USERS' TWO-DIMENSIONAL INTEREST AND ITS STORAGE METHOD

The users' interest can be mined from the web cache and can be used widely. The interest can be specialized by the two-tuple (term, weight) in the simple interest model, in which the association relations are not mined, and then the interest cannot be associated in expressing the users' interest. Based on analyzing the WWW cache model, this letter brings forward a two-dimensional interest model and gives the interrelated methods on how to store the two-dimensional interest model effectively.     

A Multilevel Security Model for Private Cloud

Towards data leak caused by misoperation and malicious inside users, we proposed a multilevel se- curity model based on Bell-lapadula （BLP） model. In our model each subject was assigned with a security level. Sub- jects can read objects only when their security levels are not less than objects＇ security levels, and subjects can write objects only when their security levels are not more than objects＇ security levels. The current security level in our model can be dynamically changed when users read sensi- tive data, since users can access data with different security levels in private cloud. Our model use mandatory access control method to control user＇s operation and can guar- antee that users can not leak sensitive data after they read them. Our model can be proved secure by mathematical method, and we implemented a prototype system of our model and the experimental results show that it is secure.     

基于卢因行为模式的个体对移动业务需求影响因素研究

How to find main influence factors of individuals to mobile service demand is investigated. The empirical research is conducted in the sample of high-value customers in China mobile market. Based on Lewin behavior model, this paper establishes factors-matrix from personal and environmental dimensions. Relationships among multiple factors are tested in the structural equation model and their impacts on customers狆demands are elaborated. Findings indicate that operational convenience and business brand image have significant effects on sample users狆demands. Furthermore, annual income, gender, occupation, the needs of access to information and the needs of enriching and improving social relationships are also important factors for high-value users. The results may provide further insights into mobile service demand and the model can be popularized to other behavior researches.     

Mining User Interest in Microblogs with a User-Topic Model

Microblogs have become an important platform for people to publish, transform information and acquire knowledge. This paper focuses on the problem of discovering user interest in microblogs. In this paper, we propose a topic mining model based on Latent Dirichlet Allocation （LDA） named user-topic model. For each user, the interests are divided into two parts by different ways to generate the microblogs： original interest and retweet interest. We represent a Gibbs sampling implementation for inference the parameters of our model, and discover not only user＇s original interest, but also retweet interest. Then we combine original interest and retweet interest to compute interest words for users. Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task. And we find that original interest and retweet interest are similar and the topics of interest contain user labels. The interest words discovered by our model reflect user labels, but range is much broader.     

A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator

The existing collaborative recommendation algorithms have lower robustness against shilling attacks. With this problem in mind, in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator. Firstly, we propose a k-distance- based method to compute user suspicion degree （USD）. The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model. The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users. Then, Tukey M-estimator is introduced to construct robust matrix factorization model, which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix. Finally, a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model. Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.     

A Service Mining Scheme Based on Semantic for Internet of Things

As the development of Internet of things （IOT）, massive sensors have been deployed as the public infrastructure. With the development of in-depth applica- tions in IOT, service discovery and composition are chal- lenges to the end users. To handle this challenge, we de- velop a service mining scheme based on semantic for IOT to provide users with interesting composite services. In this scheme~ services can be combined and recommended to users actively according to the calculation of service sim- ilarity and an updatable semantic database. By the results of service similarity, useless compositions can be filtered out so that energy consumption on service flooding will be reduced. The update strategy of semantic database is also given out, by which the composite services can keep up with time and be more applicative. The benefits of the proposed method are that all operations such as can culating, filtering, and updating are simple enough to be performed in sensor networks.     

Cache Hit Ratio Maximization in Device-to-Device Communications Overlaying Cellular Networks

This paper investigates the content placement problem to maximize the cache hit ratio in device-to-device(D2D)communications overlaying cellular networks.We consider offloading contents by users themselves,D2D communications and multicast,and we analyze the relationship between these offloading methods and the cache hit ratio.Based on this relationship,we formulate the content placement optimization as a cache hit ratio maximization problem,and propose a heuristic algorithm to solve it.Numerical results demonstrate that the proposed scheme can outperform existing schemes in terms of the cache hit ratio.     

Design of in-network caching scheme in CCN based on grey relational analysis

In-network caching is one of the most important issues in content centric networking （CCN）, which may extremely influence the performance of the caching system. Although much work has been done for in-network caching scheme design in CCN, most of them have not addressed the multiple network attribute parameters jointly during caching algorithm design. Hence, to fill this gap, a new in-network caching based on grey relational analysis （GRA） is proposed. The authors firstly define two newly metric parameters named request influence degree （RID） and cache replacement rate, respectively. The RID indicates the importance of one node along the content delivery path from the view of the interest packets arriving The cache replacement rate is used to denote the caching load of the node. Then combining hops a request traveling from the users and the node traffic, four network attribute parameters are considered during the in-network caching algorithm design. Based on these four network parameters, a GRA based in-network caching algorithm is proposed, which can significantly improve the performance of CCN. Finally, extensive simulation based on ndnSIM is demonstrated that the GRA-based caching scheme can achieve the lower load in the source server and the less average hops than the existing the betweeness （Betw） scheme and the ALWAYS scheme.     

