Aiming at the problems that Butterfly Optimization Algorithm (BOA) tends to fall into local optimum and has poor convergence, a Multi-Strategy Improved BOA (MSBOA) was proposed. Firstly, the cosine similarity position adjustment strategy was introduced to the algorithm, rotation transformation operator and scaling transformation operator were used to update the positions, so as to effectively maintain the population diversity of the algorithm. Secondly, dynamic switching probability was introduced to balance the transformation between the local phase and the global phase of the algorithm. Finally, a hybrid inertia weight strategy was added to accelerate convergence. Solving 16 benchmark test functions, as well as the Wilcoxon rank-sum test and CEC2014 test functions were to verify, the effectiveness and robustness of the proposed algorithm. Experimental results show that compared with BOA, some BOAs with different improvement strategies and some swarm intelligence algorithms, MSBOA has significant improvement in convergence accuracy and convergence speed.

Existing deep learning-based malicious code detection methods have problems such as weak deep-level feature extraction capability, relatively complex model and insufficient model generalization capability. At the same time, code reuse phenomenon occurred in large number of malicious samples of the same type, resulting in similar visual features of the code. This similarity can be used for malicious code detection. Therefore, a malicious code detection method based on multi-channel image visual features and AlexNet was proposed. In the method, the codes to be detected were converted into multi-channel images at first. After that, AlexNet was used to extract and classify the color texture features of the images, so as to detect the possible malicious codes. Meanwhile, the multi-channel image feature extraction, the Local Response Normalization(LRN) and other technologies were used comprehensively, which effectively improved the generalization ability of the model with effective reduction of the complexity of the model. The Malimg dataset after equalization was used for testing, the results showed that the average classification accuracy of the proposed method was 97.8%, and the method had the accuracy increased by 1.8% and the detection efficiency increased by 60.2% compared with the VGGNet method. Experimental results show that the color texture features of multi-channel images can better reflect the type information of malicious codes, the simple network structure of AlexNet can effectively improve the detection efficiency, and the local response normalization can improve the generalization ability and detection effect of the model.

Functional hyper-networks are widely used in brain disease diagnosis and classification studies. However, the existing research on hyper-network construction lacks the ability to interpret the grouping effect or only considers the information of group level information of brain regions, the hyper-network constructed in this way may lose some useful connections or contain some false information. Therefore, considering the group structure problem of brain regions, the sparse group Lasso (Least absolute shrinkage and selection operator) (sgLasso) method was introduced to further improve the construction of hyper-network. Firstly, the hyper-network was constructed by using the sgLasso method. Then, two groups of attribute indicators specific to the hyper-network were introduced for feature extraction and feature selection. The indictors are the clustering coefficient based on single node and the clustering coefficient based on a pair of nodes. Finally, the two groups of features with significant difference obtained after feature selection were subjected to multi-kernel learning for feature fusion and classification. The experimental results show that the proposed method achieves 87.88% classification accuracy by using the multi-feature fusion, which indicates that in order to improve the construction of hyper-network of brain function, the group information should be considered, but the whole group information cannot be forced to be used, and the group structure can be appropriately expanded.

The major drawback of existing Differential Chaos Shift Keying (DCSK) communication system is low transmission rate. To solve the problem, a Correlation Delay-Differential Chaos Shift Keying (CD-DCSK) communication scheme without inter-signal interference was proposed. At the transmitting side, two orthogonal chaotic signals were generated by an orthogonal signal generator and normalized by the sign function to keep the energy of the transmitted signal constant. Then, two chaotic signals and their chaotic signals with different delay time intervals were respectively modulated by 1 bit data information to form a frame of transmission signal. At the demodulation side, correlation demodulation was used to extract data information and the information bits were recovered by detecting the sign of correlator output. The theoretical Bit Error Rate (BER) performance of system under Additive White Gaussian Noise (AWGN) channel was analyzed by using Gaussian Approximation (GA) method, and was compared with classical chaotic communication systems. The performance analysis and experimental results indicate that, compared with DCSK system, the transmission rate of CD-DCSK system without inter-signal interference increases by 50 percentage points, and the BER performance of the proposed system is better than that of Correlation Delay Shift Keying (CDSK) system.

