• The Journal of Engineering Geology
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• v.31 no.3
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• pp.307-319
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• 2021

Although it is currently conducting slope management and research using digital technologies such as drones, big data, and artificial intelligence, it is still somewhat insufficient and is still vulnerable to slope failure. For this reason, it is inevitable to present the development direction for research on prediction and analysis of slope failure using the digital technologies to effectively deal with slope failure, which requires a preemptive understanding of prediction and analysis of slope failure. In this paper, we collected literature data based on the Web of Science for five years from January 1, 2016 to December 31, 2020 and analyzed by co-word analysis to identify the domain structure of research on prediction and analysis of slope failure. Detailed subject areas were identified through network analysis, and the domain relationships between keywords were visualized to derive global and regionally oriented keywords through relationship, centrality analysis. In addition, the clusters formed by performing cluster analysis were displayed on the multidimensional scailing map, and the domain structure according to the correlation between each keyword was presented. The results of this study reveal the domain structure of research on prediction and analysis of slope failure, and are expected to be usefully used to find future research directions.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.279-284
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• 2022

As a study of the blended learning method on design education through the blended learning method, I would like to propose that more advanced learner-led customized design education is possible. Understanding in face-to-face classes and advantages in non-face-to-face classes can be supplemented in an appropriate way in remote classes. Advanced artificial intelligence and big data technology can provide personalized and subdivided learning materials and effective learning methods tailored to learners' levels and interests based on quantified data in design classes. In this paper, it was proposed to maximize the efficiency of the class by applying a method that exceeds the limitations of time and space through the proposal of the A La Carte model (A La Carte). It is a remote class that can be heard anytime, anywhere, and it is also possible to bridge the educational quality and educational gap provided to students living in underprivileged areas. As the goal of fostering creative convergence-type future talents, it is changing with a rapid technological development speed. It is necessary to adapt to the change in learning methods in line with this. An analysis of the infographic virtual space design and construction process through the A La Carte model (A La Carte) proposal was presented. Rather than simply acquiring knowledge, it is expected that knowledge can be sorted, distinguished, learned, and easily reborn with its own knowledge.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.191-205
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• 2023

Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

• 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.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.79-92
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• 2024

ChatGPT, as a representative chatbot leveraging generative artificial intelligence technology, is used valuable not only in scientific and technological domains but also across diverse sectors such as society, economy, industry, and culture. This study conducts an explorative analysis of user sentiments and needs for ChatGPT by examining global social media discourse on Reddit. We collected 10,796 comments on Reddit from December 2022 to August 2023 and then employed keyword analysis, sentiment analysis, and need-mining-based topic modeling to derive insights. The analysis reveals several key findings. The most frequently mentioned term in ChatGPT-related comments is "time," indicative of users' emphasis on prompt responses, time efficiency, and enhanced productivity. Users express sentiments of trust and anticipation in ChatGPT, yet simultaneously articulate concerns and frustrations regarding its societal impact, including fears and anger. In addition, the topic modeling analysis identifies 14 topics, shedding light on potential user needs. Notably, users exhibit a keen interest in the educational applications of ChatGPT and its societal implications. Moreover, our investigation uncovers various user-driven topics related to ChatGPT, encompassing language models, jobs, information retrieval, healthcare applications, services, gaming, regulations, energy, and ethical concerns. In conclusion, this analysis provides insights into user perspectives, emphasizing the significance of understanding and addressing user needs. The identified application directions offer valuable guidance for enhancing existing products and services or planning the development of new service platforms.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.493-498
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• 2024

With the rapid advancement of generative artificial intelligence technology, there is growing interest in how to utilize it in practical applications. Additionally, the importance of prompt engineering to generate results that meet user demands is being newly highlighted. Exploring the new possibilities of generative AI can hold significant value. This study aims to utilize ChatGPT 4.0, a leading generative AI, to propose an effective method for evaluating user experience through the analysis of online customer review data. The user experience evaluation method was based on the six-layer elements of user experience: 'functionality', 'reliability', 'usability', 'convenience', 'emotion', and 'significance'. For this study, a literature review was conducted to enhance the understanding of prompt engineering and to grasp the clear concept of the user experience hierarchy. Based on this, prompts were crafted, and experiments for the user experience evaluation method were carried out using the analysis of collected online customer review data. In this study, we reveal that when provided with accurate definitions and descriptions of the classification processes for user experience factors, ChatGPT demonstrated excellent performance in evaluating user experience. However, it was also found that due to time constraints, there were limitations in analyzing large volumes of data. By introducing and proposing a method to utilize ChatGPT 4.0 for user experience evaluation, we expect to contribute to the advancement of the UX field.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.165-179
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• 2024

