Model Driven Architecture (MDA): Integration and Model Reuse for Open Source eLearning Platforms

Open Source (OS) community offers numerous eLearning platforms of both types: Learning Management Systems (LMS) and Learning Content Systems (LCS). General purpose OS intermediaries such as SourceForge, ObjectWeb, Apache or specialized intermediaries like CampusSource reduce the cost to locate such eLearning platforms. Still, it is impossible to directly compare the functionalities of those OS software products without performing detailed testing on each product. Some articles available from eLearning Wikipedia show comparisons between eLearning platforms which can help, but at the end they barely serve as documentation which are becoming out of date quickly [1]. The absence of integration activities between OS eLearning platforms - which are sometimes quite similar in terms of functionalities and implementation technologies - is sometimes critical since most of the OS projects possess small financial and human resources. This paper shows a possible solution for these barriers of OS eLearning platforms. We propose the Model Driven Architecture (MDA) concept to capture functionalities and to identify similarities between available OS eLearning platforms. This contribution evolved from a fruitful discussion at the 2nd CampusSource Developer Conference at the University of Muenster (27th August 2004).

Algorithms For Automatic And Robust Registration Of 3D Head Scans

wo methods for registering laser-scans of human heads and transforming them to a new semantically consistent topology defined by a user-provided template mesh are described. Both algorithms are stated within the Iterative Closest Point framework. The first method is based on finding landmark correspondences by iteratively registering the vicinity of a landmark with a re-weighted error function. Thin-plate spline interpolation is then used to deform the template mesh and finally the scan is resampled in the topology of the deformed template. The second algorithm employs a morphable shape model, which can be computed from a database of laser-scans using the first algorithm. It directly optimizes pose and shape of the morphable model. The use of the algorithm with PCA mixture models, where the shape is split up into regions each described by an individual subspace, is addressed. Mixture models require either blending or regularization strategies, both of which are described in detail. For both algorithms, strategies for filling in missing geometry for incomplete laser-scans are described. While an interpolation-based approach can be used to fill in small or smooth regions, the model-driven algorithm is capable of fitting a plausible complete head mesh to arbitrarily small geometry, which is known as "shape completion". The importance of regularization in the case of extreme shape completion is shown.

An Architecture for End-User TV Content Enrichment

This paper proposes an extension to the televisionwatching paradigm that permits an end-user to enrich broadcast content. Examples of this enriched content are: virtual edits that allow the order of presentation within the content to be changed or that allow the content to be subsetted; conditional text, graphic or video objects that can be placed to appear within content and triggered by viewer interaction; additional navigation links that can be added to structure how other users view the base content object. The enriched content can be viewed directly within the context of the TV viewing experience. It may also be shared with other users within a distributed peer group. Our architecture is based on a model that allows the original content to remain unaltered, and which respects DRM restrictions on content reuse. The fundamental approach we use is to define an intermediate content enhancement layer that is based on the W3C’s SMIL language. Using a pen-based enhancement interface, end-users can manipulate content that is saved in a home PDR setting. This paper describes our architecture and it provides several examples of how our system handles content enhancement. We also describe a reference implementation for creating and viewing enhancements.

Towards a more effective model for distance education

What is the most effective model for academic distance education, given that drop-out numbers in traditional distance education institutions are too high and the demands from the various stakeholders are changing? In this paper this question is answered from the perspective of the Open University of the Netherlands (OUNL). The OUNL has planned to redesign its educational model from the traditional guided self-study model towards a model of active online learning. In essence this means that education will be less content driven; more focus is put on activating students to engage with real world problems supported by tutors and peers using distance media. The drivers for change, the change process and the resulting redesign of the educational model are presented in this paper.

Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture

We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment.