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All Outputs (58)

Detection of false command and response injection attacks for cyber physical systems security and resilience. (2020)
Conference Proceeding
EKE, H., PETROVSKI, A. and AHRIZ, H. 2020. Detection of false command and response injection attacks for cyber physical systems security and resilience. In Proceedings of the 13th Security of information and networks international conference 2020 (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], article number 10, pages 1-8. Available from: https://doi.org/10.1145/3433174.3433615

The operational cyber-physical system (CPS) state, safety and resource availability is impacted by the safety and security measures in place. This paper focused on i) command injection (CI) attack that alters the system behaviour through injection of... Read More about Detection of false command and response injection attacks for cyber physical systems security and resilience..

Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data. (2020)
Conference Proceeding
MAJDANI, F., BATIK, L., PETROVSKI, A. and PETROVSKI, S. 2020. Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data. In Proceedings of the 13th Security of information and networks international conference 2020 (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], article number 4, pages 1-8. Available from: https://doi.org/10.1145/3433174.3433613

This paper looks at potential vulnerabilities of the Smart Grid energy infrastructure to data injection cyber-attacks and the means of addressing these vulnerabilities through intelligent data analysis. Efforts are being made by multiple groups to pr... Read More about Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data..

Forecasting meteorological analysis using machine learning algorithms. (2020)
Conference Proceeding
PAVULURI, B.L., VEJENDLA, R.S., JITHENDRA, P., DEEPIKA, T. and BANO, S. 2020. Forecasting meteorological analysis using machine learning algorithms. In Proceedings of the 2020 International conference on smart electronics and communication (ICOSEC 2020), 10-12 September 2020, Trichy, India. Piscataway: IEEE [online], pages 456-461. Available from: https://doi.org/10.1109/ICOSEC49089.2020.9215440

Weather prediction is gaining up ubiquity quickly in the current period of Machine learning and Technologies. It is fundamental to foresee the temperature of the climate for quite a while. Decision trees, K-NN, Random Forest algorithms are an integra... Read More about Forecasting meteorological analysis using machine learning algorithms..

Clood CBR: towards microservices oriented case-based reasoning. (2020)
Conference Proceeding
NKISI-ORJI, I., WIRATUNGA, N., PALIHAWADANA, C., RECIO-GARCIA, J.A. and CORSAR, D. 2020. Clood CBR: towards microservices oriented case-based reasoning. In Watson, I and Weber, R. (eds.) Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference]. Lecture notes in computer science, 12311. Cham: Springer [online], pages 129-143. Available from: https://doi.org/10.1007/978-3-030-58342-2_9

CBR applications have been deployed in a wide range of sectors, from pharmaceuticals; to defence and aerospace to IoT and transportation, to poetry and music generation; for example. However, a majority of these have been built using monolithic archi... Read More about Clood CBR: towards microservices oriented case-based reasoning..

Learning to compare with few data for personalised human activity recognition. (2020)
Conference Proceeding
WIRATUNGA, N., WIJEKOON, A. and COOPER, K. 2020. Learning to compare with few data for personalised human activity recognition. In Watson, I and Weber, R. (eds.) Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference]. Lecture notes in computer science, 12311. Cham: Springer [online], pages 3-14. Available from: https://doi.org/10.1007/978-3-030-58342-2_1

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist... Read More about Learning to compare with few data for personalised human activity recognition..

Case-based approach to automated natural language generation for obituaries. (2020)
Conference Proceeding
UPADHYAY, A., MASSIE, S. and CLOGHER, S. 2020. Case-based approach to automated natural language generation for obituaries. In Watson, I. and Weber, R. (eds.) Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference]. Lecture notes in computer science, 12311. Cham: Springer [online], pages 279-294. Available from: https://doi.org/10.1007/978-3-030-58342-2_18

Automated generation of human readable text from structured information is challenging because grammatical rules are complex making good quality outputs difficult to achieve. Textual Case-Based Reasoning provides one approach in which the text from p... Read More about Case-based approach to automated natural language generation for obituaries..

Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. (2020)
Conference Proceeding
ZAVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2020. Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. In Bäck, T., Preuss, M., Deutz, A., Wang, H., Doerr, C., Emmerich, M. and Trautmann, H. (eds.) Parallel problem solving from nature: PPSN XVI: proceedings of the 16th Parallel problem solving from nature international conference (PPSN 2020), 5-9 September 2020, Leiden, The Netherlands. Lecture notes in computer science, 12269. Cham; Springer, part 1, pages 287-300. Available from: https://doi.org/10.1007/978-3-030-58112-1_20

We propose a new class of multi-objective benchmark problems on which we analyse the performance of four well established multi-objective evolutionary algorithms (MOEAs) – each implementing a different search paradigm – by comparing run-time converge... Read More about Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems..

Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. (2020)
Conference Proceeding
MORENO-GARCÍA, C.F., DANG, T., MARTIN, K., PATEL, M., THOMPSON, A., LEISHMAN, L. and WIRATUNGA, N. 2020. Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. In Bach, K., Bunescu, R., Marling, C. and Wiratunga, N. (eds.) Knowledge discovery in healthcare data 2020: proceedings of the 5th Knowledge discovery in healthcare data international workshop 2020 (KDH 2020), co-located with 24th European Artificial intelligence conference (ECAI 2020), 29-30 August 2020, [virtual conference]. CEUR workshop proceedings, 2675. Aachen: CEUR-WS [online], pages 63-70. Available from: http://ceur-ws.org/Vol-2675/paper10.pdf

Fracture detection has been a long-standingparadigm on the medical imaging community. Many algo-rithms and systems have been presented to accurately detectand classify images in terms of the presence and absence offractures in different parts of the... Read More about Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection..

DEFeND DSM: a data scope management service for model-based privacy by design GDPR compliance. (2020)
Conference Proceeding
PIRAS, L., AL-OBEIDALLAH, M.G., PAVLIDIS, M., MOURATIDIS, H., TSOHOU, A., MAGKOS, E., PRAITANO, A., IODICE, A. and CRESPO, B. G.-N. 2020. DEFeND DSM: a data scope management service for model-based privacy by design GDPR compliance. In Gritzalis, S., Weippl, E.R., Kotsis, G., Tjoa, A.M. and Khalil, I. (eds.) Trust, privacy and security in digital business: proceedings of 17th Trust and privacy in digital business international conference 2020 (TrustBus 2020), 14-17 September 2020, Bratislava, Slovakia. Lecture notes in computer science, 12395. Cham: Springer [online], pages 186-201. Available from: https://doi.org/10.1007/978-3-030-58986-8_13

The introduction of the European General Data Protection Regulation (GDPR) has brought significant benefits to citizens, but it has also created challenges for organisations, which are facing with difficulties interpreting it and properly applying it... Read More about DEFeND DSM: a data scope management service for model-based privacy by design GDPR compliance..

Critical analysis of the suitability of surrogate models for finite element method application in catalog-based suspension bushing design. (2020)
Conference Proceeding
CERNUDA, C., LLAVORI, I., ZAVOIANU, A.-C., AGUIRRE, A., ZABALA, A. and PLAZA, J. 2020. Critical analysis of the suitability of surrogate models for finite element method application in catalog-based suspension bushing design. In Proceedings of 25th Institute of Electrical and Electronics Engineers (IEEE) Emerging technologies and factory automation international conference 2020 (ETFA 2020), 8-11 September 2020, Vienna, Austria. Piscataway: IEEE [online], article ID 9212166, pages 829-836. Available from: https://doi.org/10.1109/ETFA46521.2020.9212166

This work presents a critical analysis of the suitability of surrogate models for finite element method application. A case study of a finite element method (FEM) structural problem was selected in order to test the performance of surrogate algorithm... Read More about Critical analysis of the suitability of surrogate models for finite element method application in catalog-based suspension bushing design..

Decentralized combinatorial optimization. (2020)
Conference Proceeding
CHRISTIE, L.A. 2020. Decentralized combinatorial optimization. In Bäck, T., Preuss, M., Deutz, A., Wang, H., Doerr, C., Emmerich, M. and Trautmann, H. (eds.) Parallel problem solving from nature: PPSN XVI: proceedings of the 16th Parallel problem solving from nature international conference (PPSN 2020), 5-9 September 2020, Leiden, Netherlands. Theoretical computer science and general issues, 12269. Cham; Springer, pages 360-372. Available from: https://doi.org/10.1007/978-3-030-58112-1_25

Combinatorial optimization is a widely-studied class of computational problems with many theoretical and real-world applications. Optimization problems are typically tackled using hardware and software controlled by the user. Optimization can be comp... Read More about Decentralized combinatorial optimization..

