Most of the existing expert recommendation processes rely on manual matching, which leads to the low accuracy of expert recommendation due to that they cannot fully capture the semantic association between the subject of the project and the interests of experts. To solve this problem, a matching method for academic expertise of project peer review experts was proposed. In the method, an academic network was constructed to establish the academic entity connection, and a meta-path was designed to capture the semantic association between different nodes in the academic network. By using the random walk strategy, the node sequence of co-occurrence association between the subject of the project and the expert research interests was obtained. And through the network representation learning model training, the vector representation with semantic association of the project subject and expert research interests was obtained. On this basis, the semantic similarity was calculated layer by layer according to the hierarchical structure of project subject tree to realize multi-granularity peer review academic expertise matching. Experimental results on the crawled datasets of HowNet and Wanfang papers, an expert review dataset and Baidu Baike word vector dataset show that this method can enhance the semantic association between the project subject and expert research interests, and can be effectively applied to the academic expertise matching of project review experts.

Traditional laser localization algorithms such as Monte Carlo localization algorithm have the problems of low accuracy and poor anti-robot kidnapping performance, and traditional two-dimensional code localization algorithms have complex environmental layout and strict limitation to robot's trajectory. In order to solve these problems, a mobile robot localization algorithm based on two-dimensional code vision and laser lidar data was proposed. Firstly, the computer vision technology was used by the robot to detect two-dimensional codes in the test environment, and the poses of detecting two-dimensional codes were transformed to map coordinates respectively, and they were fused to generate the prior pose information. Then the optimized pose was obtained by the point cloud alignment with the generated information as the initial poses. At the same time, the odometry-vision supervising mechanism was introduced to effectively solve the problems brought by the environmental factors such as the information lack of two-dimensional codes and the wrong recognition of the two-dimensional codes as well as ensure the smoothness of the poses. Finally, experimental results based on mobile robot show that, the proposed algorithm has the average error of lidar sampling points reduced by 92%, the average time spent per pose calculation reduced by 88% compared with the classical Adaptive Monto Carlo Localization (AMCL) algorithm, and it solves robot kidnapping problem effectively. This algorithm can be applied to the indoor robots such as storage robot.

In order to identify photovoltaic model parameters accurately and reliably, a Modified Backtracking Search Algorithm(MBSA) was proposed. In the algorithm, firstly, some individuals were selected to learn the current population and historical population information at the same time, and the others were made to learn from the best individual in the current population and stay away from the worst solution, so as to maintain the population diversity and improve the convergence speed. Then, the performances of individuals in the population were quantified by the probability. On this basis, the individuals were able to adaptively select different evolution strategies to balance the exploration and exploitation capabilities. Finally, an elite strategy based on chaotic local search was used to further improve the quality of the population. The proposed algorithm was tested on different photovoltaic models such as single diode, double diode, and photovoltaic module. Experimental results show that the convergence speed and parameter identification accuracy of Backtracking Search Algorithm(BSA) are significantly improved by the proposed strategies. Eight advanced algorithms such as Logistic Chaotic JAYA(LCJAYA) algorithm and Multiple Learning BSA(MLBSA) were compared with the proposed algorithm. Experimental results show that the robustness of the proposed algorithm is the best among these algorithms, and the identification accuracy of the proposed algorithm is better than those of JAYA, LCJAYA, Improved JAYA(IJAYA) and Teaching-Learning-Based Optimization(TLBO) algorithms on both single diode and double diode models, and the proposed algorithm outperforms JAYA, LCJAYA, IJAYA and TLBO algorithms on photovoltaic module model in identification accuracy. Under different illumination conditions and at different temperatures, the manufacturer real data on three photovoltaic modules:thin-film, mono-crystalline and multi-crystalline were used for the actual measurement test, and the results predicted by the proposed algorithm were consistent with the actual situations in the test. Simulation results show that the proposed algorithm is accurate and stable on photovoltaic model parameter identification.

