In the last years, we have seen that the use of P2P applications has increased significantly and currently they represent a significant portion of the Internet traffic. In consequence of this growth, P2P traffic chara...
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ISBN:
(纸本)9781424413751
In the last years, we have seen that the use of P2P applications has increased significantly and currently they represent a significant portion of the Internet traffic. In consequence of this growth, P2P traffic characterization and identification are becoming increasingly important for network administrators and designers. However, this idendfication is not simple. Nowadays, P2P applications explicitly try to camouflage the original traffic in an attempt to go undetected This work presents a methodology and selection of five traffic discriminators and applies cluster analysis to identify P2P applications. Our results indicate that the accuracy is around 90% using this small number of discriminators.
We propose novel algorithms for organizing large image and video datasets using both the visual content and the associated side-information, such as time, location, authorship, and so on. Earlier research have used si...
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ISBN:
(纸本)9781424423538
We propose novel algorithms for organizing large image and video datasets using both the visual content and the associated side-information, such as time, location, authorship, and so on. Earlier research have used side-information as pre-filter before visual analysis is performed, and we design a machine learning algorithm to model the join statistics of the content and the side information. Our algorithm, Diverse-Density Contextual clustering (D2C2), starts by finding unique patterns for each sub-collection sharing the same side-info, e.g., scenes from winter. It then finds the common patterns that are shared among all subsets, e.g., persistent scenes across all seasons. These unique and common prototypes are found with Multiple Instance Learning and subsequent clustering steps. We evaluate D2C2 on two web photo collections from Flickr and one news video collection from TRECVID. Results show that not only the visual patterns found by D2C2 are intuitively salient across different seasons, locations and events, classifiers constructed from the unique and common patterns also outperform state-of-the-art bag-of-features classifiers.
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