@article{bogie_using_2024,

title = {Using virtual reality to improve verbal episodic memory in schizophrenia: A proof-of-concept trial},

author = {B. J. M. Bogie and C. Noël and F. Gu and S. Nadeau and C. Shvetz and H. Khan and M. -C. Rivard and S. Bouchard and M. Lepage and S. Guimond},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186986986&doi=10.1016%2fj.scog.2024.100305&partnerID=40&md5=a15c598b45b8f44a40b25fe5fd078a06},

doi = {10.1016/j.scog.2024.100305},

issn = {22150013 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Schizophrenia Research: Cognition},

volume = {36},

abstract = {Background: Schizophrenia is associated with impairments in verbal episodic memory. Strategy for Semantic Association Memory (SESAME) training represents a promising cognitive remediation program to improve verbal episodic memory. Virtual reality (VR) may be a novel tool to increase the ecological validity and transfer of learned skills of traditional cognitive remediation programs. The present proof-of-concept study aimed to assess the feasibility, acceptability, and preliminary efficacy of a VR-based cognitive remediation module inspired by SESAME principles to improve the use of verbal episodic memory strategies in schizophrenia. Methods: Thirty individuals with schizophrenia/schizoaffective disorder completed this study. Participants were randomized to either a VR-based verbal episodic memory training condition inspired by SESAME principles (intervention group) or an active control condition (control group). In the training condition, a coach taught semantic encoding strategies (active rehearsal and semantic clustering) to help participants remember restaurant orders in VR. In the active control condition, participants completed visuospatial puzzles in VR. Attrition rate, participant experience ratings, and cybersickness questionnaires were used to assess feasibility and acceptability. Trial 1 of the Hopkins Verbal Learning Test – Revised was administered pre- and post-intervention to assess preliminary efficacy. Results: Feasibility was demonstrated by a low attrition rate (5.88 %), and acceptability was demonstrated by limited cybersickness and high levels of enjoyment. Although the increase in the number of semantic clusters used following the module did not reach conventional levels of statistical significance in the intervention group, it demonstrated a notable trend with a medium effect size (t = 1.48},

note = {Publisher: Elsevier Inc.},

keywords = {adult, article, clinical article, clinical assessment, Cognitive remediation therapy, cybersickness, disease severity, dizziness, Ecological treatment, Episodic memory, exclusion VR criteria questionnaire, feasibility study, female, Hopkins verbal learning test, human, male, mini international neuropsychiatric interview, nausea, outcome assessment, Positive and Negative Syndrome Scale, Proof of concept, questionnaire, randomized controlled trial, schizophrenia, scoring system, Semantic encoding, Semantics, task performance, training, Verbal memory, virtual reality, vr experience questionnaire},

pubstate = {published},

tppubtype = {article}

}

@article{azzi_scoring_2023,

title = {Scoring Ontologies for Reuse: An Approach for Fitting Semantic Requirements},

author = {S. Azzi and A. Assi and S. Gagnon},

editor = {Vlachidis A. Garoufallou E.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172082147&doi=10.1007%2f978-3-031-39141-5_17&partnerID=40&md5=f2d307e9185aca3ae5a69c31e8e4440a},

doi = {10.1007/978-3-031-39141-5_17},

issn = {18650929},

year  = {2023},

date = {2023-01-01},

journal = {Communications in Computer and Information Science},

volume = {1789 CCIS},

pages = {203–208},

abstract = {The process of reusing ontologies is still challenging for the ontological community. One of the challenging efforts is to select the most relevant ontology from a set of candidates that needs a deep consideration. After the step of finding the candidates, many of them can be more appropriate than others as they fit better to the ontology requirements expressed by competency questions. First, we develop a mathematical formalisation based on Set Theory and we design the problem as an optimization problem to assist the knowledge engineer in selecting ontologies. Then, we provide formal steps to make well-founded comparison across a set of candidate ontologies. At last, we propose metrics to quantify the decision during the selection step. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {ISBN: 9783031391408 

Publisher: Springer Science and Business Media Deutschland GmbH},

keywords = {Competency question, Fitting semantics, Formalisation, Ontological communities, Ontology, Ontology engineering, Ontology reuse, Ontology's, PNADO, Reuse, Semantic requirement, Semantic Web, Semantics},

pubstate = {published},

tppubtype = {article}

}

@article{azzi_ontology-driven_2023,

title = {Ontology-Driven Parliamentary Analytics: Analysing Political Debates on COVID-19 Impact in Canada},

author = {S. Azzi and S. Gagnon},

editor = {Asemi A. Francesconi E. Ko A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172088757&doi=10.1007%2f978-3-031-39841-4_7&partnerID=40&md5=5dff3b672c36c66070042a35eed048e0},

