Home About Publications Research Teaching Consultancy Peer Esteem
JabRef references
Matching entries: 0
settings...
AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
Mitchell, A., Mazhar, M.U., Ayesh, A., Lemon, M. and Painter, B. Pedagogy for the City as an Emergent Cognitive System for Sustainability 2022 Sustainability Letters
Vol. 1(1) 
article URL 
BibTeX:
@article{Mitchell2022,
  author = {Mitchell, Andrew and Mazhar, Muhammad U. and Ayesh, Aladdin and Lemon, Mark and Painter, Birgit},
  title = {Pedagogy for the City as an Emergent Cognitive System for Sustainability},
  journal = {Sustainability Letters},
  year = {2022},
  volume = {1},
  number = {1},
  url = {https://www.sustainabilityletters.net/SL/article/view/890}
}
Wang, R., Chen, L. and Ayesh, A. Multimodal motivation modelling and computing towards motivationally intelligent E-learning systems 2022 CCF Transactions on Pervasive Computing and Interaction  article DOI URL 
Abstract: Motivation to engage in learning is essential for learning performance. Learners’ motivation is traditionally assessed using self-reported data, which is time-consuming, subjective, and interruptive to their learning process. To address this issue, this paper proposes a novel framework for multimodal assessment of learners’ motivation in e-learning environments with the ultimate purpose of supporting intelligent e-learning systems to facilitate dynamic, context-aware, and personalized services or interventions, thus sustaining learners’ motivation for learning engagement. We investigated the performance of the machine learning classifier and the most and least accurately predicted motivational factors. We also assessed the contribution of different electroencephalogram (EEG) and eye gaze features to motivation assessment. The applicability of the framework was evaluated in an empirical study in which we combined eye tracking and EEG sensors to produce a multimodal dataset. The dataset was then processed and used to develop a machine learning classifier for motivation assessment by predicting the levels of a range of motivational factors, which represented the multiple dimensions of motivation. We also proposed a novel approach to feature selection combining data-driven and knowledge-driven methods to train the machine learning classifier for motivation assessment, which has been proved effective in our empirical study at selecting predictors from a large number of extracted features from EEG and eye tracking data. Our study has revealed valuable insights for the role played by brain activities and eye movements on predicting the levels of different motivational factors. Initial results using logistic regression classifier have achieved significant predictive power for all the motivational factors studied, with accuracy of between 68.1% and 92.8%. The present work has demonstrated the applicability of the proposed framework for multimodal motivation assessment which will inspire future research towards motivationally intelligent e-learning systems.
BibTeX:
@article{Wang2022,
  author = {Wang, Ruijie and Chen, Liming and Ayesh, Aladdin},
  title = {Multimodal motivation modelling and computing towards motivationally intelligent E-learning systems},
  journal = {CCF Transactions on Pervasive Computing and Interaction},
  year = {2022},
  url = {https://doi.org/10.1007/s42486-022-00107-4},
  doi = {https://doi.org/10.1007/s42486-022-00107-4}
}
Mohammed, K., Ayesh, A. and Boiten, E. Complementing Privacy and Utility Trade-Off with Self-Organising Maps 2021 Cryptography
Vol. 5(3) 
article DOI URL 
Abstract: In recent years, data-enabled technologies have intensified the rate and scale at which organisations collect and analyse data. Data mining techniques are applied to realise the full potential of large-scale data analysis. These techniques are highly efficient in sifting through big data to extract hidden knowledge and assist evidence-based decisions, offering significant benefits to their adopters. However, this capability is constrained by important legal, ethical and reputational concerns. These concerns arise because they can be exploited to allow inferences to be made on sensitive data, thus posing severe threats to individuals’ privacy. Studies have shown Privacy-Preserving Data Mining (PPDM) can adequately address this privacy risk and permit knowledge extraction in mining processes. Several published works in this area have utilised clustering techniques to enforce anonymisation models on private data, which work by grouping the data into clusters using a quality measure and generalising the data in each group separately to achieve an anonymisation threshold. However, existing approaches do not work well with high-dimensional data, since it is difficult to develop good groupings without incurring excessive information loss. Our work aims to complement this balancing act by optimising utility in PPDM processes. To illustrate this, we propose a hybrid approach, that combines self-organising maps with conventional privacy-based clustering algorithms. We demonstrate through experimental evaluation, that results from our approach produce more utility for data mining tasks and outperforms conventional privacy-based clustering algorithms. This approach can significantly enable large-scale analysis of data in a privacy-preserving and trustworthy manner.
