Search results for "intelligence"

showing 10 items of 6959 documents

Contributeurs au Grand Débat National demandant un développement des pistes cyclables dans l'aire urbaine de Dijon

2020

Le Grand Débat National, décidé par Emmanuel Macron début 2019 pour répondre au mouvement social des Gilets Jaunes, a permis de collecter les contributions de citoyens sur la transition écologique via une plateforme en ligne. Dans cet article, nous exploitons le corpus constitué par ces contributions pour identifier des zones où les participants demandent le développement de pistes cyclables et d’équipements ferroviaires. Pour cela, nous avons créé un modèle de classification permettant d’identifier les contributions traitant de la thématique du transport et proposé une méthode d’extraction de motifs traduisant les propositions des contributeurs. A l’aide des codes postaux donnés par les co…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Pistes cyclables[SHS.GEO] Humanities and Social Sciences/GeographyGrand débat national[SHS.GEO]Humanities and Social Sciences/Geography[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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Event-Based Trajectory Prediction Using Spiking Neural Networks

2021

International audience; In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]PolynomialComputer scienceNeuroscience (miscellaneous)Neurosciences. Biological psychiatry. Neuropsychiatry02 engineering and technologyunsupervised learningSNN[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]STDP03 medical and health sciencesCellular and Molecular Neuroscience0302 clinical medicineLearning rule0202 electrical engineering electronic engineering information engineeringEvent (probability theory)Original ResearchSpiking neural networkQuantitative Biology::Neurons and Cognitionmotion selectivitybusiness.industry[SCCO.NEUR]Cognitive science/Neuroscience[SCCO.NEUR] Cognitive science/NeuroscienceProcess (computing)Pattern recognitionspiking cameraTrajectoryball trajectory predictionUnsupervised learning020201 artificial intelligence & image processingArtificial intelligencebusiness030217 neurology & neurosurgeryEfficient energy useNeuroscienceRC321-571Frontiers in Computational Neuroscience
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Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation

2022

Automatic and accurate segmentation of the left atrial (LA) cavity and scar can be helpful for the diagnosis and prognosis of patients with atrial fibrillation. However, automating the segmentation can be difficult due to the poor image quality, variable LA shapes, and small discrete regions of LA scars. In this paper, we proposed a fully-automatic method to segment LA cavity and scar from Late Gadolinium Enhancement (LGE) MRIs. For the loss functions, we propose two different losses for each task. To enhance the segmentation of LA cavity from the multicenter dataset, we present a hybrid loss that leverages Dice loss with a polynomial version of cross-entropy loss (PolyCE). We also utilize …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]SegmentationPolyLossUncertaintyCardiac MRI Late Gadolinium Enhancement MRI Left Atrium Scar quantification Segmentation Deep learning PolyLoss UncertaintyDeep learningCardiac MRILeft AtriumScar quantificationLate Gadolinium Enhancement MRI
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Artificial Potential Field Simulation Framework for Semi-Autonomous Car Conception

2017

International audience; Artificial potential field is investigated to provide a high level of synergy between driver and semi-autonomous vehicle. This article presents a framework developed to test the performances of this approach. Stand-alone performances of this system is tested for a lane keeping and cruise control application. Performances are promising and future development is discussed.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Synthèse d'image et réalité virtuelle [Informatique][INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR]Modélisation et simulation [Informatique]Autonomous car[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Intelligence artificielle [Informatique]Interface homme-machine [Informatique]Human-machine interface[INFO.INFO-MO] Computer Science [cs]/Modeling and SimulationArtificial potential field[INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC][INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC]Simulation
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Some Computational Aspects of DISTANCE-SAT

2007

In many AI fields, one must face the problem of finding a solution that is as close as possible to a given configuration. This paper addresses this problem in a propositional framework. We introduce the decision problem distance-sat, which consists in determining whether a propositional formula admits a model that disagrees with a given partial interpretation on at most d variables. The complexity of distance-sat and of several restrictions of it are identified. Two algorithms based on the well-known Davis/Logemann/Loveland search procedure for the satisfiability problem sat are presented so as to solve distance-sat for CNF formulas. Their computational behaviors are compared with the ones …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical computer scienceComputational complexity theory0102 computer and information sciences02 engineering and technologyComputer Science::Computational Complexity01 natural sciences[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]#SATArtificial IntelligenceComputer Science::Logic in Computer ScienceDPLL algorithm0202 electrical engineering electronic engineering information engineeringComputingMilieux_MISCELLANEOUSMathematicsDecision problemFunction problemSatisfiabilityPropositional formulaTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESComputational Theory and Mathematics010201 computation theory & mathematics020201 artificial intelligence & image processingBoolean satisfiability problemAlgorithmSoftware
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How to Enrich Description Logics with Fuzziness

