Traditional segmenting algorithm of time series cannot be used in the environment of data streams effectively. A new online algorithm for segmenting time series, called SC (Sequential Clustering), is presented here. T...
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Traditional segmenting algorithm of time series cannot be used in the environment of data streams effectively. A new online algorithm for segmenting time series, called SC (Sequential Clustering), is presented here. The traditional clustering methods are confronted with the large challenge in the domain of mining time series. By considering the time attribute of temporal data, a novel clustering method is designed in this present study. Through constructing an in-memory List for saving segmenting information, SC can online segments time series with a single scan of the data points. The I/O cost is linear to the size of the sequential dataset. Using several experiments, SC's time efficiency and segmentation quality are evaluated in detail. Theory analysis and experimental Results show that SC is superior to other existed segmenting algorithms.
Privacy-preserving computational geometry is a special secure multi-party computation and has many applications. Previous protocols for determining whether a point is inside a circle are not secure enough. We present ...
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Privacy-preserving computational geometry is a special secure multi-party computation and has many applications. Previous protocols for determining whether a point is inside a circle are not secure enough. We present a two-round protocol for computing the distance between two private points and develop a more efficient protocol for the point-circle inclusion problem based on the distance protocol. In comparison with previous solutions, our protocol not only is more secure but also reduces the number of communication rounds and the number of modular multiplications significantly.
Signal transduction (ST) networks simulation is important to medical research. However, owing to the complexity of the networks, most methods presented for the simulation are not desirable. Here, based on multi-agent ...
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Time synchronisation is a critical topic in wireless sensor networks for its wide applications, such as data fusion, TDMA scheduling and cooperated sleeping, etc. In this paper, we present an Accurate Time Synchronisa...
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Correlation dimension (CD) and the largest Lyapunov exponent (LLE), which are two most important nonlinear invariant measures of nonlinear system, are adopted to characterize the complexity and stability of human brai...
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The assignment of the computation and communication is one of the important problems in the design of peer-to-peer (P2P) Massively Multi-player Online Game (MMOG). A reputation mechanism for P2P MMOG is proposed in th...
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In this paper,the state-of-the-art parallel computational model research is *** will introduce various models that were developed during the past *** to their targeting architecture features,especially memory organiza...
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In this paper,the state-of-the-art parallel computational model research is *** will introduce various models that were developed during the past *** to their targeting architecture features,especially memory organization,we classify these parallel computational models into three *** models and their characteristics are discussed based on three generations *** believe that with the ever increasing speed gap between the CPU and memory systems,incorporating non-uniform memory hierarchy into computational models will become *** the emergence of multi-core CPUs,the parallelism hierarchy of current computing platforms becomes more and more *** this complicated parallelism hierarchy in future computational models becomes more and more important.A semi-automatic toolkit that can extract model parameters and their values on real computers can reduce the model analysis complexity,thus allowing more complicated models with more parameters to be *** memory and hierarchical parallelism will be two very important features that should be considered in future model design and research.
Parzen windows estimation is one of the classical non-parametric methods in the field of machine learning and pattern classification, and usually uses Gaussian density function as the kernel. Although the relation bet...
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ISBN:
(纸本)0769529097
Parzen windows estimation is one of the classical non-parametric methods in the field of machine learning and pattern classification, and usually uses Gaussian density function as the kernel. Although the relation between the kernel density estimation (KDE) and low-pass filtering is well known, it is vary difficult to setting the parameters of the other kinds of density, functions. This paper proposes a novel method to deal with the parameters of Laplace kernel through measuring the degree of exchanged information among interpolating points. Experimental results showed that the proposed method can improve the performance of Parzen windows significantly.
Historically, the empirical risk of a pattern classifier was asked to be made zero, therefor the default property of training samples were limited to a separable ones. Nowadays on the contrary, the major idea of learn...
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Many applications in wireless sensor networks like video surveillance have the requirement of timely data delivery. Real-time routing is needed in these applications. Because of the limitation of node energy, energy e...
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Many applications in wireless sensor networks like video surveillance have the requirement of timely data delivery. Real-time routing is needed in these applications. Because of the limitation of node energy, energy efficiency is also an important concern in routing protocol design in order to increase the network lifetime. In this paper, we propose a global-energy-balancing routing scheme (GEBR) for real-time traffic based on directed diffusion (DD), which balances node energy utilization to increase the network lifetime. GEBR can find an optimal path in sensor networks for data transfer considering global energy balance and limited delivery delay. Simulation results show that GEBR significantly outperforms DD in uniform energy utilization. GEBR achieves better global energy balance and longer network lifetime. The time for real-time service of the sensor networks using GEBR is prolonged by about 4.37% and the network lifetime is prolonged by 44.6%.
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