32,940 research outputs found
Sponsorship image and value creation in E-sports
.E-sports games can drive the sports industry forward and sponsorship is the best way to engage consumers of this new sport. The purpose of this study is to examine the effect of sponsorship image and consumer participation in co-creation consumption activities on fans’ sponsorship response (represented by the variables interest, purchase intention and word of mouth) in e-sports. Four antecedent variables build sponsorship image (i.e., ubiquity of sport, sincerity of sponsor, attitude to sponsor and team identification). A quantitative approach is used for the purposes of this study. Some 445 questionnaires were filled in by fans who watch e-sports in Spain; these are analyzed using partial least squares structural equation modeling (PLS-SEM). The outcomes show that sponsor antecedents are crucial factors if a sponsor wants to change their sponsorship image and influence sponsorship response, and that it is also possible to use participation to improve responsesS
Analysis of reliable deployment of TDOA local positioning architectures
.Local Positioning Systems (LPS) are supposing an attractive research topic over the last few years. LPS are ad-hoc deployments of wireless sensor networks for particularly adapt to the environment characteristics in harsh environments. Among LPS, those based on temporal measurements stand out for their trade-off among accuracy, robustness and costs. But, regardless the LPS architecture considered, an optimization of the sensor distribution is required for achieving competitive results. Recent studies have shown that under optimized node distributions, time-based LPS cumulate the bigger error bounds due to synchronization errors. Consequently, asynchronous architectures such as Asynchronous Time Difference of Arrival (A-TDOA) have been recently proposed. However, the A-TDOA architecture supposes the concentration of the time measurement in a single clock of a coordinator sensor making this architecture less versatile. In this paper, we present an optimization methodology for overcoming the drawbacks of the A-TDOA architecture in nominal and failure conditions with regards to the synchronous TDOA. Results show that this optimization strategy allows the reduction of the uncertainties in the target location by 79% and 89.5% and the enhancement of the convergence properties by 86% and 33% of the A-TDOA architecture with regards to the TDOA synchronous architecture in two different application scenarios. In addition, maximum convergence points are more easily found in the A-TDOA in both configurations concluding the benefits of this architecture in LPS high-demanded applicationS
Upgrading Urban Services Through BPL: Practical Applications for Smart Cities
Current initiatives related to smart cities in LATAM reveal an increasing interest in the improvement of cities and the wellbeing of their citizens. In addition, specific working groups have been created for this purpose. In this sense, the communication technologies set the basis for gathering, transporting, and managing the large amount of data generated in cities to provide a wide range of services. Within the many alternatives available, BPL positions as a promising technology, since smart cities can greatly benefit of its higher data rates and low latency. In addition, since the medium is already deployed and most of the assets and sensors are connected to the same medium, the cost of the communication systems will be reduced in price and simplicity. The work presents four practical applications: smart buildings, urban lighting, energy assets management and broadband access, in which the possibilities and advantages of BPL are further addressed. Finally, some conclusions and key aspects relating BPL to the success of smart cities are identified.Eusko Jaurlaritza IT-1234-19, KK-202
The influence of blockchains and internet of things on global value chain
Despite the increasing proliferation of deploying the Internet of Things (IoT) in global value chain (GVC), several challenges might lead to a lack of trust among value chain partners, e.g., technical challenges (i.e., confidentiality, authenticity, and privacy); and security challenges (i.e., counterfeiting, physical tempering, and data theft). In this study, we argue that Blockchain technology, when combined with the IoT ecosystem, will strengthen GVC and enhance value creation and capture among value chain partners. Thus, we examine the impact of Blockchain technology when combined with the IoT ecosystem and how it can be utilized to enhance value creation and capture among value chain partners. We collected data through an online survey, and 265 UK Agri-food retailers completed the survey. Our data were analyzed using structural equation modelling (SEM). Our finding reveals that Blockchain technology enhances GVC by improving IoT scalability, security, and traceability when combined with the IoT ecosystem. Which, in turn, strengthens GVC and creates more value for value chain partners – which serves as a competitive advantage. Finally, our research outlines the theoretical and practical contribution of combining Blockchain technology and the IoT ecosystem
Strung pieces: on the aesthetics of television fiction series
As layered and long works, television fiction series have aesthetic properties that are built over time, bit by bit. This thesis develops a group of concepts that enable the study of these properties, It argues that a series is made of strung pieces, a system of related elements. The text begins by considering this sequential form within the fields of film and television. This opening chapter defines the object and methodology of research, arguing for a non-essentialist distinction between cinema and television and against the adequacy of textual and contextual analyses as approaches to the aesthetics of these shows. It proposes instead that these programmes should be described as televisual works that can be scrutinised through aesthetic analysis. The next chapters propose a sequence of interrelated concepts. The second chapter contends that series are composed of building blocks that can be either units into which series are divided or motifs that unify series and are dispersed across their pans. These blocks are patterned according to four kinds of relations or principles of composition. Repetition and variation are treated in tandem in the third chapter because of their close connection, given that variation emerges from established repetition. Exception and progression are also discussed together in the fourth chapter since they both require a long view of these serial works. The former, in order to be recognised as a deviation from the patterns of repetition and variation. The latter, In order to be understood in Its many dimensions as the series advances. Each of these concepts is further detailed with additional distinctions between types of units, motifs, repetitions, variations, and exceptions, using illustrative examples from numerous shows. In contrast, the section on progression uses a single series as case study, Carnivàle (2003-05), because this is the overarching principle that encompasses all the others. The conclusion considers the findings of the research and suggests avenues for their application
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Diverging influences of usability in online authentication system: the role of culture (US vs Korea)
Purpose:
Although the use of online authentication systems in banking services is expanding globally, little is known about cultural differences in forming consumers' responses to these services. This paper examines how the usability of an online security service and culture impact consumers' behaviour.
