Lately, skeleton-based action recognition has drawn remarkable attention to graph convolutional networks (GCNs). Recent methods have focused on graph learning because graph topology is the key to GCNs. We propose to align graph learning on the channel level by introducing graph convolution with enriched topology based on careful channel-wise correlations, namely the attentive channel-wise correlation graph convolution (ACC-GC). For the model to learn channel-wise enriched topologies, ACC-GC learns a shared graph topology spanning many channels and enhances it with careful channel-wise correlations. Encoding the intra-correlation between various nodes within each channel, boosting informative channel-wise correlations, and suppressing trivial ones generates attentive channel-wise correlations. Our enhanced ACC-GCN is created by substituting our ACC-GC for the GC in a standard GCN. Extensive experiments on NTURGB60 and Northwestern-UCLA datasets demonstrate that our proposed ACC-GCN performs comparably to state-of-the-art methods while reducing the computational cost.

Virtualization is an important feature for cloud computing which has recently attracted considerable attention. In this work, we de-sign an energy efficient approach which aims to minimize the energy consumption without reducing the Quality of Service (QoS) for user's deadline requirements in cloud data centers. We also propose a VM re-placement algorithm for QoS and energy-awareness in cloud data cen-ters. We experimentally compare the performance of our algorithm with other the state-of-the-art algorithms (i.e., the random algorithm and the first fit algorithm). The experimental results show that our solution im-proves first fit and random algorithms by twice and four times respec-tively, and is also very energy efficient with reliable QoS.

This letter presents three novel contributions for the 2D laser scan data. First, a low-level point feature detector for stable interest point detection. Second, an approach for high-level B-splines features extraction, and finally, a local descriptor for the interest points based on the shape context representation of the uniformly distributed points on the B-splines features. The clues of stable interest points obtained from the detector are used in the B-splines features extraction from the scan data, which enables lightweight and accurate mapping of the environment using fewer control points instead of scan data points. The letter also proposes benchmark metrics to evaluate the performance of the extracted high-level features in comparison with the state-of-the-art methodology. A robust descriptor for the interest points offers an efficient association and correspondence matching. Consistent results compared with the state-of-the-art FALKO-BSC and FLIRT- $\beta$ -Grid features in experiments with standard datasets prove the stability of our proposed interest point detector and the proficiency of the descriptor. The results of efficient localization using the pair are also presented.