Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax...Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax or programming language technologies.However,writing Pseudo-code to each code instruction is laborious.Recently,neural machine translation is used to generate textual descriptions for the source code.In this paper,a novel deep learning-based transformer(DLBT)model is proposed for automatic Pseudo-code generation from the source code.The proposed model uses deep learning which is based on Neural Machine Translation(NMT)to work as a language translator.The DLBT is based on the transformer which is an encoder-decoder structure.There are three major components:tokenizer and embeddings,transformer,and post-processing.Each code line is tokenized to dense vector.Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network(RNN).At the post-processing step,the generated Pseudo-code is optimized.The proposed model is assessed using a real Python dataset,which contains more than 18,800 lines of a source code written in Python.The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network(RNN).The proposed DLBT records 47.32,68.49 accuracy and BLEU performance measures,respectively.展开更多
Cryogenic pre-deformation treatment has been widely used to effectively improve the comprehensive mechanical properties of steels and novel metals.However,the dislocation evolution and phase transformation induced by ...Cryogenic pre-deformation treatment has been widely used to effectively improve the comprehensive mechanical properties of steels and novel metals.However,the dislocation evolution and phase transformation induced by different degrees of deep cryogenic deformation are not yet fully elucidated.In this study,the effects of multiple cryogenic pre-treatments on the mechanical properties and deformation mechanisms of a paramagnetic Fe_(63.3)Mn_(14-)Si_(9.1)Cr_(9.8)C_(3.8)medium-entropy alloy(MEA)were investigated,leading to the discovery of a pretreated MEA that exhibits exceptional mechanical properties,including a fracture strength of 3.0 GPa,plastic strain of 26.1%and work-hardening index of 0.57.In addition,X-ray diffraction(XRD)and transmission electron microscopy(TEM)analyses revealed that multiple cryogenic pre-deformation treatments significantly increased the dislocation density of the MEA(from 9×10^(15)to 4×10^(16)m^(-2)after three pretreatments),along with a transition in the dislocation type from predominantly edge dislocations to mixed dislocations(including screw-and edge-type dislocations).Notably,this pretreated MEA retained its paramagnetic properties(μ_(r)<1.0200)even after fracture.Thermodynamic calculations showed that cryogenic pretreatment can significantly reduce the stacking fault energy of the MEA by a factor of approximately four(i.e.,from 9.7 to2.6 m J·m^(-2)),thereby activating the synergistic effects of transformation-induced plasticity,twinning-induced plasticity and dislocation strengthening mechanisms.These synergistic effects lead to simultaneous strength and ductility enhancement of the MEA.展开更多
Low-dose computed tomography(LDCT)has gained increasing attention owing to its crucial role in reducing radiation exposure in patients.However,LDCT-reconstructed images often suffer from significant noise and artifact...Low-dose computed tomography(LDCT)has gained increasing attention owing to its crucial role in reducing radiation exposure in patients.However,LDCT-reconstructed images often suffer from significant noise and artifacts,negatively impacting the radiologists’ability to accurately diagnose.To address this issue,many studies have focused on denoising LDCT images using deep learning(DL)methods.However,these DL-based denoising methods have been hindered by the highly variable feature distribution of LDCT data from different imaging sources,which adversely affects the performance of current denoising models.In this study,we propose a parallel processing model,the multi-encoder deep feature transformation network(MDFTN),which is designed to enhance the performance of LDCT imaging for multisource data.Unlike traditional network structures,which rely on continual learning to process multitask data,the approach can simultaneously handle LDCT images within a unified framework from various imaging sources.The proposed MDFTN consists of multiple encoders and decoders along with a deep feature transformation module(DFTM).During forward propagation in network training,each encoder extracts diverse features from its respective data source in parallel and the DFTM compresses these features into a shared feature space.Subsequently,each decoder performs an inverse operation for multisource loss estimation.Through collaborative training,the proposed MDFTN leverages the complementary advantages of multisource data distribution to enhance its adaptability and generalization.Numerous experiments were conducted on two public datasets and one local dataset,which demonstrated that the proposed network model can simultaneously process multisource data while effectively suppressing noise and preserving fine structures.The source code is available at https://github.com/123456789ey/MDFTN.展开更多
The Space-Air-Ground-Sea Integrated Networks(SAGSIN)significantly enhance global communication by merging satellite,aviation,terrestrial,and marine networks.Crucial to SAGSIN’s functionality and security is spectrum ...The Space-Air-Ground-Sea Integrated Networks(SAGSIN)significantly enhance global communication by merging satellite,aviation,terrestrial,and marine networks.Crucial to SAGSIN’s functionality and security is spectrum monitoring using deep learning-based Automatic Modulation Classification(AMC),essential for processing and classifying complex modulation signals.However,these AMC models are susceptible to adversarial attacks.Thus,we introduce the Deep Time-Frequency Denoising Transformation(DTFDT)defense method to mitigate the impact of adversarial attacks.The DTFDT method is comprised of a deep denoising module and a transformation module.The denoising module maps signals into the time-frequency domain,amplifying the differences between benign and adversarial examples,aiding in the elimination of adversarial perturbations.Concurrently,the transformation module develops a learnable network,generating example-specific transformation matrices suited for signal data,which diminishes the effectiveness of attacks.Extensive evaluations on two datasets,RML2016.10a and DMRadio09.real,demonstrate the superior defense capabilities of DTFDT against various attacks.展开更多
文摘Understanding the content of the source code and its regular expression is very difficult when they are written in an unfamiliar language.Pseudo-code explains and describes the content of the code without using syntax or programming language technologies.However,writing Pseudo-code to each code instruction is laborious.Recently,neural machine translation is used to generate textual descriptions for the source code.In this paper,a novel deep learning-based transformer(DLBT)model is proposed for automatic Pseudo-code generation from the source code.The proposed model uses deep learning which is based on Neural Machine Translation(NMT)to work as a language translator.The DLBT is based on the transformer which is an encoder-decoder structure.There are three major components:tokenizer and embeddings,transformer,and post-processing.Each code line is tokenized to dense vector.Then transformer captures the relatedness between the source code and the matching Pseudo-code without the need of Recurrent Neural Network(RNN).At the post-processing step,the generated Pseudo-code is optimized.The proposed model is assessed using a real Python dataset,which contains more than 18,800 lines of a source code written in Python.The experiments show promising performance results compared with other machine translation methods such as Recurrent Neural Network(RNN).The proposed DLBT records 47.32,68.49 accuracy and BLEU performance measures,respectively.
