Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Tiwari, A.; Arrabito, R.; Davoust, A.; Falk, T. H.
Bias in Physiology-Based Cognitive State Detection for Human–Autonomy Teaming: A Comprehensive Survey Proceedings Article
In: R.A., Sottilare; J., Schwarz (Ed.): Lect. Notes Comput. Sci., pp. 139–158, Springer Science and Business Media Deutschland GmbH, 2026, ISBN: 03029743 (ISSN); 978-303230014-0 (ISBN), (Journal Abbreviation: Lect. Notes Comput. Sci.).
Abstract | Links | BibTeX | Tags: Behavioral research, Bias, Biases, Biomedical signal processing, Cognitive state, Cognitive systems, Human Autonomy Teaming, Instructional system, Intelligent systems, Learning pathway, Learning systems, Personalized learning, Physiological models, Physiological signals, Population statistics, Psychophysiology, State Detection, Student feedback, Surveying, Wearable devices, Wearable technology
@inproceedings{tiwariBiasPhysiologyBasedCognitive2026,
title = {Bias in Physiology-Based Cognitive State Detection for Human–Autonomy Teaming: A Comprehensive Survey},
author = {A. Tiwari and R. Arrabito and A. Davoust and T. H. Falk},
editor = {Sottilare R.A. and Schwarz J.},
url = {https://www.scopus.com/pages/publications/105044000677?origin=resultslist},
doi = {10.1007/978-3-032-30015-7_9},
isbn = {03029743 (ISSN); 978-303230014-0 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {16737 LNCS},
pages = {139–158},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Advances in artificial intelligence (AI) are changing the role of adaptive instructional systems (AIS) from passive tools, that deliver personalized learning pathways by adapting to student feedback, to active team members and co-learners that dynamically adapt alongside human partners. In this emerging landscape of human–autonomy teaming (HAT), assessing the cognitive states of individuals interacting with highly intelligent systems has become a critical design consideration for improving team performance, collaboration, and learning outcomes. Physiological signals have emerged as a popular method for measurement of cognitive states in the past few decades. However, these signals can be strongly influenced by different user demographics, including age and biological sex. If unaccounted for, these differences could introduce systematic biases into AIS based learning pathways for different demographic groups. These biases can lead to discriminative performance of instructional systems and may also leave them vulnerable to adversarial exploitation. Aside from physiological signal-induced biases, there may also be major behavioural differences between different sex and/or age groups when interacting with and teaming with AIS, thus further confounding cognitive state monitoring. In this survey, we review emerging human-autonomy teaming studies that incorporate physiological data and analyze where biological sex- and age-related physiological or behavioral differences were documented and/or analyzed. We highlight evidence demonstrating that differences do exist during interaction with autonomous systems. We then discuss how biases can accumulate across study design and analysis modelling pipelines, and provide practical guidelines for mitigating these biases in future human-autonomy teaming research and applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.},
note = {Journal Abbreviation: Lect. Notes Comput. Sci.},
keywords = {Behavioral research, Bias, Biases, Biomedical signal processing, Cognitive state, Cognitive systems, Human Autonomy Teaming, Instructional system, Intelligent systems, Learning pathway, Learning systems, Personalized learning, Physiological models, Physiological signals, Population statistics, Psychophysiology, State Detection, Student feedback, Surveying, Wearable devices, Wearable technology},
pubstate = {published},
tppubtype = {inproceedings}
}
Advances in artificial intelligence (AI) are changing the role of adaptive instructional systems (AIS) from passive tools, that deliver personalized learning pathways by adapting to student feedback, to active team members and co-learners that dynamically adapt alongside human partners. In this emerging landscape of human–autonomy teaming (HAT), assessing the cognitive states of individuals interacting with highly intelligent systems has become a critical design consideration for improving team performance, collaboration, and learning outcomes. Physiological signals have emerged as a popular method for measurement of cognitive states in the past few decades. However, these signals can be strongly influenced by different user demographics, including age and biological sex. If unaccounted for, these differences could introduce systematic biases into AIS based learning pathways for different demographic groups. These biases can lead to discriminative performance of instructional systems and may also leave them vulnerable to adversarial exploitation. Aside from physiological signal-induced biases, there may also be major behavioural differences between different sex and/or age groups when interacting with and teaming with AIS, thus further confounding cognitive state monitoring. In this survey, we review emerging human-autonomy teaming studies that incorporate physiological data and analyze where biological sex- and age-related physiological or behavioral differences were documented and/or analyzed. We highlight evidence demonstrating that differences do exist during interaction with autonomous systems. We then discuss how biases can accumulate across study design and analysis modelling pipelines, and provide practical guidelines for mitigating these biases in future human-autonomy teaming research and applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2.
