Aiming at the problems that the low-frequency words expressed by the existing word vectors are of poor quality, the semantic information expressed by them is easy to be confused, and the existing disambiguation models cannot distinguish polysemous words accurately, a multi-feature fusion disambiguation method based on word vector fusion was proposed. In the method, the word vectors expressed by HowNet sememes and the word vectors generated by Word2vec (Word to vector) were fused to complement the polysemous information of words and improve the expression quality of low-frequency words. Firstly, the cosine similarity between the entity to be disambiguated and the candidate entity was calculated to obtain the similarity between them. After that, the clustering algorithm and HowNet knowledge base were used to obtain entity category feature similarity. Then, the improved Latent Dirichlet Allocation (LDA) topic model was used to extract the topic keywords to calculate the similarity of entity topic feature similarity. Finally, the word sense disambiguation of polysemous words was realized by weighted fusion of the above three types of feature similarities. Experimental results conducted on the test set of the Tibet animal husbandry field show that the accuracy of the proposed method (90.1%) is 7.6 percentage points higher than that of typical graph model disambiguation method.

Aiming at the application scenario of abstractive text summarization, a Transformer-based summarization model with Pointer Generator network and Coverage Loss added to the Transformer model for optimization was proposed. First, the method based on the Transformer model as the basic structure was proposed, and its attention mechanism was used to better capture the semantic information of the context. Then, the Coverage Loss was introduced into the loss function of the model to punish the distribution and coverage of repeated words, so as to solve the problem that the attention mechanism in the Transformer model continuously generates the same word in abstractive tasks. Finally, the Pointer Generator network was added to the model, which allowed the model to copy words from the source text as generated words to solve the Out of Vocabulary (OOV) problem. Whether the improved model reduced inaccurate expressions and whether the phenomenon of repeated occurrence of the same word was solved were explored. Compared with the original Transformer model, the improved model improved the score on ROUGE-L evaluation function by 1.98 percentage points, the score on ROUGE-2 evaluation function by 0.95 percentage points, and the score on ROUGE-L evaluation function by 2.27 percentage points, and improved the readability and accuracy of the summarization results. Experimental results show that Transformer can be applied to the field of abstractive text summarization after adding Coverage Loss and Pointer Generator network.

At present, most image encryption algorithms directly encrypt the plaintext image into a ciphertext image without visual meaning, which is easy to be found by hackers during the transmission process and therefore subjected to various attacks. In order to solve the problem, combining Hopfield chaotic neural network and compressive sensing technology, a visually meaningful image encryption algorithm was proposed. Firstly, the two-dimensional discrete wavelet transform was used to sparse the plaintext image. Secondly, the sparse matrix after threshold processing was encrypted and measured by compressive sensing. Thirdly, the quantized intermediate ciphertext image was filled with random numbers, and Hilbert scrambling and diffusion operations were performed to the image. Finally, the generated noise-like ciphertext image was embedded into the Alpha channel of the carrier image though the Least Significant Bit (LSB) replacement to obtain the visually meaningful steganographic image. Compared with the existing visual image encryption algorithms, the proposed algorithm demonstrates very good visual security, decryption quality and robustness, showing that it has widely application scenarios.

In view of the problem that ViBe (Visual Background extractor) algorithm sometimes produces “ghost” when detecting moving targets and the misdetection problem of moving targets caused by the interference produced by the algorithm in the target detection with dynamic background, an improved ViBe algorithm was proposed based on the techniques of three frame differencing and morphological post-processing carried on the key frames extracted by Color Layout Descriptor (CLD). Firstly, the video key frame images were extracted by CLD. Secondly, three frame differencing was performed on the selected key frame images. The background model containing the moving target was filled by the difference results, obtaining the real background image. Thirdly, the moving target was detected, so as to eliminate the “ghost”. Finally, the morphological processing technique with adaptive threshold was added in the updating stage of background model to eliminate the interference information in the dynamic background model. The experimental results show that the proposed algorithm has superiority in avoiding ghost and anti-dynamic background interference in moving target detection, and when the similar measurement threshold is selected from 0.67 to 0.72, the accuracy of the algorithm can be as high as 99.4%, indicating that the algorithm can ideally detect the position information of the moving target.

