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Surreptitious Adversarial Examples through Functioning QR Code. [PDF]

J Imaging, 2022
Chindaudom A +3 more
europepmc +1 more source

Malware Evasion Attacks Against IoT and Other Devices: An Empirical Study

Tsinghua Science and Technology
The Internet of Things (IoT) has grown rapidly due to artificial intelligence driven edge computing. While enabling many new functions, edge computing devices expand the vulnerability surface and have become the target of malware attacks.
Yan Xu, Deqiang Li, Qianmu Li, Shouhuai Xu +3 more
doaj +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source

Transcending Adversarial Perturbations: Manifold-Aided Adversarial Examples with Legitimate Semantics

Deep neural networks were significantly vulnerable to adversarial examples manipulated by malicious tiny perturbations. Although most conventional adversarial attacks ensured the visual imperceptibility between adversarial examples and corresponding raw ...
Ma, Xiaoguang, Li, Shuai, Jiang, Xiaoyu
core

Detecting Audio Adversarial Examples in Automatic Speech Recognition Systems Using Decision Boundary Patterns. [PDF]

J Imaging, 2022
Zong W, Chow YW, Susilo W, Kim J, Le NT.
europepmc +1 more source

A NEAT Approach to Evolving Neural‐Network‐Based Optimization of Chiral Photonic Metasurfaces: Application of a NeuroEvolution‐of‐Augmenting‐Topologies Pipeline

Advanced Intelligent Systems, EarlyView.
Neuro‐evolution can boost machine‐learning optimization of chiral metasurfaces. By integrating the NEAT algorithm into a deep‐learning framework, we enable the efficient design of visible‐spectrum chiroptical responses. NEAT autonomously evolves neural‐network architectures and weights, reducing manual tuning.
Davide Filippozzi, Arash Rahimi‐Iman
wiley +1 more source

Universal adversarial examples and perturbations for quantum classifiers. [PDF]

Natl Sci Rev, 2022
Gong W, Deng DL.
europepmc +1 more source

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica +38 more
wiley +1 more source

Generating adversarial examples without specifying a target model. [PDF]

PeerJ Comput Sci, 2021
Yang G, Li M, Fang X, Zhang J, Liang X.
europepmc +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source

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