All Outputs (85)

BIM solutions for construction lifecycle: a myth or a tangible future? (2018)
Conference Proceeding
PAPADONIKOLAKI, E., LEON, M. and MAHAMADU, A.M. 2018. BIM solutions for construction lifecycle: a myth or a tangible future? In Karlshoj, J. and Scherer, R (eds.) eWork and ebusiness in architecture, engineering and construction: proceedings of the 12th European conference on product and process modelling 2018 (ECPPM 2018), 12-14 September 2018, Copenhagen, Denmark. Boca Raton: CRC Press, pages 321-328. Available from: https://doi.org/10.1201/9780429506215.

Building information Modelling (BIM) lies at the centre of construction industry’s interest nowadays, with a revolutionary impact on the ways that professionals work, collaborate and conduct business. The application of BIM is not as straightforward... Read More about BIM solutions for construction lifecycle: a myth or a tangible future?.

System of systems characterisation assisting security risk assessment. (2018)
Conference Proceeding
KI-ARIES, D., FAILY, S., DOGAN, H. and WILLIAMS, C. 2018. System of systems characterisation assisting security risk assessment. In Proceedings of the 13th IEEE system of systems engineering conference (SoSE 2018), 19-22 June 2018, Paris, France. Piscataway: IEEE [online], pages 485-492. Available from: https://doi.org/10.1109/SYSOSE.2018.8428765

System of Systems (SoS) is a term often used to describe the coming together of independent systems, collaborating to achieve a new or higher purpose. However, clarity is needed when using this term given that operational areas may be unfamiliar with... Read More about System of systems characterisation assisting security risk assessment..

Creative 'tips' to integrate human factors/ergonomics principles and methods with patient safety and quality improvement clinical education. (2018)
Conference Proceeding
VOSPER, H., HIGHNETT, S. and BOWIE, P. 2018. Creative 'tips' to integrate human factors/ergonomics principles and methods with patient safety and quality improvement clinical education. In Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T. and Fujita, Y. (eds.) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018), 26-30 August 2018, Florence, Italy. Advances in intelligent systems and computing, 818. Cham: Springer [online], volume 1: healthcare ergonomics, pages 97-101. Available from: https://doi.org/10.1007/978-3-319-96098-2_14

The goal of these 12 tips is to enhance the effectiveness of safety and improvement work in frontline healthcare practice by providing a framework for integration of Human Factors and Ergonomics (HFE) theory and approaches within undergraduate curric... Read More about Creative 'tips' to integrate human factors/ergonomics principles and methods with patient safety and quality improvement clinical education..

An analysis of pupil concerns regarding transition into higher education. (2018)
Conference Proceeding
ZARB, M. and SIEGEL, A.A. 2018. An analysis of pupil concerns regarding transition into higher education. In Cristea, A.I., Bittencourt, I.I. and Lima, F. (eds.) Revised selected papers from the proceedings of the 1st Higher education for all international workshop on social, semantic, adaptive and gamification techniques and technologies for distance learning (HEFA 2017): from challenges to novel technology-enhanced solutions, 20-24 March 2017, Maceió, Brazil. Communications in computer and information science, 832. Cham: Springer [online], pages 3-16. Available from: https://doi.org/10.1007/978-3-319-97934-2_1

Transitioning to higher education is often a stressful experience, with incoming students facing similar issues year after year. This chapter presents two years of data collection regarding the concerns of Computing secondary school pupils when consi... Read More about An analysis of pupil concerns regarding transition into higher education..

