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
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.
24326022
Semantic Context Representation of Resources Using Annotation Graph
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.
7089503
Supporting MPI-2 One Sided Communication on Multi-rail InfiniBand Clusters: Design Challenges and Performance Benefits
In cluster computing, InfiniBand has emerged as a popular high performance interconnect with MPI as the de facto programming model. However, even with InfiniBand, bandwidth can become a bottleneck for clusters executing communication intensive applications. Multi-rail cluster configurations with MPI-1 are being proposed to alleviate this problem. Recently, MPI-2 with support for one-sided communication is gaining significance. In this paper, we take the challenge of designing high performance MPI-2 one-sided communication on multi-rail InfiniBand clusters. We propose a unified MPI-2 design for different configurations of multi-rail networks (multiple ports, multiple HCAs and combinations). We present various issues associated with one-sided communication such as multiple synchronization messages, scheduling of RDMA (Read, Write) operations, ordering relaxation and discuss their implications on our design. Our performance results show that multi-rail networks can significantly improve MPI-2 one-sided communication performance. Using PCI-Express with two-ports, we can achieve a peak MPI_Put bidirectional bandwidth of 2620 Million Bytes/s, compared to 1910 MB/s for single-rail implementation. For PCI-X with two HCAs, we can almost double the throughput and reduce the latency to half for large messages.
6649130
Cartop manipulation
This paper explores a novel mode of mobile manipulation using a car-like robot whose chassis does not extend above the height of its wheels. It transports an object that rests on top of the wheels, so as the robot moves forwards, the object moves forward relative to the robot. The robot must maneuver so that the object stays on top of the robot, or a second robot must be in position to receive the robot as it rolls off the first one. We describe the mechanics of this system, its basic maneuvers and manipulation techniques, present a motion planner for generating plans to transport an object, and give results of our experimental implementation of this system.
17487285
A tapping micropositioning cell
Describes the use of tapping actuators in a "micropositioning cell." Tapping actuators are fixed (both in position and orientation) about the perimeter of the cell. A part in the cell can be precisely positioned by firing a sequence of tapping actuators (sensing the position of the object after each tap for feedback control). Such a system could be used for micropositioning tasks or in parts feeding applications. We show the controllability of this system for circular parts, describe an algorithm to position these objects, and present the results of simulated experiments. We conclude with some future directions and address the issues in extending this work to more efficient algorithms that work on a broader class of parts.
29243423
Design Alternatives and Performance Trade-Offs for Implementing MPI-2 over InfiniBand
MPICH2 provides a layered architecture to achieve both portability and performance. For implementations of MPI-2 over InfiniBand, it provides the flexibility for researchers at the RDMA channel, CH3 or ADI3 layer. In this paper we analyze the performance and complexity trade-offs associated with implementations at these layers. We describe our designs and implementations, as well as optimizations at each layer. To show the performance impacts of these design schemes and optimizations, we evaluate our implementations with different micro-benchmarks, HPCC and NAS test suite. Our experiments show that although the ADI3 layers adds complexity in implementation, the benefits achieved through optimizations justify moving to the ADI layer to extract the best performance.
7757717
A kernel autoassociator approach to pattern classification
Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.