To enhance the generalization performance of radial basis function (RBF) neural networks, an RBF neural network based on a q-Gaussian function is proposed. A q-Gaussian function is chosen as the radial basis function of the RBF neural network, and a particle swarm optimization algorithm is employed to select the parameters of the network. The non-extensive entropic index q is encoded in the particle and adjusted adaptively in the evolutionary process of population. Simulation results of the function approximation indicate that an RBF neural network based on q-Gaussian function achieves the best generalization performance.

Blind source extraction (BSE) is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extraction performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.

Research on Qing Court Medical Records (RQCMR) is a large-volume book which was edited and annotated by the sixth co-author Keji, Chen and his colleagues, and consists of all the medical records of imperial families and aristocrats of the Qing dynasty. To reveal and utilize their high value both in traditional Chinese medicine research and modern clinical practice, we have developed a method of transforming the Qing Court Medical Records (QCMR) into a computer-readable, structured representation, so that statistical analysis and data mining can be accurately performed. The method consists of a frame ontology based medical language, called MedL, for representing QCMR, a parser for compiling MedL frames into a database, and an explorative pattern mining technique. With this method the entire RQCMR volume is transformed into a database and medical patterns may be mined from the database.

Contextual question answering (CQA), in which user information needs are satisfied through an interactive question answering (QA) dialog, has recently attracted more research attention. One challenge is to fuse contextual information into the understanding process of relevant questions. In this paper, a discourse structure is proposed to maintain semantic information, and approaches for recognition of relevancy type and fusion of contextual information according to relevancy type are proposed. The system is evaluated on real contextual QA data. The results show that better performance is achieved than a baseline system and almost the same performance as when these contextual phenomena are resolved manually. A detailed evaluation analysis is presented.

Marginal Fisher analysis (MFA) is a representative margin-based learning algorithm for face recognition. A major problem in MFA is how to select appropriate parameters, k₁ and k₂, to construct the respective intrinsic and penalty graphs. In this paper, we propose a novel method called nearest-neighbor (NN) classifier motivated marginal discriminant projections (NN-MDP). Motivated by the NN classifier, NN-MDP seeks a few projection vectors to prevent data samples from being wrongly categorized. Like MFA, NN-MDP can characterize the compactness and separability of samples simultaneously. Moreover, in contrast to MFA, NN-MDP can actively construct the intrinsic graph and penalty graph without unknown parameters. Experimental results on the ORL, Yale, and FERET face databases show that NN-MDP not only avoids the intractability, and high expense of neighborhood parameter selection, but is also more applicable to face recognition with NN classifier than other methods.

Rough set theory and vague set theory are powerful tools for managing uncertain, incomplete and imprecise information. This paper extends the rough vague set model based on equivalence relations and the rough fuzzy set model based on fuzzy relations to vague sets. We mainly focus on the lower and upper approximation operators of vague sets based on vague relations, and investigate the basic properties of approximation operators on vague sets. Specially, we give some essential characterizations of the lower and upper approximation operators generated by reflexive, symmetric, and transitive vague relations. Finally, we structure a parameterized roughness measure of vague sets and similarity measure methods between two rough vague sets, and obtain some properties of the roughness measure and similarity measures. We also give some valuable counterexamples and point out some false properties of the roughness measure in the paper of Wang et al.

An identity-based cryptosystem can make a special contribution to building key distribution and management architectures in resource-constrained mobile ad hoc networks since it does not suffer from certificate management problems. In this paper, based on a lightweight cryptosystem, elliptic curve cryptography (ECC), we propose an identity-based distributed key-distribution protocol for mobile ad hoc networks. In this protocol, using secret sharing, we build a virtual private key generator which calculates one part of a user’s secret key and sends it to the user via public channels, while, the other part of the secret key is generated by the user. So, the secret key of the user is generated collaboratively by the virtual authority and the user. Each has half of the secret information about the secret key of the user. Thus there is no secret key distribution problem. In addition, the user’s secret key is known only to the user itself, therefore there is no key escrow.

