- P-ISSN1738-6764
- E-ISSN2093-7504
- KCI
ISSN : 1738-6764
Vol.20 No.4
Abstract
In light of the hate crimes directed at the AAPI community during the coronavirus pandemic, a shooting in Atlanta, Georgia, in 2021 resulted in the deaths of six women of Asian descent. Following the shooting, a surge of tweets emerged with the hashtag #StopAsianHate, expressing a wide range of emotions and sentiments in response to both the shooting and the hate crimes that have occurred since the outbreak of COVID-19. The findings of the analysis reveal that negative sentiment consistently drives retweets on Twitter, underscoring the pivotal role of negative emotions in shaping tweet virality. However, positive emotions, particularly joy and fear, also demonstrate positive associations with retweets in specific instances, highlighting a nuanced relationship between emotional expressions and shareability. In summary, while negative emotions exert a significant impact, positive emotions—particularly joy and fear—also show positive correlations with retweet counts in certain contexts. This indicates that both negative and positive emotional expressions contribute to tweet shareability, although their effects vary depending on the specific emotion and situational context. Our findings provide significant insights into the literature on social media engagement, emphasizing the need for a nuanced understanding of user behavior and the intricate mechanisms governing online interactions. Future studies should investigate whether these findings are unique to AAPI members or applicable to the broader topic of racial hate crimes.
Abstract
This paper presents an innovative solution for 3D character model texture mapping based on artificial intelligence (AI) image generation models. This study explored the potential of AI-generated textures to enhance the efficiency and effectiveness of the traditional manual workflow. By leveraging state-of-the-art AI image generation techniques, we proposed a new workflow that could address existing challenges in the current texture mapping process. This research began with an introduction to AI image generation models with a review of the current status and applications of these models in texture map generation. We then identified and analyzed issues associated with the existing character model texture workflow, highlighting inefficiencies and areas for improvement. Our proposed AI-assisted workflow aimed to streamline the texture creation process, significantly reducing the time required while maintaining high-quality outputs. To validate the new workflow, we conducted a series of experiments with professional 3D artists, yielding clear and significant results. The new AI-assisted workflow significantly reduced texture creation time by an average of 76% to 89% compared to traditional manual methods, demonstrating its potential to drastically improve efficiency. Qualitative feedback from artists highlighted that the new workflow not only streamlined the process, but also made it more user-friendly and accessible. However, some limitations were identified, including the need for further refinement of AI-generated textures to achieve a quality level comparable to those created by highly skilled artists.
Abstract
Tax revenues are primary sources of funding for public expenditure in most developing countries. Therefore, tax administration as a focal point of economic policies should receive keen attention. This raises the following important questions: 1) what constitutes an efficient tax system? and 2) how can the tax system be designed to generate optimal revenue to finance public spending and promote economic development? Many developed countries, including the United States, Canada, South Korea, and China, have made significant efforts to establish effective tax management systems using available solutions. According to official reports from international organizations, African countries generally rank poorly in tax sector management. Challenges include inefficient tax collection methods, structural and functional complexities, lack of a tax culture, and crucially, underutilization of information technology as a tool to modernize the tax system. This paper strongly encourages developing countries to learn from achievements and best practices of other countries rather than reinventing the wheel. It specifically analyzes the existing tax management system of the Democratic Republic of Congo (DRC), provides a comprehensive review of the Korean tax system, and summarizes key lessons learned. A critical assessment of organizational, functional, and structural challenges was carried out using analytical and descriptive methodologies. The case study of South Korea is particularly insightful. The design of Korea's tax system reflects its unique structure, function, and policy goals, which have evolved in tandem with its economic development policies. However, the author advises caution when considering Korean fiscal policies due to their unique contexts. This paper proposed a customized tax framework for the DRC, which could also be applicable to other developing countries.
