• Journal of Information Management
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• v.36 no.3
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• pp.127-165
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• 2005

This study is focused on the quality of the Web databases which has been produced in science. For the quality evaluation of NDSL, PubMed, Scopus, and WoS, 10 evaluating criteria are developed on the basis of literature review. The evaluation results show that NDSL and PubMed are superior in the currentness and cost. Scopus and WoS are superior in the information of citing and the analysis tool. It is needed for purchasing, user training, and library service based on the above evaluation results.

• Journal of Korean Medical Science
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• v.33 no.43
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• pp.239.1-239.12
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• 2018

Background: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. Methods: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. Results: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. Conclusion: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

• Journal of Trauma and Injury
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• v.34 no.3
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• pp.147-154
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• 2021

Purpose: We analyzed the characteristics and outcomes of patients with bicycle-related injuries at a regional trauma center in northern Gyeonggi Province as a first step toward the development of improved prevention measures and treatments. Methods: The records of 239 patients who were injured in different types of bicycle-related accidents and transported to a single regional trauma center between January 2017 and December 2018 were examined. This retrospective single-center study used data from the Korea Trauma Database. Results: In total, 239 patients experienced bicycle-related accidents, most of whom were males (204, 85.4%), and 46.9% of the accidents were on roads for automobiles. Forty patients (16.7%) had an Injury Severity Score (ISS) of 16 or more. There were 125 patients (52.3%) with head/neck/face injuries, 97 patients (40.6%) with injuries to the extremities, 59 patients (24.7%) with chest injuries, and 21 patients (8.8%) with abdominal injuries. Patients who had head/neck/face injuries and an Abbreviated Injury Score (AIS) ≥3 were more likely to experience severe trauma (ISS ≥16). In addition, only 13 of 125 patients (10.4%) with head/neck/face injuries were wearing helmets, and patients with injuries in this region who were not wearing helmets had a 3.9-fold increased odds ratio of severe injury (AIS ≥2). Conclusions: We suggest that comprehensive accident prevention measures, including safety training and expansion of safety facilities, should be implemented at the governmental level, and that helmet wearing should be more strictly enforced to prevent injuries to the head, neck, and face.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.496-502
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• 2021

Recently, deep speaker embedding approach has been widely used in text-independent speaker verification, which shows better performance than the traditional i-vector approach. In this work, to improve the deep speaker embedding approach, we propose a novel method called group-based speaker embedding which incorporates group information. We cluster all speakers of the training data into a predefined number of groups in an unsupervised manner, so that a fixed-length group embedding represents the corresponding group. A Group Decision Network (GDN) produces a group weight, and an aggregated group embedding is generated from the weighted sum of the group embeddings and the group weights. Finally, we generate a group-based embedding by adding the aggregated group embedding to the deep speaker embedding. In this way, a speaker embedding can reduce the search space of the speaker identity by incorporating group information, and thereby can flexibly represent a significant number of speakers. We conducted experiments using the VoxCeleb1 database to show that our proposed approach can improve the previous approaches.

• Journal of Convergence for Information Technology
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• v.9 no.2
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• pp.148-154
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• 2019

The study was performed to prepare basic data for the development of entrepreneur education programs for nursing students through literature review and text network of relevant studies on entrepreneurship education for college students. The research was found in the database of the Korea Education and Research Information Service, the Korean Academic Information Service System, DBpia and the National Assembly Library with keywords such as 'entrepreneur', 'student', 'education', 'program' and 'training. The final selected paper was 35 studies in Korea from 2000 to September 2016. The largest number of studies have been conducted since 2011 with 85.71%, and the largest proportion of survey(88.57 %). The major independent variables were entrepreneur self-efficacy and entrepreneurship and the dependent variables were entrepreneur intention and entreprenuer self-efficacy. Based on this result, entrepreneur education programs will be developed suitable for the target, and it can promote the entrepreneur education for nursing students.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.12
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• pp.43-53
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• 2019

