• Journal of Service Research and Studies
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• v.13 no.2
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• pp.146-160
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• 2023

The spread of non-face-to-face services due to the COVID-19 pandemic has brought many changes in consumers' purchasing behavior and attracted much attention to new services. Could trying new services caused by this sudden environmental change alter consumers's choice patterns? This study proposes the research question of whether these new service experiences can change consumers' existing choice behavior, especially for risk-averse consumers who maintain their existing choice behavior or prefer safe alternatives. In this study, we examined whether trying out an unmanned payment services, one of innovative services that emerged after the pandemic crisis, can change the existing choice behavior of risk-averse consumers, i.e., make them more likely to prefer risky alternatives to safe alternatives. To accomplish these research goals, this research conducted one pilot survey and one study. The results of pilot survey showed that the stronger the prevention-focus tendency, the lower the self-efficacy to use the innovative service, with a negative relationship between them. Based on these findings, the study used an experimental method to examine the interaction effects between the use of innovation services and consumers' regulatory focus in a choice behavior and to explore the psychological mechanisms behind them. According to the results, it is found that prevention-focused consumers were more likely to choose risky alternatives and dissimilar extended brands following a trial of an unmanned payment service compared to not using that service. In contrast, promotion-focused consumers did not show different choice patterns regardless of following a trial of an innovative service. Furthermore, these results for prevention-focused consumers confirm the role of self-efficacy as a psychological mechanism. These findings shed light on the role of self-efficacy which has discussed in positive psychology into marketing area. Moreover, practical and academic implications are suggested by the finding that behavioral change occurs in risk-averse consumers, who are known to be hesitant to try new behaviors, indicating market expansion related to potential consumers for the use of the innovation services.

• Korean Journal of Heritage: History & Science
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• v.55 no.4
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• pp.278-294
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• 2022

To date, cultural property education has seen rapid quantitative growth due to national and personal needs. However, qualitative growth is lacking. The objectives of cultural property education have not been established, and therefore, even its identity is not clear. The most pressing issue at present in cultural property education is to first set objectives. This study aimed to analyze the objectives of current cultural property education, identify the problems, and set new objectives to meet significant national and personal needs in terms of education. The problems with the objectives of current cultural property education are that the persons interested in the education do not understand the concept of the education objectives clearly and that the objectives do not contain much actual content of the education. Also, the objectives of the education do not take into account the dynamic competencies and interests of the learners and do not satisfy the changes of the times. To solve these problems, new cultural property education, called 'U.V.E.C.,' was offerred. U.V.E.C. education is aimed at understanding cultural properties, recognizing their value, and enjoying them, and at creating culture. The objectives of U.V.E.C. cultural property education were set such that they can be modified flexibly in a learner-centric way with clear and practical format and contents. Based on this direction, stepwise objectives were set including overall objectives, detailed objectives, and practice objectives, and objective cases of each step were proposed. Considering the generality of the education and the distinct characteristics of the cultural properties, the U.V.E.C. education objectives took into account the diversity of behavioral objectives, clearness in statements, the objectives of problem solving, the initiative of learners and openness for expression outcomes. The U.V.E.C. objectives are clear and specific so that teachers can enhance their pedagogical efficiency and learners are able to develop interesting and diversified competencies. In addition, it is expected that the U.V.E.C. objectives will significantly affect objective setting for education on cultural properties which have not been studied widely. Further systemic and specific studies on the contents and methods of the U.V.E.C. education would help to change the overall education on cultural properties and position the field as a new academic area.