Jaberi, M.; Falk, T. H.
A Literature Survey on Potential Private User Information Leakage in Metaverse Applications Journal Article
In: Advanced Intelligent Systems, vol. 8, no. 1, 2026, ISSN: 26404567 (ISSN).
Abstract | Links | BibTeX | Tags: age, augmented reality, Brain, brain computer interface, Computer privacy, Data privacy, Electroencephalography, eye tracking, Gender, Haptic interfaces, Information leakage, Intelligent systems, Literature survey, metaverse, Metaverses, Neurophysiological signal, neurophysiological signals, Neurophysiology, Privacy, Private user information, Race, Signal analysis, User information, Virtual and augmented reality
@article{jaberiLiteratureSurveyPotential2026,
title = {A Literature Survey on Potential Private User Information Leakage in Metaverse Applications},
author = {M. Jaberi and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105012929167?origin=resultslist},
doi = {10.1002/aisy.202500263},
issn = {26404567 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Advanced Intelligent Systems},
volume = {8},
number = {1},
publisher = {John Wiley and Sons Inc},
abstract = {The Metaverse is revolutionizing various fields, including healthcare, education, social interaction, and the workplace. Commercial multisensory devices (e.g., smell diffusion and haptic technologies) are available, and virtual and augmented reality (VR/AR) headsets are increasingly integrated with brain–computer interfaces (BCI). These integrations enable adaptive, personalized virtual immersive experiences that are more engaging, interactive, and effective. As these applications become mainstream, concerns arise regarding the security and privacy of personal information. Recent studies demonstrate that users can be identified with high accuracy using the data monitored from sensors available in VR/AR headsets. This literature survey investigates the types of personal user information that can be inferred from BCI-instrumented headsets. In particular, it focuses on predicting age, gender, and ethnic/racial background from neurophysiological signals currently monitored by commercial devices. The survey highlights the predictive strength of electroencephalogram and electrocardiogram signal modalities, followed by eye tracking and iris scanning. It also considers future privacy risks posed by biometric and gesture-based monitoring using non-contact technologies such as computer vision and WiFi signal analysis. The survey concludes with recommendations for future research aimed at contributing to the development of robust frameworks that safeguard user privacy in the evolving Metaverse landscape. © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.},
keywords = {age, augmented reality, Brain, brain computer interface, Computer privacy, Data privacy, Electroencephalography, eye tracking, Gender, Haptic interfaces, Information leakage, Intelligent systems, Literature survey, metaverse, Metaverses, Neurophysiological signal, neurophysiological signals, Neurophysiology, Privacy, Private user information, Race, Signal analysis, User information, Virtual and augmented reality},
pubstate = {published},
tppubtype = {article}
}
The Metaverse is revolutionizing various fields, including healthcare, education, social interaction, and the workplace. Commercial multisensory devices (e.g., smell diffusion and haptic technologies) are available, and virtual and augmented reality (VR/AR) headsets are increasingly integrated with brain–computer interfaces (BCI). These integrations enable adaptive, personalized virtual immersive experiences that are more engaging, interactive, and effective. As these applications become mainstream, concerns arise regarding the security and privacy of personal information. Recent studies demonstrate that users can be identified with high accuracy using the data monitored from sensors available in VR/AR headsets. This literature survey investigates the types of personal user information that can be inferred from BCI-instrumented headsets. In particular, it focuses on predicting age, gender, and ethnic/racial background from neurophysiological signals currently monitored by commercial devices. The survey highlights the predictive strength of electroencephalogram and electrocardiogram signal modalities, followed by eye tracking and iris scanning. It also considers future privacy risks posed by biometric and gesture-based monitoring using non-contact technologies such as computer vision and WiFi signal analysis. The survey concludes with recommendations for future research aimed at contributing to the development of robust frameworks that safeguard user privacy in the evolving Metaverse landscape. © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH.
3.
Chabot, E.; Jaworski, E.; Renaud, P.
Pervasive Teledildonics: How AI Aims to Impact Human sexuality Proceedings Article
In: Proc. - IEEE/WIC Int. Conf. Web Intell. Intell. Agent Technol., WI-IAT, pp. 898–903, Institute of Electrical and Electronics Engineers Inc., Bangkok, Thailand, 2024, ISBN: 979-833150494-6 (ISBN).
