Aiming at the problem that the existing 3D model information hiding algorithms cannot effectively resist uneven compression, a multi-carrier low-density information hiding algorithm based on multiple fusion states was proposed. Firstly, multiple 3D models were positioned, oriented and stereotyped by translation and scaling. Secondly, the 3D models were rotated at different angles and merged by using the center point as merging point to obtain multiple fusion states. Thirdly, local height and Mean Shift clustering analysis were used to divide the energy of the vertices of the fusion state model, obtaining the vertices with different energies. Finally, by changing the vertex coordinates, the secret information changed by Arnold scrambling was quickly hidden in multiple fusion states and 3D models. Experimental results show that the proposed algorithm is robust against uneven compression attacks and has high invisibility.

To solve the problem that the influencing factors of crowdsourced testing are complex and diverse, and the test quality is difficult to assess, a method for analyzing the quality influencing factors based on Spearman correlation coefficient was proposed. Firstly, the potential quality influencing factors were obtained through the analysis of test platforms, tasks, and testers. Secondly, Spearman correlation coefficient was used to calculate the correlation degrees between potential factors and test quality and to screen out key factors. Finally, the multiple stepwise regression was used to establish a linear evaluation relationship between key factors and test quality. The experimental results show that compared with the traditional expert artificial evaluation method, the proposed method can maintain smaller fluctuations in evaluation error when facing a large number of test tasks. Therefore, the method can accurately screen out the key influencing factors of mobile application crowdsourced test quality.

Aiming at the problem that some 3D model-based information hiding algorithms are incompetent against combined attacks, a new strategy based on feature point labeling and clustering was proposed. Firstly, edge folding was adopted to achieve mesh simplification and all the vertexes were labeled in order by their energy level. Secondly, the ordered vertexes were clustered and re-ordered by using local height theory and Mean Shift clustering analysis. Lastly, hidden information and cover model carrier information were optimized, matched and modified by Logistic chaos mapping scrambling and genetic algorithm, completing the final hiding. The data in hiding areas were labeled and screened locally and globally according to the energy weight, which is good for the robustness and transparency of the algorithm. The experimental results show that, compared with 3D information hiding algorithms based on inscribed sphere and outer skeleton, the robustness of the proposed algorithm against single or joint attacks is significantly improved, and it also has the same degree of invisibility.

Aiming at the uniqueness of head feature and high detection error rate extracted by traditional zenithal pedestrian detection method, a multi-feature fusion zenithal pedestrian detection algorithm based on improved Aggregate Channel Feature (ACF) and Gray-Level Co-occurrence Matrix (GLCM) was proposed. Firstly, the extracted Hue, Sturation, Value (HSV) color features, gradient magnitude and improved Histogram of Oriented Gradients (HOG) feature were combined into ACF descriptor. Then, the improved GLCM parameter descriptor was calculated by the window method, and the texture features were extracted. The co-occurrence matrix feature descriptor was obtained by concatenating the feature vectors of each window. Finally, the aggregate channel and co-occurrence matrix features were input into Adaboost for training to get the classifier, and the final results were obtained by detection. The experimental results show that, the proposed algorithm can effectively detect targets in the presence of interference background, and improve the detection precision and recall.

To solve the problem of low invisibility and weak anti-attacking ability in the traditional information hiding algorithm, an information hiding algorithm based on compression sensing was proposed. Firstly, the carrier image was operated by first-order GHM (Geronimo Hardin Massopust) multiwavelet transform, and the obtained region in medium energy level was processed by first-order GHM transform again to get HH component, which was decomposed by the Singular Value Decomposition (SVD). Secondly, the secret image was disposed by the wavelet transform, and the obtained wavelet coefficient was processed by compressed sensing in order to get the measurement matrix. Then the elements of the matrix were decomposed by SVD. Finally, the singular value of the carrier image was replaced by the singular value of the secret image to finish the secret information embedding. The experiment shows that compared with existing two information hiding algorithms, the invisibility has been improved by 5.99% and 22.11% respectively; and the robustness against some common attacks such as low-pass filtering, salt and pepper noise, Gaussian noise and JPEG compression has been improved by 4.11% and 11.53% averagely.

