer activities on 947 undergraduates (461 males and 486 females) were analyzed in terms of self-development and sociability-development. Self-development consisted of self-evaluation, self-esteem, and quality of life. Sociability-development consisted of pother-acceptances, sense of community, and democratic citizenship. The results showed that not more volunteer activities itself but more meaningfulness of those activities had stronger relations with self-development and sociability-development. Finally, a values of the undergraduates internalized for volunteer activities and their levels of self-development and sociability-development expectation were discussed.

• Journal of Internet of Things and Convergence
• /
• v.10 no.1
• /
• pp.39-45
• /
• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

• Korean Journal of Culture and Social Issue
• /
• v.13 no.3
• /
• pp.23-63
• /
• 2007

The purpose of this study is to develop and verify the Relational Self Scale(RSS). Based on the theoretical assumptions which relational self is multi-dimensional and constructed in social contexts, 10 categories with 102 items were yielded. In the process of content analysis, item analysis, exploratory factor analysis and correlation analysis by administering 102 items to korean adults, 31 items with 7 factors are extracted. The 7 factors are consisted of 'avoidance of relation', 'consciousness of others', 'agency', 'instrumental relation', 'empathy-care', 'perceived support from relation' and 'over-dependency to relation'. Next, Confirmatory factor analysis was conducted with 649 korean adults aged from 20's to 60's. The results of confirmatory factor analysis showed the RSS as a valid scale. The 7 factors of the RSS fitted well with men and women. The internal consistency of the RSS was proved to be acceptable. The latent mean analysis indicated that the relational self was not significantly different between men and women at 7 factors. Correlation analysis showed that the construct of relational self was significantly related to relational self-construal, self-esteem and attachment to parent and intimacy person. This study has implication in that relational self is defined and assessed as multi-dimensional construct, and that by administering RSS it is possible to evaluate distinctive korean people's relational self.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.34 no.3
• /
• pp.439-449
• /
• 2024

Recent rapid advancements in voice generation technology have enabled the natural synthesis of voices using text alone. However, this progress has led to an increase in malicious activities, such as voice phishing (voishing), where generated voices are exploited for criminal purposes. Numerous models have been developed to detect the presence of synthesized voices, typically by extracting features from the voice and using these features to determine the likelihood of voice generation.This paper proposes a new model for extracting voice features to address misuse cases arising from generated voices. It utilizes a deep learning-based audio codec model and the pre-trained natural language processing model BERT to extract novel voice features. To assess the suitability of the proposed voice feature extraction model for voice detection, four generated voice detection models were created using the extracted features, and performance evaluations were conducted. For performance comparison, three voice detection models based on Deepfeature proposed in previous studies were evaluated against other models in terms of accuracy and EER. The model proposed in this paper achieved an accuracy of 88.08%and a low EER of 11.79%, outperforming the existing models. These results confirm that the voice feature extraction method introduced in this paper can be an effective tool for distinguishing between generated and real voices.

• Journal of Music and Human Behavior
• /
• v.21 no.1
• /
• pp.69-87
• /
• 2024

This study investigated whether joint attention response behaviors in children with autism spectrum disorder (ASD) change in response to musical cues and reinforcing stimulus, and compared them with neurotypically developing (NT) children. The participants included 13 children with ASD and 14 NT children aged between 3 to 5 years. The study established six task conditions, involving cues (verbal vs. musical) for responding to joint attention (RJA) behaviors and reinforcing stimulus (verbal vs. sound vs. musical) for social referencing behaviors. These tasks were presented 12 times with two repetitions each. The results of the study showed that providing musical cues during the RJA phase increased levels of RJA in children with ASD, consistent with prior studies. Subsequently, musical reinforcing stimuli increased the frequency of social referencing behaviors in these children. This indicates that musical stimuli can extend beyond mere sensory cues, helping individuals to understand and respond to social and emotional cues from others. Moreover, these musical stimuli could serve as effective social reinforcement factors for this population.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.113-127
• /
• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.5
• /
• pp.77-90
• /
• 2023

