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Found 33 papers, 7 papers with code
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
We believe that the proposed PromptCast task as well as our PISA dataset could provide novel insights and further lead to new research directions in the domain of time-series representation learning and forecasting.
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.
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.