Design/methodology/approach:
The authors conduct a 2 (usability: high vs low) × 2 (culture: US vs Korea) between-subjects, full factorial design.
Findings:
The results indicate a differential influence of the usability of a security system by culture. In particular, US consumers exhibit greater behavioural intention in a high (vs low) usability condition, whereas Korean consumers showed more favourable responses in a low-usability condition. Moreover, perceived effort is confirmed as a crucial mediator that explains the psychological mechanism of the proposed effect.
Practical implications:
This research contributes to the literature on online banking where security is an important determinant of success. Especially for managers involved in international banking services, the findings of cultural differences offer insights about the importance of local understanding and differentiation of bank services for specific target markets which can enhance consumers' response towards an online security service.
Originality/value:
The current study is one of a very few attempts to examine the role of usability of an online security system in forming consumers' behavioural intention. More importantly, this study integrates the concept of culture to explain how usability influences positive or negative behavioural intention in an international market
Implementación de un algoritmo memético para resolver el problema de corte de materiales aplicado a la producción de barras de acero para hormigón
El país ha vivido durante los últimos años un aumento en el PBI que lo ha colocado como unos
de los principales países de la región. Este crecimiento se debe al aumento sostenido de la
producción en diversos sectores. Por cifras del Ministerio de Economía y Finanzas (MEF), el
sector de construcción, a cargo del Ministerio de Vivienda y Construcción representa uno de los
principales contribuyentes al PBI interno (MEF), con un aporte del 5% al indicador. Además, se
proyecta un crecimiento de 7% sostenido en los próximos años, por encima del promedio
nacional. Esto ayudará a impulsar los sectores primarios y secundarios relacionados. Así que se
vuelve prioritaria la elaboración de soluciones que aumenten la eficiencia en el consumo de
recursos a todo nivel.
En particular, este proyecto plantea abordar la producción de las barras de acero para hormigón
a nivel industrial. La actividad productiva por la cual el acero fundido es convertido en barras de
acero grandes, que serán a su vez cortadas en longitud para conseguir barras más pequeñas,
que son usadas en la manufactura y la construcción de edificios. Estas son las conocidas como
barras de acero para hormigón.
El proceso consta de dos partes principalmente:
● La elaboración de barras largas estándar por el proceso de colada y
● Una segunda fase de corte para obtener los productos finales, barras de acero para
hormigón, según el tamaño solicitado por los clientes.
Aunque la producción de la barra grande (primaria) se realiza en una línea de ensamblaje a partir
de acero fundido, lo que la vuelve virtualmente infinita y modificable en tamaño, las barras
pequeñas se elaboran a partir de un tamaño estándar que cada fábrica utiliza para la
manufactura.
Para elaborar la producción de barras estándar, se toman en cuenta las negociaciones entre los
ejecutivos de ventas y los clientes potenciales, obteniéndose requerimientos de producción.
Dichos requerimientos son luego incluidos en la programación del periodo tomando en cuenta la
disponibilidad de material (acero fundido). Debido a esto, aunque la primera etapa de corte es
limpia por la naturaleza de la fabricación, en la segunda se pueden producir desperdicios y
pérdidas, pues las barras grandes no siempre se utilizan al cien por ciento en la creación de
barras de acero para hormigón.
El problema entonces es de corte de materiales, el cual consiste en conseguir un número de
piezas de diferentes largos que deben ser cortadas de una fuente, de tal forma que se cumpla
con la demanda de largos y se produzcan optimizando una función objetivo. Por lo mencionado este trabajo de fin de carrera, plantea implementar un algoritmo memético
para resolver el problema de corte de materiales aplicado a la producción de barras de acero
para hormigón.
