Search results for "learning"

Comparative analysis in terms of computational cost for different discrimination algorithms in implantable defibrillators

2005

Implantable defibrillators (ICDs) use very low computational cost criteria (rate, stability and onset) offering good sensitivity for arrhythmia detection. Although, the specificity of these combined criteria decreases in difficult arrhythmia discrimination as in case of discrimination between ventricular tachycardia (VT) and supraventricular tachycardia (SVT). Several morphological published algorithms enhance arrhythmia discrimination but most algorithms are developed in personal computers and cannot be used in ICDs because of computational cost requirements compared with limited ICD capabilities. A general method to determine the possibility of ICD implementation for a discrimination algo…

Formación artística en el grado de maestro de primaria de la Universitat de València. Enfoques y propuestas

2014

Este trabajo presenta una propuesta enfocada a mejorar la docencia en el actual grado de maestro de Primaria en lo que a Educación Artística se refiere, según el plan de estudios de la Facultat de Magisteri de la Universitat de València. La implantación del grado es relativamente reciente con lo cual permite plantearse mejoras que conlleven dotar a los futuros maestros de herramientas que optimicen sus conocimientos del área artística. Así, se llevará a cabo una colaboración horizontal basada en la cooperación entre profesores de las diferentes áreas artísticas: Educación Plástica y Música. Por ello, se plantea la puesta en práctica de una metodología innovadora de índole cooperativa, revis…

Modeling the insect mushroom bodies: application to a delayed match-to-sample task.

2013

Despite their small brains, insects show advanced capabilities in learning and task solving. Flies, honeybees and ants are becoming a reference point in neuroscience and a main source of inspiration for autonomous robot design issues and control algorithms. In particular, honeybees demonstrate to be able to autonomously abstract complex associations and apply them in tasks involving different sensory modalities within the insect brain. Mushroom Bodies (MBs) are worthy of primary attention for understanding memory and learning functions in insects. In fact, even if their main role regards olfactory conditioning, they are involved in many behavioral achievements and learning capabilities, as …

Methodological advances in brain connectivity

2012

Determining how distinct neurons or brain regions are connected and communicate with each other is a crucial point in neuroscience, as it allows to investigate how the functional integration of specialized neural populations enables the emergence of coherent cognitive and behavioral states. The general concept of brain connectivity encompasses different aspects: structural connectivity is related to the description of anatomical pathways and synaptic connections; functional connectivity investigates statistical dependencies between spatially separated brain regions; effective connectivity refers to models aimed at elucidating driver-response relationships. The study of these different modes…

Collecting and Using Students’ Digital Well-Being Data in Multidisciplinary Teaching

2018

This article examines how students (N=198; aged 13 to 17) experienced the new methods for sensor-based learning in multidisciplinary teaching in lower and upper secondary education that combine the use of new sensor technology and learning from self-produced well-being data. The aim was to explore how students perceived new methods from the point of view of their learning and did the teaching methods provide new information that could promote their own well-being. We also aimed to find out how to collect digital well-being data from a large number of students and how the collected big data set can be utilized to predict school success from the students’ well-being data by using machine lear…

Complexity reduction in efficient prototype-based classification

2006

Artificial Intelligence for Cybersecurity: A Systematic Mapping of Literature

2020

Due to the ever-increasing complexities in cybercrimes, there is the need for cybersecurity methods to be more robust and intelligent. This will make defense mechanisms to be capable of making real-time decisions that can effectively respond to sophisticated attacks. To support this, both researchers and practitioners need to be familiar with current methods of ensuring cybersecurity (CyberSec). In particular, the use of artificial intelligence for combating cybercrimes. However, there is lack of summaries on artificial intelligent methods for combating cybercrimes. To address this knowledge gap, this study sampled 131 articles from two main scholarly databases (ACM digital library and IEEE…

Conception d'architectures compactes pour la détection spatiotemporelle d'actions en temps réel

2022

This thesis tackles the spatiotemporal action detection problem from an online, efficient, and real-time processing point of view. In the last decade, the explosive growth of video content has driven a broad range of application demands for automating human action understanding. Aside from accurate detection, vast sensing scenarios in the real-world also mandate incremental, instantaneous processing of scenes under restricted computational budgets. However, current research and related detection frameworks are incapable of simultaneously fulfilling the above criteria. The main challenge lies in their heavy architectural designs and detection pipelines to extract pertinent spatial and tempor…

DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages

2021

Abstract Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibility of a text. ATE can affect positively several different contexts such as Finance, Health, and Education. Moreover, it can support the research on Automatic Text Simplification (ATS), a research area that deals with the study of new methods for transforming a text by changing its lexicon and structure to meet specific reader needs. In this paper, we illustrate an ATE approach named De…

Exploiting deep learning algorithms and satellite image time series for deforestation prediction

2022

In recent years, we have witnessed the emergence of Deep Learning (DL) methods, which have led to enormous progress in various fields such as automotive driving, computer vision, medicine, finances, and remote sensing data analysis. The success of these machine learning methods is due to the ever-increasing availability of large amounts of information and the computational power of computers. In the field of remote sensing, we now have considerable volumes of satellite images thanks to the large number of Earth Observation (EO) satellites orbiting the planet. With the revisit time of satellites over an area becoming shorter and shorter, it will probably soon be possible to obtain daily imag…