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With the rise of short-form content and the growing integration of generative AI (Artificial Intelligence Generated Content, AIGC) technologies in drama production, this study extends the AI Device Use Acceptance (AIDUA) model to examine the factors influencing acceptance and rejection of AIGC dramas based on Chinese traditional mythology. Drawing on survey data from 528 Chinese viewers and employing structural equation modeling, the study identifies six key factors—social influence, hedonic motivation, perceived anthropomorphism, artistic quality, novelty, and cultural traditionality—as significant predictors of performance expectancy. Emotional responses, shaped by performance expectancy, were found to differentially influence acceptance and rejection intentions. The findings underscore the importance of emotional appraisal in user responses to AIGC content and provide empirical insights for the digital transformation of traditional intellectual property (IP) and the strategic planning of culturally resonant AIgenerated content.

Despite advances in Natural Language Processing (NLP) and Large Language Models (LLMs), developing a CBT chatbot that provides emotional support and fosters continuous engagement remains a challenging task. To address this, we proposed SupportlyChat, which is a fine-tuned LLaMA with a custom dataset and provides emotion analysis visualization. We curated the custom dataset by integrating mental health conversation dataset and HappyDB dataset which collects people’s happy moments. We conducted a user study with 10 participants using conditions including standard chatbot responses, emotion analysis reports with standard responses, and emotion analysis reports with empathetic responses. The findings indicate that participants found empathetic responses helpful in shifting towards positive thinking with our proposed method. Additionally, they preferred emotional visualization for its ability to enhance professionalism and recognize inherent emotions. Based on these findings, we suggest utilizing empathetic responses in psychological counseling to emotionally support clients and using emotion analysis reports to promote continuous engagement.

We look at the business model development of a fan community platform ‘Weverse’ from two-sided platform (TSP) to multi-sided platform (MSP) and investigate its platform business model. From the Rocket Model’s theoretical perspective, the results reveal that Weverse firstly focuses on inviting artists as many as possible starting from BTS, then attracts new artists’ fans naturally. For success of this TSP, it forms MSP, ‘Weverse Shop’ to meet two sides’ relevant needs timely and filtered. In third stage of connection, various partnerships are attempted in terms of open platform strategies. For instance, by combining ‘VLive’ and Weverse, Naver’s fan platform business is transferred to Weverse. For core transaction through direct and indirect monetization, several cobranding activities are tried. Lastly, regarding optimization, newly born Weverse being launched in the first half of 2022 is supposed to create further synergies with Naver’s R&D capabilities in data, AI, and other technologies like metaverse platform ‘ZEPETO’ which already sells clothing items of Weverse artists.

The more precisely an AI system collects and analyzes user information, the more effectively it can tailor future recommendations for each user. However, gathering comprehensive information for individual users remains a significant challenge because they may have concerns about privacy or find the process bothersome. To encourage users to willingly provide diverse and meaningful personal information, we applied two widely discussed concepts in psychology and economics to conversational agents: Self-Disclosure and Utility Theory. Our study revealed that while both conversational strategies influenced user experience, the Utility Theory strategy, when combined with questions targeting opinions and emotions, enhanced users’ willingness to disclose personal information and improved their overall disclosure experience. These results highlight the importance of tailoring conversational strategies to information types to encourage self-disclosure effectively. Based on these findings, we propose design considerations for efficiently gathering user information through conversation.

This study explores the role of brand value in the global expansion of Korean food franchise companies, using big data text analysis via the Textom platform. Drawing from unstructured text collected from digital sources, the study conducted word frequency analysis and CONCOR network analysis to reveal key semantic structures in consumer and media discourse. Prominent keywords such as “brand,” “localization,” “global market,” and “Hallyu” emerged as central themes, reflecting the strategic importance of cultural identity and adaptation in overseas expansion. The CONCOR analysis identified dense keyword clusters around “brand trust,” “K-food,” and “consumer recognition,” illustrating how brand value is framed in public discussion. These findings demonstrate that strategic messaging and localized brand communication are essential to franchise success abroad. By combining qualitative nuance with quantitative depth, this study offers actionable insights for marketers and strategists aiming to strengthen brand equity in global markets. Future research should extend to platform-specific sentiment shifts and regional differences in perception.

