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2nd International Conference on Information engineering and computer science, ICIECS 2010

作者： Zuo, Baojing Chen, Kai Liang, Alei Guan, Haibing Zhang, Jun Ma, Ruhui Yang, Hongbo School of Software Shanghai Jiao Tong University Shanghai 200240 China School of Information Security Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424479412

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 the potential to provide a large application base, and to guarantee timely deterministic response to real-time applications, yet there is no convincible experimental result about its real-time property. In this paper, we analyze the interrupt latency of RTOS running on Linux KVM based on some preliminary tunings, and find out System Management Interrupt (SMI) is the main factor which makes the maximum latency unideal, so we propose a method to limit the worst-case interrupt latency in an acceptable interval. Furthermore, we also find out that boosting priority may result in wastes of CPU resources when RTOS is not executing real-time tasks, so we design a co-scheduling mechanism to improve the CPU throughput of the GPOS system. ©2010 IEEE.

关键词： Virtualization

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A Three-Stage Method to Select Informative Genes from Gene Expression Data in Classifying Cancer Classes

A Three-Stage Method to Select Informative Genes from Gene E...

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International Conference on intelligent systems, Modelling and Simulation (ISMS)

作者： Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Michifumi Yoshioka Department of Software Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Department of Computer Science and Intelligent Systems Osaka Prefecture University Sakai Osaka Japan

ISBN: (纸本)9781424459841;9780769539737

The process of gene selection for the cancer classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes, irrelevant genes, and noisy data. Hence, this paper aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, a three-stage method has been proposed. It has three stages: (1) pre-selecting genes using a filter method; (2) optimizing the gene subset using a multi-objective hybrid method; (3) analyzing the frequency of appearance of each gene. By performing experiments on three public gene expression data sets, classification accuracies and the number of selected genes of the proposed method are better than those of other experimented methods and previous works. A list of informative genes in the final gene subsets is also presented for biological usage.

关键词： Gene expression Cancer Filters Genetic algorithms Support vector machines Support vector machine classification Frequency intelligent systems Computational modeling computer simulation

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Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients

Comparing performances of logistic regression, decision tree...

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computer Information systems and Industrial Management Applications (CISIM)

作者： Anchana Khemphila Veera Boonjing Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

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.

关键词： Artificial neural networks Heart Logistics Regression tree analysis Data models Data mining

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XML Path Matching for Different Hierarchy Order of Elements in XML Documents

XML Path Matching for Different Hierarchy Order of Elements ...

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ACIS International Conference on software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Department of Mathematics and Computer Science Faculty of Science Software Systems Engineering Laboratory King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781424474226;9780769540887

XML documents allow document owners to create their documents in their own formats (element names and structure). The same information might be described in several ways. To find the similarity between XML documents which use different formats, one method is path similarity detection. The recently approach is PathSim. The previous approach can detect the similarity rate between two XML paths in case those two XML paths are in the same hierarchy order of elements that are semantic with each other. If XML documents use different elements hierarchy order, that approach has a low similarity rate. To improve the previous approach, PathMatch is introduced. PathMatch uses the edit distance algorithm to find the semantic similarity rate between element names and a cost matrix model to find the similarity rate between two XML paths. On the result, PathMatch has a higher similarity rate than the previous approach in case those two XML paths contain a different hierarchy order of elements that are semantic with each other. Moreover, in case two XML paths contain the same hierarchy order, PathMatch has the same similarity rate as the previous one.

关键词： XML Costs Change detection algorithms software engineering Artificial intelligence Distributed computing software systems systems engineering and theory Laboratories Mathematics

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XER: A Recommendation for XML Element

XER: A Recommendation for XML Element

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International Conference on Information Technology: New Generations (ITNG)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

The strong point of the XML is that it allows document owners to describe their documents in their own format (structure and element names). There might be the same information has been describe in various ways. When user wants to search some information from XML documents, user might not retrieve all related results because of the difference of elements (tag names). To retrieve all related results, user needs to have queries for all possible elements. XML Element Recommendation (XER) is an idea to make XML documents easier for searching. It receives XML document as an input and compares that input with XML documents from database. It compares word similarity and semantic of each element. For semantic similarity of each element, it checks those elements to find which element (word) should be used by using word ranking (most common words). The output is a recommendation for each element of input XML document.

关键词： XML Mobile communication Mobile ad hoc networks Routing protocols Ad hoc networks Earthquakes Network topology Motion analysis Vehicle dynamics Costs

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Optimal simulation of a perfect entangler

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Physical Review A 2010年第3期81卷 032328-032328页

作者： Nengkun Yu Runyao Duan Mingsheng Ying State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 People's Republic of China Centre for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia

A 2⊗2 unitary operation is called a perfect entangler if it can generate a maximally entangled state from some unentangled input. We study the following question: How many runs of a given two-qubit entangling unitary operation are required to simulate some perfect entangler with one-qubit unitary operations as free resources? We completely solve this problem by presenting an analytical formula for the optimal number of runs of the entangling operation. Our result reveals an entanglement strength of two-qubit unitary operations.

