Search results for "Linguistics"

showing 10 items of 8097 documents

Case and Contact Linguistics

2012

Abstract Language contact affects case categories in various ways. This article examines the effects of contacts between linguistic codes (languages, unrelated or related, or language varieties): changes in one code on the model of another. It deals with inflectional case markers, affixes, and adpositions from which they evolve. Though most adpositions express more specific relations, some are relatively desemanticised. Affixes and case-like adpositions may fulfil similar functions; the close correspondences between Dravidian case suffixes and Indic postpositions. Case markers and case functions are acquired through what is called ‘borrowing’, ‘diffusion’, ‘transfer’, ‘interference’, ‘repli…

Computer scienceLanguage contactSyncretism (linguistics)PolysemyTurkic languagesGrammaticalizationLinguisticsReplication (computing)
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A view from the periphery

2016

Computer scienceLinguistics
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Consistent Projectional Text Editors

2017

Computer scienceLinguisticsProceedings of the 5th International Conference on Model-Driven Engineering and Software Development
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SMART-ASD, model and ontology definition: a technology recommendation system for people with autism and/or intellectual disabilities

2018

There are many studies that encourage the use of mobile device solutions to improve the skills of people with an Autism Spectrum Disorder (ASD). There are a number of apps that may be useful for people with ASD, some specifically designed for them, and others not. The main goal of the SMART-ASD project is to assist in the selection of adequate technology and all related accessories. In this project, the users' data are maintained into an ontology. This ontology also includes information about devices, apps, and protection. The system is a hybrid recommendation system that guides parents and professionals in the selection of the adequate technology. This paper presents the SMART-ASD model an…

Computer scienceMulti-agent systemReuseRecommender systemLibrary and Information Sciencesmedicine.diseaseComputer Science ApplicationsWorld Wide WebAutism spectrum disordermedicineSelection (linguistics)OntologyComputingMilieux_COMPUTERSANDSOCIETYAutismMobile deviceInformation SystemsInternational Journal of Metadata, Semantics and Ontologies
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Activities using no resources

1998

Computer scienceNothingYoung learnersPronunciationLinguistics
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Object-Oriented Operational Semantics

2016

Operational semantics is one way of providing meaning to an executable language. On a high level of abstraction, operational semantics means to define an interpreter or an abstract machine for the language. In this article, we review the concept of operational semantics in the scope of meta-model-based language definitions and identify challenges and issues. We provide a clean conceptual approach using an object-oriented runtime environment and state change operations, which relies on an underlying abstract virtual machine. We present the approach using a sample language.

Computer scienceProgramming language0102 computer and information sciences02 engineering and technologycomputer.file_formatcomputer.software_genre01 natural sciencesOperational semanticsAbstract machineAction semanticsDenotational semantics010201 computation theory & mathematicsVirtual machine0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingExecutablecomputerInterpreterAbstraction (linguistics)
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Towards Diagrammatic Patterns

2008

This article presents the idea that the graphical representation (concrete syntax) of a visual language can be specified based on some pre-defined diagrammatic patterns. A diagram from the Specification and Description Language (SDL) is used as illustration.

Computer scienceProgramming languagebusiness.industryObject languageComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Specification languagecomputer.software_genreSpecification and Description LanguageVisual languageDiagrammatic reasoningLanguage Of Temporal Ordering SpecificationUniversal Networking LanguageSoftware_SOFTWAREENGINEERINGProgramming language specificationComputer Science::Programming LanguagesArtificial intelligencebusinesscomputerNatural language processingcomputer.programming_language
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Deep Convolutional Neural Network for HEp-2 fluorescence intensity classification

2019

Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the …

Computer scienceSVM02 engineering and technologyConvolutional neural networklcsh:TechnologyIIF image030218 nuclear medicine & medical imaginglcsh:Chemistry03 medical and health sciences0302 clinical medicineClassifier (linguistics)Autoimmune disease0202 electrical engineering electronic engineering information engineeringGeneral Materials Scienceautoimmune diseasesReceiver operating characteristic (ROC) curveInstrumentationlcsh:QH301-705.5AccuracyIIF imagesFluid Flow and Transfer ProcessesIndirect immunofluorescencebusiness.industrylcsh:TProcess Chemistry and TechnologyGeneral EngineeringPattern recognitionIIfGold standard (test)Convolutional Neural Network (CNN)lcsh:QC1-999Computer Science ApplicationsIntensity (physics)Support vector machineFluorescence intensitylcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040020201 artificial intelligence & image processingArtificial intelligencebusinesslcsh:Engineering (General). Civil engineering (General)lcsh:Physics
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An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification

2019

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentati…

Computer scienceSVMKNN02 engineering and technologylcsh:TechnologyIIF imageHough transformlaw.inventionlcsh:Chemistry03 medical and health scienceslawClassifier (linguistics)0202 electrical engineering electronic engineering information engineeringPreprocessorGeneral Materials ScienceSegmentationcell segmentationlcsh:QH301-705.5InstrumentationIIF images030304 developmental biologyFluid Flow and Transfer Processes0303 health sciencesIndirect immunofluorescencelcsh:Tbusiness.industryProcess Chemistry and TechnologyGeneral EngineeringPattern recognitionSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)ROC curvelcsh:QC1-999Computer Science ApplicationsSupport vector machineParameter identification problemFluorescence intensityHough transformlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040020201 artificial intelligence & image processingArtificial intelligencelcsh:Engineering (General). Civil engineering (General)businesslcsh:Physicsactive contours modelApplied Sciences
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What is the Natural Abstraction Level of an Algorithm?

2021

Abstract State Machines work with algorithms on the natural abstraction level. In this paper, we discuss the notion of the natural abstraction level of an algorithm and how ASM manage to capture this abstraction level. We will look into three areas of algorithms: the algorithm execution, the algorithm description, and the algorithm semantics. We conclude that ASM capture the natural abstraction level of the algorithm execution, but not necessarily of the algorithm description. ASM do also capture the natural abstraction level of execution semantics.

Computer scienceSemantics (computer science)Abstract state machinesNatural (music)VDP::Technology: 500::Information and communication technology: 550AlgorithmAbstraction layerAbstraction (linguistics)
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