Aiming at the problems of low accuracy, poor real-time performance and insufficient feature extraction in existing text sentiment analysis methods, a serial hybrid model based on Bi-directional Long Short-Term Memory neural network and Convolutional Neural Network (BiLSTM-CNN) was constructed. Firstly, the context information was extracted from the text by using Bi-directional Long Short Term Memory (BiLSTM) neural network. Then, the local semantic features were extracted from the context information by using Convolutional Neural Network (CNN). Finally, the emotional tendency of text was obtained by using Softmax. Compared with single models such as CNN, Long Short-Term Memory (LSTM) and BiLSTM, the proposed text sentiment analysis model increases the comprehensive evaluation index F1 by 2.02 percentage points, 1.18 percentage points and 0.85 percentage points respectively; and compared with the hybrid models such as LSTM and CNN (LSTM-CNN) and parallel features fusion of BiLSTM-CNN, the proposed text sentiment analysis model improves the comprehensive evaluation index F1 by 1.86 percentage points and 0.76 percentage points respectively. The experimental results show that the serial hybrid model based on BiLSTM-CNN has great value in practical applications.

Concerning the poor privacy and flexibility of traditional lifetime estimation for human motion, a lifetime estimation system for human motion was proposed, by analyzing the amplitude variation of WiFi Channel State Information (CSI). In this system, the continuous and complex lifetime estimation problem was transformed into a discrete and simple human motion detection problem. Firstly, the CSI was collected with filtering out the outliers and noise. Secondly, Principal Component Analysis (PCA) was used to reduce the dimension of subcarriers, obtaining the principal components and the corresponding eigenvectors. Thirdly, the variance of principal components and the mean of first difference of eigenvectors were calculated, and a Back Propagation Neural Network (BPNN) model was trained with the ratio of above two parameters as eigenvalue. Fourthly, human motion detection was achieved by the trained BP neural network model, and the CSI data were divided into some segments with equal width when the human motion was detected. Finally, after the human motion detection being performed on all CSI segments, the human motion lifetime was estimated according to the number of CSI segments with human motion detected. In real indoor environment, the average accuracy of human motion detection can reach 97% and the error rate of human motion lifetime is less than 10%. The experimental results show that the proposed system can effectively estimate the lifetime of human motion.

Focusing on the problem that serious imbalance between abnormal data and normal data in anomaly detection will lead to performance degradation of decision tree, three improved methods for C4.5 decision tree were proposed, which are C4.5+δ, UDE (Uniform Distribution Entropy) and IDEF (Improved Distribution Entropy Function). Firstly, it was deduced that the attribute selection criterion of C4.5 tends to choose the ones with imbalanced splitting. Secondly, why imbalanced splitting decreases the accuracy of anomaly (minority) detection was analyzed. Thirdly, the attribute selection criterion-information gain ratio of C4.5 was improved by introducing relaxation factor and uniform distribution entropy, or substituting distribution entropy function. Finally, three improved decision trees were verified on WEKA platform and NSL-KDD dataset. Experimental results show that three proposed improved methods can increase the accuracy of anomaly detection. Compared with C4.5, the accuracies of C4.5+7, UDE and IDEF on KDDTest-21 dataset are improved by 3.16, 3.02 and 3.12 percentage points respectively, which are better than the methods using Rényi entropy or Tsallis entropy as splitting criterion. Furthermore, using improved decision trees to detect anomalies in the industrial control system can not only improve the recall ratio of anomalies, but also reduce false positive rate.

Aiming at the poor real-time performance of mobile robot path planning in complex indoor environment, a further improvement on A ^* algorithm was proposed by analyzing and comparing Dijkstra algorithm, traditional A ^* algorithm and some improved A ^* algorithms. Firstly, the estimated path cost of the current node and its parent node were weighted in exponentially decreasing way. In this way, when the current code was far away from the target, the improved algorithm could search towards to the target quickly instead of searching around the start node. While the current code was near to the target, the algorithm could search the target carefully to ensure that the target was reachable. Secondly, the generated path was smoothed by quintic polynomia to further shorten the path and facilitate robot control. The simulation results show that compared with the traditional A ^* algorithm, the proposed algorithm can reduce the searching time by 93.8% and reduce the path length by 17.6% and get the path without quarter turning point, so that the robot could get to the destination along the planned path without a break. The proposed algorithm is verified in different scenarios, and the results show that the proposed algorithm can adapt to different environments and has good real-time performance.