关键词： Quantum entanglement

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XML Document Recommendation by Using Case Based Reasoning

XML Document Recommendation by Using Case Based Reasoning

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ACIS International Conference on software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Sutheetutt Vacharaskunee Sarun Intakosum Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9781424474226;9780769540887

Case Based Reasoning (CBR) is the process of solving new problem based on the solutions of similar previous problems. XML is a markup language that allows document owners describe their data in their own formats. There might be the same information has been described in various ways. This makes searching those information from different formats difficult. CXDR (CBR for XML Document Recommendation) is an idea to make searching XML documents easier. CXDR applies CBR framework to collect the mostly use XML document and recommend it to its user. CBR in CXDR consists of two parts, cases and knowledge. The case is XML documents and the knowledge is an XML document recommendation. The recommendation from CXDR is the most use XML element names and structure.

关键词： XML Keyword search Information retrieval Markup languages Database languages software engineering Artificial intelligence Distributed computing software systems systems engineering and theory

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An Empirical Study on Development Set Selection Strategy for Machine Translation Learning∗ 5

An Empirical Study on Development Set Selection Strategy for...

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Joint 5th Workshop on Statistical Machine Translation and MetricsMATR, WMT 2010 at the 48th Conference of the Associationfor Computational Linguistics, ACL 2010

作者： Hui, Cong Zhao, Hai Song, Yan Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China Department of Chinese Translation and Linguistics City University of Hong Kong Hong Kong

ISBN: (纸本)9781932432718

This paper describes a statistical machine translation system for our participation for the WMT10 shared task. Based on MOSES, our system is capable of translating German, French and Spanish into English. Our main contribution in this work is about effective parameter tuning. We discover that there is a significant performance gap as different development sets are adopted. Finally, ten groups of development sets are used to optimize the model weights, and this does help us obtain a stable evaluation result. © 2010 Association for Computational Linguistics

关键词： Machine translation

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intelligent Coordinated Control of a Wind Farm and Distributed Smartparks

Intelligent Coordinated Control of a Wind Farm and Distribut...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Pinaki Mitra Ganesh K. Venayagamoorthy Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA

Energy storage is generally recommended in presence of an intermittent source like wind farm for a better control over the power generation from the wind turbine with the variation of the wind speed. In this paper, the potential of plug-in electric vehicle parking lot (SmartPark) as an energy storage in a power system with a large wind farm has been investigated. Also, a fuzzy logic based coordination controller of the wind farm and the distributed SmartParks has been proposed in this paper. The fuzzy controller uses the total state-of-charge of the SmartParks and the difference between instantaneous demand and the available wind power generation as the inputs and thereby generates the charging or discharging power commands of the SmartParks and the pitch angle reference for the wind turbine. A 12-bus multimachine power system with a 400 MW wind farm is used as a test system. Six SmartParks are also connected to the same bus where the wind farm is connected. The entire model is developed in Real-Time Digital Simulator (RTDS) for power system. The results demonstrate the action of the coordinated controller to reduce the oscillations in the tie-line power flow with the sudden variations of the wind speed.

关键词： Wind farms Wind power generation Wind speed Niobium Energy storage Inverters Vehicles

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Gene Expression Classification with a Novel Coevolutionary Based Learning Classifier System on Public Clouds

Gene Expression Classification with a Novel Coevolutionary B...

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IEEE International Conference on E-science Workshops

作者： Christian Vecchiola Mani Abedini Michael Kirley Xingchen Chu Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory University of Melbourne Australia Department of Computer Science and Software Engineering University of Melbourne Australia

Microarray technology allows for the simultaneous monitoring of thousands of genes expressions per sample. Unfortunately, the classification of these samples into distinct classes is often difficult as the number of genes (features) greatly exceeds the number of samples. Consequently, there is a need to investigate new, robust machine learning techniques capable of accurately classifying microarray data. In this paper, we present a coevolutionary learning classifier system based on feature set partitioning to classify gene expression data. A distributed implementation, which leverages Cloud computing technologies, is used to address the inherent computational costs of our model. The development and execution of this application was done using the Aneka middleware on the public Cloud (Amazon EC2) infrastructure. Experiments conducted using gene expression profiles demonstrates that the proposed implementation outperforms other well-known classifiers in terms of accuracy. Preliminary analysis into the impact of different Cloud setups on the performance of the classifier are also reported.

关键词： Cloud computing Gene expression Accuracy Machine learning Containers computer architecture

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