https://scholarworks.unist.ac.kr/handle/201301/33 2026-04-08T21:08:30Z https://scholarworks.unist.ac.kr/handle/201301/91223 Title: Dynamic Modulation of Emotional Expressions in Social Robots: Effects on Liveliness and Naturalness Author(s): Park, Haeun; Hwang, Sun Jun; Kim, Hyojin; Lee, Jiyeon; Lee, Hui Sung Abstract: Humans naturally express emotions with subtle variations, and exaggerated expressions often appear as heightened intensity in facial, bodily, or vocal cues. This paper introduces a method for exaggerating robotic emotional expressions by dynamically adjusting intensity within an emotion dynamics model. By systematically manipulating the damping ratio, we generated five distinct intensity levels for each emotion, thereby producing emotional expressions that exhibited different degrees of overshoot. A user study revealed that liveliness ratings for surprise increased linearly with intensity, suggesting that exaggerated, high-energy dynamics are particularly effective for conveying surprise. In contrast, other emotions exhibited optimal points at intermediate levels, indicating that excessive exaggeration can reduce perceived naturalness. These findings highlight the need for emotion-specific and user-specific calibration of expression intensity, supporting more nuanced and engaging human-robot interactions. 2026-05-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/91222 Title: MFCC-Inspired Spectral Feature Extraction for Robust Touch Interaction in Social Robots Author(s): Kim, Ji Soo; Hwang, Sun Jun; Kim, Hyojin; Hwang, Dong Joon; Lee, Hui Sung Abstract: Touch is a fundamental modality for conveying emotions and intentions in Human–Robot Interaction. However, conventional approaches to touch pattern recognition often lack robustness to inter-user variability, whereas alternative solutions are frequently bulky or costly. This study proposes a novel feature extraction framework for touch pattern recognition, which adapts MFCC from speech processing to capacitive touch signals. The proposed method preserves the strengths of MFCC—dimensionality reduction and noise robustness—while addressing the physical differences between audio and touch signals by introducing a new frequency reference axis in place of the conventional Mel scale. To evaluate its effectiveness, a representative set of social touch patterns, including gestures traditionally difficult to classify, was defined and analyzed. The proposed framework ensures stable recognition across diverse users while reducing feature dimensionality for efficient operation in lightweight models. This efficiency highlights its suitability for real-time robotic interfaces 2026-05-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90371 Title: Understanding Compliance and Conversion Dynamics in Multi-Agent Collectives Author(s): Lee, Soohwan; Lee, Kyungho Abstract: Multi-agent AI systems are increasingly prevalent across digital environments, yet their social influence dynamics remain underexplored beyond basic compliance. This study investigates how different multi-agent configurations affect human decision-making through compliance and conversion mechanisms. We conducted a controlled experiment with 127 participants interacting with three LLM-powered agents across three conditions: Majority (all agents opposing participant), Minority (one dissenting agent), and Diffusion (gradual spread of minority position). Participants completed normative and informational tasks while reporting stance and confidence at five time points. Results demonstrate distinct influence conditions by condition and task type. In informational tasks, majority consensus drove largest immediate opinion changes, while minority dissent showed potential for delayed but deeper attitude shifts consistent with conversion-like processes. The diffusion condition revealed how temporal dynamics serve as persuasive signals. These findings extend social psychology theories to human-AI interaction, highlighting risks of synthetic consensus manipulation and opportunities for structured dissent to promote critical thinking. 2026-04-12T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/90370 Title: Creativity from Surprise: Bridging the Gap Between Fashion Designers’ Inspiration Work and AI Creative Support Tools Author(s): Jin, Yu; Kwon, Yousang; Yoon, Juhyeok; Zhan, Bowen; Lee, Kyungho Abstract: Advances in Generative AI (GenAI) enable unexpected or surprising creation in visual images. In fashion design, this capability has inten- sified demand for creativity support tools where fast-paced trends challenge fixation and drive exploration of novel creative directions. While prior work has explored interfaces that align designer in- tent with GenAI outputs, we still lack an empirical understanding of how fashion designers define, seek, and utilize AI-generated surprise as a valuable resource and actionable design direction rather than random noise. We address this gap through a qualita- tive study combining semi-structured interviews with 20 fashion professionals and a design workshop with 12 graduate students. We conceptualized surprise as a strategy that can be designed into GenAI-powered visualization tools to support traceable exploration, contextual grounding, and controllable variation across ideation stages. This work (1) reframes surprise as a designable mechanism or resource for co-creative interaction, (2) provides empirical in- sights into how fashion designers can utilize AI-generated surprise in the early stage of design, and (3) translates these insights into actionable guidance for building GenAI-driven visualization tools for fashion and related creative domains from a human-centered AI perspective. 2026-04-12T15:00:00Z