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141 results on '"Arosha K. Bandara"'

1. Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers

Author

Mohamed Bennasar, Blaine A. Price, Daniel Gooch, Arosha K. Bandara, and Bashar Nuseibeh

Subjects
tri-axial accelerometer, human activity recognition, feature selection, activity of daily living, Physical Activity, classification, Chemical technology, TP1-1185
Abstract

Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the signal features that have significant discriminative power between different human activities. It also investigates the effect of sensor placement location, the sampling frequency, and activity complexity on the selected features. A comprehensive list of 193 signal features has been extracted from accelerometer signals of four publicly available datasets, including features that have never been used before for activity recognition. Feature significance was measured using the Joint Mutual Information Maximisation (JMIM) method. Common significant features among all the datasets were identified. The results show that the sensor placement location does not significantly affect recognition performance, nor does it affect the significant sub-set of features. The results also showed that with high sampling frequency, features related to signal repeatability and regularity show high discriminative power.

Published
2022
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2. LiveBox: A Self-Adaptive Forensic-Ready Service for Drones

Author

Yijun Yu, Danny Barthaud, Blaine A. Price, Arosha K. Bandara, Andrea Zisman, and Bashar Nuseibeh

Subjects
Unmanned aerial vehicles (Drones), software engineering, self-adaptive systems, forensic readiness, flight data recorders, simulators, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly expected to operate in spaces populated by humans while avoiding injury to people or damaging property. However, incidents and accidents can, and increasingly do, happen. Traditional investigations of aircraft incidents require on-board flight data recorders (FDRs); however, these physical FDRs only work if the drone can be recovered. A further complication is that physical FDRs are too heavy to mount on light drones, hence not suitable for forensic digital investigations of drone flights. In this paper, we propose a self-adaptive software architecture, LiveBox, to make drones both forensic-ready and regulation compliant. We studied the feasibility of using distributed technologies for implementing the LiveBox reference architecture. In particular, we found that updates and queries of drone flight data and constraints can be treated as transactions using decentralised ledger technology (DLT), rather than a generic time-series database, to satisfy forensic tamper-proof requirements. However, DLTs such as Ethereum, have limits on throughput (i.e. transactions-per-second), making it harder to achieve regulation-compliance at runtime. To overcome this limitation, we present a self-adaptive reporting algorithm to dynamically reduce the precision of flight data without sacrificing the accuracy of runtime verification. Using a real-life scenario of drone delivery, we show that our proposed algorithm achieves a 46% reduction in bandwidth without losing accuracy in satisfying both tamper-proof and regulation-compliant requirements.

Published
2019
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3. Digital Detectives: Websleuthing Reduces Eyewitness Identification Accuracy in Police Lineups

Author

Camilla Elphick, Richard Philpot, Min Zhang, Avelie Stuart, Graham Pike, Ailsa Strathie, Catriona Havard, Zoe Walkington, Lara A. Frumkin, Mark Levine, Blaine A. Price, Arosha K. Bandara, and Bashar Nuseibeh

Subjects
digital detective, websleuth, eyewitness, lineup identification, police lineup, social media, Psychology, BF1-990
Abstract

Eyewitnesses to crimes sometimes search for a culprit on social media before viewing a police lineup, but it is not known whether this affects subsequent lineup identification accuracy. The present online study was conducted to address this. Two hundred and eighty-five participants viewed a mock crime video, and after a 15–20 min delay either (i) viewed a mock social media site including the culprit, (ii) viewed a mock social media site including a lookalike, or (iii) completed a filler task. A week later, participants made an identification from a photo lineup. It was predicted that searching for a culprit on social media containing the lookalike (rather than the culprit) would reduce lineup identification accuracy. There was a significant association between social media exposure and lineup accuracy for the Target Present lineup (30% more of the participants who saw the lookalike on social media failed to positively identify the culprit than participants in the other conditions), but for the Target Absent lineup (which also included the lookalike) there was no significant association with lineup identification accuracy. The results suggest that if an eyewitness sees a lookalike (where they are expecting to see the culprit) when conducting a self-directed search on social media, they are less likely to subsequently identify the culprit in the formal ID procedure.

Published
2021
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