Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an imp...Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.展开更多
Wireless Sensor Networks(WSNs)are large-scale and high-density networks that typically have coverage area overlap.In addition,a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area,whi...Wireless Sensor Networks(WSNs)are large-scale and high-density networks that typically have coverage area overlap.In addition,a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area,which leads to coverage holes in WSNs.Thus,coverage control plays an important role in WSNs.To alleviate unnecessary energy wastage and improve network performance,we consider both energy efficiency and coverage rate for WSNs.In this paper,we present a novel coverage control algorithm based on Particle Swarm Optimization(PSO).Firstly,the sensor nodes are randomly deployed in a target area and remain static after deployment.Then,the whole network is partitioned into grids,and we calculate each grid’s coverage rate and energy consumption.Finally,each sensor nodes’sensing radius is adjusted according to the coverage rate and energy consumption of each grid.Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.展开更多
The aim of information hiding is to embed the secret message in a normal cover media such as image,video,voice or text,and then the secret message is transmitted through the transmission of the cover media.The secret ...The aim of information hiding is to embed the secret message in a normal cover media such as image,video,voice or text,and then the secret message is transmitted through the transmission of the cover media.The secret message should not be damaged on the process of the cover media.In order to ensure the invisibility of secret message,complex texture objects should be chosen for embedding information.In this paper,an approach which corresponds multiple steganographic algorithms to complex texture objects was presented for hiding secret message.Firstly,complex texture regions are selected based on a kind of objects detection algorithm.Secondly,three different steganographic methods were used to hide secret message into the selected block region.Experimental results show that the approach enhances the security and robustness.展开更多
Wireless Sensor Networks(WSNs)are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring.These sensors can transmit their monitored data to the sink in a multi-hop...Wireless Sensor Networks(WSNs)are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring.These sensors can transmit their monitored data to the sink in a multi-hop communication manner.However,the‘hot spots’problem will be caused since nodes near sink will consume more energy during forwarding.Recently,mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission.Even though it is difficult to consider many network metrics such as sensor position,residual energy and coverage rate etc.,it is still very important to schedule a reasonable moving trajectory for the mobile sink.In this paper,a novel trajectory scheduling method based on coverage rate for multiple mobile sinks(TSCR-M)is presented especially for large-scale WSNs.An improved particle swarm optimization(PSO)combined with mutation operator is introduced to search the parking positions with optimal coverage rate.Then the genetic algorithm(GA)is adopted to schedule the moving trajectory for multiple mobile sinks.Extensive simulations are performed to validate the performance of our proposed method.展开更多
Distributed storage can store data in multiple devices or servers to improve data security.However,in today’s explosive growth of network data,traditional distributed storage scheme is faced with some severe challeng...Distributed storage can store data in multiple devices or servers to improve data security.However,in today’s explosive growth of network data,traditional distributed storage scheme is faced with some severe challenges such as insufficient performance,data tampering,and data lose.A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed storage.Under this scheme,the following improvements have been made in this paper.This paper first analyzes the problems faced by distributed storage.Then proposed to build a new distributed storage blockchain scheme with sharding blockchain.The proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology,which can improve the efficiency of data verification between nodes.In addition,this paper uses polynomial commitment to construct a new verifiable secret share scheme called PolyVSS.This new scheme is one of the foundations for building our improved distributed storage blockchain scheme.Compared with the previous scheme,our new scheme does not require a trusted third party and has some new features such as homomorphic and batch opening.The security of VSS can be further improved.Experimental comparisons show that the proposed scheme significantly reduces storage and communication costs.展开更多
With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapi...With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of IIoT.Blockchain technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the IIoT.In the traditional blockchain,data is stored in a Merkle tree.As data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based IIoT.Accordingly,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data.To solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC structure.Secondly,this paper uses PVC instead of the Merkle tree to store big data generated by IIoT.PVC can improve the efficiency of traditional VC in the process of commitment and opening.Finally,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments.This mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.展开更多
Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many applications.They provide pervasive computing services and techniques in various potential applications for the...Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many applications.They provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of networks.The clustering process takes sensors’location and residual energy into consideration to elect suitable cluster heads.Furthermore,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself.Related experimental results display that the presented method can avoid long distance communicate between sensor nodes.Furthermore,this algorithm reduces energy consumption effectively and improves package delivery rate.展开更多
Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action plan.However,the technologies of the digital con...Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action plan.However,the technologies of the digital continuity guarantee are still lacked.At first,this paper analyzes the requirements of digital continuity guarantee for electronic record based on data quality theory,then points out the necessity of data quality guarantee for electronic record.Moreover,we convert the digital continuity guarantee of electronic record to ensure the consistency,completeness and timeliness of electronic record,and construct the first technology framework of the digital continuity guarantee for electronic record.Finally,the temporal functional dependencies technology is utilized to build the first integration method to insure the consistency,completeness and timeliness of electronic record.展开更多
This paper first estimated the infectious capacity of COVID-19 based on the time series evolution data of confirmed cases in multiple countries. Then, a method to infer the cross-regional spread speed of COVID-19 was ...This paper first estimated the infectious capacity of COVID-19 based on the time series evolution data of confirmed cases in multiple countries. Then, a method to infer the cross-regional spread speed of COVID-19 was introduced in this paper, which took the gross domestic product(GDP) of each region as one of the factors that affect the spread speed of COVID-19 and studied the relationship between the GDP and the infection density of each region(China's Mainland, the United States, and EU countries). In addition, the geographic distance between regions was also considered in this method and the effect of geographic distance on the spread speed of COVID-19 was studied. Studies have shown that the probability of mutual infection of these two regions decreases with increasing geographic distance. Therefore, this paper proposed an epidemic disease spread index based on GDP and geographic distance to quantify the spread speed of COVID-19 in a region. The analysis results showed a strong correlation between the epidemic disease spread index in a region and the number of confirmed cases. This finding provides reasonable suggestions for the control of epidemics. Strengthening the control measures in regions with higher epidemic disease spread index can effectively control the spread of epidemics.展开更多
Recently,many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction(DSRE).These approaches generally use a softmax classifier with cross-entropy loss,which...Recently,many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction(DSRE).These approaches generally use a softmax classifier with cross-entropy loss,which inevitably brings the noise of artificial class NA into classification process.To address the shortcoming,the classifier with ranking loss is employed to DSRE.Uniformly randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss function.However,the majority of the generated negative class can be easily discriminated from positive class and will contribute little towards the training.Inspired by Generative Adversarial Networks(GANs),we use a neural network as the negative class generator to assist the training of our desired model,which acts as the discriminator in GANs.Through the alternating optimization of generator and discriminator,the generator is learning to produce more and more discriminable negative classes and the discriminator has to become better as well.This framework is independent of the concrete form of generator and discriminator.In this paper,we use a two layers fully-connected neural network as the generator and the Piecewise Convolutional Neural Networks(PCNNs)as the discriminator.Experiment results show that our proposed GAN-based method is effective and performs better than state-of-the-art methods.展开更多
The Access control scheme is an effective method to protect user data privacy.The access control scheme based on blockchain and ciphertext policy attribute encryption(CP–ABE)can solve the problems of single—point of...The Access control scheme is an effective method to protect user data privacy.The access control scheme based on blockchain and ciphertext policy attribute encryption(CP–ABE)can solve the problems of single—point of failure and lack of trust in the centralized system.However,it also brings new problems to the health information in the cloud storage environment,such as attribute leakage,low consensus efficiency,complex permission updates,and so on.This paper proposes an access control scheme with fine-grained attribute revocation,keyword search,and traceability of the attribute private key distribution process.Blockchain technology tracks the authorization of attribute private keys.The credit scoring method improves the Raft protocol in consensus efficiency.Besides,the interplanetary file system(IPFS)addresses the capacity deficit of blockchain.Under the premise of hiding policy,the research proposes a fine-grained access control method based on users,user attributes,and file structure.It optimizes the data-sharing mode.At the same time,Proxy Re-Encryption(PRE)technology is used to update the access rights.The proposed scheme proved to be secure.Comparative analysis and experimental results show that the proposed scheme has higher efficiency and more functions.It can meet the needs of medical institutions.展开更多
Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems...Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems across various fields.An increasing number of users are participating in application systems that use blockchain as their underlying architecture.As the number of transactions and the capital involved in blockchain grow,ensuring information security becomes imperative.Addressing the verification of transactional information security and privacy has emerged as a critical challenge.Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations.However,the efficiency of nodes in storing and verifying blockchain data faces unprecedented challenges.To address this issue,this paper introduces an efficient verification scheme for transaction security.Initially,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all transactions.Subsequently,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous trees.Theoretical analyses and simulation experiments conclusively demonstrate the superior performance of this scheme.When verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional methods.The findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of Things.This scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.展开更多
To address the problems of network congestion and spectrum resources shortage in multi-user large-scale scenarios,this paper proposes a twice random access OFDMA-NOMA-RA protocol combining the advantages of orthogonal...To address the problems of network congestion and spectrum resources shortage in multi-user large-scale scenarios,this paper proposes a twice random access OFDMA-NOMA-RA protocol combining the advantages of orthogonal frequency division multiple access(OFDMA)and non-orthogonal multiple access(NOMA).The idea of this protocol is that OFMDA is used to divide the entire frequency field into multiple orthogonal resource units(RUs),and NOMA is used on each RU to enable more users to access the channel and improve spectrum efficiency.Based on the protocol designed in this paper,in the case of imperfect successive interference cancellation(SIC),the probability of successful competition subchannels and the outage probability are derived for two scenarios:Users occupy the subchannel individually and users share the subchannel.Moreover,when two users share the channel,the decoding order of the users and the corresponding probabilities are considered.Then,the system throughput is obtained.To achieve better outage performance in the system,the optimal power allocation algorithm is proposed in this paper,which enables the optimal power allocation strategy to be obtained.Numerical results show that the larger the imperfect SIC coefficient,the worse the outage performance of weak users.Compared with pure OFDMA and NOMA,OFDMA-NOMA-RA always maintains an advantage when the imperfect SIC coefficient is less than a specific value.展开更多
The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tum...The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques.Computer-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor detected.The application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation tasks.This research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold segmentation.The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers.Considering this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN.The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)classifier.The study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal patients.In contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the network.It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.展开更多
Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automa...Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM.We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph.There are two kinds of path patterns in the knowledge graph:one-hop and two-hop.The one-hop path pattern maps the symptom to syndromes immediately.The two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic reasoning.Considering the different support strengths for the knowledge paths in reasoning,we design a dynamic weight mechanism.We utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score calculation.The proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path patterns.We evaluate the method with clinical records and clinical practice in hospitals.The preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in practice.Meanwhile,the method is robust and explainable under the guide of the knowledge graph.It could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.展开更多
Road traffic sign recognition is an important task in intelligent transportation system.Convolutional neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign c...Road traffic sign recognition is an important task in intelligent transportation system.Convolutional neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification.In this paper,it presents a road traffic sign recognition algorithm based on a convolutional neural network.In natural scenes,traffic signs are disturbed by factors such as illumination,occlusion,missing and deformation,and the accuracy of recognition decreases,this paper proposes a model called Improved VGG(IVGG)inspired by VGG model.The IVGG model includes 9 layers,compared with the original VGG model,it is added max-pooling operation and dropout operation after multiple convolutional layers,to catch the main features and save the training time.The paper proposes the method which adds dropout and Batch Normalization(BN)operations after each fully-connected layer,to further accelerate the model convergence,and then it can get better classification effect.It uses the German Traffic Sign Recognition Benchmark(GTSRB)dataset in the experiment.The IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning,and the spent time is also reduced greatly.展开更多
With the rapid development of information technology,the development of blockchain technology has also been deeply impacted.When performing block verification in the blockchain network,if all transactions are verified...With the rapid development of information technology,the development of blockchain technology has also been deeply impacted.When performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage problems.At the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the block.The traditional blockchain system uses theMerkle Tree method to store data.While verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and security.In order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication consumption.By realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.展开更多
Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply,which are not suitable for traditional security technology.The security technology based o...Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply,which are not suitable for traditional security technology.The security technology based on the physicalmechanism has attracted extensive attention.How to improve the key generation rate has always been one of the urgent problems to be solved in the security technology based on the physical mechanism.In this paper,superlattice technology is introduced to the security field of Internet of things,and a high-speed symmetric key generation scheme based on superlattice for Internet of things is proposed.In order to ensure the efficiency and privacy of data transmission,we also combine the superlattice symmetric key and compressive sensing technology to build a lightweight data transmission scheme that supports data compression and data encryption at the same time.Theoretical analysis and experimental evaluation results show that the proposed scheme is superior to the most closely related work.展开更多
Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy protection.And it will cause the accumulation of data on the chain and resulting in the low efficien...Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy protection.And it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification,when the whole transaction on the chain is verified.In order to improve the efficiency and privacy protection of block data verification,this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof(ZKP),which not only protects the privacy of users but also improves the speed of data block verification.There is no need to put the whole transaction on the chain when verifying block data.It just needs to generate the ZKP and root hash with the transaction information,then save them to the smart contract for verification.Moreover,the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the block.When the data is validated,the buffer accepts the complete transaction,updates the transaction status in the cloud database,and packages up the chain.So,the ZKP strengthens the privacy protection ability of blockchain,and the smart contracts save the time cost of block verification.展开更多
As the number of sensor network application scenarios continues to grow,the security problems inherent in this approach have become obstacles that hinder its wide application.However,it has attracted increasing attent...As the number of sensor network application scenarios continues to grow,the security problems inherent in this approach have become obstacles that hinder its wide application.However,it has attracted increasing attention from industry and academia.The blockchain is based on a distributed network and has the characteristics of non-tampering and traceability of block data.It is thus naturally able to solve the security problems of the sensor networks.Accordingly,this paper first analyzes the security risks associated with data storage in the sensor networks,then proposes using blockchain technology to ensure that data storage in the sensor networks is secure.In the traditional blockchain,the data layer uses a Merkle hash tree to store data;however,the Merkle hash tree cannot provide non-member proof,which makes it unable to resist the attacks of malicious nodes in networks.To solve this problem,this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and non-member proof.Moreover,the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements.This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model.Finally,this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.展开更多
基金This work is supported by the National Natural Science Foundation of China(Nos.61771154,61603239,61772454,6171101570).
文摘Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.
基金This research work was supported by the National Natural Science Foundation of China(61772454,61811530332).Professor Gwang-jun Kim is the corresponding author.
文摘Wireless Sensor Networks(WSNs)are large-scale and high-density networks that typically have coverage area overlap.In addition,a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area,which leads to coverage holes in WSNs.Thus,coverage control plays an important role in WSNs.To alleviate unnecessary energy wastage and improve network performance,we consider both energy efficiency and coverage rate for WSNs.In this paper,we present a novel coverage control algorithm based on Particle Swarm Optimization(PSO).Firstly,the sensor nodes are randomly deployed in a target area and remain static after deployment.Then,the whole network is partitioned into grids,and we calculate each grid’s coverage rate and energy consumption.Finally,each sensor nodes’sensing radius is adjusted according to the coverage rate and energy consumption of each grid.Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.
基金This work is supported,in part,by the National Natural Science Foundation of China under grant numbers U1536206,U1405254,61772283,61602253,61672294,61502242in part,by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20150925 and BK20151530+1 种基金in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundin part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘The aim of information hiding is to embed the secret message in a normal cover media such as image,video,voice or text,and then the secret message is transmitted through the transmission of the cover media.The secret message should not be damaged on the process of the cover media.In order to ensure the invisibility of secret message,complex texture objects should be chosen for embedding information.In this paper,an approach which corresponds multiple steganographic algorithms to complex texture objects was presented for hiding secret message.Firstly,complex texture regions are selected based on a kind of objects detection algorithm.Secondly,three different steganographic methods were used to hide secret message into the selected block region.Experimental results show that the approach enhances the security and robustness.
基金This work was supported by the National Natural Science Foundation of China(61772454,61811530332,61811540410)It was also supported by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications)Ministry of Education(No.JZNY201905).
