Search Results for author: Flora D. Salim

Found 53 papers, 17 papers with code

Prompt Mining for Language-based Human Mobility Forecasting

no code implementations6 Mar 2024 Hao Xue, Tianye Tang, Ali Payani, Flora D. Salim

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

no code implementations19 Feb 2024 Ruiyi Yang, Flora D. Salim, Hao Xue

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.

Knowledge Graphs Link Prediction

Measuring Misogyny in Natural Language Generation: Preliminary Results from a Case Study on two Reddit Communities

no code implementations6 Dec 2023 Aaron J. Snoswell, Lucinda Nelson, Hao Xue, Flora D. Salim, Nicolas Suzor, Jean Burgess

Generic `toxicity' classifiers continue to be used for evaluating the potential for harm in natural language generation, despite mounting evidence of their shortcomings.

Text Generation

Utilizing Language Models for Energy Load Forecasting

1 code implementation26 Oct 2023 Hao Xue, Flora D. Salim

Energy load forecasting plays a crucial role in optimizing resource allocation and managing energy consumption in buildings and cities.

Decision Making Descriptive +1

Human Mobility Question Answering (Vision Paper)

no code implementations2 Oct 2023 Hao Xue, Flora D. Salim

The aim of the task is to let the intelligent system learn from mobility data and answer related questions.

Management Question Answering +2

MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings

no code implementations15 Sep 2023 Yonchanok Khaokaew, Hao Xue, Flora D. Salim

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.

Language Modelling Large Language Model

Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse

no code implementations8 Sep 2023 Edward A. Small, Jeffrey N. Clark, Christopher J. McWilliams, Kacper Sokol, Jeffrey Chan, Flora D. Salim, Raul Santos-Rodriguez

Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable.

counterfactual

Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction

no code implementations24 Aug 2023 Yuxi Liu, Zhenhao Zhang, Shaowen Qin, Flora D. Salim, Antonio Jimeno Yepes, Jun Shen, Jiang Bian

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.

Decision Making

Designing and Evaluating Presentation Strategies for Fact-Checked Content

1 code implementation20 Aug 2023 Danula Hettiachchi, Kaixin Ji, Jenny Kennedy, Anthony McCosker, Flora D. Salim, Mark Sanderson, Falk Scholer, Damiano Spina

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.

Fact Checking Misinformation

Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRs

1 code implementation19 Aug 2023 Yuxi Liu, Zhenhao Zhang, Shaowen Qin, Flora D. Salim, Antonio Jimeno Yepes

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.

Contrastive Learning Imputation +1

Continually learning out-of-distribution spatiotemporal data for robust energy forecasting

1 code implementation10 Jun 2023 Arian Prabowo, KaiXuan Chen, Hao Xue, Subbu Sethuvenkatraman, Flora D. Salim

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)

1 code implementation9 May 2023 Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz, Flora D. Salim

During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training.

Contrastive Learning

Message Passing Neural Networks for Traffic Forecasting

no code implementations9 May 2023 Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz, Flora D. Salim

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.

Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness

1 code implementation19 Apr 2023 Edward A. Small, Kacper Sokol, Daniel Manning, Flora D. Salim, Jeffrey Chan

Group fairness is achieved by equalising prediction distributions between protected sub-populations; individual fairness requires treating similar individuals alike.

Fairness

Detecting Change Intervals with Isolation Distributional Kernel

2 code implementations30 Dec 2022 Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis.

Change Point Detection

CrossPyramid: Neural Ordinary Differential Equations Architecture for Partially-observed Time-series

no code implementations7 Dec 2022 Futoon M. Abushaqra, Hao Xue, Yongli Ren, Flora D. Salim

In this article, we introduce CrossPyramid, a novel ODE-based model that aims to enhance the generalizability of sequences representation.

Irregular Time Series Time Series +1

Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values

no code implementations11 Nov 2022 Yuxi Liu, Shaowen Qin, Antonio Jimeno Yepes, Wei Shao, Zhenhao Zhang, Flora D. Salim

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.

Decision Making Imputation

PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting

2 code implementations20 Sep 2022 Hao Xue, Flora D. Salim

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.

Language Modelling Representation Learning +6

Leveraging Language Foundation Models for Human Mobility Forecasting

1 code implementation11 Sep 2022 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.

Sequential Pattern Mining Temporal Sequences

COCOA: Cross Modality Contrastive Learning for Sensor Data

1 code implementation31 Jul 2022 Shohreh Deldari, Hao Xue, Aaqib Saeed, Daniel V. Smith, Flora D. Salim

Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data.

Contrastive Learning Self-Supervised Learning

Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data

no code implementations6 Jun 2022 Shohreh Deldari, Hao Xue, Aaqib Saeed, Jiayuan He, Daniel V. Smith, Flora D. Salim

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.

Representation Learning Self-Supervised Learning +2

Measuring disentangled generative spatio-temporal representation

no code implementations10 Feb 2022 Sichen Zhao, Wei Shao, Jeffrey Chan, Flora D. Salim

Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches.

