Results 121 to 130 of about 447,373 (304)
Adversarial Attacks on Stochastic Bandits
CoRR, 2018 We study adversarial attacks that manipulate the reward signals to control the actions chosen by a stochastic multi-armed bandit algorithm. We propose the first attack against two popular bandit algorithms: $ε$-greedy and UCB, \emph{without} knowledge of the mean rewards.
Kwang-Sung Jun +3 more
openaire +4 more sources
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
On the feasibility of Adversarial Attacks against IDSs in Automotive CAN
, 2023 LAUREA MAGISTRALELo sviluppo e la continua crescita della tecnologia si sono manifestati in ogni campo, e l’industria automobilistica non fa eccezione.
MONTALBANO, IVAN
core
Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions
Software: Practice and Experience, EarlyView.ABSTRACT Context The rapid increase in cyber threats, coupled with increasingly sophisticated attack strategies and the exponential growth of data in recent years, has exposed significant limitations in classical machine learning, rule‐based, and signature‐based defense mechanisms.
Siva Sai +5 more
wiley +1 more source
A Survey of Adversarial Attacks on SAR Target Recognition: From Digital Domain to Physical Domain
Leida xuebaoDeep Neural Network (DNN)-based Synthetic Aperture Radar (SAR) image target recognition has become a prominent area of interest in SAR applications. However, deep neural network models are vulnerable to adversarial example attacks.
Hang RUAN +6 more
doaj +1 more source
Rigid Body Adversarial Attacks
2025 International Conference on 3D Vision (3DV)Due to their performance and simplicity, rigid body simulators are often used in applications where the objects of interest can considered very stiff. However, no material has infinite stiffness, which means there are potentially cases where the non-zero compliance of the seemingly rigid object can cause a significant difference between its ...
Aravind Ramakrishnan +2 more
openaire +4 more sources
Learning to Cope with Adversarial Attacks
CoRR, 2019 The security of Deep Reinforcement Learning (Deep RL) algorithms deployed in real life applications are of a primary concern. In particular, the robustness of RL agents in cyber-physical systems against adversarial attacks are especially vital since the cost of a malevolent intrusions can be extremely high. Studies have shown Deep Neural Networks (DNN),
Xian Yeow Lee +3 more
openaire +2 more sources
Generative AI—the Transgression of Technology
Systems Research and Behavioral Science, EarlyView.ABSTRACT This article offers a systems‐theoretical analysis of generative artificial intelligence (GenAI) grounded in Niklas Luhmann's sociology of technology. It addresses a central conceptual problem: How GenAI can be understood within a theoretical framework that has traditionally defined technology as a means of stabilising action through causal ...
Jesper Tække
wiley +1 more source
Research Progress on Thermal Runaway and Thermal Management of Lithium‐Ion Batteries
Safety Science and Technology, EarlyView.This review summarizes thermal runaway mechanisms, propagation pathways, mitigation strategies, and intelligent thermal management technologies for lithium‐ion batteries, highlighting the transition from passive heat dissipation to integrated safety regulation through advanced cooling, barrier materials, sensing, machine learning, and digital‐twin ...
Songrong Li +4 more
wiley +1 more source
Informatika, 2019 This paper addresses the problem of dependence of the success rate of adversarial attacks to the deep neural networks on the biomedical image type and control parameters of generation of adversarial examples.
D. M. Voynov, V. A. Kovalev
doaj