Concerning the problem that traditional collaborative filtering algorithm just utilizes users' ratings on items when generating recommendation, without considering users' labels or comments, which can not reflect users' real preference on different items and the prediction accuracy is not high and easily overfits, a Stacked Denoising AutoEncoder (SDAE)-based improved Hybrid Recommendation (SDHR) algorithm was proposed. Firstly, SDAE was used to extract items' explicit features from users' free-text labels. Then, Latent Factor Model (LFM) algorithm was improved, the LFM's abstract item features were replaced with extracted explicit ones to train matrix decomposition model. Finally, the user-item preference matrix was used to generate recommendations. Experimental tests on the dataset MovieLens showed that the accuracy of the proposed algorithm was improved by 38.4%, 16.1% and 45.2% respectively compared to the three recommendation models (including the model based on label-based weights with collaborative filtering, the model based on SDAE and extreme learning machine, and the model based on recurrent neural networks). The experimental results show that the proposed algorithm can make full use of items' free-text label information to improve recommendation performance.

Concerning the problem that most of the existing social network growing models have negative degree correlation, considering the characteristics of positive degree correlations and high clustering coefficients, a new social network growing model was proposed based on Holme and Kim (HK) model. Firstly, the topological structure of a real-world social network was analyzed to obtain some important topological parameters of real social networks. Secondly, the HK model was improved by introducing triad formation mechanism, namely HK extended model with Turnable Degree Correlation and Clustering coefficient (HK-TDC&C), by which both clustering coefficients and degree correlations in the network could be adjusted. The model could be used to construct social networks with various topological properties. Finally, using mean field theory, the degree distribution of the model was analyzed, and Matlab was used for numerical simulation to calculate other topological parameters of the network. The results show that, by turning preferred attachment parameters and connection probabilities, the social network constructed by HK-TDC&C model can satisfy the basic characteristics of social networks, including scale-free characteristics, small world characteristics, high clustering coefficient characteristics and degree positive correlation properties, and its topology is closer to the real social network.

Focusing on the issue that the Signal-to-Clutter-and-Noise Ratio (SCNR) of echo signal is low when cognitive radar detects extended target, a waveform design method based on SCNR was proposed. Firstly, the relation between the SCNR of cognitive radar echo signal and the Energy Spectral Density (ESD) of transmitted signal, was gotten by establishing extended target detection model other than previous point target model; secondly, according to the maximum SCNR criterion, the global optimal solution of the transmitted signal ESD was deduced; finally, in order to get a meaningful time-domain signal, ESD was synthesized to be a constant amplitude based on phase-modulation after combining with the Minimum Mean-Square Error (MMSE) and iterative algorithm, which met the emission requirements of radar. In the simulation, the amplitude of time-domain synthesized signal is uniform, and its SCNR at the output of the matched filter is 19.133 dB, only 0.005 dB less than the ideal value. The results show that not only can the time-domain waveform meet the requirement of constant amplitude, but also the SCNR obtained at receiver output can achieve the best approximation to the ideal value, and it improves the performance of the extended target detection.

Concerning the problem that the weak target might be covered by the range side-lobes of the strong one and the range side-lobes could only be suppressed to a certain value, an improved Kalman-Minimum Mean-Square Error (K-MMSE) algorithm was proposed in this paper. This algorithm combined the Kalman filter with the Minimum Mean-Square Error (MMSE), and it was an effective method for suppressing range side-lobes of adaptive pulse compression. In the simulation, the proposed algorithm was compared with the traditional matched filter and other improved matched filters such as MMSE in a single target or multiple targets environments, and then found that the side-lobe levels, the Peak-SideLobe Ratio (PSLR) and Integrated SideLobe Ratio (ISLR) of the Point Spread Function (PSF) were all decreased obviously in comparison with the previous two methods. The simulation results show that the method can suppress range side-lobes well and detect the weak targets well either under both the condition of a single target and the condition of multiple targets.

Intrusion detection requires the system to identify network intrusions quickly and accurately, so it also requires high efficiency of the detection algorithm. In order to improve the efficiency and accuracy of intrusion detection system, and reduce the rate of false positives and false negatives, a H-C4.5-NB intrusion detection model combined C4.5 with Naive Bayes (NB) was proposed after fully analyzing the C4.5 and NB algorithm. The distribution of decision category was described in the form of probability in this model, and the final decision results were given in the form of C4.5 and NB probability weighted sum. Finally the performance of the model was tested by KDD 99 data set. The experimental results showed that the accuracy of Denial of Service (DoS) was improved about 9% and the accuracy of U2R and R2L was improved about 20%-30% in H-C4.5-NB compared to the traditional methods such as C4.5, NB and NBTree.

Security analysis of the certificateless signature scheme proposed by Guo L L, et al. (Guo L L, Lin C L, Zhang S Y. Attack and improvement for certificateless signature scheme. Computer Engineering, 2012, 38(16): 134-137,141) showed that the scheme was insecure against public key replacement attack. An improved scheme which can resist public key replacement attack was proposed. The scheme was proved to be existentially unforgeable against adaptive chosen message and identity attacks in random oracle model, and the security was reduced to computational Diffie-Hellman assumption. Compared with other certificateless signature schemes based on bilinear pairing, the improved scheme has better computational efficiency.

How to effectively extract the subjective information of a sentence, calculate the orientation of the subjective sentence, and analyze the fine-grained sentiment orientation, has become a hot research topic. A kind of analytical method was proposed based on sentiment words ontology, and rules were created through analyzing the semantic content of sentences. The 3-POS model was put forward to identify the subjective sentences, and the orientation value of a subjective sentence was calculated according to emotional vocabulary ontology and rules. The experimental results show that the accuracy of distinguishing and calculating the orientation of subjective sentences is 81.02 percent by using the proposed model.

Partially blind signature allows signer to add some public information negotiated by signer and user to signature, and better solves the conflict between anonymity and controllability of blind signature. Utilizing the Shim's （SHIM K A. An ID-based aggregate signature scheme with constant pairing computations. The Journal of Systems and Software, 2010, 83(10): 1873-1880 ）ID-based signature scheme, a new provable secure ID-based partially blind signature scheme was proposed. The scheme was proved to be existentially unforgeable against adaptive chosen message and identity attacks in random oracle model, and the security was reduced to computational Diffie-Hellman assumption. Compared with other ID-based partially blind signature schemes in random oracle model, the proposed scheme has better computational efficiency and less communication.

In order to improve the classification accuracy of multiclass, an algorithm called Large Margin and Fuzzy Kernel Hyper-Ball (LMFKHB) was proposed. First, the sample datasets were mapped into a high-dimensional feature space through a kernel function. Then, all decision functions were obtained using the proposed method. Meanwhile, a fuzzy membership function was introduced to solve the wrong classification issue for these samples in the dead zone, thus the flexibility was enhanced and the classification accuracy was improved. The experiments on the artificial and real data demonstrate the effectiveness of the method.

The development of the situation awareness in the world was introduced, and then the 25 indexes from security assessment and macro-network research were defined. And an index system of assessment from many data sources was designed. The use of index system in the assessment, prediction and visualization subsystems was presented. The index system covers a wide range of organizations with a reasonable organization and easily expandable features, and it can also be used in network security assessment and macro-network research.