Zugleich: Diss. Wirtschaftswiss. Zürich, 2004. / Im Buchh.: Wiesbaden : Deutscher Universitäts-Verlag. Literaturverz.

Abstract not available

Zugl.: Zürich, Universiẗat, Diss., 2004.

No description available.

In 1975, J.K. Hvistendahl and Mary R. Kahl tested 200 individuals to determine if readers preferred serif type for the body text of stories in newspapers. Subjects read stories set in serif and sans serif type. They were timed and asked for their preference as to which typeface they felt was more legible. The researchers found that test subjects preferred serif type the body text in their newsprint. After comparing the time it took subjects to read stories, researchers found readers needed less time to read stories set in serif type than sans serif.Almost 25 years later, another generation of readers has emerged, and Web pages are commonplace among many newspapers today. Yet nothing has been done to determine if these same findings are true for the World Wide Web. This study set out to do just that.Two hundred subjects were recruited for this study, each one placed into one of four groups: male student, male non-student, female student, and female non-student. Each subject was asked to read two 325-word stories, each on its own World Wide Web page. One story was set in a serif typeface, the other set in a sans serif typeface. Subjects were unobrusively timed with a stopwatch as they read each story. After reading the two stories, they were asked which typeface they felt was more legible, serif or sans serif.Overall, readers showd no statistically significant preference for serif or sans serif type in body text on the World Wide Web. The data was tested with ANOVA while frequencies and were also gathered. Only one statistically significant interaction surfaced which found that male students, who preferred sans serif type, took a statistically significant longer time to read online stories set in serif type. / Department of Journalism

The World Wide Web is one of the largest sources of information; more and more applications are being developed daily to make use of this information. This thesis presents a multi-agent architecture that deals with some of the issues related to Internet data extraction. The primary issue addresses the reliable, efficient and quick extraction of data through the use of HTTP performance monitoring agents. A second issue focuses on how to make use of available data to take decisions and alert the user when there is change in data; this is done with the help of user agents that are equipped with a Defeasible reasoning interpreter. An additional issue is the visualization of extracted data; this is done with the aid of VRML visualization agents. The cited issues are discussed using stock portfolio management as an example application.