Abstract | Links | BibTeX | Tags: Ambient intelligence, Anthropomorphic robots, Artificial intelligence, Artificial life, Capability awareness, Computing capability, Context- awareness, Cyber security, Human machine interaction, human-machine interactions, Humans-robot interactions, Industrial robots, Intelligent computing, Intelligent robots, Intelligent systems, Monitoring change, pervasive computing, Robot learning, Sexual satisfaction, sexual technologies, Sexual technology, Social robots, Teledildonic, teledildonics
@inproceedings{chabot_pervasive_2024,
title = {Pervasive Teledildonics: How AI Aims to Impact Human sexuality},
author = {E. Chabot and E. Jaworski and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007140928&doi=10.1109%2FWI-IAT62293.2024.00146&partnerID=40&md5=0e1c4d50642661b589db24542641bdf0},
doi = {10.1109/WI-IAT62293.2024.00146},
isbn = {979-833150494-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE/WIC Int. Conf. Web Intell. Intell. Agent Technol., WI-IAT},
pages = {898–903},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
address = {Bangkok, Thailand},
abstract = {Since the Covid-19 pandemic, sexual technology sales have increased all over the world, especially for devices allowing users to connect with each other or the internet. The convergence of sexualtechnology with pervasivecomputing and artificial intelligence (AI) introduces a new era of human-robot interactions. Sexual technology, ranging from smart sex toys to humanoid sex robots, now incorporates pervasive computing principles and/or AI to enhance intimate experiences. These devices leverage ubiquitous computing capabilities, adaptability, and context awareness to increase sexual satisfaction and integrate into users' lives. The integration of these technologies into intimate interactions raises questions on how these changes will affect different Webs of Life, in eluding but not limited to, the Web of People, the Web of Things and the Web of Health. From health and cybersecurity risks to the reshaping of human-human and human-robot interactions, this paper addresses the challenges at the intersection of sexual technologies, pervasive computing, AI and human-machine interactions. Emphasizing the importance of monitoring changes and increasing cybersecurity to create safe, consensual, and secure intimate experiences. © 2024 IEEE.},
keywords = {Ambient intelligence, Anthropomorphic robots, Artificial intelligence, Artificial life, Capability awareness, Computing capability, Context- awareness, Cyber security, Human machine interaction, human-machine interactions, Humans-robot interactions, Industrial robots, Intelligent computing, Intelligent robots, Intelligent systems, Monitoring change, pervasive computing, Robot learning, Sexual satisfaction, sexual technologies, Sexual technology, Social robots, Teledildonic, teledildonics},
pubstate = {published},
tppubtype = {inproceedings}
}
Since the Covid-19 pandemic, sexual technology sales have increased all over the world, especially for devices allowing users to connect with each other or the internet. The convergence of sexualtechnology with pervasivecomputing and artificial intelligence (AI) introduces a new era of human-robot interactions. Sexual technology, ranging from smart sex toys to humanoid sex robots, now incorporates pervasive computing principles and/or AI to enhance intimate experiences. These devices leverage ubiquitous computing capabilities, adaptability, and context awareness to increase sexual satisfaction and integrate into users' lives. The integration of these technologies into intimate interactions raises questions on how these changes will affect different Webs of Life, in eluding but not limited to, the Web of People, the Web of Things and the Web of Health. From health and cybersecurity risks to the reshaping of human-human and human-robot interactions, this paper addresses the challenges at the intersection of sexual technologies, pervasive computing, AI and human-machine interactions. Emphasizing the importance of monitoring changes and increasing cybersecurity to create safe, consensual, and secure intimate experiences. © 2024 IEEE.
4.
Chabot, E.; Jaworski, E.; Renaud, P.
Pervasive Teledildonics: How AI Aims to Impact Human sexuality Proceedings Article
In: Proc. - IEEE/WIC Int. Conf. Web Intell. Intell. Agent Technol., WI-IAT, pp. 898–903, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833150494-6 (ISBN).
