Concerning of the problems of high cost and unstable detection results of the traditional malicious code detection methods， a multi-neural network malicious code detection model based on depthwise separable convolution was proposed. By using the Depthwise Separable Convolution （DSC）， SENet （Squeeze-and-Excitation Network） channel attention mechanism and Grey Level Co-occurrence Matrix （GLCM）， three lightweight neural networks were connected with GLCM in parallel to detect malicious code families and their variants， then the detection results of multiple strong classifiers were fused via Naive Bayes classifier to improve the detection accuracy while reducing the computational cost. Experimental results on the hybrid dataset of MalVis + benign data show that the proposed model achieved the accuracy of 97.43% in the detection of malicious code families and their variants， which was 6.19 and 2.29 percentage points higher than those of ResNet50 and VGGNet models respectively， while its parameter quantity is only 68% of that of ResNet50 model and 13% of that of VGGNet model. On malimg dataset， the detection accuracy of this model achieved 99.31%. In conclusion， the proposed model has good detection effect with reduced parameters.

In order to deal with the problem of low pedestrian re-identification rate in video images due to small target pedestrians， occlusions and variable pedestrian postures， a dual-channel multi-scale integrated learning method was established based on efficient network EfficientNet. Firstly， EfficientNet-B1 （EfficientNet-Baseline1） network was used as the backbone structure. Secondly， a weighted Bidirectional Feature Pyramid Network （BiFPN） branch was used to integrate the extracted global features at different scales. In order to improve the identification rate of small target pedestrians， the global features with different semantic information were obtained. Thirdly， PCB （Part-based Convolutional Baseline） branch was used to extract deep local features to mine non-significant information of pedestrians and reduce the influence of pedestrian occlusion and posture variability on identification rate. Finally， in the training stage， the pedestrian features extracted by the two branch networks respectively were calculated by the Softmax loss function to obtain different subloss functions， and they were added for joint representation. In the test stage， the global features and deep local features obtained were spliced and fused， and the Euclidean distance was calculated to obtain the pedestrian re-identification matching results. The accuracy of Rank-1 of this method on Market1501 and DukeMTMC-Reid datasets reaches 95.1% and 89.1% respectively， which is 3.9 percentage points and 2.3 percentage points higher than that of the original backbone structure respectively. Experimental results show that the proposed model improves the accuracy of pedestrian re-identification effectively.

With the proliferation of Artificial Intelligence （AI） computing power to the edge of the network and even to terminal devices， the computing power network of end-edge-supercloud collaboration has become the best computing solution. The emerging new opportunities have spawned the deep integration between end-edge-supercloud computing and the network. However， the complete development of the integrated system is unsolved， including adaptability， flexibility， and valuability. Therefore， a computing power network for ubiquitous AI named ACPN was proposed with the assistance of blockchain. In ACPN， the end-edge-supercloud collaboration provides infrastructure for the framework， and the computing power resource pool formed by the infrastructure provides safe and reliable computing power for the users， the network satisfies users’ demands by scheduling resources， and the neural network and execution platform in the framework provide interfaces for AI task execution. At the same time， the blockchain guarantees the reliability of resource transaction and encourage more computing power contributors to join the platform. This framework provides adaptability for users of computing power network， flexibility for resource scheduling of networking computing power， and valuability for computing power providers. A clear description of this new computing power network architecture was given through a case.

To quickly select and organize relevant manufacturing resources and guarantee the execution of manufacturing tasks under the cloud manufacturing mode, a construction method of cloud manufacturing virtual workshop was proposed for manufacturing tasks. In this method, the manufacturing processes were abstracted into manufacturing task execution chains, in which the nodes were corresponding to manufacturing equipment cloud services or inspection cloud services and the directed edges were corresponding to logistics cloud services. At the same time, the cloud services were organized and managed through the industry domain, location domain and type domain to construct smaller candidate sets of cloud services with reducing the computing amount of function matching, performance matching, price matching and time matching, thus constructing cloud manufacturing virtual workshops rapidly. The numerical example analysis shows that compared to other methods, the proposed method can select cloud services in a shorter time and ensure that the Quality of Service (QoS) of the selected cloud services is better in the relevant domains.