by Ka-Po Ma. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 97-105). / Abstracts in English and Chinese. / Acknowledgments --- p.ii / Abstract --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Web-based Education --- p.1 / Chapter 1.2 --- Customized Web-based Learning --- p.3 / Chapter 1.3 --- Thesis Overview --- p.6 / Chapter 2 --- Web-based Education --- p.7 / Chapter 2.1 --- Impact on Traditional Learning --- p.8 / Chapter 2.2 --- Theoretical Perspectives on Teaching and Learning --- p.9 / Chapter 2.2.1 --- Behaviorism Versus Constructivism --- p.10 / Chapter 2.2.2 --- "Categorization of Individual, Group and Collaborative Learn- ing" --- p.12 / Chapter 2.3 --- On-line Eduation and Web-based Learning System --- p.15 / Chapter 2.4 --- Technologies used in Web-based Learning --- p.17 / Chapter 3 --- General Automated Timetabling --- p.21 / Chapter 3.1 --- Timtabling Problem --- p.21 / Chapter 3.2 --- Formulation and Solution Approaches --- p.22 / Chapter 4 --- "Virtual Campus, Customized Web-based Learning Environment" --- p.25 / Chapter 4.1 --- Changing Trend in Learning Process --- p.25 / Chapter 4.2 --- System Design Issue --- p.26 / Chapter 5 --- System Architecture Issue --- p.31 / Chapter 5.1 --- Client-server Model --- p.31 / Chapter 5.1.1 --- Server Side --- p.33 / Chapter 5.1.2 --- Client Side --- p.34 / Chapter 5.2 --- Functional-oriented Design --- p.35 / Chapter 5.3 --- Private Functionality Issue --- p.37 / Chapter 5.3.1 --- Access Authorizing --- p.37 / Chapter 5.3.2 --- Availability Updating --- p.40 / Chapter 5.3.3 --- Personal Information Querying and Modifying --- p.42 / Chapter 5.3.4 --- Status Selecting --- p.42 / Chapter 5.3.5 --- Current Online User Querying --- p.43 / Chapter 5.4 --- Lecture Functionality Issue --- p.44 / Chapter 5.5 --- Personal Scheduling Functionality Issue --- p.45 / Chapter 5.6 --- Collaboration Functionality Issue --- p.50 / Chapter 5.6.1 --- Chatting Room --- p.50 / Chapter 5.6.2 --- Discussion Board --- p.56 / Chapter 5.6.3 --- Personal URL-bookmark Keeping and Sharing --- p.57 / Chapter 6 --- Web-based Learning Scheduler (WL-Scheduler) --- p.59 / Chapter 6.1 --- "Web-based Customized Timetabling Problem, WCTP" --- p.60 / Chapter 6.2 --- Solution Approach - Local Search --- p.61 / Chapter 6.3 --- Algorithm for Approaching Feasible Timetables --- p.63 / Chapter 6.4 --- Evaluating The Best Timetable --- p.66 / Chapter 7 --- Multimedia Web Presentation System (MWPS) --- p.67 / Chapter 7.1 --- Overview --- p.67 / Chapter 7.2 --- System Components --- p.68 / Chapter 7.2.1 --- The MWPS Server Machine --- p.69 / Chapter 7.2.2 --- The MWPS Client Machine --- p.69 / Chapter 7.2.3 --- The Student Machine --- p.69 / Chapter 7.3 --- Presentation Flow --- p.69 / Chapter 7.4 --- Highlighed Features --- p.72 / Chapter 7.4.1 --- Slides Sequence Capturing --- p.72 / Chapter 7.4.2 --- Audio/Video Capturing --- p.72 / Chapter 7.4.3 --- Script-Text On Playback --- p.72 / Chapter 7.4.4 --- Student Feedbacking --- p.73 / Chapter 7.4.5 --- White Board Facility --- p.73 / Chapter 8 --- Illustration via Screen-shots --- p.74 / Chapter 8.1 --- Login Screen --- p.74 / Chapter 8.2 --- Functionality provided for Students --- p.75 / Chapter 8.2.1 --- Personalized Learning Timetable --- p.76 / Chapter 8.2.2 --- Lecture Delivery --- p.78 / Chapter 8.2.3 --- Checking active users in Virtual Campus --- p.78 / Chapter 8.2.4 --- View and Update Personal Information --- p.79 / Chapter 8.2.5 --- Taking An Entry Test for Interesting Subject --- p.81 / Chapter 8.2.6 --- Changing Current State --- p.84 / Chapter 8.2.7 --- Discussion Board --- p.84 / Chapter 8.2.8 --- Chatting Room --- p.85 / Chapter 8.3 --- Functionality provided for Teachers --- p.85 / Chapter 8.4 --- Functionality provided for Administrators --- p.92 / Chapter 9 --- Conclusion --- p.94 / Appendix --- p.106 / Chapter A --- Appendix --- p.106 / Chapter A.1 --- Internet Technology --- p.106 / Chapter A.2 --- Web Server --- p.107 / Chapter A.3 --- Web Client/Server Example --- p.108

No description available.

An extension of wrapper adaptation is developed to collectively extract information from multiple Web pages. There exists mutual influence between text fragments of different Web pages and hence they should be considered collectively during extraction. Extending from the dependence model, a framework which can consider the dependence between text fragments within a single Web page and the dependence between text fragments from different pages. One characteristic of this model is that additional information can be incorporated into the model and multiple tasks earl be tackled simultaneously. As a result, a global solution which can optimize the quality of the tasks, at the same time, eliminate the conflict between them can he obtained. Experiments on product feature extraction and hot item mining from multiple auction Web sites have been conducted to demonstrate the effectiveness of this framework. / One problem of most existing Web information extraction methods is that the extraction knowledge learned from a Web site can only be applied to Web pages from the same site. This thesis first investigates the problem of wrapper adaptation which aims at adapting a wrapper previously learned from a source site to new unseen sites. A dependence model that can model the dependence between text fragments in Web pages is developed. Under this model, two types of text related features are identified. The first type of features is called site invariant features. These features likely remain unchanged in Web pages from different sites in the same domain. The second type of features is called site dependent features. These features are different in Web pages collected from different Web sites, while they are similar in Web pages originated from the same site. Based on this model, two frameworks are developed to solve the wrapper adaptation problem. The first framework is called Information Extraction Knowledge Adaptation using Machine Learning approach (IEKA-ML). Machine learning methods are employed to derive site invariant features from the previously learned extraction knowledge and items previously collected or extracted from the source Web site. Both site dependent features and site invariant features in new sites are considered for learning of new information extraction knowledge tailored to the new unseen site. / The second framework, called Information Extraction Knowledge Adaptation using Bayesian learning approach (IEKA-BAYES), solves the problem of wrapper adaptation as well as the issue of new attribute discovery. The new attribute discovery problem aims at extracting new or previously unwell attributes that are not specified in the wrapper. To harness the uncertainty, a probabilistic generative model for the generation of text fragments and layout format related to attributes in Web pages is designed. Bayesian learning and expectation-maximization (EM) techniques are developed under the proposed generative model to accomplish the wrapper adaptation task. Previously unseen attributes together with their semantic labels earl be discovered via another EM-based Bayesian learning on the generative model. Extensive experiments on over 30 real-world Web sites in three different domains and comparison between existing works have been conducted to evaluate the IEKA-ML and IEKA-BAYES frameworks. / Wong Tak Lam. / "October 2006." / Adviser: Lam Wai. / Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6095. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 126-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

