Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by ut...Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism.This study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion models.We begin by discussing existing surveys of vision transformers and comparing them to this work.Then,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,etc.In the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for Segmentation.We also provide a comprehensive overview of recent transformer models for video tasks and diffusion models.We compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark datasets.Finally,we explore some future research directions to further improve the field.展开更多
A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation gradient.The MHNN is simulated and d...A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation gradient.The MHNN is simulated and dynamically analyzed,and implemented on FPGA.Then,a new pseudo-random number generator(PRNG)based on MHNN is proposed.The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of PRNG.The experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming language.The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high performance.Finally,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).展开更多
Two-phaseγ-TiAl/α_(2)-Ti_(3)Al lamellar intermetallics have attracted considerable attention because of their excellent strength and plasticity.However,the exact deformation mechanisms remain to be investigated.In t...Two-phaseγ-TiAl/α_(2)-Ti_(3)Al lamellar intermetallics have attracted considerable attention because of their excellent strength and plasticity.However,the exact deformation mechanisms remain to be investigated.In this paper,a solidified lamellar Ti-Al alloy with lamellar orientation at 0°,17°,and 73°with respect to the loading direction was stretched by utilizing molecular dynamics(MD)simulations.The results show that the mechanical properties of the sample are considerably influenced by solidified defects and tensile directions.The structure deformation and fracture were primarily attributed to an intrinsic stacking fault(ISF)accompanied by the nucleated Shockley dislocation,and the adjacent extrinsic stacking fault(ESF)and ISF formed by solidification tend to form large HCP structures during the tensile process loading at 73°.Moreover,cleavage cracking easily occurs on theγ/α_(2)interface under tensile deformation.The fracture loading mechanism at 17°is grain boundary slide whereas,at 73°and 0°,the dislocation piles up to form a dislocation junction.展开更多
A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is proposed.The theoretical and numerical results show that the reconstruction result with high quality can be obta...A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is proposed.The theoretical and numerical results show that the reconstruction result with high quality can be obtained by adjusting the bandwidth range of the light source appropriately,and the selection criterion of the bandwidth is analyzed by the power distribution of the imaging target.A proof-of-principle experiment is implemented to verify the theoretical and numerical results.In addition,the BCGI also presents better anti-noise performance when compared with some popular GI methods.展开更多
In this study,we analyzed the performance of an Unmanned Aerial Vehicle(UAV)-based mixed Underwater Power Line Communication-Radio Frequency(UPLC-RF)network.In this network,a buoy located at the sea is used as a relay...In this study,we analyzed the performance of an Unmanned Aerial Vehicle(UAV)-based mixed Underwater Power Line Communication-Radio Frequency(UPLC-RF)network.In this network,a buoy located at the sea is used as a relay to transmit signals from the underwater signal source to the UAV through the PLC link.We assume that the UPLC channel obeys a log-normal distribution and that the RF link follows the Rician distribution.Using this model,we obtained the closed-form expressions for the Outage Probability(OP),Average Bit-error-rate(ABER),and Average Channel Capacity(ACC).In addition,the asymptotic analysis of the OP and ABER was performed,and an upper bound for the average capacity was obtained.Finally,the analytical results were verified by Monte Carlo simulation thereby demonstrating the effect of impulse noise and the altitude of the UAV on network performance.展开更多
Text classification has always been an increasingly crucial topic in natural language processing.Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion,da...Text classification has always been an increasingly crucial topic in natural language processing.Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion,data sparsity,limited generalization ability and so on.Based on deep learning text classification,this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based(CNN-Based),Recurrent Neural Network-Based(RNN-based),Attention Mechanisms-Based and so on.Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets.The main reasons are text classification methods based on deep learning can avoid cumbersome feature extraction process and have higher prediction accuracy for a large set of unstructured data.In this paper,we also summarize the shortcomings of traditional text classification methods and introduce the text classification process based on deep learning including text preprocessing,distributed representation of text,text classification model construction based on deep learning and performance evaluation.展开更多
