Journal of China Universities of Posts and Telecommunications最新文献

This paper designs and implements the advertising recommending system based on user behavior under mobile Internet. By analyzing the user's online behavior, user profile that includes long-term interests and short-term interests can be obtained. In the end of this paper, the similarity between the behavior clusters and the advertisements is calculated, which is used to rank and select the most appropriate advertisements. The experiments show that the system can push the appropriate advertisements according to the user's different interest

Quality-of-service (QoS) describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. In this paper, a new hybrid user-location-aware prediction based on WAA (HUWAA) is proposed. The implicit neighbor search is optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem is validated in experiments on public real world datasets. The new algorithms outperform the existing IPCC, UPCC and WSRec algorithms.

Today, with rapid growth of apps (applications) for computers and mobile terminals, software update happens frequently. As a result, the flash crowd appearing in the early stage of a software patch release cannot be handled smoothly with abruptly arriving requests, thus considerable server bandwidth is needed. Unfortunately, it is still unknown how much bandwidth a content provider should provision to handle this case. In this paper, through measurements in one of the largest game patch distribution systems in China, we propose a simple but accurate user demand prediction model, since our finding that a highly predictable pattern of user requests exists in such a system. We further present the design of a pre-fetch based software update system. In this system, users who are idle and capable in hours before the patch release time could fetch the patches in advance and then serve other users requiring the patches. The design of this system mainly includes demand prediction and pre-fetch scale computation. After the deployment of this system, the contribution rate of users increases about 20% in the release day and is more stable than that before. Moreover, the peak of server bandwidth decreases by about 30%, which significantly saves bandwidth cost for providers.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal).

This article has been retracted at the request of the author.

Electromagnetic emission data from a simulation experiment referred to in the article cannot be depended on because some of the data was acquired from incorrect techniques taking place within the research. This has caused the author to reach incorrect conclusions and the experiment cannot be reproduced as published. The Editor and author have subsequently agreed to retract the article.

The existing online shopping researches on consumer reviews are mostly based on the attitude change model (ACM). Although the ACM is valuable, it is not easy to judge the trustworthiness of the reviews and measure the values of the reviews. Based on the online data acquisition technology, we have got the data of 360buy, a domestic large-scale business to customer (B2C) commerce website in China. With application of data-mining and the density clustering algorithm (DBSCAN), we focus on the intervals distribution and the synthetic value of consumer reviews. The distribution of review interval can be depicted by the power-law function which presents a monotonically increasing relationship between the power-exponent and the customers’ concerns with the commodity: the higher the exponent is, the more attention will be drawn. We also find that the value of online reviews can be measured by the expertise value, which is the attraction and the quality of the reviews. Based on the above results, we have constructed the online review-trust model and the synthetic value model. The relationship between the power-exponent and the consumer attention has played a vital role in the consumer attention to online-shopping, and then the synthetic value model will help people find out useful reviews more effectively.

Using the social information among users in recommender system can partly solve the data sparsely problems and significantly improve the performance of the recommendation system. However, the recommendation systems which using the users' social information have two main problems: the explicit user social connection information is not always available in real-world recommender systems, and the user social connection information is directly used in recommender systems when the user explicit social information is available. But as we know that the user social information is not all based on user interest, so this can introduce noise to the recommender systems. This paper proposes a social recommender system model called interest social recommendation (ISoRec). Based on probability matrix factorization (PMF), the model addresses the problems mentioned above by combining user-item rating matrix, explicit user social connection information and implicit user interest social connection information to make more accurately recommendation. In addition, the computational complexity of our algorithm is linear with respect to the number of observed data sets used in this algorithm, and can scalable to very large datasets.

The developing of network drive the developing of information systems in campus. More and more business information systems are built in1990's. But most of these systems in campus are developed on different platform without standard. And some data in business information system should be shared between different departments. So the target of this paper is to solve the problem of sharing data in campus the through the web service. Web service is a service-oriented architecture (SOA) to provider service for service consumer. But the traditional model of web service is not suitable in digital campus. This paper designs a new model of web service for digital campus environment. And then a public-data platform is designed to realize the data sharing in heterogeneous systems. Next, the database designing and data processing flow is introduced. Finally, some monitor information of platform is showed.

This paper proposes a method of blind multi-user detection algorithm based on signal sub-space estimation under the fading channels in the present of impulse noise. This algorithm adapts recursive least square (RLS) filter that can estimate the coefficients using only the signature waveform. In addition, to strengthen the ability of resisting the impulse noise, a new suppressive factor is induced, which can suppress the amplitude of the impulse, and improve the ability of convergence speed. Simulation results show that new RLS algorithm is more robust against consecutive impulse noise and have better convergence ability than conventional RLS. In addition, Compared to the least mean square (LMS) detector, the new robust RLS sub-space based method has better multi-address-inference (MAI) suppressing performance, especially, when channel degrades.

The amount of mobile malicious code is increasing faster every year and a serious security threat is posed by this. Hence, malware detection has become a critical topic in the field of computer security. This paper proposes a method to detect variants of known malware families in Android devices using simplify Dalvik instructions. This method is based on the sequence of instructions. A method is described in this paper to give the simplified description of each instruction and group them with n-gram patterns, which are set to be the malicious features. The result of the experiment shows that features extracted by this method are easy to be recognized, thus an effective way to detect variants of known malware families is provided.

Recently, the number of personal intelligent equipment increases rapidly which makes the internet protocol version 6 (IPv6) transition more imperative. Port control protocol (PCP) is one of the important IPv4-in-IPv6 tunnel technologies. Meanwhile, with the increasing demand to communicate or share files between terminal equipment, it is necessary to realize the peer to peer communication to support the transmission of data directly between the terminal equipment without a third-party server. In this paper, the structure of user access network and the deployment of the IPv6 transition technology based on IPv4-in-IPv6 tunnel are analyzed comprehensively, based on which the internet protocol security (IPSec) solution is chosen to protect the security of PCP message and the transport layer security (TLS) solution is chosen to protect the security of the peer-to-peer communications. A system for the authorization and distribution of digital certificates is designed for the TLS solution. Finally, the open source software Openswan is used to implement the IPSec solution, and the open source software OpenSSL is used to implement the TLS solution.