Research Center in Artificial Intelligence

Articles published by our team

Roux, L.-R., Martin, T., Valtchev, P. (2021). FGC-Stream: A novel joint miner for frequent generators and closed itemsets in data streams. 21th IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand.
Fuentes, V., Martin, T., Valtchev, P. et al. (2021). A Dairy Ontology to Support Precision Farming. 12th International Conference on Biomedical Ontologies, Bolzano, Italy.
Martin, T., Fuentes, V., Valtchev, P. et al. (2021). Towards Mining Generalized Patterns From RDF Data And A Domain Ontology. No formal publication. 3rd Workshop on Graph Embedding and Mining (GEM) with ECML-PKDD, Bilbao, Spain.
Martin, T., Fuentes, V., Valtchev, P. et al. (2021). Graph pattern mining on top of a domain ontology: Preliminary results from a dairy production application. 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Szczecin, Poland.
Benabderrahmane, S., Berrada, G., Cheney, J., Valtchev, P. (2021). A Rule Mining-Based Advanced Persistent Threats Detection System. Proceedings of the IJCA 2021. 30th International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Canada.
Wajnberg, M., Valtchev, P. et al. (2021). FCA Went (Multi-)Relational, But Does It Make Any Difference?. CEUR-WS. 9th Workshop What Can FCA Do for Artificial Intelligence? (FCA4AI) with IJCAI, Montréal, Canada.
R. Nkambou, J. Brisson, S. Robert, A. Tato (2021). Learning Logical Reasoning: Improving the Student Model with a Data Driven Approach. Proceedings of the International Conference on Intelligent Tutoring Systems, LNCS, Springer, pp. 60-67.
K. Sodoké, R. Kambou, I. Tanoubi, A. Dufresne (2021). Toward a Webcam Based ITS to Enhance Clinician Visual Situational Awareness. Proceedings of the International Conference on Intelligent Tutoring Systems, LNCS, Springer, pp. 239-243.
Wajnberg, M., Valtchev, P., Blondin Massé, A., Benmoussa, A. et al. (2020). Mining Heterogeneous Associations from Pediatric Cancer Data by Relational Concept Analysis. 2020 International Conference on Data Mining Workshops (ICDMW). Workshop on Multi-Source Data Mining (MSDM) with ICDM, Sorrento, Italy (597-604)
Martin, T., Valtchev, P., Diallo, A. (2020). Leveraging a domain ontology in (neural) learning from heterogeneous data. Proceedings of the CIKM 2020 Workshops. 1st Intl. Workshop on Combining Symbolic and Sub-symbolic methods and their Applications (CSSA) at CIKM, Galway, Ireland.
Wajnberg, M., Poulin, J.-M., Blondin Massé, A., Valtchev, P. (2020). The Dictionary Game: Toward a Characterization of Lexical Primitives Using Graph Theory and Relational Concept Analysis. COGNITIVE, Nice, France.
Martin, T., Diallo, A., Valtchev, P., Lacroix, R. (2020). Bridging the gap between an ontology and deep neural models by pattern mining. CEUR-WS, vol. 2708. 1st International Workshop "Deep Learning meets Ontologies and Natural Language Processing" in Joint Ontology Workshops (JOWO), Bolzano, Italy.
A. Tato, R. Nkambou, A. Dufresne (2020). Using AI techniques in A Serious Game for Socio-moral Reasoning Development. Proc. of The 34th AAAI Conference on Artificial Intelligence (AAAI-20). The 10th Symp. on Educational Advances in AI (EAAI-20), (13420-13427).
Tato, A., Nkambou, R. (2020). Improving First-Order Optimization Algorithms (Student Abstract). Proceedings of The Thirty- Fourth AAAI Conference on Artificial Intelligence (AAAI-20). The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), (13935-13936). AAAI.
K. Sodoké, R. Nkambou, A. Dufresne & I. Tanoubi (2020). Toward a deep convolutional LSTM for eye gaze spatiotemporal data sequence classification. Proceedings of the 13th International Conf. on Educational Data Mining. IEDM Society.
L. Pillette, C. Jeunet, B. Mansencal, R. NKambou, B. N’Kaoua, F. Lotte. (2020). A physical learning companion for Mental-Imagery BCI User Training. International Journal of Human- Computer Studies. 136.
R. Nkambou, A. Tato, J. Brisson, S. Robert, M. Sainte-Marie (2020). Une approche hybride à la modélisation de l’apprenant dans un STI pour l’apprentissage du raisonnement logique. Revue Sticef, vol. 27, numéro 2, 2020, p. 63-102, DOI :10.23709/sticef.27.2.5.