The phase transition of tungsten(W)under high pressures was investigated with molecular dynamics simulation.The structure was characterized in terms of the pair distribution function and the largest standard cluster a...The phase transition of tungsten(W)under high pressures was investigated with molecular dynamics simulation.The structure was characterized in terms of the pair distribution function and the largest standard cluster analysis(LSCA).It is found that under 40−100 GPa at a cooling rate of 0.1 K/ps a pure W melt first crystallizes into the body-centred cubic(BCC)crystal,and then transfers into the hexagonal close-packed(HCP)crystal through a series of BCC−HCP coexisting states.The dynamic factors may induce intermediate stages during the liquid−solid transition and the criss-cross grain boundaries cause lots of indistinguishable intermediate states,making the first-order BCC−HCP transition appear to be continuous.Furthermore,LSCA is shown to be a parameter-free method that can effectively analyze both ordered and disordered structures.Therefore,LSCA can detect more details about the evolution of the structure in such structure transition processes with rich intermediate structures.展开更多
Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data mu...Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.展开更多
In this paper, we consider the problems of data sharing between multiple distrusted authorities. Prior solutions rely on trusted third parties such as CAs, or are susceptible to collusion between malicious authorities...In this paper, we consider the problems of data sharing between multiple distrusted authorities. Prior solutions rely on trusted third parties such as CAs, or are susceptible to collusion between malicious authorities, which can comprise the security of honest ones. In this paper, we propose a new multi-authority data sharing scheme - Decen- tralized Multi-Authority ABE （DMA）, which is derived from CP-ABE that is resilient to these types of misbehavior. Our system distin- guishes between a data owner （DO） principal and attribute authorities （AAs）： the DO owns the data but allows AAs to arbitrate access by providing attribute labels to users. The data is protected by policy encryption over these attributes. Unlike prior systems, attributes generated by AAs are not user-specific, and neither is the system susceptible to collusion between users who try to escalate their access by sharing keys. We prove our scherne correct under the Decisional Bilinear Diffie-Hellman （DBDH） assumption; we also include a com- plete end-to-end implementation that demon- strates the practical efficacy of our technique.展开更多
Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services w...Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric vehicles.However,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust issues.In this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center.Based on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare.We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching.The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.展开更多
Wireless Body Area Networks(WBANs)refer to small sensor network that consists of sensor devices mounted on the surface of the body or implanted in the body,as such networks are employed to harvest physiological data o...Wireless Body Area Networks(WBANs)refer to small sensor network that consists of sensor devices mounted on the surface of the body or implanted in the body,as such networks are employed to harvest physiological data of the human body or to act as an assistant regulator of several specific physiological indicators of the human body.The sensor devices transmit the harvested human physiological data to the local node via a public channel.Before transmitting data,the sensor device and the local node should perform mutual authentication and key agreement.It is proposed in this paper a secure mutual authentication scheme of blockchain-based in WBANs.To analyze the security of this scheme,formal security analysis,and informal security analysis are used,then the computation and communication costs are compared with those of the relevant schemes.Relevant experimental results reveal that the proposed scheme exhibit more effective control over energy consumption and promising.展开更多
In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local...In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local label correlations can appear in real-world situation at same time.On the other hand,we should not be limited to pairwise labels while ignoring the high-order label correlation.In this paper,we propose a novel and effective method called GLLCBN for multi-label learning.Firstly,we obtain the global label correlation by exploiting label semantic similarity.Then,we analyze the pairwise labels in the label space of the data set to acquire the local correlation.Next,we build the original version of the label dependency model by global and local label correlations.After that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent model.Finally,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label learning.The experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.展开更多
