Most of the existing automatic emotion annotation methods only extract the single recognition feature from acoustic layer or language layer. While Yi language is affected by the factors such as too many branch dialects and high complexity, so the accuracy of automatic annotation of Yi language with single-layer emotion feature is low. Based on the features such as rich emotional affixes in Yi language, a double-layer feature fusion method was proposed. In the method, the emotional features from acoustic layer and language layer were extracted respectively, the methods of generating sequence and adding units as needed were applied to complete the feature sequence alignment, and the process of automatic emotion annotation was realized through the corresponding feature fusion and automatic annotation algorithm. Taking the speech and text data of Yi language in a poverty alleviation log database as samples, three different classifiers were used for comparative experiments. The results show that the classifier has no obvious effect on the automatic annotation results, and the accuracy of automatic annotation after the fusion of double-layer features is significantly improved, the accuracy is increased from 48.1% of acoustic layer and 34.4% of language layer to 64.2% of double-layer fusion.

Concerning the problem that Stack Auto-encoder (SA) easily traps to overfitting, which may reduce the accuracy of spam classification, a modified SA method based on dynamic dropout was proposed. Firstly, the specificity of the spam classification was analyzed, and the dropout algorithm was employed in SA to handle overfitting. Then according to the fault of dropout algorithm that making some nodes be in the stall state for a long time, an improved algorithm of dropout was proposed. The static dropout rate was replaced by dynamic dropout rate which decreased with training steps using dynamic function. Finally, the dynamic dropout algorithm was used to improve the pretraining model of SA. The simulation results show that compared with Support Vector Machine (SVM) and Back Propagation (BP) neural network, the average accuracy of the modified SA is 97.66%. And the Matthews correlation coefficient of every dataset is higher than 89%. Matthews correlation coefficient of the modified SA on every dataset is 3.27%, 1.68%, 2.16%, 1.51%, 1.58% and 1.07% higher than that of the conventional SA separately. The experimental results show that the modified SA using dynamic dropout has higher accuracy and better robustness.

The most existing algorithms have to build the complex model and draw a large number of training samples to achieve accurate object tracking,which will produce large amount of calculation. The proposed problem is not conducive to real-time tracking. In order to solve the problem, a real-time tracking method based on multi-channel kernel correlation filter was presented. Firstly, the target information of video frames were trained by using the nucleation ridge regression method to get the filter template. Secondly, the filter template was utilized to carry out the correlation measure for the possible area of the frame to be detected. Finally, the most relevant location was considered as the tracking result and the independent inputs of multiple channels were weighted and then added to solve the problem of multi-channel input. A large number of comparison experiments with the existing tracking methods show that, the proposed method guarantees the tracking accuracy and its tracking speed also has obvious advantages under different challenge factors. The proposed method avoids to extract a large number of samples by the correlation filter and use the dot product of frequency domain to replace the correlation operation of time-domain, which greatly reduces the computational complexity and makes the tracking speed completely meet the tracking demand of real-time scenario.

This paper proposed a novel sparse tracking method based on multi-feature fusion to compensate for incomplete description of single feature. Firstly, to fuse various features, multiple feature descriptors of dictionary templates and particle candidates were encoded as the form of kernel matrices. Secondly, every candidate particle was sparsely represented as a linear combination of all atoms of dictionary. Then the sparse representation model was efficiently solved using a Kernelizable Accelerated Proximal Gradient (KAPG) method. Lastly, in the framework of particle filter, the weights of particles were determined by sparse coefficient reconstruction errors to realize tracking. In the tracking step, a template update strategy which employed incremental subspace learning was introduced. The experimental results show that, compared with the related state-of-the-art methods, this algorithm improves the tracking accuracy under all kinds of factors such as occlusions, illumination changes, pose changes, background clutter and viewpoint variation.

Memory dependence prediction plays a very important role to reduce memory order violation and improve microprocessor performance. However, the traditional methods usually have large hardware overhead and poor realizability. Through the analysis of memory dependence's locality, this paper proposed a new memory predictor based on instruction distance. Compared to other memory dependence predictors, this predictor made full use of memory dependence's locality on instruction distance, predicted memory instruction' violation distance, controlled the speculation of a few instructions, finally deduced the number of memory order violation and improved the performance. The simulation results show that with only 1KB hardware budget, average Instruction Per Cycle (IPC) get a 1.70% speedup, and the most improvement is 5.11%. In the case of a small hardware overhead, the performance is greatly improved.

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.

In order to eliminate the inherent key escrow problem in identity-based public key cryptosystem, a new identity-based blind signature scheme without trusted Private Key Generator (PKG) was proposed. Under the random oracle model, the scheme was proved to be existentially unforgeable against adaptive chosen message and identity attacks from common attackers or semi-honest PKG, and the security was reduced to computational Diffie-Hellman assumption. For the forgery attacks from the malicious PKG, the legitimate signer can prove to the arbitration institution that the signature is forged by trace algorithm.

The cryptanalysis of the ID-based partially blind signature scheme proposed by Li et al. (LI M X, ZHAO X M, WANG H T. Security analysis and improvement of a partially blind signature scheme. Journal of Computer Applications, 2010, 30(10): 2687-2690) showed that the signature requester could change the negotiated information illegally. Therefore, an improved partially blind scheme was purposed to resist the tampering negotiated information attacks. The new scheme was proved to be existentially unforgeable against adaptive chosen message and identity attacks in random oracle model. Compared with other ID-based partially blind signature schemes, the new scheme has higher computational efficiency.

Multi-key agreement protocol can generate multi-keys in one session, so researchers are interested in it these days. In this paper, a new multi-key agreement protocol based on the traditional signature scheme was proposed. The new scheme realizes the hiding of the information between the participants and enhanced the security of our protocol by using Hash function. This paper discussed the security and computational cost of the new scheme. The result shows that the new protocol realizes the secure key agreement with lower computational cost.

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.

The effect data of actions show a significant randomness because of lots of uncertain elements in the course of action. In order to explore the rules of warfare hidden behind the data, the effectiveness evaluation was studied based on statistical analysis method. The basic concept of action and its effectiveness were analyzed. With the simulation data produced by enhanced irreducible semi-autonomous adaptive combat neural simulation toolkit (EINSTein), a single, a group and multi group experimental methods were used to study the statistical characteristics of offensive actions and find out that to a party who has a combat advantage, compared with increased number of personnel, the increased radius of firepower can achieve better operational results. On this basis, an evaluation method of action effectiveness was proposed and validated with simulation data. Therefore, a feasible resolution is provided to evaluate the action effectiveness based on actual combat data.