Focused on the problems of no protection for the number of zero elements in original matrix and no verification for the result returned by cloud in outsourcing algorithm of matrix full rank decomposition, a verifiable and secure outsourcing scheme of matrix full rank decomposition was proposed. Firstly, in the phase of encryption, a dense invertible matrix was constructed by using the Sherman-Morrison formula for encryption. Secondly, in the phase of cloud computing, the cloud computing of the full rank decomposition for the encryption matrix was required. And when the results of full rank decomposition for encryption matrix (a column full rank matrix and a row full rank matrix) were obtained, the cloud computing of the left inverse of the column full rank matrix and the right inverse of the row full rank matrix was required respectively. Thirdly, in the phase of verification, the client not only needed to verify whether these two matrices returned by cloud are row-full-rank or column-full-rank respectively, but also needed to verify whether the multiplication of these two matrices is equal to the encryption matrix. Finally, if the verification was passed, the client was able to use the private key to perform the decryption. In the protocol analysis, the proposed scheme is proved to satisfy correctness, security, efficiency, and verifiability. At the same time, when the dimension of the selected original matrix is 512×512, with different densities of non-zero elements in the matrix, the entropy of the encryption matrix calculated by this scheme is identically equal to 18, indicating that the scheme can protect the number of zero elements effectively. Experimental results show the effectiveness of the proposed scheme.

Focused on the problems of multiple expression variations, multiple pose variations as well as varying-degree missing face point cloud data in Three-Dimensional (3D) faces, 3D point cloud face data was exploratively applied to PointNet series classification networks, and the recognition results were compared and analyzed, then a new network framework named HFN (Hierarchical Feature Network) was proposed. First, the point cloud with fixed points was randomly sampled after data preprocessing. Second, the point fixed point cloud was input into SA (Set Abstraction) module in order to obtain the centroid points and neighborhood points of the local areas, and extract the features of the local areas, then the point cloud spatial structural features extracted from DSA (Directional Spatial Aggregation) module based on multi-directional convolution were mosaicked. Finally, the full connection layer was used to perform the classification of 3D faces, so as to realize the 3D face recognition. The results on CASIA database show that the average recognition rate of the proposed method is 96.34%, which is better than those of classification networks such as PointNet, PointNet++, PointCNN and Spatial Aggregation Net (SAN).

Aiming at the problem of poor accuracy of automatic region division of mouse brain slice image using traditional multimodal registration method,an unsupervised multimodal region division method of brain slice image was proposed. Firstly,based on the mouse brain map,the Atlas brain map and the Average Template brain map in the Allen Reference Atlases (ARA) database corresponding to the brain slice region division were obtained. Then the Average Template brain map and the mouse brain slices were pre-registered and modal transformed by affine transformation preprocessing and Principal Component Analysis Net-based Structural Representation(PCANet-SR)network processing. After that,according to U-net and the spatial transformation network,the unsupervised registration was realized,and the registration deformation relationship was applied to the Atlas brain map. Finally,the edge contour of the Atlas brain map extracted by the registration deformation was merged with the original mouse brain slices in order to realize the region division of the brain slice image. Compared with the existing PCANet-SR+B spline registration method,experimental results show that the Root Mean Square Error(RMSE)of the registration accuracy index of this method reduced by 1. 6%,the Correlation Coefficient(CC)and the Mutual Information(MI)increased by 3. 5% and 0. 78% respectively. The proposed method can quickly realize the unsupervised multimodal registration task of the brain slice image,and make the brain slice regions be divided accurately.

Aiming at the problem of low reconstruction quality of the existing Compressed Sensing Magnetic Resonance Imaging (CSMRI) algorithms at low sampling rates, an imaging method combining deep priors and non-local similarity was proposed. Firstly, a deep denoiser and Block Matching and 3D filtering (BM3D) denoiser were used to construct a sparse representation model that can fuse multiple priori knowledge of images. Secondly, the undersampled k-space data was used to construct a compressed sensing magnetic resonance imaging optimization model. Finally, an alternative optimization method was used to solve the constructed optimization problem. The proposed algorithm can not only use the deep priors through the deep denoiser, but also use the non-local similarity of the image through the BM3D denoiser to reconstruct the image. Compared with the reconstruction algorithms based on BM3D, experimental results show that the proposed algorithm has the average peak signal-to-noise ratio of reconstruction increased about 1 dB at the sampling rates of 0.02, 0.06, 0.09 and 0.13. Compared with the existing MRI algorithm WaTMRI (Magnetic Resonance Imaging with Wavelet Tree sparsity),DLMRI (Dictionary Learning for Magnetic Resonance Imaging), DUMRI-BM3D (Magnetic Resonance Imaging based on Dictionary Updating and Block Matching and 3D filtering), etc, the images reconstructed by the proposed algorithm contain a lot of texture information, which are the closest to the original images.