In order to solve the problems of high abstraction degree of network warfare and insufficient means of simulation and effectiveness evaluation of network warfare under the condition of informationization, a method of network warfare simulation and effectiveness evaluation integrating multiple indexes of both attack and defense sides was proposed. Firstly, for the network warfare attacker, four kinds of attack methods were introduced to attack the network; and for the network defender, the network node structure, content importance and emergency response ability were introduced as the defense indicators of the network. Then, the network warfare effectiveness evaluation model was established by integrating PageRank algorithm and fuzzy comprehensive evaluation method into Light Gradient Boosting Machine (LightGBM) algorithm. Finally, by defining the node damage effectiveness curve, the evaluation results of residual effectiveness and damage effectiveness in the whole network warfare attack and defense system were obtained. The simulation results show that the effectiveness evaluation model of network warfare can effectively evaluate the operational effectiveness of both attack and defense sides of network warfare, which verifies the rationality and feasibility of the effectiveness evaluation method of network warfare.

Considering that the microphone array is mostly used as a sound pickup device in the smart speaker,and there are distortion and complexity of the acoustic echo cancellation in the adaptive filtering technology on single channel,a fast echo cancellation algorithm for microphone array was proposed. First of all,the adaptive filtering technology was used to estimate the first channel echo,then estimate the relative echo transfer function between the arrays,and the echoes of other channels were obtained by multiplying the above two. Secondly,the estimated echo and the noise were regarded as the noise reference signals of the Generalized Sidelobe Canceller(GSC)beamforming lower branch,which were removed by the GSC beamforming algorithm. The simulation results show that the proposed algorithm has good echo cancellation and noise suppression performance under moderate reverberation,long distance,low echo to noise ratio and using music as echo environment. And the algorithm not only has small computational complexity,but also makes target speech signals have high signal distortion ratio and intelligibility.

Micro-Expression (ME) occurrence is only related to local region of face, with very short time and subtle movement intensity. There are also some unrelated muscle movements in the face during the occurrence of micro-expressions. By using existing global method of micro-expression recognition, the spatio-temporal patterns of these unrelated changes were extracted, thereby reducing the representation capability of feature vectors, and thus affecting the recognition performance. To solve this problem, the local region method was proposed to recognize micro-expression. Firstly, according to the region with the Action Units (AU) related to the micro-expression, seven local regions related to the micro-expression were partitioned by facial key coordinates. Then, the spatio-temporal patterns of these local regions were extracted and connected in series to form feature vectors for micro-expression recognition. The experimental results of leave-one-subject-out cross validation show that the micro-expression recognition accuracy of local region method is 9.878% higher than that of global region method. The analysis of the confusion matrix of each region's recognition result shows that the proposed method makes full use of the structural information of each local region of face, effectively eliminating the influence of unrelated regions of the micro-expression on the recognition performance, and its performance of micro-expression recognition can be significantly improved compared with the global region method.

A monocular vision obstacle avoidance method for quadrotor based on deep learning was proposed to help quadrotors to avoid obstacles. Firstly, the position of object in the image was obtained by object detection, and by calculating the height of the object box in the image, the distance between quadcopter and obstacle was estimated. Then, whether performing obstacle avoidance was determined by synergetic computer. Finally, experiments were conducted on a flight test platform based on Pixhawk flight control board. The results show that the proposed method can be applied to quadcoptor obstacle avoidance with low speed. Compared with traditional active sensor methods, the proposed method greatly reduces the occupied volume with only one monocular camera as sensor. This method is robust and can identify people with different postures as obstacles.

Aiming at the issue that the Integrated Production and Distribution Scheduling (IPDS) model rarely considers the complex production environment and procurement, the model of Integrated Purchase Production and Distribution Scheduling (IPPDS) with minimizing the order completion time in the job shop environment as target was established and the improved Dynamic Artificial Bee Colony (DABC) algorithm was used to solve the model. Based on characteristics of IPPDS, firstly, to realize the matching relationship between task (processing and transportation) and resource (equipment and vehicle), a coding method of two-dimensional real number matrix was adopted. Secondly, the decoding method based on the process was adopted, and the method to satisfy the constraints for different tasks were designed in the decoding process to ensure the feasibility of the decoding method. Finally, the dynamic coordination mechanism and local heuristic information of leading and following bees were designed in the process of the algorithm. Appropriate parameter intervals of DABC were obtained by experiments, and the experimental results show that:compared with piecewise scheduling and IPDS, IPPDS strategy has the scheduling time reduced by 35.59% and 30.95% respectively. DABC algorithm has the solution effect averagely improved by 2.54% compared with Artificial Bee Colony (ABC) algorithm, and averagely improved by 6.99% compared to the Adapted Genetic Algorithm (AGA). Therefore, IPPDS strategy can meet customer requirements more quickly, and DABC algorithm not only reduces the parameters to be set, but also has good exploration and development ability.

Focused on the low efficiency of verifying data on the cloud servers, to ensure correct execution of user's commands and high-efficient validation, a method supporting homomorphic MAC for arithmetic circuits on cloud environment was provided. Precise search was obtained through the following ways. Firstly, a label generation algorithm was used to represent a validation label with a polynomial. Secondly, a transformation algorithm was used to transform the validation label to satisfy homomorphic form, meanwhile, homomorphic decryption was used reduce the dimensionality of the label. Finally, a verification algorithm was used to verify the search result. Moreover, the scheme carries out infinite multiplicative homomorphism without increasing the size of verification labels, and is efficient. The drawback of the scheme is that the computational complexity increases with the increase of the input bits of enhancement circuit.

In order to study the intelligent voice control of aircraft towing vehicle, realize accurate and efficient recognition of the voice command of pilot in the airport environment, and solve the problems of large computation, high time complexity and low recognition efficiency of the traditional Dynamic Time Warping (DTW) algorithm, a new optimization algorithm of DTW with constraint of hexagonal warping window for vehicle speech recognition was proposed. Firstly, the influence of warping window on the accuracy and efficiency of DTW algorithm was analyzed from three aspects such as the principles of DTW algorithm, the speech characteristics of towing vehicle instruction and the airport environment. Then, on the basis of DTW optimization algorithm with constraint of Itakura Parallelogram rhombic warping window, a DTW global optimization algorithm with the constraint of hexagonal warping window was further proposed. Finally, by varying the optimization coefficient, the optimal DTW algorithm with the constraint of hexagonal warping window was realized. The experimental results based on isolated-word recognition show that, compared with the traditional DTW algorithm and the DTW algorithm with rhombic warping window constraint, the recognition error rate of the proposed optimal algorithm is reduced by 77.14% and 69.27% respectively, and its recognition efficiency is increased by 48.92% and 27.90% respectively. The proposed optimal algorithm is more robust and timeliness, and can be used as an ideal instruction input port for intelligent control of aircraft towing vehicle.

The malicious cloud server may send incorrect or forged query results to the user, and the authorized user may send the key information privately to a non-authorized user after completing the retrieval. In order to solve the problems, a new verifiable ciphertext retrieval scheme with user revocation was constructed. Firstly, an encryption algorithm was used to encrypt the user documents and sign the keywords. Secondly, a searching algorithm was used to retrieve documents that needed to be retrieved. Finally, a verification algorithm and user revocation algorithm were used to verify the retrieval results and encrypt the unretrieved documents again. The analysis results show that, the proposed scheme can complete the accurate retrieval on the premise of guaranteeing the integrity of data, realize the user revocation through re-encryption scheme, and guarantee the security of system. Moreover, the proposed scheme satisfies the security of Indistinguishability-Chosen Keyword Attack (IND-CKA).