Aiming at the problem of weak robustness to geometric attack in 3D model information hiding algorithms, an information hiding algorithm based on 3D model spherical segmentation was proposed. Firstly, the 3D model was preprocessed by principal component analysis, spherical coordinate transformation, spherical segmentation and partition sorting. Then, the points with larger normal vector in stereo partition are taken as feature points. The feature points were carried out wavelet transform according to the amount of secret information to be embedded. Finally, the secret information after scrambled operation was embedded into the pre-processed carrier to generate the secret 3D model. The experimental results show that the algorithm is invisible and has good robustness to random noise, heavy mesh and other common attacks.

Aiming at autonomous positioning of wheeled warehous robots, an indoor positioning method based on visual landmark and odometer data fusion was proposed. Firstly, by establishing a camera model, the rotation and translation relationship between the beacon and the camera was cleverly solved to obtain the positioning information. Then, based on the analysis of the characteristics of the angle difference between the gyroscope and the odometer, a method of angle fusion based on variance weight was proposed to deal with low update frequency and discontinuous positioning information problems. Finally, to compensate for a single sensor positioning defect, the odometer error model was designed to use a Kalman filter to integrate odometer and visual positioning information. The experiment was carried out on differential wheeled mobile robot. The results show that by using the proposed method the angle error and positioning error can be reduced obviously, and the positioning accuracy can be improved effectively. The repeat positioning error is less than 4 cm and the angle error is less than 2 degrees. This method is easy to operate and has strong practicability.

Under MapReduce computing scenarios, complex data mining algorithms typically require multiple MapReduce jobs' collaboration process to compete the task. However, serious redundant disk read and write and repeat resource request operations among multiple MapReduce jobs seriously degrade the performance of the algorithm under MapReduce. To improve the computational efficiency of ItemBased recommendation algorithm, firstly, the performance issues of the ItemBased collaborative filtering algorithm under MapReduce platform were analyzed. Secondly, the execution efficiency of the algorithm was improved by taking advantage of Spark's performance superiority on iterative computation and memory computing, and the ItemBased collaborative filtering algorithm under Spark platform was implemented. The experimental results show that, when the size of the cluster nodes is 10 and 20, the running time of the algorithm in Spark is only 25.6% and 30.8% of that in MapReduce. The algorithm's overall computing efficiency of Spark platform improves more than 3 times compared with that of MapReduce platform.

In order to solve the problems of security and capacity of confidential information during public network transmission, an information hiding algorithm based on the High Efficiency Video Coding (HEVC) background was proposed. The background image of multi-view images in HEVC was used as the carrier. Firstly, the background image was decomposed into three gray-scale components by using lαβ color space theory. Then, the image components such as α and β were transformed by Discrete Cosine Transform (DCT) method. Finally, the confidential information was embedded into the region of carrier repeatedly. With relative low energy weight, the mid-frequency coefficient regions of α and β components after DCT were both chosen as the hidden regions to be embedded repeatedly with the confidential information, which makes the proposed algorithm have good invisibility and robustness. The experimental results show that, compared with the algorithms based on intra-frame and inter-frame, the invisibility of the proposed algorithm was improved respectively by about 16.1% and 11.4% while the robustness of the proposed algorithm was increased respectively by about 55.5% and 20.2%.

Mobile application crowdsourcing testers are anonymous, non-contractual, which makes it difficult for task publishers to accurately evaluate the ability of crowdsourcing testers and quality of test results.To solve these problems, a new evaluation model of Analytic Hierarchy Process (AHP) for mobile application crowdsouring testers was proposed. The ability of crowdsourcing testers was evaluated comprehensively and hierarchically by using the multiple indexes, such as activity degree, test ability and integrity degree. The combination weight vector of each level index was calculated by constructing the judgment matrix and consistency test. Then, the proposed model was improved by introducing the requirement list and description list, which made testers and crowdsourcing tasks match better. The experimental results show that the proposed model can evaluate the ability of testers accurately, support the selection and recommendation of crowdsourcing testers based on the evaluation results, and improve the efficiency and quality of mobile application crowdsourcing testing.