文摘Wireless Sensor Networks(WSNs)are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring.These sensors can transmit their monitored data to the sink in a multi-hop communication manner.However,the‘hot spots’problem will be caused since nodes near sink will consume more energy during forwarding.Recently,mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission.Even though it is difficult to consider many network metrics such as sensor position,residual energy and coverage rate etc.,it is still very important to schedule a reasonable moving trajectory for the mobile sink.In this paper,a novel trajectory scheduling method based on coverage rate for multiple mobile sinks(TSCR-M)is presented especially for large-scale WSNs.An improved particle swarm optimization(PSO)combined with mutation operator is introduced to search the parking positions with optimal coverage rate.Then the genetic algorithm(GA)is adopted to schedule the moving trajectory for multiple mobile sinks.Extensive simulations are performed to validate the performance of our proposed method.
基金This work was supported by the National Natural Science Foundation of China under Grant 62072249,61772280,61772454,62072056.J.Wang and Y.Ren received the grants,and the URL of the sponsors’website is http://www.nsfc.gov.cn/This work was also supported by the Project of Transformation and Upgrading of Industries and Information Technologies of Jiangsu Province(No.JITC-1900AX2038/01).X.Yu received the grant,and the URL of the sponsors’website is http://gxt.jiangsu.gov.cn/.
文摘Distributed storage can store data in multiple devices or servers to improve data security.However,in today’s explosive growth of network data,traditional distributed storage scheme is faced with some severe challenges such as insufficient performance,data tampering,and data lose.A distributed storage scheme based on blockchain has been proposed to improve security and efficiency of traditional distributed storage.Under this scheme,the following improvements have been made in this paper.This paper first analyzes the problems faced by distributed storage.Then proposed to build a new distributed storage blockchain scheme with sharding blockchain.The proposed scheme realizes the partitioning of the network and nodes by means of blockchain sharding technology,which can improve the efficiency of data verification between nodes.In addition,this paper uses polynomial commitment to construct a new verifiable secret share scheme called PolyVSS.This new scheme is one of the foundations for building our improved distributed storage blockchain scheme.Compared with the previous scheme,our new scheme does not require a trusted third party and has some new features such as homomorphic and batch opening.The security of VSS can be further improved.Experimental comparisons show that the proposed scheme significantly reduces storage and communication costs.
基金supported by China’s National Natural Science Foundation(Nos.62072249,62072056)This work is also funded by the National Science Foundation of Hunan Province(2020JJ2029).
文摘With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of IIoT.Blockchain technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the IIoT.In the traditional blockchain,data is stored in a Merkle tree.As data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based IIoT.Accordingly,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data.To solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC structure.Secondly,this paper uses PVC instead of the Merkle tree to store big data generated by IIoT.PVC can improve the efficiency of traditional VC in the process of commitment and opening.Finally,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments.This mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.
基金This work is supported by the National Natural Science Foundation of China(61772454,61811530332,61811540410,U1836208).
文摘Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many applications.They provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of networks.The clustering process takes sensors’location and residual energy into consideration to elect suitable cluster heads.Furthermore,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to itself.Related experimental results display that the presented method can avoid long distance communicate between sensor nodes.Furthermore,this algorithm reduces energy consumption effectively and improves package delivery rate.