Learning With Errors (LWE)-based fully homomorphic encryption scheme was presented by Gentry, Sahai and Waters (GENTRY C, SALAHAI A, WATERS B. Homomorphic encryption from learning with errors:conceptually-simpler, asymptotically-faster, attribute-based[C]//Proceedings of the 33rd Annual Cryptology Conference. Berlin:Springer, 2013:75-92), namely GSW scheme, can only work under single-attribute settings. Aiming at this problem and introducing the concept of fully system, a fully homomorphic encryption scheme under multi-attribute settings was constructed. In the proposed scheme, whether a user was legitimate was determined through a conditional equation. Then, a new ciphertext matrix that meeting the requirements of GSW13 was constructed by using ciphertext expansion algorithm. Finally fuzzy system technology was used to complete the construction. INDistinguishability-X-Chosen Plain Attack (IND-X-CPA) security was proved under the standard model. The advantage of the proposed scheme lies in that it can be used in multi-attribute environment. The disadvantage is that the computational complexity is increased.

Considering the lack of theoretical analysis for distributed network structure under Advanced Persistent Threat (APT) attacks, a game model was proposed to solve the problem based on Nash equilibrium theory and node game theory. Firstly, a defensive framework of network security was established by analyzing the characteristics of APT attack and distributed network structure. Secondly, risky factor of vulnerability was calculated through node game model, and Oriented to APT (OAPG) was established on the basis of Nash equilibrium theory, the balance point of attack and defense was calculated, maximum return strategy of the attacker was analyzed, and then optimal defense strategy of the defender was proposed. Finally, an APT attack instance was used to verify the model. The calculation results show that the proposed model can analyze both the attack and the defense of the network from the APT attack path and provide a reasonable defense idea for the organizations using the distributed network.

In matrix permutation-based Block Compressive Sensing (BCS), matrix permutation strategy is introduced, which makes the complex sub-blocks and sparse sub-blocks change to the middle level of sparsity and reduces the blocking artifacts when sampling with the single sampling rate. However there is still a problem of poor sparsity balance among blocks. In order to get better reconstruction effect, a Ripple Matrix Permutation-based sparsity balanced BCS (BCS-RMP) algorithm was proposed. Firstly, an image was pre-processed by matrix replacement before sampling, and the sparsity of each sub-block of the image was equalized by ripple permutation matrix. Then, a same measurement matrix was used to sample the sub-blocks and reconstruct them on the decoding side. Finally, the final reconstructed image was obtained by inverse transformation of reconstruction results by the ripple permutation inverse matrix. The simulation results show that, compared with the existing matrix replacement algorithms, the proposed ripple matrix permutation algorithm can effectively improve the quality of image reconstruction, and it can reflect the details more accurately when choosing appropriate sub-block size and sampling rate.

Person Re-Identification (Re-ID) under non-overlapping multi-camera is easily affected by illumination, posture, and occlusion, and there are image mismatches in the experimental process. A Re-ID method based on siamese network and reranking was proposed. Firstly, a pair of pedestrian training images were given, a discriminative Convolutional Neural Network (CNN) feature and similarity measure could be simultaneously learned by the siamese network to predict the pedestrian identity of the two input images and determine whether they belonged to the same pedestrian. Then, the k-reciprocal neighbor method was used to reduce the image mismatches. Finally, Euclidean distance and Jaccard distance were weighted to rerank the sorted list. Several experiments were performed on the datasets Market1501 and CUHK03. The experimental results show that the Rank1 (the probability of matching successfully for the first time) reaches 83.44% and mAP (mean Average Precision) is 68.75% under Single Query on Market1501. In the case of single-shot on CUHK03, the Rank1 reaches 85.56% and mAP is 88.32%, which are significantly higher than those of the traditional methods based on feature representation and metric learning.

Cloud computing security is the key bottleneck that restricts its development, and access control on the result of cloud computing is a hot spot of current research. Based on the classical homomorphic encryption BGN (Boneh-Goh-Nissim) scheme, and combined with outsourcing the decryption of Ciphertext-Policy Attribute-Based Encryption (CP-ABE) ciphertexts, a BGN type outsourcing the decryption of ABE ciphertexts was constructed. In the scheme, partial decryption of ciphertexts was outsourced to the cloud, and only the users whose attributes meet the access policy could get the correct decryption result, thus reducing the storage and computation overhead of users. Compared with the existing outsourcing schemes of ABE, the proposed scheme can operate on ciphertexts for arbitrary additions and one multiplication. Finally, the security of the scheme was analyzed. The proposed scheme is semantically secure under the subgroup decision assumption, and its attribute security is proved under random oracle model.