In head mounted display(HMD),in order to cancel pincushion distortion,the images displayed on the mobile should be prewarped with barrel distortion.The copyright of the mobile video should be verified on both the orig...In head mounted display(HMD),in order to cancel pincushion distortion,the images displayed on the mobile should be prewarped with barrel distortion.The copyright of the mobile video should be verified on both the original view and the pre-warped virtual view.A robust watermarking resistant against barrel distortion for HMDs is proposed in this paper.Watermark mask is embedded into image in consideration of imperceptibility and robustness of watermarking.In order to detect watermark from the pre-warped image with barrel distortion,an estimation method of the barrel distortion is proposed for HMDs.Then,the same warp is enforced on the embedded watermark mask with the estimated parameters of barrel distortion.The correlation between the warped watermark and the pre-warped image is computed to predicate the existence of watermark.As shown in experimental results,watermark of mobile video can be detected not only from the original views,but also from the pre-warped virtual view.It also shows that the proposed scheme is resistant against combined barrel distortion and common post-processing,such as JPEG compression.展开更多
The exploration of the memristor model in the discrete domain is a fascinating hotspot.The electromagnetic induction on neurons has also begun to be simulated by some discrete memristors.However,most of the current in...The exploration of the memristor model in the discrete domain is a fascinating hotspot.The electromagnetic induction on neurons has also begun to be simulated by some discrete memristors.However,most of the current investigations are based on the integer-order discrete memristor,and there are relatively few studies on the form of fractional order.In this paper,a new fractional-order discrete memristor model with prominent nonlinearity is constructed based on the Caputo fractional-order difference operator.Furthermore,the dynamical behaviors of the Rulkov neuron under electromagnetic radiation are simulated by introducing the proposed discrete memristor.The integer-order and fractional-order peculiarities of the system are analyzed through the bifurcation graph,the Lyapunov exponential spectrum,and the iterative graph.The results demonstrate that the fractional-order system has more abundant dynamics than the integer one,such as hyper-chaos,multi-stable and transient chaos.In addition,the complexity of the system in the fractional form is evaluated by the means of the spectral entropy complexity algorithm and consequences show that it is affected by the order of the fractional system.The feature of fractional difference lays the foundation for further research and application of the discrete memristor and the neuron map in the future.展开更多
The personalized news recommendation has been very popular in the news recommendation field.In most research,the picture information in the news is ignored,but the information conveyed to the users through pictures is...The personalized news recommendation has been very popular in the news recommendation field.In most research,the picture information in the news is ignored,but the information conveyed to the users through pictures is more intuitive and more likely to affect the users’reading interests than the one in the textual form.Therefore,in this paper,a model that combines images and texts in the news is proposed.In this model,the new tags are extracted from the images and texts in the news,and based on these new tags,an adaptive tag(AT)algorithm is proposed.The AT algorithm selects the tags the user is interested in based on the user feedback.In particular,the AT algorithm can predict tags that a user may be interested in with the help of the tag correlation graph without any user feedback.The proposed AT algorithm is verified by experiments.The experimental results verified the AT algorithm regarding three evaluation indexes F1-score(F1),area under curve(AUC)and mean reciprocal rank(MRR).The recommended effect of the proposed algorithm is found to be better than those of the various baseline algorithms on real-world datasets.展开更多
A new four-dimensional(4D)memristive chaotic system is obtained by introducing a memristor into the Rucklidge chaotic system,and a detailed dynamic analysis of the system is performed.The sensitivity of the system to ...A new four-dimensional(4D)memristive chaotic system is obtained by introducing a memristor into the Rucklidge chaotic system,and a detailed dynamic analysis of the system is performed.The sensitivity of the system to parameters allows it obtains 16 different attractors by changing only one parameter.The various transient behaviors and excellent spectral entropy and C0 complexity values of the system can also reflect the high complexity of the system.A circuit is designed and verified the feasibility of the system from the physical level.Finally,the system is applied to image encryption,and the security of the encryption system is analyzed from multiple aspects,providing a reference for the application of such memristive chaotic systems.展开更多
