80 research outputs found

    Une mesure de similarité sémantique utilisant des résultats de psychologie

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    National audienceL'utilisation d'ontologies, c'est-à-dire de bases de connaissances, en recherche d'information est devenue une voie très explorée. Cela permet de dépasser de nombreux problèmes liés aux comparaisons terme à terme entre documents ou entre documents et requêtes, en passant à un niveau d'abstraction supérieur qui n'est pas soumis aux limitations intrinsèques à l'utilisation de mots-clés. De nombreuses techniques utilisent désormais les ontologies (expansion de requêtes, désambiguïsation sémantique, etc.) dans le but d'obtenir de meilleurs résultats en recherche d'information. Un problème récurrent de ces applications est la mesure de proximité entre concepts dans une ontologie. Elle a été étudiée par de nombreux auteurs, et deux grandes approches se sont détachées : les approches basées sur les arcs, c'est-à-dire sur la structure de l'ontologie, et les approches utilisant le contenu informatif des concepts, donc en passant par des corpus renseignant l'importance des concepts dans un document. Nous avons eu besoin de comparer les mesures classiques de distance entre concepts dans une ontologie. Des résultats de psychologie nous ont amenés à en choisir une qui respecte plus la manière dont un humain juge la proximité entre entités

    Echange d'Information grâce à des caractérisations sémantiques

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    National audienceUsing ontologies allows agents to put meaning behind the terms appearing in the information exchanges. Supporting us on this fact, we propose an entity named focus allowing to represent various kinds of information contents : documents, data bases, services, agents' capabilities, etc. A focus consists of a weighting of the concepts of an ontology in order to indicate which meanings are important for the agent which has set it up. To help the capture of the values in the focus we present a procedure to spread the weightings in the focus. Then we define a measure of relevance of a focus compared to another. The originality of our approach lies in the fact that the focus is an exchangeable entity between the agents and does not require a centralization of the data collections

    What's left can't be right -- The remaining positional incompetence of contrastive vision-language models

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    Contrastive vision-language models like CLIP have been found to lack spatial understanding capabilities. In this paper we discuss the possible causes of this phenomenon by analysing both datasets and embedding space. By focusing on simple left-right positional relations, we show that this behaviour is entirely predictable, even with large-scale datasets, demonstrate that these relations can be taught using synthetic data and show that this approach can generalise well to natural images - improving the performance on left-right relations on Visual Genome Relations

    Query interpretation to help peers understand each others in semantically heterogeneous systems

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    National audienceIn semantic web applications where query initiators and information providers do not necessarily share the same ontology, semantic interoperability generally relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the ``shared'' parts of the ontologies) but, in some sense, limited to them. Then, how the ``unshared'' parts can also contribute to and improve information exchange ? In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator's side and a query interpretation step at the document provider's. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show an important improvement of retrieval relevance when concepts of documents and queries are not shared. Even if the concepts of the initial query are not shared by the document provider, our method still ensures 90% of the precision and recall obtained when the concepts are shared

    Improving Interoperability Using Query Interpretation in Semantic Vector Spaces

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    International audienceIn semantic web applications where query initiators and information providers do not necessarily share the same ontology, semantic interoperability generally relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the ``shared'' parts of the ontologies) but, in some sense, limited to them. Then, how the ``unshared'' parts can also contribute to and improve information exchange ? In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator's side and a query interpretation step at the document provider's. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show an important improvement of retrieval relevance when concepts of documents and queries are not shared. Even if the concepts of the initial query are not shared by the document provider, our method still ensures 90% of the precision and recall obtained when the concepts are shared

    Towards the extraction of robust sign embeddings for low resource sign language recognition

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    Isolated Sign Language Recognition (SLR) has mostly been applied on datasets containing signs executed slowly and clearly by a limited group of signers. In real-world scenarios, however, we are met with challenging visual conditions, coarticulated signing, small datasets, and the need for signer independent models. To tackle this difficult problem, we require a robust feature extractor to process the sign language videos. One could expect human pose estimators to be ideal candidates. However, due to a domain mismatch with their training sets and challenging poses in sign language, they lack robustness on sign language data and image-based models often still outperform keypoint-based models. Furthermore, whereas the common practice of transfer learning with image-based models yields even higher accuracy, keypoint-based models are typically trained from scratch on every SLR dataset. These factors limit their usefulness for SLR. From the existing literature, it is also not clear which, if any, pose estimator performs best for SLR. We compare the three most popular pose estimators for SLR: OpenPose, MMPose and MediaPipe. We show that through keypoint normalization, missing keypoint imputation, and learning a pose embedding, we can obtain significantly better results and enable transfer learning. We show that keypoint-based embeddings contain cross-lingual features: they can transfer between sign languages and achieve competitive performance even when fine-tuning only the classifier layer of an SLR model on a target sign language. We furthermore achieve better performance using fine-tuned transferred embeddings than models trained only on the target sign language. The embeddings can also be learned in a multilingual fashion. The application of these embeddings could prove particularly useful for low resource sign languages in the future

    Incorporating user preferences in multi-objective feature selection in software product lines using multi-criteria decision analysis

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    Software Product Lines Engineering has created various tools that assist with the standardisation in the design and implementation of clusters of equivalent software systems with an explicit representation of variability choices in the form of Feature Models, making the selection of the most ideal software product a Feature Selection problem. With the increase in the number of properties, the problem needs to be defined as a multi-objective optimisation where objectives are considered independently one from another with the goal of finding and providing decision-makers a large and diverse set of non-dominated solutions/products. Following the optimisation, decision-makers define their own (often complex) preferences on how does the ideal software product look like. Then, they select the unique solution that matches their preferences the most and discard the rest of the solutions—sometimes with the help of some Multi-Criteria Decision Analysis technique. In this work, we study the usability and the performance of incorporating preferences of decision-makers by carrying-out Multi-Criteria Decision Analysis directly within the multi-objective optimisation to increase the chances of finding more solutions that match preferences of the decision-makers the most and avoid wasting execution time searching for non-dominated solutions that are poor with respect to decision-makers’ preferences

    Dealing with P2P semantic heterogeneity through query expansion and interpretation

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    International audienceIn P2P systems where query initiators and information providers do not necessarily share the same ontology, semantic interoperability generally relies on ontology matching or schema mappings. Information exchange is then not only enabled by the established correspondences (the "shared" parts of the ontologies) but, in some sense, limited to them. Then, to what extent the "unshared" parts can also contribute to and improve information exchange? In this paper, we address this question by considering a system where documents and queries are represented by semantic vectors. We propose a specific query expansion step at the query initiator's side and a query interpretation step at the document provider's. Through these steps, unshared concepts contribute to evaluate the relevance of documents wrt. a given query. Our experiments show that our method enables to correctly evaluate the relevance of a document even if concepts of a query are not shared. In some cases, we are able to find up to 90% of the documents that would be selected when all the central concepts are shared

    Reparation in evolutionary algorithms for multi-objective feature selection in large software product lines

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    Software Product Lines Engineering is the area of software engineering that aims to systematise the modelling, creation and improvement of groups of interconnected software systems by formally expressing possible alternative products in the form of Feature Models. Deriving a software product/system from a feature model is called Feature Configuration. Engineers select the subset of features (software components) from a feature model that suits their needs, while respecting the underlying relationships/constraints of the system–which is challenging on its own. Since there exist several (and often antagonistic) perspectives on which the quality of software could be assessed, the problem is even more challenging as it becomes a multi-objective optimisation problem. Current multi-objective feature selection in software product line approaches (e.g., SATIBEA) combine the scalability of a genetic algorithm (IBEA) with a solution reparation approach based on a SAT solver or one of its derivatives. In this paper, we propose MILPIBEA, a novel hybrid algorithm which combines IBEA with the accuracy of a mixed-integer linear programming (MILP) reparation. We show that the MILP reparation modifies fewer features from the original infeasible solutions than the SAT reparation and in a shorter time. We also demonstrate that MILPIBEA outperforms SATIBEA on average on various multi-objective performance metrics, especially on the largest feature models. The other major challenge in software engineering in general and in software product lines, in particular, is evolution. While the change in software components is common in the software engineering industry, the particular case of multi-objective optimisation of evolving software product lines is not well-tackled yet. We show that MILPIBEA is not only able to better take advantage of the evolution than SATIBEA, but it is also the one that continues to improve the quality of the solutions when SATIBEA stagnates. Overall, IBEA performs better when combined with MILP instead of SAT reparation when optimising the multi-objective feature selection in large and evolving software product lines
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