Search Results for author:
Found 28 papers, 12 papers with code
Large Language Models for Next Point-of-Interest Recommendation
Our results show that the proposed framework outperforms the state-of-the-art models in all three datasets.
Prompt Mining for Language-based Human Mobility Forecasting
Specifically, the framework includes a prompt generation stage based on the information entropy of prompts and a prompt refinement stage to integrate mechanisms such as the chain of thought.
SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding
Our framework offers a simple but comprehensive way to understand the underlying patterns and trends in dynamic KG, thereby enhancing the accuracy of predictions and the relevance of recommendations.
Measuring Misogyny in Natural Language Generation: Preliminary Results from a Case Study on two Reddit Communities
Generic `toxicity' classifiers continue to be used for evaluating the potential for harm in natural language generation, despite mounting evidence of their shortcomings.
Utilizing Language Models for Energy Load Forecasting
Energy load forecasting plays a crucial role in optimizing resource allocation and managing energy consumption in buildings and cities.
Human Mobility Question Answering (Vision Paper)
The aim of the task is to let the intelligent system learn from mobility data and answer related questions.
MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings
This study introduces a novel prediction model, Mobile App Prediction Leveraging Large Language Model Embeddings (MAPLE), which employs Large Language Models (LLMs) and installed app similarity to overcome these challenges.
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment.
Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
One of the primary reasons for this is the shift in distribution of occupancy patterns, with many people working or learning from home.
Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)
During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training.
Message Passing Neural Networks for Traffic Forecasting
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.
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study
In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data.
Multimodal Trajectory Prediction: A Survey
Trajectory prediction is an important task to support safe and intelligent behaviours in autonomous systems.
Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting
Further analysis also shows that each pair of sensors also has a unique dynamic.
CrossPyramid: Neural Ordinary Differential Equations Architecture for Partially-observed Time-series
In this article, we introduce CrossPyramid, a novel ODE-based model that aims to enhance the generalizability of sequences representation.
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
In this novel task, the numerical input and output are transformed into prompts and the forecasting task is framed in a sentence-to-sentence manner, making it possible to directly apply language models for forecasting purposes.
Leveraging Language Foundation Models for Human Mobility Forecasting
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.
COCOA: Cross Modality Contrastive Learning for Sensor Data
Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data.
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data
Unlike existing reviews of SSRL that have pre-dominately focused upon methods in the fields of CV or NLP for a single modality, we aim to provide the first comprehensive review of multimodal self-supervised learning methods for temporal data.
Event-Aware Multimodal Mobility Nowcasting
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 where societal events drive mobility behavior deviated from the normality.
MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction
Furthermore, unlike existing methods, we introduce a location prediction branch in MobTCast as an auxiliary task to model the geographical context and predict the next location.
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series
Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis.
Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification
The pre-trained feature encoder is then fine-tuned in the downstream phase to perform cough classification.
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding
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 the system.
TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting
A Transformer based long-term relation prediction module is explicitly designed to discover the periodicity and enable the three components to be jointly modeled This module predicts the periodic relation which is then used to yield the predicted urban flow tensor.
Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints
We compare our SGSG against twenty state-of-the-art pedestrian trajectory prediction methods and the results show that the proposed method achieves superior performance on two widely used trajectory prediction benchmarks.
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area.
Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories
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 pedestrians, the scene constraints, and the multimodal (multiroute) nature of predictions.