The packet generator （pktgen） is a fundamental module of the majority of soft- ware testers used to benchmark network pro- tocols and functions. The high performance of the pktgen is an important feature of Future I...The packet generator （pktgen） is a fundamental module of the majority of soft- ware testers used to benchmark network pro- tocols and functions. The high performance of the pktgen is an important feature of Future Internet Testbeds, and DPDK is a network packet accelerated platform, so we can use DPDK to improve performance. Meanwhile, green computing is advocated for in the fu- ture of the internet. Most existing efforts have contributed to improving either performance or accuracy. We, however, shifted the focus to energy-efficiency. We find that high per- formance comes at the cost of high energy consumption. Therefore, we started from a widely used high performance schema, deeply studying the multi-core platform, especially in terms of parallelism, core allocation, and fre- quency controlling. On this basis, we proposed an AFfinity-oriented Fine-grained CONtrolling （AFFCON） mechanism in order to improve energy efficiency with desirable performance. As clearly demonstrated through a series of evaluative experiments, our proposal can reduce CPU power consumption by up to 11% while maintaining throughput at the line rate.展开更多
Direct dynamics simulations are a useful and general approach for studying the atomistic properties of complex chemical systems because they do not require fitting an analytic potential energy function.Hessian-based p...Direct dynamics simulations are a useful and general approach for studying the atomistic properties of complex chemical systems because they do not require fitting an analytic potential energy function.Hessian-based predictor-corrector integrators are a widely used approach for calculating the trajectories of moving atoms in direct dynamics simulations.We employ a monodromy matrix to propose a tool for evaluating the accuracy of integrators in the trajectory calculation.We choose a general velocity Verlet as a different object.We also simulate molecular with hydrogen(CO_2) and molecular with hydrogen(H_2O) motions.Comparing the eigenvalues of monodromy matrix,many simulations show that Hessian-based predictor-corrector integrators perform well for Hessian updates and non-Hessian updates.Hessian-based predictor-corrector integrator with Hessian update has a strong performance in the H_2O simulations.Hessian-based predictor-corrector integrator with Hessian update has a strong performance when the integrating step of the velocity Verlet approach is tripled for the predicting step.In the CO_2 simulations,a strong performance occurs when the integrating step is a multiple of five.展开更多
The crowdsourcing, as a service pattern in cloud environment, usually aims at the cross-disciplinary cooperation and creating value together with customers and becomes increasingly prevalent. Software process, as a ki...The crowdsourcing, as a service pattern in cloud environment, usually aims at the cross-disciplinary cooperation and creating value together with customers and becomes increasingly prevalent. Software process, as a kind of software development and management strategy, is defined as a series of activities implemented by software life cycle and provides a set of rules for various phases of the software engineering to achieve the desired objectives. With the current software development cycle getting shorter, facing more frequent needs change and fierce competition, a new resource management pattern is proposed to respond to these issues agilely by introducing the crowdsourcing service to agile software development for pushing the agility of software process. Then, a user-oriented resource scheduling method is proposed for rational use of various resources in the process and maximizing the benefits of all parties. From the experimental results, the proposed pattern and resources scheduling method reduces greatly the resource of project resource manager and increases the team resource utilization rate, which greatly improves the agility of software process and delivers software products quickly in crowdsourcing pattern.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 61502162,61702175,and 61772184in part by the Fund of the State Key Laboratory of Geo-information Engineering under Grant SKLGIE2016-M-4-2+4 种基金in part by the Hunan Natural Science Foundation of China under Grant 2018JJ2059in part by the Key R&D Project of Hunan Province of China under Grant 2018GK2014in part by the Open Fund of the State Key Laboratory of Integrated Services Networks under Grant ISN17-14Chinese Scholarship Council(CSC)through College of Computer Science and Electronic Engineering,Changsha,410082Hunan University with Grant CSC No.2018GXZ020784.
文摘Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks(CNNs).The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism.This study aims to provide a comprehensive survey of recent transformerbased approaches in image and video applications,as well as diffusion models.We begin by discussing existing surveys of vision transformers and comparing them to this work.Then,we review the main components of a vanilla transformer network,including the self-attention mechanism,feed-forward network,position encoding,etc.In the main part of this survey,we review recent transformer-based models in three categories:Transformer for downstream tasks,Vision Transformer for Generation,and Vision Transformer for Segmentation.We also provide a comprehensive overview of recent transformer models for video tasks and diffusion models.We compare the performance of various hierarchical transformer networks for multiple tasks on popular benchmark datasets.Finally,we explore some future research directions to further improve the field.