doi = {10.1007/978-3-031-39841-4_7},

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 = {14149 LNCS},

pages = {89–102},

abstract = {Parliamentary debates are usually published in Parliament’s websites to allows citizens to be informed on the latest national debates, proposals and decisions. To enhance citizen experience and engagement, functionalities such as debates annotation and question answering are necessary. Annotating text requires semantic content and ontologies are known for their ability to describe a common vocabulary for a domain and can be a solid base for annotation and question answering. We report on an ongoing study to enhance parliamentary analytics using an ontology and knowledge graph to sharpen annotations and facilitate their query by end-users. As a salient case, a sample of debates are collected on the COVID-19 impact in Canada, as its complexity shows the relevance of using advanced knowledge representation techniques. We focused on the development of a new “Impact of COVID-19 in Canada Ontology” (ICCO) that provides contextualized semantic information on impact in numerous policy areas, as this ontology is entirely built from Canadian parliamentary debates. It has been evaluated and validated by experts. Our conclusion underscores the importance of integrating ontology-driven parliamentary analytics within the broader context of digital transformation in legislative institutions, and the need for new platforms supporting free and open Digital Humanities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {ISBN: 9783031398407 

Publisher: Springer Science and Business Media Deutschland GmbH},

keywords = {COVID-19, End-users, Knowledge graph, Knowledge graphs, Ontology, Ontology graphs, Ontology's, Parliamentary debate, Political debates, Question Answering, Semantic content, Semantic ontology, Semantics, Solid basis},

pubstate = {published},

tppubtype = {article}

}

@article{gagnon_semantic_2022,

title = {Semantic Annotation of Parliamentary Debates and Legislative Intelligence Enhancing Citizen Experience},

author = {S. Gagnon and S. Azzi},

editor = {Kotsis G. Francesconi E. Ko A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135782631&doi=10.1007%2f978-3-031-12673-4_5&partnerID=40&md5=0765bacc7d38f77896bd9adf402268b9},

doi = {10.1007/978-3-031-12673-4_5},

issn = {03029743},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {13429 LNCS},

pages = {63–76},

abstract = {The concept of “Legislative Intelligence” (LegisIntel) refers to Artificial Intelligence (AI) and semantic analytics tools implemented in parliaments to enhance citizen experience in monitoring complex interrelations among various contents of parliamentary proceedings. The integration of a suite of digital solutions can build upon the core functionality of Semantic Annotation of Parliamentary Debates. Using well-established Natural Language Processing (NLP) technologies, linked to ontologies and Knowledge Graphs (KG), it can help identify the concepts and entities throughout texts, and index sentences and summaries as per a citizen’s customized knowledge base. These annotations can then be leveraged to recommend relevant text excerpts end-users could build upon, within teams if they chose to do so, and possibly compose and customize legislative critiques and recommendations thoroughly tested for coherence, accuracy, and evidence. The present study proposes an international open-source initiative among parliaments to ensure the launch and viability of a suite of LegisIntel solutions. It reports on the completed initial phase of this initiative, aiming to prepare discussions in launching an international consultation among peers. The goals of this phase are to document the core functionality of LegisIntel solutions and formulate a proposed architecture that may serve to generate ideas from various developer communities. The Action Design Research (ADR) methodology is used in this process, with results focused on system artefacts such as an interface mockup, a functional design, and a model of infrastructure components. The conclusion addresses risks and outlines the next steps of this initiative. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {ISBN: 9783031126727 

Publisher: Springer Science and Business Media Deutschland GmbH},

keywords = {Analytic tools, Core functionality, Digital solutions, Knowledge graph, Language processing, Laws and legislation, Legislative intelligence, Natural language processing systems, Natural languages, Parliamentary debate, Parliamentary proceedings, Semantic annotations, Semantic-analytics, Semantics},

pubstate = {published},

tppubtype = {article}

}

@article{messaoudi_efficient_2021,

title = {Efficient Embedding Network for 3D Brain Tumor Segmentation},

author = {H. Messaoudi and A. Belaid and M. L. Allaoui and A. Zetout and M. S. Allili and S. Tliba and D. Ben Salem and P. -H. Conze},

editor = {Bakas S. Crimi A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107387134&doi=10.1007%2f978-3-030-72084-1_23&partnerID=40&md5=b3aa3516b0465a1bf5611db4727d95f1},

doi = {10.1007/978-3-030-72084-1_23},

issn = {03029743},

year  = {2021},

date = {2021-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {12658 LNCS},

pages = {252–262},

abstract = {3D medical image processing with deep learning greatly suffers from a lack of data. Thus, studies carried out in this field are limited compared to works related to 2D natural image analysis, where very large datasets exist. As a result, powerful and efficient 2D convolutional neural networks have been developed and trained. In this paper, we investigate a way to transfer the performance of a two-dimensional classification network for the purpose of three-dimensional semantic segmentation of brain tumors. We propose an asymmetric U-Net network by incorporating the EfficientNet model as part of the encoding branch. As the input data is in 3D, the first layers of the encoder are devoted to the reduction of the third dimension in order to fit the input of the EfficientNet network. Experimental results on validation and test data from the BraTS 2020 challenge demonstrate that the proposed method achieve promising performance. © 2021, Springer Nature Switzerland AG.},

