Search results for " network"
showing 10 items of 6428 documents
Investigating the moderating effect of information sources on cruise tourist behaviour in a port of call
2015
The aim of the study is to examine how destination knowledge acquired by cruisers through different information sources (online versus others) can moderate destination image formation and the relationship of image–satisfaction–behavioural intentions in a port of call. A multiple group analysis with partial least square method was carried out using data collected from a major tourism destination in Spain: Valencia. The findings revealed knowledge acquired through different information sources is a moderator of the image–satisfaction and satisfaction–behavioural intention relationships. The destination image formation is also significantly different from one group to the other. The findings o…
Cell state prediction through distributed estimation of transmit power
2019
Determining the state of each cell, for instance, cell outages, in a densely deployed cellular network is a difficult problem. Several prior studies have used minimization of drive test (MDT) reports to detect cell outages. In this paper, we propose a two step process. First, using the MDT reports, we estimate the serving base station’s transmit power for each user. Second, we learn summary statistics of estimated transmit power for various networks states and use these to classify the network state on test data. Our approach is able to achieve an accuracy of 96% on an NS-3 simulation dataset. Decision tree, random forest and SVM classifiers were able to achieve a classification accuracy of…
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.
A Clustering approach for profiling LoRaWAN IoT devices
2019
Internet of Things (IoT) devices are starting to play a predominant role in our everyday life. Application systems like Amazon Echo and Google Home allow IoT devices to answer human requests, or trigger some alarms and perform suitable actions. In this scenario, any data information, related device and human interaction are stored in databases and can be used for future analysis and improve the system functionality. Also, IoT information related to the network level (wireless or wired) may be stored in databases and can be processed to improve the technology operation and to detect network anomalies. Acquired data can be also used for profiling operation, in order to group devices according…
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…
I contratti di rete: valutazioni oltre l'emergenza
2018
The network contract: some evaluations after the emergency. The debate over the Italian network contract develops in the phase of the heaviest crisis for Italians firms. This article evaluates limits and opportunities of network contracts. Findings are based on 14 network cases selected among declining or growing productive sectors in the context of two regions of the South: Apulia and Sicily. Formal networks, although initiated later than in the Northern regions, are indeed now widespread also in the South. The main findings concern the inability of formal networks to support actors to come out of the crisis. Actually, the decline of the sector is one of the most relevant factors that can …