This paper first proposes HOOD+, an extension of HOOD, which has more 00 features and supports a seamless development from the requirements analysis to the systems design. Then, a CASE tool supporting HOOD+ is also de...
This paper first proposes HOOD/sup +/, an extension of HOOD, which has more object-oriented features and supports a seamless development from the requirements analysis to the systems design. Then, HPSS (HOOD/sup +/ Pr...
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This paper first proposes HOOD/sup +/, an extension of HOOD, which has more object-oriented features and supports a seamless development from the requirements analysis to the systems design. Then, HPSS (HOOD/sup +/ Project Support System), a CASE tool supporting HOOD/sup +/, is also detailed.
NDRDL is a software requirements definition language (SRDL), designed as the source language of the software requirements analysis support system NDRASS. It considers both functional and nonfunctional requirements. To...
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NDRDL is a software requirements definition language (SRDL), designed as the source language of the software requirements analysis support system NDRASS. It considers both functional and nonfunctional requirements. To avoid the impreciseness and inconsistency of its informal semantics, this paper presents formal semantics of some functional constructs of NDRDL. Different ways and means are adopted according to the inherent characteristics of different constructs.
The technique of forcing created by Cohen is adopted to discuss the semantics of medium logic program without closed-world assumption (CWA).The fixed point and complete-meet semilattice property ofprogram generic set ...
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The technique of forcing created by Cohen is adopted to discuss the semantics of medium logic program without closed-world assumption (CWA).The fixed point and complete-meet semilattice property ofprogram generic set is proved.
Edge intelligence (EI) integrates edge computing and artificial intelligence empowering service providers to deploy deep neural networks (DNNs) on edge servers in proximity to users to provision intelligent applicatio...
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Edge intelligence (EI) integrates edge computing and artificial intelligence empowering service providers to deploy deep neural networks (DNNs) on edge servers in proximity to users to provision intelligent applications (e.g., autonomous driving) for ubiquitous Internet of Things (IoT) in smart cities, which facilitates the quality of experience (QoE) of users and improves the processing and energy efficiency. However, considering DNN is typically computational-intensive and resource-hungry, conventional placement approaches ignore the influence of multi-dimensional resource requirements (processor, memory, etc.), which may degrade the real-time performance. Moreover, with the increasing scale of geo-distributed edge servers, centralized decision-making is still challenging to find the optimal strategies effectively. To overcome these shortcomings, in this paper we propose a game theoretic DNN placement approach in EI-enabled IoT. First, a DNN placement optimization problem is formulated to maximize system benefits, which is proven to be \(\mathcal {N}\mathcal {P}\)-hard and model the original problem as an exact potential game (EPG). Moreover, an EPG-based DNN model placement algorithm, named EPOL, is designed for edge servers to make sub-optimal strategies independently and theoretical analysis is possessed to guarantee the performance of EPOL. Finally, real-world dataset based experimental results corroborate the superiority and effectiveness of EPOL.
In recent years, pre-trained language models have seen significant success in natural language processing and have been increasingly applied to code-related tasks. Code intelligence tasks have shown promising performa...
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In recent years, pre-trained language models have seen significant success in natural language processing and have been increasingly applied to code-related tasks. Code intelligence tasks have shown promising performance with the support of code pre-trained language models. Pre-processing code simplification methods have been introduced to prune code tokens from the model’s input while maintaining task effectiveness. These methods improve the efficiency of code intelligence tasks while reducing computational costs. Post-prediction code simplification methods provide explanations for code intelligence task outcomes, enhancing the reliability and interpretability of model predictions. However, comprehensive evaluations of these methods across diverse code pre-trained model architectures and code intelligence tasks are lacking. To assess the effectiveness of code simplification methods, we conduct an empirical study integrating these code simplification methods with various pre-trained code models across multiple code intelligence *** empirical findings suggest that developing task-specific code simplification methods would be beneficial. Then, we recommend leveraging post-prediction methods to summarize prior knowledge, which can pre-process code simplification strategies. Moreover, establishing more evaluation mechanisms for code simplification is crucial. Finally, we propose incorporating code simplification methods into the pre-training phase of code pre-trained models to enhance their program comprehension and code representation capabilities.
This book constitutes the refereed proceedings of the 11th Annual Conference on Theory and Applications of Models of Computation, TAMC 2014, held in Chennai, India, in April 2014. The 27 revised full papers presented ...
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ISBN:
(数字)9783319060897
ISBN:
(纸本)9783319060880
This book constitutes the refereed proceedings of the 11th Annual Conference on Theory and Applications of Models of Computation, TAMC 2014, held in Chennai, India, in April 2014. The 27 revised full papers presented were carefully reviewed and selected from 112 submissions. The papers explore the algorithmic foundations, computational methods and computing devices to meet today's and tomorrow's challenges of complexity, scalability and sustainability, with wide-ranging impacts on everything from the design of biological systems to the understanding of economic markets and social networks.
This book constitutes the proceedings of the Sino-foreign-interchange Workshop on Intelligence science and Intelligent Data Engineering, IScIDE 2011, held in Xi'an, China, in October 2011. The 97 papers presented ...
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ISBN:
(数字)9783642319198
ISBN:
(纸本)9783642319181
This book constitutes the proceedings of the Sino-foreign-interchange Workshop on Intelligence science and Intelligent Data Engineering, IScIDE 2011, held in Xi'an, China, in October 2011. The 97 papers presented were carefully peer-reviewed and selected from 389 submissions. The IScIDE papers in this volume are organized in topical sections on machine learning and computational intelligence; pattern recognition; computer vision and image processing; graphics and computer visualization; knowledge discovering, data mining, web mining; multimedia processing and application.
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