• Knowledge Management Research
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• v.23 no.4
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• pp.315-338
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• 2022

In 1986, Korea established legal systems to support small and medium-sized start-ups, which becomes the main pillars of national development. The legal systems have stimulated start-up ecosystems to have more than 1 million new start-up companies founded every year during the past 30 years. To analyze the trend of Korea's start-up ecosystem, in this study, we collected 1.18 million news articles from 1991 to 2020. Then, we extracted news articles that have the keywords "start-up", "venture", and "start-up". We employed network analysis and topic modeling to analyze collected news articles. Our analysis can contribute to analyzing the government policy direction shown in the history of start-up support policy. Specifically, our analysis identifies the dynamic characteristics of government influenced by external environmental factors (e.g., society, economy, and culture). The results of our analysis suggest that the start-up ecosystems in Korea have changed and developed mainly by the government policies for corporation governance, industrial development planning, deregulation, and economic prosperity plan. Our frequency keyword analysis contributes to understanding entrepreneurial productivity attributed to activities among the networked components in industrial ecosystems. Our analyses and results provide practitioners and researchers with practical and academic implications that can help to establish dedicated support policies through forecast tasks of the economic environment surrounding the start-ups. Korean entrepreneurial productivity has been empowered by growing numbers of large companies in the mobile phone industry. The spectrum of large companies incorporates content startups, platform providers, online shopping malls, and youth-oriented start-ups. In addition, economic situational factors contribute to the growth of Korean entrepreneurial productivity the economic, which are related to the global expansions of the mobile industry, and government efforts to foster start-ups. Our research is methodologically implicative. We employ natural language processes for 30 years of media articles, which enables more rigorous analysis compared to the existing studies which only observe changes in government and policy based on a qualitative manner.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Journal of Popular Narrative
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• v.25 no.4
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• pp.191-221
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• 2019

This study aims to analyze the post-humanistic imagination that has emerged as a major academic thesis in Korean literature, especially novels. In particular, this paper focuses on Cho Ha-hyung's two novels Chimera's Morning(2004) and A Prefabricated Bodhi Tree(2008), published in the early 2000s, for intensive analysis. Post-humanism can be seen as an extension of post-modernism that tried to overcome the limitations of modernity and seek to establish a new world view. In particular, this thought pays attention to the comprehensive understanding of how the rapid development of science and technology, which has developed since the 20th century, has changed the view of humanity and human-centered civilization itself. At the concrete level, it is developing in the direction of constructing a new subject idea by reflecting and dismantling Western-, reason-, and male-centered power mechanisms that are the core of modern civilization. Cho attempts to discover and re-illuminate the surrounding figures, non-humans, and objects that were not noticed in the classic works written in the past. This ideological flow reflects the fact that the concept of human beings, which had been dominated by the humanities in recent years, has been completely changed, and the natural science and technology perspective is applied to the discourse field in various ways. From the point of view of post-humanism, objects that have not been classified as humans and objects that were considered inferior to humans should be included in human or comparable levels. These questions generate interdisciplinary research tasks by involving the large categories of philosophy, such as ontology, epistemology and empirical fields, as well as calling for the participation of the entire literature, science and social sciences. Against the backdrop of a disaster-hit world, Chimera's Morning and A Prefabricated Bodhi Tree depict human beings as variants transformed by bio-technology, and creatures made out of the artificial intelligence built by computer simulations. Post-humanistic ideas in Cho's novels provide a reflective opportunity to comprehensively reconsider the world's shape and human identity reproduced in the text, and to re-explore boundary lines and hierarchy order that distinguish between human and non-human.

• Journal of Popular Narrative
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• v.25 no.1
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• pp.9-54
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• 2019

