• The Journal of Pediatrics of Korean Medicine
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• v.34 no.4
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• pp.1-10
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• 2020

Objectives The purpose of this study is to evaluate the satisfaction and utility of a clinical training in Pediatrics of Korean Medicine after conducting participatory practices in clinical training. Methods A survey was conducted with 46 students who completed a clinical training in 2019. After completing Problem Based Learning (PBL), Objective Structured Clinical Examination (OSCE) and Clinical Performance Examination (CPX), they filled out the questionnaire composed of 15 questions. In addition, it was required to rank the preferences for clinical training items and describe what was good about, things that need to be improved or corrected, and other areas to be implemented as a part of the clinical training. Results 1. Mean of the total satisfaction score was 4.26. Mean satisfaction score of the educational method was 4.25, and mean score of the utility of educational effectiveness was 4.27. 2. Among the questions that evaluate satisfaction of the education program, 'I agree with OSCE as a part of the clinical training for juniors.' showed the highest score. Among the questions that evaluate utility of educational effectiveness, 'It will be helpful to treat patients as a Korean Medicine doctor in the future' showed the highest score. On the other hand, 'I actively participated in the clinical training' showed the lowest score. Conclusions A clinical training in Pediatrics of Korean Medicine can be highly valued from the viewpoint of the satisfaction and its utility.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.93-98
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• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.659-668
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• 2010

Collaborative filtering systems grasp behaviors for a new user and need new information for the user in order to recommend interesting items to the user. For the purpose of acquiring the information the collaborative filtering systems learn behaviors for users based on the previous data and can obtain new information from the results. In this paper, we propose an inductive inference method to obtain new information for users and rank items by using the new information in the proposed method. The proposed method clusters users into groups by learning users through NMF among inductive machine learning methods and selects the group features from the groups by using chi-square. Then, the method classifies a new user into a group by using the bayesian probability model as one of inductive inference methods based on the rating values for the new user and the features of groups. Finally, the method decides the ranks of items by applying the Rocchio algorithm to items with the missing values.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3873-3887
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• 2018

In this paper, we propose a novel method to recover images corrupted by impulse noise. The proposed method uses two stages: noise detection and filtering. In the first stage, we use pixel values, rank-ordered logarithmic difference values, and median values to train a neural-network-based impulse noise detector. After training, we apply the network to detect noisy pixels in images. In the next stage, we use group-based weighted couple sparse representation to filter the noisy pixels. During this second stage, conventional methods generally use only clean pixels to recover corrupted pixels, which can yield unsuccessful dictionary learning if the noise density is high and the number of useful clean pixels is inadequate. Therefore, we use reconstructed pixels to balance the deficiency. Experimental results show that the proposed noise detector has better performance than the conventional noise detectors. Also, with the information of noisy pixel location, the proposed impulse-noise removal method performs better than the conventional methods, through the recovered images resulting in better quality.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1059-1068
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• 2022

Recently, research using image-based deep learning is being conducted to determine similar fonts or recommend fonts. In order to increase the accuracy in judging the similarity of Hangul fonts, a previous study was conducted to calculate the similarity according to the combination of stroke elements. In this study, we tried to solve this problem by designing an integrated model that reflects the weights for each stroke element. By comparing the results of the user's font similarity calculation conducted in the previous study and the weighted model, it was confirmed that there was no difference in the ranking of the influence of the stroke elements. However, as a result of comparison by letter sizes, it was confirmed that there was a difference in the ranking of the influence of stroke elements. Accordingly, we proposed a weighted model set separately for each font size.

• Journal of Korean Academy of Nursing
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• v.48 no.4
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• pp.407-418
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• 2018

Purpose: The purpose of this study was to develop an eye-health program based on social learning theory (EPST) of preschoolers and evaluate its effectiveness. Methods: A nonequivalent control group pre-post test design was utilized and 141 six-year-old preschoolers and their parents participated (experimental group=69, control group=72) in the study. The EPST in this study included eye-health education and eye exercises. Attention, memory, replay, motivation, reinforcement, and self-efficacy were used as interventional strategies. To examine the effectiveness of EPST, proficiency in eye-health activities, refractive power, and visual acuity were measured before and after the intervention. Data were analyzed with SPSS WIN 21.0 using the Shapiro-Wilk test, ${\chi}^2$-test, Mann-Whitney U test and Wilcoxon signed rank test. Results: Following the intervention, eye-health activities, refractive power, and visual acuity significantly improved in the experimental group compared to the control group. Conclusion: The results of this study suggest that EPST is effective in improving eye-health activities, refractive power, and visual acuity in preschoolers, and its wider implementation in educational institutions will promise improved eye-health among preschoolers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2292-2309
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• 2017

