Evaluating the efficacy of Online Verbal Behaviour Training to ABA-Practitioners working with autistic individuals

During the COVID-19 pandemic, online training platforms have become more popular as a training delivery method. Given the importance of the Verbal Behaviour (VB) approach in teaching communication skills to autistic individuals, the current research was designed to evaluate the efficacy of a bespoke online VB training platform for practitioners. A quasi-experimental design was utilised to compare the effectiveness of the training platform with an experimental group (n = 20) who accessed the online VB platform and a control group (n = 20) who received hard copies of the training materials. Linear multiple regression analysis demonstrated that the online learning platform was significantly more effective in increasing practitioner knowledge of VB in comparison to receiving hard copies of the teaching materials. © 2022 Elsevier Ltd

Measuring Central Bank's Policy Effectiveness in Affecting Intention to Use New Payment Platform During The COVID-19 Pandemic

This study evaluates the effectiveness of central bank policy in influencing intention to use a new payment platform, QRIS (Quick Response code Indonesian Standard). The evaluation is hindered by the contemporaneous emergence of the COVID-19 pandemic, which acts as a confounding factor in adopting the new payment instrument. To disentangle the effect of those variables, we collected data from 617 respondents consisting of customers and merchants, employed a structural equation model with SmartPLS, asses fourteen hypotheses with demographic factors included as moderating factors. The result of the study successfully disentangles the policy impact from the pandemic impact and separates the risk of a pandemic from common risks. We verify that the pandemic and government intervention had significant direct and indirect effects on the intention to use QRIS, with the habit being the most influential component, outperforming other technology adoption determinants. This study, therefore, contributes to the advancement of the literature on the topic of technology adoption and government intervention and suggests that this measuring approach can be used as a complementary instrument to assess the impact of central bank policy on the public © International Journal of Economics and Management

Behaviour Analysis of Izmir Residents Using Public Wi-Fi Access Point Usage

The wide distribution of access points in Izmir allows the collected information to be employed in smart city algorithms. In this study, we analyze the information that has been made publicly available by Izmir Metropolitan Municipality. We first show that the data is reliable, then analyze it from the perspectives of holidays, seasonal trends, and the COVID-19 pandemic. The study also shows that the information can be used for crowd analysis and forecasting, using K-means and SARIMA algorithms, respectively. © 2022 IEEE.

Evaluating the efficacy of Online Verbal Behaviour Training to ABA-Practitioners working with autistic individuals

During the COVID-19 pandemic, online training platforms have become more popular as a training delivery method. Given the importance of the Verbal Behaviour (VB) approach in teaching communication skills to autistic individuals, the current research was designed to evaluate the efficacy of a bespoke online VB training platform for practitioners. A quasi-experimental design was utilised to compare the effectiveness of the training platform with an experimental group (n = 20) who accessed the online VB platform and a control group (n = 20) who received hard copies of the training materials. Linear multiple regression analysis demonstrated that the online learning platform was significantly more effective in increasing practitioner knowledge of VB in comparison to receiving hard copies of the teaching materials.

Analysing the Twitter Sentiments in COVID-19 Using Machine Learning Algorithms

The coronavirus emanated in Wuhan city of China, in the last month of 2019 and was even announced as a global threat. Social media could be an utterly noteworthy supply of facts during a time of crisis. User-generated texts yield perception into users' minds withinside the direction of such times, giving us insights into their critiques in addition to moods. This venture examines Twitter messages (tweets) regarding people's sentiment on the unconventional coronavirus. The essential aim of sentiment evaluation is the origin of human emotion from messages or tweets. This venture is geared toward using numerous gadgets studying type algorithms to expect the people's reception of the worldwide pandemic by reading their tweets on Twitter. In the course of this paper, we are testing our dataset on five different classifiers, namely Random Forest, Logistic regression, Multinomial naive Bayes, K-nearest neighbor, and Support vector machines classifiers. Together with precision rankings and balanced accuracy rankings, metrics are offered to gauge the fulfilment of the numerous algorithms implemented. The K-Nearest Neighbor classifier has given the highest precision score while the Logistic Regression classifier gives the highest recall, F1, accuracy and balanced accuracy scores. © 2022 IEEE.

Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets.

How different cultures react and respond given a crisis is predominant in a society's norms and political will to combat the situation. Often, the decisions made are necessitated by events, social pressure, or the need of the hour, which may not represent the nation's will. While some are pleased with it, others might show resentment. Coronavirus (COVID-19) brought a mix of similar emotions from the nations towards the decisions taken by their respective governments. Social media was bombarded with posts containing both positive and negative sentiments on the COVID-19, pandemic, lockdown, and hashtags past couple of months. Despite geographically close, many neighboring countries reacted differently to one another. For instance, Denmark and Sweden, which share many similarities, stood poles apart on the decision taken by their respective governments. Yet, their nation's support was mostly unanimous, unlike the South Asian neighboring countries where people showed a lot of anxiety and resentment. The purpose of this study is to analyze reaction of citizens from different cultures to the novel Coronavirus and people's sentiment about subsequent actions taken by different countries. Deep long short-term memory (LSTM) models used for estimating the sentiment polarity and emotions from extracted tweets have been trained to achieve state-of-the-art accuracy on the sentiment140 dataset. The use of emoticons showed a unique and novel way of validating the supervised deep learning models on tweets extracted from Twitter.

Features of Mobile Apps for People with Autism in a Post COVID-19 Scenario: Current Status and Recommendations for Apps Using AI.

The new 'normal' defined during the COVID-19 pandemic has forced us to re-assess how people with special needs thrive in these unprecedented conditions, such as those with Autism Spectrum Disorder (ASD). These changing/challenging conditions have instigated us to revisit the usage of telehealth services to improve the quality of life for people with ASD. This study aims to identify mobile applications that suit the needs of such individuals. This work focuses on identifying features of a number of highly-rated mobile applications (apps) that are designed to assist people with ASD, specifically those features that use Artificial Intelligence (AI) technologies. In this study, 250 mobile apps have been retrieved using keywords such as autism, autism AI, and autistic. Among 250 apps, 46 were identified after filtering out irrelevant apps based on defined elimination criteria such as ASD common users, medical staff, and non-medically trained people interacting with people with ASD. In order to review common functionalities and features, 25 apps were downloaded and analysed based on eye tracking, facial expression analysis, use of 3D cartoons, haptic feedback, engaging interface, text-to-speech, use of Applied Behaviour Analysis therapy, Augmentative and Alternative Communication techniques, among others were also deconstructed. As a result, software developers and healthcare professionals can consider the identified features in designing future support tools for autistic people. This study hypothesises that by studying these current features, further recommendations of how existing applications for ASD people could be enhanced using AI for (1) progress tracking, (2) personalised content delivery, (3) automated reasoning, (4) image recognition, and (5) Natural Language Processing (NLP). This paper follows the PRISMA methodology, which involves a set of recommendations for reporting systematic reviews and meta-analyses.

Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects.

Crowd behaviour analysis is an emerging research area. Due to its novelty, a proper taxonomy to organise its different sub-tasks is still missing. This paper proposes a taxonomic organisation of existing works following a pipeline, where sub-problems in last stages benefit from the results in previous ones. Models that employ Deep Learning to solve crowd anomaly detection, one of the proposed stages, are reviewed in depth, and the few works that address emotional aspects of crowds are outlined. The importance of bringing emotional aspects into the study of crowd behaviour is remarked, together with the necessity of producing real-world, challenging datasets in order to improve the current solutions. Opportunities for fusing these models into already functioning video analytics systems are proposed.