The Orc language is a concurrency calculus proposed to study the orchestration patterns in service oriented computing. Its special features, such as high concurrency and asynchronism make it a brilliant subject for studying web applications that rely on web services. The conventional semantics for Orc does not contain the execution status of services so that a program cannot determine whether a service has terminated normally or halted with a failure after it published some results. It means that this kind of failure cannot be captured by the fault handler. Furthermore, such a semantic model cannot establish an order saying that a program is better if it fails less often. This paper employs UTP methods to propose a denotational semantic model for Orc that contains execution status information. A failure handling semantics is defined to recover a failure execution back to normal. A refinement order is defined to compare two systems based on their execution failures. Based on this order, a system that introduces a failure recovery mechanism is considered better than one without. An extended operational semantics is also proposed and proven to be equivalent to the denotational semantics.

Generating test data that can expose the faults of the program is an important issue in software testing. Although previous methods of covering path can generate test data to traverse target path, the test data generated by these methods are difficult in detecting some low-probabilistic faults that lie on the covered paths. We present a method of generating test data for covering multiple paths to detect faults in this study. First, we transform the problem of covering multiple paths and detecting faults into a multi-objective optimization problem with constraint, and construct a mathematical model for it. Then, we give a strategy of solving the model based on a weighted genetic algorithm. Finally, we apply our method to several real-world programs, and compare it with several methods. The experimental results confirm that the proposed method can more efficiently generate test data that not only traverse the target paths but also detect faults lying in them than other methods.

In this paper, we propose some distance measures between type-2 fuzzy sets, and also a new family of utmost distance measures are presented. Several properties of different proposed distance measures have been introduced. Also, we have introduced a new ranking method for the ordering of type-2 fuzzy sets based on the proposed distance measure. The proposed ranking method satisfies the reasonable properties for the ordering of fuzzy quantities. Some properties such as robustness, order relation have been presented. Limitations of existing ranking methods have been studied. Further for practical use, a new method for selecting the best alternative, for group decision making problems is proposed. This method is illustrated with a numerical example.

Presently, the notion ofmultigranulation has been brought to our attention. In this paper, the multigranulation technique is introduced into incomplete information systems. Both tolerance relations and maximal consistent blocks are used to construct multigranulation rough sets. Not only are the basic properties about these models studied, but also the relationships between different multigranulation rough sets are explored. It is shown that by using maximal consistent blocks, the greater lower approximation and the same upper approximation as from tolerance relations can be obtained. Such a result is consistent with that of a single-granulation framework.

We propose a novel binary image representation algorithm using the non-symmetry and anti-packing model and the coordinate encoding procedure (NAMCEP). By taking some idiomatic standard binary images in the field of image processing as typical test objects, and by comparing our proposed NAMCEP representation with linear quadtree (LQT), binary tree (Bintree), non-symmetry and anti-packing model (NAM) with K-lines (NAMK), and NAM representations, we show that NAMCEP can not only reduce the average node, but also simultaneously improve the average compression. We also present a novel NAMCEP-based algorithm for area calculation and show experimentally that our algorithm offers significant improvements.

For stroke-order free online multi-stroke character recognition, stroke-to-stroke correspondence search between an input pattern and a reference pattern plays an important role to deal with the stroke-order variation. Although various methods of stroke correspondence have been proposed, no comparative study for clarifying the relative superiority of those methods has been done before. In this paper, we firstly review the approaches for solving the stroke-order variation problem. Then, five representative methods of stroke correspondence proposed by different groups, including cube search (CS), bipartite weighted matching (BWM), individual correspondence decision (ICD), stable marriage (SM), and deviation-expansion model (DE), are experimentally compared, mainly in regard of recognition accuracy and efficiency. The experimental results on an online Kanji character dataset, showed that 99.17%, 99.17%, 96.37%, 98.54%, and 96.59% were attained by CS, BWM, ICD, SM, and DE, respectively as their recognition rates. Extensive discussions are made on their relative superiorities and practicalities.

Linear discriminant analysis (LDA) is one of the most popular supervised dimensionality reduction (DR) techniques and obtains discriminant projections by maximizing the ratio of average-case between-class scatter to averagecase within-class scatter. Two recent discriminant analysis algorithms (DAS), minimal distance maximization (MDM) and worst-case LDA (WLDA), get projections by optimizing worst-case scatters. In this paper, we develop a new LDA framework called LDA with worst between-class separation and average within-class compactness (WSAC) by maximizing the ratio of worst-case between-class scatter to averagecase within-class scatter. This can be achieved by relaxing the trace ratio optimization to a distance metric learning problem. Comparative experiments demonstrate its effectiveness. In addition, DA counterparts using the local geometry of data and the kernel trick can likewise be embedded into our framework and be solved in the same way.

