ORCID Profile
0000-0002-6533-8104
Current Organisation
University of the Sunshine Coast
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Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: Alignment of multiple multi-relational networks, such as knowledge graphs, is vital for AI applications. Different from the conventional alignment models, we apply the graph convolutional network (GCN) to achieve more robust network embedding for the alignment task. In comparison with existing GCNs which cannot fully utilize multi-relation information, we propose a vectorized relational graph convolutional network (VR-GCN) to learn the embeddings of both graph entities and relations simultaneously for multi-relational networks. The role discrimination and translation property of knowledge graphs are adopted in the convolutional process. Thereafter, AVR-GCN, the alignment framework based on VR-GCN, is developed for multi-relational network alignment tasks. Anchors are used to supervise the objective function which aims at minimizing the distances between anchors, and to generate new cross-network triplets to build a bridge between different knowledge graphs at the level of triplet to improve the performance of alignment. Experiments on real-world datasets show that the proposed solutions outperform the state-of-the-art methods in terms of network embedding, entity alignment, and relation alignment.
Publisher: MDPI AG
Date: 17-04-2023
DOI: 10.3390/APP13085039
Abstract: The electric network frequency (ENF) is a signal that varies over time and represents the frequency of the energy supplied by a mains power system. It continually varies around a nominal value of 50/60 Hz as a result of fluctuations over time in the supply and demand of power and has been employed for various forensic applications. Based on these ENF fluctuations, the intensity of illumination of a light source powered by the electrical grid similarly fluctuates. Videos recorded under such light sources may capture the ENF and hence can be analyzed to extract the ENF. Cameras using the rolling shutter s ling mechanism acquire each row of a video frame sequentially at a time, referred to as the read-out time (Tro) which is a camera-specific parameter. This parameter can be exploited for camera forensic applications. In this paper, we present an approach that exploits the ENF and the Tro to identify the source camera of an ENF-containing video of unknown source. The suggested approach considers a practical scenario where a video obtained from the public, including social media, is investigated by law enforcement to ascertain if it originated from a suspect’s camera. Our experimental results demonstrate the effectiveness of our approach.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: SAGE Publications
Date: 05-2013
DOI: 10.1155/2013/241261
Abstract: Wireless sensor networks (WSNs) are composed of sensor nodes with limited energy which is difficult to replenish. Data aggregation is considered to help reduce communication overhead with in-network processing, thus minimizing energy consumption and maximizing network lifetime. Meanwhile, it comes with challenges for data confidentiality protection. Many existing confidentiality preserving aggregation protocols have to transfer list of sensors' ID for base station to explicitly tell which sensor nodes have actually provided measurement. However, forwarding a large number of node IDs brings overwhelming extra communication overhead. In this paper, we propose provably secure aggregation scheme perturbation-based efficient confidentiality preserving protocol (PEC2P) that allows efficient aggregation of perturbed data without transferring any ID information. In general, environmental data is confined to a certain range hence, we utilize this feature and design an algorithm to help powerful base station retrieve the ID of reporting nodes. We analyze the accuracy of PEC2P and conclude that base station can retrieve the sum of environmental data with an overwhelming probability. We also prove that PEC2P is CPA secure by security reduction. Experiment results demonstrate that PEC2P significantly reduces node congestion (especially for the root node) during aggregation process in comparison with the existing protocols.
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-669-3.CH010
Abstract: This chapter proposes a multiple-step backtracking mechanism to maintain a tradeoff between replanning and rigid backtracking for exception handling and recovery, thus enabling business process management (BPM) systems to operate robustly even in complex and dynamic environments. The concept of BDI (belief, desire and intention) agent is applied to model and construct the BPM system to inherit its advantages of adaptability and flexibility. Then, the flexible backtracking approach is introduced by utilizing the beneficial features of event-driven and means-end reasoning of BDI agents. Finally, we incorporate open nested transaction model to encapsulate plan execution and backtracking to gain the system level support of concurrency control and automatic recovery. With the ability of reasoning about task characteristics, our approach enables the system to find and commence a suitable plan prior to or in parallel with a compensation process when a failure occurs. This kind of computing allows us to achieve business goals efficiently in the presence of exceptions and failures.
Publisher: American Scientific Publishers
Date: 02-2014
DOI: 10.1166/SL.2014.3285
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: IEEE
Date: 09-2015
Publisher: Inderscience Publishers
Date: 2013
Publisher: ACM
Date: 14-05-2007
Publisher: Elsevier BV
Date: 11-2012
Publisher: IEEE
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 09-03-2021
Publisher: IGI Global
Date: 2018
DOI: 10.4018/978-1-5225-5484-4.CH005
Abstract: With the features of mobility, reality augmentation, and context sensitivity, wearable devices are widely deployed into various domains. However, the sensitivity of collected data makes security and privacy protection one of the first priority in the advancement of wearable technologies. This chapter provides a study on encryption-based confidentiality protection for data storage systems in wearable platforms. The chapter first conducts a review to storage solutions in consumer wearable products and explores a two-tier, local flash memory and remote cloud storage, storage system in wearable platforms. Then encryption-based confidentiality protection and implementation methods for both flash memory and remote cloud storage are summarized. According to the interaction and integration of these two components, a categorization of confidential storage systems in wearable platforms is proposed. In addition, the benefits and selection criteria for each category are also discussed.
