查看更多>>摘要：Super-resolution(SR)is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the decades.The main concept of SR is to reconstruct images from low-resolution(LR)to high-resolution(HR).It is an ongoing process in image technology,through up-sampling,de-blurring,and de-noising.Convolution neural network(CNN)has been widely used to enhance the resolution of images in recent years.Several alternative methods use deep learning to improve the progress of image super-resolution based on CNN.Here,we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution.,it is also to highlight the potential applications of image super-resolution in security monitoring,medical diagnosis,microscopy image processing,satellite remote sensing,communication transmission,the digital multimedia industry and video enhancement.Finally,we present the challenges and assess future trends in super-resolution based on deep learning.
查看更多>>摘要：The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data world.This approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the cloud.The IoE-based cloud computing services are located at remote locations without the control of the data owner.The data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security capabilities.The lack of knowledge about security capabilities and control over data raises several security issues.Deoxyribonucleic Acid(DNA)computing is a biological concept that can improve the security of IoE big data.The IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher algorithms.This paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access issues.The experimental results illustrated that DNACDS performs better than other DNA-based security schemes.The theoretical security analysis of the DNACDS shows better resistance capabilities.
查看更多>>摘要：A threshold signature is a special digital signature in which the N-signer share the private key x and can construct a valid signature for any subset of the included t-signer,but less than t-signer cannot obtain any information.Considering the breakthrough achievements of threshold ECDSA signature and threshold Schnorr signature,the existing threshold SM2 signature is still limited to two parties or based on the honest majority setting,there is no more effective solution for the multiparty case.To make the SM2 signature have more flexible application scenarios,promote the application of the SM2 signature scheme in the blockchain system and secure cryptocurrency wallets.This paper designs a non-interactive threshold SM2 signature scheme based on partially homomorphic encryption and zero-knowledge proof.Only the last round requires the message input,so make our scheme non-interactive,and the pre-signing process takes 2 rounds of communication to complete after the key generation.We allow arbitrary threshold t ≤ n and design a key update strategy.It can achieve security with identifiable abort under the malicious majority,which means that if the signature process fails,we can find the failed party.Performance analysis shows that the computation and communication costs of the pre-signing process grows linearly with the parties,and it is only 1/3 of the Canetti's threshold ECDSA(CCS'20).
查看更多>>摘要：Secure k-Nearest Neighbor(k-NN)query aims to find k nearest data of a given query from an encrypted database in a cloud server without revealing privacy to the untrusted cloud and has wide applications in many areas,such as privacy-preserving machine learning and secure biometric identification.Several solutions have been put forward to solve this challenging problem.However,the existing schemes still suffer from various limitations in terms of efficiency and flexibility.In this paper,we propose a new encrypt-then-index strategy for the secure k-NN query,which can simultaneously achieve sub-linear search complexity(efficiency)and support dynamical update over the encrypted database(flexibility).Specifically,we propose a novel algorithm to transform the encrypted database and encrypted query points in the cloud.By indexing the transformed database using spatial data structures such as the R-tree index,our strategy enables sub-linear complexity for secure k-NN queries and allows users to dynamically update the encrypted database.To the best of our knowledge,the proposed strategy is the first to simultaneously provide these two properties.Through theoretical analysis and extensive experiments,we formally prove the security and demonstrate the efficiency of our scheme.
查看更多>>摘要：Multiparty private set intersection(PSI)allows several parties,each holding a set of elements,to jointly compute the intersection without leaking any additional information.With the development of cloud computing,PSI has a wide range of applications in privacy protection.However,it is complex to build an efficient and reliable scheme to protect user privacy.To address this issue,we propose EMPSI,an efficient PSI(with cardinality)protocol in a multiparty setting.EMPSI avoids using heavy cryptographic primitives(mainly rely on symmetric-key encryption)to achieve better performance.In addition,both PSI and PSI with the cardinality of EMPSI are secure against semi-honest adversaries and allow any number of colluding clients(at least one honest client).We also do experiments to compare EMPSI with some state-of-the-art works.The experimental results show that proposed EMPSI(-CA)has better performance and is scalable in the number of clients and the set size.
查看更多>>摘要：Single-cell RNA sequencing reveals the gene structure and gene expression status of a single cell,which can reflect the heterogeneity between cells.However,batch effects caused by non-biological factors may hinder data integration and downstream analysis.Although the batch effect can be evaluated by visualizing the data,which actually is subjective and inaccurate.In this work,we propose a quantitative method cKBET,which considers the batch and cell type information simultaneously.The cKBET method accesses batch effects by comparing the global and local fraction of cells of different batches in different cell types.We verify the performance of our cKBET method on simulated and real biological data sets.The experimental results show that our cKBET method is superior to existing methods in most cases.In general,our cKBET method can detect batch effect with either balanced or unbalanced cell types,and thus evaluate batch correction methods.
查看更多>>摘要：Fertility is the most crucial step in the development process,which is controlled by many fertility-related proteins,including spermatogenesis-,oogenesis-and embryogenesis-related proteins.The identification of fertility-related proteins can provide important clues for studying the role of these proteins in development.Therefore,in this study,we constructed a two-layer classifier to identify fertility-related proteins.In this classifier,we first used the composition of amino acids(AA)and their physical and chemical properties to code these three fertility-related proteins.Then,the feature set is optimized by analysis of variance(ANOVA)and incremental feature selection(IFS)to obtain the optimal feature subset.Through five-fold cross-validation(CV)and independent data tests,the performance of models constructed by different machine learning(ML)methods is evaluated and compared.Finally,based on support vector machine(SVM),we obtained a two-layer model to classify three fertility-related proteins.On the independent test data set,the accuracy(ACC)and the area under the receiver operating characteristic curve(AUC)of the first layer classifier are 81.95％and 0.89,respectively,and them of the second layer classifier are 84.74％and 0.90,respectively.These results show that the proposed model has stable performance and satisfactory prediction accuracy,and can become a powerful model to identify more fertility related proteins.
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