With the popularity of mobile smart devices, Mobile Crowd Sensing (MCS) has been widely used while facing serious privacy leaks. Focusing on the issue that the existing original data privacy protection scheme is unable to resist collusion attacks and reduce the perception data availability, a Data Privacy Protection algorithm based on Mobile Node (DPPMN) was proposed. Firstly, the node manager in DPPMN was used to establish an online node list and send it to the source node. An anonymous path for data transmission was built by the source node through the list. Then, the data was encrypted by using paillier encryption scheme, and the ciphertext was uploaded to the application server along the path. Finally, the required perception data was obtained by the server using ciphertext decryption. The data was encrypted and decrypted during transmission, making sure that the attacker was not able to wiretap the content of the perception data and trace the source of the data along the path. The DPPMN ensures that the application server can access the original data without the privacy invasion of the nodes. Theoretical analysis and experimental results show that DPPMN has higher data security with increasing appropriate communication, and can resist collusion attacks without affecting the availability of data.

Cyber Physical System (CPS) involves the integration and collaboration of various computing models. Concerning the problems of inconsistent CPS design methods, poor plasticity, high complexity and difficulty in collaborative modeling and verification, a structured and descriptive heterogeneous component model was proposed. Firstly, the model was constructed by a unified component modeling method to solve the problem that the model was not open. Then, eXtensible Markup Language (XML) was used to realize the standard description of all kinds of components to resolve the inconsistency and non-extensibility of different computing model description languages. Finally, the collaborative simulation verification method of multi-level open component model was used to realize the simulation verification to solve the non-collaboration problem of verification. The medical thermostat was modeled, described and simulated by the general component modeling method, the XML component standard description language and the verification tool platform XModel. The case of medical thermostat shows that, the proposed model-driven process of building reconfigurable heterogeneous components and confirming their design correctness supports the collaborative design of cyber physics and the correction while constructing, avoiding repeated modifications when problems are found in the process of system implementation.

A Quantum-inspired Migrating Birds Co-Optimization (QMBCO) algorithm was proposed for minimizing the makespan in Lot-streaming Flow shop Scheduling Problem (LFSP). Firstly, the quantum coding based on Bloch coordinates was applied to expand the solution space. Secondly, an initial solution improvement scheme based on Framinan-Leisten (FL) algorithm was used to makeup the shortage of traditional initial solution and construct the random initial population with high quality. Finally, Migrating Birds Optimization (MBO) and Variable Neighborhood Search (VNS) algorithm were applied for iteration to achieve the information exchange between the worse individuals and superior individuals in proposed algorithm to improve the global search ability. A set of instances with different scales were generated randomly, and QMBCO was compared with Discrete Particle Swarm Optimization (DPSO), MBO and Quantum-inspired Cuckoo Co-Search (QCCS) algorithms on them. Experimental results show that compared with DPSO, MBO and QCCS, QMBCO has the Average Relative Percentage Deviation (ARPD) averagely reduced by 65%, 34% and 24% respectively under two types of running time, verifying the effectiveness and efficiency of the proposed QMBCO algorithm.

In the big data environment, the real-time processing requirement of data stream is high, and data calculations require persistence and high reliability. Distributed Data Stream Processing System (DDSPS) can solve the problem of data stream processing in big data environment. Besides, it has the advantages of scalability and fault-tolerance of distributed system, and also has high real-time processing capability. Four subsystems and their key technologies of the DDSPS based on big data were introduced in detail. The different technical schemes of each subsystem were discussed and compared. At the same time, an example of data stream processing system structure to detect Distributed Denial of Service (DDoS) attacks was introduced, which can provide the technical reference for data stream processing theory research and application technology development under big data environment.

Focusing on the issue of privacy disclosure between trajectory and non-sensitive information, a trajectory privacy preserving algorithm based on non-sensitive information analysis was proposed. Firstly, the correlation between trajectory and non-sensitive information was analyzed to build trajectory privacy disclosure decision model, and the Minimal Violating Sequence tuple (MVS) was gotten. Secondly, using common subsequences, the doublets with the minimal loss of trajectory data in MVS were selected as the suppression objects when removing the privacy risks caused by MVS, then the anonymized trajectory dataset with privacy and low data loss was obtained. In the comparison experiments with LKC-Local algorithm and Trad-Local algorithm, when the sequence length is 3, the average instance loss of the proposed algorithm is decreased by about 6% and 30% respectively, and the average MFS (Maximal Frequent Sequence) loss is decreased by about 7% and 60% respectively. The experimental results verify that the proposed algorithm can effectively improve the quality of recommend service.

To measure Chinese microblog, a framework of Credibility of Chinese Microblog based on Information Fusion (CCM-IF) was proposed by analyzing impact factors of Chinese microblog and their pedigree. Firstly, different evaluating methods were implemented for three particular features, such as text message, user, and information propagation. Secondly, a method based on Dempster-Shafer (D-S) theory was proposed to combine the features from the fuzzy nature of the credibility. Thirdly, a series of experimental validations involving two real datasets from Sina Weibo were conducted. Experimental results show that the accuracy of CCM-IF is 10%-20% higher than that of the classical ranking algorithm named LMJM (Language Modeling with Jelinek-Mercer smoothing). So, as a static indicator of quality assessment, CCM-IF can be used for microblog retrieval ordering and garbage microblog filtering.