基金This work is supported by the NSFC(Nos.61772280,61772454)the Changzhou Sci&Tech Program(No.CJ20179027)the PAPD fund from NUIST.Prof.Jin Wang is the corresponding author。
文摘Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action plan.However,the technologies of the digital continuity guarantee are still lacked.At first,this paper analyzes the requirements of digital continuity guarantee for electronic record based on data quality theory,then points out the necessity of data quality guarantee for electronic record.Moreover,we convert the digital continuity guarantee of electronic record to ensure the consistency,completeness and timeliness of electronic record,and construct the first technology framework of the digital continuity guarantee for electronic record.Finally,the temporal functional dependencies technology is utilized to build the first integration method to insure the consistency,completeness and timeliness of electronic record.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.62266030 and 61863025)International S & T Cooperation Projects of Gansu province (Grant No.144WCGA166)Longyuan Young Innovation Talents and the Doctoral Foundation of LUT。
文摘This paper first estimated the infectious capacity of COVID-19 based on the time series evolution data of confirmed cases in multiple countries. Then, a method to infer the cross-regional spread speed of COVID-19 was introduced in this paper, which took the gross domestic product(GDP) of each region as one of the factors that affect the spread speed of COVID-19 and studied the relationship between the GDP and the infection density of each region(China's Mainland, the United States, and EU countries). In addition, the geographic distance between regions was also considered in this method and the effect of geographic distance on the spread speed of COVID-19 was studied. Studies have shown that the probability of mutual infection of these two regions decreases with increasing geographic distance. Therefore, this paper proposed an epidemic disease spread index based on GDP and geographic distance to quantify the spread speed of COVID-19 in a region. The analysis results showed a strong correlation between the epidemic disease spread index in a region and the number of confirmed cases. This finding provides reasonable suggestions for the control of epidemics. Strengthening the control measures in regions with higher epidemic disease spread index can effectively control the spread of epidemics.
基金This research work is supported by the National Natural Science Foundation of China(NO.61772454,6171101570,61602059)Hunan Provincial Natural Science Foundation of China(No.2017JJ3334)+1 种基金the Research Foundation of Education Bureau of Hunan Province,China(No.16C0045)the Open Project Program of the National Laboratory of Pattern Recognition(NLPR).Professor Jin Wang is the corresponding author.
文摘Recently,many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction(DSRE).These approaches generally use a softmax classifier with cross-entropy loss,which inevitably brings the noise of artificial class NA into classification process.To address the shortcoming,the classifier with ranking loss is employed to DSRE.Uniformly randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss function.However,the majority of the generated negative class can be easily discriminated from positive class and will contribute little towards the training.Inspired by Generative Adversarial Networks(GANs),we use a neural network as the negative class generator to assist the training of our desired model,which acts as the discriminator in GANs.Through the alternating optimization of generator and discriminator,the generator is learning to produce more and more discriminable negative classes and the discriminator has to become better as well.This framework is independent of the concrete form of generator and discriminator.In this paper,we use a two layers fully-connected neural network as the generator and the Piecewise Convolutional Neural Networks(PCNNs)as the discriminator.Experiment results show that our proposed GAN-based method is effective and performs better than state-of-the-art methods.
基金This research was funded by the National Natural Science Foundation of China,Grant Number 62162039the Shaanxi Provincial Key R&D Program,China with Grant Number 2020GY-041.
文摘The Access control scheme is an effective method to protect user data privacy.The access control scheme based on blockchain and ciphertext policy attribute encryption(CP–ABE)can solve the problems of single—point of failure and lack of trust in the centralized system.However,it also brings new problems to the health information in the cloud storage environment,such as attribute leakage,low consensus efficiency,complex permission updates,and so on.This paper proposes an access control scheme with fine-grained attribute revocation,keyword search,and traceability of the attribute private key distribution process.Blockchain technology tracks the authorization of attribute private keys.The credit scoring method improves the Raft protocol in consensus efficiency.Besides,the interplanetary file system(IPFS)addresses the capacity deficit of blockchain.Under the premise of hiding policy,the research proposes a fine-grained access control method based on users,user attributes,and file structure.It optimizes the data-sharing mode.At the same time,Proxy Re-Encryption(PRE)technology is used to update the access rights.The proposed scheme proved to be secure.Comparative analysis and experimental results show that the proposed scheme has higher efficiency and more functions.It can meet the needs of medical institutions.
基金funded by the National Natural Science Foundation of China(62072056,62172058)the Researchers Supporting Project Number(RSP2023R102)King Saud University,Riyadh,Saudi Arabia+4 种基金funded by the Hunan Provincial Key Research and Development Program(2022SK2107,2022GK2019)the Natural Science Foundation of Hunan Province(2023JJ30054)the Foundation of State Key Laboratory of Public Big Data(PBD2021-15)the Young Doctor Innovation Program of Zhejiang Shuren University(2019QC30)Postgraduate Scientific Research Innovation Project of Hunan Province(CX20220940,CX20220941).