Abstract
The KJ method, a creative thinking tool, can help establish order amidst chaos, with subjectivity playing both beneficial and detrimental roles. This study aimed to enhance the original KJ method and develop a new research approach by extracting key elements from research methods that could effectively optimize subjectivity and then integrating them with steps of the KJ method. The research process included a literature review to outline advantages and challenges of the KJ method with selection and analysis of three subjective research methods and their elements, ultimately culminating in a new five-step KJ method. This study emphasizes the critical role of subjectivity in creative thinking. It provides a theoretical foundation for optimizing the KJ method, thereby offering improved strategies to foster creative thinking.
Abstract
This study attempted to analyze success factors of art technology convergence startups using the ERIS model to provide practical insights for future startups. This case study was focused on Yeolmae Company Inc, which had launched Korea's first online art collective purchase platform 'Art & Company' and issued the first arts-based fractional investment securities. This research performed literature reviews and interviews. Key findings are as follows. Entrepreneur factor showed that the founder leveraged business knowledge as an accountant and art experience from gallery work to create a unique business model. Resource factor revealed that the company secured KRW 26.2 billion (approx. USD 20 million) in investments and resources such as advanced storage facilities and a data-based pricing system, enhancing competitiveness. Industry environment factor showed positive changes due to increased MZ generation investment in art and government amendments to the Capital Market Act. Strategy factor revealed that trust was secured through blockchain-based transparent investment platforms with artworks offered at various price points.
Abstract
The rapid advancement in technology has led to the creation of interactive media across various fields, including education, entertainment, advertising, film, gaming, and animation. However, interactive animations have not achieved the same level of popularity as interactive films and games, often due to their complex story structures, additional production steps, high costs, and the necessity for expertise in game engines to enable interactivity. This paper examines the use of artificial intelligence (AI) tools, particularly Convai within Unreal Engine, to establish a more efficient workflow and reduce production costs in interactive 3D animation. The study compares traditional manual production methods using Unreal Engine and ChatGPT with AI-enhanced workflows that incorporate Convai. The findings indicate that AI tools significantly reduce production time and simplify the creation of interactive features. However, Convai has limitations in flexibility and precision, particularly when it comes to customizing features and animations. While AI tools are beneficial for beginners and those with limited programming experience in Unreal Engine due to their user-friendly nature, manual workflows provide greater flexibility for complex interactions and customizations. The research concludes that AI has substantial potential to improve the production of interactive 3D animation, although further advancements are necessary to enhance support for character and animation customization.
Abstract
The purpose of this study was to explore the effects of problem-based learning (PBL) and a mobile application on self-directed learning and learner recognition in a college course. To achieve this, the study reviewed the literature and case studies on mobile technology and problem-based learning, identifying directions and strategies for course design, including three stages of using a mobile application to enhance interaction and communication. This approach was implemented in the class over the course of a semester. The results revealed that the course effectively promoted self-directed learning among learners. Additionally, a qualitative analysis of learners' experiences with the PBL course using the mobile application highlighted the need for efficient and highly accessible IT learning tools. It also indicated that individual data exploration and integration can be quite challenging. Therefore, it is essential to select and utilize appropriate IT learning tools and provide AI literacy training to enhance the technological capabilities of both learners and instructors. This will facilitate the use of various applications to boost learner participation, motivation, and interaction, thereby improving the effectiveness of classes that employ mobile applications based on PBL.
Abstract
As society develops, the demand for security is rapidly increasing. Accordingly, there is growing interest in research on methods to detect and prevent abnormal behavior using surveillance cameras in public places and private spaces such as shopping malls and airports for human safety. Many detection techniques based on deep learning models have been researched in the field of abnormal behavior detection. However, due to the lack of labeled abnormal behavior data, there are significant difficulties in developing an effective detection system. This paper surveys methods for deep learning methods to detect abnormal human behavior in surveillance video and presents recent techniques. First, I will introduce popular datasets that have often been used in previous research. After that, we categorized the existing methods for detecting abnormal behavior using deep learning into three types: supervised learning, unsupervised learning, and partially-supervised learning. We then explained the basic concepts and advantages of each method and summarized their shortcomings. We also briefly describe future research directions based on the advantages and disadvantages of each method. Based on this, it is expected that the technology of video surveillance systems that apply abnormal behavior detection will further develop.