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The middleware is powered by the personalization engine and the content adaptation engine.
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The proposed system is characterised by the fact that the reasoning engine translates context changes as they occur into new content adaptation constraints in the operating environment, thus enabling context-aware personalized learning services.
Afterwards, the adaptation engine performs the required transformations and compositions of the selected contents for the specific usage environment using our VideoEd Editing and Composition Tool.
We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications.
Finally, multi-output SVM (MSVM) is used to build the adaptation engine for each PC module.
This method not only improves the performance of SVM-based adaptation by utilizing the mutual parameter relationships, but also reduces the computational expense of MSVM-based adaptation by partitioning the only one adaptation engine into several sub-engines.
This paper focuses on the multivariable adaptation problem in CBR adaptation, and proposes a modularized adaptation method by integrating with multiply relational analysis, case parameter clustering and adaptation engine construction.
With the variation of network bandwidth, the adaptation engine drops frames.
To enhance the user satisfaction, content adaptation is introduced.
By parsing these XML files, information which affect adaptation is conveyed between adaptation engine and the media server or media receiver instead of processing the video itself.
It also provides freedom in designing adaptation engine to parse and process information from the description in XML files.
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