基于过程控制的网络图片防盗保护方法

基于网络图片传播与浏览过程,采用URL监控和Flash实现了一种网络图片防盗保护方法,包括动态读取、访问超时、本地缓存、地址加密、看图干扰等图片防盗保护对策,可有效防范URL源下载、文件缓存和截屏抓图等侵权行为.     

Superconvergence and Extrapolation Analysis of a Nonconforming Mixed Finite Element Approximation for Time-Harmonic Maxwell’s Equations

In this paper, a nonconforming mixed finite element approximating to the three-dimensional time-harmonic Maxwell’s equations is presented. On a uniform rectangular prism mesh, superclose property is achieved for electric field E and magnetic filed H with the boundary condition E×n=0 by means of the asymptotic expansion. Applying postprocessing operators, a superconvergence result is stated for the discretization error of the postprocessed discrete solution to the solution itself. To our best knowledge, this is the first global superconvergence analysis of nonconforming mixed finite elements for the Maxwell’s equations. Furthermore, the approximation accuracy will be improved by extrapolation method.     

A localized approach for the method of approximate particular solutions

The method of approximate particular solutions (MAPS) has been recently developed to solve various types of partial differential equations. In the MAPS, radial basis functions play an important role in approximating the forcing term. Coupled with the concept of particular solutions and radial basis functions, a simple and effective numerical method for solving a large class of partial differential equations can be achieved. One of the difficulties of globally applying MAPS is that this method results in a large dense matrix which in turn severely restricts the number of interpolation points, thereby affecting our ability to solve large-scale science and engineering problems.In this paper we develop a localized scheme for the method of approximate particular solutions (LMAPS). The new localized approach allows the use of a small neighborhood of points to find the approximate solution of the given partial differential equation. In this paper, this local numerical scheme is used for solving large-scale problems, up to one million interpolation points. Three numerical examples in two-dimensions are used to validate the proposed numerical scheme.     

Perturbation Analysis and Optimization of Multiclass Multiobjective Stochastic Flow Models

Stochastic Flow Models (SFMs) are stochastic hybrid systems that abstract the dynamics of many complex discrete event systems and provide the basis for their control and optimization. SFMs have been used to date to study systems with a single user class or some multiclass settings in which performance metrics are not class-dependent. In this paper, we develop a SFM framework for multiple classes and class-dependent performance objectives, where competing classes employ threshold control policies and service is provided on a First Come First Serve (FCFS) basis. In this framework, we analyze new phenomena that result from the interaction of the different classes and give rise to a new class of “induced” events that capture delays in the SFM dynamics. We derive Infinitesimal Perturbation Analysis (IPA) estimators for derivatives of various class-dependent objectives, and use them as the basis for on-line optimization algorithms that apply to the underlying discrete event system (not the SFM). This allows us to contrast system-centric and user-centric objectives, thus putting the resource contention problem in a game framework. The unbiasedness of IPA estimators is established and numerical results are provided to illustrate the effectiveness of our method for the case where there are no constraints on the controllable thresholds and to demonstrate the gap between the results of system-centric optimization and user-centric optimization.     

基于维基百科的未登录词译文挖掘

未登录词(out of vocabulary,OOV)的查询翻译是影响跨语言信息检索(cross-language information retrieval,CLIR)性能的关键因素之一.它根据维基百科(Wikipedia)的数据结构和语言特性,将译文环境划分为目标存在环境和目标缺失环境.针对目标缺失环境下的译文挖掘难点,它采用频度变化信息和邻接信息实现候选单元抽取,并建立基于频度-距离模型、表层匹配模板和摘要得分模型的混合译文挖掘策略.实验将基于搜索引擎的未登录词挖掘技术作为baseline,并采用TOP1进行评测.实验验证基于维基百科的混合译文挖掘方法可达到0.6822的译文正确率,相对baseline取得6.98%的改进.     

基于证据理论数据融合的双效水印

提出一种新的基于证据理论的数据融合的双效水印算法.首先应用水印公式构造特殊的水印嵌入后条件,根据嵌入系数之间的定性分析提取鲁棒水印;利用嵌入系数之间的定量分析能提取脆弱水印,并能准确定位篡改的区域.其次在嵌入过程中还使用纠错码对水印信息进行调和,把水印重复嵌入到图像信息的不同位置,以便利用纠错码的辅助信息来估计出提取的...     

Proteomic examination of Leishmania chagasi plasma membrane proteins: Contrast between avirulent and virulent (metacyclic) parasite forms

Purpose: About two million new cases of leishmaniasis with 50 000 associated deaths occur worldwide each year. Promastigotes of the causative Leishmania spp. develop from the procyclic stage to the highly virulent metacyclic stage within the sand fly vector. We hypothesized that proteins important for promastigote virulence might be uniquely represented in the plasma membrane of metacyclic, but not procyclic, promastigotes. Experimental design: Procyclic (logarithmic) promastigotes and purified metacyclic promastigotes from stationary phase cultures of Leishmania chagasi were used to prepare membrane preparations either by surface biotinylation‐streptavidin affinity separation or by octyl glucoside detergent extraction. Results: These membrane fractions were enriched over 130‐ and 250‐fold, respectively, as estimated by Western blotting for the plasma membrane's major surface protease. Hundreds or dozens of proteins were identified by LC‐MS/MS in the surface biotinylation or detergent extraction, respectively. Confocal microscopy suggested the difference between the lists was due to the fact that proteins localized both on the surface membrane and within the flagellar pocket were accessible to surface biotinylation, whereas only proteins on the membrane were obtained by detergent extraction. Using detergent extraction, we found different proteins were present in membranes of the procyclic stage compared to metacyclic stage promastigotes. Several dozen were stage specific. Conclusions and clinical relevance: These data provide a foundation for identifying virulence factors in the plasma membranes of Leishmania spp. promastigotes during metacyclogenesis.     

Learning algorithm for multimodal optimization

We present a new evolutionary algorithm—“learning algorithm” for multimodal optimization. The scheme for reproducing a new generation is very simple. Control parameters, of the length of the list of historical best solutions and the “learning probability” of the current solutions being moved towards the current best solutions and towards the historical ones, are used to assign different search intensities to different parts of the feasible area and to direct the updating of the current solutions. Results of numerical tests on minimization of the 2D Schaffer function, the 2D Shubert function and the 10D Ackley function show that this algorithm is effective and efficient in finding multiple global solutions of multimodal optimization problems.     

3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video

To achieve size preserving tracking, in addition to controlling the camera’s pan and tilt motions to keep the object of interest in the camera’s field of view (FOV), the camera’s focal length is adjusted automatically to compensate for the changes in the target’s image size caused by the relative motion between the camera and the target. The estimation accuracy of these changes determines the effectiveness of the resulting zoom control. The existing method of choice for real-time target scale estimation applies structure from motion (SFM) based on the weak perspective projection model. In this paper we propose a target scale estimation algorithm with a linear solution based on the more advanced paraperspective projection model, which improves the accuracy of scale estimation by considering center offset. Another key issue in SFM based algorithms is the separation of target and background features, especially when composite camera (pan/tilt/zoom) and target motions are involved. This paper designs a fast target feature separation/grouping algorithm, the 3D affine shape method. The resulting separation automatically adapts to the target’s 3D geometry and motion and is able to accommodate a large amount of off-plane rotation, which most existing separation/grouping algorithms find difficult to achieve. Experimental results illustrate the effectiveness of the proposed scale estimation and feature separation algorithms in tracking translating and rotating objects with a PTZ camera while preserving their sizes. In comparison with the leading size preserving tracking algorithm described by Tordoff and Murray, our algorithm is able to reduce the cumulative tracking error significantly from 17.4% to 3.3%.     

TSVM-HMM: Transductive SVM based hidden Markov model for automatic image annotation

Automatic image annotation (AIA) is an effective technology to improve the performance of image retrieval. In this paper, we propose a novel AIA scheme based on hidden Markov model (HMM). Compared with the previous HMM-based annotation methods, SVM based semi-supervised learning, i.e. transductive SVM (TSVM), is triggered out for remarkably boosting the reliability of HMM with less users’ labeling effort involved (denoted by TSVM-HMM). This guarantees that the proposed TSVM-HMM based annotation scheme integrates the discriminative classification with the generative model to mutually complete their advantages. In addition, not only the relevance model between the visual content of images and the textual keywords but also the property of keyword correlation is exploited in the proposed AIA scheme. Particularly, to establish an enhanced correlation network among keywords, both co-occurrence based and WordNet based correlation techniques are well fused and are able to be helpful for benefiting from each other. The final experimental results reveal that the better annotation performance can be achieved at less labeled training images.