HCRVD: A Vulnerability Detection System Based on CST-PDG Hierarchical Code Representation Learning

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.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

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.

Degree of ontogenetic diet shift and trophic niche partitioning of Euphausia superba and Thysanoessa macrura are influenced by food availability

Euphausia superba and Thysanoessa macrura are dominant krill species in the Southern Ocean and their habitats are often overlapped reportedly.Studies of the feeding strategies of these two krill species will help us better understand the coexistence mechanisms and estimate the roles that krill played in the food web of the Southern Ocean.The trophodynamics of E.superba and T.macrura at different ontogenetic stages(furcilia,juvenile,adult)were studied using fatty acid and stable isotope biomarkers in the samples collected in Amundsen Sea during austral summer of 2017/2018 and 2018/2019.Diatoms like Fragilariopsis spp.was the most abundant phytoplankton species in the summer of 2017/2018,while the abundance of phytoplankton in the summer of 2018/2019 was dominated by Phaeocystis sp.The gradual increase of the carnivorous index 18꞉1n-9/18꞉1n-7 with ontogeny of both species in 2018/2019 indicated more carnivorous feeding of adults compared with juveniles and larvae.Meanwhile,greaterδ15N values of T.macrura than that of E.superba were more significant in the juvenile and adult stages during the summer of 2018/2019.Our results indicate that the trophic niche differentiation between the two krill species appeared in postlarval stage and can be influenced by food availability.Compared with E.superba,T.macrura was more prone to feed omnivorously or carnivorously responding to food availability.

Risks and Uncertainty Analysis on International Logistics

With the development of globalization, more and more companies are under global operation. What coming with it, on one hand is chance and opportunity; on the other hand is risk and uncertainty. This paper aims to specify the potential and current risks and uncertainty that may be met in international logistics, then seeks some practical methods to dodge and solve them.

Emissions Trading Mode in America and China and Its Future

The emissions trading is a kind of sustainable development measures that is based on market mechanism, and its growing is closely connected with the market development level. The level of American emissions permits market is relatively high, and also predicts the prospect of professional bourse pattern. Most of emissions permits transactions in China are organized by government environment authority, which make it lack of market function. Emissions permits is a kind of property rights. It is important and profitable, while the emissions trading market obviously possesses the two-class of original allocation and second-time transfer. But there are some decisive differences between emissions permits and ordinary transaction object, so the specialized bourse for emissions trading should be built.