Results 91 to 100 of about 7,737 (292)
A Comprehensive Review of AI‐Powered Energy Systems
Energy Science &Engineering, EarlyView.The role of Artificial Intelligence (AI) in developing next‐generation energy systems is getting more day by day. Therefore, incorporating AI enables real‐time decision‐making and advanced grid management, which are essential for optimizing the use of intermittent renewable sources like wind and solar power.
Armin Razmjoo +5 more
wiley +1 more source
"Defense Expenditures and Allied Cooperation" [PDF]
This paper investigates the implications of cooperative and non-cooperative defense spending of allied countries in conflicting blocs using static and leader-follower game models.
Toshihiro Ihori
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DISCO: Adversarial Defense with Local Implicit Functions
, 2022 The problem of adversarial defenses for image classification, where the goal is to robustify a classifier against adversarial examples, is considered.
Vasconcelos, Nuno, Ho, Chih-Hui
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Adversarial Attacks and Defense using Energy-Based Image Models [PDF]
, 2022 In this article we briefly review current research in adversarial attacks and defenses and form a basis for a theoretical explanation as to why a generative energy model is the solution to the defense problem as it exists for securing naturally trained ...
Mitchell, Jonathan Craig
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Text Adversarial Examples Generation and Defense Based on Reinforcement Learning
, 2021 In recent years, the neural networks are widely used in image processing, natural language processing and other fields. But there are new security issues-the adversarial examples.
Wusheng Xu +7 more
core +2 more sources
Knowledge sourcing, geopolitics, and FDI: An empirical analysis on the US green and digital sectors
Global Strategy Journal, EarlyView.Abstract Research Summary This paper examines how foreign direct investment (FDI) shapes firms' sourcing of knowledge in the digital and green domains under rising geopolitical frictions. We assemble a firm–country dyadic panel (2013–2020) linking US patent backward citations to firms' FDI, enriched with bilateral geopolitical distance and host‐country
Alberto Maria Radici
wiley +1 more source
IET CommunicationsAutomatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully
Fanghao Xu +5 more
doaj +1 more source
On the Defense of Spoofing Countermeasures Against Adversarial Attacks
, 2023 Advances in speech synthesis have exposed the vulnerability of spoofing countermeasure (CM) systems. Adversarial attacks exacerbate this problem, mainly due to the reliance of most CM models on deep neural networks.
Thien-Phuc Doan +4 more
core +1 more source
Journal of Organizational Behavior, EarlyView.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
PuVAE: A Variational Autoencoder to Purify Adversarial Examples
IEEE Access, 2019 Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise networks at inference time by applying elaborately designed perturbations to input data.
Uiwon Hwang +4 more
doaj +1 more source