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The aim of this paper is to develop some closed algebra operational laws for interval linguistic labels based on extended $\boldsymbol {t}$ -norms and $\boldsymbol {s}$ -norms. We discuss the properties of these operational laws, such as commutative law, associative law, and distribution law. Different kinds of extended $\boldsymbol {t}$ -norms and $\boldsymbol {s}$ -norms, such as the extended...
In the age of big data, information sharing paradigm has been fundamentally changed: Data collection and consumption are increasingly decentralized (partly due to the advent of personal computing devices), and sharing personal data over the Internet becomes a prominent paradigm for new applications. One of these applications is Health Information Exchange (or HIE) where a patient's electronic medical...
This paper presents a novel multi-task learning framework for the accurate prediction of spatio-temporal data at multiple locations. The framework encodes the data as a third-order tensor and performs supervised tensor decomposition to identify the latent factors that capture the inherent spatiotemporal variabilities of the data and their relationship to the target variable of interest. The framework...
In TCM theory, the syndrome is crucial to diagnose diseases and treat patients. In syndrome identification, the relation of symptoms usually correlates with syndrome and represents the pattern of syndrome at symptomatic level. Hence, we learn models for classifying syndromes in depression using 4 different algorithms, which are naive Bayes, Bayes network, SVM and C4.5. From the results of classification,...
In Scientific and Technological (S&T) papers, we first define Chinese S&T Named Entity (CSTNE) and Relation between Chinese S&T Named Entities (RCSTNE) that can offer help to solve the problem of deep knowledge mining in S&T literature database. Based on the definition of CSTNE and RCSTNE, we build their models which include semantic information. The relevant methods based on knowledge...
Understanding and modeling the function of the neurons and neural systems are primary goal of systems neuroscience. Sparse coding theory demonstrates that the neurons in primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics. In this paper, we propose a novel sparse coding model based on structural similarity (SS_SC) for natural image feature extraction...
Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we firstly extend probabilistic latent semantic analysis (PLSA) to model continuous quantity. In addition, corresponding Expectation-Maximization (EM) algorithm is derived to determine the model parameters. Furthermore, in order to deal with the data of different modalities...
The three dimensional stratum modeling provides important basis of analysis and policy-making in areas such as Geophysics, Petroleum, Minerals, Urban construction and so on. In this paper, we firstly propose a general flow of three dimensional stratum modeling, then research stratum partition algorithm and reconstruct the surface of three dimensional stratum model (3DSM), finally program the module...
Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to accomplish the tasks of semantic image annotation and retrieval. In order to model training images precisely, we employ two PLSA models to capture semantic information from visual and textual...
Along with the constant development of online services, the application of traditional RBAC model for the user-role assignment and maintaining the user-role assignment become an arduous and error-prone task. In order to solve these problems, this paper proposes a trust based user-role assignment model for assign role to users. It is based on the userspsila trustworthiness in the system, which is a...
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