查询扩展技术进展与展望

查询扩展技术是改善信息检索中查全率和查准率的关键技术之一,并成为近年来研究的热点.以查询扩展技术的发展为主线,将目前的研究方法归纳为三类:关键词查询扩展技术、查询词权重调整技术和语义概念查询扩展技术.对这三类扩展技术进行了整体性阐述和比较性研究,对一些关键性问题进行了探讨,并展望未来的发展趋势.  

一种基于Hilbert-Huang变换的基音周期检测新方法

利用Hilbert-Huang变换对语言信号处理中基于事件的基音周期检测问题提出了一种新的检测方法．该方法利用Huang等人1998年提出的具有高时频分辨能力的Hilbert-Huang变换分析语音信号，并提取其瞬时能量，通过精确定位声门脉冲发生的时刻，从而精确地跟踪基音周期的变化，达到精确检测基音周期的目的．与传统方法相比，其优点主要表现在：（1）不需要对语音信号作短时平稳性假设；（2）检测精度高，适应范围广；（3）具有跟踪基音周期变化的能力；（4）能精确区分清浊音}（5）与传统方法相比，帧长大大增加，因而，在提取连续语音信号的基音轮廓时，用于分帧和拼合的开销大大减少，帧间拼合痕迹小．仿真数据和实际语音信号检测实验均获得了相当精确的检测结果．最后，需要指出的是，Hilbert-Huang变换作为一种新的信号分析方法，被成功地用于提取语音信号的基音周期，这本身是一个有意义的探索，它为拓展Hilbert-Huang变换理论的应用给出了一个新的尝试．  

V描述子与B样条曲线

基于L2[0,1]中的一类规范正交完备函数系——V系统,建立了B样条曲线转化为V系统的表示算法,并提出B样条曲线间的“距离”概念,以此估计B样条曲线间的相近程度.该结果在造型优选、案例比对、医学检验、目标判别等相关问题中,有望得到有效的应用.  

Development, Distribution and Evaluation of Online Tourism Services in China

The development of electronic commerce (E-commerce) has led to great changes in the tourism industry in many countries around the world including China. The Chinese tourism industry has invested large amounts of money over last few years in the development of what is known as the 'Golden Tourism Project.' This study sheds more light on this project by investigating online tourism service development in China from three perspectives: the tourism website, the tourism website user and the tourism website provider. The results show that the majority of tourism website providers are regional tourism destination organizations that mainly provide comprehensive local tourism information and online services. The results also show the level of regional economic development has a significant impact on the construction of these local tourism websites. Through conducting a questionnaire survey, this paper identifies the types of web users and their evaluation for tourism websites. It assesses the level of current user satisfaction and discusses the principal barriers of implementation of online tourism services in China from a technical, financial and organizational point of view respectively. It is found that obtaining information is still the main aim of web users, however, the difficulties are slow Internet access and high fees. In conclusion, this paper proposes possible approaches to improve the quality of online tourism services in China.  

基于小波—奇异值分解的数字水印新算法*

为了有效地保护数字作品的版权，提出了一种以离散小波多级分解与奇异值分解相结合的数字水印新算法。该算法充分利用小波与奇异值的固有性质，对原始图像进行多级小波分解，并对部分子带作奇异值分解。将水印置乱来保证一定的安全性，再对其进行分块离散余弦变换，然后将它嵌入到中间奇异值及其周围的部分矩阵块中。实验表明，该方法不仅有较好的透明性，而且能抗大多数处理攻击，有较好的鲁棒性。  

Discretization for naive-Bayes learning: managing discretization bias and variance

Quantitative attributes are usually discretized in Naive-Bayes learning. We establish simple conditions under which discretization is equivalent to use of the true probability density function during naive-Bayes learning. The use of different discretization techniques can be expected to affect the classification bias and variance of generated naive-Bayes classifiers, effects we name discretization bias and variance. We argue that by properly managing discretization bias and variance, we can effectively reduce naive-Bayes classification error. In particular, we supply insights into managing discretization bias and variance by adjusting the number of intervals and the number of training instances contained in each interval. We accordingly propose proportional discretization and fixed frequency discretization, two efficient unsupervised discretization methods that are able to effectively manage discretization bias and variance. We evaluate our new techniques against four key discretization methods for naive-Bayes classifiers. The experimental results support our theoretical analyses by showing that with statistically significant frequency, naive-Bayes classifiers trained on data discretized by our new methods are able to achieve lower classification error than those trained on data discretized by current established discretization methods.  

Computing the minimum-support for mining frequent patterns

Frequent pattern mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been recognized that setting the minimum-support is a difficult task to users. This can hinder the widespread applications of these algorithms. In this paper we propose a computational strategy for identifying frequent itemsets, consisting of polynomial approximation and fuzzy estimation. More specifically, our algorithms (polynomial approximation and fuzzy estimation) automatically generate actual minimum-supports (appropriate to a database to be mined) according to users’ mining requirements. We experimentally examine the algorithms using different datasets, and demonstrate that our fuzzy estimation algorithm fittingly approximates actual minimum-supports from the commonly-used requirements. This work is partially supported by Australian ARC grants for discovery projects (DP0449535, DP0559536 and DP0667060), a China NSF Major Research Program (60496327), a China NSF grant (60463003), an Overseas Outstanding Talent Research Program of the Chinese Academy of Sciences (06S3011S01), and an Overseas-Returning High-level Talent Research Program of China Human-Resource Ministry. A preliminary and shortened version of this paper has been published in the Proceedings of the 8th Pacific Rim International Conference on Artificial Intelligence (PRICAI ’04).  

A Model for Evaluating E-Commerce Based on Cost/Benefit and Customer Satisfaction

Today's business managers face increased pressures to make sure that E-commerce (EC) investment has certain paybacks. They are attempting to find which investment will contribute effectively to their business benefits, thus determining whether money should be spent on these results. This paper develops a conceptual framework for evaluating web-based business-to-customer EC applications. The framework includes EC cost/benefit, EC functionality and customer satisfaction categories, each consisting of a set of factors. This paper then critically examines the inter-dependent and interrelated relationships among these factors through a group testing of hypotheses. The research is conducted based on a web exploration and two respective surveys: customer-oriented and company (supplier)-oriented. Through data analysis, findings are expressed into a factor-relationship model. The model shows how business organisations can use web-based EC applications to obtain further benefits by determining how EC investments can improve customer satisfaction in different ways.  

Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines

A vine is a new graphical model for dependent random variables. Vines generalize the Markov trees often used in modeling multivariate distributions. They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence. A general formula for the density of a vine dependent distribution is derived. This generalizes the well-known density formula for belief nets based on the decomposition of belief nets into cliques. Furthermore, the formula allows a simple proof of the Information Decomposition Theorem for a regular vine. The problem of (conditional) sampling is discussed, and Gibbs sampling is proposed to carry out sampling from conditional vine dependent distributions. The so-called canonical vines built on highest degree trees offer the most efficient structure for Gibbs sampling.  

负增量式关联规则更新算法

模式维护是数据挖掘中一个具有挑战性的任务.现有的增量式关联规则更新算法主要解决两种情况下的维护问题：一是最小支持度不变,而数据量增加;二是数据量不变,而改变最小支持度.本文提出了一种负增量关联规则更新算法.实验表明,该算法是有效的.