GIPSY is a platform providing a framework for the compilation and execution of programs written in intensional programming languages of the Lucid family. While maintaining its use of intenskmality, over the years, Luc...
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This paper presents our research towards efficient Demand Migration Systems (DMSs) in the General Intensional Programming System (GIPSY) environment. Basically, a DMS is the combination ofboth paradigms ofEvent-Driven...
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ISBN:
(纸本)1601320841
This paper presents our research towards efficient Demand Migration Systems (DMSs) in the General Intensional Programming System (GIPSY) environment. Basically, a DMS is the combination ofboth paradigms ofEvent-Driven Architecture and Message-Oriented Middleware. From the design perspective, a DMS is an instance ofthe Demand Migration Framework, which establishes the context to perform demand migration in the heterogeneous and distributed GIPSY environment. In this paper, we present our design and implementation approach to DMS based on JINI and JMS. Further, we benchmark these two versions by performing early experimental investigations to evaluate their behavior, capabilities and limitations for demand migration. The article concludes with a comprehensive conclusion, based on our experimental results.
We focus on defining context expressions in terms of initial syntax and semantics for an intensional MARF language, MARFL. It is there to allow scripting Modular Audio Recognition Framework (MARF)-based applications a...
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In traditional Grid resource allocation solutions, the jobs with high-quality required may not be executed when they were allocated to low-quality offered nodes because of the separation of trust mechanism and job sch...
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Grid computing is a hot research direction in computer field. Resource management is an important part of this research. In Grid environment, satisfactory services for discovering resources and allocating appropriate ...
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A link farm is a set of web pages constructed to mislead the importance of target pages in search engine results by boosting their link-based ranking scores. In this paper, we introduce a new graph grammar model for e...
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This research presents the outcomes of a study performed with the aim of identifying the issues affecting the application of Component-Oriented software Development (COSD) amongst the software developers, which consis...
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Producing software that is adaptable to the rapid environmental changes and the dynamic nature of the business life-cycle is extensively becoming a topical issue in the software evolution. In this context, change prop...
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In order to evaluate the structural complexity of class diagrams systematically and deeply, a new guiding framework of structural complexity is presented. An index system of structural complexity for class diagrams is...
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In order to evaluate the structural complexity of class diagrams systematically and deeply, a new guiding framework of structural complexity is presented. An index system of structural complexity for class diagrams is given. This article discusses the formal description of class diagrams, and presents the method of formally structural complexity metrics for class diagrams from associations, dependencies, aggregations, generalizations and so on. An applicable example proves the feasibility of the presented method.
Due to environmental mismatch, speech recognition systems often exhibit drastic performance degradation in noisy conditions. This paper presents a model-based technique termed Adaptive Parallel Model Combination (APMC...
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ISBN:
(纸本)9781424423149
Due to environmental mismatch, speech recognition systems often exhibit drastic performance degradation in noisy conditions. This paper presents a model-based technique termed Adaptive Parallel Model Combination (APMC) which compensates the initial acoustic models to reduce the discrepancy. APMC used the well-known PMC technique to composite a set of corrupted speech models, while fine tuning the mean parameter of the models using a transformation-based adaptation technique called Maximum Likelihood Spectral Transformation (MLST). Evaluated on a context-independent phone recognition task, APMC was found to be superior to both PMC and MLST, especially in non-stationary noisy conditions. On average, APMC has achieved 48.81% improvement over the initial models, whereas PMC and MLST have improved the accuracy by 34.12% and 35.23% respectively.
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