The Association for the Advancement of Artificial Intelligence was pleased to present the 2009 Fall Symposium Series, held Thursday through Saturday, November 5-7, at the Westin Arlington Gateway in Arlington, Virgini...
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GPUs are many-core processors with tremendous computational power. However, as automatic parallelization has not been realized yet, developing high-performance parallel code for GPUs is still very challenging. The pap...
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This special session aims to introduce to the hardware/software codesign community challenges and opportunities in designing high performance computing (HPC) systems. Though embedded system design and HPC system desig...
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ISBN:
(纸本)9781605589053
This special session aims to introduce to the hardware/software codesign community challenges and opportunities in designing high performance computing (HPC) systems. Though embedded system design and HPC system design have traditionally been considered as two separate areas of research, they in fact share quite some common features, especially as CMOS devices continue along their scaling trends and the HPC community hits hard power and energy limits. Understanding the similarities and differences between the design practices adopted in the two areas will help bridge the two communities and lead to design tool developments benefiting both communities.
Unified Modelling language (UML) is as a standard object-oriented modelling notation that is widely accepted and used in software development industry. In general, the UML notation is informally defined in term of nat...
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Self-organizing emergent systems, also referred to as Decentralized Autonomic Computing systems, are commonly known for their scalability, robustness, flexibility, and adaptivity rather than their efficiency. However,...
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Self-organizing emergent systems, also referred to as Decentralized Autonomic Computing systems, are commonly known for their scalability, robustness, flexibility, and adaptivity rather than their efficiency. However, certain application scenarios, in particular in industrial settings, require a high degree of efficiency from these systems as well, in order to keep operational expenditures and energy use small. In this paper, we therefore present the concept of an advisor, designed to improve the efficiency of self-organizing emergent multi-agent systems solving industrial problems with recurring tasks. The advisor autonomously identifies the recurring tasks at runtime and provides the agents with advice for better solutions in the future, if indicated. The advisor does not limit the self-organizing behavior of the underlying system, i.e., all problem-solving decisions are still locally made by the agents. Experiments with instances of dynamic pickup and delivery problems show that the advisor concept can achieve substantial efficiency improvements, even if the recurring tasks change over time.
Many web sites have features that are only accessible after registering a user account on them. These sites include forums, on-line games, corporate Intranets and many on-line shops. The sites are typically secured th...
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ISBN:
(纸本)9781424462704
Many web sites have features that are only accessible after registering a user account on them. These sites include forums, on-line games, corporate Intranets and many on-line shops. The sites are typically secured through a system whereby a user chooses both a unique user name and a password that will be used to authenticate them. An alternative system proposed here is more convenient for users, and in many cases more secure by removing the requirement for the user to choose or remember a password.
The aim of our research is to find a suitable RE process to be applied in our organisation for the development of software projects. We identified the actor and their roles through the relation practice while implemen...
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The aim of our research is to find a suitable RE process to be applied in our organisation for the development of software projects. We identified the actor and their roles through the relation practice while implementing the RE process. In order to redefine the RE process, good analysis skill is needed besides having a good understanding of the RE process itself. We believe that our experince can be understood and used by other reseachers.
Utilizing virtualization technology to combine real-time operating system (RTOS) and off-the-shelf time-sharing general purpose operating system (GPOS) is attracting much more interest recently. Such combination has t...
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In this study, performances of classification techniques were compared in order to predict the presence of the patients getting a heart disease. A retrospective analysis was performed in 303 subjects. We compared the ...
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In this study, performances of classification techniques were compared in order to predict the presence of the patients getting a heart disease. A retrospective analysis was performed in 303 subjects. We compared the performance of logistic regression(LR), decision trees(DTs), and Artificial neural networks (ANNs). The variables were medical profiles are age, Sex, Chest Pain Type, Blood Pressure, Cholesterol, Fasting Blood Sugar, Resting ECG, Maximum Heart Rate, Induced Angina, Ole Peak, Slope, Number Colored Vessels, Thal and Concept Class. We have created the model using logistic regression classifiers, artificial neural networks and decision trees that they are often used for classification problems. Performances of classification techniques were compared using lift chart and error rates. In the result, artificial neural networks have the greatest area between the model curve and the baseline curve. The error rates are 0.22, 0.198, 0.21, respectively for logistic regression, artificial neural networks and decision trees. The neural networks exhibited sensitivity of 81.1%, specificity of 78.7% and accuracy of 80.2%, while the decision tree provided the prediction performance with a sensitivity, specificity and accuracy of 81.7%, 76.0% and 79.3%. And the logistic regression provided the prediction performance with a sensitivity, specificity and accuracy of 81.2%,73.1% and 77.7% Artificial neural networks have the least of error rate and has the highest accuracy, therefore Artificial neural networks is the best technique to classify in this data set.
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