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IEEE Symposium on Computational Intelligence and Data Mining

作者： Said Abdallah, Zahraa Gaber, Mohamed Medhat Centre for Distributed Systems and Software Engineering Monash University 900 Dandenong Rd Caulfield East VIC 3145 Australia School of Computing University of Portsmouth Hampshire PO1 3HE United Kingdom

ISBN: (纸本)9781424499274

Data classification has attracted considerable research attention in the field of computational statistics and data mining due to its wide range of applications. K Best Cluster Based Neighbour (KB-CB-N) is our novel classification technique based on the integration of three different similarity measures for cluster based classification. The basic principle is to apply unsupervised learning on the instances of each class in the dataset and then use the output as an input for the classification algorithm to find the K best neighbours of clusters from the density, gravity and distance perspectives. Clustering is applied as an initial step within each class to find the inherent in-class grouping in the dataset. Different data clustering techniques use different similarity measures. Each measure has its own strength and weakness. Thus, combining the three measures can benefit from the strength of each one and eliminate encountered problems of using an individual measure. Extensive experimental results using eight real datasets have evidenced that our new technique typically shows improved or equivalent performance over other existing state-of-the-art classification methods. © 2011 IEEE.

关键词： Data mining

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CloudSec: A security monitoring appliance for Virtual Machines in the IaaS cloud model

CloudSec: A security monitoring appliance for Virtual Machin...

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International Conference on Network and System Security, NSS

作者： Amani S. Ibrahim James Hamlyn-Harris John Grundy Mohamed Almorsy Centre for Computing and Engineering Software Systems Faculty of Information & Communication Technologies Swinburne University of Technology Hawthorn VIC Australia

The Infrastructure-as-a-Service (IaaS) cloud computing model has become a compelling computing solution with a proven ability to reduce costs and improve resource efficiency. Virtualization has a key role in supporting the IaaS model. However, virtualization also makes it a target for potent rootkits because of the loss of control problem over the hosted Virtual Machines (VMs). This makes traditional in-guest security solutions, relying on operating system kernel trustworthiness, no longer an effective solution to secure the virtual infrastructure of the IaaS model. In this paper, we explore briefly the security problem of the IaaS cloud computing model, and present CloudSec, a new virtualization-aware monitoring appliance that provides active, transparent and real-time security monitoring for hosted VMs in the IaaS model. CloudSec utilizes virtual machine introspection techniques to provide fine-grained inspection of VM's physical memory without installing any monitoring code inside the VM. It actively reconstructs and monitors the dynamically changing kernel data structures instances, as a prior step to enable providing protection for kernel data structures. We have implemented a proof-of-concept prototype using VMsafe libraries on a VMware ESX platform. We have evaluated the system monitoring accuracy and the performance overhead of CloudSec.

关键词： Kernel Security Monitoring Virtual machine monitors Data structures Semantics Memory management

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RA-SAX: Resource-Aware Symbolic Aggregate Approximation for Mobile ECG Analysis

RA-SAX: Resource-Aware Symbolic Aggregate Approximation for ...

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International Conference on Mobile Data Management

作者： Hossein Tayebi Shonali Krishnaswamy Augustinus Borgy Waluyo Abhijat Sinha Mohamed Medhat Gaber Centre for Distributed Systems and Software Engineering Monash University Melbourne Australia School of Computing University of Portsmouth Portsmouth United Kingdom

There is a growing focus on 24/7 cardiac monitoring that leverages state of the art mobile phones and commercial-off-the-shelf (COTS) wearable bio-sensors. While many signal processing techniques for mobile ECG analysis have been developed, these techniques tend to be computationally intensive. In this paper, we propose, develop and evaluate a resource-aware and energy-efficient time series analysis technique for real-time ECG analysis on mobile devices based on the well-known SAX (Symbolic Aggregate Approximation) representation for time series termed RA-SAX.

