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Abstract-A new interconnection topology-the Extended Hypercube-consisting of an interconnection network of k-cubes is discussed. The extended hypercube is a hierarchical, expansive, recursive structure with a constant predefined building block. The extended hypercube retains the positive features of the k-cube at different levels of hierarchy and at the same time has some additional advantages like reduced diameter and constant degree of a node, This paper presents an introduction to the topology of the extended hypercube and analyzes its architectural potential in terms of message routing and executing a class of highly parallel algorithms. Topological properties and performance studies of the extended hypercube are presented.

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Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images.