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An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks

Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels. In a …

A Deep Reinforcement Learning Framework for Optimizing Fuel Economy of Hybrid Electric Vehicles

Hybrid electric vehicles employ a hybrid propulsion system to combine the energy efficiency of electric motor and a long driving range of internal combustion engine, thereby achieving a higher fuel economy as well as convenience compared with …