BibTeX:
@article{Mohammed2021,
  author = {Mohammed, Kabiru and Ayesh, Aladdin and Boiten, Eerke},
  title = {Complementing Privacy and Utility Trade-Off with Self-Organising Maps},
  journal = {Cryptography},
  year = {2021},
  volume = {5},
  number = {3},
  url = {https://www.mdpi.com/2410-387X/5/3/20},
  doi = {https://doi.org/10.3390/cryptography5030020}
}
Schiff, D., Rakova, B., Ayesh, A., Fanti, A. and Lennon, M. Explaining the Principles to Practices Gap in AI 2021 IEEE Technology and Society Magazine
Vol. 40(2), pp. 81-94 
article DOI  
BibTeX:
@article{Schiff2021,
  author = {Schiff, Daniel and Rakova, Bogdana and Ayesh, Aladdin and Fanti, Anat and Lennon, Michael},
  title = {Explaining the Principles to Practices Gap in AI},
  journal = {IEEE Technology and Society Magazine},
  year = {2021},
  volume = {40},
  number = {2},
  pages = {81-94},
  doi = {https://doi.org/10.1109/MTS.2021.3056286}
}
Triboan, D., Obonyo, E.A., Ayesh, A., Yerima, S.Y., Basak, B., Wang’Ombe, W., Olago, D., Olaka, L.A., Sznajder, K.K. and Madivate, C. A Transdisciplinary Framework for AI-driven Disaster Risk Reduction for Low-income Housing Communities in Kenya 2021 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 188-193  inproceedings DOI  
Abstract: In the past 50 years, natural disasters worldwide have accounted for 2.06 million deaths and US$3640 billion in economic losses. These natural disasters are heavily influenced by the composite earth system processes and human interactions. In this paper, we focus our investigation to assess the impact of flooding in rivers and coastal regions and its impact on low-income communities. For this purpose, a transdisciplinary perspective from Artificial Intelligence (AI), Climate science, Socio-economics discipline is leveraged to map and identify their inter-relationships and challenges using Soft System Methodology (SSM). A transdisciplinary framework, named ADRELO1 Disaster Support System (ADSS), is therefore proposed to (1) identify the key parameters that can influence climate change, (2) stitch together a reusable multilayered transdisciplinary knowledge model, and (3) apply the observed multivariant data to AI-based algorithm to forecast climate change, analyze the impact of climate change on socio-economic outcomes and suggest potential disaster risk reduction actions. Research-based outcomes, from the given framework, will be used for policy prescription towards making flood-affected local communities self-resilient. ADSS will be applied first in a flood-prone region, such as Nyando in Kenya and Mozambique. It will then be extrapolated in other coastal regions of Florida and North-eastern Brazil to examine the applicability of the framework.
BibTeX:
@inproceedings{Triboan2021,
  author = {Triboan, Darpan and Obonyo, Esther A. and Ayesh, Aladdin and Yerima, Suleiman Y. and Basak, Barnali and Wang’Ombe, Wangari and Olago, Daniel and Olaka, Lydia A. and Sznajder, Kristin K. and Madivate, Carvalho},
  title = {A Transdisciplinary Framework for AI-driven Disaster Risk Reduction for Low-income Housing Communities in Kenya},
  booktitle = {2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  year = {2021},
  pages = {188-193},
  doi = {https://doi.org/10.1109/SMC52423.2021.9658957}
}
Zapala, D., Hossaini, A., Kianpour, M., Sahonero-Alvarez, G. and Ayesh, A. A functional BCI model by the P2731 working group: psychology 2021 Brain-Computer Interfaces
Vol. 0(0), pp. 1-10 
article DOI URL 
Abstract: ABSTRACTThe development of Brain-Computer Interfaces (BCI) gathers experts and specialists in various fields, such as engineering, computer science, medicine, or cognitive neuroscience. Each of these disciplines has specific terminology, which makes mutual understanding and research collaboration difficult. The IEEE P2731 working group aims to improve communication between BCI researchers by developing a functional model and standards for terminology that can be used as a common description framework for all the involved knowledge fields. This work focuses on the vocabulary of mental processes involved in BCI communication and describes their role in a Functional Model that considers their influence on BCI performance. Finally, it presents potential uses of the proposed model.