Focusing on the issues that it is very difficult to test the parallel software system, and the size of state space is too large, a Colored Petri Net (CPN) model for simplifying the tested behavior of the parallel model was proposed. Firstly, the original model was divided into several sub modules according to the number of the special nodes, such as concurrent transitions, synchronous transitions, the branch places, and the confluence places. Secondly, the position of the tested behavior and the test set was created. Finally, the execution priority was set for the non-test behavior in each parallel module which met the reduction condition. By comparing the results of the state space analysis before and after simplification, the reduction rate of nodes in state space is at least 40%, and the full coverage test path generated by the tested behavior was not affected by the simplification.

In the traditional Secure Multi-party Computation (SMC), each participant can obtain the final result, but this coarse-grained access control may not be suitable for the requirements of specific users to decrypt ciphertexts, thus a new encryption scheme which has more accurate access control on the decryption authority of computation results was put forward. Combined with predicate encryption, a predicate encryption scheme with multiplicative homomorphic property for the secure multi-party computation was constructed. Compared with the existing predicate encryption, it supports the homomorphic operation, and is more accurate in access control on the decryption authority of computation results. In the current background of cloud environment, the secure multi-party computation of more fine-grained access control on computation results is realized, which is proved secure under INDistinguishable Attribute-Hiding against Chosen Plaintext Attacks (IND-AH-CPA).

With the ability to improve regression testing efficiency, test case prioritization has become a hot topic in software testing research. Since test case prioritization based on requirement is usually inefficient, a test case prioritization method based on discrete particle swarm optimization and test-point coverage, called Discrete Particle Swarm Optimization for Test Case Prioritization (TCP-DPSO) was proposed. Firstly, the various factors affecting prioritization were divided into two categories:Cost-Keys and Win-Keys, and then general test average yield index by normalizing was obtained. Then, particle's position and velocity were defined by use of switcher and basic switching sequence, the mutation operator was introduced by referencing mutation strategy of Genetic Algorithm (GA), and the exploration and development capabilities were adjusted by adopting variable inertia weight, which could promote sustainable evolution and approach optimization goals. The experimental results show that TCP-DPSO is similar to GA and dramatically better than random testing on optimal solution quality and it is superior to GA on success rate and average computing time, which indicates its better algorithm stability.

Focusing on the function defect of the traditional Identity-Based Encryption (IBE) scheme that the ciphertexts can not be calculated directly, a new IBE scheme was proposed. The homomorphism transformation mechanism proposed by Gentry was used to transform the hierarchical IBE scheme proposed by Agrawal into a homomorphic hierarchical IBE scheme. Compared with the GSW (Gentry, Sahai, Waters) scheme (GENTRY C, SAHAI A, WATERS B. Homomorphic encryption from learning with errors:conceptually-simpler, asymptotically-faster, attribute-based. CRYPTO 2013:Proceedings of the 33rd Annual Cryptology Conference on Advances in Cryptology. Berlin:Springer, 2013:75-92) and CM (Clear, Mcgoldrick) scheme (CLEAR M, MCGOLDRICK C. Bootstrappable identity-based fully homomorphic encryption. CANS 2014:Proceedings of 13th International Conference on Cryptology and Network Security. Berlin:Springer, 2014:1-19), the construction method of the proposed scheme was more natural, the level of space complexity was reduced from cubic to square with higher efficiency. In the current environment of cloud computing, the proposed scheme can contribute to the transformation from theory to practice of fully homomorphic encryption scheme based on Learning With Errors (LWE) problem. The performance analysis and the verification results under the random oracle model prove the security for Indistinguishability of the Identity-Based Encryption Scheme under Chosen-Plaintext Attack (IND-ID-CPA) of the proposed scheme.

