Search results for " function"

Emulating Human Supervision in an Intelligent Tutoring System for Arithmetical Problem Solving

2014

This paper presents an intelligent tutoring system (ITS) for the learning of arithmetical problem solving. This is based on an analysis of a) the cognitive processes that take place during problem solving; and b) the usual tasks performed by a human when supervising a student in a one-to-one tutoring situation. The ITS is able to identify the solving strategy that the student is following and offer adaptive feedback that takes into account both the problem's constraints and the decisions previously made by the user. An observational study shows the ITS's accuracy at emulating expert human supervision, and a randomized experiment reveals that the ITS significantly improves students' learning…

Chapter 3. Prosodic versatility, hierarchical rank and pragmatic function in conversational markers

2019

Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale

2021

Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: First, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Existing approaches for signal detection are usually not well suited for processing large amounts of data in parallel or rely on strong assumptions concerning the signals properties. In this study, it is shown that locali…

A novel method for network intrusion detection based on nonlinear SNE and SVM

2017

In the case of network intrusion detection data, pre-processing techniques have been extensively used to enhance the accuracy of the model. An ideal intrusion detection system (IDS) is one that has appreciable detection capability overall the group of attacks. An open research problem of this area is the lower detection rate for less frequent attacks, which result from the curse of dimensionality and imbalanced class distribution of the benchmark datasets. This work attempts to minimise the effects of imbalanced class distribution by applying random under-sampling of the majority classes and SMOTE-based oversampling of minority classes. In order to alleviate the issue arising from the curse…

Influence of the type of illumination on the measurement of the modulation transfer function in the living human eye: A theoretical study

1999

Abstract Applications such as refractive surgery demand an objective appraisal of the retinal image quality. The modulation transfer function (MTF) provides that information when measured directly. Moreover, the MTF obtained using a simple and objective method such as that described in this paper allows the neural contrast sensitivity function (CSF) to be obtained from the global CSF and the MTF. When calculating the MTF it must be borne in mind whether the applicable theory is coherent or incoherent. In the literature, the developed theory presents some approximations and incongruities. Also, it is interesting to note that the method of recording the MTF (short or long time of integration,…

Statistical properties of the capacity of Rice channels with MRC and EGC

2009

In this paper, we have studied the statistical properties of the capacity of Rice channels for both maximal ratio combining (MRC) and equal gain combining (EGC) schemes. We have analyzed the effect of the number of diversity branches and the amplitude of the line-of-sight (LOS) components in the diversity branches on the statistics of the channel capacity. Specifically, we have derived analytical expressions for the probability density function (PDF), cumulative distribution function (CDF), level-crossing rate (LCR), and average duration of fades (ADF) of the capacity of Rice channels when using MRC and EGC. It is observed that if the number of diversity branches or the amplitude of the LOS…

Dynamic Shakedown Sensitivity Analysis by Means of a Probabilistic Approach

2017

The shakedown limit load multiplier problem for elastic plastic structures subjected to a combination of fixed and seismic loads is treated. In particular, reference is firstly made to the unrestricted dynamic shakedown theory. The relevant seismic load history is modeled as a repeated one and, with reference to classically damped structures, appropriate modal analyses are utilized. With the aim of evaluating the reliability of the results arising from the application of the cited theory, a recent probabilistic approach is also utilized. This approach adopts the Monte Carlo method in order to define the necessary seismic acceleration histories and finally compute the related shakedown limit…

Semisupervised kernel orthonormalized partial least squares

2012

This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…

Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis

2014

This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. Th…

A measurement-based trajectory model for drifted motions towards a target zone

2016

Trajectory models have numerous applications in the area of wiewlwss communications. The aim of this paper is to develop an empirical trajectory model for drifted motions. Recently, a highly flexible trajectory model based on the primitives of Brownian fields (TramBrown) was proposed by A. Borhani and M. Patzold. This paper provides an empirical proof for TramBrown using global positioning system (GPS) data collected from real life user traces drifting to a particular target point or a zone. The recorded location coordinates of the mobile user are processed to compute the total travelling length and the angle-of-motion (AOM) along the drifted trajectory. It is shown that the probability den…