To solve the problem of fixed individual scale and high execution cost of weak mutation test case set generation based on Set Evolutionary Algorithm (SEA), a generation method of weak mutation test case set based on Dynamic Set Evolutionary Algorithm (DSEA) was proposed. The test case sets were used as individuals to generate some weak mutations to cover all mutant branches. In the evolutionary process, according to the minimum subset of the optimal individuals and the number of uncovered mutation branches, the minimum scale of the required test case set was calculated by the set compact operator. And the size of all individuals in the population was adjusted based on the minimum scale to generate the smallest scale of the weak mutation test case set. At the same time, a fitness function for assessing a use case set as an individual was designed. The experimental results show that when the dynamic ensemble evolution algorithm is used to guide the generation of weak mutation test cases, and the scale of the test cases was 50.15% lower than the initial size of the individuals, and the execution time is lower than that of SEA by 74.58% at most. Thus, the dynamic ensemble evolution algorithm provides a solution for generating of the weak mutation test case set with minimum scale and enhancing the algorithm speed.

Since the problem of trajectory privacy violation and homogeneous semantic location attack of moving objects in road network environment is very serious, a Privacy-aware Trajectory Data Collection (PTDC) algorithm was proposed. Firstly, through visits' entropy of Points Of Interests (POI), the sensitivity of each POI was computed; secondly, based on the mixture distance of sensitivity and Euclidean distance, θ-weight was defined and a weighted model of vertices and edges in the network environment was established to reach a k-θ-D anonymity, which can resist the semantic location homogeneity attack; finally, based on the bread-first traversal algorithm of undirected graph, an anonymous algorithm was proposed to satisfy the semantic difference of POIs, so that user's sensitive sampling location was replaced by an anonymous region. Data utility caused by PTDC algorithm was theoretically evaluated. A set of experiments were implemented to test PTDC algorithm, and compare it with the privacy-preserving algorithm named YCWA (You Can Walk Alone) in free space. In theory, the privacy level of YCWA algorithm was lower than PTDC algorithm. The experimental results show that the PTDC algorithm has an average information loss of about 15%, and average range count query error rate of about 12%, which performs slightly worse than YCWA algorithm, while the running time of PTDC algorithm is less than 5 seconds, which is much better than YCWA algorithm. PTDC algorithm meets the needs of real-time online data collection.

Samples are required to meet the manifold assumption in Spectral Embedded Clustering (SEC) algorithm, and class labels of samples can always be embedded in a linear space, which provides a new idea for spectral clustering of linearly separable data, but the linear mapping function used by the spectral embedded clustering algorithm is not available to process the nonlinear high-dimensional data. To solve this problem, this paper cored the linear mapping function, built a Spectral Embedded Clustering based on Kernel function (KSEC) model. This model can solve the problem that the linear mapping function can't deal with nonlinear data, as well as it can achieve kernel's dimension reduction synchronously. The experimental results on real data sets show that the improved algorithm can improve the clustering accuracy by 13.11% averagely, and the highest 31.62%, especially for high-dimensional data clustering accuracy can be increased by 16.53% on average. And the sensitive experiments on algorithm to parameters show the stability of the improved algorithm, so compared with traditional spectral clustering algorithms, higher accuracy and better clustering performance are obtained. And the method can be used for such complex image processing field as remote sensing image.

Concerning the huge calculation of sparse decomposition, a fast sparse decomposition algorithm with low computation complexity was proposed for first-order Polynomial Phase Signals (PPS). In this algorithm, firstly,two concatenate dictionaries including Df and Dp were constructed, and the atoms in the Df were constructed by the frequency, and the atoms in the Dp were constructed by the phase.Secondly, for the dictionary Df, the group testing was used to search the atoms that matched the signal, and the correlation values of the atoms and the signal were tested twice to achieve the reliability. Finally, according to the matching frequency atoms tested by group testing, the dictionary Dp was constructed, and the matching phase atoms were searched by Matching Pursuit (MP) algorithm. Therefore, the sparse decomposition of real first-order PPS was finished. The simulation results show that the computational efficiency of the proposed algorithm is about 604 times as high as that of matching pursuit and about 139 times as high as that of genetic algorithm, hence the presented algorithm has less computation complexity, and can finish sparse decomposition fast. The complexity of the algorithm is only O(N).