Ciphertext policy attribute-based encryption (CP-ABE) is a useful cryptographic technology for guaranteeing data confidentiality but also fine-grained access control. Typically, CP-ABE can be divided into two classes: small universe with polynomial attribute space and large universe with unbounded attribute space. Since the learning with errors over rings (R-LWE) assumption has characteristics of simple algebraic structure and simple calculations, based on R-LWE, we propose a small universe CP-ABE scheme to improve the efficiency of the scheme proposed by Zhang et al. (AsiaCCS 2012). On this basis, to achieve unbounded attribute space and improve the expression of attribute, we propose a large universe CP-ABE scheme with the help of a full-rank differences function. In this scheme, all polynomials in the R-LWE can be used as values of an attribute, and these values do not need to be enumerated at the setup phase. Different trapdoors are used to generate secret keys in the key generation and the security proof. Both proposed schemes are selectively secure in the standard model under R-LWE. Comparison with other schemes demonstrates that our schemes are simpler and more efficient. R-LWE can obtain greater efficiency, and unbounded attribute space means more flexibility, so our research is suitable in practices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.757-770
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• 2022

Palmprint recognition has drawn increasingly attentions in the past decade due to its uniqueness and reliability. Traditional palmprint recognition methods usually use high-resolution images as the identification basis so that they can achieve relatively high precision. However, high-resolution images mean more computation cost in the recognition process, which usually cannot be guaranteed in mobile computing. Therefore, this paper proposes an improved low-resolution palmprint image recognition method based on residual networks. The main contributions include: 1) We introduce a channel attention mechanism to refactor the extracted feature maps, which can pay more attention to the informative feature maps and suppress the useless ones. 2) The ResStage group structure proposed by us divides the original residual block into three stages, and we stabilize the signal characteristics before each stage by means of BN normalization operation to enhance the feature channel. Comparison experiments are conducted on a public dataset provided by the Hong Kong Polytechnic University. Experimental results show that the proposed method achieve a rank-1 accuracy of 98.17% when tested on low-resolution images with the size of 12dpi, which outperforms all the compared methods obviously.

• Korean Journal of Adult Nursing
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• v.25 no.5
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• pp.574-584
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• 2013

Purpose: The purpose of this study was to evaluate the effects of a simulation based clinical reasoning practice program on clinical competence in nursing students. The program was based on the theoretical frameworks of simulation models and experiential learning theory. Methods: The program consisted of eight scenarios which includes three main symptoms (abdominal pain, changes in mental status, dyspnea), for improvement of clinical competencies in nursing students. A nonequivalent control group pretest-posttest design was used for evaluation of the effects of the program. Fifty-two junior nursing students in Y University participated in the experimental group (n=25) or control group (n=27). Critical thinking was measured using a self-administered questionnaire. Clinical judgment and clinical performance were measured by a rater using the Rubric. Descriptive analysis, t-test, Mann-Whitney U, Wilcoxon signed rank test was used for data analyses. Results: Clinical judgment and clinical performance increased in the experimental group, but there were no significant differences in critical thinking. Conclusion: Results indicate that the program developed in this study is a useful strategy to enhance clinical judgment and clinical performance in nursing students. However, the program did not significantly enhance critical thinking disposition, and further study is needed to measure integrated clinical competence including critical thinking skills.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.69-83
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• 1995

In a large number of literature on of inspection tasks, one of the most consistent findings is the existence of large and consistent differences among inspectors. It is possible that the individual difference is described by the difference of cognitive skills, because cognitive skills are required more than manual skills in inspection tasks. Therefore, a set of cognitive factors in human information processing may underly human performance in inspection tasks. In this study, a cognitive skill was described as the relative importance of the cognitive factors involved. A hierarchical task analysis and a fuzzy hierarchical analysis were used to represent how the importance of cognitive factors contribute to inspection performance. An experiment was conducted using the computer simulations of PCB inspection tasks. The results revealed that the subject group with better performance showed the importance weights of cognitive factors in the following rank; (attention, perception, judgement, classification, recognition)<(detection)$\ll$(memory). The results of the experiment can serve as a selection criterion for efficient inspection performance and the information of skilled learning for an inspection training program. The usefullness of a hierarchical task analysis and a fuzzy hierarchical task analysis for the analysis of cognitive tasks are also confirmed.