Software Components Behavioral Adaptation Based on LTS

Compatibility is the precondition to ensure the correct interaction among components in composition process, how to make the mismatch components coordinate correctly is a vital problem in component composition. This paper first modeled component behavior by LTS and expressed action mapping as synchronous vector then defined the sequential relationship among synchronous vectors as adaptation contract. Thirdly we analyzed the different mismatch situations and corresponding adaptation strategies. At last designed adaptation algorithm to produce adaptor specification automatically and ensured the mismatch components can correct interaction under the mediation of adaptor and verified the validity of proposed method through an application system.

Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.

Chaotic behavior learning of Chua's circuit

Least-square support vector machines（LS-SVM） are applied for learning the chaotic behavior of Chua＇s circuit.The system is divided into three multiple-input single-output（MISO） structures and the LS-SVM are trained individually.Comparing with classical approaches,the proposed one reduces the structural complexity and the selection of parameters is avoided.Some parameters of the attractor are used to compare the chaotic behavior of the reconstructed and the original systems for model validation.Results show that the LS-SVM combined with the MISO can be trained to identify the underlying link among Chua＇s circuit state variables,and exhibit the chaotic attractors under the autonomous working mode.

Building a Secure Block Ciper on Small and Non- Binary Domain

We design a practical and provablysecure block ciper over small domain and non-binary inputs,which is also can be considered as a pseudorandom permutation on N elements.Our work is based on a relation we found between the small domain ciper and the negative hypergeometric probability(NHG) distribution.We prove that our block ciper achieves ideal security,that is,it is indistinguishable from a random permutation even if the adversary had already observed N plaintext-cipertext pairs.In particular,we initiate an efficient and sufficiently precise sampling algorithm for negative hypergeometric distribution.

Complex Networks from Chaotic Time Series on Riemannian Manifold

Complex networks are important paradigms for analyzing the complex systems as they allow understanding the structural properties of systems composed of different interacting entities. In this work we propose a reliable method for constructing complex networks from chaotic time series. We first estimate the covariance matrices, then a geodesic-based distance between the covariance matrices is introduced. Consequently the network can be constructed on a Riemannian manifold where the nodes and edges correspond to the covariance matrix and geodesic-based distance, respectively. The proposed method provides us with an intrinsic geometry viewpoint to understand the time series.

Anisotropic fourth-order diffusion regularization for multiframe super-resolution reconstruction

A novel rcgularization-based approach is presented for super-resolution reconstruction in order to achieve good tradeoff between noise removal and edge preservation. The method is developed by using L1 norm as data fidelity term and anisotropic fourth-order diffusion model as a regularization item to constrain the smoothness of the reconstructed images. To evaluate and prove the performance of the proposed method, series of experiments and comparisons with some existing methods including bi-cubic interpolation method and bilateral total variation method are carried out. Numerical results on synthetic data show that the PSNR improvement of the proposed method is approximately 1.0906 dB on average compared to bilateral total variation method, and the results on real videos indicate that the proposed algorithm is also effective in terms of removing visual artifacts and preserving edges in restored images.

Performance of two-way amplify-and-forward relaying with adaptive modulation over Nakagami-m fading channels

This paper investigates the performance of a two-way amplify-and-forward （AF） relay system with adaptive modulation over independent and non-identical Nakagami-m fading channels. The tight closed-form cumulative distribution function （CDF） expression of the instantaneous end-to-end signal-to-noise ratio （SNR） is provided. Further, approximate closed-form expression for the average spectral efficiency of the two-way AF system with adaptive modulation is obtained. Then, a tight lower bound of outage probability is derived. Finally, we use numerical simulations to verify the tightness of our analytical results.

SUPPORT VECTOR REGRESSION VIA MCMC WITHIN EVIDENCE FRAMEWORK

This paper proposes a novel approach, Markov Chain Monte Carlo (MCMC) sampling approximation, to deal with intractable high-dimension integral in the evidence framework applied to Support Vector Regression (SVR). Unlike traditional variational or mean field method, the proposed approach follows the idea of MCMC, firstly draws some samples from the posterior distribution on SVR's weight vector, and then approximates the expected output integrals by finite sums. Experimental results show the proposed approach is feasible and robust to noise. It also shows the performance of proposed approach and Relevance Vector Machine (RVM) is comparable under the noise circumstances. They give better robustness compared to standard SVR.