Objetivo General
Implementar un algoritmo memético para resolver el problema de corte de materiales aplicado a
la producción de barras de acero para hormigón.
Objetivos Específicos
O1. Definir la función objetivo a evaluar para los algoritmos genético y memético
O2. Diseñar un algoritmo memético como alternativa de solución para el problema de corte de
materiales unidimensional de barras de acero para hormigón
O3. Adaptar un algoritmo genético obtenido de la revisión de la literatura como alternativa de
solución para el problema de corte de materiales de barras de acero para hormigón
O4. Implementar los algoritmos propuestos en un módulo de ejecución de algoritmos
O5. Realizar experimentación mediante pruebas numéricas para comparar el desempeño entre
ambas alternativas de solución
Elaboración de un marco de trabajo para cuantificar el nivel de usabilidad y experiencia de usuario de plataformas de soporte al proceso de aprendizaje
Debido a que la Usabilidad y Experiencia de Usuario (UX) han tomado relevancia en el software con el transcurrir de los años, han aparecido métodos de evaluación para medir estos factores, garantizando una buena satisfacción en los usuarios. Sin embargo, la mayoría de estos métodos de evaluación son generales (no se enfocan en un dominio en específico), costosas, subjetivas, y – especialmente – cualitativas.
Con la finalidad de obtener resultados más objetivos, se opta por una evaluación cuantitativa. Este tipo de evaluación proporciona un valor numérico que representa el nivel de usabilidad del producto, generando un mejor análisis al momento de comparar productos de software del mismo tipo/dominio.
Por otro lado, la tecnología ha permitido la aparición de herramientas para apoyar al proceso del aprendizaje (LMS) en los estudiantes. Por ello, se propone crear un marco de trabajo que permita evaluar cuantitativamente la usabilidad y UX en este tipo de herramientas.
El marco consiste en un conjunto de ítems de verificación que evalúa las características que deben cumplir los LMS para lograr su objetivo. Para ello, se recurrió a la revisión de la literatura, entrevistas, cuestionarios, y juicio de expertos a profesionales del campo de HCI y educación. Asimismo, se recurrió a métodos estadísticos para la validación de los resultados.
Finalmente, los resultados obtenidos de la propuesta – luego de su aplicación en una plataforma de aprendizaje – fueron prometedores, ya que se acercaron mucho a los valores obtenidos por cuestionarios como SUS y SUMI
Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage
Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on theMagnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile, the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet+SVM hybrid technique. The hybrid model AlexNet+SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively
Facial expression recognition and intensity estimation.
Doctoral Degree. University of KwaZulu-Natal, Durban.Facial Expression is one of the profound non-verbal channels through which human emotion state is inferred from the deformation or movement of face components when facial muscles are activated. Facial Expression Recognition (FER) is one of the relevant research fields in Computer Vision (CV) and Human-Computer Interraction (HCI). Its application is not limited to: robotics, game, medical, education, security and marketing. FER consists of a wealth of information. Categorising the information into primary emotion states only limit its performance. This thesis considers investigating an approach that simultaneously predicts the emotional state of facial expression images and the corresponding degree of intensity. The task also extends to resolving FER ambiguous nature and annotation inconsistencies with a label distribution learning method that considers correlation among data. We first proposed a multi-label approach for FER and its intensity estimation using advanced machine learning techniques. According to our findings, this approach has not been considered for emotion and intensity estimation in the field before. The approach used problem transformation to present FER as a multilabel task, such that every facial expression image has unique emotion information alongside the corresponding degree of intensity at which the emotion is displayed. A Convolutional Neural Network (CNN) with a sigmoid function at the final layer is the classifier for the model. The model termed ML-CNN (Multilabel Convolutional Neural Network) successfully achieve concurrent prediction of emotion and intensity estimation. ML-CNN prediction is challenged with overfitting and intraclass and interclass variations. We employ Visual Geometric Graphics-16 (VGG-16) pretrained network to resolve the overfitting challenge and the aggregation of island loss and binary cross-entropy loss to minimise the effect of intraclass and interclass variations. The enhanced ML-CNN model shows promising results and outstanding performance than other standard multilabel algorithms. Finally, we approach data annotation inconsistency and ambiguity in FER data using isomap manifold learning with Graph Convolutional Networks (GCN). The GCN uses the distance along the isomap manifold as the edge weight, which appropriately models the similarity between adjacent nodes for emotion predictions. The proposed method produces a promising result in comparison with the state-of-the-art methods.Author's List of Publication is on page xi of this thesis
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