Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
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. Article d'actes
Dans: 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).
Résumé | Liens | BibTeX | Étiquettes: 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.
2.
Laib, L.; Allili, M. S.; Ait-Aoudia, S.
A probabilistic topic model for event-based image classification and multi-label annotation Article de journal
Dans: Signal Processing: Image Communication, vol. 76, p. 283–294, 2019, ISSN: 09235965 (ISSN), (Publisher: Elsevier B.V.).
Résumé | Liens | BibTeX | Étiquettes: Annotation performance, Classification (of information), Convolution, Convolution neural network, Convolutional neural nets, Event classification, Event recognition, Image annotation, Image Enhancement, Latent Dirichlet allocation, Multi-label annotation, Neural networks, Probabilistic topic models, Semantics, Statistics, Topic Modeling
@article{laib_probabilistic_2019,
title = {A probabilistic topic model for event-based image classification and multi-label annotation},
author = {L. Laib and M. S. Allili and S. Ait-Aoudia},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067936924&doi=10.1016%2fj.image.2019.05.012&partnerID=40&md5=a617885b93f3a931c6b6ce1a165f940b},
doi = {10.1016/j.image.2019.05.012},
issn = {09235965 (ISSN)},
year = {2019},
date = {2019-01-01},
journal = {Signal Processing: Image Communication},
volume = {76},
pages = {283–294},
abstract = {We propose an enhanced latent topic model based on latent Dirichlet allocation and convolutional neural nets for event classification and annotation in images. Our model builds on the semantic structure relating events, objects and scenes in images. Based on initial labels extracted from convolution neural networks (CNNs), and possibly user-defined tags, we estimate the event category and final annotation of an image through a refinement process based on the expectation–maximization (EM)algorithm. The EM steps allow to progressively ascertain the class category and refine the final annotation of the image. Our model can be thought of as a two-level annotation system, where the first level derives the image event from CNN labels and image tags and the second level derives the final annotation consisting of event-related objects/scenes. Experimental results show that the proposed model yields better classification and annotation performance in the two standard datasets: UIUC-Sports and WIDER. © 2019 Elsevier B.V.},
note = {Publisher: Elsevier B.V.},
keywords = {Annotation performance, Classification (of information), Convolution, Convolution neural network, Convolutional neural nets, Event classification, Event recognition, Image annotation, Image Enhancement, Latent Dirichlet allocation, Multi-label annotation, Neural networks, Probabilistic topic models, Semantics, Statistics, Topic Modeling},
pubstate = {published},
tppubtype = {article}
}
We propose an enhanced latent topic model based on latent Dirichlet allocation and convolutional neural nets for event classification and annotation in images. Our model builds on the semantic structure relating events, objects and scenes in images. Based on initial labels extracted from convolution neural networks (CNNs), and possibly user-defined tags, we estimate the event category and final annotation of an image through a refinement process based on the expectation–maximization (EM)algorithm. The EM steps allow to progressively ascertain the class category and refine the final annotation of the image. Our model can be thought of as a two-level annotation system, where the first level derives the image event from CNN labels and image tags and the second level derives the final annotation consisting of event-related objects/scenes. Experimental results show that the proposed model yields better classification and annotation performance in the two standard datasets: UIUC-Sports and WIDER. © 2019 Elsevier B.V.
3.
Ouyed, O.; Allili, M. S.
Feature relevance for kernel logistic regression and application to action classification Article d'actes
Dans: Proceedings - International Conference on Pattern Recognition, p. 1325–1329, Institute of Electrical and Electronics Engineers Inc., 2014, ISBN: 978-1-4799-5208-3, (ISSN: 10514651).