BibTeX:
@article{Zapala2021,
  author = {Dariusz Zapala and Ali Hossaini and Mazaher Kianpour and Guillermo Sahonero-Alvarez and Aladdin Ayesh},
  title = {A functional BCI model by the P2731 working group: psychology},
  journal = {Brain-Computer Interfaces},
  publisher = {Taylor & Francis},
  year = {2021},
  volume = {0},
  number = {0},
  pages = {1-10},
  url = {https://doi.org/10.1080/2326263X.2021.1935124},
  doi = {https://doi.org/10.1080/2326263X.2021.1935124}
}
Ishola, O., Boiten, E.A., Ayesh, A. and Albakri, A. Recognising Re-identification Attacks on Databases, by Interpreting them as SQL Queries: A Technical Study. 2020 Privacy in Statistical Databases 2020 (PSD2020)  inproceedings  
BibTeX:
@inproceedings{Ishola2020,
  author = {Ishola, Olabayo and Boiten, Eerke Albert and Ayesh, Aladdin and Albakri, Adham},
  title = {Recognising Re-identification Attacks on Databases, by Interpreting them as SQL Queries: A Technical Study.},
  booktitle = {Privacy in Statistical Databases 2020 (PSD2020)},
  year = {2020},
  note = {Arezzo, Italy, September 2020.}
}
Lim, Y.M., Ayesh, A. and Stacey, M. Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices 2020 International Journal of Human-Computer Interaction
Vol. 36(4), pp. 326-340 
article DOI URL 
Abstract: ABSTRACTThis research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real-world individual’s affective states. It is also important to ensure that the measurement can be applied regardless of the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes in duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification.
BibTeX:
@article{Lim2019,
  author = {Yee Mei Lim and Aladdin Ayesh and Martin Stacey},
  title = {Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices},
  journal = {International Journal of Human-Computer Interaction},
  publisher = {Taylor & Francis},
  year = {2020},
  volume = {36},
  number = {4},
  pages = {326-340},
  url = {https://doi.org/10.1080/10447318.2019.1642617},
  doi = {https://doi.org/10.1080/10447318.2019.1642617}
}
Mohammed, K., Ayesh, A. and Boiten, E.A. Utility promises of self-organising maps in privacy preserving data mining 2020 15th International Workshop on Data Privacy Management (ESORICS) [online]  inproceedings  
BibTeX:
@inproceedings{Mohammed2020,
  author = {Mohammed, Kabiru and Ayesh, Aladdin and Boiten, Eerke Albert},
  title = {Utility promises of self-organising maps in privacy preserving data mining},
  booktitle = {15th International Workshop on Data Privacy Management (ESORICS) [online]},
  year = {2020},
  note = {University of Surrey, UK, September 2020. https://www.youtube.com/watch?v=czGS4Sqralg&feature=youtu.be https://deic-web.uab.cat/conferences/dpm/dpm2020/program.html}
}
Schiff, D., Ayesh, A., Musikanski, L. and Havens, J.C. IEEE 7010: A New Standard for Assessing the Well-being Implications of Artificial Intelligence 2020 IEEE SMC2020 Special Session on Human Well-Being in the Context of Autonomous and Intelligent Systems  inproceedings  
BibTeX:
@inproceedings{Schiff2020,
  author = {Daniel Schiff and Aladdin Ayesh and Laura Musikanski and John C. Havens},
  title = {IEEE 7010: A New Standard for Assessing the Well-being Implications of Artificial Intelligence},
  booktitle = {IEEE SMC2020 Special Session on Human Well-Being in the Context of Autonomous and Intelligent Systems},
  year = {2020}
}
Schiff, D., Rakova, B., Ayesh, A., Fanti, A. and Lennon, M. Principles to Practices for Responsible AI: Closing the Gap 2020 The 2020 European Conference on AI (ECAI) Workshop on "Advancing Towards The SDGs: Artificial Intelligence For a Fair, Just and Equitable World (AI4EQ)"  inproceedings  
BibTeX:
@inproceedings{Schiff2020a,
  author = {Daniel Schiff and Bogdana Rakova and Aladdin Ayesh and Anat Fanti and Michael Lennon},
  title = {Principles to Practices for Responsible AI: Closing the Gap},
  booktitle = {The 2020 European Conference on AI (ECAI) Workshop on "Advancing Towards The SDGs: Artificial Intelligence For a Fair, Just and Equitable World (AI4EQ)"},
  year = {2020}
}
Arevalillo-Herráez, M., Chicote-Huete, G., Ferri, FrancescJ.., Ayesh, A., Boticario, Jes., Katsigiannis, S., Ramzan, N. and Arnau González, P. On using EEG signals for emotion modeling and biometry 2019 ESM '2019 Conference Proceedings, pp. 229-233  inproceedings URL 
Abstract: A number of previous works have adopted a subject independent approach for recognizing emotions from Electroencephalography (EEG) signals, and attempted to build a global model by treating data from different subjects as if they belong to the same individual. In this paper we visually explore the data provided in four different standard datasets when using Power Spectral Density features, and show that the subject-dependent component in the EEG signal is far stronger than the emotion-related component. In addition, the session-dependency that is also found discourages the application of this type of features from EEG signals in a biometric context.
