忘川河
发表于 2025-3-26 23:33:00
English Language Education and Assessment the challenge-response protocol. Current CRA solutions are well-suited for Internet of Things (IoT) networks, where the devices are distributed in a mesh topology and communicate only with their physical neighbours. Recent advancements on low-energy protocols, though, enabled the IoT devices to con
Bravado
发表于 2025-3-27 02:24:38
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轻信
发表于 2025-3-27 08:40:50
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不近人情
发表于 2025-3-27 12:31:12
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Pruritus
发表于 2025-3-27 15:58:24
Towards Interpreting Vulnerability of Object Detection Models via Adversarial Distillationn of adversarial examples and realize the model’s generalization on the adversarial dataset. Extensive experimental evaluations have proved the excellent generalization performance of the adversarial distillation model. Compared with the normally trained model, the mAP has increased by 2.17% on thei
配置
发表于 2025-3-27 19:30:33
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Counteract
发表于 2025-3-28 01:37:04
A Multi-agent Deep Reinforcement Learning-Based Collaborative Willingness Network for Automobile Maimputing resources. The evaluation results show that, our CWN-MADRL algorithm can converge quickly, learn effective task recommendation strategies, and achieve better system performance compared with other benchmark algorithms.
变白
发表于 2025-3-28 02:31:53
Hybrid Isolation Model for Device Application Sandboxing Deployment in Zero Trust Architecturef the subject according to the access behavior and controls the access operation of the application sandbox. Therefore, the sandbox meets the characteristics of autonomous security, domain isolation, and integrity, ensuring that the system is always in an isolated safe state and easy to use. Finally
admission
发表于 2025-3-28 08:45:50
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legislate
发表于 2025-3-28 13:50:38
Deep Learning-Based Side-Channel Analysis Against AES Inner Roundshe attack complexity in terms of the number of bits to be guessed for the hypothesis. We discuss the main limitations for obtaining predictions in inner rounds and, in particular, we compare the performance of Correlation Power Analysis (CPA) against deep learning-based profiled side-channel attacks