Aiming at the problem that the traditional entity alignment algorithm may lead to bad performance in entity alignment task of Chinese heterogeneous encyclopedia knowledge base, an entity alignment method based on entity attributes and the features of context topics was proposed. First, a Chinese heterogeneous encyclopedia knowledge base was constructed based on Baidu encyclopedia and Hudong encyclopedia data. Next, the Resource Description Framework Schema (RDFS) vocabulary list was made to normalize the entity attributes. Then the entity context information was extracted and the Chinese word segmentation was used on the contexts. The contexts were modelled by using the topic model and the parameters were computed by Gibbs sampling method. After that the topic-word probability matrix, the characteristic word collection and the corresponding feature matrix were calculated. Last, the Longest Common Subsequence (LCS) algorithm was used to compute the entity attribute similarity. When the similarity was between the lower and the upper bounds, the topic features of the entities' context were combined to resolve the entity alignment problem. Finally, according to the standard method, an entity alignment data set of Chinese heterogeneous encyclopedia was constructed for simulation experiments. In comparison with the traditional property similarity algorithm, weighted-property algorithm, context term frequency feature model and topic model algorithm, the experimental results show that the proposed method achieves 97.8% accuracy, 88.0% recall, 92.6% F-score in people class and 98.6% accuracy, 73.0% recall, 83.9% F-score in movie class. It outperformed the other entity alignment algorithms. The experimental results also indicate that the proposed method can improve the entity alignment results in constructing the Chinese heterogeneous encyclopedia knowledge base, and it can be applied to the entity alignment tasks with context information.

Aiming at the confidential communication based on information hiding technology, an information hiding algorithm using interval analysis of values on Z-axis for the vertical profile of three-dimensional (3D) models was proposed. First, the 3D model was scaled and rotated disproportionately according to fixed size and angle respectively, and the vertical profile could be obtained by horizontal mapping. Second, the vertical profile was mapped into the two-dimensional coordinate system and the values on the vertical axis could be determined using a fixed step size. Last, the vertical values were converted into binary numbers with interval constraints according to the fixed threshold. By disproportionate scaling with fixed size, the algorithm could be effective against the scaling attack. According to the fixed rotated angle and step size, the data could be embedded in redundancy of the whole model and the algorithm could be robust against cutting. The experimental results illustrate that this algorithm is of strong robustness against the random noise under 0.2%, re-meshing and non-uniform simplification.

Constant expansion and that energy consumption factors are ignored with its design process, bring the problem of high energy consumption and low efficiency of the cloud storage system. And this problem has become a main bottleneck in the development of cloud computing and big data. Most of previous studies had been mostly used to adjust the entire storage node to the low-power mode to save energy. According to the repetition of data and access rules, new storage model based on data classification was proposed. The storage area was divided into HotZone, ColdZone and ReduplicationZone so as to divisionally store the data according to the repetition and activity factor characteristics of each data file. Based on the new storage model, an energy-efficient storage algorithm was designed and a new storage model was constructed. The experimental results show that, the new storage model improves the energy utilization rate of the distributed storage system nearly 25%, especially when the system load is lower than the given threshold.

Like MapReduce, tasks under big data environment are always with data-dependent constraints. The resource selection strategy in distributed storage system trends to choose the nearest data block to requestor, which ignored the server's resource load state, like CPU, disk I/O and network, etc. On the basis of the distributed storage system's cluster structure, data file division mechanism and data block storage mechanism, this paper defined the cluster-node matrix, CPU load matrix, disk I/O load matrix, network load matrix, file-division-block matrix, data block storage matrix and data block storage matrix of node status. These matrixes modeled the relationship between task and its data constraints. And the article proposed an optimal resource selection algorithm with data-dependent constraints (ORS2DC), in which the task scheduling node is responsible for base data maintenance, MapRedcue tasks and data block read tasks take different selection strategies with different resource-constraints. The experimental results show that, the proposed algorithm can choose higher quality resources for the task, improve the task completion quality while reducing the NameNode's load burden, which can reduce the probability of the single point of failure.

