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YEF-ECE 2022 TOC
Pub Date : 2022-07-01 DOI: 10.1109/yef-ece55092.2022.9850166
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引用次数: 0
GloFood: A Community-oriented System for Knowledge Sharing and Collaboration GloFood:面向社区的知识共享和协作系统
Pub Date : 2022-07-01 DOI: 10.1109/YEF-ECE55092.2022.9849895
Pedro Alves, Majid Zamiri, L. Camarinha-Matos
In the last decades, technology has evolved exponentially and become a big part of our daily activities. The widespread use of the Internet coupled with the growth of social networks allowed a more intense search for knowledge sharing as well as the creation and maintenance of interpersonal relationships. As a result, several learning communities appeared and grew. Each community has its central topic, cooking being an example of these topics. The key idea is that some cooks from around the world might want to enhance their culinary knowledge and, to do so, they can join a related community. This paper introduces GloFood which is a community-oriented system supporting a collaborative learning community that tries to foster knowledge sharing and collaboration.
在过去的几十年里,科技呈指数级发展,成为我们日常活动的重要组成部分。互联网的广泛使用,加上社交网络的发展,使得对知识共享以及人际关系的创建和维护的搜索更加强烈。结果,几个学习社区出现并发展起来。每个社区都有自己的中心话题,烹饪就是其中一个例子。关键的想法是,来自世界各地的一些厨师可能想要提高他们的烹饪知识,为此,他们可以加入一个相关的社区。本文介绍了GloFood,这是一个面向社区的系统,支持协作学习社区,试图促进知识共享和协作。
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引用次数: 0
A scalable incremental algorithm for computing the evolution of structural virality in social networks 一种可扩展的递增算法,用于计算社交网络中结构病毒式传播的演变
Pub Date : 2022-07-01 DOI: 10.1109/YEF-ECE55092.2022.9850022
Rodrigo Calzada Haro, Felix Cuadrado Latasa, Javier Andión Jiménez
The analysis of social networks is a concern nowadays. The importance they acquired over the last few years forces one to analyze suspicious behaviors and how false information spreads. Nevertheless, there are many parameters to consider when analyzing these networks. When the content of the messages is taken into account, semantic analysis can be considered the most important element. However, the relationships established between users and their interactions can reveal the different tendencies of the social network. To analyze several aspects of the structure of the conversations, it is required to know how the cascades spread. This can be represented by the use of virality. This paper reveals a scalable algorithm that drastically reduces the complexity of the calculation of the virality evolution. With the use of graph properties, the algorithm is a key component, able to relate time and size, to characterize the shape of the conversations in social networks in a simple way.
对社交网络的分析是当今社会关注的问题。它们在过去几年中获得的重要性迫使人们分析可疑行为和虚假信息的传播方式。然而,在分析这些网络时,有许多参数需要考虑。当考虑到消息的内容时,语义分析可以被认为是最重要的元素。然而,用户之间建立的关系以及他们之间的互动可以揭示出社交网络的不同趋势。为了分析对话结构的几个方面,需要知道级联是如何传播的。这可以通过病毒式传播来体现。本文揭示了一种可扩展的算法,该算法大大降低了计算病毒进化的复杂性。通过使用图形属性,该算法是一个关键组件,能够将时间和大小联系起来,以一种简单的方式表征社交网络中对话的形状。
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引用次数: 0
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2022 International Young Engineers Forum (YEF-ECE)
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