Search results for "Neural"
showing 10 items of 2783 documents
Attention-based Model for Evaluating the Complexity of Sentences in English Language
2020
The automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep learning- based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in tw…
Deep neural attention-based model for the evaluation of italian sentences complexity
2020
In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.
Multi-class Text Complexity Evaluation via Deep Neural Networks
2019
Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results sho…
Matching research and practice: Prediction of individual patient progress and dropout risk for basic routine outcome monitoring.
2021
OBJECTIVE Despite evidence showing that systematic outcome monitoring can prevent treatment failure, the practical conditions that allow for implementation are seldom met in naturalistic psychological services. In the context of limited time and resources, session-by-session evaluation is rare in most clinical settings. This study aimed to validate innovative prediction methods for individual treatment progress and dropout risk based on basic outcome monitoring. METHODS Routine data of a naturalistic psychotherapy outpatient sample were analyzed (N = 3902). Patients were treated with cognitive behavioral therapy with up to 95 sessions (M = 39.19, SD = 16.99) and assessment intervals of 5-15…
Dropping out of a transdiagnostic online intervention: A qualitative analysis of client's experiences
2017
Introduction An important concern in Internet-based treatments (IBTs) for emotional disorders is the high dropout rate from these protocols. Although dropout rates are usually reported in research studies, very few studies qualitatively explore the experiences of patients who drop out of IBTs. Examining the experiences of these clients may help to find ways to tackle this problem. Method A Consensual Qualitative Research study was applied in 10 intentionally-selected patients who dropped out of a transdiagnostic IBT. Results 22 categories were identified within 6 domains. Among the clients an undeniable pattern arose regarding the insufficient support due to the absence of a therapist and t…
What represents a face? A computational approach for the integration of physiological and psychological data.
1997
Empirical studies of face recognition suggest that faces might be stored in memory by means of a few canonical representations. The nature of these canonical representations is, however, unclear. Although psychological data show a three-quarter-view advantage, physiological studies suggest profile and frontal views are stored in memory. A computational approach to reconcile these findings is proposed. The pattern of results obtained when different views, or combinations of views, are used as the internal representation of a two-stage identification network consisting of an autoassociative memory followed by a radial-basis-function network are compared. Results show that (i) a frontal and a…
Interpretability of Recurrent Neural Networks in Remote Sensing
2020
In this work we propose the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for multivariate time series of satellite data for crop yield estimation. Recurrent nets allow exploiting the temporal dimension efficiently, but interpretability is hampered by the typically overparameterized models. The focus of the study is to understand LSTM models by looking at the hidden units distribution, the impact of increasing network complexity, and the relative importance of the input covariates. We extracted time series of three variables describing the soil-vegetation status in agroe-cosystems -soil moisture, VOD and EVI- from optical and microwave satellites, as well as available in si…
Spiking Neural Networks models targeted for implementation on Reconfigurable Hardware
2017
La tesis presentada se centra en la denominada tercera generación de redes neuronales artificiales, las Redes Neuronales Spiking (SNN) también llamadas ‘de espigas’ o ‘de eventos’. Este campo de investigación se convirtió en un tema popular e importante en la última década debido al progreso de la neurociencia computacional. Las Redes Neuronales Spiking, que tienen no sólo la plasticidad espacial sino también temporal, ofrecen una alternativa prometedora a las redes neuronales artificiales clásicas (ANN) y están más cerca de la operación real de las neuronas biológicas ya que la información se codifica y transmite usando múltiples espigas o eventos en forma de trenes de pulsos. Este campo h…
A 3D‑scaffold of PLLA induces the morphological differentiation and migration of primary astrocytes and promotes the production of extracellular vesi…
2019
The present study analyzed the ability of primary rat astrocytes to colonize a porous scaffold, mimicking the reticular structure of the brain parenchyma extracellular matrix, as well as their ability to grow, survive and differentiate on the scaffold. Scaffolds were prepared using poly-L-lactic acid (PLLA) via thermally-induced phase separation. Firstly, the present study studied the effects of scaffold morphology on the growth of astrocytes, evaluating their capability to colonize. Specifically, two different morphologies were tested, which were obtained by changing the polymer concentration in the starting solution. The structures were characterized by scanning electron microscopy, and a…
A neural architecture for 3D segmentation
2003
An original neural scheme for segmentation of range data is presented, which is part of a more general 3D vision system for robotic applications. The entire process relies on a neural architecture aimed to perform first order image irradiance analysis, that is local estimation of magnitude and orientation of the image irradiance gradient.In the case of dense 3D data, irradiance is replaced by depth information so irradiance analysis of these pseudo-images provides knowledge about the actual curvature of the acquired surfaces. In particular, boundaries and contours due to mutual occlusions can be detected very well while there are no false contours due to rapid changing in brightness or colo…