Standard support vector machines (SVMs) training algorithms have O(l³) computational and O(l²) space complexities, where l is the training set size. It is thus computationally infeasible on very large data sets. To alleviate the computational burden in SVM training, we propose an algorithm to train SVMs on a bound vectors set that is extracted based on Fisher projection. For linear separate problems, we use linear Fisher discriminant to compute the projection line, while for non-linear separate problems, we use kernel Fisher discriminant to compute the projection line. For each case, we select a certain ratio samples whose projections are adjacent to those of the other class as bound vectors. Theoretical analysis shows that the proposed algorithm is with low computational and space complexities. Extensive experiments on several classification benchmarks demonstrate the effectiveness of our approach.

Dimensionality reduction (DR) methods based on sparse representation as one of the hottest research topics have achieved remarkable performance in many applications in recent years. However, it’s a challenge for existing sparse representation based methods to solve nonlinear problem due to the limitations of seeking sparse representation of data in the original space. Motivated by kernel tricks, we proposed a new framework called empirical kernel sparse representation (EKSR) to solve nonlinear problem. In this framework, nonlinear separable data are mapped into kernel space in which the nonlinear similarity can be captured, and then the data in kernel space is reconstructed by sparse representation to preserve the sparse structure, which is obtained by minimizing a ?1 regularization-related objective function. EKSR provides new insights into dimensionality reduction and extends two models: 1) empirical kernel sparsity preserving projection (EKSPP), which is a feature extraction method based on sparsity preserving projection (SPP); 2) empirical kernel sparsity score (EKSS), which is a feature selection method based on sparsity score (SS). Both of the two methods can choose neighborhood automatically as the natural discriminative power of sparse representation. Compared with several existing approaches, the proposed framework can reduce computational complexity and be more convenient in practice.

Frequent pattern mining discovers sets of items that frequently appear together in a transactional database; these can serve valuable economic and research purposes. However, if the database contains sensitive data (e.g., user behavior records, electronic health records), directly releasing the discovered frequent patterns with support counts will carry significant risk to the privacy of individuals. In this paper, we study the problem of how to accurately find the top-k frequent patterns with noisy support counts on transactional databases while satisfying differential privacy. We propose an algorithm, called differentially private frequent pattern (DFP-Growth), that integrates a Laplace mechanism and an exponential mechanism to avoid privacy leakage. We theoretically prove that the proposed method is (λ, δ)-useful and differentially private. To boost the accuracy of the returned noisy support counts, we take consistency constraints into account to conduct constrained inference in the post-processing step. Extensive experiments, using several real datasets, confirm that our algorithm generates highly accurate noisy support counts and top-k frequent patterns.

Certificateless public key cryptography (CLPKC) can solve the problems of certificate management in a public key infrastructure (PKI) and of key escrows in identity-based public key cryptography (ID-PKC). In CL-PKC, the key generation center (KGC) does not know the private keys of all users, and their public keys need not be certificated by certification authority (CA). At present, however, most certificateless encryption schemes are based on large integer factorization and discrete logarithms that are not secure in a quantum environment and the computation complexity is high. To solve these problems, we propose a new certificateless encryption scheme based on lattices, more precisely, using the hardness of the learning with errors (LWE) problem. Compared with schemes based on large integer factorization and discrete logarithms, the most operations are matrix-vector multiplication and inner products in our scheme, our approach has lower computation complexity. Our scheme can be proven to be indistinguishability chosen ciphertext attacks (IND-CPA) secure in the random oracle model.

In order to minimize the damage caused by key exposure in aggregate signatures, a key-insulated aggregate signature scheme is proposed in this paper. We give the definition and the security model of the key-insulated aggregate signature. We also construct a concrete key-insulated aggregate signature scheme that meets our definition. Our scheme has the properties of efficient verification and short signature length. We prove the security of our scheme in the random oracle model under the computation Diffie-Hellman assumption.

Mobile grid is a branch of grid computing that incorporates mobile devices into the grid infrastructure. It poses new challenges because mobile devices are typically resource-constrained and exhibit unique characteristics such as instability in network connections. New scheduling strategies are imperative in mobile grid to efficiently utilize the devices. This paper presents a scheduling algorithm that considers dynamic properties of mobile devices such as availability, reliability, maintainability, and usage pattern in mobile grid environments. In particular, usage patterns caused by voluntarily or involuntarily losing a connection, such as switching off the device or a network interruption could be important criteria for choosing the best resource to execute a job. The experimental results show that our scheduling algorithm provides superior performance in terms of execution time, as compared to the other methods that do not consider usage pattern. Throughout the experiments, we found it essential to consider usage pattern for improving performance in the mobile grid.