By Dr Hao Xue

Conference Papers

Abushaqra FM; Xue H; Ren Y; Salim FD, 2021, 'PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series', in Proceedings IEEE International Conference on Data Mining Icdm, pp. 976 - 981, http://dx.doi.org/10.1109/ICDM51629.2021.00109

Xue H; Salim FD, 2021, 'TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 741 - 753, http://dx.doi.org/10.1007/978-3-030-75762-5_58

Xue H; Huynh DQ; Reynolds M, 2019, 'Pedestrian tracking and stereo matching of tracklets for autonomous vehicles', in IEEE Vehicular Technology Conference, http://dx.doi.org/10.1109/VTCSpring.2019.8746329

Xue H; Huynh DQ; Reynolds M, 2019, 'Location-velocity attention for pedestrian trajectory prediction', in Proceedings 2019 IEEE Winter Conference on Applications of Computer Vision Wacv 2019, pp. 2038 - 2047, http://dx.doi.org/10.1109/WACV.2019.00221

Xue H; Huynh DQ; Reynolds M, 2018, 'SS-LSTM: A Hierarchical LSTM Model for Pedestrian Trajectory Prediction', in Proceedings 2018 IEEE Winter Conference on Applications of Computer Vision Wacv 2018, pp. 1186 - 1194, http://dx.doi.org/10.1109/WACV.2018.00135

Xue H; Huynh DQ; Reynolds M, 2017, 'Bi-prediction: Pedestrian trajectory prediction based on bidirectional LSTM classification', in Dicta 2017 2017 International Conference on Digital Image Computing Techniques and Applications, pp. 1 - 8, http://dx.doi.org/10.1109/DICTA.2017.8227412

Xue H; Huynh DQ; Reynolds M, 2017, 'Bi-Prediction: Pedestrian Trajectory Prediction Based on Bidirectional LSTM Classification', in Guo Y; Li H; Cai W; Murshed M; Wang Z; Gao J; Feng DD (eds.), 2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA), IEEE, AUSTRALIA, Sydney, pp. 307 - 314, presented at International Conference on Digital Image Computing - Techniques and Applications (DICTA), AUSTRALIA, Sydney, 29 November 2017 - 01 December 2017

Ma L; Xue H; Jia T; Tan X, 2017, 'A Fast C-GIST Based Image Retrieval Method for Vision-Based Indoor Localization', in IEEE Vehicular Technology Conference, http://dx.doi.org/10.1109/VTCSpring.2017.8108338

Xue H; Ma L; Tan X, 2016, 'A fast visual map building method using video stream for visual-based indoor localization', in 2016 International Wireless Communications and Mobile Computing Conference Iwcmc 2016, pp. 650 - 654, http://dx.doi.org/10.1109/IWCMC.2016.7577133

Preprints

Du J; Stone H; Yang Y; Desai A; Xue H; Züfle A; MacIntyre CR; Salim FD, 2025, BLUE: Bi-layer Heterogeneous Graph Fusion Network for Avian Influenza Forecasting, http://arxiv.org/abs/2505.22692v1

Wongso W; Xue H; Salim FD, 2025, Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks, http://arxiv.org/abs/2505.11239v2

Khaokaew Y; Salim FD; Züfle A; Xue H; Anderson T; MacIntyre CR; Scotch M; Heslop DJ, 2025, Evaluating the Bias in LLMs for Surveying Opinion and Decision Making in Healthcare, http://arxiv.org/abs/2504.08260v2

Ju C; Liu J; Sinha S; Xue H; Salim F, 2025, TrajLLM: A Modular LLM-Enhanced Agent-Based Framework for Realistic Human Trajectory Simulation, http://arxiv.org/abs/2502.18712v1

Lin X; Prabowo A; Razzak I; Xue H; Amos M; Behrens S; Salim FD, 2024, BiTSA: Leveraging Time Series Foundation Model for Building Energy Analytics, http://dx.doi.org/10.48550/arxiv.2412.14175

Wongso W; Xue H; Salim FD, 2024, GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems, http://arxiv.org/abs/2410.20643v2

Lin X; Prabowo A; Razzak I; Xue H; Amos M; Behrens S; Salim FD, 2024, Exploring Capabilities of Time Series Foundation Models in Building Analytics, http://dx.doi.org/10.48550/arxiv.2411.08888

Khaokaew Y; Xue H; Rahaman MS; Salim FD, 2024, WorkR: Occupation Inference for Intelligent Task Assistance, http://dx.doi.org/10.48550/arxiv.2407.18518

Yin D; Deng J; Ao S; Li Z; Xue H; Prabowo A; Jiang R; Song X; Salim F, 2024, Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned, http://dx.doi.org/10.48550/arxiv.2406.12709

