131 citations · 448 across the 23 of their papers we have counts for
23 papers
GeoFlow: Geo-Aware Modeling of Inter-Area Relationships in Origin-Destination Flow Prediction and Generation
Zherui Huang, Guanjie Zheng, Hao Xue +1
Origin-destination (OD) flow modeling underpins urban planning and mobility analysis, but prevailing graph-based methods often neglect salient geographic attributes, limiting their…
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
Arian Prabowo, Kaixuan Chen, Hao Xue +2
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment. However, this assumption bec…
MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings
Yonchanok Khaokaew, Hao Xue, Flora D. Salim
In recent years, predicting mobile app usage has become increasingly important for areas like app recommendation, user behaviour analysis, and mobile resource management. Existing…
Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
Arian Prabowo, Kaixuan Chen, Hao Xue +2
Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to optimize energy consumption and reduce costs. Thi…
Message Passing Neural Networks for Traffic Forecasting
Arian Prabowo, Hao Xue, Wei Shao +2
A road network, in the context of traffic forecasting, is typically modeled as a graph where the nodes are sensors that measure traffic metrics (such as speed) at that location. Tr…
Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)
Arian Prabowo, Hao Xue, Wei Shao +2
New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads)…
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study
Jason Liu, Shohreh Deldari, Hao Xue +2
In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several ch…
Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting
Arian Prabowo, Wei Shao, Hao Xue +2
Traffic forecasting is a critical task to extract values from cyber-physical infrastructures, which is the backbone of smart transportation. However owing to external contexts, the…
Vision-based Multi-future Trajectory Prediction: A Survey
Renhao Huang, Hao Xue, Maurice Pagnucco +2
Vision-based trajectory prediction is an important task that supports safe and intelligent behaviours in autonomous systems. Many advanced approaches have been proposed over the ye…
SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series
Futoon M. Abushaqra, Hao Xue, Yongli Ren +1
Ordinary Differential Equations (ODE) based models have become popular as foundation models for solving many time series problems. Combining neural ODEs with traditional RNN models…
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
Hao Xue, Flora D. Salim
This paper presents a new perspective on time series forecasting. In existing time series forecasting methods, the models take a sequence of numerical values as input and yield num…
Leveraging Language Foundation Models for Human Mobility Forecasting
Hao Xue, Bhanu Prakash Voutharoja, Flora D. Salim
In this paper, we propose a novel pipeline that leverages language foundation models for temporal sequential pattern mining, such as for human mobility forecasting tasks. For examp…
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data
Shohreh Deldari, Hao Xue, Aaqib Saeed +3
Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in the field of computer vision, speech, natural language processing (NLP), and recently, with…
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…
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…
MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction
Hao Xue, Flora D. Salim, Yongli Ren +1
Human mobility prediction is a core functionality in many location-based services and applications. However, due to the sparsity of mobility data, it is not an easy task to predict…
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-…
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding
Shohreh Deldari, Daniel V. Smith, Hao Xue +1
Change Point Detection (CPD) methods identify the times associated with changes in the trends and properties of time series data in order to describe the underlying behaviour of th…
TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting
Hao Xue, Flora D Salim
Urban flow forecasting is a challenging task, given the inherent periodic characteristics of urban flow patterns. To capture the periodicity, existing urban flow prediction approac…
Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints
Hao Xue, Du Q. Huynh, Mark Reynolds
Pedestrian trajectory prediction is valuable for understanding human motion behaviors and it is challenging because of the social influence from other pedestrians, the scene constr…
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Nan Gao, Hao Xue, Wei Shao +5
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…
Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories
Hao Xue, Du. Q. Huynh, Mark Reynolds
Pedestrian trajectory prediction is a challenging task as there are three properties of human movement behaviors which need to be addressed, namely, the social influence from other…