Tootfinder

Beyond Divergence: Characterizing Co-exploration Patterns in Collaborative Design Processes
Xinhui Ye, Joep Frens, Jun Hu
https://arxiv.org/abs/2510.01188 https://

Beyond Divergence: Characterizing Co-exploration Patterns in Collaborative Design Processes
Exploration is crucial in the design process and is known for its essential role in fostering creativity and enhancing design outcomes. Within design teams, exploration evolves into co-exploration, a collaborative and dynamic practice that this study aims to unpack. To investigate this experience, we conducted a longitudinal observational study with 61 students across 16 design teams. Over five months of weekly diary-interviews, we uncovered the intricate dynamics of co-exploration. Our main co…

Supporting Creative Ownership through Deep Learning-Based Music Variation
Stephen James Krol, Maria Teresa Llano, Jon McCormack
https://arxiv.org/abs/2509.25834 https://

Supporting Creative Ownership through Deep Learning-Based Music Variation
This paper investigates the importance of personal ownership in musical AI design, examining how practising musicians can maintain creative control over the compositional process. Through a four-week ecological evaluation, we examined how a music variation tool, reliant on the skill of musicians, functioned within a composition setting. Our findings demonstrate that the dependence of the tool on the musician's ability, to provide a strong initial musical input and to turn moments into complete …

Use ADAS Data to Predict Near-Miss Events: A Group-Based Zero-Inflated Poisson Approach
Xinbo Zhang, Montserrat Guillen, Lishuai Li, Xin Li, Youhua Frank Chen
https://arxiv.org/abs/2509.02614

Use ADAS Data to Predict Near-Miss Events: A Group-Based Zero-Inflated Poisson Approach
Driving behavior big data leverages multi-sensor telematics to understand how people drive and powers applications such as risk evaluation, insurance pricing, and targeted intervention. Usage-based insurance (UBI) built on these data has become mainstream. Telematics-captured near-miss events (NMEs) provide a timely alternative to claim-based risk, but weekly NMEs are sparse, highly zero-inflated, and behaviorally heterogeneous even after exposure normalization. Analyzing multi-sensor telematic…