Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Joudeh, I. O.; Cretu, A. -M.; Bouchard, S.
Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features † Journal Article
In: Sensors, vol. 24, no. 13, 2024, ISSN: 14248220 (ISSN), (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Abstract | Links | BibTeX | Tags: adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features
@article{joudeh_predicting_2024,
title = {Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features †},
author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198382238&doi=10.3390%2fs24134398&partnerID=40&md5=cefa8b2e2c044d02f99662af350007db},
doi = {10.3390/s24134398},
issn = {14248220 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Sensors},
volume = {24},
number = {13},
abstract = {The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {adult, Affective interaction, Arousal, artificial neural network, Cognitive state, Cognitive/emotional state, Collaborative interaction, computer, Convolutional neural networks, correlation coefficient, Deep learning, emotion, Emotional state, Emotions, female, Forecasting, Helmet mounted displays, human, Humans, Learning algorithms, Learning systems, Long short-term memory, Machine learning, Machine-learning, male, Mean square error, Neural networks, physiology, Regression, Root mean squared errors, Video recording, virtual reality, Visual feature, visual features},
pubstate = {published},
tppubtype = {article}
}
The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. © 2024 by the authors.
2.
Damadi, M. S.; Davoust, A.
Fairness in Socio-Technical Systems: A Case Study of Wikipedia Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14199 LNCS, pp. 84–100, 2023, ISSN: 03029743, (ISBN: 9783031421402 Publisher: Springer Science and Business Media Deutschland GmbH).
Abstract | Links | BibTeX | Tags: Algorithmics, Bias, Case-studies, Causal relationships, Cultural bias, Fairness, Gender bias, Machine learning, Machine-learning, Parallel processing systems, Sociotechnical systems, Wikipedia
@article{damadi_fairness_2023,
title = {Fairness in Socio-Technical Systems: A Case Study of Wikipedia},
author = {M. S. Damadi and A. Davoust},
editor = {Alvarez C. Marutschke D.M. Takada H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172720004&doi=10.1007%2f978-3-031-42141-9_6&partnerID=40&md5=172c8c6ae5b09536efdf983e9be965e7},
doi = {10.1007/978-3-031-42141-9_6},
issn = {03029743},
year = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {14199 LNCS},
pages = {84–100},
abstract = {Wikipedia content is produced by a complex socio-technical systems (STS), and exhibits numerous biases, such as gender and cultural biases. We investigate how these biases relate to the concepts of algorithmic bias and fairness defined in the context of algorithmic systems. We systematically review 75 papers describing different types of bias in Wikipedia, which we classify and relate to established notions of harm and normative expectations of fairness as defined for machine learning-driven algorithmic systems. In addition, by analysing causal relationships between the observed phenomena, we demonstrate the complexity of the socio-technical processes causing harm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.},
note = {ISBN: 9783031421402
Publisher: Springer Science and Business Media Deutschland GmbH},
keywords = {Algorithmics, Bias, Case-studies, Causal relationships, Cultural bias, Fairness, Gender bias, Machine learning, Machine-learning, Parallel processing systems, Sociotechnical systems, Wikipedia},
pubstate = {published},
tppubtype = {article}
}
Wikipedia content is produced by a complex socio-technical systems (STS), and exhibits numerous biases, such as gender and cultural biases. We investigate how these biases relate to the concepts of algorithmic bias and fairness defined in the context of algorithmic systems. We systematically review 75 papers describing different types of bias in Wikipedia, which we classify and relate to established notions of harm and normative expectations of fairness as defined for machine learning-driven algorithmic systems. In addition, by analysing causal relationships between the observed phenomena, we demonstrate the complexity of the socio-technical processes causing harm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
3.
Joudeh, I. O.; Cretu, A. -M.; Bouchard, S.; Guimond, S.
Prediction of Continuous Emotional Measures through Physiological and Visual Data † Journal Article
In: Sensors, vol. 23, no. 12, 2023, ISSN: 14248220, (Publisher: MDPI).
