There are a lot of security issues in block cipher algorithm.Security analysis and enhanced design of a dynamic block cipher was proposed.Firstly,the safety of ciphertext was enhanced based on confusion substitution o...There are a lot of security issues in block cipher algorithm.Security analysis and enhanced design of a dynamic block cipher was proposed.Firstly,the safety of ciphertext was enhanced based on confusion substitution of S-box,thus disordering the internal structure of data blocks by four steps of matrix transformation.Then,the diffusivity of ciphertext was obtained by cyclic displacement of bytes using column ambiguity function.The dynamic key was finally generated by using LFSR,which improved the stochastic characters of secret key in each of round of iteration.The safety performance of proposed algorithm was analyzed by simulation test.The results showed the proposed algorithm has a little effect on the speed of encryption and decryption while enhancing the security.Meanwhile,the proposed algorithm has highly scalability,the dimension of S-box and the number of register can be dynamically extended according to the security requirement.展开更多
Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of co...Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.展开更多
A biconcave particle suspended in a Poiseuille flow is investigated by the multiple-relaxation-time lattice Boltzmann method with the Galilean-invariant momentum exchange method.The lateral migration and equilibrium o...A biconcave particle suspended in a Poiseuille flow is investigated by the multiple-relaxation-time lattice Boltzmann method with the Galilean-invariant momentum exchange method.The lateral migration and equilibrium of the particle are similar to the Segré-Silberberg effect in our numerical simulations.Surprisingly,two lateral equilibrium positions are observed corresponding to the releasing positions of the biconcave particle.The upper equilibrium positions significantly decrease with the increasing Reynolds number,whereas the lower ones are almost insensitive to the Reynolds number.Interestingly,the regular wave accompanied by nonuniform rotation is exhibited in the lateral movement of the biconcave particle.It can be attributed to the fact that the biconcave shape in various postures interacts with the parabolic velocity distribution of the Poiseuille flow.A set of contours illustrate the dynamic flow field when the biconcave particle has successive postures in a rotating period.展开更多
The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can i...The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.展开更多
To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First...To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.展开更多
This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on...This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on Bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on Bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.展开更多
Boundary conditions (BCs) play an essential role in lattice Boltzmann (LB) simulations. This paper investigates several most commonly applied BCs by evaluating the relative L2-norm errors of the LB simulations for two...Boundary conditions (BCs) play an essential role in lattice Boltzmann (LB) simulations. This paper investigates several most commonly applied BCs by evaluating the relative L2-norm errors of the LB simulations for two-dimensional (2-D) Poiseuille flow. It is found that the relative L2-norm error resulting from FHML's BC is smaller than that from other BCs as a whole. Then, based on the FHML's BC, it formulates an LB model for simulating fluid flows in 2-D channel with complex geometries. Afterwards, the flows between two inclined plates, in a pulmonary blood vessel and in a blood vessel with local expansion region, are simulated. The numerical results are in good agreement with the analytical predictions and clearly show that the model is effective. It is expected that the model can be extended to simulate some real biologic flows, such as blood flows in arteries, vessels with stenosises, aneurysms and bifurcations, etc.展开更多
To better evaluate the quality of software architecture,a metrics suite is proposed to measure the coupling of software architecture models,in which CBC is used to measure the coupling between components,CBCC is used ...To better evaluate the quality of software architecture,a metrics suite is proposed to measure the coupling of software architecture models,in which CBC is used to measure the coupling between components,CBCC is used to measure the coupling of transferring message between components,CBCCT is used to measure the coupling of software architecture,WCBCC is used to measure the coupling of transferring message with weight between components,and WCBCCT is used to measure the coupling of message transmission with weight in the whole software architecture. The proposed algorithm for the coupling metrics is applied to the design of serve software architecture. Analysis of an example validates the feasibility of this metrics suite.展开更多
