55 citations · 71 across the 27 of their papers we have counts for
27 papers
Transfer Learning of Keystroke Dynamics for Cross-Device User Authentication
Nuwan Kaluarachchi, Sevvandi Kandanaarachchi, Kristen Moore +2
Keystroke dynamics (typing patterns) can be used as a behavioural biometric modality for user authentication, with applications such as fraud prevention. While the modality has bee…
A Combinatorial Optimisation Approach to Multi-factorial Gap-filling in Genome-scale Metabolic Models (GEMs)
Philip Kilby, Sevvandi Kandanaarachchi, Matthew J. Morgan +4
Genome-Scale Metabolic Models (GEMs) describe the interactions between genes, proteins, and the biochemical reactions that underpin an organism's metabolism aiming to computational…
When lookout sees crackle: Anomaly detection via kernel density estimation
Rob J Hyndman, Sevvandi Kandanaarachchi, Katharine Turner
We present an updated version of lookout -- an algorithm for detecting anomalies using kernel density estimates with bandwidth based on Rips death diameters -- with theoretical gua…
Flowette: Flow Matching with Graphette Priors for Graph Generation
Asiri Wijesinghe, Sevvandi Kandanaarachchi, Daniel M. Steinberg +1
We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework that employs a graph neural network-based transform…
Revisiting Pre-processing Group Fairness: A Modular Benchmarking Framework
Brodie Oldfield, Ziqi Xu, Sevvandi Kandanaarachchi
As machine learning systems become increasingly integrated into high-stakes decision-making processes, ensuring fairness in algorithmic outcomes has become a critical concern. Meth…
A pseudo-inverse of a line graph
Sevvandi Kandanaarachchi, Philip Kilby, Cheng Soon Ong
Line graphs are an alternative representation of graphs where each vertex of the original (root) graph becomes an edge. However not all graphs have a corresponding root graph, henc…
Predicting Graph Structure via Adapted Flux Balance Analysis
Sevvandi Kandanaarachchi, Ziqi Xu, Stefan Westerlund +1
Many dynamic processes such as telecommunication and transport networks can be described through discrete time series of graphs. Modelling the dynamics of such time series enables…
Graphon Mixtures
Sevvandi Kandanaarachchi, Cheng Soon Ong
Social networks have a small number of large hubs, and a large number of small dense communities. We propose a generative model that captures both hub and dense structures. Based o…
Fairness Evaluation with Item Response Theory
Ziqi Xu, Sevvandi Kandanaarachchi, Cheng Soon Ong +1
Item Response Theory (IRT) has been widely used in educational psychometrics to assess student ability, as well as the difficulty and discrimination of test questions. In this cont…
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs
Sevvandi Kandanaarachchi, Conrad Sanderson, Rob J. Hyndman
Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existi…
Graphons of Line Graphs
Sevvandi Kandanaarachchi, Cheng Soon Ong
We consider the problem of estimating graph limits, known as graphons, from observations of sequences of sparse finite graphs. In this paper we show a simple method that can shed l…
An Item Response Theory-based R Module for Algorithm Portfolio Analysis
Brodie Oldfield, Sevvandi Kandanaarachchi, Ziqi Xu +1
Experimental evaluation is crucial in AI research, especially for assessing algorithms across diverse tasks. Many studies often evaluate a limited set of algorithms, failing to ful…
Predicting the structure of dynamic graphs
Sevvandi Kandanaarachchi, Ziqi Xu, Stefan Westerlund
Many aspects of graphs have been studied in depth. However, forecasting the structure of a graph at future time steps incorporating unseen, new nodes and edges has not gained much…
DEFT: A new distance-based feature set for keystroke dynamics
Nuwan Kaluarachchi, Sevvandi Kandanaarachchi, Kristen Moore +1
Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboa…
Comprehensive Algorithm Portfolio Evaluation using Item Response Theory
Sevvandi Kandanaarachchi, Kate Smith-Miles
Item Response Theory (IRT) has been proposed within the field of Educational Psychometrics to assess student ability as well as test question difficulty and discrimination power. M…
Comparison of new computational methods for geostatistical modelling of malaria
Spencer Wong, Jennifer A. Flegg, Nick Golding +1
Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatis…
Detection of Anomalous Network Nodes via Hierarchical Prediction and Extreme Value Theory
Sevvandi Kandanaarachchi, Mahdi Abolghasemi, Hideya Ochiai +2
Continuously evolving cyber-attacks against industrial networks reduce the effectiveness of signature-based detection methods. Once malware has infiltrated a network (for example,…
Anomaly detection in dynamic networks
Sevvandi Kandanaarachchi, Rob J Hyndman
Detecting anomalies from a series of temporal networks has many applications, including road accidents in transport networks and suspicious events in social networks. While there a…
Short-term prediction of stream turbidity using surrogate data and a meta-model approach
Bhargav Rele, Caleb Hogan, Sevvandi Kandanaarachchi +1
Many water-quality monitoring programs aim to measure turbidity to help guide effective management of waterways and catchments, yet distributing turbidity sensors throughout networ…
Unsupervised Anomaly Detection Ensembles using Item Response Theory
Sevvandi Kandanaarachchi
Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods is challenging because the class labels or the ground truth is unknown. Thus, traditiona…
Honeyboost: Boosting honeypot performance with data fusion and anomaly detection
Sevvandi Kandanaarachchi, Hideya Ochiai, Asha Rao
With cyber incidents and data breaches becoming increasingly common, being able to predict a cyberattack has never been more crucial. The ability of Network Anomaly Detection Syste…
Singularity formation in axially symmetric mean curvature flow with Neumann boundary
John Head, Sevvandi Kandanaarachchi
We study mean curvature flow of smooth, axially symmetric surfaces in with Neumann boundary data. We show that all singularities at the first singular time must be o…
Predicting Sediment and Nutrient Concentrations in Rivers Using High Frequency Water Quality Surrogates
Catherine Leigh, Sevvandi Kandanaarachchi, James M. McGree +4
A particular focus of water-quality monitoring is the concentrations of sediments and nutrients in rivers, constituents that can smother biota and cause eutrophication. However, th…
A framework for automated anomaly detection in high frequency water-quality data from in situ sensors
Catherine Leigh, Omar Alsibai, Rob J. Hyndman +9
River water-quality monitoring is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values. However, anomalies caused by techni…