Abstract | Links | BibTeX | Tags: Affect recognition, Affective state, Arousal, Data-source, Deep learning, Electrocardiography, emotion, Emotion Recognition, Emotions, face recognition, Faces detection, Forecasting, human, Humans, Images processing, Learning systems, Machine learning, Machine-learning, mental disease, Mental Disorders, Physiological data, physiology, Signal-processing, Statistical tests, Video recording, Virtual-reality environment
@article{joudeh_prediction_2023,
title = {Prediction of Continuous Emotional Measures through Physiological and Visual Data †},
author = {I. O. Joudeh and A. -M. Cretu and S. Bouchard and S. Guimond},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163943735&doi=10.3390%2fs23125613&partnerID=40&md5=5e970f0d8c5790b85d8d77a9f3f52a2d},
doi = {10.3390/s23125613},
issn = {14248220},
year = {2023},
date = {2023-01-01},
journal = {Sensors},
volume = {23},
number = {12},
abstract = {The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments. © 2023 by the authors.},
note = {Publisher: MDPI},
keywords = {Affect recognition, Affective state, Arousal, Data-source, Deep learning, Electrocardiography, emotion, Emotion Recognition, Emotions, face recognition, Faces detection, Forecasting, human, Humans, Images processing, Learning systems, Machine learning, Machine-learning, mental disease, Mental Disorders, Physiological data, physiology, Signal-processing, Statistical tests, Video recording, Virtual-reality environment},
pubstate = {published},
tppubtype = {article}
}
The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments. © 2023 by the authors.
4.
Monthuy-Blanc, J.; Faghihi, U.; Fardshad, M. N. G.; Corno, G.; Iceta, S.; St-Pierre, M. -J.; Bouchard, S.
When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles Journal Article
In: Journal of Clinical Medicine, vol. 12, no. 16, 2023, ISSN: 20770383, (Publisher: Multidisciplinary Digital Publishing Institute (MDPI)).
Abstract | Links | BibTeX | Tags: adult, aged, article, body dissatisfaction, bulimia, causal reasoning, Cluster Analysis, controlled study, coronavirus disease 2019, feeding behavior, female, health survey, human, intuitive eating, Machine learning, major clinical study, male, online analysis, pandemic, self report
@article{monthuy-blanc_when_2023,
title = {When Eating Intuitively Is Not Always a Positive Response: Using Machine Learning to Better Unravel Eaters Profiles},
author = {J. Monthuy-Blanc and U. Faghihi and M. N. G. Fardshad and G. Corno and S. Iceta and M. -J. St-Pierre and S. Bouchard},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169079324&doi=10.3390%2fjcm12165172&partnerID=40&md5=2241ae85a40c73e19f30c5c3d10b514a},
doi = {10.3390/jcm12165172},
issn = {20770383},
year = {2023},
date = {2023-01-01},
journal = {Journal of Clinical Medicine},
volume = {12},
number = {16},
abstract = {Background: The aim of the present study was to identify eaters profiles using the latest advantages of Machine Learning approach to cluster analysis. Methods: A total of 317 participants completed an online-based survey including self-reported measures of body image dissatisfaction, bulimia, restraint, and intuitive eating. Analyses were conducted in two steps: (a) identifying an optimal number of clusters, and (b) validating the clustering model of eaters profile using a procedure inspired by the Causal Reasoning approach. Results: This study reveals a 7-cluster model of eaters profiles. The characteristics, needs, and strengths of each eater profile are discussed along with the presentation of a continuum of eaters profiles. Conclusions: This conceptualization of eaters profiles could guide the direction of health education and treatment interventions targeting perceptual and eating dimensions. © 2023 by the authors.},
note = {Publisher: Multidisciplinary Digital Publishing Institute (MDPI)},
keywords = {adult, aged, article, body dissatisfaction, bulimia, causal reasoning, Cluster Analysis, controlled study, coronavirus disease 2019, feeding behavior, female, health survey, human, intuitive eating, Machine learning, major clinical study, male, online analysis, pandemic, self report},
pubstate = {published},
tppubtype = {article}
}
Background: The aim of the present study was to identify eaters profiles using the latest advantages of Machine Learning approach to cluster analysis. Methods: A total of 317 participants completed an online-based survey including self-reported measures of body image dissatisfaction, bulimia, restraint, and intuitive eating. Analyses were conducted in two steps: (a) identifying an optimal number of clusters, and (b) validating the clustering model of eaters profile using a procedure inspired by the Causal Reasoning approach. Results: This study reveals a 7-cluster model of eaters profiles. The characteristics, needs, and strengths of each eater profile are discussed along with the presentation of a continuum of eaters profiles. Conclusions: This conceptualization of eaters profiles could guide the direction of health education and treatment interventions targeting perceptual and eating dimensions. © 2023 by the authors.
