Energy-efficient scheduling is an effective way to balance the system performance and the energy consumption. We design a polynomial-time (1 + Ε)-approximation algorithm to minimize the energy consumption for periodi...
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ISBN:
(纸本)1595930914
Energy-efficient scheduling is an effective way to balance the system performance and the energy consumption. We design a polynomial-time (1 + Ε)-approximation algorithm to minimize the energy consumption for periodic real-time tasks over such processors, where Ε is the tolerable error given by users (1 ≥ Ε > 0). It provides trade-offs between the user's tolerable error and the runtime complexity including the time complexity and the memory space complexity. System engineers could trade performance with implementation constraints. Copyright 2005 ACM.
Real-Time Process Algebra (RTPA) is an expressive mathematical means for describing cognitive behaviors and processes of human beings and softwaresystems. this paper presents the strategies and patterns for transform...
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PCS algorithm is a non-indexing method to prune non-skyline objects. Its pruning rate is more than 90% when data dimension is no more than 4, however when data dimension exceeds 4 the pruning rate falls significantly....
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Traditional engineering methods are considered unsuitable for the development of usable and engaging interactive systems such as online experimentation and simulation software. For systems involving users, user-centri...
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the proliferation of computing systems into every facet of our everyday lives raises a number of challenges for softwareengineering. Among those, we need to be able to build systemsthat take more control over their ...
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the paper discusses issues related to the development of modern computer training tools. the use of artificial intelligence methods opens up new possibilities in the creation of computer-aided training tools and knowl...
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Component-based development has become a recognized technique for building large scale distributed applications. Although the maturity of this technique, there appears to be quite a significant gap between (a) compone...
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ISBN:
(纸本)9780769528670
Component-based development has become a recognized technique for building large scale distributed applications. Although the maturity of this technique, there appears to be quite a significant gap between (a) component systemsthat are rich in advanced features (e.g., component nesting, software connectors, versioning, dynamic architectures), but which have typically only poor or even no run-time support, and (b) component systems with a solid run-time support, but which typically possess only a limited set of the advanced features. In our opinion, this is mainly due to the difficulties that arise when trying to give proper semantics to the features and reify them in development tools and an runtime platform. In this paper, we describe the implementation of the runtime environment for the SOFA 2.0 component model. In particular, we focus on the runtime support of the advanced features mentioned above. the described issues and the solution are not specific only to SOFA 2.0, but they are general and applicable to arty other component system aiming at addressing such features.
the paper is devoted to the modeling of thermal conductivity of carbon nanotubes, damping and elasticity of composite materials. In work the researches are carried out on the basis of the method of molecular dynamics ...
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ISBN:
(纸本)9780791848357
the paper is devoted to the modeling of thermal conductivity of carbon nanotubes, damping and elasticity of composite materials. In work the researches are carried out on the basis of the method of molecular dynamics and the finite elements method. the creation of the software withthe help of CASE-tools is carried out.
the main purpose of machine learning is to model the systems making predictions by using some mathematical and operational features on the data withcomputers [1].Today, there are many studies on machine learning in a...
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ISBN:
(纸本)9781728175652
the main purpose of machine learning is to model the systems making predictions by using some mathematical and operational features on the data withcomputers [1].Today, there are many studies on machine learning in all areas of the software world. software Defect Prediction is a sub-branch that progresses rapidly in machine learning. In this study, five of machine learning classification algorithms were conducted on with PYthON programming language on defect prediction data sets which are JM1, KC1, CM1, PC1 in the PROMISE repository. these data sets are created within the scope of the publicly available NASA institution's Metric Data Program. the accuracy, recall, precision and F-measure and support values of the algorithms on the data are compared. When the results are examined in terms of the accuracy of machine learning algorithms, the accuracy rates of the algorithms are quite high in all 4 data sets. the highest success rates were obtained from the classification algorithms applied in 4 data sets in CM1 and PC1 data sets. In 4 data sets, the highest success rates were seen with Random Forest algorithm.
In the present paper we use a variation of a well-known example (diningphilosophers) to illustrate how deontic logics can be used to specify, and verify, systems with fault-tolerant characteristics. Towards this goal,...
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