A NEW ADMISSION CONTROL APPROACH BASED ON PREDICTION

Admission control plays an important role in providing QoS to network users.Mo-tivated by the measurement-based admission control algorithm,this letter proposed a new ad-mission control approach for integrated service packet network based on traffic prediction .In the letter ,FARIMA(p,d,q,)models in the admission control algorithm is deployed.A method to simplify the FARIMA model fitting procedure and hence to reduce the time of traffic modeling and prediction is suggested.The feasibility-study experiments show that FARIMA models which have less number of parameters can be used to model and predict actual traffic on quite a large time scale.Simulation results validate the promising approach.     

Hybrid model of inter-stage spectrum trading in multistage game-theoretic framework

Dynamic spectrum access(DSA),consisting of spectrum sharing and spectrum trading stage,becomes a promising approach to increase the efficiency of spectrum usage and system performance.In this paper,from the perspective of individual interest optimization,we focus on strategy adaptation of network users and their interaction in spectrum trading process.Considering adverse effects on decision-making accuracy and the fairness among network users via local information acquirement,a hybrid game model based on global information of relevant spectrum is proposed to formulate intelligent behaviors of both primary and secondary users.Specifically,by using the evolutionary game theory,a spectrum-selection approach for the evolution process of secondary users is designed to converge to the evolutionary equilibrium gradually.Moreover,competition among primary users is modeled as a non-cooperative game and an iterative algorithm is employed to achieve the Nash equilibrium.The simulation results show that the proposed hybrid game model investigates network dynamics under different network parameter settings.     

一种考虑自热效应的互连线缓冲器插入优化模型

Considering the self-heating effect,an accurate expression for the global interconnection resistance per unit length in terms of interconnection wire width and spacing is presented.Based on the proposed resistance model and according to the trade-off theory,a novel optimization analytical model of delay,power dissipation and bandwidth is derived.The proposed optimal model is verified and compared based on 90 nm,65 nm and 40 nm CMOS technologies.It can be found that more optimum results can be easily obtained by the proposed model.This optimization model is more accurate and realistic than the conventional optimization models,and can be integrated into the global interconnection design of nano-scale integrated circuits.     

A Layout Method of Space-Based Pseudolite System Based on GDOP Geometry

The pseudolite system can be used to provide positioning and timing service for users in a specific area. In order to provide better positioning and timing service, a good geometric configuration needs to be formed for the pseudolite system. For the problem of pseudolite system deployment, the average and mean square values of geometric dilution of precision(GDOP)are optimized in this paper for users in a target area. We proposed a space-based pseudolite deployment method based on GDOP geometry,...     

MTBAC： A Mutual Trust Based Access Control Model in Cloud Computing

As a new computing mode, cloud computing can provide users with virtualized and scalable web services, which faced with serious security challenges, however. Access control is one of the most important measures to ensure the security of cloud computing. But applying traditional access control model into the Cloud directly could not solve the uncertainty and vulnerability caused by the open conditions of cloud computing. In cloud computing environment, only when the security and reliability of both interaction parties are ensured, data security can be effectively guaranteed during interactions between users and the Cloud. Therefore, building a mutual trust relationship between users and cloud platform is the key to implement new kinds of access control method in cloud computing environment. Combining with Trust Management（TM）, a mutual trust based access control （MTBAC） model is proposed in this paper. MTBAC model take both user＇s behavior trust and cloud services node＇s credibility into consideration. Trust relationships between users and cloud service nodes are established by mutual trust mechanism. Security problems of access control are solved by implementing MTBAC model into cloud computing environment. Simulation experiments show that MTBAC model can guarantee the interaction between users and cloud service nodes.     