5.
Ørskov, P. T.; Lichtenstein, M. B.; Ernst, M. T.; Fasterholdt, I.; Matthiesen, A. F.; Scirea, M.; Bouchard, S.; Andersen, T. E.
In: Frontiers in Psychiatry, vol. 13, 2022, ISSN: 16640640 (ISSN), (Publisher: Frontiers Media S.A.).
Abstract | Links | BibTeX | Tags: adult, aged, Alcohol Use Disorders Identification Test, anxiety assessment, Anxiety disorder, article, behavior disorder assessment, cognitive behavioral therapy, comparative effectiveness, controlled study, Depression, Drug Use Disorders Identification Test, electrodermal activity, exposure, Fear of Negative Evaluation, follow up, health economics, Heart Rate, human, Leibowitz Anxiety Scale, Machine learning, major clinical study, psychological distress assessment, psychophysiological measurements, randomized controlled trial, Simulator Sickness Questionnaire, social anxiety, Social Interaction Anxiety Scale, social phobia, Subjective Units of Distress Scale, therapy effect, treatment duration, treatment outcome, virtual reality, virtual reality exposure therapy, Working Alliance Inventory
@article{orskov_cognitive_2022,
title = {Cognitive behavioral therapy with adaptive virtual reality exposure vs. cognitive behavioral therapy with in vivo exposure in the treatment of social anxiety disorder: A study protocol for a randomized controlled trial},
author = {P. T. Ørskov and M. B. Lichtenstein and M. T. Ernst and I. Fasterholdt and A. F. Matthiesen and M. Scirea and S. Bouchard and T. E. Andersen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140322564&doi=10.3389%2ffpsyt.2022.991755&partnerID=40&md5=1dacd4e05081f4790ccd5e0d7224e0ca},
doi = {10.3389/fpsyt.2022.991755},
issn = {16640640 (ISSN)},
year = {2022},
date = {2022-01-01},
journal = {Frontiers in Psychiatry},
volume = {13},
abstract = {Background: Social anxiety disorder (SAD) has a high prevalence and an early onset with recovery taking decades to occur. Current evidence supports the efficacy of cognitive behavioral therapy (CBT) with virtual reality (VR) exposure. However, the evidence is based on a sparse number of studies with predominantly small sample sizes. There is a need for more trials investigating the optimal way of applying VR based exposure for SAD. In this trial, we will test the efficacy of CBT with adaptive VR exposure allowing adjustment of the exposure based on real-time monitoring of the participants's anxiety level. Methods: The trial is a randomized controlled, assessor-blinded, parallel-group superiority trail. The study has two arms: (1) CBT including exposure in vivo (CBT-Exp), (2) CBT including exposure therapy using individually tailored VR-content and a system to track anxiety levels (CBT-ExpVR). Treatment is individual, manual-based and consists of 10 weekly sessions with a duration of 60 min. The study includes 90 participants diagnosed with SAD. Assessments are carried out pre-treatment, mid-treatment and at follow-up (6 and 12 months). The primary outcome is the mean score on the Social Interaction Anxiety Scale (SIAS) with the primary endpoint being post-treatment. Discussion: The study adds to the existing knowledge by assessing the efficacy of CBT with adaptive VR exposure. The study has high methodological rigor using a randomized controlled trial with a large sample size that includes follow-up data and validated measures for social anxiety outcomes. Clinical trial registration: ClinicalTrials.gov, identifier: NCT05302518. Copyright © 2022 Ørskov, Lichtenstein, Ernst, Fasterholdt, Matthiesen, Scirea, Bouchard and Andersen.},
note = {Publisher: Frontiers Media S.A.},
keywords = {adult, aged, Alcohol Use Disorders Identification Test, anxiety assessment, Anxiety disorder, article, behavior disorder assessment, cognitive behavioral therapy, comparative effectiveness, controlled study, Depression, Drug Use Disorders Identification Test, electrodermal activity, exposure, Fear of Negative Evaluation, follow up, health economics, Heart Rate, human, Leibowitz Anxiety Scale, Machine learning, major clinical study, psychological distress assessment, psychophysiological measurements, randomized controlled trial, Simulator Sickness Questionnaire, social anxiety, Social Interaction Anxiety Scale, social phobia, Subjective Units of Distress Scale, therapy effect, treatment duration, treatment outcome, virtual reality, virtual reality exposure therapy, Working Alliance Inventory},
pubstate = {published},
tppubtype = {article}
}
