Cloud computing is an emerging technology and it allows users to pay as you need and has the high performance. Cloud computing is a heterogeneous system as well and it holds large amount of application data. In the process of scheduling some intensive data or computing an intensive application, it is acknowledged that optimizing the transferring and processing time is crucial to an application program. In this paper in order to minimize the cost of the processing we formulate a model for task scheduling and propose a particle swarm optimization (PSO) algorithm which is based on small position value rule. By virtue of comparing PSO algorithm with the PSO algorithm embedded in crossover and mutation and in the local research, the experiment results show the PSO algorithm not only converges faster but also runs faster than the other two algorithms in a large scale. The experiment results prove that the PSO algorithm is more suitable to cloud computing.

Although appearance based trackers have been greatly improved in the last decade, they are still struggling with some challenges like occlusion, blur, fast motion, deformation, etc. As known, occlusion is still one of the soundness challenges for visual tracking. Other challenges are also not fully resolved for the existed trackers. In this work, we focus on tackling the latter problem in both color and depth domains. Neutrosophic set (NS) is as a new branch of philosophy for dealing with incomplete, indeterminate and inconsistent information. In this paper, we utilize the single valued neutrosophic set (SVNS), which is a subclass of NS, to build a robust tracker. First, the color and depth histogram are employed as the appearance features, and both features are represented in the SVNS domain via three membership functions T , I, and F. Second, the single valued neutrosophic cross-entropy measure is utilized for fusing the color and depth information. Finally, a novel SVNS based MeanShift tracker is proposed. Applied to the video sequences without serious occlusion in the Princeton RGBD Tracking dataset, the performance of our method was compared with those by the state-of-the-art trackers. The results revealed that our method outperforms these trackers when dealing with challenging factors like blur, fast motion, deformation, illumination variation, and camera jitter.

Location-based services (LBS) have become an important part of people's daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users' privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise

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