Search results for "General Computer Science"

showing 10 items of 895 documents

The analytic hierarchy process with stochastic judgements

2014

The analytic hierarchy process (AHP) is a widely-used method for multicriteria decision support based on the hierarchical decomposition of objectives, evaluation of preferences through pairwise comparisons, and a subsequent aggregation into global evaluations. The current paper integrates the AHP with stochastic multicriteria acceptability analysis (SMAA), an inverse-preference method, to allow the pairwise comparisons to be uncertain. A simulation experiment is used to assess how the consistency of judgements and the ability of the SMAA-AHP model to discern the best alternative deteriorates as uncertainty increases. Across a range of simulated problems results indicate that, according to c…

Multicriteria decisionInformation Systems and ManagementGeneral Computer ScienceAnalytic network processAnalytic hierarchy processmulticriteriaMulticriteriaManagement Science and Operations ResearchDecision analysisIndustrial and Manufacturing EngineeringConsistency (database systems)EconometricsQA MathematicsuncertaintyQAta512ta218analytic hierarchy processMathematicsta212decision analysisStochastic multicriteria acceptability analysista214Analytic hierarchy processUncertaintysimulationRange (mathematics)Modeling and SimulationPairwise comparisonSimulationDecision analysisEuropean Journal of Operational Research
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A comprehensive skills analysis of novice software developers working in the professional software development industry

2022

Measuring and evaluating a learner’s learning ability is always the focus of every person whose aim is to develop strategies and plans for their learners to improve the learning process. For example, classroom assessments, self-assessment using computer systems such as Intelligent Tutoring Systems (ITS), and other approaches are available. Assessment of metacognition is one of these techniques. Having the ability to evaluate and monitor one’s learning is known as metacognition. An individual can then propose adjustments to their learning process based on this assessment. By monitoring, improving, and planning their activities, learners who can manage their cognitive skills are better able t…

MultidisciplinaryArticle SubjectGeneral Computer ScienceGlobal-softwareVDP::Samfunnsvitenskap: 200::Pedagogiske fag: 280VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550
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Efficient High-Order Iterative Methods for Solving Nonlinear Systems and Their Application on Heat Conduction Problems

2017

[EN] For solving nonlinear systems of big size, such as those obtained by applying finite differences for approximating the solution of diffusion problem and heat conduction equations, three-step iterative methods with eighth-order local convergence are presented. The computational efficiency of the new methods is compared with those of some known ones, obtaining good conclusions, due to the particular structure of the iterative expression of the proposed methods. Numerical comparisons are made with the same existing methods, on standard nonlinear systems and a nonlinear one-dimensional heat conduction equation by transforming it in a nonlinear system by using finite differences. From these…

MultidisciplinaryArticle SubjectGeneral Computer ScienceIterative methodMathematical analysisFinite differenceRelaxation (iterative method)010103 numerical & computational mathematics02 engineering and technologyThermal conduction01 natural sciencesExpression (mathematics)lcsh:QA75.5-76.95Local convergenceNonlinear system0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingHeat equationlcsh:Electronic computers. Computer science0101 mathematicsMATEMATICA APLICADAMathematicsComplexity
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Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models

2017

The most common approach to assess the dynamical complexity of a time series across multiple temporal scales makes use of the multiscale entropy (MSE) and refined MSE (RMSE) measures. In spite of their popularity, MSE and RMSE lack an analytical framework allowing their calculation for known dynamic processes and cannot be reliably computed over short time series. To overcome these limitations, we propose a method to assess RMSE for autoregressive (AR) stochastic processes. The method makes use of linear state-space (SS) models to provide the multiscale parametric representation of an AR process observed at different time scales and exploits the SS parameters to quantify analytically the co…

MultidisciplinaryArticle SubjectGeneral Computer ScienceMean squared errorSeries (mathematics)Computer scienceStochastic processEntropymultiscale analysis01 natural sciencesMeasure (mathematics)lcsh:QA75.5-76.95010305 fluids & plasmasEntropy; multiscale analysisAutoregressive model0103 physical sciencesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaState spacelcsh:Electronic computers. Computer science010306 general physicsRepresentation (mathematics)AlgorithmParametric statistics
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Econophysics and the challenge of efficiency

2009

MultidisciplinaryGeneral Computer ScienceEconophysiccomplx systemstochastic processes
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Measuring frequency domain granger causality for multiple blocks of interacting time series

2011

In the past years, several frequency-domain causality measures based on vector autoregressive time series modeling have been suggested to assess directional connectivity in neural systems. The most followed approaches are based on representing the considered set of multiple time series as a realization of two or three vector-valued processes, yielding the so-called Geweke linear feedback measures, or as a realization of multiple scalar-valued processes, yielding popular measures like the directed coherence (DC) and the partial DC (PDC). In the present study, these two approaches are unified and generalized by proposing novel frequency-domain causality measures which extend the existing meas…

