Community Coordinated Multimedia (CCM) provides an extended and enhanced human experience by collaboratively consuming electronic and networked content and multimedia-intensive services. Community coordinated multimed...
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Community Coordinated Multimedia (CCM) provides an extended and enhanced human experience by collaboratively consuming electronic and networked content and multimedia-intensive services. Community coordinated multimedia vision raises the challenges of multimedia content creation, interpretation, exchange, and consumption over a large range of heterogeneous services, terminals and networks. The development of CCM metamodel plays a key role in tackling these challenges by enabling the transparent multimedia aggregation and exchange crossing communities. This paper aims to design and develop the CCM metamodel. A generic terminology for CCM metamodeling is developed. A extensive study on metamodeling activities and metamodeling methodologies is presented. A combined metamodeling approach is proposed for the CCM metamodel development. Experiences and results on the CCM metamodeling and CCM metamodel-driven content annotation prototype are elaborated.
Data staging is an important data management problem for a distributed heterogeneous networking environment, where each data storage location and intermediate node may have specific data available, storage limitations...
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Data staging is an important data management problem for a distributed heterogeneous networking environment, where each data storage location and intermediate node may have specific data available, storage limitations, and communication links. Sites in the network request data items and each item is associated with a specific deadline and priority. It is assumed that not all requests can be satisfied by their deadline. The work concentrates on solving a basic version of the data staging problem in which all parameter values for the communication system and the data request information represent the best known information collected so far and stay fixed throughout the scheduling process. A mathematical model for the basic data staging problem is introduced. Then, a multiple-source shortest-path algorithm based heuristic for finding a suboptimal schedule of the communication steps for data staging is presented. A simulation study is provided, which evaluates the performance of the proposed heuristic. The results show the advantages of the proposed heuristic over two random based scheduling techniques. This research, based on the simplified static model, serves as a necessary step toward solving the more realistic and complicated version of the data staging problem involving dynamic scheduling, fault tolerance, and determining where to stage data.
In order to solve the speed problem and shallow reasoning problem met in current research in fault diagnosis expert system, this paper presents a model based parallel fault diagnosis expert system for energy managemen...
The paper presents a parallel natural language processing system implemented on a marker-passing parallel AI computer, Semantic Network Array Processor (SNAP). The system uses a memory-based parsing approach in which ...
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The paper presents a parallel natural language processing system implemented on a marker-passing parallel AI computer, Semantic Network Array Processor (SNAP). The system uses a memory-based parsing approach in which parsing is viewed as a memory search process. Linguistic information is stored as phrasal patterns in a semantic network knowledge base distributed over the memory of the parallel computer. Parsing is performed by recognizing and linking linguistic patterns that reflect a sentence interpretation. This is achieved via propagating markers over the distributed network. The authors have developed a system capable of processing newswire articles from a particular domain. The paper presents the structure of the system, the memory-based parsing method used, and performance results obtained.< >
For real-time radar processing, it is very desirable to have an algorithm that does not assume restricted statistics of the input data and can be implemented for high-speed processing (without a high cost) to meet rea...
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For real-time radar processing, it is very desirable to have an algorithm that does not assume restricted statistics of the input data and can be implemented for high-speed processing (without a high cost) to meet real-time requirements. We therefore apply the QR decomposition-based least-squares method for linear prediction to the problem of computing the reflection coefficients of a lattice predictor, instead of using the conventional Burg algorithm. We also propose a modified one-dimensional ring architecture for implementing the QR method of least-squares. The particular application considered in this case is that of surveillance radar systems for air traffic control.< >
We have designed a parallel architecture called the Semantic Network Array Processor (SNAP) for Natural Language Understanding (NLU) and other Artificial Intelligence applications. It is capable of executing large mar...
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The Semantic Network Array Processor (SNAP) is a parallel architecture for Artificial Intelligence (AI) applications. We haue implemented a first-generation hardware/soflware prototype called SNAP-1 using Digital Sign...
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The Semantic Network Array Processor (SNAP) is a parallel architecture for Artificial Intelligence (AI) applications. We haue implemented a first-generation hardware/soflware prototype called SNAP-1 using Digital Signal Processor chips and ouerlapping groups of multiport memories. The design features 32 processing clusters with four to five functionally dedicated Digital Signal Processors in each cluster. Processors within clusters share a marker-processing memo y while communication between clusters is implemented by a buffered messagepassing scheme.
The authors have designed a parallel architecture called the semantic network array processor (SNAP) for natural language understanding (NLU) and other artificial intelligence applications. It is capable of executing ...
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The authors have designed a parallel architecture called the semantic network array processor (SNAP) for natural language understanding (NLU) and other artificial intelligence applications. It is capable of executing large marker-passing programs and generating results in real-time. The design features 32 processing clusters with four to five functionally dedicated digital signal processors in each cluster. Processors within clusters share a marker-processing memory while communication between clusters is implemented by a buffered message-passing scheme. Throughout the machine, overlapping groups of multiport memories provide a direct yet visible interconnection network. The result is a low cost, flexible, and observable parallel processor capable of performing NLU operations within subsecond response time.< >
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