It is crucial to have a comprehensive understanding of inundation and water cycle in urban areas for mitigating flood risks and sustainable water resources management. In this study, we developed a Cellular Automata-based integrated Water cycle model (CAW). A comparative analysis with physics-based and conventional cellular automata-based models was performed in an urban watershed in Portland, USA, to evaluate the adequacy of spatiotemporal inundation simulation in the context of a high-resolution setup. A high similarity was found in the maximum inundation maps by CAW and Weighted Cellular Automata 2 Dimension (WCA2D) model presumably due to the same diffuse wave assumption, showing an average Root-Mean-Square-Error (RMSE) value of 1.3 cm and high scores of binary pattern indices (HR 0.91, FAR 0.02, CSI 0.90). Furthermore, through multiple simulation experiments estimating the effects of land cover and soil conditions on inundation and infiltration, as the impermeability rate increased by 41%, the infiltration decreased by 54% (4.16 mm/m²) while the maximum inundation depth increased by 10% (2.19 mm/m²). It was expected that high-resolution integrated inundation and water cycle analysis considering various land cover and soil conditions in urban areas would be feasible using CAW.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.397-405
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• 2021

With the recent phenomenon of the Intelligence Information Society, the cyber security paradigm has begun to change. In particular, the increase of the interconnectedness of the hyperlinked society has extended the scope of damage that can be caused by cyber threats to the real world. In addition to that, it can also be a risk to any given individual who could accompany a crisis that has to do with public safety or national security. Adolescents who are digital natives are more likely to be exposed to cyber threats, which is mainly due to the fact that they are significantly more involved in cyber activities and they also possess insufficient security comprehension and safety awareness. Therefore, it is necessary to strengthen cyber security capabilities of every young individual, so that they can effectively protect themselves against cyber threats and better manage their cyber activities. It examines the changes of the security paradigm and the necessity for cyber security education, which is in direct accordance to the characteristics of a connected society that further suggests directions and a basic system of cyber security education, through a detailed analysis of the current state of Domestic and Overseas Cyber Security Education. The purpose of this study was to define cybersecurity competencies that are necessary within an intelligent information society, and to propose a regular curriculum for strengthening cybersecurity competencies, through the comparison and meticulous analysis of both domestic and overseas educational systems that are pertinent to cybersecurity competencies. Accordingly, a cybersecurity competency system was constructed, by reflecting C3-Matrix, which is a cyber competency system model of digital citizens. The cybersecurity competency system consists of cyber ethics awareness, cyber ethics behavior, cyber security and cyber safety. In addition to this, based on the basic framework of the cybersecurity competency system, the relevant education that is currently being implemented in the United States, Australia, Japan and Korea were all compared and analyzed. From the insight gained through the analysis, the domestic curriculum was finally presented. The main objective of this new unified understanding, was to create a comprehensive and effective cyber security competency curriculum.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.57-75
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• 2024

This study aimed to investigate the current state and educational needs of risk education related to science and technology as perceived by secondary science teachers. A survey was conducted with a total of 366 secondary science teachers. The results are as follows. First, There were more teachers who had not provided education on risks arising from science and technology in terms of risk perception, risk assessment, and risk management than those who had not. Global warming was the most common risk taught by teachers, followed by earthquakes, artificial intelligence, and traffic accidents. Second, teachers recognized that they lacked understanding that the achievement standards of the 2022 revised science curriculum include risks that may occur due to science and technology, but they thought they were prepared to teach. Third, teachers recognized that their understanding of risk perception was higher than that of risk management and risk assessment. Fourth, the experience of teachers in training on risk was very limited, with fewer having training in risk assessment and risk management compared to risk perception. The most common training experienced was in laboratory safety. Fifth, teachers recognized that their capabilities for the 10 goals of risk education were not high. Middle school teachers or teachers majoring in integrated science education evaluated their capabilities relatively highly. Sixth, many teachers thought it was important to address risks in school science education. They prioritized 'information use', 'decision-making skills', and 'influence of mass media', in that order, for importance and called for urgent education in 'action skills', 'information use', and 'influence of risk perception'. Seventh, as a result of deriving the priorities of education needs for each of the 10 goals of risk education, 'action skills', 'influence of risk perception', and 'evaluate risk assessment' were ranked 1st, 2nd, and 3rd, respectively.