Sharding enhances blockchain scalability by partitioning nodes into multiple groups for concurrent transaction processing. Configuring a large number of small shards helps improve the transaction concurrency of a shar...
详细信息
In recent years, 3D skeleton data-based human action recognition has attracted an increasing number of researchers because of its high robustness under illumination change and scene variation. However, in 3D skeleton ...
详细信息
In a recent article[Gao et al.,***.63,120311(2020)],a two-receiver measurement-deviceindependent quantum secret sharing(MDI-QSS)protocol was *** was proven to be secure against eavesdropping and generalized to the mul...
详细信息
In a recent article[Gao et al.,***.63,120311(2020)],a two-receiver measurement-deviceindependent quantum secret sharing(MDI-QSS)protocol was *** was proven to be secure against eavesdropping and generalized to the multireceiver ***,the participant attack is a fatal threat to QSS ***,we highlight that a dishonest participant can obtain a sender’s secret message alone without introducing any detectable error,evidencing the vulnerability of the MDI-QSS protocol to the participant attack.
The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and c...
详细信息
ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and crime. A highly effective family of forgery image detection methods based on diffusion reconstruction error has emerged, as images generated by diffusion are more easily reconstructed by any diffusion model. However, we find that existing methods only use reconstruction error from a single time step, failing to fully leverage the entire reconstruction process. To this end, we propose to comprehensively consider every single time step to form the Temporal Reconstruction Error (TRE) that offers a richer feature representation. Furthermore, we design temporal aggregation and spatial focusing modules from two dimensions respectively to more effectively extract discriminative information from the TRE feature. Finally, we validate the proposed method on two popular datasets, and experimental results demonstrate that the proposed approach achieves state-of-the-art performance.
Smart contracts bring revolutionary changes to the credit landscape. However, their security remains intensely scrutinized due to numerous hacking incidents and inherent logical challenges. One well-known and represen...
详细信息
ISBN:
(数字)9798331534011
ISBN:
(纸本)9798331534028
Smart contracts bring revolutionary changes to the credit landscape. However, their security remains intensely scrutinized due to numerous hacking incidents and inherent logical challenges. One well-known and representative issue is reentrancy vulnerability, exemplified by DAO attacks that lead to substantial economic losses. Conventional approaches to detecting and repairing reentrancy vulnerability often suffer from numerous limitations, including disregarding the intricate vulnerability features and the overfitting problems associated with imbalanced datasets. Large language models are distinguished for their excellent language understanding and have achieved explosive success in artificial intelligence. However, direct prompt-based LLMs-driven approaches for reentrancy vulnerability are plagued by inefficiencies and a lack of domain-specific vulnerability knowledge. This paper proposes a hybrid framework to enhance reentrancy vulnerability detection and repair and safeguard smart contract security. This unified framework comprises two crucial modules: enhanced DL-driven vulnerability detection and knowledge-aware LLMs-driven vulnerability repair. Our approach can significantly enhance reentrancy vulnerability detection and repair efficiency by integrating advanced techniques such as feature extraction, data balancing, deep learning networks, and knowledge-aware prompting. Extensive experimental results validate the superiority of our approach over state-of-the-art baselines, emphasizing its potential to fortify the security of smart contracts and blockchain-based systems. For instance, our approach can achieve 3.51 %, 2.31 %, 0.42%, and 0.85 % improvements in accuracy, recall, precision, and F1 score while detecting reentrancy vulnerability. Additionally, our approach also can achieve a 9.62% improvement in reentrancy vulnerability repair.
We consider the quantum memory assisted quantum state verification task, where an adversary prepare independent multipartite entangled states and send to the local verifiers, who then store several copies in the quant...
详细信息
Human pose estimation has given rise to a broad spectrum of novel and compelling applications, including action recognition, sports analysis, as well as surveillance. However, accurate video pose estimation remains an...
详细信息
Human pose estimation in videos remains a challenge, largely due to the reliance on extensive manual annotation of large datasets, which is expensive and labor-intensive. Furthermore, existing approaches often struggl...
详细信息
Hyper-polarization of nuclear spins is crucial for advancing nuclear magnetic resonance (NMR) and quantum information technologies, as nuclear spins typically exhibit extremely low polarization at room temperature due...
详细信息
Worm grinding has been applied to manufacture gears to pursue high accuracy and fine surface *** the worm used to grind face gears is manufactured with multi-axis computer numerical control(CNC)machining,the machining...
详细信息
Worm grinding has been applied to manufacture gears to pursue high accuracy and fine surface *** the worm used to grind face gears is manufactured with multi-axis computer numerical control(CNC)machining,the machining accuracy is usually improved by increasing the number of tool paths with more time ***,this work proposes a generated method to improve the efficiency by dressing the worm surface with only one path,and a closed-loop manufacturing process is applied to ensure the machining *** to an advanced geometric analysis,the worm surface is practically approximated as a swept surface generated by a planar ***,this curve is applied as the profile of a dressing wheel,which is used to dress the worm *** practical machining is carried out in a CNC machine tool,which was originally used to grind helical ***,a closed-loop manufacturing process including machining,measurement,and modification is proposed to compensate the machining *** proposed method is validated with simulations and practical experiments.
暂无评论