The exponential growth of the Internet coupled with the increasing popularity of dynamically generated content on the World Wide Web, has created the need for more and faster Web servers capable of serving the over 10...The exponential growth of the Internet coupled with the increasing popularity of dynamically generated content on the World Wide Web, has created the need for more and faster Web servers capable of serving the over 100 million Internet users. To converge the control method has emerged as a promising technique to solve the Web QoS problem. In this paper, a model of adaptive session is presented and a session flow self-regulating algorism based on Kalman Filter are proposed towards Web Server. And a Web QoS self-regulating scheme is advanced. To attain the goal of on-line system identification, the optimized estimation of QoS parameters is fulfilled by utilizing Kalman Filter in full domain. The simulation results shows that the proposed scheme can guarantee the QoS with both robustness and stability .展开更多
Feature selection is an active area in data mining research and development. It consists of efforts and contributions from a wide variety of communities, including statistics, machine learning, and pattern recognition...Feature selection is an active area in data mining research and development. It consists of efforts and contributions from a wide variety of communities, including statistics, machine learning, and pattern recognition. The diversity, on one hand, equips us with many methods and tools. On the other hand, the profusion of options causes confusion.This paper reviews various feature selection methods and identifies research challenges that are at the forefront of this exciting area.展开更多
Mining important nodes in the complex network should not only consider the core nodes, but also consider the locations of the nodes in the network. Despite many researches on discovering important nodes, the importanc...Mining important nodes in the complex network should not only consider the core nodes, but also consider the locations of the nodes in the network. Despite many researches on discovering important nodes, the importance of nodes in the structural holes is still ignored easily. Therefore, this paper proposes a method of local centrality measurement based on structural holes, which evaluates the nodes importance both by direct and indirect constraints caused by the lack of structural holes around the nodes. In this method, the attributes and locations of the nodes and their first-order and second-order neighbors are taken into account simultaneously. Deliberate attack simulation is carried out through selective deletion in a certain proportion of network nodes. Calculating the decreased ratio of network efficiency is to quantitatively describe the importance of nodes in before-and-after attacks. Experiments indicate that this method has more advantages to mine important nodes compared to clustering coefficient and k-shell decomposition method. And it is suitable for the quantitative analysis of the nodes importance in large scale networks.展开更多
The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms.The traditional ste-ganalysis detector is trained on the stego images created by a ...The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms.The traditional ste-ganalysis detector is trained on the stego images created by a certain type of ste-ganographic algorithm,whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm.This phenomenon is called as steganographic algorithm mismatch in steganalysis.To resolve this pro-blem,we propose a deep learning driven feature-based approach.An advanced steganalysis neural network is used to extract steganographic features,different pairs of training images embedded with steganographic algorithms can obtain diverse features of each algorithm.Then a multi-classifier implemented as lightgbm is used to predict the matching algorithm.Experimental results on four types of JPEG steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.展开更多
The current research works and the existing problems of terminological cycles in description logics are analyzed in this paper. Referring to the works of Baader F and Nebel B,we aim in a new direction. Firstly,descrip...The current research works and the existing problems of terminological cycles in description logics are analyzed in this paper. Referring to the works of Baader F and Nebel B,we aim in a new direction. Firstly,description logic νL is defined,and the description graphs GT and GJ are redefined. A syntax condition for the satisfiability of membership relation is given. By using this syntax condition,we prove the following:The subsumption reasoning in νL with respect to gfp-model,lfp-model and descriptive model is polynomial.展开更多
An automatic detection and evaluation method of the Erhua(also called r-retroflexion or retrofex suffixation)in the Putonghua proficiency test(PSC)is proposed.Based on the framework of the computer assisted pronunciat...An automatic detection and evaluation method of the Erhua(also called r-retroflexion or retrofex suffixation)in the Putonghua proficiency test(PSC)is proposed.Based on the framework of the computer assisted pronunciation evaluation system,the present authors made an in-depth analysis of phonologic rules and acoustic characteristics of the Erhua,and solved the detection and evaluation of the Erhua as a typical classification problem.Then more representative acoustic features were selected and a variety of difierent classification algorithms were used.The results showed that the boosting classification and regression tree(Boosting CART)could make full use of the characteristics of the Erhua,and the classification accuracy was 92.41%.Based on further analysis of the acoustic feature group,it was found that formant,pronunciation confidence and duration were the most important clues of the Erhua,and these clues could effectively realize the automatic detection and evaluation of the Erhua.展开更多
Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best tim...Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.展开更多
The ever-increasing complexity of on-chip interconnection poses great challenges for the architecture of conventional system-on-chip(SoC) in semiconductor industry. The rapid development of process technology enables ...The ever-increasing complexity of on-chip interconnection poses great challenges for the architecture of conventional system-on-chip(SoC) in semiconductor industry. The rapid development of process technology enables the creation of stacked 3-dimensional(3 D) SoC by means of through-silicon-via(TSV). Stacked 3 D SoC testing consists of two major issues, test architecture optimization and test scheduling. This paper proposed game theory based optimization of test scheduling and test architecture to achieve win-win result as well as individual rationality for each player in a game. Game theory helps to achieve equilibrium between two correlated sides to find an optimal solution. Experimental results on handcrafted 3 D SoCs built from ITC'2 benchmarks demonstrate that the proposed approach achieves comparable or better test times at negligible computing time.展开更多
To provide pest technicians with a convenient way to recognize insects,a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT).The wing ...To provide pest technicians with a convenient way to recognize insects,a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT).The wing image of the lepidopteran insect is preprocessed to obtain the region of interest (ROI) whose position is then calibrated.The ROI is first segmented with the k-means algorithm into regions according to the color features,properties of all the segmented regions being used as a coarse level feature.The color image is then converted to a grayscale image,where DTCWT features are extracted as a fine level feature.The IRM scheme is undertaken to find K nearest neighbors (KNNs),out of which the nearest neighbor is searched by computing the Canberra distance of DTCWT features.The method was tested with a database including 100 lepidopteran insect species from 18 families and the recognition accuracy was 84.47%.For the forewing subset,a recognition accuracy of 92.38% was achieved.The results showed that the proposed method can effectively solve the problem of automatic species identification of lepidopteran specimens.展开更多
Aiming at the accuracy and error correction of cloud security situation prediction,a cloud security situation prediction method based on grey wolf optimization(GWO)and back propagation(BP)neural network is proposed.Fi...Aiming at the accuracy and error correction of cloud security situation prediction,a cloud security situation prediction method based on grey wolf optimization(GWO)and back propagation(BP)neural network is proposed.Firstly,the adaptive disturbance convergence factor is used to improve the GWO algorithm,so as to improve the convergence speed and accuracy of the algorithm.The Chebyshev chaotic mapping is introduced into the position update formula of GWO algorithm,which is used to select the features of the cloud security situation prediction data and optimize the parameters of the BP neural network prediction model to minimize the prediction output error.Then,the initial weights and thresholds of BP neural network are modified by the improved GWO algorithm to increase the learning efficiency and accuracy of BP neural network.Finally,the real data sets of Tencent cloud platform are predicted.The simulation results show that the proposed method has lower mean square error(MSE)and mean absolute error(MAE)compared with BP neural network,BP neural network based on genetic algorithm(GA-BP),BP neural network based on particle swarm optimization(PSO-BP)and BP neural network based on GWO algorithm(GWO-BP).The proposed method has better stability,robustness and prediction accuracy.展开更多
基金supported in part by National Natural Science Fundation of China under Grant No.61202458,61403109
文摘There are a lot of security issues in block cipher algorithm.Security analysis and enhanced design of a dynamic block cipher was proposed.Firstly,the safety of ciphertext was enhanced based on confusion substitution of S-box,thus disordering the internal structure of data blocks by four steps of matrix transformation.Then,the diffusivity of ciphertext was obtained by cyclic displacement of bytes using column ambiguity function.The dynamic key was finally generated by using LFSR,which improved the stochastic characters of secret key in each of round of iteration.The safety performance of proposed algorithm was analyzed by simulation test.The results showed the proposed algorithm has a little effect on the speed of encryption and decryption while enhancing the security.Meanwhile,the proposed algorithm has highly scalability,the dimension of S-box and the number of register can be dynamically extended according to the security requirement.
基金Supported by the National Natural Science Foundation of China(No.61305042,61202098)Projects of Center for Remote Sensing Mission Study of China National Space Administration(No.2012A03A0939)Science and Technological Research of Key Projects of Education Department of Henan Province of China(No.13A520071)
文摘Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.