Gonçalves Frasco, C., Radmacher, M., Lacroix, R., Cue, R., Valtchev, P., Robert, C., Boukadoum, M., Sirard, M.- A., Diallo, A. (2020). Towards an Effective Decision-making System based on Cow Profitability using Deep Learning. Vol. (2). International Conference on Agents and Artificial Intelligence, Malta (949-958).
Massardi, J., Gravel, M., Beaudry, E., 2020. PARC: A Plan and Activity Recognition Component for Assistive Robots", 2020 IEEE International Conference on Robotics and Automation (ICRA)
Champagne Gareau, J., Beaudry, É., Makarenkov, V. , 2020. An Efficient Electric Vehicle Path-Planner That Considers the Waiting Time. To be published in Proc. of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019). 9 p.https://dl.acm.org/doi/abs/10.1145/3347146.3359064
Belainine, B., Sadat, F., Boukadoum, M., Lounis, H., 2020. Towards a Multi-Dataset for Complex Emotions Learning Based on Deep Neural Networks, in: Second Workshop on Linguistic and Neurocognitive Resources. pp. 50–58.
Drown, D.J., Villemaire, R., Robert, S., 2020. Big Players: Emotion in Twitter Communities Tweeting About Global Warming, in: Goutte, C., Zhu, X. (Eds.), Advances in Artificial Intelligence, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 189–200. https://doi.org/10.1007/978-3-030-47358-7_18
Martin, T., Francoeur, G., Valtchev, P., 2020. CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams, in: Proceedings of the ACM SIGKDD 2020. Sheridan Communications, San Diego (CA), p. 10 p.
Pillette, L., Jeunet, C., Mansencal, B., N’Kambou, R., N’Kaoua, B., Lotte, F., 2020. A physical learning companion for Mental-Imagery BCI User Training. International Journal of Human-Computer Studies 136, 102380. https://doi.org/10.1016/j.ijhcs.2019.102380
Tam, S., Boukadoum, M., Campeau-Lecours, A., Gosselin, B., 2020. A Fully Embedded Adaptive Real-Time Hand Gesture Classifier Leveraging HD-sEMG and Deep Learning. IEEE Transactions on Biomedical Circuits and Systems 14, 232–243. https://doi.org/10.1109/TBCAS.2019.2955641
P. Fournier-Viger, J. Lin, R. Nkambou, B. Vo, V. Tseng (2019). High-Utility Pattern Mining: Theory, Algorithms & Applications. Springer.
C. M. Desmarais, C. F. Lynch, A. Merceron, R. Nkambou. (2019). Proceedings of the 12th Internat. Conf. on Educational Data Mining.
A. Tato, R. Nkambou, A. Dufresne (2019). Hybrid Deep Neural Networks to Predict Socio-Moral Reasoning Skills. 12th International Conference on Educational Data Mining, Montreal, Canada (623-626).
R. Ghali, A. Tato, R. Nkambou (2019). Using EEG Features and Machine Learning to Predict Gifted Children. Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conf. (FLAIRS2019), (120-123). AAAI.
Massardi, J., Gravel, M., Beaudry, E., 2019. Error-Tolerant Anytime Approach to Plan Recognition Using a Particle Filter. ICAPS 2019: 284-291 https://www.aaai.org/ojs/index.php/ICAPS/article/view/3490
Aksenova, G., Kiviniemi, A., Kocaturk, T., Lejeune, A., 2019. From Finnish AEC knowledge ecosystem to business ecosystem: lessons learned from the national deployment of BIM. Construction Management and Economics 37, 317–335. https://doi.org/10.1080/01446193.2018.1481985
Dekhili, G., Le, N.T., Sadat, F., 2019. Improving Named Entity Recognition with Commonsense Knowledge Pre-training, in: Ohara, K., Bai, Q. (Eds.), Knowledge Management and Acquisition for Intelligent Systems, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 10–20. https://doi.org/10.1007/978-3-030-30639-7_2
Fournier-Viger, P., Chun-Wei Lin, J., Truong-Chi, T., Nkambou, R., 2019. A Survey of High Utility Itemset Mining, in: Fournier-Viger, P., Lin, J.C.-W., Nkambou, R., Vo, B., Tseng, V.S. (Eds.), High-Utility Pattern Mining, Studies in Big Data. Springer International Publishing, Cham, pp. 1–45. https://doi.org/10.1007/978-3-030-04921-8_1
Lazli, Boukadoum, Ait Mohamed, 2019. Computer-Aided Diagnosis System of Alzheimer’s Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model. Brain Sciences 9, 289. https://doi.org/10.3390/brainsci9100289
Le, N.T., Sadat, F., Menard, L., Dinh, D., 2019. Low-Resource Machine Transliteration Using Recurrent Neural Networks. ACM Transactions on Asian and Low-Resource Language Information Processing 18, 1–14. https://doi.org/10.1145/3265752
Legault, M., Bourdon, J.-N., Poirier, P., 2019. Neurocognitive Variety in Neurotypical Environments: The Source of “Deficit” in Autism. Journal of Behavioral and Brain Science 09, 246–272. https://doi.org/10.4236/jbbs.2019.96019
Meftah, S., Tamaazousti, Y., Semmar, N., Essafi, H., Sadat, F., 2019. Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging, in: Proceedings of the 2019 Conference of the North. Presented at the Proceedings of the 2019 Conference of the North, Association for Computational Linguistics, Minneapolis, Minnesota, pp. 4107–4112. https://doi.org/10.18653/v1/N19-1416
Pérez-Gay Juárez, F., Sicotte, T., Thériault, C., Harnad, S., 2019. Category learning can alter perception and its neural correlates. PLOS ONE 14, e0226000. https://doi.org/10.1371/journal.pone.0226000
Poirier, P., Faucher, L., Bourdon, J.-N., 2019. Cultural Blankets: Epistemological Pluralism in the Evolutionary Epistemology of Mechanisms. Journal for General Philosophy of Science. https://doi.org/10.1007/s10838-019-09472-8
Potvin, B., Villemaire, R., 2019. Robust Web Data Extraction Based on Unsupervised Visual Validation, in: Nguyen, N.T., Gaol, F.L., Hong, T.-P., Trawiński, B. (Eds.), Intelligent Information and Database Systems, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 77–89. https://doi.org/10.1007/978-3-030-14799-0_7
Remita, A.M., Diallo, A.B., 2019. Statistical Linear Models in Virus Genomic Alignment-free Classification: Application to Hepatitis C Viruses. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 474–481. https://doi.org/10.1109/BIBM47256.2019.8983375
Sarray, I., Salah, A., 2019. Assisted Composition of Linked Data Queries:, in: Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Presented at the 11th International Conference on Knowledge Engineering and Ontology Development, SCITEPRESS - Science and Technology Publications, Vienna, Austria, pp. 185–194. https://doi.org/10.5220/0008169601850194
Tato, A., Nkambou, R., 2019. Some Improvements of Deep Knowledge Tracing, in: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). Presented at the 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, Portland, OR, USA, pp. 1520–1524. https://doi.org/10.1109/ICTAI.2019.00217
Wajnberg, M., Valtchev, P., Lezoche, M., Masse, A.B., Panetto, H., 2019a. Concept Analysis-Based Association Mining From Linked Data: A Case In Industrial Decision Making, in: Proceedings of the JOWO 2019. Presented at the 2nd International Workshop on Data meets Applied Ontologies in Open Science and Innovation, DAO-SI 2019, CEUR-WS, p. 10.
Wajnberg, M., Valtchev, P., Lezoche, M., Panetto, H., Blondin Massé, A., 2019b. Mining Process Factor Causality Links with Multi-relational Associations, in: Proceedings of the 10th International Conference on Knowledge Capture. Presented at the K-CAP ’19: Knowledge Capture Conference, ACM, Marina Del Rey CA USA, pp. 263–266. https://doi.org/10.1145/3360901.3364446
Wajnberg, M., Valtchev, P., Lezoche, M., Panetto, H., Blondin Massé, A., 2019c. Mining Process Factor Causality Links with Multi-relational Associations, in: Proceedings of the 10th International Conference on Knowledge Capture. Presented at the K-CAP ’19: Knowledge Capture Conference, ACM, Marina Del Rey CA USA, pp. 263–266. https://doi.org/10.1145/3360901.3364446
Wu, C.-J., Remita, A.M., Diallo, A.B., 2019. MirLibSpark: A Scalable NGS Plant MicroRNA Prediction Pipeline for Multi-Library Functional Annotation, in: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. Presented at the BCB ’19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, ACM, Niagara Falls NY USA, pp. 669–674. https://doi.org/10.1145/3307339.3343463
