13283914
ConKMeL: a contextual knowledge management framework to support multimedia e-Learning
As the latest stage of learning and training evolution, e-Learning is supposed to provide intelligent functionalities not only in processing multi-media education resources but also in supporting context-sensitive pedagogical education processes. Towards providing an integrated solution for intelligent multimedia e-Learning, this paper presents a context-aware knowledge management framework named ConKMeL. Proposed framework features a semantic context-based approach for representing and integrating information and knowledge in e-Learning. Requirement analysis in university e-Learning environments shows that knowledge communications are usually in a hybrid mode across different conceptual levels. Based on the fact, a multi-layer contextual knowledge representation model called KG (knowledge graph) is presented. Corresponding key issues in development such as context-based knowledge retrieval and logical knowledge interpretation are discussed. On the application side, a scenario-based learning case study is shown to demonstrate the concepts and techniques developed in the ConKMeL framework.
18025478
Facilitate knowledge communications
With current multimedia information management techniques, the knowledge communications among users in multimedia e-Learning environments are still limited at a relative low single type media servicing level. New developments in multimedia knowledge discovery, representation and integration are needed to improve the intelligence of the knowledge management and communications at the semantic level. This paper proposes a novel contextual knowledge management framework to improve the current isolated learning information retrieval and communication status, by enabling flexible knowledge representation beyond heterogeneous multimedia learning resources and facilitating multilevel knowledge communications between instructors and learners. Based on knowledge communication model analysis in university e-Learning environments, a contextual knowledge representation model is presented. Corresponding knowledge retrieval techniques are discussed afterwards. To demonstrate the proposed concepts and techniques, a case study in a virtual scenario-based learning environment shows how the presented framework works with existing e-Learning content description standards and multimedia information retrieval techniques, and consequently enables a semantic-based interactive learning environment.
21933462
Multi-media semantics contextualisation for knowledge-oriented e-learning
Existing e-learning technical solutions are more focused on content packaging rather than semantics management, which is one of the keys to knowledge-oriented e-learning. This paper presents a context-based approach to address the challenging semantics management issue by contextualising the semantics of multimedia learning resources at different levels. A prototype of semi-automatic multimedia semantics generation and editing tool - CONMAN shows the basic idea of the approach in practice.
40011370
Contextual Knowledge Representation, Retrieval and Interpretation in Multimedia e-Learning
In highly interactive modern multimedia e-learning environments, current e-learning support products based on basic information management technologies are not able to support semantic-based knowledge management and communication beyond heterogeneous multimedia learning resources. Much of desired intelligent e-learning functionality needs further research and technical developments with semantic context-awareness. Proposed framework in this paper is expected to improve the current isolated learning management status, by making the most use of related learning information, and enabling flexible knowledge retrieval, communication and transfer between instructors and learners. To work with related existing e-learning content description standards and enable a semantic-based interactive learning environment, this paper explores a set of novel contextual knowledge discovery and reuse techniques. Based on knowledge communication model analysis in university e-learning, a contextual knowledge representation model is presented. Corresponding knowledge retrieval and semantic knowledge interpretation techniques are presented afterwards towards supporting pedagogical learning approaches such as scenario-based learning.
33589927
Towards Context-Aware Knowledge Management in e-Enterprises
2376785
Designing Efficient Asynchronous Memory Operations Using Hardware Copy Engine: A Case Study with I/OAT
Memory copies for bulk data transport incur large overheads due to CPU stalling, small register-size data movement, etc. Intel's I/O Acceleration Technology offers an asynchronous memory copy engine in kernel space which alleviates such overheads. In this paper, we propose a set of designs for asynchronous memory operations in user space for both single process (as an offloaded memcpy()) and lPC using the copy engine. We analyze our design based on overlap efficiency, performance and cache utilization. Our microbenchmark results show that using the copy engine for performing memory copies can achieve close to 87% overlap with computation. Further, the copy engine improves the copy latency of bulk memory data transfers by 50% and avoids cache pollution effects. With the emergence of multi-core architectures, the support for asynchronous memory operations holds a lot of promise in reducing the gap between the memory and processor performance.
17604511
POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome
One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.
62635072
Kernel autoassociator with applications to visual classification
Autoassociator is an important issue in concept learning, and the learned concept of a particular class can be used to distinguish the class from the others. For nonlinear autoassociation, This work presents a new model referred to as kernel autoassociator. Using kernel feature space as a potential nonlinear manifold, the model formulates the autoassociation as a special reconstruction problem from kernel feature space to input space. Two methods are developed to solve the problem. We evaluate the autoassociator with artificial data, and apply it to handwritten digit recognition and multiview face recognition, yielding positive experimental results.
11481204
Scheduling of MPI-2 one sided operations over InfiniBand
MPI-2 provides interfaces for one sided communication, which is becoming increasingly important in scientific applications. MPI-2 semantics provide the flexibility to reorder the one sided operations within an access epoch. Based on this flexibility, in this paper we try to improve the performance of one sided communication by scheduling one sided operations. We have come up with several re-ordering and aggregating schemes to achieve better network utilization. We have evaluated these schemes on both PCI-X and PCI-Express platforms. With re-ordering scheme, we see an improvement in the throughput up to 76%, latency up to 40%. With aggregation scheme, we observe an improvement of 44% and 42% for MPI/spl I.bar/Put and MPI/spl I.bar/Get latency respectively on PCI-Express platform.
12648516
Experiments in impulsive manipulation
In this paper, we present the results of our experimental effort in one form of impulsive manipulation: tapping. Our previous work studied the mechanics of tapping a planar object which then slides on a support surface, coming to rest due to friction. This work addresses the practical issues in creating a system which uses this mode of manipulation. We begin with the design of tapping devices-end effecters designed to deliver an impulse to an object, and report some of the issues we have found to be important in their design. Our next step was to perform single-tap experiments in order to fit and evaluate the models of impact and sliding. These experiments have shown that objects rotate less than predicted; we have found that the addition of a scaling factor for the torque due to friction enables the models to predict object motion reasonably well. In order to do positioning experiments, we developed a number of planning methods (or feedback control strategies) to compensate for errors in modeling, parameters, and actuation. These planning methods were successfully used to demonstrate a positioning task. We also have experimentally demonstrated that tapping can be used to position an object more precisely than the manipulator can position the tapping device. We offer some sensitivity analysis in support of this result.