Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representation...Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations.However,due to limitations in code representation and neural network design,the validity and practicality of the model still need to be improved.Additionally,due to differences in programming languages,most methods lack cross-language detection generality.To address these issues,in this paper,we analyze the shortcomings of previous code representations and neural networks.We propose a novel hierarchical code representation that combines Concrete Syntax Trees(CST)with Program Dependence Graphs(PDG).Furthermore,we introduce a Tree-Graph-Gated-Attention(TGGA)network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection(HCRVD)system.This system enables cross-language vulnerability detection at the function-level.The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities.It benefits from the hierarchical code representation learning method,and outperforms baseline in cross-language vulnerability detection by 9.772%and 11.819%in the C/C++and Java datasets,respectively.Moreover,HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects.HCRVD shows good validity,generality and practicality.展开更多
With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and ...With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.展开更多
As cloud quantum computing gains broader acceptance,a growing quantity of researchers are directing their focus towards this domain.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources ha...As cloud quantum computing gains broader acceptance,a growing quantity of researchers are directing their focus towards this domain.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity,which in turn hampers users from achieving optimal satisfaction.Therefore,cloud quantum computing service providers require a unified analysis and scheduling framework for their quantumresources and user jobs to meet the ever-growing usage demands.This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment.The framework addresses the issue of limited quantum computing resources in cloud environments and ensures a satisfactory user experience.It introduces three innovative designs:1)Our framework automatically allocates tasks to different quantum backends while ensuring fairness among users by considering both the cloud-based quantum resources and the user-submitted tasks.2)Multi-programming mechanism is employed across different quantum backends to enhance the overall throughput of the quantum cloud.In comparison to conventional task schedulers,our proposed framework achieves a throughput improvement of more than two-fold in the quantum cloud.3)The framework can balance fidelity and user waiting time by adaptively adjusting scheduling parameters.展开更多
目的探讨十二指肠镜治疗无明显胆管扩张型胰胆管合流异常(pancreaticobiliary maljunction without obvious biliary dilatation,PBM-nonOBD)患儿的手术疗效及预后不良相关因素。方法回顾性分析复旦大学附属儿科医院自2020年1月至2022...目的探讨十二指肠镜治疗无明显胆管扩张型胰胆管合流异常(pancreaticobiliary maljunction without obvious biliary dilatation,PBM-nonOBD)患儿的手术疗效及预后不良相关因素。方法回顾性分析复旦大学附属儿科医院自2020年1月至2022年12月收治的内镜治疗PBM-nonOBD患儿的临床资料(包括人口学资料、临床症状、实验室检查及影像学资料),并对患儿进行随访。采用单因素分析及多因素Logistic回归分析十二指肠镜治疗PBM-nonOBD患儿不良预后的危险因素,并绘制受试者工作特征(receiver operating characteristic,ROC)曲线分析相关危险因素的预测价值。结果本研究共纳入44例患儿,随访时间(19.7±8.6)个月,治愈率为54.5%(24/44),其中治疗有效24例(为治疗有效组),治疗无效20例(为治疗无效组)。术后不良事件以十二指肠镜逆行性胆胰管造影术后胰腺炎最常见(7/44,15.9%),其中27.3%(12/44)的患儿最终需接受根治术,15.9%(7/44)的患儿需接受再次内镜治疗。治疗有效组胰胆管合流异常(pancreaticobiliary maljunction,PBM)分型以B型和D型为主,占比分别为41.7%(10/24)和37.5%(9/24)。单因素分析结果显示,年龄偏小、胰胆共同管直径较长、胆总管最宽直径较宽是PBM-nonOBD患儿内镜手术后预后不良的相关因素(P<0.05);多因素Logistic回归分析发现,年龄偏小(OR=1.645,95%CI:1.645~2.309)及胰胆共同管直径较长(OR=0.720,95%CI:0.720~0.968)是PBM-nonOBD患儿预后不良的独立危险因素(P<0.05),曲线下面积(area under the ROC curve,AUC)分别为0.838(95%CI:0.719~0.958)和0.731(95%CI:0.567~0.894),最佳截断值分别为4.9岁和8.8 mm。结论十二指肠镜手术创伤小,不会导致严重并发症,可有效缓解部分PBM-nonOBD患儿症状;年龄偏小和胰胆共同管长度较长可能与十二指肠镜治疗PBM-nonOBD预后不良相关。展开更多
基金funded by the Major Science and Technology Projects in Henan Province,China,Grant No.221100210600.
