Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy

The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users' intents, due to updates in guidelines or in response to new information, we highlight the important task of intent discovery in this use-case. We introduce a novel automatic evaluation framework for intent discovery, leveraging the existing intent classifier of VIRA. We use this framework to report baseline intent-discovery results over VIRADialogs, that highlight the difficulty of this task. © 2023 Association for Computational Linguistics.

BIG DATA FRAMEWORK TO EVALUATE AND ANALYSE NOTATIONAL COVID-19 IMMUNIZATION PROGRAMMED (NCIP) IN MALAYSIA: A COMPARATIVE STUDY

Many governments around the world have launched their open government data (OGD) portal to improve the government's transparency by sharing their data with the public such as National Covid-19 Immunization Programmed (NCIP), which has been published at https://github.com/CITF-Malaysia/citf-public. However, increasing the number of datasets, data types, volume and complexity will be raised the integration issues. There-fore, it is essential to evaluate and analyses those huge amounts of these datasets. NCIP provides multiple data sources and datasets. These may raise the Big Data (BD) issues and pose various evaluation and analysis problems to produce valuable information. To generate meaningful linked data to support the purposes of this research study, the relationship between these disparate datasets needs to be identified and construct a comprehensive framework. In order to understand the causes of OGD development of big data, this study involves a detailed examination and comparison of existing theories and actual approaches to handle public sector open data concerns. According to the review, the framework was dominantly adopted over architecture, infrastructures, theoretical and conceptual framework in previous research to examine the revolution of government public accessible data. According to the findings, most existing frameworks do not con-sider the demand for public open data in health such as NCPI. Previous re-search on OGD for health has a lesser number of advanced BD frameworks. In the public sector, there is still a lack of investment and use of Big Data. The findings will aid academics in doing empirical research on the revealed need, as well as offer decision-makers with a better understanding of how to leverage OGD adoption in health by taking relevant actions. © School of Engineering, Taylor's University.

COVID Detection using X-Ray images through Machine Learning

This electronic document is a 'live' template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. Many organizations have been forced to undergo significant change as a result of the COVID-19 pandemic, including rethinking key aspects of their business cycles and utilizing innovation to stay up with duties while adhering to a shifting scene of regulations and new techniques. This procedure can offer complete knowledge covering points of interest and after effects which influence society from COVID19, by using data frameworks and innovative viewpoints. The viewpoints by various welcomed subject specialists are examined and cross referenced by internet learning, AI brainpower, data board, social communication, network safety, huge information, block chain, innovation and methodology through the perspective of the current emergency and effect on these particular regions. The viewpoints offers and ideal understanding of the scope of points, distinguishing central questions and proposals for hypothesis and practice by utilizing chest X-ray pictures using ML-approach. In the paper, the use of these ML methods to cope with the COVID-19 pandemic flow situation is a promising aspect, just as the prevention of the Covid infection model is proposed. © 2021 IEEE.