131 citations · 862 across the 65 of their papers we have counts for
13 papers · 1 filter
Event-Aware Multimodal Mobility Nowcasting
Zhaonan Wang, Renhe Jiang, Hao Xue +3
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios…
Translating Human Mobility Forecasting through Natural Language Generation
Hao Xue, Flora D. Salim, Yongli Ren +1
Existing human mobility forecasting models follow the standard design of the time-series prediction model which takes a series of numerical values as input to generate a numerical…
Individual and Group-wise Classroom Seating Experience: Effects on Student Engagement in Different Courses
Nan Gao, Mohammad Saiedur Rahaman, Wei Shao +2
Seating location in the classroom can affect student engagement, attention and academic performance by providing better visibility, improved movement, and participation in discussi…
GCCN: Global Context Convolutional Network
Ali Hamdi, Flora Salim, Du Yong Kim
In this paper, we propose Global Context Convolutional Network (GCCN) for visual recognition. GCCN computes global features representing contextual information across image patches…
Signature-Graph Networks
Ali Hamdi, Flora Salim, Du Yong Kim +1
We propose a novel approach for visual representation learning called Signature-Graph Neural Networks (SGN). SGN learns latent global structures that augment the feature representa…
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series
Futoon M. Abushaqra, Hao Xue, Yongli Ren +1
Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been…
CoSEM: Contextual and Semantic Embedding for App Usage Prediction
Yonchanok Khaokaew, Mohammad Saiedur Rahaman, Ryen W. White +1
App usage prediction is important for smartphone system optimization to enhance user experience. Existing modeling approaches utilize historical app usage logs along with a wide ra…
Evolutionary Ensemble Learning for Multivariate Time Series Prediction
Hui Song, A. K. Qin, Flora D. Salim
Multivariate time series (MTS) prediction plays a key role in many fields such as finance, energy and transport, where each individual time series corresponds to the data collected…
Investigating the Reliability of Self-report Data in the Wild: The Quest for Ground Truth
Nan Gao, Mohammad Saiedur Rahaman, Wei Shao +1
Inferring human mental state (e.g., emotion, depression, engagement) with sensing technology is one of the most valuable challenges in the affective computing area, which has a pro…
Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification
Hao Xue, Flora D. Salim
The usage of smartphone-collected respiratory sound, trained with deep learning models, for detecting and classifying COVID-19 becomes popular recently. It removes the need for in-…
Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map
Wei Shao, Arian Prabowo, Sichen Zhao +2
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor d…
Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables
Nan Gao, Max Marschall, Jane Burry +2
We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Ga…
Spatiotemporal Data Mining: A Survey on Challenges and Open Problems
Ali Hamdi, Khaled Shaban, Abdelkarim Erradi +3
Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth o…