Résumé | Liens | BibTeX | Étiquettes: Action classifications, Action recognition, Classification (of information), Classification methods, Classification performance, Feature relevance, Kernel logistic regression, Logistic regression, Multinomial kernels, Pattern Recognition, Supervised classification, Support vector machines
@inproceedings{ouyed_feature_2014,
title = {Feature relevance for kernel logistic regression and application to action classification},
author = {O. Ouyed and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919884283&doi=10.1109%2fICPR.2014.237&partnerID=40&md5=616038cd7924614fd633061dfed32903},
doi = {10.1109/ICPR.2014.237},
isbn = {978-1-4799-5208-3},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
pages = {1325–1329},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {An approach is proposed for incorporating feature relevance in mutinomial kernel logistic regression (MKLR) for classification. MKLR is a supervised classification method designed for separating classes with non-linear boundaries. However, it assumes all features are equally important, which may decrease classification performance when dealing with high-dimensional or noisy data. We propose a feature weighting algorithm for MKLR which automatically tunes features contribution according to their relevance for classification and reduces data over-fitting. The proposed algorithm produces more interpretable models and is more generalizable than MKLR, Kernel-SVM and LASSO methods. Application to simulated data and video action classification has provided very promising results compared to the aforementioned classification methods. © 2014 IEEE.},
note = {ISSN: 10514651},
keywords = {Action classifications, Action recognition, Classification (of information), Classification methods, Classification performance, Feature relevance, Kernel logistic regression, Logistic regression, Multinomial kernels, Pattern Recognition, Supervised classification, Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}
An approach is proposed for incorporating feature relevance in mutinomial kernel logistic regression (MKLR) for classification. MKLR is a supervised classification method designed for separating classes with non-linear boundaries. However, it assumes all features are equally important, which may decrease classification performance when dealing with high-dimensional or noisy data. We propose a feature weighting algorithm for MKLR which automatically tunes features contribution according to their relevance for classification and reduces data over-fitting. The proposed algorithm produces more interpretable models and is more generalizable than MKLR, Kernel-SVM and LASSO methods. Application to simulated data and video action classification has provided very promising results compared to the aforementioned classification methods. © 2014 IEEE.
4.
Gagnon, S.; Messaoudi, S.; Charbonneau, A.
Automated Text Classification based on an ontology standard: Application of the Extensible Business Reporting Language (XBRL) to Reuters Corpus Volume 1 (RCV1) Article de journal
Dans: CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications, p. 151–158, 2011, ISSN: 978-235768024-1 (ISBN).
Résumé | Liens | BibTeX | Étiquettes: Administrative data processing, Automated Text Classification, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Reuters Corpus Volume 1 (RCV1), Text classification, Text processing
@article{gagnon_automated_2011,
title = {Automated Text Classification based on an ontology standard: Application of the Extensible Business Reporting Language (XBRL) to Reuters Corpus Volume 1 (RCV1)},
author = {S. Gagnon and S. Messaoudi and A. Charbonneau},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869423599&partnerID=40&md5=1d12a436b5acb9f715fd6f1669e37be4},
issn = {978-235768024-1 (ISBN)},
year = {2011},
date = {2011-01-01},
booktitle = {CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications},
journal = {CORIA 2011: COnference en Recherche d'Information et Applications - Conference on Information Retrieval and Applications},
pages = {151–158},
address = {Avignon},
abstract = {We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.},
keywords = {Administrative data processing, Automated Text Classification, Automation, Classification (of information), Domain-specific ontologies, Extensible Business Reporting Language (XBRL), F measure, Financial news, Information retrieval, Ontology, Reuters, Reuters Corpus Volume 1 (RCV1), Text classification, Text processing},
pubstate = {published},
tppubtype = {article}
}
We demonstrate that applying a domain-specific ontology standard significantly improves Automated Text Classification (ATC). We use the Extensible Business Reporting Language (XBRL) to define a standard ontology and compare the performance of an ACT engine (IBM Classification Module v.8.6) against 2 other list of concepts, namely simple and hierarchical. Our sample of financial news is extracted from the Reuters Corpus Volume 1 (RCV1), where 2 experts in finance help us code 1000 of the 45000 news dealing with mergers and acquisitions. We report recall, precision, the F measure, and in addition a hierarchical measure adjusted for classification relevance in parent classes, as well as a more detailed measure evaluating the classification improvements at the level of each text.