基于WebService Behavior的Web页面数据实时显示技术研究与实现

指出了现有Web页面数据实时显示实现技术存在的不足,提出了利用WebService Behavior技术实现Web页面数据实时显示的新方法.研究了.NET环境下利用WebService Behavior技术实现Web页面数据实时显示的关键过程.     

多屏显示技术及其在图像处理系统中的应用

阐述了Windows98环境下多屏显示技术的原理及其实现方法，并将该技术应用在图像处理系统中充利用用户的显示资源，扩大观察视野，改善了用户的操作环境。     

高校图书馆OPAC系统中书目扩展信息应用情况研究

利用MASHUP技术,可以实现书目扩展信息应用,在OPAC系统中显示图书封面、章节目录等内容。现在在大部分的OPAC系统中都可以实现,但应用的情况比较混乱,缺乏统一的标准规范。     

提高汉字显示速度的途径及优化方法

分析了合理利用显示系统的硬件资源,提高汉字显示速度的可能性。结合示例,给出了速度优化的技术路线和汉字字模扫描线直接写屏技术的实现过程。     

基于虚拟技术和大型机硬件平台的Linux服务器系统实现

针对企业级计算服务器平均负载率低的问题,探究了采用虚拟化技术和Linux平台相结合的解决方案,分析了利用VM虚拟化技术创建Linux服务器的方法,介绍了利用VM系统虚拟化技术,实现在大型机硬件平台上动态分配,并行运行若干Linux服务器系统资源的方法.同时,探究了采用VM虚拟化技术实现Linux集群的基本方法.实际运行情况表明,采用虚拟化技术和Linux相结合,能够大幅度提高大型机资源利用率,有效解决企业级大型计算服务器平均负载率低的问题.     

Internet/Intranet上多媒体数据库的建立与检索技术研究

文章介绍了在 Ｉｎｔｅｒｎｅｔ／Ｉｎｔｒａｎｅｔ上建立多媒体数据库的一种方法,并着重讨论了利用 ＡＳＰ／ＡＤＯ技术,实现与数据库的连接及多媒体数据的显示与控制。     

UIA技术的内部实现和使用技巧

本文为“微软UI自动化技术揭秘”下篇，上篇刊登于2010年1月刊，介绍了UI自动化测试的技术演变，本篇将着重介绍UI Automation（UIA）技术适用场景、内部细节以及实现方法。     

Amazon排行榜岁末回顾与展望

“Amazon技术图书每月观察”专栏自今年4月份开辟以来,已经陪伴读者走到了岁末。在感谢广大读者的同时,也要感谢陈冀康、刘铁锋两位专栏作者的支持和协作。本期中,不列举月度排行,而是从不同的技术领域切入,对年度排行做一个综述。     

软测量系统的数据通信实现

本文提出了一种软测量系统数据通信的具体实现方法.利用可视化编程工具Visual Basic(vB)通过AcfiveX控件技术实现对PHD数据对象的访问,进行数据的实时存取,为软测量模型计算提供数据来源;采用二进制文本的方法实现Matlab与VB应用程序的数据交换;并利用组态王实现Access数据库与外部程序的数据交换.在系统界面上实时显示各类采集和运算结果.最后整体实现了系统从数据采集、模型运算和界面显示的正常运行.     

航空保障场景可控可视化关键技术研究

在对航空装备保障场景进行可控可视化建模时,由于场景中包含的设施量大、场景复杂程度高、运动目标多,造成对其进行可控可视化的技术方法成为当前的难点和重点,探讨了利用Vega软件多通道显示技术实现视点切换、地势追踪、自然环境变换等功能的方法,研究了通过拟合运动路线实现装备的可控可视化运动的技术.实践表明,文中所用方法可行、有效.     

Manifold Ranking using Hessian Energy

In recent years, learning on manifolds has attracted much attention in the academia community. The idea that the distribution of real-life data forms a low dimensional manifold embedded in the ambient space works quite well in practice, with applications such as ranking, dimensionality reduction, semi-supervised learning and clustering. This paper focuses on ranking on manifolds. Traditional manifold ranking methods try to learn a ranking function that varies smoothly along the data manifold by using a Laplacian regularizer. However, the Laplacian regularization suffers from the issue that the solution is biased towards constant functions. In this work, we propose using second-order Hessian energy as regularization for manifold ranking. Hessian energy overcomes the above issue by only penalizing accelerated variation of the ranking function along the geodesics of the data manifold. We also develop a manifold ranking framework for general graphs/hypergraphs for which we do not have an original feature space (i.e. the ambient space). We evaluate our ranking method on the COREL image dataset and a rich media dataset crawled from Last.fm. The experimental results indicate that our manifold ranking method is effective and outperforms traditional graph Laplacian based ranking method.     

基于查询性能预测的鲁棒检索排序研究

信息检索技术致力于从海量的信息资源中为用户获取所需的信息。相较于传统的简单模型,近些年来的大量研究工作在提升了检索结果平均质量的同时,往往忽略了鲁棒性的问题,即造成了很多查询的性能下降,导致用户满意度的显著下降。本文提出了一种基于排序学习的查询性能预测方法,针对每一个查询,对多种模型得到的检索结果列表进行预测,将其中预测性能最优的检索结果列表展示给用户。在LETOR的三个标准数据集OHSUMED、MQ2008和MSLR-WEB10K上的一系列对比实验表明,在以经典的BM25模型作为基准的情况下,与当前最好的检索模型之一LambdaMART相比,该方法在提升了检索结果平均质量的同时,显著地减少了性能下降的查询的数量,具备较好的鲁棒性。
     

Web2．0社区搜索模块排序算法的设计与实现

网页排序技术是搜索引擎的核心技术之一。描述Web2.0社区构建语义搜索的必要性,分析影响网页排序的因素,将搜索引擎的排序算法借鉴到基于Web2.0社区的搜索模块中,以改进的TF/IDF和PageRank算法为基础,在一个Web2.0开源社区开发平台上实现基于语义排序的搜索模块。测试结果表明,该排序算法具有内容定位精确、有效结果靠前的特点。     

A stochastic approach to group recommendations in social media systems

Recommender systems are becoming increasingly important not only to individual users but also to groups of people. This study focuses on the issue of recommending items to communities of interest (i.e., groups) that are specifically formed in social media systems. To deal with this issue, we introduce a new graph model that profits from fruitful tagging information. By using the proposed graph model, we present a stochastic method that makes recommendations based on link-structure analysis in a probabilistic manner. This method supports two ways of computing group ranking scores for items—via a preference aggregation approach and via a ranking aggregation approach, but ensures the same ranking results. We also explore the influence of users and items associated with a group in the facilitation of more accurate recommendations. Our empirical evaluations with the Last.fm dataset corroborate the benefits of our graph model on group recommendations, and demonstrate that the proposed group recommendation method performs better than existing alternatives.     

基于语义距离度量模型的语义Web服务匹配排序机制

随着语义Web服务技术研究工作的不断深入,因特网上语义Web服务数量急剧增加。如何快速便捷地定位可用语义Web服务已经成为一个迫切且关键的问题。在语义Web服务匹配技术研究中,其中一个重要的研究主题就是语义Web服务匹配结果的排序机制。本文在综合概括和分析各种关于语义Web服务匹配结果排序机制的基础上,提出了一种基于语义距离度量模型的语义Web服务匹配结果排序机制,利用该排序机制,计算待匹配语义Web服务的语义相似度量,并依据此度量对语义Web服务匹配结果进行排序。该度量模型将语义Web服务引用概念间的语义关系转换成可精确比较的量化度量值,对属于相同语义匹配类型的匹配候选服务也能够分别计算语义距离,区分出相同匹配类型的候选服务与服务请求的匹配程度,从而达到改善用户对语义Web服务的搜索体验的目的。     

