arXiv subjects

Pengcheng

Publications and source records attributed to Pengcheng.

2 recordsLinked to original sources

arXiv2023

CONTRACTFIX: A Framework for Automatically Fixing Vulnerabilities in Smart Contracts

The increased adoption of smart contracts in many industries has made them an attractive target for cybercriminals, leading to millions of dollars in losses. Thus, deploying smart contracts with detected vulnerabilities (known to developers) are not acceptable, and fixing all the detected vulnerabilities is needed, which incurs high manual labor cost without effective tool support. To fill this need, in this paper, we propose ContractFix, a novel framework that automatically generates security patches for vulnerable smart contracts. ContractFix is a general framework that can incorporate different fix patterns for different types of vulnerabilities. Users can use it as a security fix-it tool that automatically applies patches and verifies the patched contracts before deploying the contracts. To address the unique challenges in fixing smart contract vulnerabilities, given an input smart contract, \tool conducts our proposed ensemble identification based on multiple static verification tools to identify vulnerabilities that are amenable for automatic fix. Then, ContractFix generates patches using template-based fix patterns and conducts program analysis (program dependency computation and pointer analysis) for smart contracts to accurately infer and populate the parameter values for the fix patterns. Finally, ContractFix performs static verification that guarantees the patched contract is free of vulnerabilities. Our evaluations on $144$ real vulnerable contracts demonstrate that \tool can successfully fix $94\%$ of the detected vulnerabilities ($565$ out of $601$) and preserve the expected behaviors of the smart contracts.

cs.SE↗

arXiv2022

AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition

Affective computing is an important branch of artificial intelligence, and with the rapid development of brain computer interface technology, emotion recognition based on EEG signals has received broad attention. It is still a great challenge to effectively explore the multi-dimensional information in the EEG data in spite of a large number of deep learning methods. In this paper, we propose a deep model called Attention-based Multiple Dimensions EEG Transformer (AMDET), which can exploit the complementarity among the spectral-spatial-temporal features of EEG data by employing the multi-dimensional global attention mechanism. We transformed the original EEG data into 3D temporal-spectral-spatial representations and then the AMDET would use spectral-spatial transformer encoder layer to extract effective features in the EEG signal and concentrate on the critical time frame with a temporal attention layer. We conduct extensive experiments on the DEAP, SEED, and SEED-IV datasets to evaluate the performance of AMDET and the results outperform the state-of-the-art baseline on three datasets. Accuracy rates of 97.48%, 96.85%, 97.17%, 87.32% were achieved in the DEAP-Arousal, DEAP-Valence, SEED, and SEED-IV datasets, respectively. We also conduct extensive experiments to explore the possible brain regions that influence emotions and the coupling of EEG signals. AMDET can perform as well even with few channels which are identified by visualizing what learned model focus on. The accuracy could achieve over 90% even with only eight channels and it is of great use and benefit for practical applications.

eess.SP↗