Abdenbi, M., Masse, A.B., Goupil, A., 2018. On the maximal number of leaves in induced subtrees of series-parallel graphs, in: GASCom. pp. 24–31.
Ange, T., Roger, N., Aude, D., Claude, F., 2018. Semi-Supervised Multimodal Deep Learning Model for Polarity Detection in Arguments, in: 2018 International Joint Conference on Neural Networks (IJCNN). Presented at the 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, Rio de Janeiro, pp. 1–8. https://doi.org/10.1109/IJCNN.2018.8489342
Blin, G., Blondin Massé, A., Gasparoux, M., Hamel, S., Vandomme, É., 2018. Nearest constrained circular words 14 pages. https://doi.org/10.4230/LIPICS.CPM.2018.6
Blondin Massé, A., de Carufel, J., Goupil, A., Lapointe, M., Nadeau, É., Vandomme, É., 2018. Leaf realization problem, caterpillar graphs and prefix normal words. Theoretical Computer Science 732, 1–13. https://doi.org/10.1016/j.tcs.2018.04.019
Brisson, J., Markovits, H., Robert, S., Schaeken, W., 2018. Reasoning from an incompatibility: False dilemma fallacies and content effects. Memory & Cognition 46, 657–670. https://doi.org/10.3758/s13421-018-0804-x
Faucher, L., Poirier, P., 2018. Mother Culture, Meet Mother Nature. Oxford University Press. https://doi.org/10.1093/oso/9780199367511.003.0017
Gisiger, T., Boukadoum, M., 2018. A loop-based neural architecture for structured behavior encoding and decoding. Neural Networks 98, 318–336. https://doi.org/10.1016/j.neunet.2017.11.019
Halioui, A., Valtchev, P., Diallo, A.B., 2018. Bioinformatic Workflow Extraction from Scientific Texts based on Word Sense Disambiguation. IEEE/ACM Transactions on Computational Biology and Bioinformatics 15, 1979–1990. https://doi.org/10.1109/TCBB.2018.2847336
Lazli, L., Boukadoum, M., Ait Mohamed, O., 2018. Computer-Aided Diagnosis System for Alzheimer’s Disease Using Fuzzy-Possibilistic Tissue Segmentation and SVM Classification, in: 2018 IEEE Life Sciences Conference (LSC). Presented at the 2018 IEEE Life Sciences Conference (LSC), IEEE, Montreal, QC, pp. 33–36. https://doi.org/10.1109/LSC.2018.8572122
Le, N.T., Sadat, F., 2018. Improving the neural network-based machine transliteration for low-resourced language pair. Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation 8.
Massé, A.B., de Carufel, J., Goupil, A., Lapointe, M., Nadeau, É., Vandomme, É., 2018. Fully leafed induced subtrees. arXiv:1709.09808 [cs, math].
Mili, H., Valtchev, P., Szathmary, L., Boubaker, A., Leshob, A., Charif, Y., Martin, L., 2018. Ontology-based model-driven development of a destination management portal: Experience and lessons learned: Model-driven development: Experience and lessons learned - Mili et al. Software: Practice and Experience 48, 1438–1460. https://doi.org/10.1002/spe.2581
Potvin, B., Villemaire, R., 2018. When Different Is Wrong: Visual Unsupervised Validation for Web Information Extraction, in: Perner, P. (Ed.), Machine Learning and Data Mining in Pattern Recognition, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 132–146. https://doi.org/10.1007/978-3-319-96133-0_10
Sharma, R., Le Tan, N., Sadat, F., 2018. Multimodal Sentiment Analysis Using Deep Learning, in: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Presented at the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, Orlando, FL, pp. 1475–1478. https://doi.org/10.1109/ICMLA.2018.00240
Tato, A., Nkambou, R., Frasson, C., 2018. Predicting Emotions From Multimodal Users’ Data, in: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization. Presented at the UMAP ’18: 26th Conference on User Modeling, Adaptation and Personalization, ACM, Singapore Singapore, pp. 369–370. https://doi.org/10.1145/3209219.3209264
Thériault, C., Pérez-Gay, F., Rivas, D., Harnad, S., 2018. Learning-induced categorical perception in a neural network model. TopiCS in Cognitive Science 1, 20.
  1. N. Ghazzawi, B. Robichaud, P. Drouin and F. Sadat. Automatic extraction of specialized verbal units - A comparative study on Arabic, English and French. In Terminology | International Journal of Theoretical and Applied Issues in Specialized Communication(to appear).

