DEFT: A new distance-based feature set for keystroke dynamics

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 keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person's typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices.Comment: 12 pages, 5 figures, 3 tables, conference pape

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors

River water-quality monitoring is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values. However, anomalies caused by technical issues confound these data, while the volume and velocity of data prevent manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data. After identifying end-user needs and defining anomalies, we ranked their importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, using other water-quality variables as covariates reduced performance due to complex relationships among variables. Classification of drift and periods of anomalously low or high variability improved when we applied replaced anomalous measurements with forecasts, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies, but were also less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, all feature-based methods produced low false positive rates, but did not and require training or optimization. Rule-based methods successfully detected impossible values and missing observations. Thus, we recommend using a combination of methods to improve anomaly detection performance, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and analysts for optimal outcomes with respect to both detection performance and end-user needs. Our framework is applicable to other types of high frequency time-series data and anomaly detection applications

Axially symmetric volume preserving mean curvature flow

In this thesis we study two problems of axially symmetric volume preserving mean curvature flow. We study the singularities of the flow for the major part of the thesis. We consider an axially symmetric hypersurface contained between the two parallel planes, meeting the planes at right angles along its boundary. We study the first singularity that develop in this flow. We prove that given a certain lower height bound on the boundary of a specific region, the first singularity is of type I. As part of the methodology used in this thesis we also derive extension theorems: we prove that no singularities can develop during a finite time interval, if the mean curvature is bounded within that time interval on the entire hypersurface. Finally we include new convergence results: Assuming the surface is not pinching off along the axis at any time during the flow, and without any additional conditions, as for example on the curvature, we prove that it converges to a hemisphere, when the hypersurface has a free boundary and satisfies Neumann boundary data, and to a sphere when it is compact without boundary

Axially symmetric volume preserving mean curvature flow

In this thesis we study two problems of axially symmetric volume preserving mean curvature flow. We study the singularities of the flow for the major part of the thesis. We consider an axially symmetric hypersurface contained between the two parallel planes, meeting the planes at right angles along its boundary. We study the first singularity that develop in this flow. We prove that given a certain lower height bound on the boundary of a specific region, the first singularity is of type I. As part of the methodology used in this thesis we also derive extension theorems: we prove that no singularities can develop during a finite time interval, if the mean curvature is bounded within that time interval on the entire hypersurface. Finally we include new convergence results: Assuming the surface is not pinching off along the axis at any time during the flow, and without any additional conditions, as for example on the curvature, we prove that it converges to a hemisphere, when the hypersurface has a free boundary and satisfies Neumann boundary data, and to a sphere when it is compact without boundary

Early classification of spatio-temporal events using partial information

This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event extraction algorithm and an early event classification algorithm. We apply this framework to synthetic and real problems and demonstrate its reliability and broad applicability. The algorithms and data are available in the R package eventstream, and other code in the supplementary material