Aiming at the problem that the existed host Security Risk Assessment(SRA) index is not complete,difficult to operate and the result is hard to understand,this paper proposes a method for host SRA based on Analytic Hierarchy Process(AHP) and cloud model.It integrates the national information security classified protection policy,designs a multi-level index system using AHP and cloud model to assess the risk of host security fuzzily and quantifiably.Experimental results show that the proposed method achieves a satisfactory result in quantitative evaluation of complex host system,and effectively improves the accuracy and scientificity of the detection results.
Audit of data possession is the key technique of ensuring the cloud data integrity,but the concurrent update operation makes the audit system efficiency dramatically decreased.Aiming at this problem,this paper proposes an audit method of cloud storage data possession supporting concurrent update.By improving Merkle Hash Tree(MHT) structure,it makes multiple updated requests of MHT intermediate node delay execution and generates updated state tree.It separates multiple leaf nodes and combines them to execute,which can significantly eliminate duplicate nodes in MHT,and effectively reduce the update cost of cloud storage data integrity audit system.Both formal analysis and the experimental results indicate that the proposed method can efficiently reduce the number of updating MHT nodes,and improve the update efficiency for the audit of cloud storage data possession.
To solve the no-structuring problem of Term Frequency-Inverse Document Frequency(TF-IDF) model in topical crawler,this paper proposes a novel topical crawler based on Classified Keyword Term Frequency (CKTF) model.A Webpage is divided into five parts,according to the Webpage document structure characteristics and the distribution information of topical works.Geopolitical topical words and their correlative rates are calculated based on Wikipedia and Sougou internet corpus.Then,Webpage vector classification are learned and classified by Support Vector Machine(SVM).Experimental result shows that geopolitical topical crawler based on CKTF model can mine the rich meaning of the geopolitical topic,and measure effectively correlation between a Webpage and a topic with a higher accuracy and stability.
To solve the problems of the difficulties of most smart home control system installation deployment and high cost,the paper puts forward a kind of family-oriented WiFi smart power plug scheme.This scheme uses an embedded PIC32 microcontroller,uses applications of smart phones which are based on Android,and uses IP to connect remote access control and devices which link smart power plug.It utilizes Representational State Transfer(REST) based Web services to monitor home appliances.The user uses point to point mode and basic networking mode to visit smart power plug.Experimental results show that the hardware intelligent of smart power plug is simple,the cost is low,reliability is high,and it is easy to extend.
Based on the typical M form of the block volatility,this paper puts forward a ridge regression stock market trend prediction algorithm based on causality.Stock form reflects the stock fluctuations of energy change.According to the characteristics of the fluctuations in the form of M introducing energy ideas,based on edge,peaks and troughs in the form of M nodes,it builds a Bayesian network structure model in the form of M.By using Markov blanket algorithm and asymmetric information entropy,it gets a local causal structure in the form of M.The introduction of the strength of causal metrics is introduced to the M shape causality in ridge regression model for its stock market trend prediction of the model through stock form and causation of energy fluctuations,which can effectively find the abrupt change point of the stock market.Results on real data sets show that,compared with ridge regression algorithm and radial basis neural network algorithm,the proposed algorithm has better prediction effect.
In a hybrid scene of local and remote rendering,in order to make the scene more realistic,a shadow rendering method is proposed based on the characteristics of simple mobile terminal scene,and strong object interaction.On the server side,a combination of improved Parallel Split Shadow Mapping(PSSM)and Percentage Closer Filtering(PCF) algorithm is used to improve the unity of the reality and real-time shadow rendering.A combination of classic Shadow Mapping(SM) and PCF algorithm is used to draw a shadow on mobile terminals.Then the integration is done on mobile terminal based on the actual relative position of the two scenes which are processed on server side and mobile side.Experimental results show that the seamless fusion of scene can be realized on the mobile terminal through the proposed method,and it brings higher efficiency when drawing graph.
Syntactic and semantic composition has problem in achieving the integrity,validity and practicability of simulation component composition.In view of this,this paper analyzes pragmatic composition and simulation context,and proposes the formal definition of Simulation Context Space(SCS) as well as the approach of static pragmatic composition verification based on SCS matching index calculation,including concept semantic matching index and value matching index.It analyzes the features of the Extended Finite State Machine(EFSM),and designs the formal description of Simulation Component Model(SCM) including context constraint based on EFSM.It develops the mapping between SCM and Color Petri Net (CPN) model,so the dynamic pragmatic composition verification of the composed model can be done by CPN Tools.Application result of the static and dynamic analysis of the simulation component composition shows that,it can provide quantifiable,intuitive basis for component discovery,model optimization,and composition verification on pragmatic level.
Aiming at the collision problem of Radio Frequency Identification(RFID) multi-tag identification system,the multi-cycle collision tree algorithm for RFID tag identification based on parity packet is proposed.It divides all the identification tags into two groups according to the parity by the sum of each tag’s bits in order to reduce the probability of collision.After making use of odd group or even group and the respond of the first bit either 0 or 1,it can be divided into two sub periods in response to reader queries.Using the feature of binary either 0 or 1 and properties of parity,two collision bits can be identified by the reader in one time.Mathematics analysis and simulation results show that the proposed algorithm can reduce the times of reader request and improve recognition efficiency,compared with Collision Tree(CT),Query Tree(QT) and Binary Search(BS) algorithms.