The feature representation extracted from the functional connection network based on single brain map template is not sufficient to reveal complex topological differences between patient group and Normal Control (NC) group. However, the traditional multi-template-based functional brain network definitions mostly use independent templates, ignoring the potential topological association information in functional brain networks built with each template. Aiming at the above problems, a multi-level brain map template and a method of Relationship Induced Sparse (RIS) feature selection model were proposed. Firstly, an associated multi-level brain map template was defined, and the potential relationship between templates and network structure differences between groups were mined. Then, the RIS feature selection model was used to optimize the parameters and extract the differences between groups. Finally, the Support Vector Machine (SVM) method was used to construct classification model and was applied to the diagnosis of patients with depression. The experimental results on the clinical diagnosis database of depression in the First Hospital of Shanxi University show that the functional brain network based on multi-level template achieves 91.7% classification accuracy by using the RIS feature selection method, which is 3 percentage points higher than that of traditional multi-template method.

Traditional Fuzzy C-Means (FCM) clustering algorithm only considers the characteristics of a single pixel when applied to liver CT image segmentation, and it can not overcome the influence of uneven gray scale and the problem of boundary leakage caused by blurred liver boundary. In order to solve the problems, a Spatial Fuzzy C-Means (SFCM) clustering segmentation algorithm combined with spatial constraints was proposed. Firstly, the convolution kernel was constructed by using two-dimensional Gauss distribution function, and the feature matrix could be obtained by using the convolution kernel to extract the spatial information of the source image. Then, the penalty term of spatial constraint was introduced to update and optimize the objective function to obtain a new iteration equation. Finally, the liver CT image was segmented by using the new algorithm. As shown in results, the shape of liver contour splited by SFCM is more regular when segmenting liver CT images with gray unevenness and boundary leakage. The accuracy of SFCM reaches 92.8%, which is 2.3 and 4.3 percentage points higher than that of FCM and Intuitionistic Fuzzy C-Means (IFCM). Also, over-segmentation rate of SFCM is 4.9 and 5.3 percentage points lower than that of FCM and IFCM.

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

The use of resting-state functional magnetic resonance imaging to study the functional connectivity network of the brain is one of the important methods of current brain disease research. This method can accurately detect a variety of brain diseases, including Alzheimer's disease. However, the traditional network only studies the correlation between the two brain regions, and lacks a deeper interaction between the brain regions and the association between functional connections. In order to solve these problems, a method was proposed to construct a functional connectivity network of high-order minimum spanning tree, which not only ensured the physiological significance of functional connectivity network, but also studied more complex interactive information in the network and improves the accuracy of classification. The classification results show that the resting-state functional magnetic resonance imaging classification method based on the functional connectivity network of high-order minimum spanning tree greatly improves the accuracy of Alzheimer's disease detection.

In order to solve the low-quality and degeneration problem of particles in the process of particle filtering, a multisensor information fusion algorithm based on intelligent particle filtering was proposed. The process of the proposed algorithm was divided into two steps. Firstly, the multisensor data was sent to the appropriate particle filtering calculation module, and the proposal distribution density was updated for the purpose of optimizing the particle distribution. Then, the integrated likelihood function model was structured by using the multisensor data in intelligent particle filtering module, meanwhile, the small-weight particles were modified into large-weight ones according to the designed genetic operators. The posterior distribution was more sufficiently approximated, thus large-weight particles were reserved in the process of resampling, which avoided the problem of exhausting particles, further maintained the diversity of the particles and improved the filtering precision. Finally, the optimal accurate estimated value was obtained. The proposed algorithm was applied to the GPS/SINS/LOG integrated navigation system according to the prototype testing data, and its effectiveness was verified by the simulation calculation. The simulation results show that, the proposed algorithm can get accurate informations of location, speed and heading, and effectively improve the filtering performance, which can improve the calculating precision of the integrated navigation system and meet the requirement of high precision navigation and positioning of the ship.

