In this paper, we present a scalable implementation of a topic modeling (Adaptive Link-IPLSA) based method for online event analysis, which summarize the gist of massive amount of changing tweets and enable users to e...
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PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of ...
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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.
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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We study the visual learning models that could work efficiently with little ground-truth annotation and a mass of noisy unlabeled data for large scale Web image applications, following the subroutine of semi-supervise...
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We study the visual learning models that could work efficiently with little ground-truth annotation and a mass of noisy unlabeled data for large scale Web image applications, following the subroutine of semi-supervised learning (SSL) that has been deeply investigated in various visual classification tasks. However, most previous SSL approaches are not able to incorporate multiple descriptions for enhancing the model capacity. Furthermore, sample selection on unlabeled data was not advocated in previous studies, which may lead to unpredictable risk brought by real-world noisy data corpse. We propose a learning strategy for solving these two problems. As a core contribution, we propose a scalable semi-supervised multiple kernel learning method (S 3 MKL) to deal with the first problem. The aim is to minimize an overall objective function composed of log-likelihood empirical loss, conditional expectation consensus (CEC) on the unlabeled data and group LASSO regularization on model coefficients. We further adapt CEC into a group-wise formulation so as to better deal with the intrinsic visual property of real-world images. We propose a fast block coordinate gradient descent method with several acceleration techniques for model solution. Compared with previous approaches, our model better makes use of large scale unlabeled images with multiple feature representation with lower time complexity. Moreover, to address the issue of reducing the risk of using unlabeled data, we design a multiple kernel hashing scheme to identify the “informative” and “compact” unlabeled training data subset. Comprehensive experiments are conducted and the results show that the proposed learning framework provides promising power for real-world image applications, such as image categorization and personalized Web image re-ranking with very little user interaction.
A group key assignment scheme based on multi-dimensional space circle properties is proposed, which enable the members of a group to obtain the group key by both the public information published on the notice board an...
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A group key assignment scheme based on multi-dimensional space circle properties is proposed, which enable the members of a group to obtain the group key by both the public information published on the notice board and the secret information of their own. The proposed scheme can be divided into three phases: user registration, group key assignment, and group key computation. The proposed scheme has such advantages as easy operations of group key update and member's join/leave, and the security properties of forward secrecy and backward secrecy.
Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorith...
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Some expanded fuzzy rough sets models have been investigated to handle fuzzy databases with uncertain, imprecise and incomplete real-valued information. In this paper, we make further research on fuzzy rough sets mode...
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Some expanded fuzzy rough sets models have been investigated to handle fuzzy databases with uncertain, imprecise and incomplete real-valued information. In this paper, we make further research on fuzzy rough sets models in fuzzy environment, and we generalize rough fuzzy sets model based on a covering to fuzzy rough sets model based on a fuzzy covering. The lower and upper approximations of fuzzy subsets are defined based on a fuzzy covering, and basic properties are investigated. Then, the axiom definition of the lower approximation operator is given. It is shown that the rough fuzzy sets model based on a covering is a special instance of the fuzzy rough sets model based on a fuzzy covering.
Relational Database Model (RDM) has been proven to be a very useful data-storage technique. As information is stored as data in relational databases, the induction of concepts from data is a pivotal topic in the data ...
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Using Particle Swarm Optimization to handle complex functions with high-dimension it has the problems of low convergence speed and sensitivity to local convergence. The convergence of particle swarm algorithm is studi...
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Using Particle Swarm Optimization to handle complex functions with high-dimension it has the problems of low convergence speed and sensitivity to local convergence. The convergence of particle swarm algorithm is studied from the dynamic system theory, and the condition for the convergence of particle swarm algorithm is given. The analysis provided qualitative guidelines for the general algorithm parameter selection. Results of numerical tests show the efficiency of the results.
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