Search results for "computer.software_genre"

Automated quality control of next generation sequencing data using machine learning

2019

AbstractControlling quality of next generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterized common NGS quality features and developed a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal data and external disease diagnostic datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at the following …

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…

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…

MansOS

2010

Often software for wireless sensor networks (WSNs) is developed using a specific event based operating system (OS) such as TinyOS. However, this requires steep learning curve for the new developers. Other operating systems for embedded devices have limited support for new hardware platforms. Our goal is to provide an operating system for resource constrained devices that is easy to use for the wide range of researchers and developers familiar with C programming language and Unix operating system concepts. In addition, we provide a framework for agile portability to new hardware platforms, due to the nature of WSN systems that require specific hardware or features for the sensing tasks at ha…

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…

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…

Saliency Based Aesthetic Cut of Digital Images

2013

Aesthetic cut of photos is a process well known to professional photographers. It consists of cutting the original photo to remove less relevant parts close to the borders leaving in this way the interesting subjects in a position that is perceived by the observer as more pleasant. In this paper we propose a saliency based technique to automatically perform aesthetic cut in images. We use a standard method to estimate the saliency map and propose some post processing on the map to make it more suitable for our scope. We then apply a greedy algorithm to determine the cut (i.e. the most important part of the original image) both in the cases of free and fixed aspect ratio. Experimental result…

Acquisition of Higher Order Knowledge by a Dynamic Modeling Environment Based on the Educational Concept of Self-Regulated Learning

2013

I aim to show that learning with this modeling based Educational Learning System (ELS) can accomplish the target of achieving higher order knowledge. The ELS is a system consisting of internal and external elements. The external prerequisites consist of technical and physical elements and the internal ones are shaped by the students pre-knowledge and the instructors teaching competencies including his/her social, emotional, and disciplinary knowledge necessary for teaching. The ELS is based on a theoretical framework of different theories and models such as concept mapping, elaboration of mental models, cognitive tool-approach, and self-regulated learning (SRL). Different features for visua…

Diversity in search strategies for ensemble feature selection

2005

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subse…