When do data visualizations persuade? The impact of prior attitudes on learning about correlations from scatterplot visualizations

Data visualizations are vital to scientific communication on critical issues such as public health, climate change, and socioeconomic policy. They are often designed not just to inform, but to persuade people to make consequential decisions (e.g., to get vaccinated). Are such visualizations persuasive, especially when audiences have beliefs and attitudes that the data contradict? In this paper we examine the impact of existing attitudes (e.g., positive or negative attitudes toward COVID-19 vaccination) on changes in beliefs about statistical correlations when viewing scatterplot visualizations with different representations of statistical uncertainty. We find that strong prior attitudes are associated with smaller belief changes when presented with data that contradicts existing views, and that visual uncertainty representations may amplify this effect. Finally, even when participants' beliefs about correlations shifted their attitudes remained unchanged, highlighting the need for further research on whether data visualizations can drive longer-term changes in views and behavior. © 2023 ACM.

iAssist: An online wellness platform to elevate the physical and mental health of the elderly

The physical and mental health of older adults is a critical issue that is often overlooked. With the recent increase in the number of people infected with the new variants of coronavirus, we are facing several problems, including a dearth of high-quality medical care. iAssist aims to be a platform that primarily focuses on the social benefit of promptly delivering medical aid to the elderly in our nation. It enables a variety of functions, such as doctor appointments, medicine orders, and lab appointments under one roof, with the goal of assisting caregivers, such as family members and healthcare professionals. Additionally, it offers a chatbot component that uses a social media messaging service, to inform users of new developments and assist in swiftly answering user questions. The technology stack used in iAssist makes the platform efficient and user-friendly for everyone involved. © 2022 IEEE.

Performance of Traffic Accidents' Prediction Models

Modeling traffic-accident frequency is a critical issue to better understand the accident trends and the effectiveness of current traffic policies and practices in different countries. The main objectives of this study are to model traffic road accidents, fatalities and injuries in Jordan, using different modeling techniques, including regression, artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models and to evaluate the safety impact of travel-restriction strategies during Covid-19 pandemic on traffic-accident statistics for the year 2020. To accomplish these objectives, data of traffic accidents, registered vehicles (REGV), population (POP) and economic gross domestic product (GDP) from 1995 through 2020 were obtained from related sources in Jordan. The analysis revealed that accidents, fatalities and injuries have an increasing trend in Jordan. Root mean of square error (RMSE), mean absolute error (MAE) and coefficient of multiple determination (R2) were sued to evaluate the performance of the developed prediction models. Based on model performance, the ANN models are the best, followed by the ARIMA models and then the regression models. Finally, it was concluded that the strategies undertaken by the government of Jordan to combat Covid-19, including complete and partial banning of travel, resulted in a considerable reduction of accidents, injuries and fatalities by about 35%, 37% and 50%, respectively. © 2023, Jordan University of Science and Technology. All rights reserved.

Driving innovation through project based learning: A pre-university STEAM for Social Good initiative

The Covid pandemic is a clarion call for increased sensitivity to the interconnected nature of social problems facing our world today. A future-oriented education on critical issues, such as those outlined in the United Nations Sustainable Development Goals (UN SDGs) and designing potential solutions for such problems is an imperative skill that must be imparted to children to help them navigate their future in today's unpredictable world. Towards this goal, we have been conducting 3.5 month-long mentoring sessions for pre-university students in India to participate in a STEAM for Social Good innovation challenge conducted annually by the Government of India. Using digital and physical computing skills, we helped children explore creative solutions for social problems through a constructionist approach to learning, wherein they ideated and reflected upon the problems in their communities. The children learnt the Engineering Design Thinking process and worked in online groups of two or three, from concept to completion. Despite the constraints posed by the pandemic, they explored creative ways to think about design and innovation. They completed a variety of tasks by making, tinkering, engineering, assembling, and programming to grasp the intricate relationship between software and hardware. Subsequently, the children showcased their creative abilities through video storytelling to a panel of domain experts. In this paper, we present the children's perspective of their experiences through this journey, the evaluation metrics based on IEEE design principles, and our learnings from conducting this initiative as a university-school partnership model for 84 middle and high school students. The aspirational intent of this initiative is to make the children better social problem solvers and help them perceive social problems as opportunities to enhance life for themselves and their communities. © 2022 IEEE.

