Learnable antinoise-receiver algorithm based on a quantum feedforward neural network in optical quantum communication

Qu, Zhiguo; Liu, Xinzhu; Sun, Le

doi:10.1103/physreva.105.052427

Cited by 12 publications

(2 citation statements)

References 41 publications

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“…A sound legal judgment prediction algorithm can provide corresponding legal guidance and assistance to people without legal background knowledge at a lower labor cost. Hiring a professional lawyer or knowing the legal knowledge on your own will have a certain capital or time cost for the parties who are not engaged in the legal related industry [ 8 ]. Through the legal judgment prediction algorithm, an intelligent litigation guidance system can be built, and by deeply mining judicial big data, a relatively complete information coverage of various crimes and laws can be constructed, providing professional case prediction and litigation guidance, and assisting the parties involved in litigation, or help litigation participants to make rational predictions.…”

Section: Introductionmentioning

confidence: 99%

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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Legal judgment prediction is the most typical application of artificial intelligence technology, especially natural language processing methods, in the judicial field. In a practical environment, the performance of algorithms is often restricted by the computing resource conditions due to the uneven computing performance of the devices. Reducing the computational resource consumption of the model and improving the inference speed can effectively reduce the deployment difficulty of the legal judgment prediction model. To improve the prediction accuracy, enhance the model inference speed, and reduce the model memory consumption, we propose a BERT knowledge distillation-based legal decision prediction model, called KD-BERT. To reduce the resource consumption in the model inference process, we use the BERT pretraining model with lower memory requirements to be the encoder. Then, the knowledge distillation strategy transfers the knowledge to the student model of the shallow transformer structure. Experiment results show that the proposed KD-BERT has the highest F1-score compared with traditional BERT models. Its inference speed is also much faster than the other BERT models.

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Section: Introductionmentioning

confidence: 99%

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Zheng

Liu

Sun

2022

Computational Intelligence and Neuroscience

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“…Deep learning (DL) is being extensively used in various fields [7][8][9][10][11]. Since 2020, significant advancements have been made in DL-based vulnerability detection.…”

Section: Vulnerability Detection Based On Deep Learningmentioning

confidence: 99%

E-GVD: Efficient Software Vulnerability Detection Techniques Based on Graph Neural Network

Wang,

Qu,

Sun

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: Vulnerability detection is crucial for preventing severe security incidents like hacker attacks, data breaches, and network paralysis. Traditional methods, however, face challenges such as low efficiency and insufficient detail in identifying code vulnerabilities. OBJECTIVES: This paper introduces E-GVD, an advanced method for source code vulnerability detection, aiming to address the limitations of existing methods. The objective is to enhance the accuracy of function-level vulnerability detection and provide detailed, understandable insights into the vulnerabilities. METHODS: E-GVD combines Graph Neural Networks (GNNs), which are adept at handling graph-structured data, with residual connections and advanced Programming Language (PL) pre-trained models. RESULTS: Experiments conducted on the real-world vulnerability dataset CodeXGLUE show that E-GVD significantly outperforms existing baseline methods in detecting vulnerabilities. It achieves a maximum accuracy gain of 4.98%, indicating its effectiveness over traditional methods. CONCLUSION: E-GVD not only improves the accuracy of vulnerability detection but also contributes by providing fine-grained explanations. These explanations are made possible through an interpretable Machine Learning (ML) model, which aids developers in quickly and efficiently repairing vulnerabilities, thereby enhancing overall software security.

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Universal Hierarchical Quantum Information Splitting Schemes of an Arbitrary Multi-Qubit State

Tang

2022

Int J Theor Phys

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Learnable antinoise-receiver algorithm based on a quantum feedforward neural network in optical quantum communication

Cited by 12 publications

References 41 publications

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era

E-GVD: Efficient Software Vulnerability Detection Techniques Based on Graph Neural Network

Universal Hierarchical Quantum Information Splitting Schemes of an Arbitrary Multi-Qubit State

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