Concerning the problem that Particle Swarm Optimization (PSO) falls into local minima easily and converges slowly at the last stage, a kind of hybrid PSO algorithm with cooperation of multiple particle roles (MPRPSO) was proposed. The concept of particle roles was introduced into the algorithm to divide the population into three roles: Exploring Particle (EP), Patrolling Particle (PP) and Local Exploiting Particle (LEP). In each iteration, EP was used to search the solution space by the standard PSO algorithm, and then PP which was based on chaos was used to strengthen the global search capability and replace some EPs to restore population vitality when the algorithm trapped in local optimum. Finally, LEP was used to strengthen the local search to accelerate convergence by unidimensional asynchronous neighborhood search. The 30 times independent runs in the experiment show that, the proposed algorithm in the conditions that particle roles ratio is 0.8∶〖KG-*3〗0.1∶〖KG-*3〗0.1 has the mean value of 2.352E-72,4.678E-29,7.780E-14 and 2.909E-14 respectively in Sphere, Rosenbrock, Ackley and Quadric, and can converge to the optimal solution of 0 in Rastrigrin and Griewank, which is better than the other contrastive algorithms. The experimental results show that proposed algorithm improves the optimal performance with certain robustness.

Oblivious transfer plays an important role in the field of cryptography. A provably secure k-out-of-n oblivious transfer scheme was analyzed in this paper. This scheme was based on a novel method and was efficient in computation and communication. However, it was found not secure at all after deep analysis. The main fault is that the receiver can easily acquire all the secret messages sent by sender. Thus it does not satisfy the secure requirement of oblivious transfer. Finally, by adding a random number the fault of the scheme was fixed. The improved k-out-of-n oblivious transfer scheme keeps the same communicational overhead and computational overhead as the original one. The security of the improved scheme is also based on Decisional Diffie-Hellman (DDH) assumption.

It is often difficult to classify Electrocardiogram （ECG） waveform automatically due to high similarity. A new feature representation of ECG waveform was proposed — window-slope method. In this method, an ECG waveform was divided into different windows in a plane, and the slope of maximum and minimum amplitude in a window was extracted as feature information to perform similarity analysis. The experimental results show that the method can not only reduce the dimension, but also can enlarge the difference between different types of waveforms under distance-based classification. The classification accuracy and efficiency can be improved by using the method; meanwhile the sensitivity and specificity of classification can be stabilized at a higher level.