Abstract | Links | BibTeX | Tags: Ambient intelligence, Anthropomorphic robots, Artificial intelligence, Artificial life, Capability awareness, Computing capability, Context- awareness, Cyber security, Human machine interaction, human-machine interactions, Humans-robot interactions, Industrial robots, Intelligent computing, Intelligent robots, Intelligent systems, Monitoring change, pervasive computing, Robot learning, Sexual satisfaction, sexual technologies, Sexual technology, Social robots, Teledildonic, teledildonics
@inproceedings{chabotPervasiveTeledildonicsHow2024,
title = {Pervasive Teledildonics: How AI Aims to Impact Human sexuality},
author = {E. Chabot and E. Jaworski and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007140928&doi=10.1109%2FWI-IAT62293.2024.00146&partnerID=40&md5=0e1c4d50642661b589db24542641bdf0},
doi = {10.1109/WI-IAT62293.2024.00146},
isbn = {979-833150494-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE/WIC Int. Conf. Web Intell. Intell. Agent Technol., WI-IAT},
pages = {898–903},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Since the Covid-19 pandemic, sexual technology sales have increased all over the world, especially for devices allowing users to connect with each other or the internet. The convergence of sexualtechnology with pervasivecomputing and artificial intelligence (AI) introduces a new era of human-robot interactions. Sexual technology, ranging from smart sex toys to humanoid sex robots, now incorporates pervasive computing principles and/or AI to enhance intimate experiences. These devices leverage ubiquitous computing capabilities, adaptability, and context awareness to increase sexual satisfaction and integrate into users' lives. The integration of these technologies into intimate interactions raises questions on how these changes will affect different Webs of Life, in eluding but not limited to, the Web of People, the Web of Things and the Web of Health. From health and cybersecurity risks to the reshaping of human-human and human-robot interactions, this paper addresses the challenges at the intersection of sexual technologies, pervasive computing, AI and human-machine interactions. Emphasizing the importance of monitoring changes and increasing cybersecurity to create safe, consensual, and secure intimate experiences. © 2024 IEEE.},
keywords = {Ambient intelligence, Anthropomorphic robots, Artificial intelligence, Artificial life, Capability awareness, Computing capability, Context- awareness, Cyber security, Human machine interaction, human-machine interactions, Humans-robot interactions, Industrial robots, Intelligent computing, Intelligent robots, Intelligent systems, Monitoring change, pervasive computing, Robot learning, Sexual satisfaction, sexual technologies, Sexual technology, Social robots, Teledildonic, teledildonics},
pubstate = {published},
tppubtype = {inproceedings}
}
Since the Covid-19 pandemic, sexual technology sales have increased all over the world, especially for devices allowing users to connect with each other or the internet. The convergence of sexualtechnology with pervasivecomputing and artificial intelligence (AI) introduces a new era of human-robot interactions. Sexual technology, ranging from smart sex toys to humanoid sex robots, now incorporates pervasive computing principles and/or AI to enhance intimate experiences. These devices leverage ubiquitous computing capabilities, adaptability, and context awareness to increase sexual satisfaction and integrate into users' lives. The integration of these technologies into intimate interactions raises questions on how these changes will affect different Webs of Life, in eluding but not limited to, the Web of People, the Web of Things and the Web of Health. From health and cybersecurity risks to the reshaping of human-human and human-robot interactions, this paper addresses the challenges at the intersection of sexual technologies, pervasive computing, AI and human-machine interactions. Emphasizing the importance of monitoring changes and increasing cybersecurity to create safe, consensual, and secure intimate experiences. © 2024 IEEE.
5.
Khosrojerdi, F.; Akhigbe, O.; Gagnon, S.; Ramirez, A.; Richards, G.
Integrating artificial intelligence and analytics in smart grids: a systematic literature review Journal Article
In: International Journal of Energy Sector Management, vol. 16, no. 2, pp. 318–338, 2022.
Abstract | Links | BibTeX | Tags: Advanced Analytics, Automation, Building energy consumption, Data Analytics, Design/methodology/approach, Dynamic energy managements, Electric power system control, Electric power transmission networks, Energy management, Energy management systems, Energy utilization, Extract, Home energy management systems, Information management, Intelligent systems, Project management, quality control, Real time systems, SCADA systems, Smart power grids, Solar buildings, Supervisory control and dataacquisition systems (SCADA), System stability, Systematic literature review, transform and loads, Voltage stability assessment
@article{khosrojerdi_integrating_2022,
title = {Integrating artificial intelligence and analytics in smart grids: a systematic literature review},
author = {F. Khosrojerdi and O. Akhigbe and S. Gagnon and A. Ramirez and G. Richards},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112559126&doi=10.1108%2fIJESM-06-2020-0011&partnerID=40&md5=7052f94c993368405955c1d33d87043c},
doi = {10.1108/IJESM-06-2020-0011},
year = {2022},
date = {2022-01-01},
journal = {International Journal of Energy Sector Management},
volume = {16},
number = {2},
pages = {318–338},
abstract = {Purpose: The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects. Design/methodology/approach: A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects? Findings: The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control. Originality/value: Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources. © 2021, Emerald Publishing Limited.},
keywords = {Advanced Analytics, Automation, Building energy consumption, Data Analytics, Design/methodology/approach, Dynamic energy managements, Electric power system control, Electric power transmission networks, Energy management, Energy management systems, Energy utilization, Extract, Home energy management systems, Information management, Intelligent systems, Project management, quality control, Real time systems, SCADA systems, Smart power grids, Solar buildings, Supervisory control and dataacquisition systems (SCADA), System stability, Systematic literature review, transform and loads, Voltage stability assessment},
pubstate = {published},
tppubtype = {article}
}
Purpose: The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects. Design/methodology/approach: A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects? Findings: The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control. Originality/value: Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources. © 2021, Emerald Publishing Limited.