There exist many logics. Some of them may be only used to analyze security protocols, and the others may be only used to design security protocols. This paper presents a new logic for analysis and design of security p...
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There exist many logics. Some of them may be only used to analyze security protocols, and the others may be only used to design security protocols. This paper presents a new logic for analysis and design of security protocols. The logic can be used not only to analyze security protocols, but also to design security protocols. Security protocol analysis and design may proceed in the same logic. At the same time, the logic may get rid of non-consistence in different ways of analysis and design. During analyzing a security protocol, the security protocol is formalized by the logic and then it is deduced by the reasoning rule. The security protocol is found to have bugs or leaks if the logic can not reason out the protocol goal. During designing a security protocol, the protocol designer may use a systematic way to construct the required protocol. This paper uses the logic to analyze Woo-Lam protocol, indicates the impossibility to reach the protocol goal, redesigns the Woo-Lam protocol and makes it success.
Image clustering, an important technology for image processing, has been actively researched for a long period of time. Especially in recent years, with the explosive growth of the Web, image clustering has even been ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. ...
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Trajectory prediction is a crucial challenge in autonomous vehicle motion planning and decision-making techniques. However, existing methods face limitations in accurately capturing vehicle dynamics and interactions. To address this issue, this paper proposes a novel approach to extracting vehicle velocity and acceleration, enabling the learning of vehicle dynamics and encoding them as auxiliary information. The VDI-LSTM model is designed, incorporating graph convolution and attention mechanisms to capture vehicle interactions using trajectory data and dynamic information. Specifically, a dynamics encoder is designed to capture the dynamic information, a dynamic graph is employed to represent vehicle interactions, and an attention mechanism is introduced to enhance the performance of LSTM and graph convolution. To demonstrate the effectiveness of our model, extensive experiments are conducted, including comparisons with several baselines and ablation studies on real-world highway datasets. Experimental results show that VDI-LSTM outperforms other baselines compared, which obtains a 3% improvement on the average RMSE indicator over the five prediction steps.
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