关键词： Electrocardiography Mobile communication Clustering algorithms Classification algorithms Smart phones Accuracy

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KB-CB-N classification: Towards unsupervised approach for supervised learning

KB-CB-N classification: Towards unsupervised approach for su...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Zahraa Said Abdallah Mohamed Medhat Gaber Centre of Distributed Systems and Software Engineering Monash University Caulfield VIC Australia School of Computing University of Portsmouth Portsmouth Hampshire UK

关键词： Clustering algorithms Gravity Training Prediction algorithms Density measurement Classification algorithms Euclidean distance

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Automatic diagram layout support for the Marama meta-toolset

Automatic diagram layout support for the Marama meta-toolset

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IEEE Symposium on Visual Languages and Human Centric computing (VL/HCC)

作者： Pei Shan Yap John Hosking John Grundy Department of Computer Science University of Auckland Auckland New Zealand Centre of Computing and Engineering Software Systems Swinburne University of Technology Melbourne Australia

Automatic layout can be a crucial support feature for complex diagramming tools. Adding suitable layout algorithms to diagramming tools is a complex task and meta-tools should incorporate these for reuse. We present MaramaALM, a generalised set of automatic layout mechanisms. This has been incorporated in the Eclipse-based Marama meta-toolset to support automatic layout in Marama diagrams. It provides an easy-to-use mechanism for tool developers to add such layouts to their generated tools. We describe our motivation for MaramaALM, our approach to its implementation and an example case study of using these tool extensions.

关键词： Layout Visualization Shape Generators Measurement Connectors Algorithm design and analysis

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Using Automated Control Charts for the Runtime Evaluation of QoS Attributes

Using Automated Control Charts for the Runtime Evaluation of...

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IEEE International Symposim on High Assurance systems engineering

作者： Ayman Amin Alan Colman Lars Grunske Centre for Computing & Engineering Software Systems Faculty of Information and Communication Technologies Swinburne University of Technology Hawthorn VIC Australia Department of Computer Science Software Engineering: AG AQUA University of Kaiserslautern Kaiserslautern Germany

As modern software systems operate in a highly dynamic context, they have to adapt their behaviour in response to changes in their operational environment or/and requirements. Triggering adaptation depends on detecting quality of service (QoS) violations by comparing observed QoS values to predefined thresholds. These threshold-based adaptation approaches result in late adaptations as they wait until violations have occurred. This may lead to undesired consequences such as late response to critical events. In this paper we introduce a statistical approach CREQA - Control Charts for the Runtime Evaluation of QoS Attributes. This approach estimates at runtime capability of a system, and then it monitors and provides early detection of any changes in QoS values allowing timely intervention in order to prevent undesired consequences. We validated our approach using a series of experiments and response time datasets from real world web services.

关键词： Quality of service Control charts Web services Monitoring Runtime Time factors Estimation

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Energy-aware data processing techniques for wireless sensor networks: A review

Energy-aware data processing techniques for wireless sensor ...

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3rd International Conference on Data Management in Grid and Peer-to-Peer systems, Globe 2010

作者： Chong, Suan Khai Gaber, Mohamed Medhat Krishnaswamy, Shonali Loke, Seng Wai Centre for Distributed Systems and Software Engineering Monash University 900 Dandenong Rd Caulfield EastVIC3145 Australia School of Computing University of Portsmouth Portsmouth HampshirePO1 3HE United Kingdom Department of Computer Science and Computer Engineering La Trobe University VIC3086 Australia

ISBN: (纸本)9783642230738

Extensive data generated by peers of nodes in wireless sensor networks (WSNs) needs to be analysed and processed in order to extract information that is meaningful to the user. Data processing techniques that achieve this goal on sensor nodes are required to operate while meeting resource constraints such as memory and power to prolong a sensor network’s lifetime. This survey serves to provide a comprehensive examination of such techniques, enabling developers of WSN applications to select and implement data processing techniques that perform efficiently for their intended WSN application. It presents a general analysis of the issue of energy conservation in sensor networks and an up-to-date classification and evaluation of data processing techniques that have factored in energy constraints of sensors. © Springer-Verlag Berlin Heidelberg 2011.