Background: Social anxiety disorder (SAD) has a high prevalence and an early onset with recovery taking decades to occur. Current evidence supports the efficacy of cognitive behavioral therapy (CBT) with virtual reality (VR) exposure. However, the evidence is based on a sparse number of studies with predominantly small sample sizes. There is a need for more trials investigating the optimal way of applying VR based exposure for SAD. In this trial, we will test the efficacy of CBT with adaptive VR exposure allowing adjustment of the exposure based on real-time monitoring of the participants's anxiety level. Methods: The trial is a randomized controlled, assessor-blinded, parallel-group superiority trail. The study has two arms: (1) CBT including exposure in vivo (CBT-Exp), (2) CBT including exposure therapy using individually tailored VR-content and a system to track anxiety levels (CBT-ExpVR). Treatment is individual, manual-based and consists of 10 weekly sessions with a duration of 60 min. The study includes 90 participants diagnosed with SAD. Assessments are carried out pre-treatment, mid-treatment and at follow-up (6 and 12 months). The primary outcome is the mean score on the Social Interaction Anxiety Scale (SIAS) with the primary endpoint being post-treatment. Discussion: The study adds to the existing knowledge by assessing the efficacy of CBT with adaptive VR exposure. The study has high methodological rigor using a randomized controlled trial with a large sample size that includes follow-up data and validated measures for social anxiety outcomes. Clinical trial registration: ClinicalTrials.gov, identifier: NCT05302518. Copyright © 2022 Ørskov, Lichtenstein, Ernst, Fasterholdt, Matthiesen, Scirea, Bouchard and Andersen.
6.
Cote, S. S. -P.; Paquette, G. R.; Neveu, S. -M.; Chartier, S.; Labbe, D. R.; Renaud, P.
Combining electroencephalography with plethysmography for classification of deviant sexual preferences. Proceedings Article
In: Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021, Institute of Electrical and Electronics Engineers Inc., 2021, ISBN: 978-172819556-8 (ISBN), (Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF).
Abstract | Links | BibTeX | Tags: Biometrics, Classification, Classification (of information), Decision trees, Deviant sexual preferences, Dimensionality reduction, Electroencephalography, Electrophysiology, extraction, Extraction method, Machine learning, Plethysmography, Proof of concept, Psychophysiological measures, Standard protocols, Variable selection and extraction
@inproceedings{cote_combining_2021,
title = {Combining electroencephalography with plethysmography for classification of deviant sexual preferences.},
author = {S. S. -P. Cote and G. R. Paquette and S. -M. Neveu and S. Chartier and D. R. Labbe and P. Renaud},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113855965&doi=10.1109%2fIWBF50991.2021.9465078&partnerID=40&md5=b545b2a6d22e32115ac179399188960e},
doi = {10.1109/IWBF50991.2021.9465078},
isbn = {978-172819556-8 (ISBN)},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings - 9th International Workshop on Biometrics and Forensics, IWBF 2021},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Evaluating sexual preferences is a difficult task. Past researchrelied mostly on penile plethysmography (PPG). Even though this technique is the standard protocol used in most currentforensic settings, its usage showed mixed results. One way to improve PPG is the addition of other psychophysiological measures such as electroencephalography (EEG). However, EEG generates significant amount of data that hinders classification. Machine learning (ML) is nowadays an excellent tool to identify most discriminating variables and for classification. Therefore, it is proposed to use ML selection and extraction methods for dimensionality reduction and then to classify sexual preferences. Evidence from this proof of concept shows that using EEG and PPG together leads to better classification (85.6%) than using EEG (82.2%) or PPG individually (74.4%). The Random Forest (RF) classifier combined with the Principal Component Analysis (PCA) extraction method achieves a slightly higher general performance rate. This increase in performances opens the door for using more reliable biometric measures in the assessment of deviant sexual preferences. © 2021 IEEE.},
note = {Journal Abbreviation: Proc. - Int. Workshop Biom. Forensics, IWBF},
keywords = {Biometrics, Classification, Classification (of information), Decision trees, Deviant sexual preferences, Dimensionality reduction, Electroencephalography, Electrophysiology, extraction, Extraction method, Machine learning, Plethysmography, Proof of concept, Psychophysiological measures, Standard protocols, Variable selection and extraction},
pubstate = {published},
tppubtype = {inproceedings}
}
Evaluating sexual preferences is a difficult task. Past researchrelied mostly on penile plethysmography (PPG). Even though this technique is the standard protocol used in most currentforensic settings, its usage showed mixed results. One way to improve PPG is the addition of other psychophysiological measures such as electroencephalography (EEG). However, EEG generates significant amount of data that hinders classification. Machine learning (ML) is nowadays an excellent tool to identify most discriminating variables and for classification. Therefore, it is proposed to use ML selection and extraction methods for dimensionality reduction and then to classify sexual preferences. Evidence from this proof of concept shows that using EEG and PPG together leads to better classification (85.6%) than using EEG (82.2%) or PPG individually (74.4%). The Random Forest (RF) classifier combined with the Principal Component Analysis (PCA) extraction method achieves a slightly higher general performance rate. This increase in performances opens the door for using more reliable biometric measures in the assessment of deviant sexual preferences. © 2021 IEEE.