文摘Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems across various fields.An increasing number of users are participating in application systems that use blockchain as their underlying architecture.As the number of transactions and the capital involved in blockchain grow,ensuring information security becomes imperative.Addressing the verification of transactional information security and privacy has emerged as a critical challenge.Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations.However,the efficiency of nodes in storing and verifying blockchain data faces unprecedented challenges.To address this issue,this paper introduces an efficient verification scheme for transaction security.Initially,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all transactions.Subsequently,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous trees.Theoretical analyses and simulation experiments conclusively demonstrate the superior performance of this scheme.When verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional methods.The findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of Things.This scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.
基金funded in part by the National Natural Science Foundation of China under Grant 61663024in part by the Hongliu First Class Discipline Development Project of Lanzhou University of Technology(25-225305).
文摘To address the problems of network congestion and spectrum resources shortage in multi-user large-scale scenarios,this paper proposes a twice random access OFDMA-NOMA-RA protocol combining the advantages of orthogonal frequency division multiple access(OFDMA)and non-orthogonal multiple access(NOMA).The idea of this protocol is that OFMDA is used to divide the entire frequency field into multiple orthogonal resource units(RUs),and NOMA is used on each RU to enable more users to access the channel and improve spectrum efficiency.Based on the protocol designed in this paper,in the case of imperfect successive interference cancellation(SIC),the probability of successful competition subchannels and the outage probability are derived for two scenarios:Users occupy the subchannel individually and users share the subchannel.Moreover,when two users share the channel,the decoding order of the users and the corresponding probabilities are considered.Then,the system throughput is obtained.To achieve better outage performance in the system,the optimal power allocation algorithm is proposed in this paper,which enables the optimal power allocation strategy to be obtained.Numerical results show that the larger the imperfect SIC coefficient,the worse the outage performance of weak users.Compared with pure OFDMA and NOMA,OFDMA-NOMA-RA always maintains an advantage when the imperfect SIC coefficient is less than a specific value.
基金the Researchers Supporting Project Number(RSP2023R 509)King Saud University,Riyadh,Saudi ArabiaThis work was supported in part by the Higher Education Sprout Project from the Ministry of Education(MOE)and National Science and Technology Council,Taiwan,(109-2628-E-224-001-MY3)in part by Isuzu Optics Corporation.Dr.Shih-Yu Chen is the corresponding author.
文摘The medical community has more concern on lung cancer analysis.Medical experts’physical segmentation of lung cancers is time-consuming and needs to be automated.The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques.Computer-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor detected.The application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation tasks.This research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold segmentation.The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers.Considering this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN.The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)classifier.The study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal patients.In contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the network.It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.
基金This work is supported by the National Key Research and Development Program of China under Grant 2017YFB1002304the China Scholarship Council under Grant 201906465021.
文摘Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM.We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph.There are two kinds of path patterns in the knowledge graph:one-hop and two-hop.The one-hop path pattern maps the symptom to syndromes immediately.The two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic reasoning.Considering the different support strengths for the knowledge paths in reasoning,we design a dynamic weight mechanism.We utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score calculation.The proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path patterns.We evaluate the method with clinical records and clinical practice in hospitals.The preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in practice.Meanwhile,the method is robust and explainable under the guide of the knowledge graph.It could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.
文摘Road traffic sign recognition is an important task in intelligent transportation system.Convolutional neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification.In this paper,it presents a road traffic sign recognition algorithm based on a convolutional neural network.In natural scenes,traffic signs are disturbed by factors such as illumination,occlusion,missing and deformation,and the accuracy of recognition decreases,this paper proposes a model called Improved VGG(IVGG)inspired by VGG model.The IVGG model includes 9 layers,compared with the original VGG model,it is added max-pooling operation and dropout operation after multiple convolutional layers,to catch the main features and save the training time.The paper proposes the method which adds dropout and Batch Normalization(BN)operations after each fully-connected layer,to further accelerate the model convergence,and then it can get better classification effect.It uses the German Traffic Sign Recognition Benchmark(GTSRB)dataset in the experiment.The IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning,and the spent time is also reduced greatly.