To solve the problem of image reconstruction of incomplete projection data from cone-beam CT, a fast cone-beam CT reconstruction algorithm was proposed. In this work, the cone-beam CT reconstruction problem was reduced to an unconstrained optimization problem of minimizing an objective function which included a squared error term combined with a sparseness-inducing regularization term. The Lipschitz continuity of the objective function was analyzed and the Lipschitz constant was estimated based on its definition. The gradient descent step-size was calculated by the Lipschitz constant and the reconstructed image was updated by gradient method. Finally simultaneous algebraic reconstruction technique was used to reconstruct image from limited-angle projections and to meet the constraint of the projection data. An adaptive step-size technique was accommodated as so to accelerate the convergence of proposed algorithm. Simulation with noiseless Shepp-Logan shows: In comparison with simultaneous algebraic reconstruction technique, adaptive steepest descent-projection onto convex sets algorithm and gradient-projection Barzilari-Borwein algorithm, the proposed algorithm has higher SNR (Signal-to-Noise Ratio) by 13.7728dB, 12.8205dB, and 7.3580dB respectively. The algorithm has better performance in convergence speed and reconstruction accuracy, and can greatly improve the quality of images reconstructed from few projection data.

Concerning the situation that the ecological security has a lot of uncertaint factors and is dynamic, the Set Pair Analysis (SPA) theory and Markov chain were combined for dynamic assessment of ecological security, and a dynamic assessment method of ecological security was proposed combining the state assessment and trend analysis. The method adopted connection degree of SPA to represent uncertain knowledge, used the connection number of SPA to classify ecological security level, and built the comprehensive evaluation model of the system. Through the analysis of the development trend of ecological security, using the ergodicity of Markov chain and statistical quality of Monte Carlo method, the state of ecological security in the next moment could be predicted. This method uses limited assessment data and historical data, dynamically assesses the development and changes of ecological security, and provides a theoretical basis for the safety management.

The current blind detection techniques do not consider how the contents of different images influence the steganalysis performance. In this paper, a new approach based on image content and classifier fusion was proposed. In the training phase of the proposed method, the input images were first divided into several classes according to the image complexity, the training process was specialized and then the fuzzy measure was calculated for each class. In the testing phase, the class of image was first obtained, and various classified results were acquired by classifiers and then a fuzzy integral was used to fuse different classes in the decision making process. The experimental results on several sets of images demonstrate that the proposed steganalyzer significantly enhances the detection accuracy of prior art.

In view of the contradiction between capability, invisibility and robustness of the existing information hiding algorithms, a preprocessing algorithm for digital image based on CL (Chui-Lian) multi-wavelet transform and Combination Bit Plane (CBP) was proposed. Then the digital image preprocessed by CL multi-wavelet and CBP was taken as the cover image to embed secret information for confidential communication and image sharing. CL multi-wavelet transform could divide the cover image into four lowest resolution sub-images with different energy level. And CBP method could analyze the above four sub-images into different bit planes as the final embedding regions. During the hiding procedure, robust information, secret information and fragile information could be embedded according to the energy and robustness characteristics of the embedding regions. The experimental results show that the robustness against several common attacks described in this paper has certain enhancement compared with other two methods when the embedding rate is 25%. The Peak Signal-to-Noise Ratio (PSNR) is increased by 37.16% and 20.00% respectively compared with Discrete Cosine Transform-Least Significant Bit (DCT-LSB) and Discrete Wavelet Transform -Least Significant Bit (DWT-LSB) algorithm.

In order to study the influence of multipath, shadows and the weather variations on meteorological satellite channel, the Suzuki and extension Suzuki channel models were studied according to the analyses on the different weather conditions. Then the two-state Markov model was introduced into satellite channel model, which could describe the transformation between two kinds of channel state models caused by the changes of weather. Finally, the meteorological satellite channel simulator was designed and simulated based on the the shaping filter method. The results show that the proposed meteorological satellite channel simulator can be used in the description of actual meteorological satellite channel propagation characteristics.

In order to protect Android platforms from the application-level privilege escalation attacks, this paper analyzed the XManDroid access control model, which has better ability on fighting these attacks, especially the collusion attack on the covert channel. To address the problem that XManDroid could not detect the multi-application and multi-permissions collusion attacks, this paper proposed an improved mandatory access control model which recorded the communication history of applications by building an IPC links colored diagram. At last, the test result on the prototype system show that the new model can solve the problem in the XManDroid well.