To solve the problem of large amount of calculation and nonlinear programming in the process of service composition optimization, a Cost Benefit Coefficient (CBC) approach was proposed for Web services composition reliability optimization in the situation of a given cost investment. First, the structure patterns of service composition and related reliability function were analyzed. Furthermore, the Web service composition method of reliability calculation was proposed and a nonlinear optimization model was established accordingly. And then the cost benefit coefficient was computed through the relationship between the cost and the reliability of component services, and the optimization schemes of Web service composition were decided. According to the nonlinear optimization model, the results of optimization were computed. Finally, given cost investment, the higher reliability of the approach to optimize the reliability of Web service composition was verified through the comparison of this approach and the traditional method on the reliable data of component service. The experimental results show that the proposed algorithm is effective and reasonable for reliability optimization of Web services composition.

In order to improve the remote authentication efficiency for integrity measurement of computing platforms, this paper proposed a platform remote authentication mechanism based on unbalanced-Hash trees. Hash values of platform's trusted entities were stored in the structure of leaf nodes of unbalanced-Hash trees. Effectiveness of the metrics was verified through seeking corresponding leaf nodes of measured entities, recording the validation paths from leaf nodes to root nodes, passing from root nodes to the prover and finally recalculating the root nodes according to validation paths. The experimental results show that the proposed mechanism can effectively reduce time and space overhead of storing Hash values and the time complexity of integrity measurement authentication is O(lb N).

With the development of cloud computing and big data, traditional single storage medium based data storage cannot meet the demands of large data processing, thus distributed data storage has been widely used recently. However, some existing distributed ways of storage cannot perfectly meet the needs of the distribution system. In order to achieve the distributed data storage and redundancy more effectively, a new distributed method and backup strategy based on Consistent Tree Distribution (CTD) was proposed to achieve the location mapping of data indexing and storage. The new method featured load balancing, no single point of failure, high scalability and easy programming. In addition, a method based on Consistent Binary Tree Distribution (CBTD) was also put forward. Analysis of the application system verifies that the method can satisfy the data balance, load balance and high scalability requirements of the distributed system.

An adaptive Virtual Machine (VM) dynamic migration strategy of soft energy-saving was put forward to optimize energy consumption and load balance in cloud computing. The energy-saving strategy adopted Dynamic Voltage Frequency Scaling (DVFS) as the static energy-aware technology to achieve the sub-optimized static energy saving, and used online VM migration to achieve an adaptive dynamic soft energy-saving in cloud platform. The two energy-saving strategies were simulated and compared with each other in CloudSim platform, and the data were tested on PlanetLab platform. The results show that: Firstly, the adaptive soft and hard combination strategy in energy-saving can significantly save 96% energy; secondly, DVFS+MAD_MMT strategy using Median Absolute Deviation (MAD) to determine whether the host is overload, and choosing VM to remove based on Minimum Migration Time (MMT), which can save energy about 87.15% with low-load in PlanetLab Cloudlets than that of experimental environment; finally, security threshold of 2.5 in MAD_MMT algorithm can consume the energy efficiently and achieve the adaptive load balancing of virtual machines migration dynamically.

To distribute the score of composite service obtained from customer to each component service based on actual and historical performance of component services, Analytic Hierarchy Process (AHP) was used to calculate the distribution weight of each component service, in which a method was presented to convert Web service process into structure tree process, and the weight matrix was used to calculate the weight of each node in the tree structure. The relationship between actual Quality of Service (QoS) of component services and its advertised utility interval of QoS were taken into consideration, and through deviation function, the deviation proportion between actual QoS utility value of component service and actual QoS average utility value of all component services was calculated, meanwhile the influence on score distribution by history performance of each component service was considered. The experimental results show that actual QoS and history performance of component services have some influence on score which was distributed, and demonstrate that the proposed approach can achieve a reasonable and fair score distribution.

