• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.426-429
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• 2007

CrossRef operates a cross-publisher citation linking system based on the DOI(R) global identifier. The number of organization building a reference citations linking structure through CrossRef is increasing. This paper concentrates on developing a Citation Matcher Solution to effectively build the reference linking structure. Citation Matcher automatically builds and processes the reference citation and identifier mapping which used to be handled manually. After the copy & paste of the reference citation, analyzation is processed to parse the journal title, author name, volume, issue, and start pages from the free style text. CrossRef, PubMed, and YesKISTI's identifiers are collected by through a standardized method. Renovation of the building process for domestic scholastic resources' reference linking and matching will be made possible by using a Citation Matcher. The connection between resources and seamless access for the electronic full-text will enhance the usability.

• Journal of the Korean Society for Library and Information Science
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• v.41 no.1
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• pp.345-372
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• 2007

In this study, the intellectual structure of Records Management & Archival Science in Korea was analyzed using document clustering, a widely used method of text mining, and document similarity network analysis. The data used in this study were 145 articles written on the subject of Records Management & Archival Science selected from five major representative journals in the field of Library & Information Science in Korea, published from 2001 to 2006. The results of cluster analysis show that the core subject areas are "electronic records management and digital Preservation," "records management policy and institution," "records description and catalogues." and "records management domain and education." The results of document analysis, which is more detailed than cluster analysis, show that "digital archiving," a specialized subject in digital preservation, plays a central role. The results of serial analysis, which proceeds according to a timeline, show the emergence of "archival services" as a new subject area.

• Smart Media Journal
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• v.10 no.4
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• pp.21-27
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• 2021

Recently, the importance of counseling is increasing due to the Corona Blue caused by COVID-19. Also, with the increase of non-face-to-face services, researches on chatbots that have changed the counseling media are being actively conducted. In non-face-to-face counseling through chatbot, it is most important to accurately understand the client's emotions. However, since there is a limit to recognizing emotions only in sentences written by the client, it is necessary to recognize the dimensional emotions embedded in the sentences for more accurate emotion recognition. Therefore, in this paper, the vector and sentence VAD (Valence, Arousal, Dominance) generated by learning the Word2Vec model after correcting the original data according to the characteristics of the data are learned using a deep learning algorithm to learn the multi-dimensional We propose an emotion recognition model. As a result of comparing three deep learning models as a method to verify the usefulness of the proposed model, R-squared showed the best performance with 0.8484 when the attention model is used.

• Smart Media Journal
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• v.12 no.9
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• pp.81-88
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• 2023

Recently, communication through online is increasing due to the spread of non-face-to-face services due to COVID-19. In non-face-to-face situations, the other person's opinions and emotions are recognized through modalities such as text, speech, and images. Currently, research on multimodal emotion recognition that combines various modalities is actively underway. Among them, emotion recognition using speech data is attracting attention as a means of understanding emotions through sound and language information, but most of the time, emotions are recognized using a single speech feature value. However, because a variety of emotions exist in a complex manner in a conversation, a method for recognizing multiple emotions is needed. Therefore, in this paper, we propose a multi-emotion regression model that extracts feature vectors after preprocessing speech data to recognize complex, inherent emotions and takes into account the passage of time.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.489-498
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• 2012

This paper consists of two parts: In the first part, we describe our work to build hierarchical knowledge base of digital library patron's research interests and learning topics in various scholarly areas through analyzing well classified Electronic Theses and Dissertations (ETDs) of NDLTD Union catalog. Journal articles from ACM Transactions and conference web sites of computing areas also are added in the analysis to specialize computing fields. This hierarchical knowledge base would be a useful tool for many social computing and information service applications, such as personalization, recommender system, text mining, technology opportunity mining, information visualization, and so on. In the second part, we compare four grouping algorithms to select best one for our data mining researches by testing each one with the hierarchical knowledge base we described in the first part. From these two studies, we intent to show traditional verification methods for social community miming researches, based on interviewing and answering questionnaires, which are expensive, slow, and privacy threatening, can be replaced with systematic, consistent, fast, and privacy protecting methods by using our suggested hierarchical knowledge base.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.97-107
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• 2020

This study aims to conduct a comprehensive meta-study from the perspective of content analysis to explore trends in Korean academic research on the sharing economy by using the big data analytics. Comprehensive meta-analysis methodology can examine the entire set of research results historically and wholly to illuminate the tendency or properties of the overall research trend. Academic research related to the sharing economy first appeared in the year in which Professor Lawrence Lessig introduced the concept of the sharing economy to the world in 2008, but research began in earnest in 2013. In particular, between 2006 and 2008, research improved dramatically. In order to grasp the overall flow of domestic academic research of trends, 8 years of papers from 2013 to the present have been selected as target analysis papers, focusing on titles, keywords, and abstracts using database of electronic journals. Big data analysis was performed in the order of cleaning, analysis, and visualization of the collected data to derive research trends and insights by year and type of literature. We used Python3.7 and Textom analysis tools for data preprocessing, text mining, and metrics frequency analysis for key word extraction, and N-gram chart, centrality and social network analysis and CONCOR clustering visualization based on UCINET6/NetDraw, Textom program, the keywords clustered into 8 groups were used to derive the typologies of each research trend. The outcomes of this study will provide useful theoretical insights and guideline to future studies.

• Journal of the Korean Society for Library and Information Science
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• v.56 no.4
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• pp.161-180
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• 2022

This study aims to improve the accessibility of websites that can search for online e-journals and check the original text, and the accessibility of web contents in the form of article papers. In order to publish online e-journals in Korea, article contribution management system is used, and services are provided through public or private academic DB companies. There was no content related to accessibility in the publishing and editing stage of online e-journals. In the case of foreign countries, objective to comply with Level AA of WCAG 2.1 to improve accessibility of websites and web content. In addition, the level of accessibility of academic journals is guided through VPAT. In order to improve access to web content in online journals, Accessibility matters are added to the academic society's editorial and publication regulations. Accessibility education should be provided to journal editors. Accessibility checklists should be developed and researchers should verify themselves. To improve the accessibility of online e-journals to websites, For equal use, various convenience functions should be provided when using the website. It guides the accessibility function to the article contribution management system. Each academic and academic DB company should be required to submit a Korean VPAT.

• Smart Media Journal
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• v.11 no.10
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• pp.30-35
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• 2022

Previous studies in various medical specialties have shown that fewer than 50% of abstracts presented at meetings are subsequently published, but only a few studies have been performed in pediatric dentistry. The purpose of this study was to investigate the rate of publication of articles based on abstracts presented at the Korean Academy of Pediatric Dentistry (K.A.D.P) spring and fall Congress for 2001 to 2011. The abstracts for both oral and poster presentation were collected. A RISS search was then performed to identify the publication of full-length articles based on those titles of the abstracts. A total of 706 abstract presentations were done at the 24 meetings (477 as oral presentation, 229 as poster presentations). Of these, from 45.2%(319) in 2011 to 82.9%(585) in 2022 was subsequently published. The publication ratio for orally presented abstracts was from 52.2%(249) in 2011 to 86.6%(413) in 2022, poster presentations from 30.6%(70) in 2011 to 75.1%(172) in 2022. We suggest that presenters at these meetings should expand their abstracts into full manuscripts and seek to publish them in peer-reviewed journals for the benefit of the profession. We believe that the results of changes in the publication rate over the past 12 years are attributable to the digitalized environment such as electronic literature search and electronic publication.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.