Human Behavior Analysis for Highlight Ranking in Broadcast Racket Sports Video

The majority of existing work on sports video analysis concentrates on highlight extraction. Little work focuses on the important issue as how the extracted highlights should be organized. In this paper, we present a multimodal approach to organize the highlights extracted from racket sports video grounded on human behavior analysis using a nonlinear affective ranking model. Two research challenges of highlight ranking are addressed, namely affective feature extraction and ranking model construction. The basic principle of affective feature extraction in our work is to extract sensitive features which can stimulate user's emotion. Since the users pay most attention to player behavior and audience response in racket sport highlights, we extract affective features from player behavior including action and trajectory, and game-specific audio keywords. We propose a novel motion analysis method to recognize the player actions. We employ support vector regression to construct the nonlinear highlight ranking model from affective features. A new subjective evaluation criterion is proposed to guide the model construction. To evaluate the performance of the proposed approaches, we have tested them on more than ten-hour broadcast tennis and badminton videos. The experimental results demonstrate that our action recognition approach significantly outperforms the existing appearance-based method. Moreover, our user study shows that the affective highlight ranking approach is effective.     

A novel group decision making method with intuitionistic fuzzy preference relations for RFID technology selection

A more scientific decision making process for radio frequency identification (RFID) technology selection is important to increase success rate of RFID technology application. RFID technology selection can be formulated as a kind of group decision making (GDM) problem with intuitionistic fuzzy preference relations (IFPRs). This paper develops a novel method for solving such problems. First, A technique for order preference by similarity to ideal solution (TOPSIS) based method is presented to rank intuitionistic fuzzy values (IFVs). To achieve higher group consensus as well as possible, we construct an intuitionistic fuzzy linear programming model to derive experts’ weights. Depending on the construction of membership and non-membership functions, the constructed intuitionistic fuzzy linear programming model is solved by three kinds of approaches: optimistic approach, pessimistic approach and mixed approach. Then to derive the ranking order of alternatives from the collective IFPR, we extend quantifier guided non-dominance degree (QGNDD) and quantifier guided dominance degree (QGDD) to intuitionistic fuzzy environment. A new two-phase ranking approach is designed to generate the ordering of alternatives based on QGNDD and QGDD. Thereby, the corresponding method is proposed for the GDM problems with IFPRs. Some generalizations on the constructed intuitionistic fuzzy linear programming model are further discussed. At length, the validity of the proposed method is illustrated with a real-world RFID technology selection example.     

多媒体在辅助教学困境中的探讨

随着信息技术的飞速发展以及电脑的广泛普及,人们利用网络、多媒体技术进行教育教学已经成为评价体系中的一个重要环节。多媒体在现代课堂教学中的应用走向普及,其最大的优势在于将传统课堂的单一教学转变为现代课堂的多样展示。但人们对多媒体教学在认识上的误区不断加深,因此多媒体教学的问题也日渐暴露,逐步陷入教学的困境。本文通过对多媒体在现代教学应用中的实践考察与分析,对在应用中的困境做了客观阐述和说明,并提出可行的解决办法,借此来探讨多媒体在辅助教学应用中的创造性以及实用价值。     

基于复合幂函数修正G1法的商户综合评价

针对多指标综合评价问题中主客观权重相悖时客观权重淹没主观权重的问题,以G1法和客观赋权法为基础,提出了复合幂函数修正G1法的组合赋权模型。首先,建立指标体系并通过G1法确定各指标主观排序和主观初始向量;然后,利用客观赋权法计算各指标客观向量;其次,在不改变主观排序的情况下利用复合幂函数算出主客观结合的综合权重;最后,利用各指标标准化后的值和综合权重计算综合评价值。采用大众点评网的商户数据进行综合评价实验：该模型的均方根误差（RMSE）为3.891,均低于G1-熵权法的8.818和标准差修正G1法的4.752,且覆盖率优于两种对比方法;分别修改主观初始向量和主观排序进行对比实验,修改主观排序的均方根误差为5.430,高于修改主观初始向量的1.17。实验结果表明,该模型得到的评价值与大众点评网官方的评分的一致性较高,且该模型弱化了主观初值对评分结果的影响,体现了主观排序的基础作用。     

基于GDI＋的WebCAD技术研究

WebCAD是指运行在Internet上的网络化CAD系统，是利用互连网技术扩展和完善CAD的一项新技术．GDI＋是微软．NET框架中的下一代图形设备接口，论文探讨了利用GDI＋开发WebCAD的基本方法：CAD图形元素的GDI＋表达、基本交互功能的实现、数据网上发布与传输的实现。