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Data outsourcing has become quite popular in recent years. While the rapid development of cloud storage service, it brings about many new security challenges. One of the biggest concerns with cloud data storage is that of data integrity at untrusted cloud servers. For example, when individual users store important data files in cloud servers, the data files may be damaged or corrupted because of cloud...
With the growing adoption of virtualized data centers and cloud services, nowadays multiple resource scheduling is increasingly attractive to researchers. Some previous studies achieved progresses in this area. However, these heuristics have obvious limitations in complex software defined cloud environment. A real multi-dimensional model is needed to solve this NP hard problem. Our approach emphasizes...
Autonomous operation of blast hole drill rigs requires monitoring of drilling parameters known as “Measurement While Drilling” (MWD) data. From these data, rock properties can be inferred. A supervised classification scheme is usually used to map MWD data inputs to rock type outputs given some labeled training data. However, the geology has no definite ground truth that can allow a reliable labeling...
This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function...
A neural network based asset evaluation method is proposed in this paper. The total amount value of remained asset can be evaluated by using the neural network prediction model, which obtained by training the disposed asset data. Examples show the effectiveness of algorithm. The method proposed has been successfully used for remaining assets evaluation in banking non-performing assets disposal.
We improve Gaussian processes (GP) classification by reorganizing the (non-stationary and anisotropic) data to better fit to the isotropic GP kernel. First, the data is partitioned into two parts: along the feature with the highest frequency bandwidth. Secondly, for each part of the data, only the spectrally homogeneous features are chosen and used (the rest discarded) for GP classification. In this...
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