Multi-Agent collaboration as a complementary architecture for AI-generated medical examination items. [PDF]
Jiang Z.
europepmc +1 more source
Dual-Domain Adaptive Input Perturbation Sensitivity for Adversarial Example Detection. [PDF]
Yue L, Gao H, Wang H, Yang M, Xu D.
europepmc +1 more source
Sharing unwritten rules of negotiation: An effort to help leaders thrive
Abstract Negotiation is a skill essential for leader success yet not often trained in the academic pathway. Ideal negotiation is between at least two parties with overlapping goals where mutual benefit is achieved in a solidified agreement. For long‐term success in partnership, integrity, shared value, mutual respect, and clarity should be demonstrated
Julie Byerley +2 more
wiley +1 more source
Adversarial Systems and Adversarial Mindsets: Do We Need Either? [PDF]
William van Caenegem
doaj
Trust-Aware Environmental State Consensus for Smart Agriculture with TEE-Enabled Sensing and Byzantine-Resilient Blockchain Coordination. [PDF]
Li L +7 more
europepmc +1 more source
Mapping the Landscape of Over‐Scanning in CT Imaging: A Scoping Review
Over‐scanning in CT is highly prevalent and contributes to unnecessary radiation exposure, with notable impact on radiosensitive organs. Standardised protocols and AI‐assisted planning show strong potential to optimise scan range and reduce excess dose.
Mo'men Bani‐Ahmad +5 more
wiley +1 more source
Perceived rivalry in intergroup conflict. [PDF]
Reinhard DA +3 more
europepmc +1 more source
ABSTRACT As organizations increasingly adopt human‐AI teams (HATs), understanding how to enhance team performance is paramount. A crucially underexplored area for supporting HATs is training, particularly helping human teammates to work with these inorganic counterparts.
Caitlin M. Lancaster +5 more
wiley +1 more source
Evaluating the safety of large language models in healthcare and dentistry: adversarial testing approaches. [PDF]
Umer F, Shaikh MM, Ur Rahman A.
europepmc +1 more source
Raman Spectroscopy and Generative Variational Autoencoders for Metallurgical Coke Quality Prediction
An integrated soft sensor framework combining standardized Raman spectroscopy, synergy‐vector feature selection, variational autoencoder‐based data augmentation, and regression models (kNN, PLS, and SVR) predicts metallurgical coke quality (CSR and DI).
Pedro Henrique Agrimpio Coutinho +2 more
wiley +1 more source

