Abstract. We describe several Markov models derived from the behaviour patterns of many users, which predict which documents a user is likely to request next. We then present comparative results of the predictive accuracy of the different models, and, based on these results, build hybrid models which combine the individual models in different ways. These hybrid models generally have a greater predictive accuracy than the individual models. The best models will be incorporated in a system for pre-sending WWW documents.
Abstract-Intention recognition is an important topic in human-robot cooperation that can be tackled using probabilistic model-based methods. A popular instance of such methods are Bayesian networks where the dependencies between random variables are modeled by means of a directed graph. Bayesian networks are very efficient for treating networks with conditionally independent parts. Unfortunately, such independence sometimes has to be constructed by introducing so called hidden variables with an intractably large state space. An example are human actions which depend on human intentions and on other human actions. Our goal in this paper is to find models for intention-action mapping with a reduced state space in order to allow for tractable on-line evaluation. We present a systematic derivation of the reduced model and experimental results of recognizing the intention of a real human in a virtual environment.
Purpose: Advances in additive manufacturing processes are enabling the fabrication of surrogate bone structures for applications including use in high-resolution anthropomorphic phantoms. In this research, a simple numerical model is proposed that enables the generation of microarchitecture with similar statistical distribution to trabecular bone. Methods: A human humerus, radius, ulna, and several vertebrae were scanned on the Imaging and Medical beamline at the Australian Synchrotron and the proposed numerical model was developed through the definition of two complex functions that encode the trabecular thickness and positiondependant spacing to generate volumetric surrogate trabecular structures. The structures reproduced those observed at 19 separate axial locations through the experimental bone volumes. The applicability of the model when incorporating a two-material approximation to absorption-and phase-contrast CT was also investigated through simulation. Results: The synthetic structures, when compared with the real trabecular microarchitecture, yielded an average mean thickness error of 2 lm, and a mean difference in standard deviation of 33 lm for the humerus, 24 lm for the ulna and radius, and 15 lm for the vertebrae. Simulated absorption-and propagation-based phase contrast CT projection data were generated and reconstructed using the derived mathematical simplifications from the two-material approximation, and the phase-contrast effects were successfully demonstrated. Conclusions: The presented model reproduced trabecular distributions that could be used to generate phantoms for quality assurance and validation processes. The implication of utilizing a twomaterial approximation results in simplification of the additive manufacturing process and the generation of synthetic data that could be used for training of machine learning applications.
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