A Novel Quantum Differential Evolutionary (NQDE) algorithm was proposed for the Blocking Flowshop Scheduling Problem (BFSP) to minimize the makespan. The NQDE algorithm combined Quantum Evolutionary Algorithm (QEA) with Differential Evolution (DE) algorithm, and a novel quantum rotating gate was designed to control the evolutionary trend and increase the diversity of population. An effective Quantum-inspired Evolutionary Algorithm-Variable Neighborhood Search (QEA-VNS) co-evolutionary strategy was also developed to enhance the global search ability of the algorithm and to further improve the solution quality. The proposed algorithm was tested on the Taillard's benchmark instances, and the results show that the number of optimal solutions obtained by NQDE is bigger than the current better heuristic algorithm-Improved Nawaz-Enscore-Ham Heuristic (INEH) evidently. Specifically, the optimal solutions of 64 instances in the 110 instances are improved by NQDE. Moreover, the performance of NQDE is superior to the valid meta-heuristic algorithm-New Modified Shuffled Frog Leaping Algorithm (NMSFLA) and Hybrid Quantum DE (HQDE), and the Average Relative Percentage Deviation (ARPD) of NQDE algorithm decreases by 6% contrasted with the latter ones. So it is proved that NQDE algorithm is suitable for the large scale BFSP.

As to the low clustering quality and high communication cost of the existed distributed clustering algorithm, a distributed data stream clustering algorithm (DAPDC) which combined the density with the idea of representative points clustering was proposed. The concept of the class cluster representative point to describe the local distribution of data flows was introduced in the local sites using affinity propagation clustering, while the global site got the global model by merging the summary data structure that was uploaded from the local site by the improved density clustering algorithm. The simulation results show that DAPDC can improve the clustering quality of data streams in distributed environment significantly. Simultaneously, the algorithm can find the clusters of different shapes and reduce the amount of data transferred significantly by using class cluster representative points.

The evolution analysis of network public opinion becomes rather complex because of its interaction, diversity and derivability. At first, this paper analyzed the features of the source, content, dissemination and influence of network public opinion,and introduced G (Galam) model which was applied to the study of the evolution of network public opinion. Through experimental analysis, it is concluded that the final polarization direction of the evolution of network public opinion has certain regularity and controllability. Through the intervention in public communication between individuals and the influence on public in public preferences, the direction of the ultimate evolution of network public opinion can be guided.

In order to overcome the problems of poor local search and premature convergence on Particle Swarm Optimization (PSO) algorithm, an improved particle swarm optimization approach based on Dynamic Random Search Technique (DRST) and good-point set was proposed in this paper. DRST was introduced to optimize the current best position of the swarm. On the other hand, reinitialization with a good-point set manner was employed for the swarm falling into premature convergence to go out of the local optimum. Linear search in the negative gradient direction was also applied to accelerate the optimization. In the end, an experiment was given and the results show that the improved algorithm has rapid convergence, great ability of preventing premature convergence and better performance than Standard Particle Swarm Optimization (SPSO) and Dissipative Particle Swarm Optimization (DPSO).

 In virtual reality, a fundamental motion library is built by using motion capture system. Then appropriate fundamental motions are selected from the library, and desired motions are synthesized from these fundamental ones. One of these motion editing techniques, motion blending, is the most practical and complex one. A real-time synchronization algorithm was presented based on blending module. The basic idea is to make the data set more flexible and to dynamically blend motions to avoid unexpected ones. As a result, complex virtual animations are created from above.

A human face recognition technique based on modular 2DPCA was presented. First, the original images were divided into modular images in proposed approach. Then the 2DPCA method could be directly used to the sub-images obtained from the previous step. There are three advantages for this way: 1)dimension reduction of discriminant features can be done conveniently; 2)singular value decomposition of matrix is fully avoided in the process of feature extraction, so the features for recognition can be gained easily; 3)as opposed to 2DPCA, the feature matrix of lower dimension can be employed, and higher (not less at least) correct recognition rate can be reached. Moreover, 2DPCA is the special case of modular 2DPCA. To test modular 2DPCA and evaluate its performance, a series of experiments were performed on three human face image databases: ORL and NJUST603 human face databases. The experimental results indicated that the performance of modular 2DPCA is superior to that of 2DPCA.

The method of modeling was discussed to support the construction of networked standard parts in this paper. Moreover, a web-based modeling environment was provided that integrates products’ standard information, functions and techniques. Programming and modeling methods and flows were analyzed particularly. The item management and workflow methods was implement in the system to construct the standard parts information for users in Internet. Besides, a platform was constructed to management the standard project and faced with products development.