24326022
Semantic Context Representation of Resources Using Annotation Graph
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.
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.
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.
2799741
Towards integrating semantics of multi-media resources and processes in e-Learning
Internet-based e-Learning has experienced a boom and bust situation in the past 10 years [32]. To bring in new forces to knowledge-oriented e-Learning, this paper addresses the semantic integration issue of multi-media resources and learning processes with theoretical learning supports in an integrated framework. This paper proposes a context-mediated approach that aims to enable semantic-based inter-operations across knowledge domains, even across the WWW and the Semantic Web [8]. The proposed semantic e-Learning framework enables intelligent operations of heterogeneous multi-media contents based on a generic semantic context intermediation model. This framework supports intelligent e-Learning with a knowledge network for knowledge object visualization, an enhanced Kolb's learning cycle [31] to guide learning practices, and a learning health care framework for personalized learning.
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.
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.
10270257
Layout Effects on Sociogram Perception
This paper describes a within-subjects experiment in which we compare the relative effectiveness of five sociogram drawing conventions in communicating underlying network substance, based on user task performance and usability preference, in order to examine effects of different spatial layout formats on human sociogram perception. We also explore the impact of edge crossings, a widely accepted readability aesthetic. Subjective data were gathered based on the methodology of Purchase et al.[14] Objective data were collected through an online system. 
 
We found that both edge crossings and conventions pose significant affects on user preference and task performance of finding groups, but either has little impact on the perception of actor status. On the other hand, the node positioning and angular resolution might be more important in perceiving actor status. In visualizing social networks, it is important to note that the techniques that are highly preferred by users do not necessarily lead to best task performance.
416932
Parts Feeding on a Conveyor with a One Joint Robot
Abstract. This paper explores a method of manipulating a planar rigid part on a conveyor belt using a robot with just one joint. This approach has the potential of offering a simple and flexible method for feeding parts in industrial automation applications. In this paper we develop a model of this system and of a variation which requires no sensing. We have been able to characterize these systems and to prove that they can serve as parts feeding devices for planar polygonal parts. We present the planners for these systems and describe our implementations.
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.