基金supported by the Scientific Research Fund of Hunan Provincial Education Department(Grant No.21B0345)the Postgraduate Scientific Research Innovation Project of Changsha University of Science and Technology(Grant Nos.CX2021SS69 and CX2021SS72)+3 种基金the Postgraduate Scientific Research Innovation Project of Hunan Province,China(Grant No.CX20200884)the Natural Science Foundation of Hunan Province,China(Grant Nos.2019JJ50648,2020JJ4622,and 2020JJ4221)the National Natural Science Foundation of China(Grant No.62172058)the Special Funds for the Construction of Innovative Provinces of Hunan Province,China(Grant Nos.2020JK4046 and 2022SK2007)。
文摘A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation gradient.The MHNN is simulated and dynamically analyzed,and implemented on FPGA.Then,a new pseudo-random number generator(PRNG)based on MHNN is proposed.The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of PRNG.The experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming language.The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high performance.Finally,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51761004,51661005,and 11964005)Industry and Education Combination Innovation Platform of Intelligent Manufacturing and Graduate Joint Training Base at Guizhou University(Grant No.2020520000-83-01-324061)+2 种基金the Guizhou Province Science and Technology Fund,China(Grant Nos.ZK[2021]051,[2017]5788,and J[2015]2050)High Level Creative Talent in Guizhou Education Department of Chinathe Cooperation Project of Science and Technology of Guizhou Province,China(Grant No.LH[2016]7430)。
文摘Two-phaseγ-TiAl/α_(2)-Ti_(3)Al lamellar intermetallics have attracted considerable attention because of their excellent strength and plasticity.However,the exact deformation mechanisms remain to be investigated.In this paper,a solidified lamellar Ti-Al alloy with lamellar orientation at 0°,17°,and 73°with respect to the loading direction was stretched by utilizing molecular dynamics(MD)simulations.The results show that the mechanical properties of the sample are considerably influenced by solidified defects and tensile directions.The structure deformation and fracture were primarily attributed to an intrinsic stacking fault(ISF)accompanied by the nucleated Shockley dislocation,and the adjacent extrinsic stacking fault(ESF)and ISF formed by solidification tend to form large HCP structures during the tensile process loading at 73°.Moreover,cleavage cracking easily occurs on theγ/α_(2)interface under tensile deformation.The fracture loading mechanism at 17°is grain boundary slide whereas,at 73°and 0°,the dislocation piles up to form a dislocation junction.
基金the National Natural Science Foundation of China(Grant Nos.61871431,61971184,and 62001162).
文摘A scheme to improve the quality in ghost imaging(GI)by controlling the bandwidth of light source(BCGI)is proposed.The theoretical and numerical results show that the reconstruction result with high quality can be obtained by adjusting the bandwidth range of the light source appropriately,and the selection criterion of the bandwidth is analyzed by the power distribution of the imaging target.A proof-of-principle experiment is implemented to verify the theoretical and numerical results.In addition,the BCGI also presents better anti-noise performance when compared with some popular GI methods.
基金supported in part by the National Natural Science Foundation of China(No.62272153)the Changsha Natural Science Foundation under Grant(No.kq2202172)+3 种基金the Hunan Natural ScienceFoundation(2022JJ30008)the Key Research and Development in Hunan Province under Grant(No.2022GK2051)the Hunan High-tech Industry Science and Technology Innovation Leading Program Project under Grant(No.2022GK4004)the Project Supported by Scientific Research Fund of Hunan Provincial Education Department(No.20C1489)。
文摘In this study,we analyzed the performance of an Unmanned Aerial Vehicle(UAV)-based mixed Underwater Power Line Communication-Radio Frequency(UPLC-RF)network.In this network,a buoy located at the sea is used as a relay to transmit signals from the underwater signal source to the UAV through the PLC link.We assume that the UPLC channel obeys a log-normal distribution and that the RF link follows the Rician distribution.Using this model,we obtained the closed-form expressions for the Outage Probability(OP),Average Bit-error-rate(ABER),and Average Channel Capacity(ACC).In addition,the asymptotic analysis of the OP and ABER was performed,and an upper bound for the average capacity was obtained.Finally,the analytical results were verified by Monte Carlo simulation thereby demonstrating the effect of impulse noise and the altitude of the UAV on network performance.
