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Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 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
A New Type of Adversarial Examples
CoRRMost machine learning models are vulnerable to adversarial examples, which poses security concerns on these models. Adversarial examples are crafted by applying subtle but intentionally worst-case modifications to examples from the dataset, leading the model to output a different answer from the original example. In this paper, adversarial examples are
Xingyang Nie +5 more
openaire +2 more sources
Explaining and Harnessing Adversarial Examples
, 2014 Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence.
Ian J. Goodfellow +2 more
openaire +2 more sources
Verifying the causes of adversarial examples [PDF]
, 2020 The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence.
Yezzi, Anthony +4 more
core
Large Language Model‐Based Chatbots in Higher Education
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci +4 more
wiley +1 more source
Компьютерная оптикаAdversarial examples, in the context of computer vision, are inputs deliberately crafted to deceive or mislead artificial neural networks. These examples exploit vulnerabilities in neural networks, resulting in minimal alterations to the original input ...
A.V. Trusov +2 more
doaj +1 more source
Beware the Black-Box: On the Robustness of Recent Defenses to Adversarial Examples. [PDF]
Entropy (Basel), 2021 Mahmood K +3 more
europepmc +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
Universal adversarial defense in remote sensing based on pre-trained denoising diffusion models
International Journal of Applied Earth Observations and GeoinformationDeep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO). However, their susceptibility to adversarial examples poses a critical challenge, compromising the reliability of AI4EO ...
Weikang Yu, Yonghao Xu, Pedram Ghamisi
doaj +1 more source