Machine learning methods are widely used to evaluate the risk of small-and mediumsized enterprises(SMEs)in supply chain finance(SCF).However,there may be problems with data scarcity,feature redundancy,and poor predict...Machine learning methods are widely used to evaluate the risk of small-and mediumsized enterprises(SMEs)in supply chain finance(SCF).However,there may be problems with data scarcity,feature redundancy,and poor predictive performance.Additionally,data collected over a long time span may cause differences in the data distribution,and classic supervised learning methods may exhibit poor predictive abilities under such conditions.To address these issues,a domain-adaptation-based multistage ensemble learning paradigm(DAMEL)is proposed in this study to evaluate the credit risk of SMEs in SCF.In this methodology,a bagging resampling algorithm is first used to generate a dataset to address data scarcity.Subsequently,a random subspace is applied to integrate various features and reduce feature redundancy.Additionally,a domain adaptation approach is utilized to reduce the data distribution discrepancy in the cross-domain.Finally,dynamic model selection is developed to improve the generalization ability of the model in the fourth stage.A real-world credit dataset from the Chinese securities market was used to validate the effectiveness and feasibility of the multistage ensemble learning paradigm.The experimental results demonstrated that the proposed domain-adaptation-based multistage ensemble learning paradigm is superior to principal component analysis,joint distribution adaptation,random forest,and other ensemble and transfer learning methods.Moreover,dynamic model selection can improve the model generalization performance and prediction precision of minority samples.This can be considered a promising solution for evaluating the credit risk of SMEs in SCF for financial institutions.展开更多
Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize ...Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains.For each new domain,it is required to collect and annotate a large amount of data,and the training of the algorithm does not benefit from prior knowledge,leading to redundant calculation workload and excessive time investment.To address this problem,the paper proposes a method that could transfer gesture data in different domains.We use a red-green-blue(RGB)Camera to collect images of the gestures,and use Leap Motion to collect the coordinates of 21 joint points of the human hand.Then,we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning.This paper compares the effects of three classification algorithms,i.e.,support vector machine(SVM),broad learning system(BLS)and deep learning(DL).We also compare learning performances with and without using the joint distribution adaptation(JDA)algorithm.The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion.In addition,we found that when using DL to classify the data,excessive training on the source domain may reduce the accuracy of recognition in the target domain.展开更多
Reversible data hiding in encrypted images(RDHEI)is essential for safeguarding sensitive information within the encrypted domain.In this study,we propose an intelligent pixel predictor based on a residual group block ...Reversible data hiding in encrypted images(RDHEI)is essential for safeguarding sensitive information within the encrypted domain.In this study,we propose an intelligent pixel predictor based on a residual group block and a spatial attention module,showing superior pixel prediction performance compared to existing predictors.Additionally,we introduce an adaptive joint coding method that leverages bit-plane characteristics and intra-block pixel correlations to maximize embedding space,outperforming single coding approaches.The image owner employs the presented intelligent predictor to forecast the original image,followed by encryption through additive secret sharing before conveying the encrypted image to data hiders.Subsequently,data hiders encrypt secret data and embed them within the encrypted image before transmitting the image to the receiver.The receiver can extract secret data and recover the original image losslessly,with the processes of data extraction and image recovery being separable.Our innovative approach combines an intelligent predictor with additive secret sharing,achieving reversible data embedding and extraction while ensuring security and lossless recovery.Experimental results demonstrate that the predictor performs well and has a substantial embedding capacity.For the Lena image,the number of prediction errors within the range of[-5,5]is as high as 242500 and our predictor achieves an embedding capacity of 4.39 bpp.展开更多
An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVD...An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.展开更多
基金supported by grants from the National Natural Science Foundation of China(No.72361014)the Technical Field Fund of Basic Research Strengthening Program(Project No.2021-JCJQ-JJ-0003)+1 种基金the Major Program of the National Social Science Foundation of China(No.19ZDA103)the Science and Technology Project of Jiangxi Provincial Department of Education(No.GJJ2200526).
