-based representation permits very efficient matching for face recognition and is also suited to be employed for face identification in very large datasets with the support of appropriate index structures. The method obtained the best ranking at the SHREC 2008 contest for 3D face recognition. We present an extensive

(MMOGs) can include millions of concurrent players spread across the world. To keep these highly-interactive virtual environments online, a MMOG operator may need to provision tens of thousands of computing resources from various data centers. Faced with large resource demand variability

characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular dataset. In order to scale to very large datasets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbour matching framework that can be used

at approximately 50 kW and with a thermal efficiency of about 30 percent, are well suited to meeting the energy needs of small users such as schools, apartment buildings, restaurants, offices, and small businesses. New markets will open up when such a microturbine is coupled with a SOFC, to give an output of about

variant of ‘groupwise ’ registration to obtain the necessary image correspondences. We evaluate the method on a large dataset, and demonstrate that it achieves results comparable with some of the best published. 1

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i This page intentionally left blank. This paper proposes using active learning combined with rare class discovery and uncertainty identification to statistically train a network traffic classifier. For ingress traffic, a classifier can be trained for a network intrusion detection or prevention

by computing the optical flow of only the foreground pixels. The motion and size features are modelled by an approximated version of kernel density estimation, which is computationally efficient even for large training datasets. Texture features are modelled by an adaptively grown codebook, with the number

macroinvertebrate classifier using evolutionary RBF networks. The best classifier, which is trained over a dataset of river macroinvertebrate specimens, is then used in the MUVIS framework for the efficient search and retrieval of particular macroinvertebrate peculiars. Classification and retrieval results present

to approximate; (ii) identification of computationally simple and efficient strategies that perform surprisingly well; and (iii) a feasibility study of order-theoretic feature selection for large scale datasets. 1