Search Results for author:
Found 53 papers, 18 papers with code
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.
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable.
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.
Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction
In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction.
Designing and Evaluating Presentation Strategies for Fact-Checked Content
We address this research gap by exploring the critical design elements in fact-checking reports and investigating whether credibility and presentation-based design improvements can enhance users' ability to interpret the report accurately.
Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRs
Existing approaches focus on exploiting the variable correlations in patient medical records to impute missing values and establishing time-decay mechanisms to deal with such irregularity.
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.
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
Group fairness is achieved by equalising prediction distributions between protected sub-populations; individual fairness requires treating similar individuals alike.
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.
Multiple-level Point Embedding for Solving Human Trajectory Imputation with Prediction
Sparsity is a common issue in many trajectory datasets, including human mobility data.
Detecting Change Intervals with Isolation Distributional Kernel
Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis.
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.
Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values
Our model can capture both long- and short-term temporal patterns within each patient journey and effectively handle the high degree of missingness in EHR data without any imputation data generation.
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.
Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction
Building models for health prediction based on Electronic Health Records (EHR) has become an active research area.
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.
Measuring disentangled generative spatio-temporal representation
Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches.
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.
Spatio-temporal Disentangled representation learning for mobility prediction
In this work, we propose a VAE-based architecture for learning the disentangled representation from real spatio-temporal data for mobility forecasting.
CoSEM: Contextual and Semantic Embedding for App Usage Prediction
App usage prediction is important for smartphone system optimization to enhance user experience.
Evolutionary Ensemble Learning for Multivariate Time Series Prediction
In this framework, a specific pipeline is encoded as a candidate solution and a multi-objective evolutionary algorithm is applied under different population sizes to produce multiple Pareto optimal sets (POSs).
MoParkeR : Multi-objective Parking Recommendation
Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only.
Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map
To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas.
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.
Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables
We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia.
Drone-as-a-Service Composition Under Uncertainty
Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts.
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.
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors
This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators.
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.
Multi-officer Routing for Patrolling High Risk Areas Jointly Learned from Check-ins, Crime and Incident Response Data
In this paper, we formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information.
flexgrid2vec: Learning Efficient Visual Representations Vectors
We compare the performance of flexgrid2vec with a set of state-of-the-art visual representation learning models on binary and multi-class image classification tasks.
G-CREWE: Graph CompREssion With Embedding for Network Alignment
In this paper, we propose a framework, called G-CREWE (Graph CompREssion With Embedding) to solve the network alignment problem.
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
Federated learning provides a compelling framework for learning models from decentralized data, but conventionally, it assumes the availability of labeled samples, whereas on-device data are generally either unlabeled or cannot be annotated readily through user interaction.
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data
Extracting informative and meaningful temporal segments from high-dimensional wearable sensor data, smart devices, or IoT data is a vital preprocessing step in applications such as Human Activity Recognition (HAR), trajectory prediction, gesture recognition, and lifelogging.
Ranked #3 on
Change Point Detection
on TSSB
(Covering metric)
An Ambient-Physical System to Infer Concentration in Open-plan Workplace
One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks.
Transfer Learning for Thermal Comfort Prediction in Multiple Cities
The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage.
Relation Embedding for Personalised POI Recommendation
Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services.
COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference
This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments.
DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones
Our experiments on CelebA and LFW datasets show that the quality of the reconstructed images from the obfuscated features of the raw image is dramatically decreased from 0. 9458 to 0. 3175 in terms of multi-scale structural similarity.
Modelling Regional Crime Risk using Directed Graph of Check-ins
The mobility dynamics inferred from Foursquare helps us understanding urban social events like crime In this paper, we propose a directed graph from the aggregated movement between regions using Foursquare data.
Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network
Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques.
