Results 201 to 210 of about 16,308 (300)
Industrial Relations: A Journal of Economy and Society, Volume 65, Issue 2, Page 103-120, April 2026.ABSTRACT While scholars have often debated the outcomes of partnership strategy, they have rarely studied the conditions in which trade unions opt to pursue partnerships, especially social partnerships in liberal market economies (‘LMEs’). By comparing five trade unions' social partnership responses in the US‐American and English healthcare systems ...
Nick Krachler
wiley +1 more source
Mobile applications for skin cancer detection are vulnerable to physical camera-based adversarial attacks. [PDF]
Sci RepOda J, Takemoto K.
europepmc +1 more source
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks. [PDF]
Nat Commun, 2021 Schwalbe-Koda D +2 more
europepmc +1 more source
Machine learning models, and in particular deep neural networks, are now widely deployed in applications that demand high levels of accuracy and reliability. However, over the past decade, researchers have shown that these systems are not inherently robust, as they are vulnerable to adversarial interventions that can manipulate their behavior in subtle
openaire +1 more source
An Overview of Deep Learning Techniques for Big Data IoT Applications
International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur +2 more
wiley +1 more source
GEAAD: generating evasive adversarial attacks against android malware defense. [PDF]
Sci RepAhmad N +6 more
europepmc +1 more source
Derivative-free optimization adversarial attacks for graph convolutional networks. [PDF]
PeerJ Comput Sci, 2021 Yang R, Long T.
europepmc +1 more source
Distribution shifts in trustworthy machine learning
AI Magazine, Volume 47, Issue 1, Spring 2026.Abstract This article investigates the impact of distribution shifts in trustworthy machine learning. To this end, we start by summarizing fine‐grained types of distribution shifts commonly studied in machine learning communities. To tackle distribution shifts across domains, we present our research across various learning scenarios by enforcing ...
Jun Wu
wiley +1 more source
Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks. [PDF]
Sensors (Basel)Stein Z, Hazan A, Stern A.
europepmc +1 more source
Comprehensive Analysis on Laser Spots Adversarial Attacks Using Genetic Algorithm
Youssef Mansour +6 more
openalex +1 more source