Since the birth of melodrama in the early Enlightenment era, it has flowed through various cultures and media. In order to grasp the principle of differentiation of melodrama and the direction of its change, a developmental approach to the formation process of melodrama is necessary. In this regard, this paper examines the formation process of modern melodrama and its aesthetic features around the time of the French Revolution. The modern melodrama was formed in the period between the end of the 18th century and the start of the 19th century. It was born at the intersectional point of the contradictions of the modern imagination and the political paradox of the French Revolution, which demanded an autonomous citizenship but did not recognize a woman as a citizen. The aesthetic of women's sacrifice and tears reproduced in the modern melodrama is a political aspiration to restore a corrupt society by glamorizing a woman as a moral icon. This was an icon to save a society under divide and crisis and a coordination of emotions to conceal sexist violence in the politics of the exclusion of women. The aesthetic of women's sacrifice and tears reproduced in modern melodrama has consistently been considered under negative evaluation such as a play of moral hypocrisy and vulgar drama. However, the academic interest in melodrama in the 1970s has been amplified due to the "Sirk-melo" which is a transition to the new aesthetic of women's sacrifice and tears, encompassing not only women, but also races and classes. In modern society, entering the era of uncertainty, where various social problems, national disasters, and global disasters have become commonplace, 'the aesthetic of women's sacrifice and tears' are shifting from gender differences to various victim narratives. Reviewing new theoretical trends and changes of recent melodrama as well as analyzing specific works are left as follow-up tasks.Since the birth of the melodrama in the early Enlightenment era, it has flowed through various cultures and media. In order to grasp the principle of differentiation of melodrama and the direction of its change, a developmental approach to the formation process of melodrama is basically necessary. In this regard, this paper examines the formation process of modern melodrama and its aesthetic features around the time of the French Revolution.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.265-276
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• 2023

The purpose of this study was to develop a plan to ensure that students lacking basic science skills acquire the minimum needed science learning ability while completing the common curriculum. We surveyed 27 elementary and secondary science teachers with experience in research and teaching related to basic skills support to investigate their perceptions of the criteria for minimum achievement standards using Consensual Qualitative Research (CQR) and Analytic Hierarchy Process (AHP). The results indicated that the science teachers tended to describe low achievers as lacking science learning competency, accumulating a science learning deficit, and lacking prerequisite knowledge. However, there were some differences in the characteristics that the elementary and secondary teachers paid attention to in students with insufficient science and basic academic skills. Specifically, the secondary teachers demonstrated greater sensitivity towards low learning motivation and difficulties in using scientific symbols, whereas the elementary teachers were more sensitive towards students' attitudes towards science or lack of experience. Furthermore, it has been observed that the prioritization of items, categorized by school level, differs in terms of setting minimum achievement standards to ensure basic skill support. This implies the need to develop minimum achievement standards considering various variables based on the school level. As there are diverse opinions among science teachers, depending on their expertise, regarding the factors to be considered when developing these standards to guarantee science and basic skill support. Based on the findings of the study, policy support is required to enhance teachers' professionalism in developing students' basic skills while considering the individual context and diversity of low achievers. Additionally, it is crucial to establish a shared vision for students lacking basic skills to reduce the gap between national policy and the practices of science teachers in ensuring support for basic skills.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.57-84
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• 2023

With the development of information and communications technology (ICT) and big data technology, anyone can easily obtain and utilize vast amounts of data through the Internet. Therefore, the capability of selecting high-quality data from a large amount of information is becoming more important than the capability of just collecting them. This trend continues in academia; literature reviews, such as systematic and non-systematic reviews, have been conducted in various research fields to construct a healthy knowledge structure by selecting high-quality research from accumulated research materials. Meanwhile, after the COVID-19 pandemic, remote healthcare services, which have not been agreed upon, are allowed to a limited extent, and new healthcare services such as health recommender systems (HRS) equipped with artificial intelligence (AI) and big data technologies are in the spotlight. Although, in practice, HRS are considered one of the most important technologies to lead the future healthcare industry, literature review on HRS is relatively rare compared to other fields. In addition, although HRS are fields of convergence with a strong interdisciplinary nature, prior literature review studies have mainly applied either systematic or non-systematic review methods; hence, there are limitations in analyzing interactions or dynamic relationships with other research fields. Therefore, in this study, the overall network structure of HRS and surrounding research fields were identified using citation network analysis (CNA). Additionally, in this process, in order to address the problem that the latest papers are underestimated in their citation relationships, the GraphSAGE algorithm was applied. As a result, this study identified 'recommender system', 'wireless & IoT', 'computer vision', and 'text mining' as increasingly important research fields related to HRS research, and confirmed that 'personalization' and 'privacy' are emerging issues in HRS research. The study findings would provide both academic and practical insights into identifying the structure of the HRS research community, examining related research trends, and designing future HRS research directions.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.