Modelling the generalised median correspondence through an edit distance. (2018)
Conference Proceeding
MORENO-GARCÍA, C.F. and SERRATOSA, F. 2018. Modelling the generalised median correspondence through an edit distance. In Bai, X., Hancock, E.R., Ho, T.K., Wilson, R.C., Biggio, B. and Robles-Kelly, A. (eds.) Structural, syntactic, and statistical pattern recognition: proceedings of the 2018 Joint International Association for Pattern Recognition (IAPR) international workshops on structural and syntactic pattern recognition (SSPR 2018), and statistical techniques in pattern recognition (SPR 2018) (S+SSPR 2018), 17-19 August 2018, Beijing, China. Lecture notes in computer science, 11004. Cham: Springer [online], pages 271-281. Available from: https://doi.org/10.1007/978-3-319-97785-0_26

On the one hand, classification applications modelled by structural pattern recognition, in which elements are represented as strings, trees or graphs, have been used for the last thirty years. In these models, structural distances are modelled as th... Read More about Modelling the generalised median correspondence through an edit distance..

Qualitative adaptation: informing design for risk-based decision-making. (2018)
Conference Proceeding
M'MANGA, A., FAILY, S., MCALANEY, J., WILLIAMS, C., KADOBAYASHI, Y. and MIYAMOTO, D. 2018. Qualitative adaptation: informing design for risk-based decision-making. In Proceedings of the 2nd Workshop on the challenges and opportunities for qualitative data research methods in HCI, co-located with the 32nd International BCS human computer interaction conference (HCI 2018), 3 July 2018, Belfast, UK. Swindon: BCS [online], article number 216. Available from: https://doi.org/10.14236/ewic/HCI2018.216

Research on decision-making during risk and uncertainty facilitates risk-based decision-making, by understanding techniques that decision-makers use to arrive at informed decisions. Approaches to the research usually involve a mix of cognitive techni... Read More about Qualitative adaptation: informing design for risk-based decision-making..

Eliciting persona characteristics for risk-based decision making. (2018)
Conference Proceeding
M'MANGA, A., FAILY, S., MCALANEY, WILLIAMS, C., KADOBAYASHI, Y. and MIYAMOTO, D. 2018. Eliciting persona characteristics for risk-based decision making. In Proceedings of the 32nd International BCS human computer interaction conference (HCI 2018), 4-6 July 2018, Belfast, UK. Swindon: BCS [online], article number 158. Available from: https://doi.org/10.14236/ewic/HCI2018.158

Personas are behavioural specifications of archetypical users in Human Factors Engineering and User Interaction research, aimed at preventing biased views system designers may have of users. Personas are therefore nuanced representations of goals and... Read More about Eliciting persona characteristics for risk-based decision making..

Deep imitation learning with memory for robocup soccer simulation. (2018)
Conference Proceeding
HUSSEIN, A., ELYAN, E. and JAYNE, C. 2018. Deep imitation learning with memory for robocup soccer simulation. In Pimenidis, E. and Jayne, C. (eds.) Proceedings of the 19th International conference on engineering applications of neural networks (EANN 2018), 3-5 September 2018, Bristol, UK. Communications in computer and information science, 893. Cham: Springer [online], pages 31-43. Available from: https://doi.org/10.1007/978-3-319-98204-5_3

Imitation learning is a field that is rapidly gaining attention due to its relevance to many autonomous agent applications. Providing demonstrations of effective behaviour to teach the agent is useful in real world challenges such as sparse rewards a... Read More about Deep imitation learning with memory for robocup soccer simulation..

Toward video tampering exposure: inferring compression parameters from pixels. (2018)
Conference Proceeding
JOHNSTON, P., ELYAN, E. and JAYNE, C. 2018. Toward video tampering exposure: inferring compression parameters from pixels. In Pimenidis, E. and Jayne, C. (eds.) Proceedings of the 19th International conference on engineering applications of neural networks (EANN 2018), 3-5 September 2018, Bristol, UK. Communications in computer and information science, 893. Cham: Springer [online], pages 44-57, Available from: https://doi.org/10.1007/978-3-319-98204-5_4

Video tampering detection remains an open problem in the field of digital media forensics. Some existing methods focus on recompression detection because any changes made to the pixels of a video will require recompression of the complete stream. Rec... Read More about Toward video tampering exposure: inferring compression parameters from pixels..

Matching networks for personalised human activity recognition. (2018)
Conference Proceeding
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2018. Matching networks for personalised human activity recognition. In Bichindaritz, I., Guttmann, C., Herrero, P., Koch, F., Koster, A., Lenz, R., López Ibáñez, B., Marling, C., Martin, C., Montagna, S., Montani, S., Reichert, M., Riaño, D., Schumacher, M.I., ten Teije, A. and Wiratunga, N. (eds.) Proceedings of the 1st Joint workshop on artificial intelligence in health, organized as part of the Federated AI meeting (FAIM 2018), co-located with the 17th International conference on autonomous agents and multiagent systems (AAMAS 2018), the 35th International conference on machine learning (ICML 2018), the 27th International joint conference on artificial intelligence (IJCAI 2018), and the 26th International conference on case-based reasoning (ICCBR 2018), 13-19 July 2018, Stockholm, Sweden. CEUR workshop proceedings, 2142. Aachen: CEUR-WS [online], pages 61-64. Available from: http://ceur-ws.org/Vol-2142/short4.pdf

Human Activity Recognition (HAR) has many important applications in health care which include management of chronic conditions and patient rehabilitation. An important consideration when training HAR models is whether to use training data from a gene... Read More about Matching networks for personalised human activity recognition..

Monitoring health in smart homes using simple sensors. (2018)
Conference Proceeding
MASSIE, S., FORBES, G., CRAW, S., FRASER, L. and HAMILTON, G. 2018. Monitoring health in smart homes using simple sensors. In Bach, K., Bunescu, R., Farri, O., Guo, A., Hasan, S., Ibrahim, Z.M., Marling, C., Raffa, J., Rubin, J. and Wu, H. (eds.) Proceedings of the 3rd International workshop on knowledge discovery in healthcare data (KDH), co-located with the 27th International joint conference on artificial intelligence and the 23rd European conference on artificial intelligence (IJCAI-ECAI 2018), 13 July 2018, Stockholm, Sweden. CEUR workshop proceedings, 2148. Aachen: CEUR-WS [online], pages 33-37. Available from: http://ceur-ws.org/Vol-2148/paper05.pdf

We consider use of an ambient sensor network, installed in Smart Homes, to identify low level events taking place which can then be analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to bot... Read More about Monitoring health in smart homes using simple sensors..

Maintenance of case bases: current algorithms after fifty years. (2018)
Conference Proceeding
JUAREZ, J.M., CRAW, S., LOPEZ-DELGADO, J.R. and CAMPOS, M. 2018. Maintenance of case bases: current algorithms after fifty years. In Lang, J. (ed.) Proceedings of the 27th International joint conference on artificial intelligence and the 23rd European conference on artificial intelligence (IJCAI-ECAI 2018), 13-19 July 2018, Stockholm, Sweden. Freiburg: IJCAI [online], pages 5457-5463. Available from: https://doi.org/10.24963/ijcai.2018/770

Case-Based Reasoning (CBR) learns new knowledge from data and so can cope with changing environments. CBR is very different from modelbased systems since it can learn incrementally as new data is available, storing new cases in its casebase. This mea... Read More about Maintenance of case bases: current algorithms after fifty years..

Reasoning with multi-modal sensor streams for m-health applications. (2018)
Conference Proceeding
WIJEKOON, A. 2018. Reasoning with multi-modal sensor streams for m-health applications. In Minor, M. (ed.) Workshop proceedings for the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Stockholm: ICCBR [online], pages 234-238. Available from: http://iccbr18.com/wp-content/uploads/ICCBR-2018-V3.pdf#page=234

Musculoskeletal Disorders have a long term impact on individuals as well as on the community. They require self-management, typically in the form of maintaining an active lifestyle that adheres to prescribed exercises regimes. In the recent past m-he... Read More about Reasoning with multi-modal sensor streams for m-health applications..

