ClassRoute: An English to Punjabi Educational Video Translation Pipeline for Supporting Punjabi Mother-Tongue Education

Information Communication Technology (ICT) permeates almost every aspect of our daily lives and has become one of the most important priorities for formal and informal education. However, many people particularly those in least developed countries, are unable to reap the benefits due to lack of access to ICT but also due to lack of access to quality educational material. Additionally, in Punjab India, due to a shortage of resources and lack of infrastructure, the education system suffers from massive gaps including high student to teacher ratios, shortage of qualified teachers, and poor teacher training programs. This all has also been further exacerbated due to the COVID19 Pandemic as schools shut down globally and all teaching/learning activities moved online where possible or were canceled otherwise. In an effort to help relieve some of the burden on the Punjabi education system, and motivated by the proven efficiency of mother-tongue based education as well as the importance of visual-based learning, this paper introduces a pipeline for translating English educational videos into Punjabi equivalents which seeks to go beyond simple translation and in future iterations take into consideration the cultural needs of the learners in order to better connect them with the topics being taught. This pipeline is among a series of under construction pipelines aimed at translating English educational videos into other languages, dubbed as ClassRoute.

EVision: Epidemic Forecasting on COVID-19

Vaccination is the primary strategy to prevent COVID-19 illness and hospitalization. However, supplies are scarce and due to the regional mutations of the virus, new vaccines or booster shots will need to be administered potentially regularly. Hence, the prediction of the rate of growth of COVID-19 cases is paramount to ensuring the ample supply of vaccines as well as for local, state, and federal government measures to ensure the availability of hospital beds, supplies, and staff. eVision is an epidemic forecaster aimed at combining Machine Learning (ML) - in the form of a Long Short-Term Memory (LSTM) Recursive Neural Network (RNN) - and search engine statistics, in order to make accurate predictions about the weekly number of cases for highly communicable diseases. By providing eVision with the relative popularity of carefully selected keywords searched via Google along with the number of positive cases reported from the US Centers for Disease Control and Prevention (CDC) and/or the World Health Organization (WHO) the model can make highly accurate predictions about the trend of the outbreak by learning the relationship between the two trends. Thus, in order to predict the trend of the outbreak in a specific region, eVision is provided with a weekly count of the number of COVID-19 cases in a region along with statistics surrounding common symptom search phrases such as "loss of smell"and "loss of taste"that have been searched on Google in that region since the start of the pandemic. eVision has, for instance, been able to achieve an accuracy of %89 for predicting the trend of the COVID-19 outbreak in the United States © 2021 by Walter de Gruyter Berlin/Boston.

eVision: Epidemic forecasting on COVID-19

Vaccination is the primary strategy to prevent COVID-19 illness and hospitalization. However, supplies are scarce and due to the regional mutations of the virus, new vaccines or booster shots will need to be administered every so often. Hence, the prediction of the rate of growth in reported COVID-19 cases is paramount to ensuring the ample supply of vaccines as well as for local/state/federal government measures to ensure the availability of hospital beds, supplies, and staff. eVision is an epidemic forecaster aimed at combining AI-in the form of a Long Short-Term Memory (LSTM) Recursive Neural Network (RNN)-and search engine statistics, in order to make accurate predictions about the weekly number of cases for highly communicable diseases. Starting on replicating an older Google model and then improving upon it, predictions are accurately made as far as seven weeks into the future with an accuracy rate of %91 for seasonal influenza. While many different kinds of forecasting models have been created to track the COVID-19 pandemic, they have missed the insight discovered by eVision on influenza: by simply providing the AI model with the relative popularity of carefully selected key phrases searched via Google along with the number of positive cases reported from the CDC and/or WHO the model can make highly accurate predictions about the trend of the outbreak by learning the relationship between the two trends. eVision is thus provided with a weekly count of the number of COVID-19 cases in a region along with statistics surrounding common search phrases such as “loss of smell” and “loss of taste” that have been searched via Google in that region since the start of the pandemic. It has, for instance, been able to achieve an accuracy of %89 for predicting the trend of the COVID-19 outbreak in the United States.

Twitter Sentiment Analysis and Political Approval Ratings for Situational Awareness

The rally around the flag effect is a political science concept used to explain increased, yet short-lived, popular support of a country's government or political leader(s) during periods of crisis such as wars. The effect is teased out through solicited public opinion surveys which reach a limited sub-sample of willing participants and are expensive to conduct, leading to a slow response rate and after-the-fact results that are more suitable for historical studies than for situational awareness or crisis management. On the other hand, on social media platforms such as Twitter, millions of users provide their unsolicited opinion on almost any topic, including politics.This paper aims to initiate the conversation around the question: Can social media be used to observe the rally around the flag effect in action as it occurs, and thus help increase situational awareness? This first study utilizes the Twitter social media platform as, at the time of this writing, Twitter is used for political discourse more frequently than other mainstream social media platforms. Furthermore, Donald Trump, the 45th President of the United States of America was a staunch Twitter user who almost religiously used the platform for communicating to the public, more than and even some times in place of the regular established main stream media channels such as radio and television broadcasts and long standing processes such as white house press conferences and media releases. The study was conducted on tweets from the entire 4 years of Trump's presidency with a focus on the biggest crisis during his presidency: The COVID-19 global pandemic which lead to the only, and exceptionally short-lived, rally around the flag effect for his presidency.Ultimately, the study found that sentiment towards the president on Twitter did not mimic the nation's sentiment toward the president except for a brief moment during the peak of the rally around the flag effect;which is not enough for obtaining situational awareness nor for acting decisively in a timely manner that is in accordance with public sentiment. This result is to be expected, as while Twitter remains one of the dominate social media sites in the United States, it's users do not accurately reflect the demographics of the nation. © 2021 IEEE.