基金This work supported in part by the National Natural Science Foundation of China under Grant 61872134,in part by the Natural Science Foundation of Hunan Province under Grant 2018JJ2062in part by Science and Technology Development Center of the Ministry of Education under Grant 2019J01020in part by the 2011 Collaborative Innovative Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province。
文摘Text classification has always been an increasingly crucial topic in natural language processing.Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion,data sparsity,limited generalization ability and so on.Based on deep learning text classification,this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based(CNN-Based),Recurrent Neural Network-Based(RNN-based),Attention Mechanisms-Based and so on.Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets.The main reasons are text classification methods based on deep learning can avoid cumbersome feature extraction process and have higher prediction accuracy for a large set of unstructured data.In this paper,we also summarize the shortcomings of traditional text classification methods and introduce the text classification process based on deep learning including text preprocessing,distributed representation of text,text classification model construction based on deep learning and performance evaluation.
基金Projects(51661005,U1612442)supported by the National Natural Science Foundation of ChinaProject(QKHJC[2017]1025)supported by the Natural Science Foundation of Guizhou Province,ChinaProject(2018JJ3560)supported by the Natural Science Foundation of Hunan Province,China。
文摘The phase transition of tungsten(W)under high pressures was investigated with molecular dynamics simulation.The structure was characterized in terms of the pair distribution function and the largest standard cluster analysis(LSCA).It is found that under 40−100 GPa at a cooling rate of 0.1 K/ps a pure W melt first crystallizes into the body-centred cubic(BCC)crystal,and then transfers into the hexagonal close-packed(HCP)crystal through a series of BCC−HCP coexisting states.The dynamic factors may induce intermediate stages during the liquid−solid transition and the criss-cross grain boundaries cause lots of indistinguishable intermediate states,making the first-order BCC−HCP transition appear to be continuous.Furthermore,LSCA is shown to be a parameter-free method that can effectively analyze both ordered and disordered structures.Therefore,LSCA can detect more details about the evolution of the structure in such structure transition processes with rich intermediate structures.
基金This study was supported by National Key Research and Development Project(Project No.2017YFD0301506)National Social Science Foundation(Project No.71774052)+1 种基金Hunan Education Department Scientific Research Project(Project No.17K04417A092).
文摘Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.
基金supported by the National Natural Science Foundation of China under grant 61402160Hunan Provincial Natural Science Foundation of China under grant 2016JJ3043Open Funding for Universities in Hunan Province under grant 14K023
文摘In this paper, we consider the problems of data sharing between multiple distrusted authorities. Prior solutions rely on trusted third parties such as CAs, or are susceptible to collusion between malicious authorities, which can comprise the security of honest ones. In this paper, we propose a new multi-authority data sharing scheme - Decen- tralized Multi-Authority ABE （DMA）, which is derived from CP-ABE that is resilient to these types of misbehavior. Our system distin- guishes between a data owner （DO） principal and attribute authorities （AAs）： the DO owns the data but allows AAs to arbitrate access by providing attribute labels to users. The data is protected by policy encryption over these attributes. Unlike prior systems, attributes generated by AAs are not user-specific, and neither is the system susceptible to collusion between users who try to escalate their access by sharing keys. We prove our scherne correct under the Decisional Bilinear Diffie-Hellman （DBDH） assumption; we also include a com- plete end-to-end implementation that demon- strates the practical efficacy of our technique.
基金supported in part by the National Natural Science Foundation of China (No.62002113)the Natural Science Foundation of Hunan Province (No. 2021JJ40122).
文摘Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric vehicles.However,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust issues.In this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center.Based on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare.We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching.The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.
基金supported by the National Natural Science Foundation of China(Grant Nos.61872138&61572188)。
文摘Wireless Body Area Networks(WBANs)refer to small sensor network that consists of sensor devices mounted on the surface of the body or implanted in the body,as such networks are employed to harvest physiological data of the human body or to act as an assistant regulator of several specific physiological indicators of the human body.The sensor devices transmit the harvested human physiological data to the local node via a public channel.Before transmitting data,the sensor device and the local node should perform mutual authentication and key agreement.It is proposed in this paper a secure mutual authentication scheme of blockchain-based in WBANs.To analyze the security of this scheme,formal security analysis,and informal security analysis are used,then the computation and communication costs are compared with those of the relevant schemes.Relevant experimental results reveal that the proposed scheme exhibit more effective control over energy consumption and promising.
