• Journal of the Korea Society for Simulation
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• v.18 no.2
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• pp.39-48
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• 2009

This study reviews the limit of image search by considering on the image search methods related to content-based image retrieval and suggests a user interface for more efficient content-based image retrieval and the ways to utilize image properties. For now, most studies on image search are being performed focusing on content-based image retrieval; they try to search based on the image's colors, texture, shapes, and the overall form of the image. However, the results are not satisfactory because there are various technological limits. Accordingly, this study suggests a new retrieval system which adapts content-based image retrieval and the conventional keyword search method. This is about a way to attribute properties to images using texts and a fast way to search images by expressing the attribute of images as keywords and utilizing them to search images. Also, the study focuses on a simulation for a user interface to make query language on the Internet and a search for clothes in an online shopping mall as an application of the retrieval system based on image attribute. This study will contribute to adding a new purchase pattern in online shopping malls and to the development of the area of similar image search.

• Korean Journal of Communication Studies
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• v.20 no.2
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• pp.105-132
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• 2012

This study is designed to explore the persuasion strategies applied to the student recruitment advertising of the Korean universities. Advertising is a "mirror" of a consumption culture in the sense that an advertising message is composed of the major consumption values (as the persuasive appeals) and the major premises of the consumption culture (as the persuasive premises). Furthermore, the analysis of the persuasive appeals and premises in advertising reveals the ideologies that govern the consumption culture. Thus, this study attempts to explicate the value systems and ideologies of Korean universities in the society by a semiotic decoding of their advertising text. Semiotic approach to "decoding advertising text" allows us to classify advertising signs and sign systems in relation to the way they are transmitted. To achieve this goal, this study investigates three research questions: ① What are the major persuasive appeals appeared in the university advertising? ② What are the persuasive premises underlying those persuasive appeals? ③ What are the ideologies that govern those persuasive appeals and premises in Korean university advertising? The study result reveals 15 major persuasive values and premises along with the four major ideologies governing the symbolism of Korean universities.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.197-216
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• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.10
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• pp.449-456
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• 2016

In this study we recomposed the life story of Min Tatyana who is a Koryo-saram, or in other words, an ethnic Korean residing Kazakhstan. The life story of an overseas Korean, or Koryo-saram to be exact, includes in itself their multi-level identity which consists of the period of the immigration of Koryo-sarams, the special incident of deportation, as well as life as people of the Soviet Union and Kazakhstan, and life as a Korean race. In this study, we were confirmed the texture of the Korean race thought ordinary korean Min Tatyana life who living in Kazakhstan. The life story of Min Tatyana was reconstituted into two stories as "growing up in a multicultural society as Koryo-Saram" and "to live a life as the descendants of independence activist". She was born in historically region Kyzylorda, where living Korean groups and now she is living in Almaty, where living the many koreans ethnic groups of Kazakhstan. Also, her parents were respected to the local residents and her grandfather of husband was famous independent activist. These aspects have helped keep her Korean ethnic identity. The life story of Min Tatyana is personal story and qualitative text, which shows social, cultural background of korean ethnic who living in Kazakhstan. This study is expected to help to confirm the Koryo-Saram characteristics and aspect of their multilayer life.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.259-268
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• 2023

The purpose of this study was to verify the effectiveness of mental simulation practice training using standardized patients for nursing students. This study is a single-group pre- and post-design study, and for data collection, a structured questionnaire was provided to 95 nursing students from a university located in J. The collected data was analyzed using the SPSS/WIN 27.0 program. Results of the study The mental simulation practice training program using standardized patients improved the subject's learning motivation (t=-2.011, p=.046), learning self-efficacy (t=-2.225, p=.027), and learning satisfaction (t=-). 3.428, p=.001) and transfer motivation (t=-2.628, p=.009). In addition, as a result of analyzing the self-assessment contents by text mining, words related to mental simulation practice education using standardized patients included situation, experience, acting, communication, scenario, and mental nursing clinical practice, and words related to satisfaction were actual, There was help, response, understanding, variety, etc. As a result of this study, an environment similar to the actual situation was implemented, and the mental simulation training program applying various cases was found to be effective in practical education of nursing students, so it is necessary to actively utilize it to improve the ability to adapt to the field in the future.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.311-320
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• 2023

This paper analyzes viewers' perception of investigative journalism before and after COVID-19, and examines the direction of investigative journalism using big data. Based on the previous research set as a social science model, the relationship between words related to big data TV current affairs programs and investigative journalism in this paper was investigated before and after the appearance of COVID-19. We visualized changes in viewers' perception of investigative journalism by analyzing text data obtained through the use of Textom, with TV current affairs programs and investigative journalism as keywords. Data was collected from 2017 to June 2022 and refined for analysis. We visualized connectivity centrality using Ucinet 6.0 and Netdraw, and clustered the number of keywords and their frequency using Concor analysis. Our study found a clear change in viewer perception before and after the pandemic. As an implication of this thesis, big data analysis was conducted with the investigative journalism as the main keyword, and the direction of the investigative journalism was presented based on the analysis. Furthermore, based on previous research, we suggest effective approaches for investigative journalism after the pandemic to better engage viewers.

• Journal of Industrial Convergence
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• v.21 no.11
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• pp.1-12
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• 2023

With the recent explosive popularity of SNS, it is increasingly important to utilize SNS marketing, and in this process, the importance of image and caption order in SNS layout is also growing. This research aims to analyze the impact of SNS layouts (Image First vs. Caption First) on the user's attitude toward SNS shopping. A survey was conducted targeting 350 general public and college(graduate) students living in Daegu City and Gyeongbuk Province. The data was analyzed using PROCESS, regression analysis, and t-test by SPSS 21.0 program. The result of this study, it was confirmed that the Image First was more accessible than the Caption First. The Caption First was confirmed to be more diagnostic than the Image First. Moreover, from three specific mediation paths, only two were confirmed, named is through diagnosticity and usefulness, and through accessibility, diagnosticity, and usefullness. The path through diagnosticity and usefulness were stronger than another. Additionally, the impact of accessibility on diagnosticity was found to be higher when involvement was high rather than when involvement was low.

• Information Systems Review
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• v.23 no.1
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• pp.155-172
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• 2021

With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.18-36
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• 2024

As interest in ESG has been increased, it is easy to find papers that empirically study that a company's ESG activities have a positive impact on the company's performance. However, research on what ESG activities companies should actually engage in is relatively lacking. Accordingly, this study systematically classifies ESG activities of companies and seeks to provide insight to companies seeking to plan new ESG activities. This study analyzes how Chinese manufacturing companies perform ESG activities based on their dynamic capabilities in the global economy and how they differ in their activities. This study used the ESG annual reports of 151 Chinese manufacturing listed companies on the Shanghai & Shenzhen Stock Exchange and ESG indicators of China Securities Index Company (CSI) as data. This study focused on the following three research questions. The first is to determine whether there are any differences in ESG activities between companies with high ESG scores (TOP-25) and companies with low ESG scores (BOT-25), and the second is to determine whether there are any changes in ESG activities over a 10-year period (2010-2019), focusing only on companies with high ESG scores. The results showed that there was a significant difference in ESG activities between high and low ESG scorers, while tracking the year-to-year change in activities of the top-25 companies did not show any difference in ESG activities. In the third study, social network analysis was conducted on the keywords of E/S/G. Through the co-concurrence matrix technique, we visualized the ESG activities of companies in a four-quadrant graph and set the direction for ESG activities based on this.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.35-49
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• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.