ORCID Profile
0000-0002-9873-8331
Current Organisations
Arizona State University
,
The University of Hong Kong
,
RMIT University
,
Data61
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Publisher: ACM
Date: 09-05-2023
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 10-2020
Publisher: Association for Computing Machinery (ACM)
Date: 21-04-2020
DOI: 10.1145/3380966
Abstract: The smart parking system is one of the most important problems in smart cities. Recently, an increasing number of sensors installed in parking spaces have provided big spatio-temporal data that be used to analyze parking situations in the city and help parking officers monitor parking violations. The traveling officer problem was customized to formulate a path-finding problem that aims to maximize the probability of catching overstayed cars before they leave. One of the challenges is to extract effective features from the big spatio-temporal data and provide a data-driven solution to replace conventional solutions such as a simple rule-based system or single optimization methods. In this article, we propose a seamless end-to-end learning and optimization framework that combines the long short-term memory auto-encoder neural network, clustering, and path-finding methods to solve the traveling officer problem. Our extensive comparison experiments on a large-scale real-world dataset have shown that our proposed solution outperforms any other single-step or optimization methods.
Publisher: ACM
Date: 06-07-2021
Publisher: Elsevier BV
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: IEEE
Date: 06-2017
Publisher: IEEE
Date: 06-2019
Publisher: Hindawi Limited
Date: 2018
DOI: 10.1155/2018/7087239
Publisher: IEEE
Date: 03-2019
Publisher: Association for Computing Machinery (ACM)
Date: 02-05-2023
DOI: 10.1145/3582427
Abstract: Sparsity is a common issue in many trajectory datasets, including human mobility data. This issue frequently brings more difficulty to relevant learning tasks, such as trajectory imputation and prediction. Nowadays, little existing work simultaneously deals with imputation and prediction on human trajectories. This work plans to explore whether the learning process of imputation and prediction could benefit from each other to achieve better outcomes. And the question will be answered by studying the coexistence patterns between missing points and observed ones in incomplete trajectories. More specifically, the proposed model develops an imputation component based on the self-attention mechanism to capture the coexistence patterns between observations and missing points among encoder-decoder layers. Meanwhile, a recurrent unit is integrated to extract the sequential embeddings from newly imputed sequences for predicting the following location. Furthermore, a new implementation called Imputation Cycle is introduced to enable gradual imputation with prediction enhancement at multiple levels, which helps to accelerate the speed of convergence. The experimental results on three different real-world mobility datasets show that the proposed approach has significant advantages over the competitive baselines across both imputation and prediction tasks in terms of accuracy and stability.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: ACM
Date: 19-10-2020
Publisher: Springer Science and Business Media LLC
Date: 27-09-2023
Publisher: American Association for the Advancement of Science (AAAS)
Date: 17-04-2020
Abstract: 3D vectorial holography reconstructs an arbitrary 3D vectorial field–carrying wavefront using machine learning inverse design.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: Association for Computing Machinery (ACM)
Date: 11-12-2019
DOI: 10.1145/3369811
Publisher: Association for Computing Machinery (ACM)
Date: 06-09-2022
DOI: 10.1145/3550335
Abstract: Seating location in the classroom can affect student engagement, attention and academic performance by providing better visibility, improved movement, and participation in discussions. Existing studies typically explore how traditional seating arrangements (e.g. grouped tables or traditional rows) influence students' perceived engagement, without considering group seating behaviours under more flexible seating arrangements. Furthermore, survey-based measures of student engagement are prone to subjectivity and various response bias. Therefore, in this research, we investigate how in idual and group-wise classroom seating experiences affect student engagement using wearable physiological sensors. We conducted a field study at a high school and collected survey and wearable data from 23 students in 10 courses over four weeks. We aim to answer the following research questions: 1. How does the seating proximity between students relate to their perceived learning engagement? 2. How do students' group seating behaviours relate to their physiologically-based measures of engagement (i.e. physiological arousal and physiological synchrony)? Experiment results indicate that the in idual and group-wise classroom seating experience is associated with perceived student engagement and physiologically-based engagement measured from electrodermal activity. We also find that students who sit close together are more likely to have similar learning engagement and tend to have high physiological synchrony. This research opens up opportunities to explore the implications of flexible seating arrangements and has great potential to maximize student engagement by suggesting intelligent seating choices in the future.
Publisher: Springer Nature Singapore
Date: 2022
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 06-12-2022
Publisher: IEEE
Date: 06-12-2022
Publisher: Springer Science and Business Media LLC
Date: 23-07-2019
Publisher: MDPI AG
Date: 23-08-2021
DOI: 10.3390/RS13163338
Publisher: Elsevier BV
Date: 05-2021
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: ACM
Date: 05-11-2018
Publisher: IEEE
Date: 22-03-2021
Publisher: IEEE
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: arXiv
Date: 2022
Publisher: IEEE
Date: 09-2012
Publisher: Association for Computing Machinery (ACM)
Date: 06-02-2022
DOI: 10.1145/3474838
Abstract: Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation, and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this article, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.
Publisher: IEEE
Date: 09-2011
Publisher: MDPI AG
Date: 21-11-2022
DOI: 10.3390/S22229011
Abstract: Inertial attitude estimation is a crucial component of many modern systems and applications. Attitude estimation from commercial-grade inertial sensors has been the subject of an abundance of research in recent years due to the proliferation of Inertial Measurement Units (IMUs) in mobile devices, such as the smartphone. Traditional methodologies involve probabilistic, iterative-state estimation however, these approaches do not generalise well over changing motion dynamics and environmental conditions, as they require context-specific parameter tuning. In this work, we explore novel methods for attitude estimation from low-cost inertial sensors using a self-attention-based neural network, the Attformer. This paper proposes to part ways from the traditional cycle of continuous integration algorithms, and formulate it as an optimisation problem. This approach separates itself by leveraging attention operations to learn the complex patterns and dynamics associated with inertial data, allowing for the linear complexity in the dimension of the feature vector to account for these patterns. Additionally, we look at combining traditional state-of-the-art approaches with our self-attention method. These models were evaluated on entirely unseen sequences, over a range of different activities, users and devices, and compared with a recent alternate deep learning approach, the unscented Kalman filter and the iOS CoreMotion API. The inbuilt iOS had a mean angular distance from the true attitude of 117.31∘, the GRU 21.90∘, the UKF 16.38∘, the Attformer 16.28∘ and, finally, the UKF-Attformer had mean angular distance of 10.86∘. We show that this plug-and-play solution outperforms previous approaches and generalises well across different users, devices and activities.
Publisher: IOP Publishing
Date: 05-08-2022
Publisher: ACM
Date: 21-09-2021
Publisher: IEEE
Date: 18-07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: ACM
Date: 05-11-2019
Publisher: Springer Science and Business Media LLC
Date: 17-05-2020
Publisher: IEEE
Date: 18-07-2022
Publisher: Elsevier BV
Date: 06-2022
No related grants have been discovered for Wei Shao.