2017

International audience; The paper describes the relation between fuzzy and non-fuzzy description logics. It gives an overview about current research in these areas and describes the difference between tasks for description logics and fuzzy logics. The paper also deals with the transformation properties of description logics to fuzzy logics and backwards. While the process of transformation from a description logic to a fuzzy logic is a trivial inclusion, the other way of reducing information from fuzzy logic to description logic is a difficult task, that will be topic of future work.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Theoretical computer science[ INFO ] Computer Science [cs]Relation (database)Process (engineering)Computer scienceMathematics::General Mathematics0102 computer and information sciences02 engineering and technology[INFO] Computer Science [cs]01 natural sciencesFuzzy logicTask (project management)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Knowledge-based systemsFuzzy Description LogicDescription logicComputer Science::Logic in Computer Science0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]Semantic WebSemantic WebUncertaintyTransformation (function)TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES010201 computation theory & mathematics020201 artificial intelligence & image processingComputingMethodologies_GENERALHardware_LOGICDESIGN
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Attempts to produce minimal Resolution refutations

2018

We address the challenge of searching minimal refutations proofs of inconsistent CNF formulae using the Resolution rule. We propose two algorithms which can only afford formulae of at most 5 variables with a desktop computer. A faster but incomplete algorithm is used to produce "hard" 5 variables 3CNF formulae though a stochastic greedy search. It allowed us to find formulae that can be refuted by producing clauses of at most 3 literals, but whose all minimal refutations contain at least one clause of 4 literals.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES
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Propagation d'informations le long d'une ligne de transmission non linéaire structurée en super réseau et simulant un neurone myélinisé

2019

Non-linear systems are almostly described by partial differential equations that characterize them. We have some systems such as the chain of coupled pebdelums, the protein chain comprising molecules with hydrogen bonds, atomic lattice, and so on .These systems are most often characterized by anharmonic inter particulate interactions and and then immersed in deformable potential substrates. In addition to nonlinearity and dispersion, these other phenomena namely anharmonicity and deformability are responsible for certain properties of propagation of solitary waves such as (compactons, kinks and anti-kinks, peackons, ...etc) and also the ability of the systems to transmit a signal . We used …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]TransmitivitySoliton solutionsEquations aux dérivées partiellesTransmissivitéPartial differential equations[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSimulationSolution soliton[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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Customizing Semantic Profiling for Digital Advertising

2014

International audience; Personalization is the new magic buzzword of application development. To make the complexity of today's application functionalities and information spaces "digestible", customization has become the new go-to technique. But while those technologies aim to ease the consumption of media for their users, they suffer from the same problematic: in the age of Big Data, applications have to cope with a conundrum of heterogeneous information sources that have to be perceived, processed and interpreted. Researchers tend to aim for a maximum degree of integration to create the perfect, all-embracing personalization. The results are wide-range, but overly complex systems that su…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO ] Computer Science [cs]Computer scienceBig dataComplex systemsemantic technologies02 engineering and technology[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]Personalization[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]World Wide Web020204 information systems0202 electrical engineering electronic engineering information engineeringProfiling (information science)Heterogeneous information[ INFO.INFO-CL ] Computer Science [cs]/Computation and Language [cs.CL][INFO]Computer Science [cs]user profiles[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]OWLuser profilingbusiness.industryScalabilitySemantic technology020201 artificial intelligence & image processingbusinessDigital advertisingcustomization
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Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO

2016

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations,…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO ] Computer Science [cs]Matching (graph theory)Feature vectorComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technology[INFO] Computer Science [cs][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Kernel (linear algebra)[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Discriminative modelRobustness (computer science)0202 electrical engineering electronic engineering information engineeringFeature (machine learning)[INFO]Computer Science [cs][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ComputingMilieux_MISCELLANEOUSMathematicsbusiness.industryParticle swarm optimization[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringPattern recognition020201 artificial intelligence & image processingArtificial intelligencebusinessEnergy (signal processing)
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