文摘Machine learning methods are widely used to evaluate the risk of small-and mediumsized enterprises(SMEs)in supply chain finance(SCF).However,there may be problems with data scarcity,feature redundancy,and poor predictive performance.Additionally,data collected over a long time span may cause differences in the data distribution,and classic supervised learning methods may exhibit poor predictive abilities under such conditions.To address these issues,a domain-adaptation-based multistage ensemble learning paradigm(DAMEL)is proposed in this study to evaluate the credit risk of SMEs in SCF.In this methodology,a bagging resampling algorithm is first used to generate a dataset to address data scarcity.Subsequently,a random subspace is applied to integrate various features and reduce feature redundancy.Additionally,a domain adaptation approach is utilized to reduce the data distribution discrepancy in the cross-domain.Finally,dynamic model selection is developed to improve the generalization ability of the model in the fourth stage.A real-world credit dataset from the Chinese securities market was used to validate the effectiveness and feasibility of the multistage ensemble learning paradigm.The experimental results demonstrated that the proposed domain-adaptation-based multistage ensemble learning paradigm is superior to principal component analysis,joint distribution adaptation,random forest,and other ensemble and transfer learning methods.Moreover,dynamic model selection can improve the model generalization performance and prediction precision of minority samples.This can be considered a promising solution for evaluating the credit risk of SMEs in SCF for financial institutions.
基金supported by National Nature Science Foundation of China(NSFC)(Nos.U20A20200,61811530281,and 61861136009)Guangdong Regional Joint Foundation(No.2019B1515120076)+1 种基金Fundamental Research for the Central Universitiesin part by the Foshan Science and Technology Innovation Team Special Project(No.2018IT100322)。
文摘Gesture recognition has been widely used for human-robot interaction.At present,a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains.For each new domain,it is required to collect and annotate a large amount of data,and the training of the algorithm does not benefit from prior knowledge,leading to redundant calculation workload and excessive time investment.To address this problem,the paper proposes a method that could transfer gesture data in different domains.We use a red-green-blue(RGB)Camera to collect images of the gestures,and use Leap Motion to collect the coordinates of 21 joint points of the human hand.Then,we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning.This paper compares the effects of three classification algorithms,i.e.,support vector machine(SVM),broad learning system(BLS)and deep learning(DL).We also compare learning performances with and without using the joint distribution adaptation(JDA)algorithm.The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion.In addition,we found that when using DL to classify the data,excessive training on the source domain may reduce the accuracy of recognition in the target domain.
基金Project supported by the Scientific Research Project of Liaoning Provincial Department of Education,China(No.JYTMS20231039)the Liaoning Provincial Educational Science Planning Project,China(No.JG22CB252)。
文摘Reversible data hiding in encrypted images(RDHEI)is essential for safeguarding sensitive information within the encrypted domain.In this study,we propose an intelligent pixel predictor based on a residual group block and a spatial attention module,showing superior pixel prediction performance compared to existing predictors.Additionally,we introduce an adaptive joint coding method that leverages bit-plane characteristics and intra-block pixel correlations to maximize embedding space,outperforming single coding approaches.The image owner employs the presented intelligent predictor to forecast the original image,followed by encryption through additive secret sharing before conveying the encrypted image to data hiders.Subsequently,data hiders encrypt secret data and embed them within the encrypted image before transmitting the image to the receiver.The receiver can extract secret data and recover the original image losslessly,with the processes of data extraction and image recovery being separable.Our innovative approach combines an intelligent predictor with additive secret sharing,achieving reversible data embedding and extraction while ensuring security and lossless recovery.Experimental results demonstrate that the predictor performs well and has a substantial embedding capacity.For the Lena image,the number of prediction errors within the range of[-5,5]is as high as 242500 and our predictor achieves an embedding capacity of 4.39 bpp.
基金supported by the National Science&Technology Pillar Program(2013BAF07B03)Zhejiang Provincial Natural Science Foundation of China(LY13F010009)
文摘An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.
