Students cheating in exams destroys the fair principle of evaluation and affects the normal teaching order of the school. Therefore, the examination cheating detection has the vital significance. The existing cheating...
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ISBN:
(纸本)9781728137988
Students cheating in exams destroys the fair principle of evaluation and affects the normal teaching order of the school. Therefore, the examination cheating detection has the vital significance. The existing cheating detection methods have disadvantages such as insufficient modeling accuracy for students, lag in cognitive diagnosis, difficulty in detecting multi-source plagiarism and low accuracy. In order to solve the disadvantages of traditional methods, this paper proposes a method for detecting cheating in multi-index examinations based on feed -forward neural network. This paper first proposes RAE algorithm which combines linear regression and em algorithm for students' cognitive diagnosis. We use RAE algorithm and LSTM neural network to obtain the knowledge point mastery degree of each student based on the history problem solving and the knowledge point mastery degree based on the exam problem solving. Then, according to the information of students' cognitive level, seat distribution in the examination room, students' habit of guessing answers at normal times, similarity of examination papers, etc., we get several indicators to judge whether students cheat. Finally, we take various indicators obtained through various methods as characteristics and use feed -forward neural network to classify whether students cheat or not. The experimental results show that the accuracy and recall of this method are significantly higher than those of several popular methods.
Transmuted geometric distribution (TGD) was recently introduced and investigated by Chakraborty and Bhati [Stat. Oper. Res. Trans. 40 (2016), 153–176]. This is a flexible extension of geometric distribution having an...
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Re-parametrization is often done to make a constrained optimization problem an unconstrained one. This paper focuses on the non-parametric maximum likelihood estimation of the sub-distribution functions for current st...
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Re-parametrization is often done to make a constrained optimization problem an unconstrained one. This paper focuses on the non-parametric maximum likelihood estimation of the sub-distribution functions for current status data with competing risks. Our main aim is to propose a method using re-parametrization, which is simpler and easier to handle with compared to the constrained maximization methods discussed in Jewell and Kalbfleisch (Biostatistics. 5, 291-306, 2004) and Maathuis (2006), when both the monitoring times and the number of individuals observed at these times are fixed. Then the Expectation-Maximization (em) algorithm is used for estimating the unknown parameters. We have also established some asymptotic results of these maximum likelihood estimators. Finite sample properties of these estimators are investigated through an extensive simulation study. Some generalizations have been discussed.
This paper investigates mixture of multilayer perceptron (MLP) regressions. Although mixture of MLP regressions (MoMR) can be a strong fitting model for noisy data, the research on it has been rare. We employ soft mix...
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ISBN:
(纸本)9789897583513
This paper investigates mixture of multilayer perceptron (MLP) regressions. Although mixture of MLP regressions (MoMR) can be a strong fitting model for noisy data, the research on it has been rare. We employ soft mixture approach and use the Expectation-Maximization (em) algorithm as a basic learning method. Our learning method goes in a double-looped manner;the outer loop is controlled by the em and the inner loop by MLP learning method. Given data, we will have many models;thus, we need a criterion to select the best. Bayesian Information Criterion (BIC) is used here because it works nicely for MLP model selection. Our experiments showed that the proposed MoMR method found the expected MoMR model as the best for artificial data and selected the MoMR model having smaller error than any linear models for real noisy data.
Parametric Operational Modal Analysis consists on fitting a mathematical model to the vibration data, and in a second phase, to compute the modal parameters from the mathematical model applying closed formulas. One po...
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ISBN:
(纸本)9788409049004
Parametric Operational Modal Analysis consists on fitting a mathematical model to the vibration data, and in a second phase, to compute the modal parameters from the mathematical model applying closed formulas. One possibility is to use the state space model and the maximum likelihood method to estimate this model. But maximum likelihood method relies in maximizing the likelihood function, and this is in turn defined using the probabilistic distribution of the error terms. It is usual to admit that the error terms are uncorrelated, but taking into account the equation of vibrations these errors must be correlated. In this work, the effect of this correlation in the accuracy of the modal parameters is analyzed.
The nonlinear nature of the quantization operation performed by the ADCs imposes great challenges to the systems employing coarse quantization. Systems operating in moderate to high SNR regimes experience a pronounced...