基金Supported by the National Natural Science Foundation of China under Grant Nos 10825520 and 11162002the National Basic Research Program of China under Grant No 2012CB932400.
文摘A biconcave particle suspended in a Poiseuille flow is investigated by the multiple-relaxation-time lattice Boltzmann method with the Galilean-invariant momentum exchange method.The lateral migration and equilibrium of the particle are similar to the Segré-Silberberg effect in our numerical simulations.Surprisingly,two lateral equilibrium positions are observed corresponding to the releasing positions of the biconcave particle.The upper equilibrium positions significantly decrease with the increasing Reynolds number,whereas the lower ones are almost insensitive to the Reynolds number.Interestingly,the regular wave accompanied by nonuniform rotation is exhibited in the lateral movement of the biconcave particle.It can be attributed to the fact that the biconcave shape in various postures interacts with the parabolic velocity distribution of the Poiseuille flow.A set of contours illustrate the dynamic flow field when the biconcave particle has successive postures in a rotating period.
基金This work is supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Teaching reform project of higher education in Heilongjiang Province under Grant No.SJGY20210456in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038.
文摘The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.
基金supported by the Provincial Universities Basic Business Expense Scientific Research Projects of Heilongjiang Province(No.2021-KYYWF-0179)the Science and Technology Project of Henan Province(No.212102310991)+2 种基金the Opening Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security(No.AGK2015003)the Key Scientific Research Project of Henan Province(No.21A413001)the Postgraduate Innovation Project of Harbin Normal University(No.HSDSSCX2021-121).
文摘To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.
基金Project (No. 20070593) supported by the Scientific Research Fund of Zhejiang Provincial Education Department, China
文摘This paper presents a quantitative method for automatic identification of human pulse signals. The idea is to start with the extraction of characteristic parameters and then to construct the recognition model based on Bayesian networks. To identify depth, frequency and rhythm, several parameters are proposed. To distinguish the strength and shape, which cannot be represented by one or several parameters and are hard to recognize, the main time-domain feature parameters are computed based on the feature points of the pulse signal. Then the extracted parameters are taken as the input and five models for automatic pulse signal identification are constructed based on Bayesian networks. Experimental results demonstrate that the method is feasible and effective in recognizing depth, frequency, rhythm, strength and shape of pulse signals, which can be expected to facilitate the modernization of pulse diagnosis.
基金Project supported by the National Natural Science Foundation of China (Grant No 10765002)Guangxi Natural Science Foundation (Grant No 0542045)
文摘Boundary conditions (BCs) play an essential role in lattice Boltzmann (LB) simulations. This paper investigates several most commonly applied BCs by evaluating the relative L2-norm errors of the LB simulations for two-dimensional (2-D) Poiseuille flow. It is found that the relative L2-norm error resulting from FHML's BC is smaller than that from other BCs as a whole. Then, based on the FHML's BC, it formulates an LB model for simulating fluid flows in 2-D channel with complex geometries. Afterwards, the flows between two inclined plates, in a pulmonary blood vessel and in a blood vessel with local expansion region, are simulated. The numerical results are in good agreement with the analytical predictions and clearly show that the model is effective. It is expected that the model can be extended to simulate some real biologic flows, such as blood flows in arteries, vessels with stenosises, aneurysms and bifurcations, etc.
基金Sponsored by the Science and Technology Department Term of Education of Heilongjiang Province(Grant No. 10541098)
文摘To better evaluate the quality of software architecture,a metrics suite is proposed to measure the coupling of software architecture models,in which CBC is used to measure the coupling between components,CBCC is used to measure the coupling of transferring message between components,CBCCT is used to measure the coupling of software architecture,WCBCC is used to measure the coupling of transferring message with weight between components,and WCBCCT is used to measure the coupling of message transmission with weight in the whole software architecture. The proposed algorithm for the coupling metrics is applied to the design of serve software architecture. Analysis of an example validates the feasibility of this metrics suite.
