To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modi...To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.展开更多
For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs)....For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations.Inspired from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for EMOPs.More specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate model.The extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization algorithms.The experimental results show that MOEA-EC outperforms the compared algorithms.展开更多
We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchic...We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.展开更多
In this paper, we discuss building an information dissemination model based on individual behavior. We analyze the individual behavior related to information dissemination and the factors that affect the sharing behav...In this paper, we discuss building an information dissemination model based on individual behavior. We analyze the individual behavior related to information dissemination and the factors that affect the sharing behavior of individuals, and we define and quantify these factors. We consider these factors as characteristic attributes and use a Bayesian classifier to classify individuals. Considering the forwarding delay characteristics of information dissemination, we present a random time generation method that simulates the delay of information dissemination. Given time and other constraints, a user might not look at all the information that his/her friends published. Therefore, this paper proposes an algorithm to predict information visibility, i.e., it estimates the probability that an individual will see the information. Based on the classification of individual behavior and combined with our random time generation and information visibility prediction method, we propose an information dissemination model based on individual behavior. The model can be used to predict the scale and speed of information propagation. We use data sets from Sina Weibo to validate and analyze the prediction methods of the individual behavior and information dissemination model based on individual behavior. A previously proposedinformation dissemination model provides the foundation for a subsequent study on the evolution of the network and social network analysis. Predicting the scale and speed of information dissemination can also be used for public opinion monitoring.展开更多
基金supported in part by the National Natural Science Foundation of China (NO. 61401004, 61271233, 60972038)Plan of introduction and cultivation of university leading talents in Anhui (No.gxfxZ D2016013)+3 种基金the Natural Science Foundation of the Higher Education Institutions of Anhui Province, China (No. KJ2010B357)Startup Project of Anhui Normal University Doctor Scientific Research (No.2016XJJ129)the US National Science Foundation under grants CNS1702957 and ACI-1642133the Wireless Engineering Research and Education Center at Auburn University
文摘To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.
文摘For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations.Inspired from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for EMOPs.More specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate model.The extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization algorithms.The experimental results show that MOEA-EC outperforms the compared algorithms.
基金supported by the National High Technology Research and Development Program of China under Grant No.2012AA011104the Fundamental Research Funds for the Center Universities
文摘We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.
基金sponsored by the National Natural Science Foundation of China under grant number No. 61100008 the Natural Science Foundation of Heilongjiang Province of China under Grant No. LC2016024
文摘In this paper, we discuss building an information dissemination model based on individual behavior. We analyze the individual behavior related to information dissemination and the factors that affect the sharing behavior of individuals, and we define and quantify these factors. We consider these factors as characteristic attributes and use a Bayesian classifier to classify individuals. Considering the forwarding delay characteristics of information dissemination, we present a random time generation method that simulates the delay of information dissemination. Given time and other constraints, a user might not look at all the information that his/her friends published. Therefore, this paper proposes an algorithm to predict information visibility, i.e., it estimates the probability that an individual will see the information. Based on the classification of individual behavior and combined with our random time generation and information visibility prediction method, we propose an information dissemination model based on individual behavior. The model can be used to predict the scale and speed of information propagation. We use data sets from Sina Weibo to validate and analyze the prediction methods of the individual behavior and information dissemination model based on individual behavior. A previously proposedinformation dissemination model provides the foundation for a subsequent study on the evolution of the network and social network analysis. Predicting the scale and speed of information dissemination can also be used for public opinion monitoring.
