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Prediction based on observed data is one of the major purposes in (big) data analytics, and has shown great impacts in many applications, including engineering, social science, and medical treatments. Statistical machine learning has been widely adopted to deal with such problem. In this paper, we analogize the relationship among data variables as a sort of generalized social network [1], that is,...
Self-organizing dense sensor networks are expected to resolve the challenges of spectral efficiency, energy efficiency, and device management. This paper explores the use of information dependence among sensors to form a community structure of sensor data, and uses this structure to develop an information-centric processing methodology to achieve self-organizing dense sensor networks. Moreover, a...
In this paper we address the problem of cognition (inference and decision) on data collected from a system which is characterized by a stochastic network topology. The decision problem is formulated as a data fusion problem and a data model that only requires stochastic network topology, marginal probability densities and pairwise correlations is proposed. Based on such model, we can mitigate the...
This paper introduces the general framework of statistical information fusion on social network, which plays an important role on understanding the human interaction and cooperation in the society. On many existing information platforms on the social network, people's relationship affects their decision on both static and dynamic way. We consider the scenario that agents refer others decisions which...
Trust among Internet users and thus social networks plays an important role in e-commerce and other Internet applications. However, the precise mathematical model of trust and thus applications based on trust in e-commerce system has not been satisfactorily established yet. In this paper, we present a probability theoretic framework to quantitatively measure trust as mathematical reasoning and to...
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