@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}

}

@inproceedings{nabelsi_detecting_2015,

title = {Detecting constraints in supply chain reengineering projects: Case study of data and process integration in a hospital pharmacy},

author = {V. Nabelsi and S. Gagnon},

editor = {Zaremba M. Sasiadek J. Dolgui A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953887968&doi=10.1016%2fj.ifacol.2015.06.066&partnerID=40&md5=ce9be2cbe2fdcfc4872793c13f4228a2},

doi = {10.1016/j.ifacol.2015.06.066},

isbn = {24058963 (ISSN)},

year  = {2015},

date = {2015-01-01},

booktitle = {IFAC-PapersOnLine},

volume = {28},

pages = {106–111},

abstract = {This paper discusses how messy data may be a hidden failure factor that Business Process Reengineering (BPR) projects typically cannot detect during the planning phase. Our case study deals with Supply Chain Management (SCM) within two major urban hospitals, involving $2 million in minimum stocks for drug inventory. Our project addresses the feasibility of the hospital's data warehousing integration, especially at the stage of Extract, Transform, and Load (ETL). We conclude with a proposed system architecture audit and verification method that may serve to guide reengineering project planning and execution. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},

note = {Issue: 3 

Journal Abbreviation: IFAC-PapersOnLine},

keywords = {Administrative data processing, Artificial intelligence, Business Process, Business process management, Business process management (BPM), Business process re-engineering, Case-studies, Data integration, Data mining, Data models, Data structures, Data warehouses, Decision support system, Decision support system (dss), Decision support systems, Enterprise resource management, Extract transform and load, Extract Transform and Load (ETL), Hospitals, Information management, Integration, Process management, Project case, Re-engineering projects, Reengineering, Supply chain management, Supply Chain Management (SCM), Supply chain managements (SCM), System architectures, Verification method, Verification of information system},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{murtaza_trace_2015,

title = {A trace abstraction approach for host-based anomaly detection},

author = {S. S. Murtaza and W. Khreich and A. Hamou-Lhadj and S. Gagnon},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953310135&doi=10.1109%2fCISDA.2015.7208644&partnerID=40&md5=30e47f711b04bc6c44be9f6daea5ebf1},

doi = {10.1109/CISDA.2015.7208644},

isbn = {978-146737557-3 (ISBN)},

year  = {2015},

date = {2015-01-01},

booktitle = {2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2015 - Proceedings},

pages = {170–177},

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

abstract = {High false alarm rates and execution times are among the key issues in host-based anomaly detection systems. In this paper, we investigate the use of trace abstraction techniques for reducing the execution time of anomaly detectors while keeping the same accuracy. The key idea is to represent system call traces as traces of kernel module interactions and use the resulting abstract traces as input to known anomaly detection techniques, such as STIDE (the Sequence Time-Delay Embedding) and HMM (Hidden Markov Models). We performed experiments on three datasets, namely, the traditional UNM dataset as well as two modern datasets, Firefox and ADFA-LD. The results show that kernel module traces can lead to similar or fewer false alarms and considerably smaller execution times compared to raw system call traces for host-based anomaly detection systems. © 2015 IEEE.},

note = {Journal Abbreviation: IEEE Symp. Comput. Intell. Secur. Def. Appl., CISDA - Proc.},

keywords = {Abstracting, Abstraction techniques, Alarm systems, Anomaly detection, Anomaly detection systems, Anomaly detector, Artificial intelligence, Chemical detection, Computer programming languages, Distributed computer systems, Errors, Hidden Markov models, Host-based Anomaly Detection System, Markov processes, Signal detection, Software dependability, Software security, System call traces, Time delay, Time delay embedding, Trace analysis, Trace Analysis and Abstraction},

pubstate = {published},

tppubtype = {inproceedings}

}