Aiming at the problem of the lack of accurate recommended and selected courses in the new form of higher education, a high-dimensional multi-objective evolutionary algorithm based course guidance and recommendation method was proposed. First, a multi-dimensional fact data warehouse model was designed to save storage space, and the related attributes in the data warehouse such as courses, students, teachers, course difficulty and course recommendation index were formally defined and stipulated. Second, a recommendation model based on high-dimensional R2-MODE (R2 based Multi-Objective Differential Evolution) algorithm was constructed, which improved the search ability in the high-dimensional complex space. Finally, the optimizations of 4 performances, the professionalism of the course teacher, the professional relevance of the course, the degree of the course difficulty and the comprehensive evaluation of the course, were achieved at the same time. Experimental results showed that the proposed algorithm improved the convergence by 50% compared with the reference point-based NSGA-Ⅲ (Third version of Non-dominated Sorting Genetic Algorithm), and had the increase of 5% in the distribution compared with the dominant relationship-based ε-MOEA ( ε-dominance based Multi Objective Evolutionary Algorithm). The designed method had the best overall effect on the convergence and distribution of datasets. In the experiment, the accurate recommendation of courses according to the individual characteristics and wishes of students was successfully performed by using the proposed algorithm. The proposed algorithm provided the necessary theoretical support for the accurate guidance and recommendation of course selection on the network platform, and a new method for intelligent course selection.

In view of the subjective arbitrariness of objective weight in multi-objective decision making based on Vague sets and the monotong problem of evaluation function, a novel approach to multi-objective decision making based on entropy weighted-Vague sets was presented. Firstly, the decision matrix was transformed into the objective-grade-membership matrix. Then the objective weight of each objective was calculated by entropy coefficient method, and the weight vector interval of each objective was obtained by considering objective weight and subjective weight. Next the Vague evaluation was obtained by computing the sets of objectives being in favor, against and neutral. Finally, a new evaluation function was defined to sort the alternatives and select the optimal scheme. The rationality and effectiveness of the method were verified by an example.

To effectively control large-scale outbreak, the propagation properties of the leeching P2P (Peer-to-Peer) botnet was studied using dynamics theory. Firstly, a delayed differential-equation model was proposed according to the formation of the botnet. Secondly, the threshold expression of controlling botnet was obtained by the explicit mathematical analysis. Finally, the numerical simulations verified the correctness of theoretical analysis. The theoretical analysis and experimental results show that the botnet can be completely eliminated if the basic reproduction number is less than 1. Otherwise, the defense measures can only reduce the scale of botnet. The simulation results show that decreasing the infection rate of bot programs or increasing the immune rate of nodes in the network can effectively inhibit the outbreak of botnet. In practice, the propagation of bot programs can be controlled by some measures, such as uneven distribution of nodes in the network, timely downloading patch and so on.

Traditional hypernetwork model is biased towards the majority class, which leads to much higher accuracy on majority class than the minority when being tackled on imbalanced data classification problem. In this paper, a Boosting ensemble of cost-sensitive hypernetworks was proposed. Firstly, the cost-sensitive learning was introduced to hypernetwork model, to propose cost-sensitive hyperenetwork model. Meanwhile, to make the algorithm adapt to the cost of misclassification on positive class, cost-sensitive hypernetworks were integrated by Boosting. The proposed model revised the bias towards the majority class when traditional hypernetwork model was tackled on imbalanced data classification, and improved the classification accuracy on minority class. The experimental results show that the proposed scheme has advantages in imbalanced data classification.

The arithmetic correlation function is a new method for studying the cryptographic properties of Boolean functions. Based on the basic definitions of addition and multiplication of multi-2-adic integer, the study constructed a new algebraic ring and realized the arithmetic or “with-carry” analogs of classic correlation functions. In this paper the definition of arithmetic autocorrelation function was introduced. The arithmetic correlation value of symmetric Boolean functions was studied. The results show that the arithmetic autocorrelation function of symmetric Boolean functions is a real symmetric function with at most n1 values.

Based on the business process, a composite model of manufacturing cloud services was proposed to improve the successful rate of the manufacturing cloud service composition and match the composite cloud service with the user business demand correctly. As the foundation, formal descriptions were given about concepts such as the manufacturing cloud service, the process node task, the service composability and the process matching. The model consisted of the business process engine, the business process, the selection logic, the evaluation logic, the monitoring logic, the knowledge base and the atomic cloud service set. With the function matching, the composability of optional services was checked. The load, the Quality of Service (QoS) and the business process information were also considered. Then suitable cloud services were selected and integrated into the business process to form the composite manufacturing cloud service. The process of the service composition was described in detail, and the realization method of it was offered. The composite service model was verified through an example. The results prove that the valid cloud service entities can be selected and combined effectively with the model, the successful rate of the service composition is raised, and users' manufacturing activities can be carried out smoothly.