Informed Deep Learning for Epidemics Forecasting

The SARS-CoV-2 pandemic has galvanized the interest of the scientific community toward methodologies apt at predicting the trend of the epidemiological curve, namely, the daily number of infected individuals in the population. One of the critical issues, is providing reliable predictions based on interventions enacted by policy-makers, which is of crucial relevance to assess their effectiveness. In this paper, we provide a novel data-driven application incorporating sub-symbolic knowledge to forecast the spreading of an epidemic depending on a set of interventions. More specifically, we focus on the embedding of classical epidemiological approaches, i.e., compartmental models, into Deep Learning models, to enhance the learning process and provide higher predictive accuracy. © 2022 The authors and IOS Press.

An Analysis of the Integration of DRT Services with Local Public Transport in Post-pandemic Period: Some of the Preliminary Insights in the Italian Context

Italy was one of the first country in Europe which was severely affected by COVID-19 pandemic. Several critical issues emerged during the different pandemic phases, especially in the health and mobility sector. Restrictions on public transport reduced the supply of transport, highlighting the need to rethink complementary transport systems. Since May 2020, in the post-lockdown phase, the provision of local public transport has been based on ordinary services, such as bus services, which are mainly intended to meet the needs of systematic travel between the places of residence and work on main development routes of the territory. These services have undergone reductions both in the on-board capacity and in some cases the complete elimination of transit routes. The rebalancing in favour of sustainable modes of transport and the reduction of the share of road mobility is pursued through the encouragement of ad-hoc measures aimed at balancing-off the supply-demand mechanism and improving the quality of services. The application of an on-demand responsive transit system has the ability to improve the transit needs in order to reach the places where personal or family services are provided or to enjoy the resources distributed within desired territory. In Italy since March 2020, new areas of weak demand for transport have been created, i.e. areas with a certain number of users that need to be transferred to and from places that have generally never had access to public transport or have had it restricted. The Demand Responsive Transport (DRT) system is, therefore, used in both urban and suburban areas, allowing even those who do not have their own means of transport (for example, disadvantaged social categories or users with a short stay in the area) or who are suitably equipped (people with reduced or no motor skills), to move around in areas easily. The present work focuses on an analysis of the current state of affairs, starting from the literature and regulations concerning the diffusion of the DRT systems in Italy, and offers some ideas for the optimisation of an integrated public transport service. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Material Design for Next-Generation mRNA Vaccines Using Lipid Nanoparticles

Vaccine development is among the critical issues for ceasing the COVID-19 pandemic. This review discusses the current usage of biomaterials in vaccine development and provides brief descriptions of the vaccine types and their working mechanisms. New types of vaccine platforms (next-generation vaccines and DNA- or mRNA-based vaccines) are discussed in detail. The mRNA vaccine encoding the spike protein viral antigen can be produced in a cell-free system, suggesting that mRNA vaccines are safer than “classic vaccines” using live or inactivated virus. The mRNA vaccine efficacy is typically high at approximately 95%. However, most mRNA vaccines need to be maintained at −20 or −70 degrees for storage for long periods (half a year) and their transportation because of mRNA vaccine instability in general, although mRNA vaccines with unmodified and self-amplifying RNA (ARCT-154, Arcturus), which have a lyophilized form, have recently been reported to be kept at room temperature. mRNA vaccines are typically entrapped in lipid nanoparticles composed of ionizable lipids, polyethylene glycol (PEG)-lipids, phospholipids, and cholesterol. These components and their composition affect mRNA vaccine stability and efficacy and the size of the mRNA vaccine. The development of an improved mRNA vaccine entrapped in sophisticated biomaterials, such as novel lipid nanoparticles, using new types of biopolymers or lipids is necessary for high efficacy, safe transportation and long-term storage of the next generation of mRNA vaccines under mild conditions. © 2022 Taylor & Francis Group, LLC.

Applications of Coversational AI in Mental Health: A Survey

Online mode during COVID-19 has raised mental health issues among students along with the stigma around it that continues to exist. As a result of which, there are people who are not comfortable in disclosing their personal details to outsiders for help. This brings in the use of AI applications that can help in this critical issue requiring regular monitoring. In this paper, a survey is conducted to know about awareness about this topic. A review about many conversational AI chatbots is provided that are helpful for handling mental health issues such as stress, anxiety, and depression in a number of ways. These include voice and text based chatbots developed in the last decade. The strengths and limitations of these are also discussed. © 2022 IEEE.

