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Multiagent Dynamics of Gradual Argumentation Semantics
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In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022) ; 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022) ; https://hal.archives-ouvertes.fr/hal-03584238 ; 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), May 2022, Auckland (virtual), New Zealand (2022)
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Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
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In: ISSN: 2561-326X ; JMIR Formative Research ; https://hal.archives-ouvertes.fr/hal-03614832 ; JMIR Formative Research, JMIR Publications 2022, 6 (2), pp.e18539. ⟨10.2196/18539⟩ ; https://formative.jmir.org/2022/2/e18539 (2022)
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Emotional Speech Recognition Using Deep Neural Networks
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In: ISSN: 1424-8220 ; Sensors ; https://hal.archives-ouvertes.fr/hal-03632853 ; Sensors, MDPI, 2022, 22 (4), pp.1414. ⟨10.3390/s22041414⟩ (2022)
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From Biological Synapses to “Intelligent” Robots
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In: ISSN: 2079-9292 ; Electronics ; https://hal.archives-ouvertes.fr/hal-03590998 ; Electronics, MDPI, 2022, 11 (5), pp.707. ⟨10.3390/electronics11050707⟩ (2022)
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Multiagent Dynamics of Gradual Argumentation Semantics
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In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022) ; 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022) ; https://hal.archives-ouvertes.fr/hal-03584238 ; 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), May 2022, Auckland (virtual), New Zealand (2022)
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Question-Based Explainability in Abstract Argumentation
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In: https://hal-univ-tlse3.archives-ouvertes.fr/hal-03647896 ; [Research Report] IRIT/RR--2022--01--FR, IRIT : Institut de Recherche en Informatique de Toulouse, France. 2022, pp.1-64 (2022)
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Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming
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In: https://hal.archives-ouvertes.fr/hal-03590714 ; 2022 (2022)
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Towards combined semantic and lexical scores based on a new representation of textual data to extract experimental data from scientific publications
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In: ISSN: 1751-5858 ; EISSN: 1751-5866 ; International Journal of Intelligent Information and Database Systems ; https://hal.inrae.fr/hal-03616243 ; International Journal of Intelligent Information and Database Systems, Inderscience, 2022, 15 (1), pp.78. ⟨10.1504/IJIIDS.2022.120146⟩ (2022)
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Integrating a Phrase Structure Corpus Grammar and a Lexical-Semantic Network: the HOLINET Knowledge Graph
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In: Proceedings of LREC 2022 ; https://hal-amu.archives-ouvertes.fr/hal-03655636 ; Proceedings of LREC 2022, Jun 2022, Marseille, France (2022)
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Caveats of Measuring Semantic Change of Cognates and Borrowings using Multilingual Word Embeddings
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In: LChange'22 - 3rd International Workshop on Computational Approaches to Historical Language Change 2022 ; https://hal.inria.fr/hal-03635005 ; LChange'22 - 3rd International Workshop on Computational Approaches to Historical Language Change 2022, May 2022, Dublin, Ireland (2022)
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Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-03578503 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapour, Singapore (2022)
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DeepL et Google Translate face à l'ambiguïté phraséologique
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In: https://hal.archives-ouvertes.fr/hal-03583995 ; 2022 (2022)
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An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
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In: ISSN: 2375-4699 ; EISSN: 2375-4702 ; ACM Transactions on Asian and Low-Resource Language Information Processing ; https://hal.inria.fr/hal-03616853 ; ACM Transactions on Asian and Low-Resource Language Information Processing, ACM, In press, ⟨10.1145/3523179⟩ (2022)
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The contextual logic
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In: https://hal.archives-ouvertes.fr/hal-03195162 ; 2022 (2022)
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Morphology in the Corsican Language Database (BDLC) : assessment and perspectives ; La morphologie dans la Banque de Données Langue Corse : bilan et perspectives
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In: ISSN: 1638-9808 ; EISSN: 1765-3126 ; Corpus ; https://hal.archives-ouvertes.fr/hal-03591866 ; Corpus, Bases, Corpus, Langage - UMR 7320, 2022, Corpus et données en morpholgie, ⟨10.4000/corpus.7115⟩ ; https://journals.openedition.org/corpus/7115 (2022)
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Automatic generation of the complete vocal tract shape from the sequence of phonemes to be articulated
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In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.univ-lorraine.fr/hal-03650212 ; Speech Communication, Elsevier : North-Holland, 2022, ⟨10.1016/j.specom.2022.04.004⟩ (2022)
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VEREINDEUTIGUNG ZUR KLASSIFIZIERUNG LEXIKALISCHER OBJEKTE ; DISAMBIGUATION FOR THE CLASSIFICATION OF LEXICAL ITEMS ; DÉSAMBÏGUISATION POUR LA CLASSIFICATION DE LEXÈMES
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In: https://hal.archives-ouvertes.fr/hal-03598242 ; France, Patent n° : EP3937059A1. 2022 (2022)
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Genetic Neural Architecture Search for automatic assessment of human sperm images
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In: ISSN: 0957-4174 ; Expert Systems with Applications ; https://hal.archives-ouvertes.fr/hal-03585035 ; Expert Systems with Applications, Elsevier, 2022 (2022)
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Abstract:
International audience ; Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. However, manual SMA is an inexact, subjective, nonreproducible, and hard to teach process. Therefore, in this paper, we introduce a novel automatic SMA technique that is based on the neural architecture search algorithm, named Genetic Neural Architecture Search (GeNAS). For this purpose, we used a collection of images termed MHSMA dataset, which contains 1, 540 sperm images that have been collected from 235 patients with infertility problems. In detail, GeNAS consists of a special genetic algorithm that acts as a meta-controller which explores the constrained search space of plain convolutional neural network architectures. Every individual of this genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome). The fitness of each individual is calculated by a novel proposed method, named GeNAS Weighting Factor (GeNAS-WF). This technique is specially designed to evaluate the fitness of neural networks which, during their learning process, validation accuracy highly fluctuates. To speed up the algorithm, a hashing method is practiced to save each trained neural architecture fitness, so we could reuse them during fitness evaluation. In terms of running time and computational power, our proposed architecture search method is far more efficient than most of the other existing neural architecture search algorithms. Moreover, whereas most of the existing neural architecture search algorithms are designed to work well with well-prepared benchmark datasets, the overall paradigm of GeNAS is specially designed to address the challenges of real-world datasets, particularly shortage of data and class imbalance. In our experiments, the best neural architecture found by GeNAS has reached an accuracy of 91.66%, 77.33%, and 77.66% in the vacuole, head, and acrosome abnormality detection, respectively. In comparison to other proposed algorithms for MHSMA dataset, GeNAS achieved state-of-the-art results.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]; Deep Learning; Genetic Algorithm; Human Sperm Morphometry; Infertility; Neural Architecture Search
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URL: https://hal.archives-ouvertes.fr/hal-03585035
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Assessing the impact of OCR noise on multilingual event detection over digitised documents
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In: ISSN: 1432-5012 ; EISSN: 1432-1300 ; International Journal on Digital Libraries ; https://hal.archives-ouvertes.fr/hal-03635985 ; International Journal on Digital Libraries, Springer Verlag, 2022, ⟨10.1007/s00799-022-00325-2⟩ (2022)
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Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0
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In: Proceedings of the International Workshop on Challenges & Perspectives in Creating Large Language Models 2022 (BigScience 2022) ; https://hal.inria.fr/hal-03639144 ; Proceedings of the International Workshop on Challenges & Perspectives in Creating Large Language Models 2022 (BigScience 2022), May 2022, Dublin, France (2022)
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