关键词： Sensor nodes

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Long term bank failure prediction using Fuzzy Refinement-based Transductive Transfer learning

Long term bank failure prediction using Fuzzy Refinement-bas...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Vahid Behbood Jie Lu Guangquan Zhang Decision Systems & E-Service Intelligence Research Laboratory Centre of Qua ntum Computing and Intelligent System School of Software Faculty of Engineering and Information Technology University of Technology-Sydney Broadway NSW Australia

Machine learning algorithms, which have been considered as robust methods in different computational fields, assume that the training and test data are drawn from the same distribution. This assumption may be violated in many real world applications like bank failure prediction because training and test data may come from different time periods or domains. An efficient novel algorithm known as Fuzzy Refinement (FR) is proposed in this paper to solve this problem and improve the performance. The algorithm utilizes the fuzzy system and similarity concept to modify the instances' labels in target domain which was initially predicted by shift-unaware Fuzzy Neural Network (FNN) proposed by [1]. The experiments are performed using bank failure financial data of United States to evaluate the algorithm performance. The results address a significant improvement in the predictive accuracy of FNN due to applying the proposed algorithm.

关键词： Prediction algorithms Fuzzy neural networks Pragmatics Training Inference algorithms Accuracy Machine learning algorithms

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No-go theorem for one-way quantum computing on naturally occurring two-level systems

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Physical Review A 2011年第5期83卷 050301(R)-050301(R)页

作者： Jianxin Chen Xie Chen Runyao Duan Zhengfeng Ji Bei Zeng Department of Computer Science and Technology Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts USA Centre for Quantum Computation and Intelligent Systems (QCIS) Faculty of Engineering and Information Technology University of Technology Sydney New South Wales Australia Perimeter Institute for Theoretical Physics Waterloo Ontario Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Institute for Quantum Computing and Department of Combinatorics and Optimization University of Waterloo Waterloo Ontario Canada

The ground states of some many-body quantum systems can serve as resource states for the one-way quantum computing model, achieving the full power of quantum computation. Such resource states are found, for example, in spin-52 and spin-32 systems. It is, of course, desirable to have a natural resource state in a spin-12, that is, qubit system. Here, we give a negative answer to this question for frustration-free systems with two-body interactions. In fact, it is shown to be impossible for any genuinely entangled qubit state to be a nondegenerate ground state of any two-body frustration-free Hamiltonian. What is more, we also prove that every spin-12 frustration-free Hamiltonian with two-body interaction always has a ground state that is a product of single- or two-qubit states. In other words, there cannot be any interesting entanglement features in the ground state of such a qubit Hamiltonian.

关键词： QUANTUM computers QUANTUM theory HAMILTONIAN operator HAMILTONIAN systems PHYSICS

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Energy-aware data processing techniques for wireless sensor networks: A review

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2011年 6790 LNCS卷 117-137页

作者： Chong, Suan Khai Gaber, Mohamed Medhat Krishnaswamy, Shonali Loke, Seng Wai Centre for Distributed Systems and Software Engineering Monash University 900 Dandenong Rd Caulfield East VIC 3145 Australia School of Computing University of Portsmouth Portsmouth Hampshire PO1 3HE United Kingdom Department of Computer Science and Computer Engineering La Trobe University Victoria 3086 Australia

ISBN: (纸本)9783642230738

Extensive data generated by peers of nodes in wireless sensor networks (WSNs) needs to be analysed and processed in order to extract information that is meaningful to the user. Data processing techniques that achieve this goal on sensor nodes are required to operate while meeting resource constraints such as memory and power to prolong a sensor network's lifetime. This survey serves to provide a comprehensive examination of such techniques, enabling developers of WSN applications to select and implement data processing techniques that perform efficiently for their intended WSN application. It presents a general analysis of the issue of energy conservation in sensor networks and an up-to-date classification and evaluation of data processing techniques that have factored in energy constraints of sensors. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Sensor nodes

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