Multivariate statisticsTime FactorsGeneral Computer ScienceLogarithmScalar (mathematics)Complex systemTopologyModels BiologicalNeurophysiological time serieBlock-based connectivity analysiGranger causalityStatisticsHumansComputer SimulationDirected coherenceMathematicsNumerical analysisPartial directed coherenceBrainElectroencephalographyVector autoregressive (VAR) modelBrain WavesCausalityAutoregressive modelFrequency domainComputer ScienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityAlgorithmsBiotechnologyBiological Cybernetics
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Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

2010

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. M…

Multivariate statisticsTime FactorsGeneral Computer ScienceModels NeurologicalPattern Recognition AutomatedCardiovascular Physiological PhenomenaElectrocardiographyGranger causalityArtificial IntelligenceEconometricsCoherence (signal processing)AnimalsHumansComputer SimulationEEGPartial Directed CoherenceMathematicsCausal modelMultivariate autoregressive modelComputer Science (all)Linear modelElectroencephalographySignal Processing Computer-AssistedCardiovascular variabilityAutoregressive modelFrequency domainParametric modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityMultivariate time serieLinear ModelsNeural Networks ComputerBiotechnologyBiological cybernetics
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Archetypal analysis: contributions for estimating boundary cases in multivariate accommodation problem

2013

[EN] The use of archetypal analysis is proposed in order to determine a set of representative cases that entail a certain percentage of the population, in the accommodation problem. A well-known anthropometric database has been used in order to compare our methodology with the common used PCA-approach, showing the advantages of our methodology: the level of accommodation is reached unlike the PCA approach, no more adjustments are necessary, the user can decide the number of archetypes to consider or leave the selection by a criterion. Unlike PCA, the objective of the archetypal analysis is obtaining extreme individuals, so it is the appropriate statistical technique for solving this type of…

Multivariate statisticsrepresentative human model generationGeneral Computer ScienceComputer scienceBoundary (topology)Type (model theory)Anthropometry [Percentile]computer.software_genrearchetypepercentileSet (abstract data type)Archetypal analysisStatisticsArchetypeSelection (genetic algorithm)Archetypeanthropometryrepresentative casebusiness.industryGeneral EngineeringRepresentative humanPercentile: AnthropometryModel generationRepresentative caseData miningbusinesscomputerAccommodation
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A description based on languages of the final non-deterministic automaton

2014

The study of the behaviour of non-deterministic automata has traditionally focused on the languages which can be associated to the different states. Under this interpretation, the different branches that can be taken at every step are ignored. However, we can also take into account the different decisions which can be made at every state, that is, the branches that can be taken, and these decisions might change the possible future behaviour. In this case, the behaviour of the automata can be described with the help of the concept of bisimilarity. This is the kind of description that is usually obtained when the automata are regarded as labelled transition systems or coalgebras. Contrarily t…

Nested wordTheoretical computer scienceGeneral Computer ScienceTimed automatonLlenguatges de programacióω-automatonTheoretical Computer ScienceDeterministic pushdown automatonCoalgebraFinal automatonDeterministic automatonQuantum finite automataAutomatitzacióComputer Science::DatabasesMathematicsDiscrete mathematicsNonlinear Sciences::Cellular Automata and Lattice GasesNon-deterministic automatonMobile automatonBisimilarityComputer Science::Programming LanguagesAutomata theoryFormal languageÀlgebraMATEMATICA APLICADAComputer Science::Formal Languages and Automata Theory
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A real-time network architecture for biometric data delivery in Ambient Intelligence

2012

Ambient Intelligent applications involve the deployment of sensors and hardware devices into an intelligent environment surrounding people, meeting users’ requirements and anticipating their needs (Ambi- ent Intelligence-AmI). Biometrics plays a key role in surveillance and security applications. Fingerprint, iris and voice/speech traits can be acquired by contact, contact-less, and at-a-distance sensors embedded in the environment. Biometric traits transmission and delivery is very critical and it needs real-time transmission net- work with guaranteed performance and QoS. Wireless networks become suitable for AmI if they are able to satisfy real-time communication and security system requi…

Network architectureAmbient intelligenceGeneral Computer ScienceBiometricsbusiness.industryComputer scienceWireless networkQuality of serviceComputational intelligenceAutomationAmbient Intelligence Efficient wireless sensor networks Real-time scheduling Biometric traits processingSoftware deploymentEmbedded systemWirelessIntelligent environmentbusinessJournal of Ambient Intelligence and Humanized Computing
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