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Stream clustering methods, which group continuous, temporally ordered dynamic data instances, have been used in a number of applications such as stock market analysis, network analysis, and cosmological analysis. Most of the popular stream clustering algorithms are linear in nature, i.e. they assume that the data is linearly separable in the input space and use measures such as the Euclidean distance...
In this paper we investigate the problem of user authentication using keystroke biometrics. A new distance metric that is effective in dealing with the challenges intrinsic to keystroke dynamics data, i.e., scale variations, feature interactions and redundancies, and outliers is proposed. Our keystroke biometrics algorithms based on this new distance metric are evaluated on the CMU keystroke dynamics...
We propose a novel method of analyzing human interactions based on the walking trajectories of human subjects, which provide elementary and necessary components for understanding and interpretation of complex human interactions in visual surveillance tasks. Our principal assumption is that an interaction episode is composed of meaningful small unit interactions, which we call “sub-interactions.” We...
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