Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
Learning outcome assessment is of great significance in the field of traditional on-campus teaching especially on the courses of programming languages. In this work, we take advantage of the data offered by our programming assignment judge system and propose a new IRT-BKT model for estimation of learning outcome. This new framework: Item Response Theory (IRT) model that estimates students' initial...
When working with multiple independent mobile robots, each has a different knowledge about its environment, based on its available sensors. This paper proposes an approach that allows working with these different views by independently modeling the common logical relationships between the elements in the scene and the meaning of device-specific sensor data. Using these models, for each robot an estimation...
Software effort estimation (SEE) is a crucial step in software development. Effort data missing usually occurs in real-world data collection. Focusing on the missing data problem, existing SEE methods employ the deletion, ignoring, or imputation strategy to address the problem, where the imputation strategy was found to be more helpful for improving the estimation performance. Current imputation methods...
A new Software Reliability model based on a pure birth pro- cess is proposed. In our novel approach, the birth (failure) rate of the process is considered to be non dependent on time but dependent non linearly on the previous number of births (failures), contrarily to non homogeneous pure birth processes, as it is usually done in the literature. We use the empirical Bayes framework in order to get...
Software Estimation is an important part of every Software Engineers’ skill set.At Stevens Institute of Technology, we have taught Estimation as part of our Software Engineering Masters Program since 2001.Over the past few years, we have evolved our teaching style to be more experiential and engaging. This case study describes an evolving software engineering pedagogical method using LEGOs, which...
Although high dimensionality and heavy censoring may cause difficulties for model selection, many literatures concern the accelerated failure time (AFT) model. We perform variable selection and statistical inference for high-dimensional censoring data based on the AFT model by directly controlling the false discovery rate. We also perform some numerical simulations to evaluate the performance of the...
Cognitive radio offers a novel solution to overcome the problem of spectrum underutilization by providing spectral access to secondary users. The television (TV) bands are of particular interest for secondary usage due to their high penetration power and greater coverage. These licensed bands are occupied only in few regions while in most of the other regions they are unoccupied and are termed as...
This paper describes a comparison of proposed model with simulation software and measurement data of PV power plant in Cambodia. The proposed model is based on behavior of PV module at a particular site. The parameters which affected to PV power output as solar irradiance and module temperature were used as input in this model. Weight function technics were used to improve accuracy of single diode...
Regularized Tyler Estimator's (RTE) have raised attention over the past years due to their attractive performance over a wide range of noise distributions and their natural robustness to outliers. Developing adaptive methods for the selection of the regularisation parameter α is currently an active topic of research. Indeed, the bias-performance compromise of RTEs highly depends on the considered...
Signal processing in spherical harmonic domain has the ability to decouple frequency dependent and location dependent components of the signal received. A method for low dimensional spherical harmonic feature extraction is proposed in this work for DOA estimation in noisy and reverberant environments. The features are extracted using frequency smoothing and a transformation which makes them frequency...
Most of the tests proposed in the literature to verify if a given random multivariate dataset fits a spherical or elliptical distribution are designed for real valued data and rely on the estimation of high order moment matrices. Recently, a test that considers complex random vectors, derived based on the Schott spherical symmetry test was proposed aiming in a more proper analysis of PolSAR data....
The existence of complementary information across multiple sensors has driven the proliferation of multivariate datasets. Exploitation of this common information, while minimizing the assumptions imposed on the data has led to the popularity of data-driven methods. Independent vector analysis (IVA), in particular, provides a flexible and effective approach for the fusion of multivariate data. In many...
Non-parametric Bayesian methods have recently gained popularity in several research areas dealing with unsupervised learning. These models are capable of simultaneously learning the cluster models as well as their number based on properties of a dataset. The most commonly applied models are using Dirichlet process priors and Gaussian models, called as Dirichlet process Gaussian mixture models (DPGMMs)...
This paper considers the problem of estimating an unknown high dimensional signal from (typically low-dimensional) noisy linear measurements, where the desired unknown signal is assumed to possess a group-sparse structure, i.e. given a (pre-defined) partition of its entries into groups, only a small number of such groups are non-zero. Assuming the unknown group-sparse signal is generated according...
In this paper we discuss a class of models for time series of low count data based on the Generalized Linear Model (GLM) approach. Unlike the traditional Auto-Regressive Moving-Average (ARMA) models for continuous Gaussian data, these models capture both the temporal correlation structure and the discrete marginal distribution of count data. We focus on the properties, parameter estimation, and model...
When noise is directional instead of diffuse, the majority of conventional direction of arrival (DOA) estimation techniques suffer from performance degradation because of mismatched noise models. In this paper, a novel robust DOA estimation algorithm is developed as an initial investigation into DOA estimation of speech under directional non-speech interference (DNSI) and non-directional background...
Scale-free dynamics commonly appear in individual components of multivariate data. Yet, while the behavior of cross-components is crucial in modeling real-world multivariate data, their examination often suggests departures from exact multivariate self-similarity (also termed fractal connectivity). The present paper introduces a multivariate Gaussian stochastic process with Hadamard (i.e., entry-wise)...
We present an algorithm that finds planar structures in a Manhattan world from two pictures taken from different viewpoints with unknown baseline. The Manhattan world assumption constrains the homographies induced by the visible planes on the image pair, thus enabling robust reconstruction. We extend the T-linkage algorithm for multistructure discovery to account for constrained homographies, and...
This paper proposes a method that estimatesconsumer family-structure attributesby focusing on purchasing behavior. The method generatesa relevance model for each product type between family-structure attributes and purchasing histories beforehand based on a consumer panel survey. Random Forest, a machine learning method, is employed to generate the model. The proposed method facilitates provisioning...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.