基金supported by the National Natural Science Foundation of China(Nos.52061027 and 52130108)Zhejiang Provincial Natural Science Foundation of China(No.LY23E010002)+1 种基金the Science and Technology Program Project of Gansu Province(Nos.22YF7GA155 and 22ZD6GA008)Lanzhou Youth Science and Technology Talent Innovation Project(No.2023-QN-91)。
文摘Cryogenic pre-deformation treatment has been widely used to effectively improve the comprehensive mechanical properties of steels and novel metals.However,the dislocation evolution and phase transformation induced by different degrees of deep cryogenic deformation are not yet fully elucidated.In this study,the effects of multiple cryogenic pre-treatments on the mechanical properties and deformation mechanisms of a paramagnetic Fe_(63.3)Mn_(14-)Si_(9.1)Cr_(9.8)C_(3.8)medium-entropy alloy(MEA)were investigated,leading to the discovery of a pretreated MEA that exhibits exceptional mechanical properties,including a fracture strength of 3.0 GPa,plastic strain of 26.1%and work-hardening index of 0.57.In addition,X-ray diffraction(XRD)and transmission electron microscopy(TEM)analyses revealed that multiple cryogenic pre-deformation treatments significantly increased the dislocation density of the MEA(from 9×10^(15)to 4×10^(16)m^(-2)after three pretreatments),along with a transition in the dislocation type from predominantly edge dislocations to mixed dislocations(including screw-and edge-type dislocations).Notably,this pretreated MEA retained its paramagnetic properties(μ_(r)<1.0200)even after fracture.Thermodynamic calculations showed that cryogenic pretreatment can significantly reduce the stacking fault energy of the MEA by a factor of approximately four(i.e.,from 9.7 to2.6 m J·m^(-2)),thereby activating the synergistic effects of transformation-induced plasticity,twinning-induced plasticity and dislocation strengthening mechanisms.These synergistic effects lead to simultaneous strength and ductility enhancement of the MEA.
基金supported in part by the National Key Research and Development Program of China,No.2022YFC2404103in part by the Jiangsu Provincial Key Research and Development Program Social Development Project,No.BE2022720+1 种基金in part by the Natural Science Foundation of China,No.62001471in part by the Suzhou Science and Technology Plan Project,No.SYG202345.
文摘Low-dose computed tomography(LDCT)has gained increasing attention owing to its crucial role in reducing radiation exposure in patients.However,LDCT-reconstructed images often suffer from significant noise and artifacts,negatively impacting the radiologists’ability to accurately diagnose.To address this issue,many studies have focused on denoising LDCT images using deep learning(DL)methods.However,these DL-based denoising methods have been hindered by the highly variable feature distribution of LDCT data from different imaging sources,which adversely affects the performance of current denoising models.In this study,we propose a parallel processing model,the multi-encoder deep feature transformation network(MDFTN),which is designed to enhance the performance of LDCT imaging for multisource data.Unlike traditional network structures,which rely on continual learning to process multitask data,the approach can simultaneously handle LDCT images within a unified framework from various imaging sources.The proposed MDFTN consists of multiple encoders and decoders along with a deep feature transformation module(DFTM).During forward propagation in network training,each encoder extracts diverse features from its respective data source in parallel and the DFTM compresses these features into a shared feature space.Subsequently,each decoder performs an inverse operation for multisource loss estimation.Through collaborative training,the proposed MDFTN leverages the complementary advantages of multisource data distribution to enhance its adaptability and generalization.Numerous experiments were conducted on two public datasets and one local dataset,which demonstrated that the proposed network model can simultaneously process multisource data while effectively suppressing noise and preserving fine structures.The source code is available at https://github.com/123456789ey/MDFTN.
文摘The Space-Air-Ground-Sea Integrated Networks(SAGSIN)significantly enhance global communication by merging satellite,aviation,terrestrial,and marine networks.Crucial to SAGSIN’s functionality and security is spectrum monitoring using deep learning-based Automatic Modulation Classification(AMC),essential for processing and classifying complex modulation signals.However,these AMC models are susceptible to adversarial attacks.Thus,we introduce the Deep Time-Frequency Denoising Transformation(DTFDT)defense method to mitigate the impact of adversarial attacks.The DTFDT method is comprised of a deep denoising module and a transformation module.The denoising module maps signals into the time-frequency domain,amplifying the differences between benign and adversarial examples,aiding in the elimination of adversarial perturbations.Concurrently,the transformation module develops a learnable network,generating example-specific transformation matrices suited for signal data,which diminishes the effectiveness of attacks.Extensive evaluations on two datasets,RML2016.10a and DMRadio09.real,demonstrate the superior defense capabilities of DTFDT against various attacks.
