Hanyang Cyber University

This study uses network analysis to examine how self-differentiation sub-factors relate to marital satisfaction among 400 married adults in South Korea, a society undergoing rapid cultural transition. A network model was estimated to map the relationships between differentiation components (e.g., Emotional Reactivity, I-Position, Fusion) and marital satisfaction items. The analysis identified Emotional Reactivity and Emotional Fusion as the most central drivers of marital dissatisfaction. In contrast, a rigid "I-Position" (self-assertion) was negatively associated with marital satisfaction, suggesting its role is complex within a cultural context that prioritizes relational harmony. This may reflect "reactive individualism" against traditional suppression. The findings highlight a dynamic interplay between emotional regulation and culturally shaped self-expression, demonstrating network analysis's utility for developing targeted, culturally sensitive clinical interventions that promote a balanced sense of self.

Antennas mounted on masts located at high positions on ships require lightweight design to ensure the stability of the ship. One way to reduce the weight of the mast-mounted antennas is to design array antennas that operate in multiple frequency bands to share the common aperture, thereby reducing the overall array antenna size. However, antennas sharing a common aperture often face performance degradation due to coupling between antennas operating in different frequency bands.

The purpose of this study is to explore the client service experience of medicaid case managers in Korea and prepare health protection measures. The research method was in-depth interviews, and data was collected twice in total from 04 September to 06 September, 2023. The study participants were five medicaid care managers belonging to cities, counties, and districts across the country. As a result of this study, participants experienced problem behaviors such as unreasonable demands, verbal abuse and assault, a threatening environment, a passive attitude toward expressing emotions, and a defensive attitude when performing work. In addition, the participants appeared to be unaware of the organization’s management policy, the absence of a healing program for health disorders, and the follow-up management system to resolve client problem behavior. In conclusion, the organization must establish a safety and health management policy, establish a grievance handling committee, conduct safety and health education, investigate job stress and prepare countermeasures, prepare follow-up measures to protect health, and prepare a job stress relief program.

Image block matching is one of the representative methods for image detection and motion compensation in MPEG. Block matching between two images is a problem of finding symmetry between two images by matching macro blocks that are symmetrical to each other in two given images. The greater the PSNR value, the greater the symmetry of the two images. In the given two images, the two macro blocks with the minimum matching error values are regarded as symmetrical to each other. The classical method of calculating the matching error function for every pixel in the entire search area and choosing the smallest of them guarantees global convergence but requires a lot of computation, especially for large intensities. For this reason, many sparse search methods have been developed to reduce the amount of computation. In this paper, we introduce a gradient descent vector optimization algorithm with guaranteed global convergence to the image block matching problem by utilizing the conceptual symmetry of the vector optimization problem, which is a continuous variable, and the image block matching problem, which is a discrete variable. By blurring the image, we transform the matching cost function closer to being unimodal so that the descent-type algorithm works well. As a result, although the proposed method is simple, it can reduce the amount of computation remarkably and has more robustness for the large displacement of image blocks compared to existing sparse search methods.

Despite the increasing use of conversational artificial intelligence (AI) in language learning, few studies explored how to develop collaborative partnership between AIs and humans. This systematic review examines empirical evidence of human-computer collaboration from 24 studies conducted in an AI-integrated language learning environment and published between 2015 and 2021. The roles of conversational AIs and teachers in each language learning phase with challenges of and suggestions for conversational AI-integrated language learning were identified. Although limited evidence for collaboration between conversational AIs and human teachers was found, future language education should integrate conversational AIs to promote intelligence amplification and decrease human teachers’ workload through classroom orchestration. The study concludes with guidelines and recommendations for teachers and AI researchers.

As the use of devices in mobile environments increases, network attacks such as DDoS have a malicious attempt to flood the network's regular traffic to overload the target and surrounding infrastructure. This research proposed machine learning and deep learning approaches to dealing with DDoS attacks, and the results are described as follows. First, this research successfully detected DDoS attacks through an LGBM with a 100% accuracy score. Second, the proposed model (GRU-LM), which consists of a trained Word2vec layer with the Python dataset, is far more effective than the standard GRU model. Since Python is quite similar to English, language model-based GRU yields superior results. Various preprocessing steps were performed through the NLTK package, and each number was assigned to the tokenized one for constructing the GRU language model. The result reveals that the proposed model achieved an accuracy score of 87% for predicting the following words in the source code, while the rest achieved below 30% accuracy. This conclusion is significant because its relatively simple and light structure overcomes tradeoff problems between time and accuracy and is adaptable to the mobile setting. Discovering traffic patterns for the underlying data of DDOS assaults and retrieving them using statistical data analysis is the value of this research. Furthermore, since public cloud application vulnerability assaults are rising due to expanding cloud infrastructure, this finding could be used in such attacks.