Substitution boxes (S-boxes) are often used as the most important nonlinear components in many symmetric encryption algorithms. The cryptographic properties of an S-box directly affect the security of the whole cipher system. Recently, generalized global avalanche characteristics (GGAC) were introduced to measure the correlation between two arbitrary Boolean functions. In this paper, to better evaluate the security of an S-box, we present two cross-correlation indicators for it. In addition, by studying the related properties of the cross-correlation between two balanced Boolean functions, we propose the lower bounds on the sum-of-squares indicator related to GGAC for two balanced functions and also for an S-box.

Two-thirds simulation provides a kind of abstract description of an implementation with respect to a specification. In order to characterize the approximate two-thirds simulation, we propose the definition of a two-thirds simulation index which expresses the degree to which a binary relation between processes is two-thirds simulation. λ–two-thirds simulation and its substitutivity laws are given in this paper. And, based on λ–two-thirds simulation, we present a measure model for describing the degree of approximation between processes. In particular, we give the modal logical characterization of λ–two-thirds simulation.

In this paper, we introduce a new way of certifying assembly programs. Unlike previous program logics, we extract the control-flow information from the code and generate an intermediate trail between the specification and the real code. Trails are auxiliary specifications and treated as modules in the certification process. We define a simple modular program logic called trail-based certified assembly programming (TCAP) to certify and link different parts of a program using the corresponding trails. Because the control flow information in trails is explicit, the rules are easier to design. We show that our logic is powerful enough to prove partial correctness of assembly programs with features including stack-based abstractions and self-modifying code. We also provide a semantics for TCAP and prove that the logic is sound with respect to the semantics.

Backlogged empty containers have gradually turned into a serious burden to shipping networks. Empty container allocation has become an urgent settlement issue for the container shipping industry on a global scale. Therefore, this paper proposes an improved immune algorithm based recursive model for optimizing static empty container allocation which integrates with the global maritime container shipping network. This model minimizes the operating and capital costs during container shipping considering 0-1 mixed-integer programming. So an immune algorithm procedure based on a special two-dimensional chromosome encoding is proposed. Finally, computational experiments are performed to optimize a 10-port static empty container shipping system. The results indicate that the proposed recursive model for static empty container allocation is effective in making an optimal strategy for empty container allocation.

In this paper, we formulate a non-cooperative optimization game in market-oriented overlay networks where participating peers share their own computing resources to earn virtual money called energy. We model an overlay network as a set of non-cooperative resource providing peers, called platforms, that perform resource pricing and topology management to maximize their own energy gains. Resource consuming peers, called agents, are simply designed to migrate platform-to-platform to find the least expensive resources in the network. Simulation results are presented to demonstrate the market dynamics as well as the global properties of the network, i.e., resource price and network topology, that emerge from local interactions among the group of peers.

Resource allocation for multi-tier web applications in virtualization environments is one of the most important problems in autonomous computing. On one hand, the more resources that are provisioned to a multi-tier web application, the easier it is to meet service level objectives (SLO). On the other hand, the virtual machine which hosts the multi-tier web application needs to be consolidated as much as possible in order to maintain high resource utilization. This paper presents an adaptive resource controller which consists of a feedback utilization controller and an auto-regressive and moving average model (ARMA)-based model estimator. It can meet application-level quality of service (QoS) goals while achieving high resource utilization. To evaluate the proposed controllers, simulations are performed on a testbed simulating a virtual data center using Xen virtual machines. Experimental results indicate that the controllers can improve CPU utilization and make the best trade-off between resource utilization and performance for multi-tier web applications.

Grid computing is the combination of computer resources in a loosely coupled, heterogeneous, and geographically dispersed environment. Grid data are the data used in grid computing, which consists of large-scale data-intensive applications, producing and consuming huge amounts of data, distributed across a large number of machines. Data grid computing composes sets of independent tasks each of which require massive distributed data sets that may each be replicated on different resources. To reduce the completion time of the application and improve the performance of the grid, appropriate computing resources should be selected to execute the tasks and appropriate storage resources selected to serve the files required by the tasks. So the problem can be broken into two sub-problems: selection of storage resources and assignment of tasks to computing resources. This paper proposes a scheduler, which is broken into three parts that can run in parallel and uses both parallel tabu search and a parallel genetic algorithm. Finally, the proposed algorithm is evaluated by comparing it with other related algorithms, which target minimizing makespan. Simulation results show that the proposed approach can be a good choice for scheduling large data grid applications.