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Jara A.J.,University of Murcia | Kafle V.P.,National Institution of Information and Communications Technology | Skarmeta A.F.,University of Murcia
International Journal of Ad Hoc and Ubiquitous Computing | Year: 2013

Internet of Things is becoming a reality with the rapid development of communication technologies. This evolution presents an enrichment of the users' experiences, but also challenges regarding network scalability, security, privacy vulnerabilities, and mobility support. Mobility support for the Future Internet is focused on ID/Locator split architectures since the limitations of the current internet. This work analyses the security challenges for the HIMALIS (Heterogeneity Inclusion and Mobility Adaptation through Locator ID Separation) architecture for the particularities from the Internet of Things and the ID/Locator management messages vulnerable to attacks. This work proposes a secure and scalable mobility management scheme that considers the constraints from the Internet of Things, solving the possible security and privacy vulnerabilities of the HIMALIS architecture. The proposed scheme supports scalable interdomain authentication and secure location update and binding transfer for the mobility process. The proposed scheme has been verified and evaluated successfully with the AVISPA framework. Source


Pang S.,Unitec Institute of Technology | Liu F.,Auckland University of Technology | Kadobayashi Y.,Nara Institute of Science and Technology | Ban T.,National Institution of Information and Communications Technology | Inoue D.,National Institution of Information and Communications Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

This paper proposes a learner independent multi-task learning (MTL) scheme such that M L = L(T i, KT(T i, T j)), for i, j = 1, 2, i ≠ j, where KT is independent to the learner , and MTL is conducted for arbitrary learner combinations. In the proposed solution, we use Minimum Enclosing Balls (MEBs) as knowledge carriers to extract and transfer knowledge from one task to another. Since the knowledge presented in MEB can be decomposed as raw data, it can be incorporated into any learner as additional training data for a new learning task and thus improve its learning rate. The effectiveness and robustness of the proposed KT is evaluated on multi-task pattern recognition (MTPR) problems derived from UCI datasets, using classifiers from different disciplines for MTL. The experimental results show that multi-task learners using KT via MEB carriers perform better than learners without-KT, and it is successfully applied to all type of classifiers. © 2012 Springer-Verlag. Source


Pang S.,Unitec Institute of Technology | Liu F.,Auckland University of Technology | Kadobayashi Y.,Nara Institute of Science and Technology | Ban T.,National Institution of Information and Communications Technology | Inoue D.,National Institution of Information and Communications Technology
Cognitive Computation | Year: 2014

This paper proposes a learner-independent multi-task learning (MTL) scheme in which knowledge transfer (KT) is running beyond the learner. In the proposed KT approach, we use minimum enclosing balls (MEBs) as knowledge carriers to extract and transfer knowledge from one task to another. Since the knowledge presented in MEB can be decomposed as raw data, it can be incorporated into any learner as additional training data for a new learning task to improve the learning rate. The effectiveness and robustness of the proposed KT is evaluated, respectively, on multi-task pattern recognition problems derived from synthetic datasets, UCI datasets, and real face image datasets, using classifiers from different disciplines for MTL. The experimental results show that multi-task learners using KT via MEB carriers perform better than learners without-KT, and this has been successfully applied to different classifiers such as k nearest neighbor and support vector machines. © 2013 Springer Science+Business Media New York. Source


Pang S.,Unitec Institute of Technology | Ban T.,National Institution of Information and Communications Technology | Kadobayashi Y.,National Institution of Information and Communications Technology | Kadobayashi Y.,Nara Institute of Science and Technology | Kasabov N.K.,Auckland University of Technology
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | Year: 2012

To adapt linear discriminant analysis (LDA) to real-world applications, there is a pressing need to equip it with an incremental learning ability to integrate knowledge presented by one-pass data streams, a functionality to join multiple LDA models to make the knowledge sharing between independent learning agents more efficient, and a forgetting functionality to avoid reconstruction of the overall discriminant eigenspace caused by some irregular changes. To this end, we introduce two adaptive LDA learning methods: LDA merging and LDA splitting. These provide the benefits of ability of online learning with one-pass data streams, retained class separability identical to the batch learning method, high efficiency for knowledge sharing due to condensed knowledge representation by the eigenspace model, and more preferable time and storage costs than traditional approaches under common application conditions. These properties are validated by experiments on a benchmark face image data set. By a case study on the application of the proposed method to multiagent cooperative learning and system alternation of a face recognition system, we further clarified the adaptability of the proposed methods to complex dynamic learning tasks. © 2006 IEEE. Source


Zhu L.,Unitec Institute of Technology | Pang S.,Unitec Institute of Technology | Sarrafzadeh A.,Unitec Institute of Technology | Ban T.,National Institution of Information and Communications Technology | Inoue D.,National Institution of Information and Communications Technology
IEEE Transactions on Knowledge and Data Engineering | Year: 2016

Max-flow has been adopted for semi-supervised data modelling, yet existing algorithms were derived only for the learning from static data. This paper proposes an online max-flow algorithm for the semi-supervised learning from data streams. Consider a graph learned from labelled and unlabelled data, and the graph being updated dynamically for accommodating online data adding and retiring. In learning from the resulting non stationary graph, we augment and de-augment paths to update max-flow with a theoretical guarantee that the updated max-flow equals to that from batch retraining. For classification, we compute min-cut over current max-flow, so that minimized number of similar sample pairs are classified into distinct classes. Empirical evaluation on real-world data reveals that our algorithm outperforms state-of-the-art stream classification algorithms. © 1989-2012 IEEE. Source

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