基金This work is supported by the Fundamental Research Funds for the central Universities(Zhejiang University NGICS Platform),Xiaofeng Yu receives the grant and the URLs to sponsors’websites are https://www.zju.edu.cn/.And the work are supported by China’s National Natural Science Foundation(No.62072249,62072056)JinWang and Yongjun Ren receive the grant and the URLs to sponsors’websites are https://www.nsfc.gov.cn/.This work is also funded by the National Science Foundation of Hunan Province(2020JJ2029)Jin Wang receives the grant and the URLs to sponsors’websites are http://kjt.hunan.gov.cn/.
文摘With the rapid development of information technology,the development of blockchain technology has also been deeply impacted.When performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage problems.At the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the block.The traditional blockchain system uses theMerkle Tree method to store data.While verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and security.In order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication consumption.By realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.
基金This work was supported by the Humanities and Social Science Youth Fund of Ministry of Education of China(19YJCZH254)the Innovation driven plan project of Hunan University of Technology and Business in 2020,the Scientific Research Fund of Hunan Provincial Education Department(19B315)this work was funded by the Researchers Supporting Project No.(RSP-2021/102)King Saud University,Riyadh,Saudi Arabia.
文摘Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply,which are not suitable for traditional security technology.The security technology based on the physicalmechanism has attracted extensive attention.How to improve the key generation rate has always been one of the urgent problems to be solved in the security technology based on the physical mechanism.In this paper,superlattice technology is introduced to the security field of Internet of things,and a high-speed symmetric key generation scheme based on superlattice for Internet of things is proposed.In order to ensure the efficiency and privacy of data transmission,we also combine the superlattice symmetric key and compressive sensing technology to build a lightweight data transmission scheme that supports data compression and data encryption at the same time.Theoretical analysis and experimental evaluation results show that the proposed scheme is superior to the most closely related work.
基金This work was supported by China’s National Natural Science Foundation(No.62072249,62072056).Jin Wang and Yongjun Ren received the grant and the URLs to sponsors’websites are https://www.nsfc.gov.cn/.This work was also funded by the Researchers Supporting Project No.(RSP-2021/102)King Saud University,Riyadh,Saudi Arabia.
文摘Since transactions in blockchain are based on public ledger verification,this raises security concerns about privacy protection.And it will cause the accumulation of data on the chain and resulting in the low efficiency of block verification,when the whole transaction on the chain is verified.In order to improve the efficiency and privacy protection of block data verification,this paper proposes an efficient block verification mechanism with privacy protection based on zeroknowledge proof(ZKP),which not only protects the privacy of users but also improves the speed of data block verification.There is no need to put the whole transaction on the chain when verifying block data.It just needs to generate the ZKP and root hash with the transaction information,then save them to the smart contract for verification.Moreover,the ZKP verification in smart contract is carried out to realize the privacy protection of the transaction and efficient verification of the block.When the data is validated,the buffer accepts the complete transaction,updates the transaction status in the cloud database,and packages up the chain.So,the ZKP strengthens the privacy protection ability of blockchain,and the smart contracts save the time cost of block verification.
基金supported by the NSFC(61772454)the Researchers Supporting Project No.RSP-2020/102 King Saud University,Riyadh,Saudi Arabiafunded by National Key Research and Development Program of China(2019YFC1511000).
文摘As the number of sensor network application scenarios continues to grow,the security problems inherent in this approach have become obstacles that hinder its wide application.However,it has attracted increasing attention from industry and academia.The blockchain is based on a distributed network and has the characteristics of non-tampering and traceability of block data.It is thus naturally able to solve the security problems of the sensor networks.Accordingly,this paper first analyzes the security risks associated with data storage in the sensor networks,then proposes using blockchain technology to ensure that data storage in the sensor networks is secure.In the traditional blockchain,the data layer uses a Merkle hash tree to store data;however,the Merkle hash tree cannot provide non-member proof,which makes it unable to resist the attacks of malicious nodes in networks.To solve this problem,this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and non-member proof.Moreover,the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements.This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model.Finally,this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.
