3,623 research outputs found

    Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model

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    Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time applications or massive real world networks. Thus, approximation techniques have been developed and used to compute the measures in such scenarios. In this paper, we demonstrate and analyze the use of neural network learning algorithms to tackle such task and compare their performance in terms of solution quality and computation time with other techniques from the literature. Our work offers several contributions. We highlight both the pros and cons of approximating centralities though neural learning. By empirical means and statistics, we then show that the regression model generated with a feedforward neural networks trained by the Levenberg-Marquardt algorithm is not only the best option considering computational resources, but also achieves the best solution quality for relevant applications and large-scale networks. Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models, Machine Learning, Regression ModelComment: 8 pages, 5 tables, 2 figures, version accepted at IJCNN 2018. arXiv admin note: text overlap with arXiv:1810.1176

    Exploring the relationship between multiple team membership and team performance: the role of social networks and collaborative technology

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    Firms devoted to research and development and innovative activities intensively use teams to carry out knowledge intensive work and increasingly ask their employees to be engaged in multiple teams (e.g. R&D project teams) simultaneously. The literature has extensively investigated the antecedents of single teams performance, but has largely overlooked the effects of multiple team membership (MTM), i.e., the participation of a focal team\u2019s members in multiple teams simultaneously, on the focal team outcomes. In this paper we examine the relationships between team performance, MTM, the use of collaborative technologies (instant messaging), and work-place social networks (external advice receiving). The data collected in the R&D unit of an Italian company support the existence of an inverted U-shaped relationship between MTM and team performance such that teams whose members are engaged simultaneously in few or many teams experience lower performance. We found that receiving advice from external sources moderated this relationship. When MTM is low or high, external advice receiving has a positive effect, while at intermediate levels of MTM it has a negative effect. Finally, the average use of instant messaging in the team also moderated the relationship such that at low levels of MTM, R&D teams whose members use instant messaging intensively attain higher performance while at high levels of MTM an intense use of instant messaging is associated with lower team performance. We conclude with a discussion of theoretical and practical implications for innovative firms engaged in multitasking work scenarios

    Investigating Multitasking with Technology in Academic Settings

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    The presence of multitasking has become more and more prevalent in most if not all aspects of today’s society. This reoccurring display of multitasking is extremely prominent within the classrooms of our nation’s colleges and universities. While supposedly paying attention to lectures and taking efficient notes, students can be seen texting and/or using social media on their phones, or having a wide variety of possible tabs and windows pulled up on their laptops or tablets. This apparent habit of almost every higher-education level student has raised a multitude of questions in various fields of study over the years. It has also provided professors with yet another obstacle that they must overcome to effectively teach their students. In this study, we explore these issues and develop an extensive conceptual model outlining the factors that may impact multitasking with technology in academic settings

    Social media in the english classroom: a study on the use of whatsapp messenger by english teaching training program students of Universidad Andrés Bello Casona de Las Condes campus

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    Tesis (Profesor de Inglés para la Enseñanza Básica y Media y al grado académico de Licenciado en Educación)The reason behind the use of WhatsApp Messenger (WM) by the English Teaching Training Program (ETTP) students and its possible effects on their engagement is a problem that has not been addressed in the Chilean context. The present study was designed to fill this gap. The purpose of this study was to examine the dynamics of the English class regarding the use of mobile devices. Moreover, this study aimed at examining the reasons behind the use of WM by ETTP students of UNAB Casona Las Condes Campus and its possible effects on their engagement in the English class. The method used in this investigation followed the characteristics of a sequential explanatory design. The results were obtained through two observations, a questionnaire, and a focus group. This research study concluded that the use of smartphones and specifically WM has grown exponentially as it is constantly affecting our daily routine and habits, and also what happens inside the classroom. The results revealed there were several themes attributed to disengagement that might trigger students to use WM in the English class, such as boredom, short attention span, and demotivation.Las razones de los estudiantes de Pedagogía en Inglés para usar WhatsApp Messenger (WM) y sus posibles efectos sobre el involucramiento que estos tienen en las clases de inglés es un problema que aún no ha sido tratado en el contexto chileno. El presente estudio fue diseñado para suplir esta falencia. El propósito de esta investigación fue examinar las dinámicas de la clase de inglés en relación con el uso de dispositivos móviles. Además, este estudio tenía el propósito de examinar las razones de los estudiantes de Pedagogía en Inglés de UNAB Campus Casona de Las Condes para usar WM y los posibles efectos que su involucramiento pudiera tener en la sala de inglés. El método usado en esta investigación siguió las características de un diseño secuencial explanatorio. Los resultados se obtuvieron a través de dos observaciones, un cuestionario y un grupo focal. Este estudio de investigación nos permitió concluir que el uso de smartphones y específicamente el uso de WM han crecido de forma exponencial de manera que este afecta constantemente nuestras rutinas diarias y hábitos. Los resultados revelaron que existen varios temas que se pueden atribuir al desenganche y que pueden causar que los estudiantes usen WM en la clase de inglés, como el aburrimiento, el corto periodo de concentración y la desmotivación

    You Can't Hide Behind Your Headset: User Profiling in Augmented and Virtual Reality

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    Virtual and Augmented Reality (VR, AR) are increasingly gaining traction thanks to their technical advancement and the need for remote connections, recently accentuated by the pandemic. Remote surgery, telerobotics, and virtual offices are only some examples of their successes. As users interact with VR/AR, they generate extensive behavioral data usually leveraged for measuring human behavior. However, little is known about how this data can be used for other purposes. In this work, we demonstrate the feasibility of user profiling in two different use-cases of virtual technologies: AR everyday application (N=34N=34) and VR robot teleoperation (N=35N=35). Specifically, we leverage machine learning to identify users and infer their individual attributes (i.e., age, gender). By monitoring users' head, controller, and eye movements, we investigate the ease of profiling on several tasks (e.g., walking, looking, typing) under different mental loads. Our contribution gives significant insights into user profiling in virtual environments
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