Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities, the patterns of HFS crowd collaboration process, and the characteristics of HFS participant networks. A survey of HFS participants was conducted to provide an in-depth understanding of the HFS community and various factors that motivate these participants to contribute. This article also advocates a new stream of Web science and social computing research that will be important in predicting the future growth and use of the World Wide Web.
Fei-Yue WangDaniel ZengQingpeng ZhangJames A. HendlerJianping Cao
The acquired hyperspectral images （HSIs） are inherently attected by noise wlm Dano-varylng level, which cannot be removed easily by current approaches. In this study, a new denoising method is proposed for removing such kind of noise by smoothing spectral signals in the transformed multi- scale domain. Specifically, the proposed method includes three procedures： 1 ） applying a discrete wavelet transform （DWT） to each band; 2） performing cubic spline smoothing on each noisy coeffi- cient vector along the spectral axis; 3 ） reconstructing each band by an inverse DWT. In order to adapt to the band-varying noise statistics of HSIs, the noise covariance is estimated to control the smoothing degree at different spectra｜ positions. Generalized cross validation （GCV） is employed to choose the smoothing parameter during the optimization. The experimental results on simulated and real HSIs demonstrate that the proposed method can be well adapted to band-varying noise statistics of noisy HSIs and also can well preserve the spectral and spatial features.
In this paper,we aim to solve the finite horizon optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming(ADP) approach.A new-optimal control algorithm based on the iterative ADP approach is proposed which makes the performance index function converge iteratively to the greatest lower bound of all performance indices within an error according to within finite time.The optimal number of control steps can also be obtained by the proposed-optimal control algorithm for the situation where the initial state of the system is unfixed.Neural networks are used to approximate the performance index function and compute the optimal control policy,respectively,for facilitating the implementation of the-optimal control algorithm.Finally,a simulation example is given to show the results of the proposed method.
