@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{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{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}

}

@inproceedings{davoust_user_2016,

title = {User participation and honesty in online rating systems: What a social network can do},

author = {A. Davoust and B. Esfandiari},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021919921&partnerID=40&md5=6a33a1ab6d3b6ddd037240f4f664b6fe},

isbn = {978-1-57735-759-9},

year  = {2016},

date = {2016-01-01},

booktitle = {AAAI Workshop - Technical Report},

volume = {WS-16-01 - WS-16-15},

pages = {477–483},

publisher = {AI Access Foundation},

abstract = {An important problem with online communities in general, and online rating systems in particular, is uncooperative behavior: lack of user participation, dishonest contributions. This may be due to an incentive structure akin to a Prisoners' Dilemma (PD). We show that introducing an explicit social network to PD games fosters cooperative behavior, and use this insight to design a new aggregation technique for online rating systems. Using a dataset of ratings from Yelp, we show that our aggregation technique outperforms Yelp's proprietary filter, as well as baseline techniques from recommender systems. Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org).},

keywords = {Aggregation techniques, Artificial intelligence, Behavioral research, Big data, Co-operative behaviors, Cognitive systems, Computer games, Computer programming, Computer systems programming, Data mining, Hybrid systems, Incentive structure, On-line communities, Online rating systems, Online systems, Population statistics, Prisoners' Dilemma, Rating, Social networking (online), User participation},

pubstate = {published},

tppubtype = {inproceedings}

}