Search results for "Artificial"

A learning model for the dual evolution of human social behaviors

2003

In this work we modelize, with an abstract mathematical model by computer simulation, the processes that have made to appear in the world a strong duality between orient and occident, by combining changes in conditions of initialization, natural system and the opposition gregarious/individualism of the social behaviors. Finally we present a statistical study of the influence of the repression adaptability, resignation and recycling on the ecological destruction and social evolution. This model can help us to analyze if the current capitalist globalization can be stopped, changed or regulated, and if it is possible to overcome it towards a Free Scientific Society.

Métricas epistemológicas para modelos basados en fractales lingüísticos de PLN

2016

This work is part of a wider research named BIOTECH that intends to assure the quality of linguistic modeling activity for automatic systems, making it possible to automate the management of words and natural language. Words are considered part of the complex articulation of language expressions. BIOTECH aims to take it as a tool to evaluate and track linguistic and verbal communication distorsion in patients with Autistic Spectrum Disorder. The main contribution of this paper is to discuss the validity of fractals when used to model linguistic reasoning, and the relevance of considering not only statistics but also epistemology-related metrics. Furthermore, a set of metrics is introduced a…

Anchoring by Imitation Learning in Conceptual Spaces

2005

In order to have a robotic system able to effectively learn by imitation, and not merely reproduce the movements of a human teacher, the system should have the capabilities of deeply understanding the perceived actions to be imitated. This paper deals with the development of a cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. The purpose of the following discussion is to show how the same conceptual representation can be used both in a bottom-up approach, in order to learn sequences of actions by imitation learning paradigm, and in a top-down approach, in order to anchor the symbolical representations to the perceptu…

Using System Dynamics to Model Student Performance in an Intelligent Tutoring System

2017

One basic adaptation function of an Intelligent Tutoring System (ITS) consists of selecting the most appropriate next task to be offered to the learner. This decision can be based on estimates, such as the expected performance of the student, or the probability that the student successfully solves each particular task. However, the computation of these values is intrinsically difficult, as they may depend on other complex latent variables that also need to be estimated from observable quantities, e.g. the current student's ability. In this work, we have used system dynamics to model learning and predict the student's performance in a given exercise, in an existing ITS that was developed to …

<title>Expanding context against weighted voting of classifiers</title>

2000

In the paper we propose a new method to integrate the predictions of multiple classifiers for Data Mining and Machine Learning tasks. The method assumes that each classifier stands in it's own context, and the contexts are partially ordered. The order is defined by monotonous quality function that maps each context to the value from the interval [0,1]. The classifier that has the context with better quality is supposed to predict better than the classifier from worse quality. The objective is to generate the opinion of `virtual' classifier that stands in the context with quality equal to 1. This virtual classifier must have the best accuracy of predictions due to the best context. To do thi…

Hybrid 3D-ResNet Deep Learning Model for Automatic Segmentation of Thoracic Organs at Risk in CT Images

2020

In image radiation therapy, accurate segmentation of organs at risk (OARs) is a very essential task and has clinical applications in cancer treatment. The segmentation of organs close to lung, breast, or esophageal cancer is a routine and time-consuming process. The automatic segmentation of organs at risk would be an essential part of treatment planning for patients suffering radiotherapy. The position and shape variation, morphology inherent and low soft tissue contrast between neighboring organs across each patient’s scans is the challenging task for automatic segmentation of OARs in Computed Tomography (CT) images. The objective of this paper is to use automatic segmentation of the orga…

The Effectiveness of LDOCE Definitions for Concrete and Abstract Nouns in Headword- and Picture-Identification Tasks

2021

Abstract LDOCE uses a defining vocabulary to make their definitions intelligible to the user. Critics claim this policy may result in imprecise definitions, something especially noticeable in certain concrete and abstract words that are difficult to define by a definition only. This paper examines to what extent LDOCE definitions of such words help learners identify the objects and words being defined. In our experiment on 381 learners of English as a foreign language, three groups of participants viewed different definition types: simplified definitions of LDOCE, unsimplified definitions of MWC, and definitions written in the learners’ mother tongue (UDPL/TR). The results show that the LDO…

Emotions in Motion

2017

Depression is a disabling medical illness characterized by persistent and all-encompassing feelings of sadness, loss of interest, or pleasure in normally enjoyable activities, as well as problems in emotion regulation. Medication, sometimes in combination with verbal psychotherapy or counselling, is the predominant method of treatment for depression. This article argues that body movement, being fundamental to the perception and production of emotion, should also be considered in approaches and methods utilized in the treatment of depression. This chapter introduces motion capture technology as a method for studying dance movement, and provides a short overview of related studies. Recent fi…

2020

Abstract. Despite the availability of both commercial and open-source software, an ideal tool for digital rock physics analysis for accurate automatic image analysis at ambient computational performance is difficult to pinpoint. More often, image segmentation is driven manually, where the performance remains limited to two phases. Discrepancies due to artefacts cause inaccuracies in image analysis. To overcome these problems, we have developed CobWeb 1.0, which is automated and explicitly tailored for accurate greyscale (multiphase) image segmentation using unsupervised and supervised machine learning techniques. In this study, we demonstrate image segmentation using unsupervised machine le…

Motion estimation and reconstruction of piecewise planar scenes from two views

2010

The task of recovering the camera motion relative to the environment (motion estimation) is fundamental to many computer vision applications. We present an algorithm for reconstruction of piece-wise planar scenes from only two views and based on minimum line correspondences. We first recover camera rotation by matching vanishing points based on the methods already exist in the literature and then recover the camera translation by searching among a family of hypothesized planes passing through one line. Unlike algorithms based on line segments, the presented algorithm does not require an overlap between two line segments or more than one line correspondence across more than two views to reco…