Results 171 to 180 of about 229,925 (327)
Robust detection framework for adversarial threats in Autonomous Vehicle Platooning. [PDF]
Ness S.
europepmc +1 more source
Social connections to neighbors and NIMBYism among public housing residents in Seoul
Abstract The study examines whether and how transitions into and out of social connections with neighbors have asymmetric effects on residents' attitudes toward the siting of locally unwanted land uses—commonly referred to as “Not In My Backyard” (NIMBY) responses.
Gum‐Ryeong Park, Jinho Kim
wiley +1 more source
Hybrid framework for image forgery detection and robustness against adversarial attacks using vision transformer and SVM. [PDF]
Abdelmaksoud M +3 more
europepmc +1 more source
The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems [PDF]
Hongru Song +6 more
openalex +1 more source
ABSTRACT This study explores youth violence towards police officers in Australia through the Power Threat Meaning Framework (PTMF) to better understand the underlying factors contributing to such violence; focusing on power dynamics, childhood adversity, and trauma.
Dimitra Lattas +4 more
wiley +1 more source
Dual-targeted adversarial noise for 3D point cloud classification model. [PDF]
Lee T, Lee S, Kwon H.
europepmc +1 more source
ABSTRACT For adults with intellectual disability and their families, future planning and moving out of the family home in Australia will increasingly occur within the context of the National Disability Insurance Scheme (NDIS). As a market‐based, individualised funding system its impact on this transition remains largely unknown. This paper reports on a
I. Belperio +5 more
wiley +1 more source
Energy-Efficient and Adversarially Resilient Underwater Object Detection via Adaptive Vision Transformers. [PDF]
Li L, Zhang G, Zhou Y.
europepmc +1 more source
SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems [PDF]
Oubo Ma +7 more
openalex +1 more source
Laser‐induced breakdown spectroscopy (LIBS), an atomic emission technique, is widely applied in fields like geology and biology. This rapid elemental analysis method leverages computational tools to boost precision and speed up data processing. This review explores machine learning and deep learning methods for analyzing LIBS spectral data, tackling ...
Pegah Dehbozorgi +3 more
wiley +1 more source