基金Supported by the National Natural Science Funda-tion of China (60272024) ,the National Natural Science Foundation ofHenan Province (0411014100)
文摘The exponential growth of the Internet coupled with the increasing popularity of dynamically generated content on the World Wide Web, has created the need for more and faster Web servers capable of serving the over 100 million Internet users. To converge the control method has emerged as a promising technique to solve the Web QoS problem. In this paper, a model of adaptive session is presented and a session flow self-regulating algorism based on Kalman Filter are proposed towards Web Server. And a Web QoS self-regulating scheme is advanced. To attain the goal of on-line system identification, the optimized estimation of QoS parameters is fulfilled by utilizing Kalman Filter in full domain. The simulation results shows that the proposed scheme can guarantee the QoS with both robustness and stability .
文摘Feature selection is an active area in data mining research and development. It consists of efforts and contributions from a wide variety of communities, including statistics, machine learning, and pattern recognition. The diversity, on one hand, equips us with many methods and tools. On the other hand, the profusion of options causes confusion.This paper reviews various feature selection methods and identifies research challenges that are at the forefront of this exciting area.
基金The work was supported by The National Natural Science Foundation of China (Nos. 61402126, 61073043, 61370083).
文摘Mining important nodes in the complex network should not only consider the core nodes, but also consider the locations of the nodes in the network. Despite many researches on discovering important nodes, the importance of nodes in the structural holes is still ignored easily. Therefore, this paper proposes a method of local centrality measurement based on structural holes, which evaluates the nodes importance both by direct and indirect constraints caused by the lack of structural holes around the nodes. In this method, the attributes and locations of the nodes and their first-order and second-order neighbors are taken into account simultaneously. Deliberate attack simulation is carried out through selective deletion in a certain proportion of network nodes. Calculating the decreased ratio of network efficiency is to quantitatively describe the importance of nodes in before-and-after attacks. Experiments indicate that this method has more advantages to mine important nodes compared to clustering coefficient and k-shell decomposition method. And it is suitable for the quantitative analysis of the nodes importance in large scale networks.
基金supported by the National Natural Science Foundation of China (NSFC)under grant No.U1836102Anhui Science and Technology Key Special Program under the grant No.201903a050200162020 Domestic Visiting and Training Program for Outstanding Young Backbone Talents in Colleges and Universities under the grant No.gxgnfx2020132.
文摘The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms.The traditional ste-ganalysis detector is trained on the stego images created by a certain type of ste-ganographic algorithm,whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm.This phenomenon is called as steganographic algorithm mismatch in steganalysis.To resolve this pro-blem,we propose a deep learning driven feature-based approach.An advanced steganalysis neural network is used to extract steganographic features,different pairs of training images embedded with steganographic algorithms can obtain diverse features of each algorithm.Then a multi-classifier implemented as lightgbm is used to predict the matching algorithm.Experimental results on four types of JPEG steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 60496320, 60573010 and 60663001)the National Natural Science Foundation of Guangxi Province, China (Grant No. 0447032)the Youth Science Foundation of Guangxi Province of China (Grant No. 0640030)
文摘The current research works and the existing problems of terminological cycles in description logics are analyzed in this paper. Referring to the works of Baader F and Nebel B,we aim in a new direction. Firstly,description logic νL is defined,and the description graphs GT and GJ are redefined. A syntax condition for the satisfiability of membership relation is given. By using this syntax condition,we prove the following:The subsumption reasoning in νL with respect to gfp-model,lfp-model and descriptive model is polynomial.
基金supported by the National Natural Science Foundation of China(41071262,61171186)the Natural Science Foundation of Heilongjiang Province of China(F201321)
文摘An automatic detection and evaluation method of the Erhua(also called r-retroflexion or retrofex suffixation)in the Putonghua proficiency test(PSC)is proposed.Based on the framework of the computer assisted pronunciation evaluation system,the present authors made an in-depth analysis of phonologic rules and acoustic characteristics of the Erhua,and solved the detection and evaluation of the Erhua as a typical classification problem.Then more representative acoustic features were selected and a variety of difierent classification algorithms were used.The results showed that the boosting classification and regression tree(Boosting CART)could make full use of the characteristics of the Erhua,and the classification accuracy was 92.41%.Based on further analysis of the acoustic feature group,it was found that formant,pronunciation confidence and duration were the most important clues of the Erhua,and these clues could effectively realize the automatic detection and evaluation of the Erhua.