In recent years, multiscale pedestrian detection received extensive attentions in the field of computer vision. In traditional methods, the input image must be resized with different scales to compute the features, which significantly reduces the detection speed. Color Self-Similarity Feature (CSSF) was presented to overcome this problem. An improved CSSF with lower dimension was proposed for the CSSF whose dimension is too high and time-consuming in the training process of the classifiers. Combined with pedestrian structural similarity, a fixed-size window was defined at first, and then the improved CSSF was extracted by sliding the fixed-size window in different color space. Finally, the pedestrian detection classifier was constructed by combining with AdaBoost algorithm. Test shows that compared with the traditional CSSF whose dimension is ten millions, new feature dimension is only a few thousand, and it can be extracted and trained faster, but detection effect decreases slightly; compared with the Histogram of Oriented Gradient (HOG), feature extraction speed improves 5 times, detection effect is essentially the same. The new method has a good application value in real-time pedestrian detection and monitoring systems.

There are many illegal Web pages on the Internet, which may have pornographic, violent, gambling or reactionary content. Without being filtered effectively, they will exercise a malign influence on the searching services. An improved K-Nearest Neighbors (KNN) classification algorithm to promote the recognition accuracy was proposed and implemented on a virtualized platform following the MapReduce model provided by the open source software Hadoop, which made it distributed and parallel. Through experiments and comparison with the existing work, it is proved that the proposed recognition method improves the accuracy and efficiency greatly. The algorithm is implemented on a virtualized platform following the MapReduce model provided by the open source software Hadoop, which makes it distributed and parallel. Through experiments and comparison with existing work, it is proved that the recognition method we propose improves the accuracy and efficiency greatly.

In order to overcome the defects of traditional rigid load balancing mechanism that it can not adapt to changing network environment, and to solve the problem of load balancing mechanisms in cloud environment that it can not take full advantage of elastic characteristics and Quality of Service (QoS) would be unstable, this paper proposed a new load balancing mechanism in cloud environment based on green computing resource pool strategy. It quantified the load according to the utilization rate of system resources and the quantization decided distribution of virtual machines. On the basis of the use of the virtual machines, resources would be recycled to improve resource utilization. The experimental results show that the response time stablizes around 2.5 seconds, the overall QoS has been obviously improved with the power consumption reduced, and the effectiveness of the mechanisms has been verified.

A neural network classifier with the hidden neurons activated by unipolar Sigmoid function was constructed and investigated in this paper. The thresholds of hidden neurons and weights between the input layer and the hidden layer of the neural network were randomly generated. The psedoinverse-type Weights Direct Determination (WDD) method was applied to determining the weights between the hidden layer and the output layer. Moreover, a Structure Automatic Determination (SAD) algorithm with pruning-while-growing and twice-pruning policies was proposed to determine the optimal structure of the neural network. The numerical experimental results demonstrate that the SAD algorithm can determine the optimal structure of the neural network quickly and effectively and the neural network classifier has a satisfactory performance.

The community detecting algorithm was applied to human functional network to explore the mechanism of human brain. The brain functional data of 28 healthy subjects were collected by functional Magnetic Resonance Imaging (fMRI), and the brain functional network of human beings based on time series was constructed. A threshold range of vertices in the network was designated according to modularity and full connected network theory. The community structures were detected by using the hierarchical clustering algorithm and the greedy algorithm respectively, and the experimental results show that similar community structures have been obtained. Then different performances can be explored across the threshold by analyzing the modularity. An effective threshold range of vertices between 180 to 320 in brain network was proposed. Exploring the community structure is helpful to comprehend the mechanism of brain lesions, which provides a tool for diagnosis and treatment of brain diseases.

