The Web Services Business Process Execution Language (BPEL) is a language used to specify compositions of web services. In the last few years, a considerable amount of work has been done on modeling (parts of) BPE...
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The Web Services Business Process Execution Language (BPEL) is a language used to specify compositions of web services. In the last few years, a considerable amount of work has been done on modeling (parts of) BPEL and developing verification techniques and tools for BPEL. Petri nets and formal languages have been widely used to model Web services composition, but temporal value passing calculus of communicating systems (TVPCCS) language seems to be more adequate for several reasons. Generally BPEL programs are mapped to other languages and then the verification is performed, A more promising way is to directly build TVPCCS model, to check it and then map it to a BPEL process model. This paper describes a mapping from TVPCCS onto BPEL process model.
The Web Services Business Process Execution Language (BPEL) is a language used to specify compositions of web *** the last few years,a considerable amount of work has been done on modeling (parts of) BPEL and developi...
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The Web Services Business Process Execution Language (BPEL) is a language used to specify compositions of web *** the last few years,a considerable amount of work has been done on modeling (parts of) BPEL and developing verification techniques and tools for *** nets and formal languages have been widely used to model Web services composition,but temporal value passing calculus of communicating systems (TVPCCS) language seems to be more adequate for several *** BPEL programs are mapped to other languages and then the verification is performed,A more promising way is to directly build TVPCCS model,to check it and then map it to a BPEL process *** paper describes a mapping from TVPCCS onto BPEL process model.
Most of quantum codes have been constructed by using classical linear codes over finite field. However, little is known about the construction of quantum codes from symmetric designs. In this work, we present the cons...
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The motivation for this work was that little is known about the construction of asymmetric quantum error-correcting codes from linear codes over finite rings. In this work, attempts are made to construct asymmetric qu...
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The motivation for this work was that little is known about the construction of asymmetric quantum error-correcting codes from linear codes over finite rings. In this work, attempts are made to construct asymmetric quantum error-correcting codes from linear codes over finite rings Zp2, where p is any prime. Furthermore, we present explicit parameters for infinite families of asymmetric quantum error correcting codes which derived from linear over finite rings.
TWSVM(Twin Support Vector Machines) is based on the idea of GEPSVM (Proximal SVM based on Generalized Eigenvalues), which determines two nonparallel planes by solving two related SVM-type problems, so that its computi...
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TWSVM(Twin Support Vector Machines) is based on the idea of GEPSVM (Proximal SVM based on Generalized Eigenvalues), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. In order to further improve the speed and accuracy of TWSVM, this paper proposes the twin support vector machines based on rough sets. Firstly, using the rough sets theory to reduce the attributes, and then using TWSVM to train and predict the new datasets. The final experimental results and data analysis show that the proposed algorithm has higher accuracy and better efficiency compared with the traditional twin support vector machines.
At present, most of the attribute reduction algorithms based on granularity are simply computing the granularity of knowledge. Repeated calculation will increase the time complexity. Binary discernibility matrix is us...
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At present, most of the attribute reduction algorithms based on granularity are simply computing the granularity of knowledge. Repeated calculation will increase the time complexity. Binary discernibility matrix is used to express binary form, which has a clear ascension whether in space or in time than the traditional discernibility matrix efficiency. On the basis of granularity-based attribute reduction, a method is proposed to preprocess the dataset by using binary discernibility matrix. Firstly, find the core attribute and a minimal reduction. Then use the granularity thought to calculate each particle of the importance of attributes. Most important is joined to the reduction set, thereby achieving the attributes reduction. The example analysis shows that the method can improve the performance of the traditional attribute reduction algorithms effectively. It is a feasible approach to reduce attributes.
Traditional support vector machine has disadvantages of slow training speed and great time consumption when dealing with large-scale datasets. This paper proposes a support vector extraction method based on clustering...
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Traditional support vector machine has disadvantages of slow training speed and great time consumption when dealing with large-scale datasets. This paper proposes a support vector extraction method based on clustering membership, which preprocesses the training datasets and extracts all possible support vectors for SVM training according to the memberships. Considering the training datasets may be linear or nonlinear, this paper severally uses FCM and KFCM to extract support vectors. Experiment results show that the method proposed in this paper can improve the training speed greatly in the condition of maintaining the classification accuracy.
In delay tolerant networks (DTNs), message delivery is operated in an opportunistic way through store-carry and forward relaying, and every DTN node is in anticipation of cooperation for data forwarding from others. U...
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In delay tolerant networks (DTNs), message delivery is operated in an opportunistic way through store-carry and forward relaying, and every DTN node is in anticipation of cooperation for data forwarding from others. Unfortunately, there always exist some selfish nodes that are reluctant to contribute to this cooperative data forwarding procedure so as to save their valuable storage buffer, limited computation power and precious energy. In order to stimulate nodes' willingness to participate in data forwarding, a number of incentive schemes have been proposed recently. However, most existing incentive schemes simply ignore efforts of nodes involved in message delivery if messages delivered fail to reach their destinations. Due to the nature of DTN, such as intermittent connectivity, it is not unusual to have unreliable message delivery, which results in unrewarded or wasted efforts for participating nodes and may discourage them from participating in future data forwarding. Therefore, it is crucial to recognize contribution of every node involved in a data forwarding procedure even the message it helps to forward doesn't successfully reach its destination. However, how to track all delivery paths so as to give every intermediate node some incentive for their cooperative efforts of data forwarding is still an open research problem. To address this problem, we propose a secure message forwarding scheme with path tracking. The proposed method is end-to-end secure with data source and identity authentication. In addition, it can thwart some well known attacks including edge inserting attack, sibling inserting attack and free riding attack.
In this paper, we propose a method to improve adaptive loop filter (ALF) efficiency with temporal prediction. For one frame, two sets of adaptive loop filter parameters are adaptively selected by rate distortion optim...
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In this paper, we propose a method to improve adaptive loop filter (ALF) efficiency with temporal prediction. For one frame, two sets of adaptive loop filter parameters are adaptively selected by rate distortion optimization. The first set of ALF parameters is estimated by minimizing the mean square error between the original frame and the current reconstructed frame. The second set of filter parameters is the one that is used in the latest prior frame. The proposed algorithm is implemented in HM3.0 software. Compared with the HM3.0 anchor, the proposed method achieves 0.4%, 0.3% and 0.3% BD bitrate reduction in average for high efficiency low delay B, high efficiency low delay P and high efficiency random access configuration, respectively. The encoding and decoding time increase by 1% and 2% on average, respectively.
Linear Discriminant Analysis (LDA) is an efficient image feature extraction technique by supervised dimensionality reduction. In this paper, we extend LDA to Structured Sparse LDA (SSLDA), where the projecting vectors...
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Linear Discriminant Analysis (LDA) is an efficient image feature extraction technique by supervised dimensionality reduction. In this paper, we extend LDA to Structured Sparse LDA (SSLDA), where the projecting vectors are not only constrained to sparsity but also structured with a pre-specified set of shapes. While the sparse priors deal with small sample size problem, the proposed structure regularization can also encode higher-order information with better interpretability. We also propose a simple and efficient optimization algorithm to solve the proposed optimization problem. Experiments on face images show the benefits of the proposed structured sparse LDA on both classification accuracy and interpretability.
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