a novel Dynamic multi-Species Particle Swarm Optimization (DSPSO) algorithm based on food chain mechanism was proposed aiming at the problem that the basic Particle Swarm Optimization (PSO) algorithm is easy fall into local optimal solution when solving multimodal problems. Inspired by the natural ecosystem, a food chain mechanism and a reproduction mechanism were employed to keep the swarm diversity and good performance. In food chain mechanism, the swarm was divided into several sub-swarms, and each sub-swarm could prey on the others. The memory leader swarm was evolved and the less contributed particle was eliminated through predation, and then the new particle was generated through reproduction mechanism. The diversity was kept through the evaluation of the swarm, and the efficiency of the algorithm was enhanced through eliminating the misleading effect of the less contributed particles. In order to verify the effectiveness of the algorithm, ten benchmark problems including shifted problems and rotated problems were chose to test the performance of DSPSO. The experimental results show that DSPSO has a well optimizing performance. Compared with PSO algorithm, Local version Particle Swarm Optimization (LPSO) algorithm, Dynamic Multi-Swarm Particle Swarm Optimization (DMS-PSO) algorithm and Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm, DSPSO algorithm not only obtains more accurate solutions, but also has higher reliability.

Concerning the low efficiency of calculating flow accumulation on high resolution digital terrain data, a parallel algorithm was put forward based on the Compute Unified Device Architecture (CUDA) and flooding model. Based on the technology of Graphic Processing Unit (GPU), two strategies were designed to improve the speed of the extraction. Firstly, the calculation of flow accumulation was divided into a plurality of independent tasks for parallel processing. Secondly, the time of data exchange was reduced through the asynchronous data transmission. The experimental results show that the efficiency of the parallel algorithm is superior of the serial algorithm. The acceleration of river network extraction reached 62 times in NVIDIA Geforce GTX660 for 600 MB DEM data with 9784×8507 grid size.

Aiming at not satisfactory defogging effect of the traditional defogging algorithm based on polarized characteristics in heavy fog, a new color space conversion algorithm using dark channel prior for polarization image dehazing was proposed. Compared with the traditional imaging technology, polarization imaging detection technology has remarkable advantages in the target detection and recognition of complex environment. Intensity, polarization degree and polarization angel information are usually used to describe target's polarization information for polarization images. In order to combine the polarization information and defogging model, a method of color space transformation was adopted. Firstly, the polarization information was converted into the components of the brightness, hue, saturation in Hue-Intensity-Saturation (HIS) color space and then the HIS color space was mapped to the Red-Green-Blue (RGB) space. Secondly, the dark channel prior principle was applied to get the dark channel image with the combination of the atmospheric scattering model in haze weather. Finally, the atmospheric transmission rate was elaborated by using softmatting algorithm based on sparse prior of the image. The experimental results show that, compared with the existing polarization defogging algorithm, many technical specifications of defogged images such as standard deviation, entropy, average gradient of the proposed algorithm have been greatly improved in very low visibility conditions. The proposed algorithm can effectively enhance the global contrast in heavy fog weather and improve the identification capability for the polarized images.

To get a path for a continuum robot in the environment like the aircraft fuel tank, a path planning algorithm based on regional-advance strategy was proposed. By combining with the mechanical constraints of the robot, the method could ensure that arbitrary points can be reached in the single cabin. With the flexibility of movement, but the hyper-redundant freedom degree of the continuum robot brings about both the multiple path solutions in three-dimensional space and high time complexity. The approach based on dimension reduction, which is transforming the planning in three-dimensional space into that in two-dimensional plane, was presented to reduce the computing complexity. The single cabin of the aircraft fuel tank was divided to two regions, and the planning strategy was determined by the regional location of the target point. Finally, the Matlab simulation experiments were carried out, and the practicability and effectiveness of the proposed method were verified.

