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研究生： 黃薇勳
Wei-Shiun Huang
論文名稱： 應用於情境導向內容調適技術之資訊區塊自動偵測方法
An Automatic Fragment Detection Method in Context-Oriented Content Adaptation
指導教授： 楊鎮華
Stephen J.H. Yang
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 網路學習科技研究所
Graduate Institute of Network Learning Technology
畢業學年度： 94
語文別： 英文
論文頁數： 84
中文關鍵詞： 區塊偵測 、情境感知 、資訊區塊 、內容調適
外文關鍵詞： Information Fragment, Context Aware, Fragment Detection, Content Adaptation
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    • 行動裝置所帶來的可攜性與普及化，使得透過行動裝置來存取網際內容成為獲取網際資訊的管道之一，同時由於行動裝置性能的獨特性能，致使透過行動裝置呈現的網際內容，常常出現排版怪異以及大小不一致的狀況，內容調適技術主要用來解決在不同使用者情境瀏覽內容的呈現問題，透過依據使用者特性設計的調適規則對網際內容進行轉換，使得調適後的內容能以較佳的呈現方式送達使用者，爲達此目的，首先我們必須要能夠對組成網際內容的媒體物件進行識別、歸類與群組，以作為調適的依據。因此，我們在論文中提出一套新的資訊區塊偵測方法，透過網際內容中媒體物件的樣式特性，將語意相似的物件區塊偵測出，再對區塊內的媒體物件進行調適，使得該區塊內的媒體物件可以群組方式呈現在使用者面前，以提升調適後內容的可讀性。

    Ubiquitous Web accessibility is considered to provide integrated, interoperable, pervasive and seamless content access experiences in terms of users’ surrounding environment and social situation. We propose a content adaptation mechanism to facilitate user browsing Web contents comfortably with diverse devices and analyze the structure of Web contents to extract unit of information (UOI). UOI is the smallest unit that can be displayed and adapted during content adaptation. In addition, we define four types of users’ contexts including situation, accessibility, device and network. We then design the strategies of content adaptation based on the analysis of content structure and users’ contexts to enhance our previous content adaptation mechanism. The proposed mechanism can help users get more accurate browses of Web contents in ubiquitous environments.

    Contents 摘要 I Abstract II Acknowledgements III Contents IV List of Figures VI List of Tables VIII Chapter 1 Introduction 1 1.1 Overview 1 1.2 Motivation and Challenges 2 1.3 Solutions and Contributions 5 1.4 Organization of This Thesis 7 Chapter 2 Related Works 8 2.1 Introduction of Content Adaptation 8 2.2 Challenges in Content Adaptation 10 2.3 State of the Art in Content Adaptation 11 Chapter 3 Context-Oriented Content Adaptation Framework 17 3.1 Challenges in Adapted Content Generation 17 3.2 Framework Description 18 3.3 Client Context Description 21 3.4 Content Process 24 3.5 Information Fragment Detection 30 3.5.1 Fundamental of UOI 32 3.5.2 Detection of UOI 35 3.6 Adaptation Process 40 3.6.1 Adaptation Strategy Generation 40 3.6.2 Trascoding Utilities 47 3.7 Adapted Content Generation 48 Chapter 4 Experiments and Evaluation 51 4.1 Background 51 4.2 Experiment 1: Detecting UOI from Content 52 4.2.1 Experiment objective 52 4.2.2 The result and analysis of the experiment 54 4.3 Experiment 2: Accessing Content from a Website 63 Chapter 5 Concluding Remarks and Future Work 66 References 68 Appendixes 73

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