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dissimilar visual semantic spaces for diverse keyword based queries through keyword enlargements (expansion). Visual structures of pictures are projected into their associated visual semantic area to acquire sense (semantic) signatures. At online phase, pictures are re-ranked by matching their semantic signs acquired from
system (Fexpert) for a research university. Data were undertaken from three sources: (1) researcher's personal profile, (2) graduate school profile and (3) research project profiles. The data were preprocessed and clustered according to each expert's keywords using K-Means algorithm. The proposed system can be used to find
vocabulary. A group-LASSO regularizer is used to drive as many feature weights to zero as possible. We evaluate the quality of the pruned vocabulary by clustering the data using the resulting feature subset. Experiments on PASCAL VOC 2007 dataset using 5000 visual keywords, resulted in around 80% reduction in the number of
base stations to determine the location of the mobile. The holder's trajectory can get through the statistics of large-scale location about anonymous user. Even in the special position the mall can push information keywords and make the service management more intelligent. When mobile phones become an indispensable things
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