文摘Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations.However,due to limitations in code representation and neural network design,the validity and practicality of the model still need to be improved.Additionally,due to differences in programming languages,most methods lack cross-language detection generality.To address these issues,in this paper,we analyze the shortcomings of previous code representations and neural networks.We propose a novel hierarchical code representation that combines Concrete Syntax Trees(CST)with Program Dependence Graphs(PDG).Furthermore,we introduce a Tree-Graph-Gated-Attention(TGGA)network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection(HCRVD)system.This system enables cross-language vulnerability detection at the function-level.The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities.It benefits from the hierarchical code representation learning method,and outperforms baseline in cross-language vulnerability detection by 9.772%and 11.819%in the C/C++and Java datasets,respectively.Moreover,HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects.HCRVD shows good validity,generality and practicality.
基金supported by Major Science and Technology Projects in Henan Province,China,Grant No.221100210600.
文摘With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.
文摘As cloud quantum computing gains broader acceptance,a growing quantity of researchers are directing their focus towards this domain.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity,which in turn hampers users from achieving optimal satisfaction.Therefore,cloud quantum computing service providers require a unified analysis and scheduling framework for their quantumresources and user jobs to meet the ever-growing usage demands.This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment.The framework addresses the issue of limited quantum computing resources in cloud environments and ensures a satisfactory user experience.It introduces three innovative designs:1)Our framework automatically allocates tasks to different quantum backends while ensuring fairness among users by considering both the cloud-based quantum resources and the user-submitted tasks.2)Multi-programming mechanism is employed across different quantum backends to enhance the overall throughput of the quantum cloud.In comparison to conventional task schedulers,our proposed framework achieves a throughput improvement of more than two-fold in the quantum cloud.3)The framework can balance fidelity and user waiting time by adaptively adjusting scheduling parameters.
文摘目的探讨十二指肠镜治疗无明显胆管扩张型胰胆管合流异常(pancreaticobiliary maljunction without obvious biliary dilatation,PBM-nonOBD)患儿的手术疗效及预后不良相关因素。方法回顾性分析复旦大学附属儿科医院自2020年1月至2022年12月收治的内镜治疗PBM-nonOBD患儿的临床资料(包括人口学资料、临床症状、实验室检查及影像学资料),并对患儿进行随访。采用单因素分析及多因素Logistic回归分析十二指肠镜治疗PBM-nonOBD患儿不良预后的危险因素,并绘制受试者工作特征(receiver operating characteristic,ROC)曲线分析相关危险因素的预测价值。结果本研究共纳入44例患儿,随访时间(19.7±8.6)个月,治愈率为54.5%(24/44),其中治疗有效24例(为治疗有效组),治疗无效20例(为治疗无效组)。术后不良事件以十二指肠镜逆行性胆胰管造影术后胰腺炎最常见(7/44,15.9%),其中27.3%(12/44)的患儿最终需接受根治术,15.9%(7/44)的患儿需接受再次内镜治疗。治疗有效组胰胆管合流异常(pancreaticobiliary maljunction,PBM)分型以B型和D型为主,占比分别为41.7%(10/24)和37.5%(9/24)。单因素分析结果显示,年龄偏小、胰胆共同管直径较长、胆总管最宽直径较宽是PBM-nonOBD患儿内镜手术后预后不良的相关因素(P<0.05);多因素Logistic回归分析发现,年龄偏小(OR=1.645,95%CI:1.645~2.309)及胰胆共同管直径较长(OR=0.720,95%CI:0.720~0.968)是PBM-nonOBD患儿预后不良的独立危险因素(P<0.05),曲线下面积(area under the ROC curve,AUC)分别为0.838(95%CI:0.719~0.958)和0.731(95%CI:0.567~0.894),最佳截断值分别为4.9岁和8.8 mm。结论十二指肠镜手术创伤小,不会导致严重并发症,可有效缓解部分PBM-nonOBD患儿症状;年龄偏小和胰胆共同管长度较长可能与十二指肠镜治疗PBM-nonOBD预后不良相关。
