131 citations · 862 across the 65 of their papers we have counts for
70 papers
Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition
Hada Melino Muhammad, Luan Pham, Laure Barrière +3
Identifying the root cause of an anomaly among hundreds of sensors is critical for preventing safety incidents and costly downtime in complex monitored systems. Existing studies ev…
RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data
Luan Pham, Hongyu Zhang, Huong Ha +2
Root cause analysis (RCA) for microservice systems has gained significant attention in recent years. However, there is still no standard benchmark that includes large-scale dataset…
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
Arian Prabowo, Kaixuan Chen, Hao Xue +2
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment. However, this assumption bec…
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Edward A. Small, Jeffrey N. Clark, Christopher J. McWilliams +4
Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable. Conceptually, given an individual clas…
How Crowd Worker Factors Influence Subjective Annotations: A Study of Tagging Misogynistic Hate Speech in Tweets
Danula Hettiachchi, Indigo Holcombe-James, Stephanie Livingstone +4
Crowdsourced annotation is vital to both collecting labelled data to train and test automated content moderation systems and to support human-in-the-loop review of system decisions…
MAPLE: Mobile App Prediction Leveraging Large Language Model Embeddings
Yonchanok Khaokaew, Hao Xue, Flora D. Salim
In recent years, predicting mobile app usage has become increasingly important for areas like app recommendation, user behaviour analysis, and mobile resource management. Existing…
i-Align: an interpretable knowledge graph alignment model
Bayu Distiawan Trisedya, Flora D Salim, Jeffrey Chan +3
Knowledge graphs (KGs) are becoming essential resources for many downstream applications. However, their incompleteness may limit their potential. Thus, continuous curation is need…
Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRs
Yuxi Liu, Zhenhao Zhang, Shaowen Qin +2
Predicting the risk of in-hospital mortality from electronic health records (EHRs) has received considerable attention. Such predictions will provide early warning of a patient's h…
Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction
Yuxi Liu, Zhenhao Zhang, Shaowen Qin +4
The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patie…
Designing and Evaluating Presentation Strategies for Fact-Checked Content
Danula Hettiachchi, Kaixin Ji, Jenny Kennedy +5
With the rapid growth of online misinformation, it is crucial to have reliable fact-checking methods. Recent research on finding check-worthy claims and automated fact-checking hav…
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Shohreh Deldari, Dimitris Spathis, Mohammad Malekzadeh +3
Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field. Self-supervised learning (SSL) is a promising approac…
Towards Mobility Data Science (Vision Paper)
Mohamed Mokbel, Mahmoud Sakr, Li Xiong +45
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracki…
A System of Monitoring and Analyzing Human Indoor Mobility and Air Quality
Kyle K. Qin, Mohammad S. Rahaman, Yongli Ren +3
Human movements in the workspace usually have non-negligible relations with air quality parameters (e.g., CO, PM2.5, and PM10). We establish a system to monitor indoor human mo…
Continually learning out-of-distribution spatiotemporal data for robust energy forecasting
Arian Prabowo, Kaixuan Chen, Hao Xue +2
Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to optimize energy consumption and reduce costs. Thi…
Message Passing Neural Networks for Traffic Forecasting
Arian Prabowo, Hao Xue, Wei Shao +2
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. Tr…
Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)
Arian Prabowo, Hao Xue, Wei Shao +2
New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads)…
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study
Jason Liu, Shohreh Deldari, Hao Xue +2
In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several ch…
Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities
Kaixin Ji, Damiano Spina, Danula Hettiachchi +2
Physiological signals can potentially be applied as objective measures to understand the behavior and engagement of users interacting with information access systems. However, the…
Are footpaths encroached by shared e-scooters? Spatio-temporal Analysis of Micro-mobility Services
Hiruni Kegalle, Danula Hettiachchi, Jeffrey Chan +2
Micro-mobility services (e.g., e-bikes, e-scooters) are increasingly popular among urban communities, being a flexible transport option that brings both opportunities and challenge…
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
Edward A. Small, Kacper Sokol, Daniel Manning +2
Group fairness is achieved by equalising prediction distributions between protected sub-populations; individual fairness requires treating similar individuals alike. These two obje…
Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting
Arian Prabowo, Wei Shao, Hao Xue +2
Traffic forecasting is a critical task to extract values from cyber-physical infrastructures, which is the backbone of smart transportation. However owing to external contexts, the…
Vision-based Multi-future Trajectory Prediction: A Survey
Renhao Huang, Hao Xue, Maurice Pagnucco +2
Vision-based trajectory prediction is an important task that supports safe and intelligent behaviours in autonomous systems. Many advanced approaches have been proposed over the ye…
Multiple-level Point Embedding for Solving Human Trajectory Imputation with Prediction
Kyle K. Qin, Yongli Ren, Wei Shao +3
Sparsity is a common issue in many trajectory datasets, including human mobility data. This issue frequently brings more difficulty to relevant learning tasks, such as trajectory i…
Detecting Change Intervals with Isolation Distributional Kernel
Yang Cao, Ye Zhu, Kai Ming Ting +4
Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. Although many unsupervised Change-Point Detection (CPD) methods have…