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ISBN:
(纸本)9781479981311
The nonlinear nature of the quantization operation performed by the ADCs imposes great challenges to the systems employing coarse quantization. Systems operating in moderate to high SNR regimes experience a pronounced capacity loss and also impairments in the quality of the channel estimates, compared to the infinite precision case. This work proposes a multiuser MIMO channel estimation algorithm that operates with low-resolution receiving ADCs. The strategy is based on an coarse quantizer followed by an em-ML estimator, that takes as input the observation vector subtracted by an offset value produced by a first rough channel estimate. The approach performed by this feedback-controlled double-stage estimator aims to make the quantizer thresholds based on the low-SNR Cramer-Rao lower bound approximation valid in the high SNR region. Numerical analysis reveals that reliable estimation is achieved in coarse quantization for a wide range of SNR with a very small and constant gap in relation to infinite precision quantized estimation.
Maintaining the desired interface level between the top froth layer and the liquid layer plays an important role in achieving high recovery of products in oil sands and related process industries. As varying throughpu...
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Maintaining the desired interface level between the top froth layer and the liquid layer plays an important role in achieving high recovery of products in oil sands and related process industries. As varying throughputs and downstream disturbances tend to change the interface level over time, it is an important indicator of the process behavior. In this paper, we propose an approach based on Gaussian mixture model and Markov Random Field (MRF) based unsupervised image segmentation to achieve the real-time accurate measurement of the interface. The image processing problem is solved as a Maximum a Posteriori (MAP) estimation problememploying the MRF framework and the parameters are estimated using the em algorithm. The proposed approach is validated using the images captured from a laboratory scale equipment designed to simulate the industrial PSV interface. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Advances in Gaussian methodology for spatio-temporal data have made it possible to develop sophisticated non-stationary models for very large data sets. The literature on non-Gaussian spatio-temporal models is compara...
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Advances in Gaussian methodology for spatio-temporal data have made it possible to develop sophisticated non-stationary models for very large data sets. The literature on non-Gaussian spatio-temporal models is comparably sparser and strongly focused on distributing the uncertainty across layers of a hierarchical model. This choice allows to model the data conditionally, to transfer the dependence structure at the process level via a link function, and to use the familiar Gaussian framework. Conditional modeling, however, implies an (unconditional) distribution function that can only be obtained through integration of the latent process, with a closed form only in special cases. In this work, we present a spatio-temporal non-Gaussian model that assumes an (unconditional) skew-t data distribution, but also allows for a hierarchical representation by defining the model as the sum of a small and a large scale spatial latent effect. We provide semi-closed form expressions for the steps of the Expectation-Maximization algorithm for inference, as well as the conditional distribution for spatial prediction. We demonstrate how it outperforms a Gaussian model in a simulation study, and show an example of application to precipitation data in Colorado. (C) 2019 Elsevier B.V. All rights reserved.
This paper describes a practical application of the Expectation Maximization algorithm for computing the static load model of large electricity consumers. Such a model is necessary for effective and reliable operation...
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ISBN:
(纸本)9781538651421
This paper describes a practical application of the Expectation Maximization algorithm for computing the static load model of large electricity consumers. Such a model is necessary for effective and reliable operational control of electric power systems. The possibility of estimating basic states of electrical load using arrays of measured voltage and power data is shown. The criteria for selecting the most appropriate clusters are formulated and a step by step linear load model parameter estimation method is proposed. A practical method application is illustrated by using measurements of voltage and real power obtained at a large industrial facility. As a result, a load model with two basic states and corresponding linear models is obtained.
This paper investigates localization of an arbitrary number of simultaneously active speakers in an acoustic enclosure. We propose an algorithm capable of estimating the number of speakers, using reliability informati...
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ISBN:
(纸本)9781479981311
This paper investigates localization of an arbitrary number of simultaneously active speakers in an acoustic enclosure. We propose an algorithm capable of estimating the number of speakers, using reliability information to obtain robust estimation results in adverse acoustic scenarios and estimating individual probability distributions describing the position of each speaker using convex geometry tools. To this end, we start from an established algorithm for localization of acoustic sources based on the em algorithm. There, the estimation of the number of sources as well as the handling of reverberation has not been addressed sufficiently. We show improvement in the localization of a higher number of sources and in the robustness in adverse conditions including interference from competing speakers, reverberation and noise.
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