Applied Mechanics and Materials Vols. 719-720

Abstract: Customer Churn is a pesky problem that continues to haunt telecommunication companies. Social influence analysis has recently been introduced in churn prediction, motivated by the fact thatsome users can churn due to accumulated churn influence that they’ve received from other churners.We’ve collected call data records of about 10 thousands mobile phone users of one the largest mobile network operators in China,and have built a Multi-relational call network which is a graph constructed by mobile phone users considered as nodes and the interactive calls between them considered as the relationships or edges. We’veapplied Linear Threshold (LT) to model the diffusion of churners influence in theobtainedsocial network, in order to analyze the relevance of social affinities in the diffusion of churn influence.The results indicate that churn influence diffusion depends not only on the number of initial churners but also on the existingaffinities between users.

Abstract: In this paper, based on the analysis of ordinary visual modeling method, with the purpose of instantly and conveniently providing a more effective modeling method, a visual modeling method based on .NET is proposed. The visual modeling method based on .NET is easier to implement, because of the. NET design environment support. Besides, its interactivity and strong operability make software develop more conveniently.

Abstract: Hybrid clouds integrate different cloud solutions. Its inherent complexity and short of standard urge for a careful analysis, systematizing and understanding of monitoring. In this context, this paper provides a deep insight into hybrid cloud monitoring. It proposes a layered monitoring model for hybrid clouds, identifying the multiple layers of monitoring, focusing on physical infrastructure layer, virtual infrastructure layer, network, application/service layer, while combining the perspectives of service providers and clients. This process involves the identification of relevant parameters and metrics for each layer. Due to its flexibiliity and intelligence, using Agent technology, an agent-based monitoring architecture is presented. It enables to eliminate the complexity among different cloud platforms.This study contributes to achieve a clearer and more efficient approach to hybrid cloud monitoring.

Abstract: With the thorough development and utilization of electronic government information resources, some phenomena are presented, such as: a large amount of data and data concentration, insufficient sharing of information resources and unsafe data exchange and so on. The construction and operation of traditional e-government data management can't meet those needs. Cloud computing is mainly aimed at intensive work load of mass data. It can allocate resources dynamically and adjust the load. This paper mainly deals with how to use cloud computing model to reduce the cost and risk of information sharing in e-government data management, so as to improve the operational efficiency of e-government data management.

Abstract: Instrument discovery as defined by the LXI standard implements the VXI-11 instrument discovery protocol. This mechanism allows the user to quickly discover instruments on a local subnet. The paper describes how to implement the auto-discovery RPC server compatible with LXI standard. For better understanding auto discovery process, it starts by introducing the VXI-11 protocol stack and follows with the deep analysis of auto discovery process. Finally, it discusses how to implement the RPC server. The RPC server supports both windows XP and windows CE operating system. The auto-discovery RPC server passes the tests on the Elgar LXI Browser, Agilent IO Library Suite and NI Measurement and Automation Explorer.

Abstract: In this paper, gives the concept of functional dependency for XML based on Tree Tuples, furthermore, infers a inference rules set of functional dependency by using Armstrong axiom-system of database system.

Abstract: Cloud computing is computing tasks distribution resources of a large number of computers in the subnet, to provide users with cheap and efficient computing power, storage capacity and service capabilities. Data mining is to find useful information in large data repository. Frequent flow of large amounts of data quickly and accurately find important basis for forecasting and decision, therefore, under the cloud computing environment parallelization frequent item data mining strategy to provide efficient solutions to store and analyze vast amounts of data has important theoretical significanceand application value.

Abstract: Beginning with the meaning and characteristics of cloud computing, author discusses the library is integrated with cloud computing technology. Cloud computing plays a very important role in resource development and services for the library. Cloud computing provide the sharing of hardware and software on the data storage, data management, it also has the capabilities with the database resource sharing, unified digital resources, one-stop entrance, a full range of user interactive services, the expansion of the complex network applications and services. This paper proposes several suggestions about the characteristics of cloud computing technology in library applications.

Abstract: Semantic Gap problem is the essence of the SQL Injection Attacks vulnerability in Web applications. Web application loses the semantic information while the SQL statement is constructed dynamically. This paper analyzes the cause of the SQLIA vulnerability. And then it analyzes several suggested techniques, such as the filtering techniques and the static analysis, and points out their drawbacks in the SOLIA prevention, which leads to the conclusion that the key problem for the eradication of SQLIA is to solve the semantic gap problem causing by the unstructured SQL statement in the process of constructing a Web system dynamically.

Abstract: Because of RF power amplifier’s nonlinearity and the discrete distribution of electronic components value, it is possible to form individual RF fingerprints. How to identify individual RF fingerprints is the key problem because received RF signals are weak to be affected by channel noises. In order to identify steady RF signal fingerprints, this paper proposed a tensors-based method for obtaining identification patterns from high order cummulants. Firstly, high order cummulants tensors of received RF signal were formed as distinguished patterns of RF Fingerprints. Secondly, these cummulants tensors were passed to classifiers as training data and testing data. The RF Fingerprint classifiers based on SVM and RBF networks are compared with their performance. Finally, the identification results of two classifiers were obtained. The results showed that the method is effective for RF Fingerprints Identification.