A Trustworthy Group Identifying Trust Metric for P2P Service Sharing Economy Based on Personal Social Network of Users

With the quick growth of sharing economy, service sharing becomes a popular phenomenon in daily lives. However, some service providers give exaggerated information about their services on the Peer-to-Peer（P2 P） service sharing platforms to get more profits. How to identify a reliable service provider becomes a difficult challenge for users. In this paper, we propose a trustworthy group trust metric for P2 P service sharing（TMPSS） economy based on personal social network（PSN） of users. Deriving from Advogato group trust metric, it considers factors such as social circle similarity, preference similarity, interaction degree, ranks the reliable nodes in a target user＇s PSN, outputs an ordered set of reliable nodes, and prevents unreliable nodes from access PSN of honest users. Experimental results show that TMPSS has advantages over existing representative methods because it finds more reliable nodes, and counts against malicious nodes＇ attacks more effectively, and it is suitable for mobile transaction circumstances.

Greedy Randomized Adaptive Search Procedure with Path-Relinking for the Vertex p-Center Problem

The p-center problem consists of choosing a subset of vertices in an undirected graph as facilities in order to minimize the maximum distance between a client and its closest facility. This paper presents a greedy randomized adaptive search procedure with path-relinking （GRASP/PR） algorithm for the p-center problem, which combines both GRASP and path-relinking. Each iteration of GRASP/PR consists of the construction of a randomized greedy solution, followed by a tabu search procedure. The resulting solution is combined with one of the elite solutions by path-relinking, which consists in exploring trajectories that connect high-quality solutions. Experiments show that GRASP/PR is competitive with the state-of-the-art algorithms in the literature in terms of both solution quality and computational efficiency. Specifically, it virtually improves the previous best known results for 10 out of 40 large instances while matching the best known results for others.

Worst-Input Mutation Approach to Web Services Vulnerability Testing Based on SOAP Messages

The growing popularity and application of Web services have led to increased attention regarding the vulnerability of software based on these services. Vulnerability testing examines the trustworthiness and reduces the security risks of software systems. This paper proposes a worst-input mutation approach for testing Web service vulnerability based on Simple Object Access Protocol （SOAP） messages. Based on characteristics of SOAP messages, the proposed approach uses the farthest neighbor concept to guide generation of the test suite. The corresponding automatic test case generation algorithm, namely, the Test Case generation based on the Farthest Neighbor （TCFN）, is also presented. The method involves partitioning the input domain into sub-domains according to the number and type of SOAP message parameters in the TCFN, selecting the candidate test case whose distance is the farthest from all executed test cases, and applying it to test the Web service. We also implement and describe a prototype Web service vulnerability testing tool. The tool was applied to the testing of Web services on the Internet. The experimental results show that the proposed approach can find more vulnerability faults than other related approaches.

Optimal distributed power allocation for a new spectrum sharing paradigm in cognitive radio networks

The problem of power allocation in cognitive radio networks plays an important role to improve the efficiency of spectrum utilization. However, most of previous works focus on the power allocation for secondary users in spectrum sharing overlay or spectrum sharing underlay, which needs to frequently handoff between the idle spectrum bands or considers the interference constraints in all spectrum bands respectively. In order to reduce the handoff and fully utilize the spectrum resource, we propose a new spectrum sharing paradigm which not only can just need to adjust the transmit power in spectrum bands instead of frequently handoff between idle spectrum bands, but can fully utilize the spectrum resource as we only consider the interference power constraints in active spectrum bands rather than in all spectrum bands. Then based on this new spectrum sharing paradigm and the constraint conditions, we study the distributed power allocation for secondary users and formulate the optimization problem as a non-cooperative game problem, after that the variational inequality approach is used to solve this game problem and a Nash equilibria solution is got, finally simulation results are illustrated to demonstrate the performance of the proposed scheme.