121 research outputs found

    A grammar for standardized wiki markup

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    Fake news identification on Twitter with hybrid CNN and RNN models

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    The problem associated with the propagation of fake news continues to grow at an alarming scale. This trend has generated much interest from politics to academia and industry alike. We propose a framework that detects and classifies fake news messages from Twitter posts using hybrid of convolutional neural networks and long-short term recurrent neural network models. The proposed work using this deep learning approach achieves 82% accuracy. Our approach intuitively identifies relevant features associated with fake news stories without previous knowledge of the domain

    Impalpable Hits: indeterminacy in the searching of tagged Shakespearian texts

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    In Shakespeare studies, as in the rest of early modern literary studies, the new information technologies have been neither rapidly nor effectively adopted in research. One reason is a misplaced attention upon the notion of hypertext and the seeking of spurious analogies with the early modern printed codex. This essay is concerned with machine applications of textual searching technologies, which is where we should be focussing our energies, and it argues that important recent products for Shakespearian research are weak, and more importantly non-standard, in their searching mechanisms. The desirability of adopting an existing standard, called 'regular expressions', is argued

    Scholarly event characteristics in four fields of science : a metrics-based analysis

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    One of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences.However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution.In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics.In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy.This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work.Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org. © 2020, The Author(s)
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