文摘In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local label correlations can appear in real-world situation at same time.On the other hand,we should not be limited to pairwise labels while ignoring the high-order label correlation.In this paper,we propose a novel and effective method called GLLCBN for multi-label learning.Firstly,we obtain the global label correlation by exploiting label semantic similarity.Then,we analyze the pairwise labels in the label space of the data set to acquire the local correlation.Next,we build the original version of the label dependency model by global and local label correlations.After that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent model.Finally,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label learning.The experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.
基金partially supported by Fundamental Research Funds for the Central Universities of China(2016JKF01203)National Natural Science Foundation of China(61401408,61402484,and 61502160)
文摘In head mounted display(HMD),in order to cancel pincushion distortion,the images displayed on the mobile should be prewarped with barrel distortion.The copyright of the mobile video should be verified on both the original view and the pre-warped virtual view.A robust watermarking resistant against barrel distortion for HMDs is proposed in this paper.Watermark mask is embedded into image in consideration of imperceptibility and robustness of watermarking.In order to detect watermark from the pre-warped image with barrel distortion,an estimation method of the barrel distortion is proposed for HMDs.Then,the same warp is enforced on the embedded watermark mask with the estimated parameters of barrel distortion.The correlation between the warped watermark and the pre-warped image is computed to predicate the existence of watermark.As shown in experimental results,watermark of mobile video can be detected not only from the original views,but also from the pre-warped virtual view.It also shows that the proposed scheme is resistant against combined barrel distortion and common post-processing,such as JPEG compression.
基金supported by the Major Research Plan of the National Natural Science Foundation of China(Grant No.91964108)the National Natural Science Foundation of China(Grant No.61971185)the Natural Science Foundation of Hunan Province,China(Grant No.2020JJ4218).
文摘The exploration of the memristor model in the discrete domain is a fascinating hotspot.The electromagnetic induction on neurons has also begun to be simulated by some discrete memristors.However,most of the current investigations are based on the integer-order discrete memristor,and there are relatively few studies on the form of fractional order.In this paper,a new fractional-order discrete memristor model with prominent nonlinearity is constructed based on the Caputo fractional-order difference operator.Furthermore,the dynamical behaviors of the Rulkov neuron under electromagnetic radiation are simulated by introducing the proposed discrete memristor.The integer-order and fractional-order peculiarities of the system are analyzed through the bifurcation graph,the Lyapunov exponential spectrum,and the iterative graph.The results demonstrate that the fractional-order system has more abundant dynamics than the integer one,such as hyper-chaos,multi-stable and transient chaos.In addition,the complexity of the system in the fractional form is evaluated by the means of the spectral entropy complexity algorithm and consequences show that it is affected by the order of the fractional system.The feature of fractional difference lays the foundation for further research and application of the discrete memristor and the neuron map in the future.
基金The authors gratefully acknowledge support from National Key R&D Program of China(No.2018YFC0831800)National Natural Science Foundation of China(No.61872134)+2 种基金Natural Science Foundation of Hunan Province(No.2018JJ2062)Science and Technology Development Center of the Ministry of Educationthe 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province.
文摘The personalized news recommendation has been very popular in the news recommendation field.In most research,the picture information in the news is ignored,but the information conveyed to the users through pictures is more intuitive and more likely to affect the users’reading interests than the one in the textual form.Therefore,in this paper,a model that combines images and texts in the news is proposed.In this model,the new tags are extracted from the images and texts in the news,and based on these new tags,an adaptive tag(AT)algorithm is proposed.The AT algorithm selects the tags the user is interested in based on the user feedback.In particular,the AT algorithm can predict tags that a user may be interested in with the help of the tag correlation graph without any user feedback.The proposed AT algorithm is verified by experiments.The experimental results verified the AT algorithm regarding three evaluation indexes F1-score(F1),area under curve(AUC)and mean reciprocal rank(MRR).The recommended effect of the proposed algorithm is found to be better than those of the various baseline algorithms on real-world datasets.
