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Found 2 papers, 1 papers with code
Knowledge Migration Framework for Smart Contract Vulnerability Detection
The approach markedly enhances the model's capacity for feature extraction and cross-class adaptation, while concurrently reducing computational overhead. In order to further enhance the extraction of vulnerability features, an adaptive fusion module is proposed in this paper, which aims to strengthen the interaction and fusion of feature information. The experimental results demonstrate that the STip model attains an average F1 value detection score of 91. 16% for the four vulnerabilities without disclosing the original smart contract data.
Flying through a narrow gap using neural network: an end-to-end planning and control approach
To this end, we propose an end-to-end policy network, which imitates from the traditional pipeline and is fine-tuned using reinforcement learning.
Robotics
