Search results for "Neural Networks"
showing 10 items of 599 documents
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…
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 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…
Focal Cortical Lesions Induce Bidirectional Changes in the Excitability of Fast Spiking and Non Fast Spiking Cortical Interneurons
2014
A physiological brain function requires neuronal networks to operate within a well-defined range of activity. Indeed, alterations in neuronal excitability have been associated with several pathological conditions, ranging from epilepsy to neuropsychiatric disorders. Changes in inhibitory transmission are known to play a key role in the development of hyperexcitability. However it is largely unknown whether specific interneuronal subpopulations contribute differentially to such pathological condition. In the present study we investigated functional alterations of inhibitory interneurons embedded in a hyperexcitable cortical circuit at the border of chronically induced focal lesions in mouse …
Learning automata based energy-efficient AI hardware design for IoT applications
2020
Energy efficiency continues to be the core design challenge for artificial intelligence (AI) hardware designers. In this paper, we propose a new AI hardware architecture targeting Internet of Things applications. The architecture is founded on the principle of learning automata, defined using propositional logic. The logic-based underpinning enables low-energy footprints as well as high learning accuracy during training and inference, which are crucial requirements for efficient AI with long operating life. We present the first insights into this new architecture in the form of a custom-designed integrated circuit for pervasive applications. Fundamental to this circuit is systematic encodin…
Towards a Comprehension of University Dropout in Catalonia: the Case of Universitat Autònoma de Barcelona
2015
The article presents the results of a research that aims to unfold the reasons behind students’ dropout at the Autonomous University of Barcelona (UAB). Data is analyzed, and interviews and focus groups with students who have dropout, study degree’s coordinators and institutional authorities are carried out. Results indicate that part of the abandonment taking place is voluntary, natural and even necessary, when the student does not enter the desired option or discovers that the studies do not fi t with their vocational interests, but it also represents a misalignment between the academic offer and the students’ capabilities, performance and motivation.
Acoustic characterization of Silica aerogel clamped plates for perfect absorption purpose
2017
International audience; Silica aerogel has been widely studied as bulk material for its extremely low density and thermal conductivity. Plates or membranes made of this extremely soft materials exhibits interesting properties for sound absorption. A novel signal processing method for the characterization of an acoustic metamaterial made of silica aerogel clamped plates is presented. The acoustic impedance of a silica aerogel clamped plate is derived from the elastic theory for the flexural waves, while the transfer matrix method is used to model reflection and transmission coefficients of a single plate. Experimental results are obtained by using an acoustic impedance tube. The difference b…