The construction industry has made great strides in the past decades by utilizing computer programs including CAD. However, compared to other manufacturing sectors, labor productivity is low due to the high proportion of workers' knowledge-based task in addition to simple repetitive task. Therefore, the knowledge-based task efficiency of workers should be improved by recognizing the visual information of computers. A computer needs a lot of training data, such as the ImageNet project, to recognize visual information. This study, aim at proposing building facade datasets that is efficiently constructed by quickly collecting building facade data through portal site road view and automatically labeling using deep learning as part of construction of image dataset for visual recognition construction by the computer. As a method proposed in this study, we constructed a dataset for a part of Dongseong-ro, Daegu Metropolitan City and analyzed the utility and reliability of the dataset. Through this, it was confirmed that the computer could extract the significant facade information of the portal site road view by recognizing the visual information of the building facade image. Additionally, In contribution to verifying the feasibility of building construction image datasets. this study suggests the possibility of securing quantitative and qualitative facade design knowledge by extracting the facade design knowledge from any facade all over the world.

• Journal of Korean Physical Therapy Science
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• v.28 no.1
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• pp.33-45
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• 2021

Background: Children with cerebral palsy have difficulty acquiring motor skills through motor learning due to lack of motor planning of the central nervous system and musculoskeletal dysfunction. Motor learning is the acquisition or modification of movements with the aim of developing skilled movements and behaviors. Cerebral palsy improve motor function through motor learning, and effective motor learning mainly depends on practice parameters such as learning feedback. Therefore, we investigate the effect of motor learning in children with cerebral palsy and try to present the possibility of clinical application. Design: A systemic review. Methods: Research papers were published from Jan, 2010 to Dec, 2020 and were searched using PubMed and Medline. The search terms are 'task specific training' OR 'motor learning' OR 'feedback(Mesh term)' OR 'goal activity' AND 'cerebral palsy(Mesh term)'. A total of eight papers were analyzed in this study. The paper presented the quality level based on the research evidence, and also presented PEDro (Physiotherapy Evidence Database) scores to evaluate the quality of design studies in randomized clinical trials. Results: The results showed that motor learning coaching in children with cerebral palsy improved motor function in post and follow up tests. Also, self-control feedback of motor learning is more effective than external control feedback. 100% external control feedback of motor learning is effective in the acquisition phase and 50% external feedback of motor learning is effective in the retain phase. Conclusion: These results suggest that it will be an important data for establishing evidence on the effect of motor learning arbitration methods in children with cerebral palsy to develop clinical applicability and protocols.

• The Korean Journal of Pain
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• v.34 no.1
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• pp.82-93
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• 2021

Background: This study used bibliometric analysis of articles published about the topic of regional anesthesia from 1980-2019 with the aim of determining which countries, organizations, and authors were effective, engaged in international cooperation, and had the most cited articles and journals. Methods: All articles published from 1980-2019 included in the Web of Science database and found using the keywords regional anesthesia/anaesthesia, spinal anesthesia/anaesthesia, epidural anesthesia/anaesthesia, neuraxial anesthesia/anaesthesia, combined spinal-epidural, and peripheral nerve block in the title section had bibliometric analysis performed. Correlations between the number of publications from a country with gross domestic product (GDP), gross domestic product (at purchasing power parity) per capita (GDP PPP), and human development index (HDI) values were investigated with the Spearman correlation coefficient. The number of articles that will be published in the future was estimated with linear regression analysis. Results: Literature screening found 11,156 publications. Of these publications, 6,452 were articles. The top 4 countries producing articles were United States of America (n = 1,583), Germany (585), United Kingdom (510), and Turkey (386). There was a significant positive correlation found between the GDP, GDP PPP, and HDI markers for global countries with publication productivity (r = 0.644, P < 0.001; r = 0.623, P < 0.001, r = 0.542, P < 0.001). The most productive organizations were Harvard University and the University of Toronto. Conclusions: This comprehensive study presenting a holistic summary and evaluation of 6,452 articles about this topic may direct anesthesiologists, doctors, academics, and students interested in this topic.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.255-277
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• 2021

This study aims to analyze the current status of continuing education for academic librarians based on the records of education. For this purpose, the study investigated the academic librarian's preferences for the topics of continuing education in terms of regional and monthly differences using the 30,404 education records in university and college library statistics of Korean Education and Research Information Service (KERIS). The results shows that 'academic database system' and 'cataloging' were the most preferred topics, and there were topical differences between the academic librarians in university and college.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.109-126
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• 2022

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.