@inproceedings{brideau-duquette_forensic_2025,

title = {Forensic Psychiatry and Big Data: Towards a Cyberphysical System in Service of Clinic, Research and Cybersecurity},

author = {M. Brideau-Duquette and S. S. -P. Côté and T. Boukhalfi and P. Renaud},

editor = {Boulay B. and Di Mascio T. and Tovar E. and Meinel C.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003621528&doi=10.5220%2f0013496100003932&partnerID=40&md5=ee442c9113f1104973b40ef698bc5d7d},

doi = {10.5220/0013496100003932},

isbn = {21845026 (ISSN); 978-989758746-7 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {International Conference on Computer Supported Education, CSEDU - Proceedings},

volume = {1},

pages = {856–864},

publisher = {Science and Technology Publications, Lda},

abstract = {The advent of big data and artificial intelligence has led to the elaboration of computational psychiatry. In parallel, great progress has been made with extended reality (XR) technologies. In this article, we propose to build a forensic cyberphysical system (CPS) that, with a data lake as its computational and data repository core, will support clinical and research efforts in forensic psychiatry, this in both intramural and extramural settings. The proposed CPS requires offender's data (notably clinical, behavioural and physiological), but also emphasises the collection of such data in various XR contexts. The same data would be used to train machine and deep learning, artificial intelligence, algorithms. Beyond the direct feedback these algorithms could give to forensic specialists, they could help build forensic digital twins. They could also serve in the fine tuning of XR usage with offenders. This paper concludes with human-centered cybersecurity concerns and opportunities the same CPS would imply. The proximity between a forensic and XR-supported CPS and social engineering will be addressed, and special consideration will be given to the opportunity for situational awareness training with offenders. We conclude by sketching ethical and implementation challenges that would require future inquiring. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.},

note = {Journal Abbreviation: International Conference on Computer Supported Education, CSEDU - Proceedings},

keywords = {Artificial intelligence, Artificial life, Behavioral research, Computer forensics, Cyber security, Cyber-physical systems, Cyberphysical System, Cybersecurity, Data Lake, Data repositories, Extended Reality, Forensic engineering, Forensic psychiatry, Intelligent computing, prediction, Prevention, Research efforts, Social Engineering, Sociology, System in services},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lapointe_field_2025,

title = {Field Trials of an AI-AR-Based System for Remote Bridge Inspection by Drone},

author = {J. -F. Lapointe and M. S. Allili and N. Hammouche},

editor = {Harris D. and Li W.-C. and Krömker H.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213387549&doi=10.1007%2f978-3-031-76824-8_20&partnerID=40&md5=565ae5dded9cfdf27632e79e702c7718},

doi = {10.1007/978-3-031-76824-8_20},

isbn = {03029743 (ISSN); 978-303176823-1 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15381 LNCS},

pages = {278–287},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Bridge inspections are important to ensure the safety of users of these critical transportation infrastructures and avoid tragedies that could be caused by the collapse of these infrastructures. This paper describes the results of field trials of an advanced system for remotely guided inspection of bridges by a drone, which relies on artificial intelligence and augmented reality to achieve it. Results indicate that a high speed network link is critical to achieve good performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

note = {Journal Abbreviation: Lect. Notes Comput. Sci.},

keywords = {Advanced systems, Air navigation, Artificial intelligence, artificial intelligence (AI), augmented reality, augmented reality (AR), Bridge inspection, Concrete bridges, Drone, Drones, Field trial, HIgh speed networks, High-speed Networks, Network links, Performance, Remote guidance, Transportation infrastructures, UAV},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nosrati_ai_2025,

title = {The AI Complacency Model: Integrating Bounded Rationality and Information Processing},

author = {S. Nosrati and H. Motaghi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105025349267&partnerID=40&md5=14d676bedd3ea18921ed24a830889a8b},

isbn = {979-833132774-3 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Am. Conf. Inf. Syst., AMCIS},

volume = {7},

pages = {4590–4599},

publisher = {Association for Information Systems},

abstract = {This study addresses a critical gap in understanding why users exhibit reduced oversight when interacting with generative AI systems despite their known limitations. While existing research documents AI errors across domains, theoretical frameworks explaining the underlying psychological mechanisms remain underdeveloped. We propose a comprehensive model of AI complacency that extends beyond traditional automation complacency theories by integrating bounded rationality constraints with dual-process information processing models. Our framework demonstrates how Perceived AI Reliability triggers a shift from systematic to heuristic information processing, subsequently reducing vigilance and impairing task performance. The relationship between Perceived AI Reliability and information processing is moderated by three key factors derived from bounded rationality theory: knowledge limitations, cognitive processing capabilities, and time constraints. The proposed conceptual model of this study contributes to the literature by identifying unique psychological mechanisms underlying AI complacency, explaining the processing shifts in human-AI interaction, positioning vigilance as a critical mediating mechanism, and introducing the Vigilance-Reliability Matrix as a diagnostic tool for identifying different interaction patterns. This framework offers both theoretical insights and practical guidance for maintaining appropriate human oversight as AI systems become increasingly sophisticated and widespread across domains. Copyright © 2025 by Association for Information Systems (AIS). All rights reserved.},