by Yao Jian. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 109-112). / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Basic Concepts And Related Work --- p.6 / Chapter 2.1 --- Codeset and Codeset Conversion --- p.7 / Chapter 2.2 --- HTML Language --- p.10 / Chapter 2.3 --- HTTP Protocol --- p.13 / Chapter 2.4 --- I18N And LION --- p.18 / Chapter 2.5 --- Proxy Server --- p.19 / Chapter 2.6 --- Related Work --- p.20 / Chapter 3 --- Design Principles And System Architecture --- p.23 / Chapter 3.1 --- Use of Existing Web System --- p.23 / Chapter 3.1.1 --- Protocol --- p.23 / Chapter 3.1.2 --- Avoid Duplication of Documents for Different Codesets --- p.25 / Chapter 3.1.3 --- Support On-line Codeset Conversion Facility --- p.27 / Chapter 3.1.4 --- Provide Internationalized Interface of Web Browser --- p.28 / Chapter 3.2 --- Our Approach --- p.29 / Chapter 3.2.1 --- Enhancing the Existing Browsers and Servers --- p.30 / Chapter 3.2.2 --- Incorporating Proxies in Our Scheme --- p.32 / Chapter 3.2.3 --- Automatic Codeset Conversion --- p.34 / Chapter 3.3 --- Overall System Architecture --- p.38 / Chapter 3.3.1 --- Architecture of Our Web System --- p.38 / Chapter 3.3.2 --- Flexibility of Our Design --- p.40 / Chapter 3.3.3 --- Which side do the codeset conversion? --- p.42 / Chapter 3.3.4 --- Caching --- p.42 / Chapter 4 --- Design Details of An Enhanced Server --- p.44 / Chapter 4.1 --- Architecture of The Enhanced Server --- p.44 / Chapter 4.2 --- Procedure on Processing Client's Request --- p.45 / Chapter 4.3 --- Modifications of The Enhanced Server --- p.48 / Chapter 4.3.1 --- Interpretation of Client's Codeset Announcement --- p.48 / Chapter 4.3.2 --- Codeset Identification of Web Documents on the Server --- p.49 / Chapter 4.3.3 --- Codeset Notification to the Web Client --- p.52 / Chapter 4.3.4 --- Codeset Conversion --- p.54 / Chapter 4.4 --- Experiment Results --- p.54 / Chapter 5 --- Design Details of An Enhanced Browser --- p.58 / Chapter 5.1 --- Architecture of The Enhanced Browser --- p.58 / Chapter 5.2 --- Procedure on Processing Users' Requests --- p.61 / Chapter 5.3 --- Event Management and Handling --- p.63 / Chapter 5.3.1 --- Basic Control Flow of the Browser --- p.63 / Chapter 5.3.2 --- Event Handlers --- p.64 / Chapter 5.4 --- Internationalization of Browser Interface --- p.75 / Chapter 5.4.1 --- Locale --- p.76 / Chapter 5.4.2 --- Resource File --- p.77 / Chapter 5.4.3 --- Message Catalog System --- p.79 / Chapter 5.5 --- Experiment Result --- p.85 / Chapter 6 --- Another Scheme - CGI --- p.89 / Chapter 6.1 --- Form and CGI --- p.90 / Chapter 6.2 --- CGI Control Flow --- p.96 / Chapter 6.3 --- Automatic Codeset Detection --- p.96 / Chapter 6.3.1 --- Analysis of code range for GB and Big5 --- p.98 / Chapter 6.3.2 --- Control Flow of Automatic Codeset Detection --- p.99 / Chapter 6.4 --- Experiment Results --- p.101 / Chapter 7 --- Conclusions and Future Work --- p.104 / Chapter 7.1 --- Current Status --- p.105 / Chapter 7.2 --- System Efficiency --- p.106 / Chapter 7.3 --- Future Work --- p.107 / Bibliography --- p.109 / Chapter A --- Programmer's Guide --- p.113 / Chapter A.1 --- Data Structure --- p.113 / Chapter A.2 --- Calling Sequence of Functions --- p.114 / Chapter A.3 --- Modification of Souce Code --- p.116 / Chapter A.4 --- Modification of Resources --- p.133 / Chapter B --- User Manual --- p.135