note = {ISBN: 9783030720834 

Publisher: Springer Science and Business Media Deutschland GmbH},

keywords = {3D medical image processing, Brain, Brain tumor segmentation, Classification networks, Convolutional neural networks, Deep learning, Embedding network, Image segmentation, Large dataset, Large datasets, Medical imaging, Natural images, Net networks, Semantic segmentation, Semantics, Signal encoding, Tumors},

pubstate = {published},

tppubtype = {article}

}

@article{saidani_semantic_2020,

title = {Semantic Representation Based on Deep Learning for Spam Detection},

author = {N. Saidani and K. Adi and M. S. Allili},

editor = {Barbeau M. Laborde R. Benzekri A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083960781&doi=10.1007%2f978-3-030-45371-8_5&partnerID=40&md5=95eba44c33557354be0900bfd2565ca9},

doi = {10.1007/978-3-030-45371-8_5},

issn = {03029743},

year  = {2020},

date = {2020-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {12056 LNCS},

pages = {72–81},

abstract = {This paper addresses the email spam filtering problem by proposing an approach based on two levels text semantic analysis. In the first level, a deep learning technique, based on Word2Vec is used to categorize emails by specific domains (e.g., health, education, finance, etc.). This enables a separate conceptual view for spams in each domain. In the second level, we extract a set of latent topics from email contents and represent them by rules to summarize the email content into compact topics discriminating spam from legitimate emails in an efficient way. The experimental study shows promising results in term of the precision of the spam detection. © 2020, Springer Nature Switzerland AG.},

note = {ISBN: 9783030453701 

Publisher: Springer},

keywords = {Conceptual views, Deep learning, E-mail spam, Electronic mail, Email content, Learning techniques, Second level, Semantic analysis, Semantic representation, Semantics, Spam detection},

pubstate = {published},

tppubtype = {article}

}

@article{saidani_semantic-based_2020,

title = {A semantic-based classification approach for an enhanced spam detection},

author = {N. Saidani and K. Adi and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084283123&doi=10.1016%2fj.cose.2020.101716&partnerID=40&md5=539ac0fc0a7144fe983f514175a138e2},

doi = {10.1016/j.cose.2020.101716},

issn = {01674048 (ISSN)},

year  = {2020},

date = {2020-01-01},

journal = {Computers and Security},

volume = {94},

abstract = {In this paper, we explore the use of a text semantic analysis to improve the accuracy of spam detection. We propose a method based on two semantic level analysis. In the first level, we categorize emails by specific domains (e.g., Health, Education, Finance, etc.) to enable a separate conceptual view for spams in each domain. In the second level, we combine a set of manually-specified and automatically-extracted semantic features for spam detection in each domain. These features are meant to summarize the email content into compact topics discriminating spam from non-spam emails in an efficient way. We show that the proposed method enables a better spam detection compared to existing methods based on bag-of-words (BoW) and semantic content, and leads to more interpretable results. © 2020},

note = {Publisher: Elsevier Ltd},

keywords = {Classification, Classification approach, Conceptual views, Domain-specific analysis, Electronic mail, Email content, Multilevel analysis, Semantic analysis, Semantic content, Semantic features, Semantic levels, Semantics, Spam detection},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{saidani_supervised_2017,

title = {A supervised approach for spam detection using text-based semantic representation},

author = {N. Saidani and K. Adi and M. S. Allili},

editor = {Aimeur E. Weiss M. Ruhi U.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019905686&doi=10.1007%2f978-3-319-59041-7_8&partnerID=40&md5=f416f274d5e08603fa6d1ec9a4cf9c43},

doi = {10.1007/978-3-319-59041-7_8},

issn = {18651348},

year  = {2017},

date = {2017-01-01},

journal = {Lecture Notes in Business Information Processing},

volume = {289},

pages = {136–148},

abstract = {In this paper, we propose an approach for email spam detection based on text semantic analysis at two levels. The first level allows categorization of emails by specific domains (e.g., health, education, finance, etc.). The second level uses semantic features for spam detection in each specific domain. We show that the proposed method provides an efficient representation of internal semantic structure of email content which allows for more precise and interpretable spam filtering results compared to existing methods. © Springer International Publishing AG 2017.},

note = {ISBN: 9783319590400 

Publisher: Springer Verlag},

keywords = {Domain categorization, E-mail spam, Electronic mail, Feature extraction, Semantic analysis, Semantic features, Semantic representation, Semantic structures, Semantics, Spam detection, Spam filtering},

pubstate = {published},

tppubtype = {article}

}