In order to solve the problem of low and non-uniform resolution in catadioptric omnidirectional imaging, a new image fusion method based on wavelet multi-scale decomposition was proposed in this paper according to the characteristics of complementary panoramic images. Using wavelet transform, the two complementary source images were decomposed into components with different resolutions and different directions first. And then based on specific fusion rules, low frequency was fused by average operator. With high frequency fusion, the exchanging by scales principle was utilized. At last, the fused image was reconstructed by inverse wavelet transform. The experimental results show that the fusion algorithm is simple and effective in the fusion of complementary panoramic images, and has good fusion results.

In order to achieve fast collision detection in robot assisted virtual surgery, an algorithm based on changeable direction hull was proposed. It combined fixed direction hull algorithm with the two characteristics in virtual scene, complex motion of surgical instrument end and continuous deformation of soft tissues. Action mode between instruments and soft tissue was analyzed and deformation of soft tissue was predicted. Then the set of box directions was changed to improve the tightness of bounding volume trees. Accordingly, collision detection was accelerated with decreased interaction tests. The simulation results show that collision information can be obtained through the proposed algorithm and the new method implements faster compared with fixed direction hull algorithm.

Software tamper resistance is an important method for software protection. Concerning the control-flow tampering invoked by buffer overflow as well as some other software attacks, a software tamper-proofing method based on Function-Level Control-Flow (FLCF) monitoring was proposed. This method described the software's normal behaviors by FLCF and instrumented one guard at every entrance of functions by binary rewriting technology. The monitoring module decided whether the software was tampered or not by comparing the running status received from the guards' reports with the expected condition. A prototype system was realized and its performance was analyzed. The experimental results show that this method can effectively detect the control-flow tampering with less overhead and no false positives. It can be easily deployed and transplanted as its implementation does not need source code or any modifications of underlying devices, and system security is strengthened by isolating the monitoring module with the software being protected.

In response to the issue that music-light show control system cannot automatically obtain the required music characteristic information, a kind of rough-emotion model which can be used for music-light performance was presented combined with the traditional Arousal-Valence model. In this model, the Mallat algorithm of wavelet analysis was used to extract comparative items and the ratio of judgment method of strength and rhythm was used to take two "soft-cutting" actions on music. Then the classification of music in the rough-emotion model and the extraction of music characteristic parameters could be achieved by the corresponding production rules of expert system. The simulation results show that the method can effectively classify music clips according to music emotion and extract characteristic elements. Meanwhile, these characteristic elements can satisfy the precision requirement of the music-light show control system on the time domain.

Node scheduling scheme is an effective approach to solve problems of energy constraints and high coverage redundancy in Wireless Sensor Network (WSN). However, it should satisfy the requirements of coverage rate as well as energy saving. To solve the problems of unbalanced and inefficient energy consumption of nodes in random node scheduling schemes, a node scheduling scheme based on spatial resolution was proposed. This scheme maintained coverage rate by controlling the number of active nodes in a deployed area, and balanced the remaining energy of every node. Meanwhile, a neighbor nodes protection mechanism was adopted to ensure that the dormant nodes closed real-time listening to reduce energy consumption, and the demand of coverage rate was fulfilled effectively due to relieving the situation of coverage holes which may occur when nodes take turns to rest. The simulation results indicate the performance of this scheme is superior to other similar schemes in coverage rate, lifetime and balance of energy consumption among nodes.

Basic Input Output System (BIOS) trapdoor has huge impact on computer system, and it is difficult to detect the existence of BIOS trapdoor effectively with the existing tools. After researching BIOS structure and BIOS code obfuscation technique based on reverse analysis, BIOS trapdoors were divided into module-level BIOS trapdoor and instruction-level BIOS trapdoor according to implementation granularity, followed by analyzing the implementation principle and characteristics of these two BIOS trapdoors in detail. Finally the detection method of module-level trapdoor based on analyzing module structure and the detection method of instruction-level trapdoor based on integrity measurement were presented. The experimental results show that these two methods can detect the existence of their corresponding BIOS trapdoors effectively.

