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StreamAR: Incremental and Active Learning with Evolving Sensory Data for Activity Recognition

StreamAR: Incremental and Active Learning with Evolving Sens...

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International Conference on Tools with Artificial Intelligence

作者： Zahraa Said Abdallah Mohamed Medhat Gaber Bala Srinivasan Shonali Krishnaswamy Centre for Distributed Systems and Software Engineering Monash University School of Computing University of Portsmouth Institute for Infocomm Research (I2R)

ISBN: (纸本)9781479902279

Activity recognition focuses on inferring current user activities by leveraging sensory data available on today's sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel cluster-based classification for activity recognition systems, termed StreamAR. The system incorporates incremental and active learning for mining user activities in data streams. The novel approach processes activities as clusters to build a robust classification framework. StreamAR integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition datasets have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.

关键词： Experiential Learning Activity recognition Incremental Sensation

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Resource-aware ECG analysis on mobile devices 11

Resource-aware ECG analysis on mobile devices

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Proceedings of the 2011 ACM Symposium on Applied Computing

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

ISBN: (纸本)9781450301138

关键词： Time series analysis

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Role of context-awareness for demand response mechanisms

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Lecture Notes in Computer Science

作者： Haghighi, Pari Delir Krishnaswamy, Shonali Centre for Distributed Systems and Software Engineering Monash University 900 Dandenong Rd Caulfield East VIC 3145 Australia

ISBN: (纸本)9783642234460

Recently due to major changes in the structure of electricity industry and the rising costs of power generation, many countries have realized the potential and benefits of smart metering systems and demand response programs in balancing between the supply and the demand. DR mechanisms are capable of controlling the user energy consumption according to load conditions and providing effective energy management. However, they are typically performed regardless of user's situation and current activities. Factoring in the user's contextual information which is relevant to their current or future energy consumption can significantly increase the effectiveness of DR programs and enable adaptive and personalized execution of DR control actions. In this paper, we review current DR techniques and discuss the state-of-the-art smart energy management approaches that take into account contextual information. An overview of context reasoning and learning techniques for smart homes are presented to demonstrate how knowledge of user activities can be utilized in context-aware DR mechanisms. Our aim is to provide a better understanding of DR programs and highlight the importance of the context-awareness in improving smart energy management. © 2011 Springer-Verlag.

关键词： Energy 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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2011 12th IEEE International Conference on Mobile Data Management, MDM 2011

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

ISBN: (纸本)9780769544366

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. © 2011 IEEE.

关键词： Aggregates

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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

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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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

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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A probabilistic context-aware approach for quality of experience measurement in pervasive systems 11

A probabilistic context-aware approach for quality of experi...

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Proceedings of the 2011 ACM Symposium on Applied Computing

作者： Mitra, Karan Zaslavsky, Arkady Åhlund, Christer Center for Distributed Systems and Software Engineering Monash University 900 Dandenong Road Caulfield East VIC 3145 Australia Computer Science and Electrical Engineering Luleå University of Technology SE-97187 Luleå Sweden Division of Mobile Networking and Computing SE-93187 Skellefteå Sweden

ISBN: (纸本)9781450301138

In this paper, we develop a novel context-aware approach for quality of experience (QoE) modeling, reasoning and inferencing in mobile and pervasive computing environments. The proposed model is based upon a state-space approach and Bayesian networks for QoE modeling and reasoning. We further extend this context model to incorporate influence diagrams for efficient QoE inferencing. Our approach accommodates user, device and quality of service (QoS) related context parameters to determine the overall QoE of the user. This helps in user-related media, network and device adaptation. We perform experimentation to validate the proposed approach and the results verify its modeling and inferencing capabilities. © 2011 ACM.

关键词： Bayesian networks

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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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Two-Party Mobile Authentication Protocols for Wireless Roaming Networks

Two-Party Mobile Authentication Protocols for Wireless Roami...

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IEEE International Symposium on Network Computing and Applications

作者： Abdullah Almuhaideb Phu Dung Le Bala Srinivasan College of Computer Science and Information Technology King Faisal University Al-Ahsa Saudi Arabia Centre for Distributed Systems and Software Engineering Monash University Melbourne Australia

Mobile Internet access has significantly increased over the past few years. Roaming into foreign networks becomes necessary in order to access mobile-services. Traditionally, this done based on roaming agreement between the home network and the foreign network to extend trust for mobile users. However, secured mobile authentication is a challenge especially in two-party authentication environment where only the visited user and the foreign network get involved. This paper proposes two-party secure roaming protocols based on direct negotiation with potential foreign networks regarding quality of service, pricing and other billing related features, in order to establish service agreement and get the authorization token. The paper provides better performance to check the revocation status of the mobile user using recency evidence compare to the previously used revocation list. Identity Based Signature is used to stop impersonation attack. The security and performance analysis indicates that our protocol is resistant to well-known attacks, and it ensures efficient roaming.

关键词： Protocols Authentication Mobile communication Cryptography Roaming Mobile computing

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