7.
Barrad, S.; Gagnon, S.; Valverde, R.
An analytics architecture for procurement Journal Article
In: International Journal of Information Technologies and Systems Approach, vol. 13, no. 2, pp. 73–98, 2020, ISSN: 1935570X (ISSN), (Publisher: IGI Global).
Abstract | Links | BibTeX | Tags: Big data, Business process management, Complex event processing, Complex event processing (CEP), Computer science, Cost reduction, Digital transformation, Emerging technologies, Enterprise Architecture, Information technology, Machine learning, Predictive analytics, Procurement, Procurement organizations, Proposed architectures, Rules based systems, Skill shortage, Supply chain management, Technology limitations
@article{barrad_analytics_2020,
title = {An analytics architecture for procurement},
author = {S. Barrad and S. Gagnon and R. Valverde},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083561161&doi=10.4018%2fIJITSA.2020070104&partnerID=40&md5=79790ea0afaa8174f59e639d7c9ce917},
doi = {10.4018/IJITSA.2020070104},
issn = {1935570X (ISSN)},
year = {2020},
date = {2020-01-01},
journal = {International Journal of Information Technologies and Systems Approach},
volume = {13},
number = {2},
pages = {73–98},
abstract = {Procurement transformation and pure cost reduction are no longer a novelty in today's modern business world. Procurement, as a core business function, plays a key role given its ability to generate value for the firm. From maximizing supplier value to minimizing contract leakage, challenges seldomly lack in this department. In fact, both resource and skill shortages and technology limitations are typically "top-of-mind" for Procurement Executives. Many research articles around the concept of cost reduction however, limited literature has been published in the areas of Artificial Intelligence, analytics and Rules-Based Systems and their specific application in Procurement. This article proposes a new enterprise architecture, leveraging emerging technologies to guide procurement organizations in their digital transformation. Our intent is to discuss how analytics, business rules and complex event processing (CEP) can be explored and adapted to the world of procurement to help reduce costs. This article concludes by suggesting an approach to implement the proposed architecture. Copyright © 2020, IGI Global.},
note = {Publisher: IGI Global},
keywords = {Big data, Business process management, Complex event processing, Complex event processing (CEP), Computer science, Cost reduction, Digital transformation, Emerging technologies, Enterprise Architecture, Information technology, Machine learning, Predictive analytics, Procurement, Procurement organizations, Proposed architectures, Rules based systems, Skill shortage, Supply chain management, Technology limitations},
pubstate = {published},
tppubtype = {article}
}
Procurement transformation and pure cost reduction are no longer a novelty in today's modern business world. Procurement, as a core business function, plays a key role given its ability to generate value for the firm. From maximizing supplier value to minimizing contract leakage, challenges seldomly lack in this department. In fact, both resource and skill shortages and technology limitations are typically "top-of-mind" for Procurement Executives. Many research articles around the concept of cost reduction however, limited literature has been published in the areas of Artificial Intelligence, analytics and Rules-Based Systems and their specific application in Procurement. This article proposes a new enterprise architecture, leveraging emerging technologies to guide procurement organizations in their digital transformation. Our intent is to discuss how analytics, business rules and complex event processing (CEP) can be explored and adapted to the world of procurement to help reduce costs. This article concludes by suggesting an approach to implement the proposed architecture. Copyright © 2020, IGI Global.