A Hypothesis Testing Approach to Semifragile Watermark-Based Authentication

This paper studies the problem of achieving watermark semifragility in watermark-based authentication systems through a composite hypothesis testing approach. Embedding a semifragile watermark serves to distinguish legitimate distortions caused by signal-processing manipulations from illegitimate ones caused by malicious tampering. This leads us to consider authentication verification as a composite hypothesis testing problem with the watermark as side information. Based on the hypothesis testing model, we investigate effective embedding strategies to assist the watermark verifier to make correct decisions. Our results demonstrate that quantization-based watermarking is more appropriate than spread-spectrum-based methods to achieve the semifragility tradeoff between two error probabilities. This observation is confirmed by a case study of an additive Gaussian white noise channel with a Gaussian source using two figures of merit: 1) relative entropy of the two hypothesis distributions and 2) the receiver operating characteristic. Finally, we focus on common signal-processing distortions, such as JPEG compression and image filtering, and investigate the discrimination statistic and optimal decision regions to distinguish legitimate and illegitimate distortions. The results of this paper show that our approach provides insights for authentication watermarking and allows for better control of semifragility in specific applications.      

编程初学者程序调试能力的现象描述法研究

程序调试能力是学生程序设计能力的一个非常重要的方面。运用现象描述分析学的方法从学生对程序调试能力的总体评价、程序出错的原因分析、出错后的解决办法等方面进行深入的研究,得到一些非常重要的信息,并提出改进学生程序调试能力的办法。     

Diagnostic analysis of inventory systems: A Statistical Approach

In this paper we introduce a statistical approach to estimate the performance of inventory systems. We briefly survey the existing methods, present a stratified sampling methodology, and describe a new technique to estimate seasonal factors and safety stocks. The paper concludes with an example based on real life data.     

A Statistical Approach to Scheduling Software Development

Given certain facts about a project that are known early, this macro-estimating technique generates an expected life-cycle curve of manpower against time.     

一种基于组合测试的软件故障调试方法

在研究了组合测试基本模型的基础上,提出了一种基于组合测试的故障原因诊断方法．该方法基于组合测试的结果,补充一些附加测试用例进行重新测试,并对其结果作进一步分析和验证,从而迅速将故障原因锁定在很小的范围内,这样可为软件的调试和测试工作提供更方便、更有价值的线索和参考．     

基于统计假设检验的传感器网络目标探测机制

多传感器组网对目标探测是当前军事领域的研究热点,也是传感器网络的基本功能。但是,现有的基于目标输出信号阈值机制难以精确描述目标发现事件。根据数理统计理论将目标探测问题建模为统计推断过程,本文提出一种基于统计假设检验的传感器网络目标探测机制,通过样本训练确定检验参数,采用数据融合方法优化探测结果。实验显示,该方法能有效解决探测过程的噪声干扰问题,提高目标捕捉的正确率。     

传感器网络调试研究综述

越来越多面向不同应用领域的传感器网络被部署在真实环境中,帮助人们以新的方式观测周围的物理世界.然而,这些系统常常会出现各种不可预期的故障,能否快速有效地对这些故障进行检测、定位和修复,是传感器网络调试需要研究的重要内容.在概述了传感器网络调试问题之后,文章总结、比较了传感器网络调试过程中常用的系统状态信息获取技术,然后从故障检测、故障定位和故障修复3个方面综述了代表性关键技术及相关工具,最后探讨了该领域未来的研究方向.     