  2. Tato, A., Nkambou, R., Brisson, J., Kenfack, C., Robert, S., & Kissok, P. (2016, September). A Bayesian Network for the Cognitive Diagnosis of Deductive Reasoning. In European Conference on Enhanced Learning Technology (pp. 627-631). Springer International Publishing.

  3. Fonseca, A., Sadat, F., & Lareau, F. (2016). Lexfom: a lexical functions ontology model. COLING 2016, 145.Link.

  4. NT, Mallek, F., & Sadat, F. (2016). UQAM-NTL: Named entity recognition in Twitter messages. WNUT 2016, 197.Link.

  5. Alexsandro Fonseca, Fatiha Sadat and François Lareau (2016). Lexical Ontology to Represent Lexical Functions. Proceedings of the 2nd Language and Ontology Workshop (LangOnto2, LREC-2016), pp. 69-73, Portoroz, Slovenia, May 23rd 2016.Link.

  6. Billal, B., Fonseca, A., & Sadat, F. (2016). Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets. WNUT 2016, 102.Link.

  7. Kenfack, C., Nkambou, R., Robert, S., Tato, AAN, Brisson, J., & Kissok, P. (2016). A Brief Overview of Logic-Muse, an Intelligent Tutoring System for Logical Reasoning Skills. In Intelligent Tutoring Systems (ITS) 2016. LNCS 9684, (pp. 511-513). (p.511). Springer International Publishing.

