Results 71 to 80 of about 5,384 (200)

Multi‐Agent Reinforcement Learning for Joint Police Patrol and Dispatch

Naval Research Logistics (NRL), EarlyView.
ABSTRACT Police patrol units need to split their time between performing preventive patrol and being dispatched to serve emergency incidents. In the existing literature, patrol and dispatch decisions are often studied separately. We consider joint optimization of these two decisions to improve police operations efficiency and reduce response time to ...
Matthew Repasky, He Wang, Yao Xie
wiley   +1 more source

Unraveling Patch Size Effects in Vision Transformers: Adversarial Robustness in Hyperspectral Image Classification

Remote Sensing
Vision Transformers (ViTs) have demonstrated strong performance in hyperspectral image (HSI) classification; however, their robustness is highly sensitive to patch size.
Shashi Kiran Chandrappa, Sidike Paheding, Abel A. Reyes-Angulo   +2 more
doaj   +1 more source

SAR-to-Optical Image Translation Using Supervised Cycle-Consistent Adversarial Networks

IEEE Access, 2019
Optical remote sensing (RS) data suffer from the limitation of bad weather and cloud contamination, whereas synthetic aperture radar (SAR) can work under all weather conditions and overcome this disadvantage of optical RS data.
Lei Wang, Xin Xu, Yue Yu, Rui Yang, Rong Gui, Zhaozhuo Xu, Fangling Pu   +6 more
doaj   +1 more source

Vision transformers: the threat of realistic adversarial patches

Artificial Intelligence for Security and Defence Applications III
The increasing reliance on machine learning systems has made their security a critical concern. Evasion attacks enable adversaries to manipulate the decision-making processes of AI systems, potentially causing security breaches or misclassification of targets. Vision Transformers (ViTs) have gained significant traction in modern machine learning due to
Kasper Cools, Clara Maathuis, Alexander M. van Oers, Claudia S. Hübner, Nikos Deligiannis, Marijke Vandewal, Geert De Cubber   +6 more
openaire   +2 more sources

Lacking data? No worries! How synthetic images can alleviate image scarcity in wildlife surveys: A case study with muskox (Ovibos moschatus)

Remote Sensing in Ecology and Conservation, EarlyView.
This study investigates the integration of synthetic imagery, created with diffusion‐based models, to supplement limited training data and improve muskox (Ovibos moschatus) detection in zero‐shot (ZS) and few‐shot (FS) settings. ZS models detected more than 80% of muskoxen in real images, confirming the potential of synthetic data as a substitute for ...
Simon Durand, Samuel Foucher, Alexandre Delplanque, Joëlle Taillon, Jérôme Théau   +4 more
wiley   +1 more source

Physical adversarial attack in artificial intelligence of things

IET Communications
With the continuous development of wireless communication and artificial intelligence technology, Internet of Things (IoT) technology has made great progress. Deep learning methods are currently used in IoT technology, but deep neural networks (DNNs) are
Xin Ma, Kai Yang, Chuanzhen Zhang, Hualing Li, Xin Zheng   +4 more
doaj   +1 more source

MAS-PD: Transferable Adversarial Attack Against Vision-Transformers-Based SAR Image Classification Task

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Synthetic aperture radar (SAR) is widely used in civil and military fields. With advancements in vision transformer (ViT) research, these models have become increasingly important in SAR image classification due to their remarkable performance. Therefore,
Boshi Zheng, Jiabin Liu, Yunjie Li, Yan Li, Zhen Qin   +4 more
doaj   +1 more source

Integrating Image Segmentation and Deep Learning to Improve Radio Frequency Propagation Models

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT This paper proposes a multi‐sensor approach to improve radio frequency (RF) propagation models, which play a key role in the rapidly expanding field of connected vehicle technology. Focusing on the 1‐ to 20‐GHz frequency range, which is critical for both satellite‐to‐vehicle and base station‐to‐vehicle communications, our study introduces a ...
Jonathan Israel, Xavier Boulanger, Vegard Arneson   +2 more
wiley   +1 more source

Pattern Corruption-Assisted Physical Attacks Against Object Detection in UAV Remote Sensing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep neural networks (DNNs) have attained remarkable success in aerial detection tasks, yet they remain susceptible to adversarial samples, posing a significant challenge for their practical applications.
Yu Zhang, Zhiqiang Gong, Hao Wen, Xikun Hu, Xiaoyan Xia, Hejun Jiang, Ping Zhong   +6 more
doaj   +1 more source

Integrating multimodal data and machine learning for entrepreneurship research

Strategic Entrepreneurship Journal, EarlyView.
Abstract Research Summary Extant research in neuroscience suggests that human perception is multimodal in nature—we model the world integrating diverse data sources such as sound, images, taste, and smell. Working in a dynamic environment, entrepreneurs are expected to draw on multimodal inputs in their decision making.
Yash Raj Shrestha, Vivianna Fang He
wiley   +1 more source