ElectroCardioGram （ECG） data usually contain many diseases， and ECG diagnosis is a typical multi-label classification problem. In RAndom k-labELsets （RAKEL） algorithm， one of multi-label classification methods， all labels are randomly decomposed into several labelsets of size k， and a Label Powerset （LP） classifier is established for training； however， the lack of sufficient consideration of correlation between labels makes the LP classifier obtain quite few samples corresponding to certain label combinations， which affects the prediction performance. To fully consider the correlation between labels， a Bayesian Network-based RAKEL （BN-RAKEL） algorithm was proposed. Firstly， the correlation between labels was found by Bayesian network to determine the candidate labelsets. Then， a feature selection method based on information gain was applied to construct the optimal feature space for each label， and the optimal feature space similarity was used for each candidate label subset to detect its correlation degree， determing the final labelsets with strong correlation. Finally， the LP classifiers were trained in the optimal feature space of the corresponding labelsets. A comparison with K-Nearest Neighbors for Multi-label Learning （ML-KNN）， RAKEL， Classifier Chains （CC） and FP-Growth based RAKEL algorithm named FI-RAKEL on the real ECG dataset showed that the proposed algorithm achieved a minimum improvement of 3.6 percentage points and 2.3percentage points in recall and F-score， respectively. Experimental results show that BN-RAKEL algorithm has good prediction performance， and can effectively improve the ECG diagnosis accuracy.

The classical Monarch Butterfly Optimization （MBO） algorithm cannot handle continuous data well， and the rough set model cannot sufficiently process large-scale， high-dimensional and complex data. To address these problems， a new feature selection algorithm based on Neighborhood Rough Set （NRS） and MBO was proposed. Firstly， local disturbance， group division strategy and MBO algorithm were combined， and a transmission mechanism was constructed to form a Binary MBO （BMBO） algorithm. Secondly， the mutation operator was introduced to enhance the exploration ability of this algorithm， and a BMBO based on Mutation operator （BMBOM） algorithm was proposed. Then， a fitness function was developed based on the neighborhood dependence degree in NRS， and the fitness values of the initialized feature subsets were evaluated and sorted. Finally， the BMBOM algorithm was used to search the optimal feature subset through continuous iterations， and a meta-heuristic feature selection algorithm was designed. The optimization performance of the BMBOM algorithm was evaluated on benchmark functions， and the classification performance of the proposed feature selection algorithm was evaluated on UCI datasets. Experimental results show that， the proposed BMBOM algorithm is significantly better than MBO and Particle Swarm Optimization （PSO） algorithms in terms of the optimal value， worst value， average value and standard deviation on five benchmark functions. Compared with the optimized feature selection algorithms based on rough set， the feature selection algorithms combining rough set and optimization algorithms， the feature selection algorithms combining NRS and optimization algorithms， the feature selection algorithms based on binary grey wolf optimization， the proposed feature selection algorithm performs well in the three indicators of classification accuracy， the number of selected features and fitness value on UCI datasets， and can select the optimal feature subset with few features and high classification accuracy.

Signcryption as a cryptographic primitive is a splendid combination of signature with authentication and encryption with confidentiality simultaneously. Online/offline signcryption, with the online/offline technique, provides higher efficiency for the system. However, most of the present signcryption schemes are implemented in the identity-based setting in which there exists key escrow problem. Based on the certificateless cryptography system's advantages with revocation of certificate management and without key escrow problem, a secure online/offline certificateless signcryption scheme was proposed. The proposed scheme satisfied the requirement that there is no need to determine the recipient's information in the offline stage. Moreover, its security was proved in the Random Oracle Model (ROM).

Traditional Linear Discriminant Analysis (LDA) faces the problem of tending to keep the separability of the class pairs having large within-class distances, while discarding the separability of those having small within-class distances. Based on the viewpoint that the feature subspace should uniformly keep the separability of each class pair, a new criterion, i.e., the Proportion of Divergence (PD), was presented. PD criterion was the mean of the proportion of the subspace divergence to original space divergence of each class pair. The solution of the Linear Discriminant Analysis (LDA) maximizing PD criterion (PD-LDA) was also presented. PD-LDA was used to perform feature extraction in the amplitude spectrum space of High Resolution Range Profile (HRRP). Shortest Euclidian distance classifier and Support Vector Machine (SVM) classifier were designed to evaluate the recognition performance. The experimental results for measured data show that, compared with traditional LDA, PD-LDA reduces data dimension remarkably and improves recognition rate effectively.

Currently stealth malware is becoming a major threat to the PC computers. Process hiding is the technique commonly used by stealth malware to evade detection by anti-malware scanners. In this paper, we presented a new VM-based approach called Gemini that accurately reproduced the software environment of the underlying preinstalled OS within the Gemini VM. With our new local-booting technology, Gemini VM just booted from the underlying host OS but not a newly installed OS image. In addition, Gemini adopted a unique technique to implicitly construct the Trusted View of Process List (TVPL) from within the virtualized hardware layer. Thus, Gemini provided a way to detect the existing process-hiding stealth malware in the host OS. Our evaluation results with real-world hiding-process rootkits, which are widely used by stealth malware, demonstrate its practicality and effectiveness.

Due to the mismatch between mandarin and Sichuan dialect in training and test stages, the performance of speaker verification system degrades dramatically. To solve this problem, a combined Gaussian Mixture Model （GMM）, which is trained by proportional pooling mandarin and Sichuan dialect, was presented in this paper. Compared with the Gaussian mixture model trained solely using mandarin/Sichuan dialect, the combined Gaussian mixture model described the characteristic of speaker from both mandarin and Sichuan dialect. Experiments on a self-built mandarin-Sichuan dialect speech database demonstrate that the introduced combined Gaussian mixture model is more robust for speech mismatching between mandarin and Sichuan dialect. A proper proportion between pooling mandarin and Sichuan dialect speech was also provided.