The emergency of quantum computing is a threat to the foundations of classical cryptographic systems such as RSA and ECC. Currently these algorithms underpin modern data security. Quantum computing based attacks when executed can render these cryptographic systems obsolete. Thus, there is need to transition to quantum-resistant cryptography to protect critical infrastructure including in smart cities where secure and resilient data exchange is paramount. However, this transiting is faced with significant challenges due to legacy systems. To solve this issue, our paper introduces Hybrid Quantum-Safe Encryption (HQSE) that is a dual layer encryption integrating classical cryptography with Quantum computing resistant techniques. Specifically, we evaluate our approach using an integrated classical RSA and Kyber512 that is a lattice based post-quantum algorithm. Multiple files ranging from 256 kb to 2048 kb were then encrypted and decrypted using each algorithm, The performance of our approach was evaluated using metrics such as encryption and decryption time, integrity verification, matching ratio and error rate. Results indicated that Kyber512 consistently outperformed RSA in terms of efficiency. On the other hand, the hybrid model demonstrated middle ground behavior signifying enhanced security without substantial performance loss. Notably our solution achieved near-zero integrity verification delays, matching ratio of 1 and error rate of 0 across all the algorithms. These insights validate the HQSE as a viable transitional solution for protecting legacy cryptographic systems against quantum-era threats. Furthermore, this research contributes to global cybersecurity by introducing a hybrid encryption model that enhances digital resilience particularly in the next generation smart cities.

We present a systematic review of paradigm shifts in computer vision from 2020 to 2025. The survey centers on Vision Transformers(ViT), large-scale self-supervised learning contrastive, MAE/BEiT, multimodal pretraining CLIP, SAM, diffusion-based generation, and 3D representations via NeRF. Using a literature-synthesis framework, we compare architectures, training regimes, and transfer benefits and limits across major tasks. Evidence shows transformer families rival or surpass CNNs on dense-prediction task detection, segmentation, while diffusion models enable stabler training and higher-quality generation than GANs. Self-supervised learning reduces labeling cost and improves generalization in low-label regimes. Multimodal models unlock zero-shot and open-vocabulary recognition; foundation models such as SAM demonstrate general-purpose segmentation. Persisting challenges include data bias, substantial compute/energy demand, and limited explainability. We recommend efficiency-oriented compression distillation, pruning, quantization, green-AI practices, and guidelines for responsible use of foundation models. The outlook highlights edge/embedded realtime vision, 3D/video understanding, and applications in healthcare, remote sensing, and AR/metaverse. Overall, the period is defined by large-scale pretraining, a shift to transformers, multimodal integration, and advances in 3D—pointing to the next goal: responsible and efficient vision AI.

This study analyzes AI education policies in South Korea and Japan between 2017 and 2024, a period when the necessity of AI education gained increasing prominence. Both countries recognize AI education as strategically important, yet their policies diverge in underlying assumptions, orientations, and implementation trajectories. South Korea advances a strategy-driven, technology-oriented approach, positioning AI education as an instrument for economic growth and global competitiveness through sequential reforms such as national roadmaps, curriculum revisions, and digital textbooks. In contrast, Japan pursues a vision-driven, holistic trajectory under the framework of Society 5.0, embedding AI education within a broader ethical and socially oriented context. These contrasting assumptions shape how each country defines core competencies, structures curricula, and conceptualizes the role of AI in society. South Korea emphasizes technical skills such as coding, AI literacy, and data science, whereas Japan foregrounds social and ethical dimensions, including digital citizenship and the early integration of AI ethics. Taken together, the findings underscore the need for AI education policies that balance technical, cognitive, social, and ethical competencies. Ultimately, this comparative analysis contributes to the policy discourse of AI education by demonstrating how distinct national approaches can offer complementary insights for future-oriented AI education strategies.

This study aims to understand the determinants and effects of SNS photo sharing on festival satisfaction among teenage festivalgoers, to derive strategies that festival organizers could employ to maintain long-term relationships with their audience. In this paper, we followed the festival attribute and SNS photo sharing behavior approaches for developing measurement constructs, by investigating previous related studies. Statistical analyses revealed that festival attributes positively impact consumer perceptions of festival value, which in turn influences SNS photo sharing behavior and festival satisfaction. In addition, SNS photo sharing behavior has a significant mediation effect on the process. Moreover, the differing intensities of SNS usage among the festivalgoers resulted in consumers presenting significantly different SNS photo sharing behavior. Our findings offer insights for marketers in the festival business who are interested in gaining a better understanding of the behavior of teenage festivalgoers.

Genshin Impact is an open-world action role-playing game launched by miHoYo in 2020 and has been released on multiple platforms. Due to the excellent character design, it has received widespread attention once it was released, and it has been ranked in the top 10 best-selling lists in several countries after a few weeks of launch. This article will first analyze the map design of the game's open world, and what elements are used on the game map to allow players to immerse themselves in it. Then, in the gameplay of the game, a rich and varied combat system is formed by using the relationship between the elements to restrain each other.There is also a card drawing system added to it, which makes the acquisition of characters full of randomness and allows more players to participate in it. It is concluded that the novel design and rich and varied play methods have made the game Genshin Impact high in popularity, occupying the forefront of the app download list for a long time.