  8. Nkambou, R., Tato, AAN, Brisson, J., Kenfack, C., Robert, S., & Kissok, P. (2016). On the Evaluation of the Expert and the Learner Models of Logic-Muse Tutoring System. In Intelligent Tutoring Systems (ITS) 2016 LNCS 9684, (pp. 506-508) (p.506). Springer International Publishing.

  9. Bilal Belainine, Alexsandro Fonseca, Fatiha Sadat. Efficient Natural Language Pre-processing for large data sets. In Proc. of Big Data and Natural Language Processing Workshop, IEEE Big Data 2016. Washington DC, USA, Dec. 5-8, 2016.

  10. Nkambou, R., Brisson, J., Kenfack, C., Robert, S., Kissok, P., Tato, A. (2015). The Participatory Design of Logic-Muse, an Intelligent Tutoring System for Logical Reasoning in Multiple Contexts. In S. Carliner, C. Fulford & N. Ostashewski (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2015 (pp. 171029-171938). Association for the Advancement of Computing in Education (AACE).

  11. Sellami, R., Deffaf, F., Sadat, F., and Hadrich Belguith, L. (2015). Improved statistical machine translation. Computación y Sistemas, 19 (4), 701-711.Link.

  12. Mohamed, E., & Sadat, F. (2015). Hybrid Arabic-French translation machine using syntactic re-ordering and morphological pre-processing. Computer Speech & Language, 32 (1), 135-144.Link.

  13. SELLAMI, R., & SADAT, F. (2015). IMPROVING NAMED ENTITY TRANSLATION BY EXPLOITING NOISY PARALLEL CORPORA. Papers in Translation Studies, 179.Link.

  14. Fennouh, S., Nkambou, R., Valchev, P., & Rouane-Hacene, M. (2015, November). On the Assessment of Concept Relevance in FCA-Based Ontology Restructuring. In Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on (pp. 566-574).

  15. Nkambou, R., Brisson, J., Kenfack, C., Robert, S., Kissok, P., & Tato, A. (2015). Towards an Intelligent Tutoring System for Logical Reasoning in Multiple Contexts. In Design for Teaching and Learning in a Networked World, LNCS 9307, (pp. 460-466). Springer International Publishing.

  16. Sadat, F., Mallek, F., Sellami, R., Boudabous, MM, & Farzindar, A. (2014, August). Collaboratively constructed linguistic resources for language vari-ants and their exploitation in nlp applications-the case of tunisian arabic and the social media. In Workshop on lexical and grammatical resources for language processing (p.Link.

  17. Fonseca, A., & Sadat, F. (2014). A Comparative Study of Different Classification Methods for the Identification of Brazilian Portuguese Multiword Expressions. ComAComA 2014, 53 ..Link.

  18. Fonseca, A., Sadat, F., & Massé, AB (2014). Identifying Portuguese Multiword Expressions Using Different Algorithms-A Comparative Analysis. COLING 2014, 104.Link.

  19. FENNOUH, S., NKAMBOU, R., VALTCHEV, P., & ROUANE-HACENE, MOHAMED. (2014). STABILITY-BASED FILTERING FOR ONTOLOGY RESTRUCTURING. Studia Universitatis Babes-Bolyai, Informatica, 59.

  20. Sadat, F., Kazemi, F., & Farzindar, A. (2014, July). Automatic identification of Arabic dialects in social media. In Proceedings of the first international workshop on social media retrieval and analysis (pp. 35-40). ACM.Link.

  21. Sellami, R., Sadat, F., & Belguith, LH (2014, May). Mining Named Entity Translation from Non Parallel Corpora. In FLAIRS Conference (pp. 219-224).Link.

  22. Rouane-Hacene, M., Fennouh, S., Nkambou, R., & Valchev, P. (2010, October). Refactoring of ontologies: Improving the design of ontological models with concept analysis. In Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on (Vol 2, pp. 167-172). IEEE.

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