Prabowo A; Lin X; Razzak I; Xue H; Yap EW; Amos M; Salim FD, 2024, BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics, http://arxiv.org/abs/2406.08990v2

Li L; Xue H; Song Y; Salim F, 2024, T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation, http://dx.doi.org/10.48550/arxiv.2406.12913

Lin X; Prabowo A; Razzak I; Xue H; Amos M; Behrens S; Salim FD, 2024, A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings, http://dx.doi.org/10.48550/arxiv.2405.14267

Li P; Rijke MD; Xue H; Ao S; Song Y; Salim FD, 2024, Large Language Models for Next Point-of-Interest Recommendation, http://dx.doi.org/10.1145/3626772.3657840

Xue H; Tang T; Payani A; Salim FD, 2024, Prompt Mining for Language-based Human Mobility Forecasting, http://arxiv.org/abs/2403.03544v1

Yang R; Salim FD; Xue H, 2024, SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding, http://arxiv.org/abs/2402.12132v1

Xue H; Salim FD, 2023, Utilizing Language Models for Energy Load Forecasting, http://arxiv.org/abs/2310.17788v1

Xue H; Salim FD, 2023, Human Mobility Question Answering (Vision Paper), http://arxiv.org/abs/2310.04443v2

Khaokaew Y; Xue H; Salim FD, 2023, MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings, http://dx.doi.org/10.48550/arxiv.2309.08648

Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning, http://dx.doi.org/10.1145/3600100.3623726

Prabowo A; Chen K; Xue H; Sethuvenkatraman S; Salim FD, 2023, Continually learning out-of-distribution spatiotemporal data for robust energy forecasting, http://dx.doi.org/10.1007/978-3-031-43430-3_1

Prabowo A; Xue H; Shao W; Koniusz P; Salim FD, 2023, Message Passing Neural Networks for Traffic Forecasting, http://arxiv.org/abs/2305.05740v1

Prabowo A; Xue H; Shao W; Koniusz P; Salim FD, 2023, Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT), http://dx.doi.org/10.1007/s10618-023-00982-0

Liu J; Deldari S; Xue H; Nguyen V; Salim FD, 2023, Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study, http://dx.doi.org/10.48550/arxiv.2305.00619

Huang R; Xue H; Pagnucco M; Salim F; Song Y, 2023, Vision-based Multi-future Trajectory Prediction: A Survey, http://dx.doi.org/10.48550/arxiv.2302.10463

Prabowo A; Shao W; Xue H; Koniusz P; Salim FD, 2023, Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting, http://dx.doi.org/10.1145/3576842.3582362

Abushaqra FM; Xue H; Ren Y; Salim FD, 2022, SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series, http://arxiv.org/abs/2212.03560v3

Xue H; Salim FD, 2022, PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting, http://arxiv.org/abs/2210.08964v5

Xue H; Voutharoja BP; Salim FD, 2022, Leveraging Language Foundation Models for Human Mobility Forecasting, http://arxiv.org/abs/2209.05479v2

Deldari S; Xue H; Saeed A; Smith DV; Salim FD, 2022, COCOA: Cross Modality Contrastive Learning for Sensor Data, http://dx.doi.org/10.1145/3550316

Deldari S; Xue H; Saeed A; He J; Smith DV; Salim FD, 2022, Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data, http://arxiv.org/abs/2206.02353v2

Wang Z; Jiang R; Xue H; Salim FD; Song X; Shibasaki R, 2021, Event-Aware Multimodal Mobility Nowcasting, http://arxiv.org/abs/2112.08443v1

Xue H; Salim FD; Ren Y; Clarke CLA, 2021, Translating Human Mobility Forecasting through Natural Language Generation, http://arxiv.org/abs/2112.11481v1

Abushaqra FM; Xue H; Ren Y; Salim FD, 2021, PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series, http://arxiv.org/abs/2110.00071v2

Xue H; Salim FD; Ren Y; Oliver N, 2021, MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction, http://arxiv.org/abs/2110.01401v1

Xue H; Salim FD, 2021, Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification, http://dx.doi.org/10.1145/3447548.3467263

Deldari S; Smith DV; Xue H; Salim FD, 2020, Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding, http://dx.doi.org/10.1145/3442381.3449903

Xue H; Salim FD, 2020, TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting, http://dx.doi.org/10.1007/978-3-030-75762-5_58

Xue H; Huynh DQ; Reynolds M, 2020, Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints, http://arxiv.org/abs/2010.05507v1

Gao N; Xue H; Shao W; Zhao S; Qin KK; Prabowo A; Rahaman MS; Salim FD, 2020, Generative Adversarial Networks for Spatio-temporal Data: A Survey, http://dx.doi.org/10.1145/3474838

Xue H; Huynh DQ; Reynolds M, 2020, Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories, http://arxiv.org/abs/2004.09760v1

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