keywords = {AI complacency, AI systems, Artificial intelligence, Behavioral research, Bounded rationality, Cognitive bias, cognitive biases, decision-making, Decisions makings, Heuristic processing, Human computer interaction, Human-AI interaction, information processing, Information systems, Information use, Perceived AI reliability, Reliability theory, Systematic processing, Vigilance},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{von_lucke_using_2024,

title = {Using Artificial Intelligence in Parliament - Initial Results from the Canadian House of Commons},

author = {J. Lucke and F. Fitsilis and S. Gagnon},

editor = {Ubacht J. and Crompvoets J. and Csaki C. and Danneels L. and Janssen M. and Johannessen M.R. and Lampoltshammer T. and Lee H. and Lindgren I. and Hofmann S. and Parycek P. and Parycek P. and Pereira G.V. and Pereira G.V. and Schwabe G. and Schwabe G. and Susha I. and Tambouris E. and Tambouris E. and Zuiderwijk A. and Zuiderwijk A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200734306&partnerID=40&md5=5d929fa3bd78c93c5ae02a29099f918e},

isbn = {16130073 (ISSN)},

year  = {2024},

date = {2024-01-01},

booktitle = {CEUR Workshop Proc.},

volume = {3737},

publisher = {CEUR-WS},

abstract = {Parliaments are already exploring the integration of artificial intelligence (AI) technology for specific tasks. Reflecting on possible tools, application areas, usage scenarios, and requirements, it is reasonable to anticipate that AI-driven changes will manifest in parliamentary operations. Though Canada has been championing AI, additional research is necessary for its seamless integration and use in the parliamentary workspace. This research paper contributes to the bridging of this gap by presenting empirical evidence for the future use of AI-based tools and services, along with addressing open questions for their implementation within the Canadian Parliament. The data were collected during a brainstorming exercise in July 2020 and a virtual workshop in September 2023. An examination was conducted to investigate the relevance and priority of 210 applications and topics related to parliamentary AI. Copyright 2024 for this paper by its authors.},

note = {Journal Abbreviation: CEUR Workshop Proc.},

keywords = {Application area, Artificial intelligence, Artificial intelligence technologies, Canada, Canadian House of Commons, Canadian Parliament, Parliament, Parliamentary Workspace, Specific tasks, Tool applications, Usage scenarios},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{khosrojerdi_applications_2021,

title = {Applications of Artificial Intelligence in Smart Grids: Present and Future Research Domains},

author = {F. Khosrojerdi and S. Gagnon and R. Valverde},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115988707&doi=10.1109%2fSEGE52446.2021.9534914&partnerID=40&md5=a55eba3a0bc91b6381600a3a24de963f},

doi = {10.1109/SEGE52446.2021.9534914},

isbn = {978-073814535-8 (ISBN)},

year  = {2021},

date = {2021-01-01},

booktitle = {2021 9th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2021},

pages = {7–12},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {In the last decade, Artificial Intelligence (AI) have been applied overwhelmingly in various research domains in the context of smart grid. It has been one of the main streams of advanced technological approaches that the research community offered for developing smart grids. However, the broad scope of the subject matter has launched complexity for scholars to identify effective research approaches. In this paper, we present a literature review about utilizing AI in the key elements of smart grids including grid-connected vehicles, data-driven components, and the power system network. This will result in highlighting technical challenges of the integration of electric vehicles to the grid and the power network operation as well. Moreover, we discuss the four key research areas in the context of AI and its applications in intelligent power grids. The proposed research fields aid PhD candidates to consider these areas as the promising domains for investigation. © 2021 IEEE.},

note = {Journal Abbreviation: IEEE Int. Conf. Smart Energy Grid Eng., SEGE},

keywords = {AI research, Artificial intelligence, Artificial intelligence research, Electric network analysis, Electric power transmission networks, EV, Network operations, Power network operation, Power networks, Research domains, Research opportunities, Scholar, scholars, Smart grid, Vehicle to grids, Vehicle-to-grid, Vehicles},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ouyed_recognizing_2018,

title = {Recognizing human interactions using group feature relevance in multinomial kernel logistic regression},

author = {O. Ouyed and M. S. Allili},

editor = {Rus V. Brawner K.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067977954&partnerID=40&md5=1c8d720de570fc565bca3741c107bc9a},

isbn = {978-1-57735-796-4},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018},

pages = {541–545},

publisher = {AAAI press},

abstract = {We propose a supervised approach incorporating group feature sparsity in multi-class kernel logistic regression (GFR-MKLR). The need for group sparsity arises in several practical situations where a subset of a set of factors can explain a predicted variable and each factor consists of a group of variables. We apply our approach for predicting human interactions based on body parts motion (e.g., hands, legs, head, etc.) where image features are organised in groups corresponding to body parts. Our approach, leads to sparse models by assigning weights to groups of features having the highest discrimination between different types of interactions. Experiments conducted on the UT-Interaction dataset have demonstrated the performance of our method with regard to stat-of-art methods. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.},

keywords = {Art methods, Artificial intelligence, Feature relevance, Group sparsities, Human interactions, Image features, Kernel logistic regression, Multinomial kernels, regression analysis, Sparse models},

pubstate = {published},

tppubtype = {inproceedings}

}