BibTeX:
@inproceedings{Arevalillo-Herraez2019,
  author = {Miguel Arevalillo-Herráez and Guillermo Chicote-Huete and Ferri, Francesc J. and Aladdin Ayesh and Boticario, Jesús G. and Stamos Katsigiannis and Naeem Ramzan and Arnau González, Pablo},
  title = {On using EEG signals for emotion modeling and biometry},
  booktitle = {ESM '2019 Conference Proceedings},
  publisher = {European Multidisciplinary Society for Modelling and Simulation Technology},
  year = {2019},
  pages = {229--233},
  note = {33rd European Simulation and Modelling Conference, ESM'2019 ; Conference date: 28-10-2019 Through 30-10-2019},
  url = {https://www.eurosis.org/conf/esm/2019/index.html}
}
Ayesh, A. Cognitive Systems Approach to Smart Cities 2019 CoRR - arxiv.org
Vol. abs/1906.11032 
article URL 
BibTeX:
@article{Ayesh2019,
  author = {Aladdin Ayesh},
  title = {Cognitive Systems Approach to Smart Cities},
  journal = {CoRR - arxiv.org},
  year = {2019},
  volume = {abs/1906.11032},
  url = {http://arxiv.org/abs/1906.11032}
}
Ayesh, A. Turing Test Revisited: A Framework for an Alternative 2019 CoRR - arxiv.org
Vol. abs/1906.11068 
article URL 
BibTeX:
@article{Ayesh2019a,
  author = {Aladdin Ayesh},
  title = {Turing Test Revisited: A Framework for an Alternative},
  journal = {CoRR - arxiv.org},
  year = {2019},
  volume = {abs/1906.11068},
  url = {http://arxiv.org/abs/1906.11068}
}
Mishael, Q., Ayesh, A. and Yevseyeva, I. Users Intention Based on Twitter Features Using Text Analytics 2019 Intelligent Data Engineering and Automated Learning -- IDEAL 2019, pp. 121-128  inproceedings  
Abstract: Online Social networks are widely used in current times. In this paper, we investigate twitter posts to identify features that feed in intention mining calculation. The posts features are divided into three sets: tweets textual features, users features, and network contextual features. In this paper, our focus is on tweets analysing textual features. As a result of this paper, we were able to create intentions profiles for 2960 users based on textual features. The prediction accuracy of three classifiers was compared for the data set, using ten intention categories to test the features. The best accuracy was achieved for SVM classifier. In the future, we plan to include user and network contextual features aiming at improving the prediction accuracy.
BibTeX:
@inproceedings{Mishael2019,
  author = {Mishael, Qadri and Ayesh, Aladdin and Yevseyeva, Iryna},
  title = {Users Intention Based on Twitter Features Using Text Analytics},
  booktitle = {Intelligent Data Engineering and Automated Learning -- IDEAL 2019},
  publisher = {Springer International Publishing},
  year = {2019},
  pages = {121--128}
}
Muhammad, M.A. and Ayesh, A. A Behaviour Profiling Based Technique for Network Access Control Systems 2019 International Journal of Cyber-Security and Digital Forensics (IJCSDF)
Vol. 8(1), pp. 23-30 
article  
BibTeX:
@article{Muhammad2019,
  author = {Musa Abubakar Muhammad and Aladdin Ayesh},
  title = {A Behaviour Profiling Based Technique for Network Access Control Systems},
  journal = {International Journal of Cyber-Security and Digital Forensics (IJCSDF)},
  year = {2019},
  volume = {8},
  number = {1},
  pages = {23-30}
}
Muhammad, M.A., Ayesh, A. and Wagner, I. Behavior-Based Outlier Detection for Network Access Control Systems 2019 Proceedings of the 3rd International Conference on Future Networks and Distributed Systems, pp. 14:1-14:6  inproceedings DOI URL 
BibTeX:
@inproceedings{Muhammad2019a,
  author = {Muhammad, Musa Abubakar and Ayesh, Aladdin and Wagner, Isabel},
  title = {Behavior-Based Outlier Detection for Network Access Control Systems},
  booktitle = {Proceedings of the 3rd International Conference on Future Networks and Distributed Systems},
  publisher = {ACM},
  year = {2019},
  pages = {14:1--14:6},
  url = {http://doi.acm.org/10.1145/3341325.3342004},
  doi = {https://doi.org/10.1145/3341325.3342004}
}
Wang, R., Chen, L., Ayesh, A., Shell, J. and Solheim, I. Gaze-based Assessment of Dyslexic Students' Motivation within an E-learning Environment 2019 The 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation UIC  inproceedings DOI  
BibTeX:
@inproceedings{Wang2019,
  author = {R. Wang and L. Chen and A. Ayesh and J. Shell and I. Solheim},
  title = {Gaze-based Assessment of Dyslexic Students' Motivation within an E-learning Environment},