WEC: weighted ensemble of text classifiers. (2020)
Conference Proceeding
UPADHYAY, A., NGUYEN, T.T., MASSIE, S. and MCCALL, J. 2020. WEC: weighted ensemble of text classifiers. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2020), part of the 2020 (IEEE) World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 International joint conference on neural networks (IJCNN 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, Glasgow, UK [virtual conference]. Piscataway: IEEE [online], article ID 9185641. Available from: https://doi.org/10.1109/CEC48606.2020.9185641

Text classification is one of the most important tasks in the field of Natural Language Processing. There are many approaches that focus on two main aspects: generating an effective representation; and selecting and refining algorithms to build the c... Read More about WEC: weighted ensemble of text classifiers..

Racing strategy for the dynamic-customer location-allocation problem. (2020)
Conference Proceeding
ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A., CONWAY, A. and OWUSU, G. 2020. Racing strategy for the dynamic-customer location-allocation problem. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2020), part of the 2020 (IEEE) World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 International joint conference on neural networks (IJCNN 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, Glasgow, UK [virtual conference]. Piscataway: IEEE [online], article 9185918. Available from: https://doi.org/10.1109/CEC48606.2020.9185918

In previous work, we proposed and studied a new dynamic formulation of the Location-allocation (LA) problem called the Dynamic-Customer Location-allocation (DC-LA) prob­lem. DC-LA is based on the idea of changes in customer distribution over a define... Read More about Racing strategy for the dynamic-customer location-allocation problem..

Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. (2020)
Conference Proceeding
MORENO-GARCÍA, C.F., JOHNSTON, P. and GARKUWA, B. 2020. Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207479. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207479

One of the key features of most document image digitisation systems is the capability of discerning between the main components of the printed representation at hand. In the case of engineering drawings, such as circuit diagrams, telephone exchanges... Read More about Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks..

Locality sensitive batch selection for triplet networks. (2020)
Conference Proceeding
MARTIN, K., WIRATUNGA, N. and SANI, S. 2020. Locality sensitive batch selection for triplet networks. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207538. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207538

Triplet networks are deep metric learners which learn to optimise a feature space using similarity knowledge gained from training on triplets of data simultaneously. The architecture relies on the triplet loss function to optimise its weights based u... Read More about Locality sensitive batch selection for triplet networks..

Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition. (2020)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N. and COOPER, K. 2020. Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9206941. Available from: https://doi.org/10.1109/IJCNN48605.2020.9206941

Exercise adherence is a key component of digital behaviour change interventions for the self-management of musculoskeletal pain. Automated monitoring of exercise adherence requires sensors that can capture patients performing exercises and Machine Le... Read More about Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition..

Representing temporal dependencies in smart home activity recognition for health monitoring. (2020)
Conference Proceeding
FORBES, G., MASSIE, S., CRAW, S., FRASER, L. and HAMILTON, G. 2020. Representing temporal dependencies in smart home activity recognition for health monitoring. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207480. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207480

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which m... Read More about Representing temporal dependencies in smart home activity recognition for health monitoring..

Deep learning for text detection and recognition in complex engineering diagrams. (2020)
Conference Proceeding
JAMIESON, L, MORENO-GARCIA, C.F. and ELYAN, E. 2020. Deep learning for text detection and recognition in complex engineering diagrams. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207127. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207127

Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper... Read More about Deep learning for text detection and recognition in complex engineering diagrams..

Evolved ensemble of detectors for gross error detection. (2020)
Conference Proceeding
NGUYEN, T.T., MCCALL, J., WILSON, A., OCHEI, L., CORBETT, H. and STOCKTON, P. 2020. Evolved ensemble of detectors for gross error detection. In GECCO '20: proceedings of the Genetic and evolutionary computation conference companion (GECCO 2020), 8-12 July 2020, Cancún, Mexico. New York: ACM [online], pages 281-282. Available from: https://doi.org/10.1145/3377929.3389906

In this study, we evolve an ensemble of detectors to check the presence of gross systematic errors on measurement data. We use the Fisher method to combine the output of different detectors and then test the hypothesis about the presence of gross err... Read More about Evolved ensemble of detectors for gross error detection..

ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks. (2020)
Conference Proceeding
HAJAR, M.S., AL-KADRI, M.O. and KALUTARAGE, H. 2020. ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) Communications and network security conference (CNS 2020), 29 June - 1 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9162325. Available from: https://doi.org/10.1109/CNS48642.2020.9162325

Wireless Medical Sensor Networks (WMSN) will play a significant role in the advancements of modern healthcare applications. Security concerns are still the main obstacle to the widespread adoption of this technology. Conventional security approaches,... Read More about ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks..