All Outputs (115)

Crowdsourced quality assessment of enhanced underwater images: a pilot study. (2022)
Conference Proceeding
LIN, H., MEN, H., YAN, Y., REN, J. and SAUPE, D. 2022. Crowdsourced quality assessment of enhanced underwater images: a pilot study. In Proceedings of 14th International conference on quality of multimedia experience 2022 (QoMEX 2022), 5-7 September 2022, Lippstadt, Germany. Piscataway: IEEE [online], article 9900904. Available from: https://doi.org/10.1109/QoMEX55416.2022.9900904

Underwater image enhancement (UIE) is essential for a high-quality underwater optical imaging system. While a number of UIE algorithms have been proposed in recent years, there is little study on image quality assessment (IQA) of enhanced underwater... Read More about Crowdsourced quality assessment of enhanced underwater images: a pilot study..

Deep learning based short-term total cloud cover forecasting. (2022)
Conference Proceeding
BANDARA, I., ZHANG, L. and MISTRY, K. 2022. Deep learning based short-term total cloud cover forecasting. In Proceedings of the 2022 International joint conference on neural networks (IJCNN 2022), co-located with the 2022 conference proceedings of Institute of Electrical and Electronics Engineers (IEEE) World congress on computational intelligence (IEEE WCCI 2022), 18-23 July 2022, Padua, Italy. Piscataway: IEEE [online], article 9892773. Available from: https://doi.org/10.1109/IJCNN55064.2022.9892773

In this research, we conduct deep learning based Total Cloud Cover (TCC) forecasting using satellite images. The proposed system employs the Otsu's method for cloud segmentation and Long Short-Term Memory (LSTM) variant models for TCC prediction. Spe... Read More about Deep learning based short-term total cloud cover forecasting..

Obesity and its association with mental health among Mexican children and adolescents: systematic review. (2022)
Journal Article
GODINA-FLORES, N.L., GUTIERREZ-GÓMEZ, Y.Y., GARCÍA-BOTELLO, M., LÓPEZ-CRUZ, L., MORENO-GARCÍA, C.F. and ACEVES-MARTINS, M. 2023. Obesity and its association with mental health among Mexican children and adolescents: systematic review. Nutrition reviews [online], 81(6). pages 658-669. Available from: https://doi.org/10.1093/nutrit/nuac083

Obesity and mental health issues increasingly affect children and adolescents, but whether obesity is a risk factor for mental health issues is unclear. To systematically review the association between obesity and mental health issues (ie, anxiety a... Read More about Obesity and its association with mental health among Mexican children and adolescents: systematic review..

Bayesian gravitation-based classification for hyperspectral images. (2022)
Journal Article
ZHANG, A., SUN, G., PAN, Z., REN, J., JIA, X., ZHANG, C., FU, H. and YAO, Y. 2022. Bayesian gravitation-based classification for hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 60, article 5542714. Available from: https://doi.org/10.1109/TGRS.2022.3203488

Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between... Read More about Bayesian gravitation-based classification for hyperspectral images..

Multiscale voting mechanism for rice leaf disease recognition under natural field conditions. (2022)
Journal Article
TANG, Y., ZHAO, J., HUANG, H., ZHUANG, J., TAN, Z., HOU, C., CHEN, W. and REN, J. 2022. Multiscale voting mechanism for rice leaf disease recognition under natural field conditions. International journal of intelligent systems [online], 37(12), pages 12169-12191. Available from: https://doi.org/10.1002/int.23081

Rice leaf disease (RLD) is one of the major factors that cause the decline in production, and the automatic recognition of such diseases under natural field conditions is of great significance for timely targeted rice management. Although many machin... Read More about Multiscale voting mechanism for rice leaf disease recognition under natural field conditions..

Addressing trust and mutability issues in XAI utilising case based reasoning. (2022)
Conference Proceeding
SALIMI, P. 2022. Addressing trust and mutability issues in XAI utilising case based reasoning. In Reuss, P. and Schönborn, J. (eds.) Proceedings of the 30th Doctoral consortium of the international conference on case-based reasoning (ICCBR-DC 2022), co-located with the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3418. Aachen: CEUR-WS [online], pages 22-27. Available from: https://ceur-ws.org/Vol-3418/ICCBR_2022_DC_paper19.pdf

Explainable AI (XAI) research is required to ensure that explanations are human readable and understandable. The present XAI approaches are useful for observing and comprehending some of the most important underlying properties of any Black-box AI mo... Read More about Addressing trust and mutability issues in XAI utilising case based reasoning..

Explaining and upsampling anomalies in time-series sensor data. (2022)
Conference Proceeding
PIRIE, C. 2022. Explaining and upsampling anomalies in time-series sensor data. In Reuss, P. and Schönborn, J. (eds.) Proceedings of the 30th Doctoral consortium of the international conference on case-based reasoning (ICCBR-DC 2022), co-located with the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3418. Aachen: CEUR-WS [online], pages 16-21. Available from: https://ceur-ws.org/Vol-3418/ICCBR_2022_DC_paper13.pdf

The aims of this research was to improve anomaly detection methods in multi-sensor data by extending current re-sampling and explanation methods to work in a time-series setting. While there is a plethora of literature surrounding XAI for tabular dat... Read More about Explaining and upsampling anomalies in time-series sensor data..

CBR for interpretable response selection in conversational modelling. (2022)
Conference Proceeding
SURESH, M. 2022. CBR for interpretable response selection in conversational modelling. In Reuss, P. and Schönborn, J. (eds.) Proceedings of the 30th Doctoral consortium of the international conference on case-based reasoning (ICCBR-DC 2022), co-located with the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3418. Aachen: CEUR-WS [online], pages 28-33. Available from: https://ceur-ws.org/Vol-3418/ICCBR_2022_DC_paper25.pdf

Current state-of-the-art dialogue systems are increasingly complex. When used in applications such as motivational interviewing, the lack of interpretability is a concern. CBR offers to bridge this gap by using the most similar past cases to decide t... Read More about CBR for interpretable response selection in conversational modelling..

Cross domain evaluation of text detection models. (2022)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E., MORENO-GARCÍA, C. and JAYNE, C. 2022. Cross domain evaluation of text detection models. In Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A. and Aydin, M. (eds.) Artificial neural networks and machine learning - ICANN 2022: proceedings of the 31st International conference on artificial neural networks (ICANN 2022), 6-9 September 2022, Bristol, UK, part III. Lecture notes in computer science, 13531. Cham: Springer [online], pages 50-61. Available from: https://doi.org/10.1007/978-3-031-15934-3_5

Text detection is a very common task across a wide range of domains, such as document image analysis, remote identity verification, amongst others. It is also considered an integral component of any text recognition system, where the performance of r... Read More about Cross domain evaluation of text detection models..

Reconsidering RepStat rules in dialectic games. (2022)
Conference Proceeding
WELLS, S. and SNAITH, M. 2022. Reconsidering RepStat rules in dialectical games. In Grasso, F., Green, N.L., Schneider, J. and Wells, S. (eds.) Proceedings of the 22nd Workshop on computational models of natural argument (CMNA 2022), 12 September 2022, Cardiff, UK. CEUR workshop proceedings, 3205. Aachen: CEUR-WS [online], pages 18-28. Available from: http://ceur-ws.org/Vol-3205/paper3.pdf

Prohibition of repeated statements has benefits for the tractability and predictability of dialogues carried out by machines, but doesn't match the real world behaviour of people. This gap between human and machine behaviour leads to problems when fo... Read More about Reconsidering RepStat rules in dialectic games..

Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset] (2022)
Dataset
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PIRAS, L. and PETROVSKI, A. 2023. Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models [Dataset]. Hosted on GitHub (online). Available from: https://github.com/softwaresec-labs/LVDAndro

Many of the Android apps get published without appropriate security considerations, possibly due to not verifying code or not identifying vulnerabilities at the early stages of development. This can be overcome by using an AI based model trained on a... Read More about Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset].

DRL-RNP: deep reinforcement learning-based optimized RNP flight procedure execution. (2022)
Journal Article
ZHU, L., WANG, J., WANG, Y., JI, Y. and REN, J. 2022. DRL-RNP: deep reinforcement learning-based optimized RNP flight procedure execution. Sensors [online], 22(17), article 6475. Available from: https://doi.org/10.3390/s22176475

The required navigation performance (RNP) procedure is one of the two basic navigation specifications for the performance-based navigation (PBN) procedure as proposed by the International Civil Aviation Organization (ICAO) through an integration of t... Read More about DRL-RNP: deep reinforcement learning-based optimized RNP flight procedure execution..

An approach to emotion recognition using brain rhythm sequencing and asymmetric features. (2022)
Journal Article
LI, J.W., CHEN, R.J., BARMA, S., CHEN, F., PUN, S.H., MAK, P.U., WANG, L.J., ZENG, X.X., REN, J.C. and ZHAO, H.M. 2022. An approach to emotion recognition using brain rhythm sequencing and asymmetric features. Cognitive computation [online], 14(6), pages 2260-2273. Available from: https://doi.org/10.1007/s12559-022-10053-z

Emotion can be influenced during self-isolation, and to avoid severe mood swings, emotional regulation is meaningful. To achieve this, efficiently recognizing emotion is a vital step, which can be realized by electroencephalography signals. Previousl... Read More about An approach to emotion recognition using brain rhythm sequencing and asymmetric features..

Adapting semantic similarity methods for case-based reasoning in the Cloud. (2022)
Conference Proceeding
NKISI-ORJI, I., PALIHAWADANA, C., WIRATUNGA, N., CORSAR, D. and WIJEKOON, A. 2022. Adapting semantic similarity methods for case-based reasoning in the Cloud. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 125-139. Available from: https://doi.org/10.1007/978-3-031-14923-8_9

CLOOD is a cloud-based CBR framework based on a microservices architecture, which facilitates the design and deployment of case-based reasoning applications of various sizes. This paper presents advances to the similarity module of CLOOD through the... Read More about Adapting semantic similarity methods for case-based reasoning in the Cloud..

How close is too close? Role of feature attributions in discovering counterfactual explanations. (2022)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., NKISI-ORJI, I., PALIHAWADANA, C., CORSAR, D. and MARTIN, K. 2022. How close is too close? Role of feature attributions in discovering counterfactual explanations. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 33-47. Available from: https://doi.org/10.1007/978-3-031-14923-8_3

Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, counterfactuals are "actionable" explanations that help users to understand how model decisions can be changed by adapting features of an input. A cas... Read More about How close is too close? Role of feature attributions in discovering counterfactual explanations..

A case-based approach for content planning in data-to-text generation. (2022)
Conference Proceeding
UPADHYAY, A. and MASSIE, S. 2022. A case-based approach for content planning in data-to-text generation. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 380-394. Available from: https://doi.org/10.1007/978-3-031-14923-8_25

The problem of Data-to-Text Generation (D2T) is usually solved using a modular approach by breaking the generation process into some variant of planning and realisation phases. Traditional methods have been very good at producing high quality texts b... Read More about A case-based approach for content planning in data-to-text generation..

Analysing the fitness landscape rotation for combinatorial optimisation. (2022)
Conference Proceeding
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2022. Analysing the fitness landscape rotation for combinatorial optimisation. In Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G. and Tušar, T. (eds.) Parallel problem solving from nature (PPSN XVII): proceedings of 17th Parallel problem solving from nature international conference 2022 (PPSN 2022), 10-14 September 2022, Dortmund, Germany. Lecture notes in computer science, 13398. Cham: Springer [online], pages 533-547. Available from: https://doi.org/10.1007/978-3-031-14714-2_37

Fitness landscape rotation has been widely used in the field of dynamic combinatorial optimisation to generate test problems with academic purposes. This method changes the mapping between solutions and objective values, but preserves the structure o... Read More about Analysing the fitness landscape rotation for combinatorial optimisation..

Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022). (2022)
Conference Proceeding
KEANE, M.T. and WIRATUNGA, N. (eds.) 2022. Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online]. Available from: https://doi.org/10.1007/978-3-031-14923-8

This volume contains the papers presented at the 30th International Conference on Case-Based Reasoning (ICCBR 2022), which was held during September 12–15, 2022, at LORIA in Nancy, France. ICCBR is the premier annual meeting of the Case-Based Reasoni... Read More about Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022)..

Framework for detecting APTs based on steps analysis and correlation. (2022)
Book Chapter
EKE, H.N., PETROVSKI, A., AHRIZ, H. and AL-KADRI, M.O. 2022. Framework for detecting APTs based on steps analysis and correlation. In Abbaszadeh, M. and Zemouche, A. (eds.) Security and resilience in cyber-physical systems: detection, estimation and control. Cham: Springer [online], chapter 6, pages 119-147. Available from: https://doi.org/10.1007/978-3-030-97166-3_6

An advanced persistent threatAdvanced persistent threat, (APTAPT), is an attack that uses multiple attack behavior to penetrate a system, achieve specifically targeted and highly valuable goals within a system. This type of attack has presented an in... Read More about Framework for detecting APTs based on steps analysis and correlation..

MIRATAR: a virtual caregiver for active and healthy ageing. (2022)
Conference Proceeding
SANTOFIMIA, M.J., VILLANUEVA, F.J., DORADO, J., RUBIO, A., FERNÁNDEZ-BERMEJO, J., LLUMIGUANO, H., DEL TORO, X., WIRATUNGA, N. and LOPEZ, J.C. 2022. MIRATAR: a virtual caregiver for active and healthy ageing. In Mazzeo, P.L., Frontoni, E., Sclaroff, S. and Distante, C. (eds.) Image analysis and processing: ICIAP 2022 workshops; revised selected papers from the proceedings of the 21st International conference on image analysis and processing (ICIAP 2022) international workshops, 23-27 May 2022, Lecce, Italy, part I. Lecture notes in computer science, 13373. Cham: Springer [online], pages 49-58. Available from: https://doi.org/10.1007/978-3-031-13321-3_5

Despite the technology advances in the field of virtual assistant and activity monitoring devices, older adults are still reluctant to embrace this technology, specially when it comes to employ it to manage health-related issues. This paper presents... Read More about MIRATAR: a virtual caregiver for active and healthy ageing..