Content adaptation

About: Content adaptation is a research topic. Over the lifetime, 742 publications have been published within this topic receiving 10642 citations.

Adapting multimedia Internet content for universal access

Abstract: Content delivery over the Internet needs to address both the multimedia nature of the content and the capabilities of the diverse client platforms the content is being delivered to. We present a system that adapts multimedia Web documents to optimally match the capabilities of the client device requesting it. This system has two key components. 1) A representation scheme called the InfoPyramid that provides a multimodal, multiresolution representation hierarchy for multimedia. 2) A customizer that selects the best content representation to meet the client capabilities while delivering the most value. We model the selection process as a resource allocation problem in a generalized rate distortion framework. In this framework, we address the issue of both multiple media types in a Web document and multiple resource types at the client. We extend this framework to allow prioritization on the content items in a Web document. We illustrate our content adaptation technique with a web server that adapts multimedia news stories to clients as diverse as workstations, PDA's and cellular phones.

Detecting web page structure for adaptive viewing on small form factor devices

Abstract: Mobile devices have already been widely used to access the Web. However, because most available web pages are designed for desktop PC in mind, it is inconvenient to browse these large web pages on a mobile device with a small screen. In this paper, we propose a new browsing convention to facilitate navigation and reading on a small-form-factor device. A web page is organized into a two level hierarchy with a thumbnail representation at the top level for providing a global view and index to a set of sub-pages at the bottom level for detail information. A page adaptation technique is also developed to analyze the structure of an existing web page and split it into small and logically related units that fit into the screen of a mobile device. For a web page not suitable for splitting, auto-positioning or scrolling-by-block is used to assist the browsing as an alterative. Our experimental results show that our proposed browsing convention and developed page adaptation scheme greatly improve the user's browsing experiences on a device with a small display.

A context-aware decision engine for content adaptation

Abstract: Building a good content adaptation service for mobile devices poses many challenges. To meet these challenges, this quality-of-service-aware decision engine automatically negotiates for the appropriate adaptation decision for synthesizing an optimal content version.

Annotation-based Web content transcoding

Abstract: Users are increasingly accessing the Internet from information appliances such as PDAs, cell phones, and set-top boxes. Since these devices do not have the same rendering capabilities as desktop computers, it is necessary for Web content to be adapted, or transcoded, for proper presentation on a variety of client devices. In this paper, we propose an annotation-based system for Web content transcoding. First, we introduce a framework of external annotation, in which existing Web documents are associated with content adaptation hints as separate annotation files. We then explain an annotation-based transcoding system with particular focus on the authoring-time integration between a WYSIWYG annotation tool and a transcoding module. Finally, after giving an example of content adaptation using a page fragmentation module for small-screen devices, we compare our approach with related work.

Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

Abstract: With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) for delivering adapted learning content to mobile learners. The ANFIS model was designed using trial and error based on various experiments. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the adaptation of learning content according to learners' needs. Study results show that ANFIS has been successfully implemented for learning content adaptation within different learning context scenarios. The performance of the ANFIS model was evaluated using standard error measurements which revealed the optimal setting necessary for better predictability. The MATLAB simulation results indicate that the performance of the ANFIS approach is valuable and easy to implement. The study results are based on analysis of different model settings; they confirm that the m-learning application is functional. However, it should be noted that an increase in the number of inputs being considered by the model will increase the system response time, and hence the delay for the mobile learner.