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Graph Processing has been widely used to capture complex data dependency and uncover relationship insights. Due to the ever-growing graph scale and algorithm complexity, distributed graph processing has become more and more popular. In this paper, we investigate how to balance performance and cost for large scale graph processing on configurable virtual machines (VMs). We analyze the system architecture...
We herein propose an evolutionary multi-agent system (EMAS for short) to build an ensemble of surrogates for prediction. In our EMAS, we employ six kinds of basic surrogates, including Gaussian process, Kriging model, polynomial response surface, radial basis function, radial basis function neural network, and support vector regression machine. We define each surrogate as one agent and co-evolve parameters...
To deal with multi-modality in human pose estimation, mixture models or local models are introduced. However, problems with over-fitting and generalization are caused by our necessarily limited data, and the regression parameters need to be determined without resorting to slow and processor-hungry techniques, such as cross validation. To compensate these problems, we have developed a semi-parametric...
Pop-up threats usually appear or disappear randomly in a battle field. If the next pop-up threat locations could be predicted it would assist a search or attack team, such as in a persistent area denial (PAD) mission, in getting a team of unmanned air vehicles (UAVs) to the threats sooner. We present a Markov model for predicting pop-up ground threats in military operations. We first introduce a general...
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