Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring.However,the existing methods are time-c...Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring.However,the existing methods are time-consuming for even moderate-scale networks.In this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries.The effectiveness of the proposed method is validated through synthetic and real-world networks.The experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.展开更多
In the past decades, various neural network models have been developed for modeling the behavior of human brain or performing problem-solving through simulating the behavior of human brain. The recurrent neural networ...In the past decades, various neural network models have been developed for modeling the behavior of human brain or performing problem-solving through simulating the behavior of human brain. The recurrent neural networks are the type of neural networks to model or simulate associative memory behavior of human being. A recurrent neural network (RNN) can be generally formalized as a dynamic system associated with two fundamental operators: one is the nonlinear activation operator deduced from the input-output properties of the involved neurons, and the other is the synaptic connections (a matrix) among the neurons. Through carefully examining properties of various activation functions used, we introduce a novel type of monotone operators, the uniformly pseudo-projectionanti-monotone (UPPAM) operators, to unify the various RNN models appeared in the literature. We develop a unified encoding and stability theory for the UPPAM network model when the time is discrete. The established model and theory not only unify but also jointly generalize the most known results of RNNs. The approach has lunched a visible step towards establishment of a unified mathematical theory of recurrent neural networks.展开更多
SQL injection poses a major threat to the application level security of the database and there is no systematic solution to these attacks.Different from traditional run time security strategies such as IDS and fire-wa...SQL injection poses a major threat to the application level security of the database and there is no systematic solution to these attacks.Different from traditional run time security strategies such as IDS and fire-wall,this paper focuses on the solution at the outset;it presents a method to find vulnerabilities by analyzing the source codes.The concept of validated tree is developed to track variables referenced by database operations in scripts.By checking whether these variables are influenced by outside inputs,the database operations are proved to be secure or not.This method has advantages of high accuracy and efficiency as well as low costs,and it is universal to any type of web application platforms.It is implemented by the software code vulnerabilities of SQL injection detector(CVSID).The validity and efficiency are demonstrated with an example.展开更多
This article presents the formal definition and description of popular topics on the Internet,analyzes the relationship between popular words and topics,and finally introduces a method that uses statistics and correla...This article presents the formal definition and description of popular topics on the Internet,analyzes the relationship between popular words and topics,and finally introduces a method that uses statistics and correlation of the popular words in traffic content and network flow characteristics as input for extracting popular topics on the Internet.Based on this,this article adapts a clustering algorithm to extract popular topics and gives formalized results.The test results show that this method has an accuracy of 16.7%in extracting popular topics on the Internet.Compared with web mining and topic detection and tracking(TDT),it can provide a more suitable data source for effective recovery of Internet public opinions.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(61471101)the National Natural Science Foundation of China(U1736205).
文摘Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring.However,the existing methods are time-consuming for even moderate-scale networks.In this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries.The effectiveness of the proposed method is validated through synthetic and real-world networks.The experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.
基金Supported by the National Basic Research Program of China (973 Program) (Grant No. 2007CB311002), the National Nature Science Foundation of China (Grant No. 61075054) and the Fundamental Research Funds for the Central Universities (Grant No. xjj20100087)
文摘In the past decades, various neural network models have been developed for modeling the behavior of human brain or performing problem-solving through simulating the behavior of human brain. The recurrent neural networks are the type of neural networks to model or simulate associative memory behavior of human being. A recurrent neural network (RNN) can be generally formalized as a dynamic system associated with two fundamental operators: one is the nonlinear activation operator deduced from the input-output properties of the involved neurons, and the other is the synaptic connections (a matrix) among the neurons. Through carefully examining properties of various activation functions used, we introduce a novel type of monotone operators, the uniformly pseudo-projectionanti-monotone (UPPAM) operators, to unify the various RNN models appeared in the literature. We develop a unified encoding and stability theory for the UPPAM network model when the time is discrete. The established model and theory not only unify but also jointly generalize the most known results of RNNs. The approach has lunched a visible step towards establishment of a unified mathematical theory of recurrent neural networks.
基金supported by the National Natural Science Foundation of China (Grant No.60574087)the Hi-Tech Research and Development Program of China (Nos.2007AA01Z475,2007AA01Z480,2007AA01Z464)the 111 International Collaboration Program of China.
文摘SQL injection poses a major threat to the application level security of the database and there is no systematic solution to these attacks.Different from traditional run time security strategies such as IDS and fire-wall,this paper focuses on the solution at the outset;it presents a method to find vulnerabilities by analyzing the source codes.The concept of validated tree is developed to track variables referenced by database operations in scripts.By checking whether these variables are influenced by outside inputs,the database operations are proved to be secure or not.This method has advantages of high accuracy and efficiency as well as low costs,and it is universal to any type of web application platforms.It is implemented by the software code vulnerabilities of SQL injection detector(CVSID).The validity and efficiency are demonstrated with an example.
基金was supported by the National Natural Science Foundation of China (Grant No.60574087)the Hi-Tech Research and Development Program of China (2007AA01Z475,2007AA01Z480,2007A-A01Z464)the 111 International Collaboration Program of China.
文摘This article presents the formal definition and description of popular topics on the Internet,analyzes the relationship between popular words and topics,and finally introduces a method that uses statistics and correlation of the popular words in traffic content and network flow characteristics as input for extracting popular topics on the Internet.Based on this,this article adapts a clustering algorithm to extract popular topics and gives formalized results.The test results show that this method has an accuracy of 16.7%in extracting popular topics on the Internet.Compared with web mining and topic detection and tracking(TDT),it can provide a more suitable data source for effective recovery of Internet public opinions.