In view of the limitation of the traditional Transmission Control Protocol (TCP) used in the heterogeneous network, a network cognitive model based on fuzzy comprehensive evaluation was proposed. This model, by building the membership functions and different dynamic weight distribution under the different network environment, distinguished the wireless error loss from congestion loss according to fuzzy comprehensive evaluation. The simulation results show: compared with the traditional TCP, under different network conditions, the model can more accurately distinguish the wireless error loss from congestion loss, and improve the TCP throughput and network performance.

Embedded system is vulnerable to buffer overflow attack. In order to solve this problem, a block based protection scheme was proposed after analyzing the memory management of μC/OS-Ⅱ. By making a combination of all the memory blocks which belong to one task and managing it through the established block_table, the introduced scheme protected the safety through creating isolation between task memories, checking and controlling the access of memory blocks. Then, an effective analysis about this scheme was given. In addition, a buffer overflow attack experiment was operated on Nios Ⅱ with the improved uC/OS-Ⅱ, and the results show that the proposed scheme is feasible.

Through analyzing the advantages and disadvantages of traditional immersive display systems, a new immersive display system was designed and implemented. In this system, the light illuminated by one projector was reflected by a cylindrical reflector to a cambered rear projection screen. Seamless projection picture can be obtained and coherent displaying of wideangle virtual scene could be implemented. By properly designing the surface of cylindrical reflector, it implemented uniform enlargement of the image on the horizontal direction. The distortion of the projection picture caused by the curvature of the screen could be basically removed through prior distortion of projected image and participants could acquire more moving freedom by means of rear projection. This system overcame the problem of image mosaics in traditional multiprojector and multidisplay immersive display systems. It is simple for manufacturing and can achieve satisfying immersive display quality, which is verified by the experimental result.

The cluster head election of current Wireless Sensor Network (WSN) mainly relies on energy and location, but ignores the trust. In this paper, using the trust as the basis of reliability and with the energy as the priority, trust value was considered to improve the system reliability. Meanwhile, an election generates more candidate nodes, every node bears cluster head in turn, so it reduces the frequency of the election and improves the efficiency of the election of cluster head. Thus, a Cluster Head Trust Elections (CHTE) based on trust and energy was proposed. The experiments show that the electing algorithm is efficient on data packet correctness of sink node and Mean Time Between Failures (MTBF) within WSN environments.

First,DC map of I frame was extracted for rough detection of the shots.And then the forward motion compensation vector of P frame was extracted, and Extended Vector Median (EVM) filtering was used for the preprocessing of motion vector.Finally the characteristics of three sports features,exercise intensity value, exercise intensity difference and the direction of motion vector histogram, absolute difference were calculated. Fuzzy inference was introduced to synthesize these three characteristics and classified shots to abrupt-change,gradual-change and no-change ones.The MPEG video shot boundary detection method do not need to decompress the video fully,and extracts information directly from MPEG compressed bit stream,so it is of low computation complexity and high extration speed,which is verified by the experimental results.

The existing uneven clustering routing algorithms do not consider the optimal path selection between cluster heads and base station, which leads to unbalanced energy consumption. In order to balance energy consumption of transmission paths, this paper proposed an uneven clustering routing algorithm based on minimum spanning tree. The algorithm utilized residual energy of nodes and the distance between nodes and base station to select cluster heads, and then generated minimum spanning tree to search the optimal transmission paths, which reduced energy consumption on the transmission paths and effectively solved unbalanced energy consumption. The theoretical analysis and experimental results show that the algorithm is better than the existing Energy Efficient Uneven Clustering (EEUC) and Energy Balancing Clustering Algorithm (EBCA) in terms of the number of live nodes and energy consumption.

The algebraic reconstruction technique family, including Algebraic Reconstruction Techniques (ART), Multiplicative Algebraic Reconstruction Techniques (MART), Simultaneous Iterative Reconstruction Techniques (SIRT), was studied to reconstruct the three-dimensional distribution of water vapor in this article. The simulation experiment was carried out on Shanghai GPS network. The results show that the algebraic reconstruction technology family can work effectively in water vapor tomography, get rapid convergence, and implement more easily. The range of relaxation factor and the initial value of iteration were also given in the article.