Cryptomining Malware Detection Based on Edge Computing-Oriented Multi-Modal Features Deep Learning

In recent years,with the increase in the price of cryptocurrencies,the number of malicious cryptomining software has increased significantly.With their powerful spreading ability,cryptomining malware can unknowingly occupy our resources,harm our interests,and damage more legitimate assets.However,although current traditional rule-based malware detection methods have a low false alarm rate,they have a relatively low detection rate when faced with a large volume of emerging malware.Even though common machine learning-based or deep learning-based methods have certain ability to learn and detect unknown malware,the characteristics they learn are single and independent,and cannot be learned adaptively.Aiming at the above problems,we propose a deep learning model with multi-input of multi-modal features,which can simultaneously accept digital features and image features on different dimensions.The model in turn includes parallel learning of three sub-models and ensemble learning of another specific sub-model.The four sub-models can be processed in parallel on different devices and can be further applied to edge computing environments.The model can adaptively learn multi-modal features and output prediction results.The detection rate of our model is as high as 97.01%and the false alarm rate is only 0.63%.The experimental results prove the advantage and effectiveness of the proposed method.     

Reservation Based Optimal Parking Lot Recommendation Model in Internet of Vehicle Environment

In order to solve the problem that the drivers can＇t find the optimal parking lot timely, a reservation based optimal parking lot recommendation model in Internet of Vehicle （IoV） environment is designed. Based on the users oriented parking information recommendation system, the model considers subjective demands of drivers comprehensively, makes a deeply analysis of the evaluation indicators. This recommendation model uses a phased selection method to calculate the optimal objective parking lot. The first stage is screening which based on the users＇ subjective parking demands; the second stage is processing the candidate parking lots through multiple attribute decision making. Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot, save the driver＇s parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.     

Heterogeneous Network Selection Optimization Algorithm Based on a Markov Decision Model

A network selection optimization algorithm based on the Markov decision process(MDP)is proposed so that mobile terminals can always connect to the best wireless network in a heterogeneous network environment.Considering the different types of service requirements,the MDP model and its reward function are constructed based on the quality of service(QoS)attribute parameters of the mobile users,and the network attribute weights are calculated by using the analytic hierarchy process(AHP).The network handoff decision condition is designed according to the different types of user services and the time-varying characteristics of the network,and the MDP model is solved by using the genetic algorithm and simulated annealing(GA-SA),thus,users can seamlessly switch to the network with the best long-term expected reward value.Simulation results show that the proposed algorithm has good convergence performance,and can guarantee that users with different service types will obtain satisfactory expected total reward values and have low numbers of network handoffs.     

Simultaneous image classification and annotation based on probabilistic model

The paper proposes a novel probabilistic generative model for simultaneous image classification and annotation.The model considers the fact that the category information can provide valuable information for image annotation.Once the category of an image is ascertained,the scope of annotation words can be narrowed,and the probability of generating irrelevant annotation words can be reduced.To this end,the idea that annotates images according to class is introduced in the model.Using variational methods,the approximate inference and parameters estimation algorithms of the model are derived,and efficient approximations for classifying and annotating new images are also given.The power of our model is demonstrated on two real world datasets:a 1 600-images LabelMe dataset and a 1 791-images UIUC-Sport dataset.The experiment results show that the classification performance is on par with several state-of-the-art classification models,while the annotation performance is better than that of several state-of-the-art annotation models.     

Optical Fiber Type Slide Tactile Sensor Used for Underwater Robot

Bexause of the special underwater environment, many sensors used well in robots working in space or on the hand can not be used in the underwater.So an optical fiber type slide tactile sensor is designed by the inner modulation mechanism of the intensity type optical fiber.The principle and structure of the sensor are introduced in detail.The static and dynamic characteristics are analyzed theoretically and experimentally.The dynamic characteristic model is built and the simulation is made by using genetic algorithm based on neural network.In order to use the sensor perfectly, the recognition model of the sensor is built on the basis of the principle of “inverse solution”using neural networks.The control precision and sensitivity of the manipulator are improved.     

Variational Image Restoration and Decomposition in Shearlet Smoothness Spaces

We present the shearlet-based variational model for image restoration and decomposition.The new model can be seen as generalizations of Daubechies-Teschke's model.By using regularization term in shearlets smoothness spaces,and writing the problem in a shearlet framework,we obtain elegant shearlet shrinkage schemes.Furthermore,the model allows us to incorporate general bounded linear blur operators into the problem.The experiments on denoising,deblurring and decomposition of images show that our algorithm is very efficient.     

Development of Trusted Network and Challenges It Faces

As the information network plays a more and more important role globally, the traditional network theories and technologies, especially those related to network security, can no longer meet the network development requirements. Offering the system with secure and trusted services has become a new focus in network research. This paper first discusses the meaning of and aspects involved in the trusted network. According to this paper, the trusted network should be a network where the network’s and users’ behaviors and their results are always predicted and manageable. The trustworthiness of a network mainly involves three aspects: service provider, information transmission and terminal user. This paper also analyzes the trusted network in terms of trusted model for network/user behaviors, architecture of trusted network, service survivability and network manageability, which is designed to give ideas on solving the problems that may be faced in developing the trusted network.