Publisher: IEEE
Date: 12-2013
Publisher: Springer Science and Business Media LLC
Date: 28-12-2015
Publisher: Association for Computing Machinery (ACM)
Date: 19-09-2019
DOI: 10.1145/3354187
Abstract: Next and next new point-of-interest (POI) recommendation are essential instruments in promoting customer experiences and business operations related to locations. However, due to the sparsity of the check-in records, they still remain insufficiently studied. In this article, we propose to utilize personalized latent behavior patterns learned from contextual features, e.g., time of day, day of week, and location category, to improve the effectiveness of the recommendations. Two variations of models are developed, including GPDM, which learns a fixed pattern distribution for all users and PPDM, which learns personalized pattern distribution for each user. In both models, a soft-max function is applied to integrate the personalized Markov chain with the latent patterns, and a sequential Bayesian Personalized Ranking (S-BPR) is applied as the optimization criterion. Then, Expectation Maximization (EM) is in charge of finding optimized model parameters. Extensive experiments on three large-scale commonly adopted real-world LBSN data sets prove that the inclusion of location category and latent patterns helps to boost the performance of POI recommendations. Specifically, our models in general significantly outperform other state-of-the-art methods for both next and next new POI recommendation tasks. Moreover, our models are capable of making accurate recommendations regardless of the short/long duration or distance.
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Most existing solutions for the alignment of multi-relational networks, such as multi-lingual knowledge bases, are ``translation''-based which facilitate the network embedding via the trans-family, such as TransE. However, they cannot address triangular or other structural properties effectively. Thus, we propose a non-translational approach, which aims to utilize a probabilistic model to offer more robust solutions to the alignment task, by exploring the structural properties as well as leveraging on anchors to project each network onto the same vector space during the process of learning the representation of in idual networks. The extensive experiments on four multi-lingual knowledge graphs demonstrate the effectiveness and robustness of the proposed method over a set of state-of-the-art alignment methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Wiley
Date: 12-02-2013
DOI: 10.1002/SEC.715
Publisher: Informa UK Limited
Date: 19-01-2021
Publisher: IEEE
Date: 12-2016
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IGI Global
Date: 2016
DOI: 10.4018/978-1-5225-1016-1.CH004
Abstract: With the features of mobility, reality augmentation, and context sensitivity, wearable devices are widely deployed into various domains. However, the sensitivity of collected data makes security and privacy protection one of the first priority in the advancement of wearable technologies. This chapter provides a study on encryption-based confidentiality protection for data storage systems in wearable platforms. The chapter first conducts a review to storage solutions in consumer wearable products and explores a two-tier, local flash memory and remote cloud storage, storage system in wearable platforms. Then encryption-based confidentiality protection and implementation methods for both flash memory and remote cloud storage are summarized. According to the interaction and integration of these two components, a categorization of confidential storage systems in wearable platforms is proposed. In addition, the benefits and selection criteria for each category are also discussed.
Publisher: Association for Computing Machinery (ACM)
Date: 08-09-2022
DOI: 10.1145/3464300
Abstract: Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial ersity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.
Publisher: Wiley
Date: 25-01-2013
DOI: 10.1002/CPE.2989
Publisher: Inderscience Publishers
Date: 2019
Publisher: Wiley
Date: 31-07-2009
DOI: 10.1002/CPE.1456
Publisher: Institution of Engineering and Technology
Date: 28-10-2021
Publisher: NADIA
Date: 30-09-2014
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 08-2016
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 06-2016
Publisher: Springer International Publishing
Date: 2016
Publisher: Informa UK Limited
Date: 08-03-2017
Publisher: IEEE
Date: 11-2012
DOI: 10.1109/CGC.2012.46
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 25-03-2016
Publisher: IEEE
Date: 11-2012
DOI: 10.1109/CGC.2012.82
Publisher: IEEE
Date: 05-2012
Publisher: ACM
Date: 31-01-2017
Publisher: Elsevier BV
Date: 02-2016
Publisher: IEEE
Date: 11-2007
DOI: 10.1109/IAT.2007.62
Publisher: American Scientific Publishers
Date: 09-2013
DOI: 10.1166/SL.2013.3041
Publisher: Elsevier BV
Date: 05-2014
Publisher: Association for Computing Machinery (ACM)
Date: 16-02-2023
DOI: 10.1145/3579827
Abstract: The alignment of multiple multi-relational networks, such as knowledge graphs, is vital for many AI applications. In comparison with existing GCNs which cannot fully utilize relational information of multiple types, we propose a relation-aware graph convolutional network (ERGCN), which is equipped with both entity convolution and relation convolution to learn the entity embeddings and relation embeddings simultaneously. The role discrimination and translation property of knowledge graphs are adopted in the entity convolutional process to incorporate the relation information. To facilitate the relation convolution, we construct quadruples to model the connection between a pair of relations thus to determine their neighborhood, which also enables the relation convolution to be conducted in an efficient way. Thereafter, AERGCN, the alignment framework based on ERGCN, is developed for multi-relational network alignment tasks. Anchors are used to supervise the objective function, which aims at minimizing the distances between anchors and to generate new cross-network triplets to build a bridge between different knowledge graphs at the level of triplet to improve the performance of alignment. Experiments on real-world datasets show that the proposed solutions outperform the competitive baselines in terms of link prediction, entity alignment, and relation alignment.
No related grants have been discovered for Mingzhong Wang.