Dimensionality Reduction Representation Learning

Event-Aware Multimodal Mobility Nowcasting

1 code implementation14 Dec 2021 Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D. Salim, Xuan Song, Ryosuke Shibasaki

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

no code implementations NeurIPS 2021 Hao Xue, Flora D. Salim, Yongli Ren, Nuria Oliver

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.

Trajectory Forecasting

Spatio-temporal Disentangled representation learning for mobility prediction

no code implementations29 Sep 2021 Sichen Zhao, Wei Shao, Jeffrey Chan, Flora D. Salim

In this work, we propose a VAE-based architecture for learning the disentangled representation from real spatio-temporal data for mobility forecasting.

Management Representation Learning

CoSEM: Contextual and Semantic Embedding for App Usage Prediction

no code implementations26 Aug 2021 Yonchanok Khaokaew, Mohammad Saiedur Rahaman, Ryen W. White, Flora D. Salim

App usage prediction is important for smartphone system optimization to enhance user experience.

Evolutionary Ensemble Learning for Multivariate Time Series Prediction

no code implementations22 Aug 2021 Hui Song, A. K. Qin, Flora D. Salim

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).

Ensemble Learning Time Series +1

MoParkeR : Multi-objective Parking Recommendation

no code implementations10 Jun 2021 Mohammad Saiedur Rahaman, Wei Shao, Flora D. Salim, Ayad Turky, Andy Song, Jeffrey Chan, Junliang Jiang, Doug Bradbrook

Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only.

Recommendation Systems

Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

no code implementations19 May 2021 Wei Shao, Arian Prabowo, Sichen Zhao, Piotr Koniusz, Flora D. Salim

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

no code implementations17 May 2021 Hao Xue, Flora D. Salim

The pre-trained feature encoder is then fine-tuned in the downstream phase to perform cough classification.

Classification Self-Supervised Learning

Drone-as-a-Service Composition Under Uncertainty

no code implementations11 Mar 2021 Ali Hamdi, Flora D. Salim, Du Yong Kim, Azadeh Ghari Neiat, Athman Bouguettaya

Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts.

Scheduling Service Composition

Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding

2 code implementations28 Nov 2020 Shohreh Deldari, Daniel V. Smith, Hao Xue, Flora D. Salim

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.

Anomaly Detection Change Point Detection +4

Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors

no code implementations26 Aug 2020 Manpreet Kaur, Flora D. Salim, Yongli Ren, Jeffrey Chan, Martin Tomko, Mark Sanderson

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.

intent-classification Intent Classification

Generative Adversarial Networks for Spatio-temporal Data: A Survey

no code implementations18 Aug 2020 Nan Gao, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, Flora D. Salim

Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area.

Imputation Time Series +2

Multi-officer Routing for Patrolling High Risk Areas Jointly Learned from Check-ins, Crime and Incident Response Data

no code implementations31 Jul 2020 Shakila Khan Rumi, Kyle K. Qin, Flora D. Salim

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.

point of interests

flexgrid2vec: Learning Efficient Visual Representations Vectors

no code implementations30 Jul 2020 Ali Hamdi, Du Yong Kim, Flora D. Salim

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.

Image Classification Representation Learning +1

G-CREWE: Graph CompREssion With Embedding for Network Alignment

1 code implementation30 Jul 2020 Kyle K. Qin, Flora D. Salim, Yongli Ren, Wei Shao, Mark Heimann, Danai Koutra

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

no code implementations25 Jul 2020 Aaqib Saeed, Flora D. Salim, Tanir Ozcelebi, Johan Lukkien

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.

Federated Learning Self-Supervised Learning +1

ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data

1 code implementation24 Jul 2020 Shohreh Deldari, Daniel V. Smith, Amin Sadri, Flora D. Salim

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)

Change Point Detection Gesture Recognition +4

An Ambient-Physical System to Infer Concentration in Open-plan Workplace

no code implementations27 May 2020 Mohammad Saiedur Rahaman, Jonathan Liono, Yongli Ren, Jeffrey Chan, Shaw Kudo, Tim Rawling, Flora D. Salim

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

no code implementations29 Apr 2020 Nan Gao, Wei Shao, Mohammad Saiedur Rahaman, Jun Zhai, Klaus David, Flora D. Salim

The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage.

Transfer Learning

Relation Embedding for Personalised POI Recommendation

no code implementations9 Feb 2020 Xianjing Wang, Flora D. Salim, Yongli Ren, Piotr Koniusz

Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services.

Knowledge Graph Embedding Relation +1

COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference

no code implementations24 Sep 2019 Arian Prabowo, Piotr Koniusz, Wei Shao, Flora D. Salim

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

no code implementations9 Sep 2019 Ang Li, Jiayi Guo, Huanrui Yang, Flora D. Salim, Yiran Chen

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.

General Classification Image Classification +3

Modelling Regional Crime Risk using Directed Graph of Check-ins

no code implementations25 Jul 2019 Shakila Khan Rumi, Flora D. Salim

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.

regression

Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network

no code implementations8 Mar 2019 Wei Shao, Flora D. Salim, Jeffrey Chan, Sean Morrison, Fabio Zambetta

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.

General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.