Titlebook: AI 2021: Advances in Artificial Intelligence; 34th Australasian Jo Guodong Long,Xinghuo Yu,Sen Wang Conference proceedings 2022 Springer Na

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Osmosis

Kittiphop Phalakarn,Toru Nakamuras have been focusing on. Many applications have used reinforcement learning, such as robotics, recommendation systems, and healthcare systems. These systems could collect data about the environment or users, which may contain sensitive information that posed a real risk when these data were disclose

Gesture

Xavier Carpent,Seoyeon Hwang,Gene Tsudikth and autonomous vehicles. It is important to understand why particular predictions are made by a sub-symbolic machine learning (ML) model, because humans use these predictions in their decision making process. In this paper, we introduce HESIP, a hybrid system that combines symbolic and sub-symbol

folliculitis

不要不誠(chéng)實(shí)

How Not to Create an Isogeny-Based PAKEted in AI systems through a combination of Knowledge Representation,?Monte Carlo Tree Search and Deep Reinforcement Learning: Generalised AlphaZero?[.] provides a method for building general game-playing agents that can learn any game describable in a formal specification language. We investigate ho

招人嫉妒

Classical Misuse Attacks on NIST Round 2 PQCeconstructing training data of a target model. However, the performances of current works are highly rely on auxiliary datasets. In this paper, we investigate the model inversion problem under a strict restriction, where the adversary aims to reconstruct plausible samples of the target class without

債務(wù)

Liliya Kraleva,Tomer Ashur,Vincent Rijmencated. Computing Shapley Values are one of the best approaches so far to find the importance of each feature in a model, at the instance (data point) level. In other words, Shapley values represent the importance of a feature for a particular instance or observation, especially for classification or

宣稱(chēng)

Lo?s Huguenin-Dumittan,Serge Vaudenayive benefits of such systems, there is potential for exploitation by invading user privacy. In this work, we analyse the privacy invasiveness of face biometric systems by predicting privacy-sensitive soft-biometrics using masked face images. We train and apply a CNN based on the ResNet-50 architectu

節(jié)省

Endemic