基金Project supported by the National Natural Science Foundation of China(Grant No.U1612442)Science and Technology Special Foundation Project of Guizhou Water Resources Department(Grant No.KT202236)。
文摘A new four-dimensional(4D)memristive chaotic system is obtained by introducing a memristor into the Rucklidge chaotic system,and a detailed dynamic analysis of the system is performed.The sensitivity of the system to parameters allows it obtains 16 different attractors by changing only one parameter.The various transient behaviors and excellent spectral entropy and C0 complexity values of the system can also reflect the high complexity of the system.A circuit is designed and verified the feasibility of the system from the physical level.Finally,the system is applied to image encryption,and the security of the encryption system is analyzed from multiple aspects,providing a reference for the application of such memristive chaotic systems.
基金supported by the National Science Foundation of China (No. 61472130, Research on Graphic Processing Units-based High-performance Packet Processing)the China Postdoctoral Science Foundation funded project (No. 61702174)
文摘The packet generator （pktgen） is a fundamental module of the majority of soft- ware testers used to benchmark network pro- tocols and functions. The high performance of the pktgen is an important feature of Future Internet Testbeds, and DPDK is a network packet accelerated platform, so we can use DPDK to improve performance. Meanwhile, green computing is advocated for in the fu- ture of the internet. Most existing efforts have contributed to improving either performance or accuracy. We, however, shifted the focus to energy-efficiency. We find that high per- formance comes at the cost of high energy consumption. Therefore, we started from a widely used high performance schema, deeply studying the multi-core platform, especially in terms of parallelism, core allocation, and fre- quency controlling. On this basis, we proposed an AFfinity-oriented Fine-grained CONtrolling （AFFCON） mechanism in order to improve energy efficiency with desirable performance. As clearly demonstrated through a series of evaluative experiments, our proposal can reduce CPU power consumption by up to 11% while maintaining throughput at the line rate.
基金Project(2016JJ2029)supported by Hunan Provincial Natural Science Foundation of ChinaProject(2016WLZC014)supported by the Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational TechnologyProject(2015HNWLFZ059)supported by the Open Research Fund of Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges,China
文摘Direct dynamics simulations are a useful and general approach for studying the atomistic properties of complex chemical systems because they do not require fitting an analytic potential energy function.Hessian-based predictor-corrector integrators are a widely used approach for calculating the trajectories of moving atoms in direct dynamics simulations.We employ a monodromy matrix to propose a tool for evaluating the accuracy of integrators in the trajectory calculation.We choose a general velocity Verlet as a different object.We also simulate molecular with hydrogen(CO_2) and molecular with hydrogen(H_2O) motions.Comparing the eigenvalues of monodromy matrix,many simulations show that Hessian-based predictor-corrector integrators perform well for Hessian updates and non-Hessian updates.Hessian-based predictor-corrector integrator with Hessian update has a strong performance in the H_2O simulations.Hessian-based predictor-corrector integrator with Hessian update has a strong performance when the integrating step of the velocity Verlet approach is tripled for the predicting step.In the CO_2 simulations,a strong performance occurs when the integrating step is a multiple of five.
基金Projects(61304184,61672221)supported by the National Natural Science Foundation of ChinaProject(2016JJ6010)supported by the Hunan Provincial Natural Science Foundation of China
文摘The crowdsourcing, as a service pattern in cloud environment, usually aims at the cross-disciplinary cooperation and creating value together with customers and becomes increasingly prevalent. Software process, as a kind of software development and management strategy, is defined as a series of activities implemented by software life cycle and provides a set of rules for various phases of the software engineering to achieve the desired objectives. With the current software development cycle getting shorter, facing more frequent needs change and fierce competition, a new resource management pattern is proposed to respond to these issues agilely by introducing the crowdsourcing service to agile software development for pushing the agility of software process. Then, a user-oriented resource scheduling method is proposed for rational use of various resources in the process and maximizing the benefits of all parties. From the experimental results, the proposed pattern and resources scheduling method reduces greatly the resource of project resource manager and increases the team resource utilization rate, which greatly improves the agility of software process and delivers software products quickly in crowdsourcing pattern.