In order to meet the high requirements of real-time collision detection in increasingly complex virtual environment, a fast collision detection algorithm based on image space was proposed. It made efficiently use of the Graphics Processing Unit (GPU). Based on the hierarchical binary tree and the collision detection between Oriented Bounding Boxes (OBB), the algorithm could quickly eliminate disjoint bumps of the virtual scene. With the potential collision set, the efficiency of the algorithm has a significantly improvement on the basis of RECODE algorithm. The experimental results show that the algorithm achieves good results, and has a higher efficiency, especially in a highly complex virtual environment.

According to the analysis on the effect of the texture complexity on steganalysis, the authors proposed a data hiding algorithm based on texture complexity and pixel-value difference. Firstly, the image was decomposed into blocks. Then, the texture complexity of each block was calculated and blocks with high complexity were selected. Then the pixel-value difference on two directions of the selected blocks was computed to determine the edge areas. The secret messages were only embedded in the pixel-value difference above the threshold. When compared with several steganographic methods on different image databases, the experimental results show that the proposed method outperforms previous steganographic methods such as LSB matching and adaptive data hiding in edge areas of images on the capability of resisting universal blind detection, such as wavelet high-order statistics analysis.

The data block storage mechanism and downloading process in Hadoop Distributed File System (HDFS) cluster were analyzed. In combination with multi-point and multi-threaded Peer-to-Peer (P2P) download idea, an efficiency optimization algorithm was proposed from the aspects of data-block, file and cluster. Concerning the possible imbalanced load problem caused by multi-thread download in HDFS cluster, a download-point selection algorithm was put forward to optimize the download-point selection. The mathematical analysis and experiments prove that the three methods can improve the download efficiency and download-point selection algorithm can achieve loading balance among DataNodes in HDFS cluster.

Concerning the problems of low precision ratio and low recall ratio of function match in the research of workflow integration, the authors implemented the matching mechanism based on formal semantic of extract pre/post match pattern, and proposed matching principles on the basis of algebraic expressions in high level programming languages. The specific algorithm was raised up and also an example was given to analyze and illustrate the algorithm. The proposed algorithm is suitable for function matching in workflow integration and it is founded on strict formal method, so that it can be analyzed and verified conveniently with mathematical methods. The limitation is that it is based on elementary algebraic expression.

In order to enhance the statistical security of a data hiding system, the factors that influenced the security of a steganography were analyzed. Three ways to reduce the statistical distortion of the stego-image were found: Increasing embedding efficiency, controlling modifying amplitude and choosing embedding position adaptively. According to the three ways, a new adaptive steganography based on double-layered embedding method was proposed. The new scheme chose pixels under strong noise background as message carrier and embedded secrets in their least significant and second least significant bit-planes. The experimental results on uncompressed images show that the proposed steganography outperforms the prior algorithm for resisting blind steganalysis.

The available tools for detecting network information can hardly meet the demands of acquiring the completeness and precision of network information for the researchers.The information fusion technology was applied to collect the network information using several detecting tools.The information from different detecting tools was fused in different layers.In data layer,the fuzzy logical statistic method was adopted to identify system type and network device,and in logic layer,the most credible information was obtained with the support of system knowledge database.

A storing systematic configuration of spatial metadata based on XDR Schema was proposed and a XML data reduced schema was created. The spatial metadata expressed by XML was mapped to SQL Server 2000 RDBMS.  The annotated XDR schema corresponded with XML view, so we could query database using annotated XDR schema and get result in XML form.

 In traditional monolithic kernel operating systems, all kernel codes run within a common and shared address space, and any vulnerabilities in kernel or any untrusted modules loaded in kernel would compromise the whole system security. The development of a layered and separated secure kernel was described in this paper. Since the powers of kernel are partitioned, the vulnerabilities of kernel are confined, and arbitrarily tampering of kernel by malice codes was prevented. The prototype system is entirely developed from beginning for the i386 architecture.

The biological Immune System(IS) is highly complicated and aimed at detecting and removing the viruses. There’re many similarities between the computer security system and living organism’s IS. So the researches on the similarities could provide important clues about how to construct robust computer security system. Based on the theory of lymphocyte activation, a behavioral characteristic detecting model based on the self-replicating behavior of the viruses is brought forward. A validity analysis and experiment was made. The results of the experiment show that this model could become a new try to detect the viruses according to the replication behavior of the viruses. And at the same time it effectively reduces the problem of false negative and false positive in the process of distinguish between self and non-self.