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Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by spiking neurons, where the spiking rules are usually used in a sequential way (an applicable rule is applied one time at a step) or an exhaustive way (an applicable rule is applied as many times as possible at a step). In this letter, we consider a generalized way of using spiking rules by "combining" the sequential way and the exhaustive way: if a rule is used at some step, then at that step, it can be applied any possible number of times, nondeterministically chosen. The computational power of SN P systems with a generalized use of rules is investigated. Specifically, we prove that SN P systems with a generalized use of rules consisting of one neuron can characterize finite sets of numbers. If the systems consist of two neurons, then the computational power of such systems can be greatly improved, but not beyond generating semilinear sets of numbers. SN P systems with a generalized use of rules consisting of three neurons are proved to generate at least a non-semilinear set of numbers. In the case of allowing enough neurons, SN P systems with a generalized use of rules are computationally complete. These results show that the number of neurons is crucial for SN P systems with a generalized use of rules to achieve a desired computational power.

Community detection has received a great deal of attention, since it could help to reveal the useful information hidden in complex networks. Although most previous modularity-based and local modularity-based community detection algorithms could detect strong communities, they may fail to exactly detect several weak communities. In this work, we define a network with clear or ambiguous community structures based on the types of its communities. A seed-expanding method based on random walks is proposed to detect communities for networks, especially for the networks with ambiguous community structures. We identify local maximum degree nodes, and detect seed communities in a network. Then, the probability of a node belonging to each community is calculated based on the total probability model and random walks, and each community is expanded by repeatedly adding the node which is most likely to belong to it. Finally, we use the community optimization method to ensure that each node is in a community. Experimental results on both computer-generated and real-world networks demonstrate that the quality of the communities detected by the proposed algorithm is superior to the- state-of-the-art algorithms in the networks with ambiguous community structures.

Community detection problem in networks has received a great deal of attention during the past decade. Most of community detection algorithms took into account only positive links, but they are not suitable for signed networks. In our work, we propose an algorithm based on random walks for community detection in signed networks. Firstly, the local maximum degree node which has a larger degree compared with its neighbors is identified, and the initial communities are detected based on local maximum degree nodes. Then, we calculate a probability for the node to be attracted into a community by positive links based on random walks, as well as a probability for the node to be away from the community on the basis of negative links. If the former probability is larger than the latter, then it is added into a community; otherwise, the node could not be added into any current communities, and a new initial community may be identified. Finally, we use the community optimization method to merge similar communities. The proposed algorithm makes full use of both positive and negative links to enhance its performance. Experimental results on both synthetic and real-world signed networks demonstrate the effectiveness of the proposed algorithm.

Random numbers play a crucial role in modern security schemes. Couple to the rapid development of cryptography, the strength of security protocols and encryption algorithms consumingly relies on the quality of random number. With simple architecture and faster speed, linear feedback shift register often is selected in many applications. However, the random sequence generated by LFSR can not meet the demand of unpredictability for secure mechanism. Genetic algorithm improves the linear property of LFSR and constructs a novel random sequence generator with longer period and complex architecture.With the development of cryptography, the need for random numbers of high quality is sharply growing. Public/private keypairs for asymmetric algorithms are generated from random bit streams [1,2] ; random numbers are also used for key generation in symmetric algorithms for generating challenges in authentication protocols to create padding bytes and blinding values [3] . In the Smart Card, countermeasures against side-channel attacks can also require the availability of good quality random number source [4] . The security of many cryptographic systems depends on the assumption that future values in the random sequence can be unpredictable [5] .Linear feedback shift registers (LFSR) are one class of generators for random sequence, and are suited to low power or high speed requirements [6] . LFSR sequence are not considered on their own to provide an adequate strength random bit stream because the dependency between the output bits and the inner state can be modeled by a system of linear equations. However, LFSR is a useful building block for pseudo random number generation when complemented by further complexity to prevent attacks based on its inherent linearity. From a hardware perspective, LFSR can be created by using a chain of flip-flops.It is well known that genetic algorithm possess very interesting function approximation capabilities [7,8] . Their most important and intriguing property are their optimization capabilities. This operation is also a kind of nonlinear operation, which is needed in the nonlinear design of LFSR. In this paper, exploiting genetic algorithm, a novel random bit sequence based on LFSR is constructed.