Results 61 to 70 of about 82,924 (315)

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +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, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

A Robust CycleGAN-L2 Defense Method for Speaker Recognition System

IEEE Access, 2023
With the rapid development of voice technology, speaker recognition is becoming increasingly prevalent in our daily lives. However, with its increased usage, security issues have become more apparent.
Lingyi Yang, Yang Xu, Sicong Zhang, Xinyu Zhang   +3 more
doaj   +1 more source

Adversarial Backdoor Defense in CLIP

CoRR
Multimodal contrastive pretraining, exemplified by models like CLIP, has been found to be vulnerable to backdoor attacks. While current backdoor defense methods primarily employ conventional data augmentation to create augmented samples aimed at feature alignment, these methods fail to capture the distinct features of backdoor samples, resulting in ...
Junhao Kuang, Siyuan Liang, Jiawei Liang, Kuanrong Liu, Xiaochun Cao   +4 more
openaire   +2 more sources

Understanding and Improving Ensemble Adversarial Defense

Advances in Neural Information Processing Systems 36, 2023
The strategy of ensemble has become popular in adversarial defense, which trains multiple base classifiers to defend against adversarial attacks in a cooperative manner. Despite the empirical success, theoretical explanations on why an ensemble of adversarially trained classifiers is more robust than single ones remain unclear.
Deng, Yian, Mu, Tingting
openaire   +4 more sources

An Intelligent Feature Engineering‐Driven Hybrid Framework for Adversarial Domain Name System Tunneling Detection

Advanced Intelligent Systems, EarlyView.
This study presents a novel framework that enhances the reliability of DNS traffic monitoring using a hybrid long short‐term memory‐deep neural network (LSMT‐DNN) architecture, enabling robust detection of adversarial DNS tunneling. The proposed framework leverages feature extraction from DNS traffic patterns, including domain request sequences, query ...
Ahmad Almadhor, Shtwai Alsubai, Monji Mohamed Zaidi, Natalia Kryvinska, Abdullah Al Hejaili, Sidra Abbas   +5 more
wiley   +1 more source

Care and COVID 19: Lessons for liberals and neoliberals

Child &Family Social Work, EarlyView., 2023
Abstract Within the liberal political traditions, care is regarded as a private matter, a problem of ethics rather than justice. Social justice is framed as an issue of economics (re/distribution), culture (recognition) and/or politics (representation).
Kathleen Lynch
wiley   +1 more source

Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense

Applied Sciences
Escalating advancements in artificial intelligence (AI) has prompted significant security concerns, especially with its increasing commercialization. This necessitates research on safety measures to securely utilize AI models.
Inpyo Hong, Sokjoon Lee
doaj   +1 more source

Adversarial Example Detection and Restoration Defensive Framework for Signal Intelligent Recognition Networks

Applied Sciences, 2023
Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques.
Chao Han, Ruoxi Qin, Linyuan Wang, Weijia Cui, Dongyang Li, Bin Yan   +5 more
doaj   +1 more source

Detecting adversarial examples with inductive Venn-ABERS predictors [PDF]

, 2019
Inductive Venn-ABERS predictors (IVAPs) are a type of probabilistic predictors with the theoretical guarantee that their predictions are perfectly calibrated.
Goossens, Bart, Peck, Jonathan, Saeys, Yvan   +2 more
core   +1 more source