Pandemic rumor identification on social networking sites: A case study of COVID-19

Digital Rumors, because of the ease and innovations in social networking technologies, has become an important issue. These rumors become a critical issue in a disaster, epidemic, or pandemic. Considering classification power of conventional and deep learning techniques, we propose a hybrid learning technique that identifies rumors effectively. For this, TF-IDF description has been used to build a stack of multiple conventional learning techniques;logistic regression, Naïve Bayes, and random forest. Whereas, word-embedding features have been used for purpose of deep learning;LSTM and LSTM-RNN. The combination of LSTM and RNN makes this study unique in the field of rumor detection. With LSTM and RNN gated architectures, huge series rumor tweets may be efficiently managed. To aggregate the decisions, the labels of deep learning and the stack of conventional learning have been combined using majority voting based ensemble classification. To evaluate the performance of the proposed technique, we used publically available standard COVID-19 RUMOR dataset. The proposed technique obtains 99.02% accuracy, which shows its effectiveness. The dataset utilized and the ensemble model created for rumor identification distinguish our work from existing methods. © 2021 ACM.

LionKeeper: Automatically Discovering Timely Cybercrime Dynamics in the Age of COVID-19

As more activities are moving to digital platforms in the age of COVID-19 pandemic, cyber security becomes an increasingly critical issue. Thus, understanding how the recent pandemic has changed the Singapore cyber security landscape gains importance in unearthing potential weaknesses present in the infrastructure, which unfortunately is very challenging. In this paper, we propose, LionKeeper, an automated system for discovering the cyber security dynamics timely in Lion City - Singapore through social media data analytics. In particular, considering that the social media platforms like news websites provide immediate reports on local and global cybercrime incidents, in our system, we first crawl all the news articles from mainstream news sources such as CNA and Strait Times. Then, we analyze these news articles to identify those related to cybercrimes, the date and the location of cybercrime incidents, and employ a scoring system to detect the cyber security attack types and their significance. Additionally, based on the extracted information, we perform various analyses to generate meaningful insights for users to understand the cyber attack landscape dynamics before and during the COVID-19 pandemic automatically and intelligently. To the best of our knowledge, this is the first automated solution to understand the Singapore cyber landscape via social media analytics. © 2021 IEEE.

The effects of self-monitoring of physical activity among elderly people with the impact of Covid-19 pandemic

With the growth of aging societies, the health of the elderly is considered one of the critical issues. Regular physical activity is linked to improving physical and mental functions. Therefore, there is an urgent need to motivate the elderly to be physically active. The self-monitoring of physical activity may positively impact the awareness of exercise and health and increase activity levels. In this study, we conducted a 12-week trial with thirty Japanese elderly to investigate the effects of self-monitoring on their attitudes, awareness, and activity levels. During the trial, the participants wore activity trackers daily and responded to repeated questionnaires weekly. The Covid-19 pandemic has begun to appear a few weeks after starting this trial. Therefore, we explored the impact of this pandemic on the participants' activity and psychological status. Overall, the participants increased their perception of the benefits of self-monitoring and willingness to check the activity tracker's feedback. Despite there was a significant decrease (p < 0.05) in the participant number of steps due to the Covid-19 pandemic, the percentage of reduction was small (11%). The self-monitoring of activity may help the elderly maintain activity level during the pandemic. Furthermore, the participants agreed with the importance of monitoring physical activity and the necessity to maintain activity level during the Covid-19 pandemic. © 2021 Association for Computing Machinery. All rights reserved.

Challenges of Online Learning During the COVID-19: What Can We Learn on Twitter?

The COVID-19 pandemic is an ongoing global pandemic. With schools shut down abruptly in mid-March 2020, education has changed dramatically. With the phenomenal rise of online learning, teaching is undertaken remotely and on digital platforms, making schools, teachers, parents, and students face a steep learning curve. This unplanned and rapid move to online learning with little preparation results in a poor experience for everyone involved. Thus, this study explores how people perceive that online learning during the COVID-19 pandemic is challenging. We focus on tweets in English scraped from March to April 2020 with keywords related to the COVID-19 pandemic and online learning. We applied the latent Dirichlet allocation to discover the topics that occur in the data collection. We analyzed representative tweets from the qualitative perspective to explore and augment quantitative findings. Our findings reveal that most challenges identified align with previous studies. We also shed light on several critical issues, including mental health, the digital divide, and cyberbullying. Future work includes investigating these critical issues to enhance teaching and learning practices in the post-digital era. © 2021, Springer Nature Switzerland AG.