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Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of classical Fisher's Linear Discriminant Analysis in supervised metric learning, feature selection and classification. With the increasing needs of distributed data collection, storage and processing, enabling the Sparse Discriminant Learning to embrace the Multi-Party distributed computing environments becomes an...
Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
Total number of failures of a software system can help practitioners to have a better understanding of the software quality. In this paper, we propose a model to predict the total number of software failures in a software system by analyzing the failure data from testing using models based on Zipf's law together with the information on code coverage. Failure data and code coverage are combined in...
The article considers the problem of complex estimation of the product state, associated with the need to help decision-makers in managing the life cycle of space facilities. A system analysis of the subject area was conducted, which showed the presence of limitations in the existing information system of the technical state and reliability of space facilities. The article presents a new intellectual...
This paper proposes an estimation method for the latent variable Rasch model based on the method of least squares which allows a continuous data set using. The research suggests the application of original approaches within the method for the solution of some applied problems. The authors explain how to use it for task assignment and work organization, decision-making under certainty and the securities...
The article considers methods of processing uncertainties in solving dynamic planning problems. Various types of uncertainties are considered, such as stochastic uncertainties, uncertainties in the parameters and structure of models, the uncertainty of the amplitude type and the probabilistic type. Methods for processing data for reducing uncertainties are proposed.
This paper proposes a real-time identification method for auto-regressive with exogenous inputs and state-dependent parameters (ARX-SDP). This model is always non-linear. We conveniently adapt Young's off-line approach to an real-time approach with reduced computational cost. Young's approach focuses on discovering state-parameter dependence. This implies to unveil the nonlinear structure of the system...
One of the challenges in the development of high-performance closed-loop anesthetic drug delivery systems is the lack of accurate models. Physiological models have limited accuracy and drug effect varies largely between patients, while data-driven modeling of individual responses is challenging due to limited excitation and disturbances. This paper proposes a multi-input single-output (MISO) approach...
This study is motivated by the problem of evaluating reliable false alarm (FA) rates for sinusoid detection tests applied to unevenly sampled time series involving colored noise, when a (small) training data set of this noise is available. While analytical expressions for the FA rate are out of reach in this situation, we show that it is possible to combine specific periodogram standardization and...
Granger causality approaches have been widely used to estimate effective connectivity in complex dynamic systems. These techniques are based on the building of predictive models which not only depend on a proper selection of the predictive vectors size but also on the chosen class of regression functions. The question addressed in this paper is the estimation of the model order in the computation...
Traditional matrix factorization methods approximate high dimensional data with a low dimensional subspace. This imposes constraints on the matrix elements which allow for estimation of missing entries. A lower rank provides stronger constraints and makes estimation of the missing entries less ambiguous at the cost of measurement fit. In this paper we propose a new factorization model that further...
Different from representation learning models using deep learning to project original feature space into lower density ones, we propose a feature space learning (FSL) model based on a semi-supervised clustering framework. There are three main contributions in our approach: (1) Inspired by Zipf's law and word bursts, the feature space learning processes not only select trusted unlabeled samples and...
Visual attention is a dynamic search process of acquiring information. However, most previous studies have focused on the prediction of static attended locations. Without considering the temporal relationship of fixations, these models usually cannot explain the dynamic saccadic behavior well. In this paper, an iterative representation learning framework is proposed to predict the saccadic scanpath...
We present a method for developing executable algorithms for quantitative cyber-risk assessment. Exploiting techniques from security risk modeling and actuarial approaches, the method pragmatically combines use of available empirical data and expert judgments. The input to the algorithms are indicators providing information about the target of analysis, such as suspicious events observed in the network...
This paper presents a novel two-stage regularized moving-horizon algorithm for PieceWise Affine (PWA) regression. At the first stage, the training samples are processed iteratively, and a Mixed-Integer Quadratic-Programming (MIQP) problem is solved to find the sequence of active modes and the model parameters which best match the training data, within a relatively short time window in the past. According...
Precise radius estimation is of high interest for rebar and pipe characterization but very challenging. In this work, we present a novel 3D frequency-domain full-waveform inversion (FWI) approach with which the geometrical information of subsurface cylindrical objects and the dielectric properties of the penetrating medium are simultaneously extracted from ground penetrating radar (GPR) data. The...
The optimization of a model that expresses time series data for a given period is a problem associated with the development of a regression model that estimates future data on the extension of the past data time series. This is a two-step optimization problem where the order of past data used in the regression model (number of orders of the solution space) is decided, and weighted coefficients for...
Condition monitoring data have been widely used to evaluate the health state and reliability, as well as estimate the remaining useful life (RUL) for degrading systems. Among various degradation modeling and RUL estimating methods, Wiener process based models is recognized by both scholars and engineers as the one of the most effect tools, and thus becomes very popular nowadays. In this paper, a prognostic...
Photoplethysmographic signals are synthesized using a Fourier representation with a fundamental and 2 harmonic components, and an accurate method for parameter estimation using this synthesis model is discussed. The estimated parameters enable to determine physiologically meaningful features related to arterial stiffness, heart rate variability, blood pressure variations, etc. The method allows signal...
The paper considers the problem of decision support systems constructing for solving the problems of modeling and estimating selected types of risks with the possibility for application of alternative data processing techniques, modeling and estimation of parameters and states for the processes under study. The system proposed has a modular architecture that provides a possibility for easy extension...
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