基金Natural Science Foundation of China(grant Nos.61473237,61202170,and 61402331)It is also supported by the Shaanxi Provincial Natural Science Foundation Research Project(2014JM2-6096)+3 种基金Tianjin Research Program of Application Foundation and Advanced Technology(14JCYBJC42500)Tianjin science and technology correspondent project(16JCTPJC47300)the 2015 key projects of Tianjin science and technology support program(No.15ZCZDGX00200)the Fund of Tianjin Food Safety&Low Carbon Manufacturing Collaborative Innovation Center.
文摘Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.
基金supported by the Support Project of High-Level Teachers in Beijing Municipal Universities in the Period of the13th Five-Year Plan(CIT&TCD 201704069)the Advanced Research Project for Science and Technology Development of Harbin Normal University(901-220601094)the Natural ScienceFoundationofHeilongjiangProvince(JJ2019LH0418)
文摘The ever-increasing complexity of on-chip interconnection poses great challenges for the architecture of conventional system-on-chip(SoC) in semiconductor industry. The rapid development of process technology enables the creation of stacked 3-dimensional(3 D) SoC by means of through-silicon-via(TSV). Stacked 3 D SoC testing consists of two major issues, test architecture optimization and test scheduling. This paper proposed game theory based optimization of test scheduling and test architecture to achieve win-win result as well as individual rationality for each player in a game. Game theory helps to achieve equilibrium between two correlated sides to find an optimal solution. Experimental results on handcrafted 3 D SoCs built from ITC'2 benchmarks demonstrate that the proposed approach achieves comparable or better test times at negligible computing time.
基金Project (No.2006AA10Z211) supported by the National High-Tech Research and Development Program (863) of China
文摘To provide pest technicians with a convenient way to recognize insects,a novel method is proposed to classify insect images by integrated region matching (IRM) and dual tree complex wavelet transform (DTCWT).The wing image of the lepidopteran insect is preprocessed to obtain the region of interest (ROI) whose position is then calibrated.The ROI is first segmented with the k-means algorithm into regions according to the color features,properties of all the segmented regions being used as a coarse level feature.The color image is then converted to a grayscale image,where DTCWT features are extracted as a fine level feature.The IRM scheme is undertaken to find K nearest neighbors (KNNs),out of which the nearest neighbor is searched by computing the Canberra distance of DTCWT features.The method was tested with a database including 100 lepidopteran insect species from 18 families and the recognition accuracy was 84.47%.For the forewing subset,a recognition accuracy of 92.38% was achieved.The results showed that the proposed method can effectively solve the problem of automatic species identification of lepidopteran specimens.
基金This work was supported by the National Natural Science Foundation of China(61202458,61403109)the Natural Science Foundation of Heilongjiang Province of China(LH2020F034).
文摘Aiming at the accuracy and error correction of cloud security situation prediction,a cloud security situation prediction method based on grey wolf optimization(GWO)and back propagation(BP)neural network is proposed.Firstly,the adaptive disturbance convergence factor is used to improve the GWO algorithm,so as to improve the convergence speed and accuracy of the algorithm.The Chebyshev chaotic mapping is introduced into the position update formula of GWO algorithm,which is used to select the features of the cloud security situation prediction data and optimize the parameters of the BP neural network prediction model to minimize the prediction output error.Then,the initial weights and thresholds of BP neural network are modified by the improved GWO algorithm to increase the learning efficiency and accuracy of BP neural network.Finally,the real data sets of Tencent cloud platform are predicted.The simulation results show that the proposed method has lower mean square error(MSE)and mean absolute error(MAE)compared with BP neural network,BP neural network based on genetic algorithm(GA-BP),BP neural network based on particle swarm optimization(PSO-BP)and BP neural network based on GWO algorithm(GWO-BP).The proposed method has better stability,robustness and prediction accuracy.
