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How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear stateof-the-art against other RGB and RGBT tracking methods.

Cloud computing is a suitable platform to execute the deadline-constrained scientific workflows which are typical big data applications and often require many hours to finish. Moreover, the problem of energy consumption has become one of the major concerns in clouds. In this paper, we present a cost and energy aware scheduling (CEAS) algorithm for cloud scheduler to minimize the execution cost of workflow and reduce the energy consumption while meeting the deadline constraint. The CEAS algorithm consists of five sub-algorithms. First, we use the VM selection algorithm which applies the concept of cost utility to map tasks to their optimal virtual machine (VM) types by the sub-makespan constraint. Then, two tasks merging methods are employed to reduce execution cost and energy consumption of workflow. Further, In order to reuse the idle VM instances which have been leased, the VM reuse policy is also proposed. Finally, the scheme of slack time reclamation is utilized to save energy of leased VM instances. According to the time complexity analysis, we conclude that the time complexity of each sub-algorithm is polynomial. The CEAS algorithm is evaluated using Cloudsim and four real-world scientific workflow applications, which demonstrates that it outperforms the related well-known approaches.

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