Concerning the problem that Fireworks Explosion Optimization (FEO) algorithm is easy to be premature and has low solution precision, an elite Opposition-Based Learning (OBL) was proposed. In every iteration, OBL was executed by the current best individual to generate an opposition search populations in its dynamic search boundaries, thus the search space of the algorithm was guided to approximate the optimum space. This mechanism is helpful to improve the balance and exploring ability of the FEO. For keeping the diversity of population, the sudden jump probability of the individual to the current best individual was calculated, and based on it, the roulette mechanism was adopted to choose the individual which entered into the child population. The experimental simulation on five classical benchmark functions show that, compared with the related algorithm, the improved algorithm has higher convergence rate and accuracy for numerical optimization, and it is suitable to solve the high dimensional optimization problem.

In order to recommend trustworthy Web services, the differences between Web service recommendation and electronic commerce recommendation were analyzed, and then based on the collaborative filtering recommendation algorithm, a trustworthy Web service recommendation approach was proposed. At first, non-functional requirements of trustworthy software were evaluated. According to the evaluation results, similar users were filtered for the first time. Then, by using the rating information and basic information, the similar users were filtered for the second time. After finishing these two filtering procedures, the final recommendation users were determined. When using users' ratings information to calculate the similarity between the users, the similarity of the different services to the users was taken into consideration. When using users' basic information to calculate the similarity between the users, the Euclidean distance formula was introduced because of the nonlinear characteristics of the users. The problems of the dishonesty and insufficient number of users were also considered in the approach. At last, the experimental results show that the recommendation approach for trustworthy Web services is effective.

Multiple feature fusion based tracking is one of the most active research topic in tracking field, but the tracking accuracy needs improving in complex environment and most of them use single fusion rule. In this paper, a new adaptive fusion strategy was proposed for multi-feature fusion. First, the local background information was introduced to strengthen the description of the target, and then the feature weight was calculated by a variety of criteria in the fusion process. In addition, the framework of mean shift was considered to realize target tracking. An extensive number of comparative experimental results show that the proposed algorithm is more stable and robust than the single fusion rule.

For cloud manufacturing environment, many manufacturing cloud services have the same or similar function, so it is difficult to get the most suitable cloud services. This study designed a selection method of the manufacturing cloud services based on trust evaluation. How to select cloud services was described by abstraction; the reliability, usability, timeliness, price and honesty were used as the trust characteristics together; and the evaluation time and effect of estimators' honesty on the service's credibility were also taken into account; and then the overall credibility was calculated from all above data by weighted average method. Furthermore, with all factors such as the cloud services' function, workload, current state and physical distance considered in package, the method was built to guide the cloud service selection by matching the services' function, workload and price and combining the trust evaluation. The results of simulation experiments show that the service selection method is able to recognize entities of manufacturing cloud services, and it improves the rate of the cloud service trades and meets users' functional and non-functional requests better.

This paper introduced a low-cost platform for locating bluetooth cell phones and releasing position information. The authors improved the scheme of measuring Received Signal Strength Indication (RSSI) of bluetooth devices to locate multiple cell phones simultaneously, developed cell phone program to receive position information via Wi-Fi and display it on map. The experimental results show that the system is accurate in positioning and easy to use, and it provides a platform support for applications of Internet of Things (IOT) under current hardware condition.

Task assignment problem is a typical NP problem. Particle Swarm Optimization (PSO) algorithm was used to solve task assignment problem. The model of task assignment problem was formulated and the detailed solution for solving task assignment problem based on PSO algorithm was illuminated. To get better optimization results, an improved PSO algorithm named IPSO including variance mechanism and local updating mechanism was presented. Examples and simulation experiences demonstrate that the IPSO algorithm is effective in solving task assignment problem.

The theory of IPID(IP Identification) covert network scanning was introduced,then the design and implementation of IPID covert network scanning under the operation Windows was proposed.In order to improve the efficiency of scanning, the chunk binary algorithm was introduced, then its performance was analyzed and was compared with other algorithm. The results show that the chunk binary algorithm is a good algorithm of IPID covert scanning,and the correctness and speed of IPID covert scanning is decided by setting delay time between getting two IPID.