  booktitle = {The 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation UIC},
  publisher = {IEEE},
  year = {2019},
  doi = {https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00142}
}
Ayesh, A., Arevalillo-Herraéz, M. and Arnau-González, P. SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset 2018 International Journal of Software Science and Computational Intelligence (IJSSCI)
Vol. 10 (1)(1), pp. 15-26 
article DOI  
Abstract: This paper investigates the possibility of identifying classes by clustering. This study includes employing Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be mapped to emotional classes. Beginning by training varying sizes of SOM with the EEG data provided from the public dataset: DEAP. The produced graphs showing Neighbor Distance, Sample Hits, and Weight Position are examined. Following that, the ground-truth label provided in DEAP is tested, in order to identify correlations between the label and the clusters produced by the SOM. The results show that there is a potential of class discovery using SOM-based clustering. It is then concluded that by evaluating the implications of this work and the difficulties in evaluating its outcome.
BibTeX:
@article{Ayesh2018,
  author = {Ayesh, A. and Arevalillo-Herraéz, M. and Arnau-González, P.},
  title = {SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset},
  journal = {International Journal of Software Science and Computational Intelligence (IJSSCI)},
  year = {2018},
  volume = {10 (1)},
  number = {1},
  pages = {15-26},
  doi = {https://doi.org/10.4018/IJSSCI.2018010102}
}
Shenfield, A., Day, D. and Ayesh, A. Intelligent intrusion detection systems using artificial neural networks 2018 ICT Express
Vol. 4(2), pp. 95 - 99 
article DOI URL 
Abstract: This paper presents a novel approach to detection of malicious network traffic using artificial neural networks suitable for use in deep packet inspection based intrusion detection systems. Experimental results using a range of typical benign network traffic data (images, dynamic link library files, and a selection of other miscellaneous files such as logs, music files, and word processing documents) and malicious shell code files sourced from the online exploit and vulnerability repository exploitdb [1], have shown that the proposed artificial neural network architecture is able to distinguish between benign and malicious network traffic accurately. The proposed artificial neural network architecture obtains an average accuracy of 98%, an average area under the receiver operator characteristic curve of 0.98, and an average false positive rate of less than 2% in repeated 10-fold cross-validation. This shows that the proposed classification technique is robust, accurate, and precise. The novel approach to malicious network traffic detection proposed in this paper has the potential to significantly enhance the utility of intrusion detection systems applied to both conventional network traffic analysis and network traffic analysis for cyber–physical systems such as smart-grids.
BibTeX:
@article{Shenfield2018,
  author = {Alex Shenfield and David Day and Aladdin Ayesh},
  title = {Intelligent intrusion detection systems using artificial neural networks},
  journal = {ICT Express},
  year = {2018},
  volume = {4},
  number = {2},
  pages = {95 - 99},
  note = {SI on Artificial Intelligence and Machine Learning},
  url = {http://www.sciencedirect.com/science/article/pii/S2405959518300493},
  doi = {https://doi.org/10.1016/j.icte.2018.04.003}
}
Wang, Y., Raskin, V., Rayz, J., Baciu, G., Ayesh, A., Mizoguchi, F., Tsumoto, S., Patel, D. and Howard, N. Cognitive Computing: Methodologies for Neural Computing and Semantic Computing in Brain-Inspired Systems 2018 International Journal of Software Science and Computational Intelligence (IJSSCI)
Vol. 10 (1)(1), pp. 1-14 
article DOI  
BibTeX:
@article{Wang2018,
  author = {Wang, Y. and Raskin, V. and Rayz, J. and Baciu, G. and Ayesh, A. and Mizoguchi, F. and Tsumoto, S. and Patel, D. and Howard, N.},
  title = {Cognitive Computing: Methodologies for Neural Computing and Semantic Computing in Brain-Inspired Systems},
  journal = {International Journal of Software Science and Computational Intelligence (IJSSCI)},
  year = {2018},
  volume = {10 (1)},
  number = {1},
  pages = {1-14},
  doi = {https://doi.org/10.4018/IJSSCI.2018010101}
}
Created by JabRef on 11/10/2022.