arXiv:2608. 14651v1 Announce Type: new Abstract: Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individuals with access and functional needs, including hard-of-hearing individuals, pregnant women, mothers with toddlers, and elderly individuals with dementia.
By Anuridhi Gupta, Samara Mansoor, Hemant Purohit
arXiv:2604. 14163v2 Announce Type: replace-cross Abstract: Maritime distress communications transmitted over very high frequency (VHF) radio are safety-critical voice messages used to report emergencies at sea.
By Tomer Atia, Yehudit Aperstein, Alexander Apartsin
Effective crisis response requires spatially grounded communication that bridges linguistic guidance of civilians with the physical environment, accounting for structural bottlenecks, evolving threats, and agent-specific contexts. Yet, current NLP research in crisis communication remains mainly limited to static, text-only classification settings, overlooking the critical communicative role of AI operators in dynamic, embodied scenarios.
arXiv:2602.01163v2 Announce Type: replace
Abstract: Safe UAV emergency landing requires more than just identifying flat terrain; it demands understanding complex semantic risks (e.g., crowds, tempora...
By Chunliang Hua, Lei Zhang, Jiayang Sun, Chunlan Zeng, Xiao Hu
arXiv:2606. 18319v1 Announce Type: cross Abstract: Air Traffic Control Operators (ATCOs) are vital in ensuring the safe, orderly, and efficient flow of air traffic, yet training capacity is constrained by reliance on specialized human trainers known as simpilots, who must role-play both pilots and ATCOs in a simulated airspace.
By Ethan Chew, Enjia Wu, Iruss Eng Wei Yeow, Ian Weiqin Lim, Ranen Sim, Brandon Koh Ziheng, Kaleb Nim, Caden Toh Jun Yi, Wei Dong Soin, Darius Kai Keat Koh, Galen King Yu Tay, Prannaya Gupta, Jonathan Ee Fang Koong, Yong Zhi Lim
The paper presents an LLM-based framework for automatically classifying crisis levels in psychological support hotlines, addressing variability in human judgments and staffing constraints. It introduces a paralinguistic injection method that embeds non‑verbal emotional cues into transcripts, allowing the model to consider acoustic nuances. A reasoning‑enhanced training strategy encourages the model to produce diagnostic reasoning chains, which regularizes and improves classification, achieving a macro F1‑score of 0.802 and accuracy of 0.805 in 5‑fold cross‑validation.
By Terumi Chiba, Yang Luo, Ziyun Cui, Yongsheng Tong, Chao Zhang