Seung Won Lee

Yejin Yun, Seung Won Lee, Jiin Choi et al.

Infinite canvas platforms are becoming central to contemporary design practice, enabling designers to externalize cognition through the spatial arrangement of multimodal artifacts. As AI agents increasingly generate and organize content within these environments, their impact on designers' externalization processes remains underexplored. We report a field study with eight professional designers comparing workflows with and without an AI organizing agent. Through a sequence analysis of 5,838 design actions, we identify three key shifts: (1) AI integration reallocates cognitive effort from spatial management to content curation and relational structuring, without increasing active time; (2) a characteristic generate-and-curate cycle emerges in which designers' demands on the agent intensify while the agent's functional role adapts; and (3) AI's role evolves from a divergent catalyst in early stages to a convergent curator in later phases. These findings offer a behavioral model for designing phase-adaptive AI tools that support human-AI co-evolution on infinite canvases.

Ryan Donghan Kwon, Dohyun Lim, Yoonha Lee et al.

The advent of deep learning has significantly propelled the capabilities of automated medical image diagnosis, providing valuable tools and resources in the realm of healthcare and medical diagnostics. This research delves into the development and evaluation of a Deep Residual Convolutional Neural Network (CNN) for the multi-class diagnosis of chest infections, utilizing chest X-ray images. The implemented model, trained and validated on a dataset amalgamated from diverse sources, demonstrated a robust overall accuracy of 93%. However, nuanced disparities in performance across different classes, particularly Fibrosis, underscored the complexity and challenges inherent in automated medical image diagnosis. The insights derived pave the way for future research, focusing on enhancing the model's proficiency in classifying conditions that present more subtle and nuanced visual features in the images, as well as optimizing and refining the model architecture and training process. This paper provides a comprehensive exploration into the development, implementation, and evaluation of the model, offering insights and directions for future research and development in the field.

Ryan Donghan Kwon, Gangjoo Robin Nam, Jisoo Tak et al.

In this study, we present an advanced convolutional neural network (CNN) architecture for ship classification based on optical satellite imagery, which significantly enhances performance through the integration of a convolutional block attention module (CBAM) and additional architectural innovations. Building upon the foundational ResNet50 model, we first incorporated a standard CBAM to direct the model's focus toward more informative features, achieving an accuracy of 87% compared to 85% of the baseline ResNet50. Further augmentations involved multiscale feature integration, depthwise separable convolutions, and dilated convolutions, culminating in an enhanced ResNet model with improved CBAM. This model demonstrated a remarkable accuracy of 95%, with precision, recall, and F1 scores all witnessing substantial improvements across various ship classes. In particular, the bulk carrier and oil tanker classes exhibited nearly perfect precision and recall rates, underscoring the enhanced capability of the model to accurately identify and classify ships. Attention heatmap analyses further validated the efficacy of the improved model, revealing more focused attention on relevant ship features regardless of background complexities. These findings underscore the potential of integrating attention mechanisms and architectural innovations into CNNs for high-resolution satellite imagery classification. This study navigates through the class imbalance and computational costs and proposes future directions for scalability and adaptability in new or rare ship-type recognition. This study lays the groundwork for applying advanced deep learning techniques in remote sensing, offering insights into scalable and efficient satellite image classification.

Seung Won Lee, Semin Jin, Kyung Hoon Hyun

AI-based creativity support tools (CSTs) are evaluated through domain-specific metrics, limiting cross-domain comparison of creative processes. Embedding-based protocol analysis offers a potential domain-agnostic analytical layer. However, we argue that fixed embedding similarity can misrepresent creative dynamics: it may not detect creative pivots that occur within superficially similar language, treating shifts in the problem being addressed as continued elaboration. We identify three open challenges stemming from this gap: aligning similarity measures with creative significance, segmenting and representing multimodal design traces, and evaluating agentic systems where embedding-based metrics enter the generation loop and shape agent behavior. We propose context-aware interventions using large language models as a direction for making trace analysis sensitive to session-specific creative dynamics.