Improving human activity recognition with neural translator models. (2018)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N. and SANI, S. 2018. Improving human activity recognition with neural translator models. In Minor, M. (ed.) Workshop proceedings for the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Stockholm: ICCBR [online], pages 96-100. Available from: http://iccbr18.com/wp-content/uploads/ICCBR-2018-V3.pdf#page=96

Multiple sensor modalities provide more accurate Human Activity Recognition (HAR) compared to using a single modality, yet the latter is more convenient and less intrusive. It is advantages to create a model which learns from all available sensors; a... Read More about Improving human activity recognition with neural translator models..

Few-shot classifier GAN. (2018)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E., SAVOYE, Y. and JAYNE, C. 2018. Few-shot classifier GAN. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489387. Available from: https://doi.org/10.1109/IJCNN.2018.8489387

Fine-grained image classification with a few-shot classifier is a highly challenging open problem at the core of a numerous data labeling applications. In this paper, we present Few-shot Classifier Generative Adversarial Network as an approach for fe... Read More about Few-shot classifier GAN..

Spatial effects of video compression on classification in convolutional neural networks. (2018)
Conference Proceeding
JOHNSTON, P., ELYAN, E. and JAYNE, C. 2018. Spatial effects of video compression on classification in convolutional neural networks. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489370. Available from: https://doi.org/10.1109/IJCNN.2018.8489370

A collection of Computer Vision application reuse pre-learned features to analyse video frame-by-frame. Those features are classically learned by Convolutional Neural Networks (CNN) trained on high quality images. However, available video content is... Read More about Spatial effects of video compression on classification in convolutional neural networks..

Symbols classification in engineering drawings. (2018)
Conference Proceeding
ELYAN, E., MORENO GARCIA, C. and JAYNE, C. 2018. Symbols classification in engineering drawings. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489087. Available from: https://doi.org/10.1109/IJCNN.2018.8489087

Technical drawings are commonly used across different industries such as Oil and Gas, construction, mechanical and other types of engineering. In recent years, the digitization of these drawings is becoming increasingly important. In this paper, we p... Read More about Symbols classification in engineering drawings..

Generic application of deep learning framework for real-time engineering data analysis. (2018)
Conference Proceeding
MAJDANI, F., PETROVSKI, A. and PETROVSKI, S. 2018. Generic application of deep learning framework for real-time engineering data analysis. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489356. Available from: https://doi.org/10.1109/IJCNN.2018.8489356

The need for computer-assisted real-time anomaly detection in engineering data used for condition monitoring is apparent in various applications, including the oil and gas, automotive industries and many other engineering domains. To reduce the relia... Read More about Generic application of deep learning framework for real-time engineering data analysis..

Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry. (2018)
Conference Proceeding
VILELA, M., OLUYEMI, G. and PETROVSKI, A. 2018. Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry. In Proceedings of the 2018 IEEE international conference on fuzzy systems (FUZZ-IEEE 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article ID 8491628. Available from: https://doi.org/10.1109/FUZZ-IEEE.2018.8491628

To manage uncertainty in reservoir development projects, the Value of Information is one of the main factors on which the decision is based to determine whether it is necessary to acquire additional data. However, subsurface data is not always precis... Read More about Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry..

Botnet detection in the Internet of Things using deep learning approaches. (2018)
Conference Proceeding
MCDERMOTT, C.D., MAJDANI, F. and PETROVSKI, A.V. 2018. Botnet detection in the Internet of Things using deep learning approaches. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489489. Available from: https://doi.org/10.1109/IJCNN.2018.8489489

The recent growth of the Internet of Things (IoT) has resulted in a rise in IoT based DDoS attacks. This paper presents a solution to the detection of botnet activity within consumer IoT devices and networks. A novel application of Deep Learning is u... Read More about Botnet detection in the Internet of Things using deep learning approaches..