解决多音字字-音转换的一种统计学习方法

字-音转换是语音合成系统中的一个重要模块,其中多音词和以单字词形式存在的多音字读音的确定一直是个没有很好解决的问题。本文通过对大量标注有正确拼音的语料的统计,指出着重解决41个重点多音字和22个重点多音词就可基本解决字-音转换的问题。本文采用基于扩展的随机复杂度的随机决策列表方法自动提取多音字(词)的读音规则,将字-音转换的错误率由8.8‰降低到4.4‰。规则的训练和测试的材料的标注是一个耗费人力和时间的工作,而训练材料的数量和质量又直接影响最终的结果。本文提出一种半自动的语料标注流程,可以节省将近一半的人工和时间。     

怪引子重构的统计熵方法

用Boltzmann统计状态熵度量怪引子的展开程度,从而提出选取延迟时间变量的第一极大熵准则。实际使用时,仅需等价地计算重构图所及的状态个数。通过Rossler吸引子的重构说明了本文方法的有效性。     

A Component-Based Debugging Approach for Detecting Structural Inconsistencies in Declarative Equation Based Models

Object-oriented modeling with declarative equation based languages often unconsciously leads to structural inconsistencies. Component-based debugging is a new structural analysis approach that addresses this problem by analyzing the structure of each component in a model to separately locate faulty components. The analysis procedure is performed recursively based on the depth-first rule. It first generates fictitious equations for a component to establish a debugging environment, and then detects structural defects by using graph theoretical approaches to analyzing the structure of the system of equations resulting from the component. The proposed method can automatically locate components that cause the structural inconsistencies, and show the user detailed error messages. This information can be a great help in finding and localizing structural inconsistencies, and in some cases pinpoints them immediately.     

Statistical Mechanics of Online Learning of Drifting Concepts: A Variational Approach

We review the application of statistical mechanics methods to the study of online learning of a drifting concept in the limit of large systems. The model where a feed-forward network learns from examples generated by a time dependent teacher of the same architecture is analyzed. The best possible generalization ability is determined exactly, through the use of a variational method. The constructive variational method also suggests a learning algorithm. It depends, however, on some unavailable quantities, such as the present performance of the student. The construction of estimators for these quantities permits the implementation of a very effective, highly adaptive algorithm. Several other algorithms are also studied for comparison with the optimal bound and the adaptive algorithm, for different types of time evolution of the rule.     

面向无线传感器网络应用的自适应调试方法

传感网技术是物联网得以实现的重要基础.然而,受到资源有限以及程序行为不确定等因素的影响,无线传感器网络上编程和调试的难度尤甚于普通的分布式程序.文中提出了一种面向无线传感器网络程序的源码级错误诊断方法.该方法采用基于全局量计数器的方法进行程序追踪,然后根据追踪日志重放错误执行轨迹,支持属性违反错误的分析和调试.同时,通...     

A Statistical Approach to Texture Classification from Single Images

We investigate texture classification from single images obtained under unknown viewpoint and illumination. A statistical approach is developed where textures are modelled by the joint probability distribution of filter responses. This distribution is represented by the frequency histogram of filter response cluster centres (textons). Recognition proceeds from single, uncalibrated images and the novelty here is that rotationally invariant filters are used and the filter response space is low dimensional.Classification performance is compared with the filter banks and methods of Leung and Malik [IJCV, 2001], Schmid [CVPR, 2001] and Cula and Dana [IJCV, 2004] and it is demonstrated that superior performance is achieved here. Classification results are presented for all 61 materials in the Columbia-Utrecht texture database.We also discuss the effects of various parameters on our classification algorithm—such as the choice of filter bank and rotational invariance, the size of the texton dictionary as well as the number of training images used. Finally, we present a method of reliably measuring relative orientation co-occurrence statistics in a rotationally invariant manner, and discuss whether incorporating such information can enhance the classifiers performance.     

基于统计的网页正文信息抽取方法的研究

为了把自然语言处理技术有效的运用到网页文档中,本文提出了一种依靠统计信息,从中文新闻类网页中抽取正文内容的方法。该方法先根据网页中的HTML 标记把网页表示成一棵树,然后利用树中每个结点包含的中文字符数从中选择包含正文信息的结点。该方法克服了传统的网页内容抽取方法需要针对不同的数据源构造不同的包装器的缺点,具有简单、准确的特点,试验表明该方法的抽取准确率可以达到95%以上。采用该方法实现的网页文本抽取工具目前为一个面向旅游领域的问答系统提供语料支持,很好的满足了问答系统的需求。     

A Statistical Language Modeling Approach to Online Deception Detection

Online deception is disrupting our daily life, organizational process, and even national security. Existing approaches to online deception detection follow a traditional paradigm by using a set of cues as antecedents for deception detection, which may be hindered by ineffective cue identification. Motivated by the strength of statistical language models (SLMs) in capturing the dependency of words in text without explicit feature extraction, we developed SLMs to detect online deception. We also addressed the data sparsity problem in building SLMs in general and in deception detection in specific using smoothing and vocabulary pruning techniques. The developed SLMs were evaluated empirically with diverse datasets. The results showed that the proposed SLM approach to deception detection outperformed a state-of-the-art text categorization method as well as traditional feature-based methods.     

A Statistical Approach to Material Classification Using Image Patch Exemplars

In this paper, we investigate material classification from single images obtained under unknown viewpoint and illumination. It is demonstrated that materials can be classified using the joint distribution of intensity values over extremely compact neighborhoods (starting from as small as 3times3 pixels square) and that this can outperform classification using filter banks with large support. It is also shown that the performance of filter banks is inferior to that of image patches with equivalent neighborhoods. We develop novel texton-based representations which are suited to modeling this joint neighborhood distribution for Markov random fields. The representations are learned from training images and then used to classify novel images (with unknown viewpoint and lighting) into texture classes. Three such representations are proposed and their performance is assessed and compared to that of filter banks. The power of the method is demonstrated by classifying 2,806 images of all 61 materials present in the Columbia-Utrecht database. The classification performance surpasses that of recent state-of-the-art filter bank-based classifiers such as Leung and Malik (IJCV 01), Cula and Dana (IJCV 04), and Varma and Zisserman (IJCV 05). We also benchmark performance by classifying all of the textures present in the UIUC, Microsoft Textile, and San Francisco outdoor data sets. We conclude with discussions on why features based on compact neighborhoods can correctly discriminate between textures with large global structure and why the performance of filter banks is not superior to that of the source image patches from which they were derived.     

统计机器翻译中短语切分的新方法

基于短语的统计机器翻译是目前主流的一种统计机器翻译方法,但是目前基于短语的翻译系统都没有对短语切分作专门处理,认为一个句子的所有短语切分都是等概率的。本文提出了一种短语切分方法,将句子的短语切分概率化: 首先,识别出汉语语料库中所有出现次数大于2次的词语串,将其作为汉语短语; 其次,用最短路径方法进行短语切分,并利用Viterbi算法迭代统计短语的出现频率。在2005年863汉英机器翻译评测测试集上的实验结果(BLEU4)是: 0.1764(篇章),0.2231(对话)。实验表明,对于长句子(如篇章),短语切分模型的加入有助于提高翻译质量,比原来约提高了0.5个百分点。     

Statistical Approach for Voice Personality Transformation

A voice transformation method which changes the source speaker's utterances so as to sound similar to those of a target speaker is described. Speaker individuality transformation is achieved by altering the LPC cepstrum, average pitch period and average speaking rate. The main objective of the work involves building a nonlinear relationship between the parameters for the acoustical features of two speakers, based on a probabilistic model. The conversion rules involve the probabilistic classification and a cross correlation probability between the acoustic features of the two speakers. The parameters of the conversion rules are estimated by estimating the maximum likelihood of the training data. To obtain transformed speech signals which are perceptually closer to the target speaker's voice, prosody modification is also involved. Prosody modification is achieved by scaling excitation spectrum and time scale modification with appropriate modification factors. An evaluation by objective tests and informal listening tests clearly indicated the effectiveness of the proposed transformation method. We also confirmed that the proposed method leads to smoothly evolving spectral contours over time, which, from a perceptual standpoint, produced results that were superior to conventional vector quantization (VQ)-based methods