The technology of Digital Time-Stamping (DTS) is widely used in digital signature, electronic commerce and patents and property right protection of various software and hardware. For some unreliable networks, of which the network situation is poor, the net speed changes greatly and the net links are usually intermittent, there is no necessary technological means to guarantee the normal and effective operation of DTS service. According to the characteristics of the unreliable networks, a new time-stamping scheme was proposed. In the scheme, it did not need to communicate with Time Stamp Authority (TSA) each time when a time-stamping service was required. The local trusted platform would offer the time-stamping service itself. A new DTS service protocol based on Trusted Platform Module (TPM) was also proposed under the circumstances of unreliable networks. The results of the security analysis of the protocol show that the protocol is secure and the time error in the protocol can be kept under control. The adaptability of the protocol for the unreliable network is excellent.

Argumentation model is the theoretical basis to establish group argumentation environment. Based on Issue-Based Information System (IBIS) model and Toulmin' argument schema, a group argumentation model was proposed, which was able to evaluate the argumentative utterance. With this model, the group argumentative information could be structured as a graph which consisted of utterance nodes and semantic links. A method of evaluating utterance nodes based on Language Weighted Aggregation (LWA) operator and node reduction was proposed. A group argumentation on the issue of system architecture design was illustrated as an example to show the usability and effectiveness of the proposed model.

Web service greatly facilitates the application-to-application integration based on heterogeneous platform, but its core components are faced with threats of malicious attacks. Currently, the Intrusion Detection System (IDS) is usually used to prevent these attacks. However, the IDSs distributed throughout the network may be developed by different vendors and there is not a common vocabulary understandable among them. Therefore, the IDSs stopped people from cooperatively preventing the multi-phased and distributed attacks easily. In this paper, a new method based on ontology and OWL to classify and describe the Web services attack was presented. Through constructing a Web services attack ontology, the common understandable vocabulary could be provided for different IDSs. Then, an intrusion detection system based on the Web Service Attack ontology (called O-IDS) was presented as well, which could efficiently overcome the shortage of the existed IDS and enhance the ability to detect the multi-phased and distributed attacks.

A distributed simulation approach was proposed for discrete-events simulation with considerable amounts of events between logical processes. The proposed approach employed a time-driven method to simulate occurrence of discrete-events, using Remote Procedure Call (RPC) to describe the interaction between simulation members. In this approach, barrier synchronization objects were deployed for time synchronization in simulation advancement, in order to ensure the correctness of the causal ordering. Results obtained from the experiments show that the proposed approach can correctly and promptly handle large number of events, providing accuracy guarantee and efficiency improvement of the simulation model.

In order to fully use IPv4/IPv6 heterogeneous network resources and provide resource requirement for cloud computation platform, the authors designed an IPv4/IPv6 virtual machine migration transition framework for cloud computation based on tunnel technology, prefix management, address pool management and mobile IP. The framework used the designed cloud computation control engine as a core to translate and link heterogeneous network, and needed Network Address Translation-Protocol Translation (NAT-PT) and tunnel technology collaboration. The framework was established for IPv4/IPv6 virtual machine seamless live migration in the early, middle, late period of IPv4 to IPv6 transition, and IPv4/IPv6 cloud computation service was provided for client. The framework could be applied to construct cloud computation platform in the IPv4/IPv6 transition period.

Particle swarm optimization has the disadvantages of being easily trapped into a local optimal solution and searching with lower efficiency in multi-dimensional space. With reference to the strategy of concave function to the inertia weight, the authors proposed a method of multidimensional asynchronism and stochastic disturbance to improve the ability to search for global optimum as well as solve the limitation of dimensionality problem. The experimental results of four classic benchmark functions show that the algorithm can keep the balance between the global search and local search, which effectively improves the success probability of searching with higher precision.