OpenFlow enhances the Quality of Service (QoS) of traditional networks, but it has disadvantage that its network session identification efficiency is low and the network packet forwarding path is poor and so on. On the basis of the current study of the OpenFlow, GPU OpenFlow Massive Data Network Analysis (GOMDI) model was proposed by this paper, through integrating the biological sequence algorithm, GPU parallel computing algorithm and machine learning methods. The network session matching algorithm and path selection algorithm of GOMDI were designed. The experimental results show that the speedup of the GOMDI network session matching algorithm is over 300 higher than the CPU environment in real network, and the network packet loss rate of its path selection algorithm is lower than 5%, the network delay is less than 20ms. Thus, the GOMDI model can effectively improve network performance and meet the needs of the real-time processing for massive information in big data environment.

To deal with the under-resourced labeled pronunciation data in mispronunciation detection, some other data were used to improve the discriminability of feature in the framework of Tandem system. Taking Chinese learning of English as object, unlabeled data, native Mandarin data and native English data which can be relatively easily accessed were selected as the assisted data. The experiments show that these types of data can effectively improve the performance of system, and the unlabeled data performs the best. And the effect to system performance was discussed with different length of frame context, the shallow and deep neural network typically represented by Multi-Layer Perception (MLP) and Deep Neural Network (DNN), and different structure of Tandem feature. Finally the strategy of merging multiple data streams was used to further improve the system performance, and the best system performance was achieved by combining the DNN based unlabeled data stream and native English stream. Compared with the baseline system, the recognition accuracy is increased by 7.96%, and the diagnostic accuracy of mispronunciation type is increased by 14.71%.

Context-aware computing is one of the indispendable key technologies for developing and deploying intelligent applications. Whether the context can really contribute to the applications mainly depends on the following two aspects: the first is how to continuously and steadily monitor/capture high-quality context information from the dynamic interaction environment, the second is how to reason on contexts and make adaptation decisions for applications. A layered middleware infrastructure was designed to achieve the above objectives. It afforded effective supports for not only gathering, managing, interpreting and making use of context information to dynamically adapt applications, but also selecting the most appropriate context sources dynamically at runtime based upon Qualities of Context (QoC). Finally, the experimental results show that the middleware can quickly and efficiently support the development and deployment of context-aware applications, and has better computing performance in comparison with others.

In the field of social influence propagation, social network as the media plays a fundamental role in interaction between social individuals and disseminating information or views. First, the definition of social influence and the essential attribute of social influences as the social relevance were discussed. Then, the independent cascade model and the linear threshold model were expounded, as well as greedy algorithm and heuristic algorithms which can confirm the influential people. Finally, the new trend of research on social influence, such as community-based influence maximization algorithm and research on the influence of multiple subjects and multiple behaviors were deeply analyzed.

Concerning the influence of resident's psychological factors on travel mode choice in the actual travel, a travel mode choice model based on prospect theory was established and a choice method more according to human thinking habits was put forward. Considering psychological reference points of travel time and travel cost comprehensively, satisfied travel mode to resident was obtained. The influence of reference point on travel mode was analyzed by comparing changes of comprehensive prospect value under different reference points. Finally an example gave the application of this travel mode choice method. The experimental results show that residents in the minority whose expectation of travel time is lower prefer bus travel, although the comprehensive prospect value changes of taxi and private car are identical. More residents tend to use private car mode, which is consistent with the fact. The proposed method provides a new way to predict resident travel mode.

Concerning the present situation that Quality of Service (QoS) evaluation methods ignore the implicit service quality assessment and lead to inaccurate results, a service evaluation method that comprehensively considered explicit and implicit quality attributes was put forward. Explicit quality attributes were expressed in vector form, using service quality assessment model, after quantization, normalization, then evaluation values were calculated; and implicit quality attributes were expressed according to the evaluation on similar users' recommendation. The users' credibility and difference between old and new users were considered in the evaluation process. Finally the explicit and implicit quality evaluation was regarded as the QoS evaluation results. The experiments were performed in comparison with three algorithms by using one million Web Service QoS data. The simulation results show that the proposed method has certain feasibility and accuracy.