This study aims to investigate the potential of alternative credit assessment through Social Networking Sites (SNS) as a complementary tool to conventional loan review processes. It seeks to discern the impact of SNS usage characteristics and loan product attributes on credit loan repayment. To achieve this objective, we conducted a binomial logistic regression analysis examining the influence of SNS usage patterns, loan characteristics, and personal attributes on credit loan conditions, utilizing data from Company A's credit loan program, which integrates SNS data into its actual loan review processes. Our findings reveal several noteworthy insights. Firstly, with respect to profile photos that reflect users' personalities and individual characteristics, individuals who choose to upload photos directly connected to their personal lives, such as images of themselves, their private circles (e.g., family and friends), and photos depicting social activities like hobbies, which tend to be favored by individuals with extroverted tendencies, as well as character and humor-themed photos, which are typically favored by individuals with conscientious traits, demonstrate a higher propensity for diligently repaying credit loans. Conversely, the utilization of photos like landscapes or images concealing one's identity did not exhibit a statistically significant causal relationship with loan repayment. Furthermore, a positive correlation was observed between the extent of SNS usage and the likelihood of loan repayment. However, the level of SNS interaction did not exert a significant effect on the probability of loan repayment. This observation may be attributed to the passive nature of the interaction variable, which primarily involves expressing sympathy for other users' comments rather than generating original content. The study also unveiled the statistical significance of loan duration and the number of loans, representing key characteristics of loan portfolios, in influencing credit loan repayment. This underscores the importance of considering loan duration and the quantity of loans as crucial determinants in the design of microcredit products. Among the personal characteristic variables examined, only gender emerged as a significant factor. This implies that the loan program scrutinized in this analysis does not exhibit substantial discrimination based on age and credit scores, as its customer base predominantly consists of individuals in their twenties and thirties with low credit scores, who encounter challenges in securing loans from traditional financial institutions. This research stands out from prior studies by empirically exploring the relationship between SNS usage and credit loan repayment while incorporating variables not typically addressed in existing credit rating research, such as profile pictures. It underscores the significance of harnessing subjective, unstructured information from SNS for loan screening, offering the potential to mitigate the financial disadvantages faced by borrowers with low credit scores or those ensnared in short-term liquidity constraints due to limited credit history a group often referred to as "thin filers." By utilizing such information, these individuals can potentially reduce their credit costs, whereas they are supposed to accrue a more substantial financial history through credit transactions under conventional credit assessment system.

• Journal of Animal Science and Technology
• /
• v.55 no.1
• /
• pp.7-11
• /
• 2013

The purpose of this study was to estimate (co) variance components of three milk production traits for genetic evaluation using a multiple lactation model. Each of the first five lactations was treated as different traits. For the parameter estimation study, a data set was set up including lactations from cows calved from 2001 to 2009. The total number of raw lactation records in first to fifth parities reached 1,416,589. At least 10 cows were required for each contemporary group, herd-year-season effect. Sires with fewer than 10 daughters were discarded. Lactations with 305d milk yield exceeding 15,000 kg were removed. In total, 1,456 sires of cows were remained after all the selection steps. A complete pedigree consisting of 292,382 records was used for the study. A sire model containing herd-year-season, caving age, and sire additive genetic effects was applied to the selected lactation data and pedigree for estimating (co) variance components via VCE. Heritabilities and genetic or residual correlations were then derived from the (co) variance estimates using R package. Genetic correlations between lactations ranged from 0.76 to 0.98 for milk yield, 0.79~1.00 for fat yield, 0.75~1.00 for protein yield. On individual lactation basis, relatively low heritability values were obtained 0.14~0.23, 0.13~0.20 and 0.14~0.19 for milk, fat, and protein yields, respectively. For the combined lactation heritability values were 0.29, 0.28, and 0.26 for milk, fat, and protein yields. The estimated parameters will be used in national genetic evaluations for production traits.

• Korean Journal of Cognitive Science
• /
• v.29 no.2
• /
• pp.121-135
• /
• 2018

Dancers' motions are perceived by observers through visual processes with visual information forming the basis for the observers' appreciation and evaluation of the dancers' motions. There have been many discussions as to whether or not observers' personal athletic capabilities form an essential basis for accurate assessment of the motions of others but, so far, no valid conclusions have been reached. The purpose of this study is to investigate how the ability to predict motions of others varies depending on the athletic expertise of the observers. Participants of this research were ballet dancers of varying athletic expertise. Twenty seven participants were divided into three groups with nine in each: beginners, intermediate experts and experts. The participants watched the same dance video and then evaluated whether the motion would be successful or not. The movement related visual information required to evaluate the success of the motion was systematically adjusted by controlling the length of the video. Using the temporal occlusion method, this study measured the response accuracy of the participants by category of expertise. Under the circumstance with insufficient visual information to utilize, the experts showed higher rates of correct response than the intermediate experts and the beginners. The beginners showed higher rates of wrong response than the experts and the intermediate experts. These results showed that the ability to predict success or failure of a dance motion varied depending on motion expertise of the observers, although they had similar level of expertise in perception. Participants considered to have high athletic expertise showed high prediction ability on the result of the motion. In addition, high expertise in perception reduced the likelihood that participants would make hasty responses under the circumstance with insufficient information and helped to reduce wrong response rate. In conclusion, this study showed that motor expertise and perceptual expertise contribute to prediction accuracy of observed motions.