ABSTRACT
Prominent social media sites such as Facebook and Twitter use content and filter algorithms that play a significant role in creating filter bubbles that may captivate many users. These bubbles can expose users to dangerous or extremist content as seen with QAnon rhetoric, leading to the January 6, 2021 attack on the U.S. Capitol, and the unprecedented propaganda surrounding COVID-19 vaccinations. This paper hypothesizes that the secrecy around content algorithms and their ability to perpetuate filter bubbles creates an environment where dangerous false information is pervasive and not easily mitigated with the existing algorithms designed to provide false information warning messages. Both Facebook and Twitter provide various forms of false information warning messages which sometimes include fact-checked research to provide a counter viewpoint to the information presented. The results of a survey administered by the authors indicate that users would spend less time on Facebook or Twitter once they understood how their data is used to influence their behavior on the sites and the information that is fed to them via algorithmic recommendations. Further analysis of the survey data indicates a possible correlation between having seen a false information warning message and COVID-19 vaccination status. © 2022 WMSCI.All rights reserved.
ABSTRACT
Under the influence of COVID-19, various studies have shown that the most important transmission of the epidemic is droplet infection, it is the most effective way to control the epidemic by wearing a mask in a safe range. To confirm the situation of masks-wearing in public, a useful way is to use image-recognition technology to detect the people in the field. On the other hand, with the continued development of wearable devices, smart glasses have been widely used in many files such as handicapped person support. Based on the previous researches, it is already possible to incorporate facial recognition technology into smart glasses. Especially, the application of Augmented Reality (AR) technology on smart glasses can provide users with a lot of additional information, for example, to highlight the targets who been identified. Therefore, to identify the people who are not wearing masks more effectively, in this paper we try to design and wearable mask recognition warning system by using the AR smart glasses. The system can supply the warning messages about the person without masks in both visual and auditory way to the user to support the users including the handicapped persons who not being able to hear or see. The results of this study may provide guidelines to develop the epidemic prevention system and offers useful insights for the supporting of handicapped persons. © 2021 IEEE.
ABSTRACT
This paper presents the role and limitations of Unmanned Aerial Vehicles in emergency situations especially during the Covid-19 outbreak. The world had faced lot of disasters through the years leading to a flurry of innovation. New business opportunities have merged especially in the automation revolution. UAVs are small aircraft, mainly known as drones, that are used as technology for public safety companies. A Flying Ad Hoc Network is a network composed by UAVs. They are used in different military and civilian applications especially in areas where there is no infrastructure or where it is difficult to access. In emergency situations, like Covid-19 pandemic that many countries fight nowadays, these technologies help the authorities to control and limit the propagation of the virus. Spraying of disinfectants, temperature scanning, broadcasting of warning messages or detection of suspicious movements, many situations where UAVs can be used. In some countries, UAVs are also used to transport medical supplies including vaccines or deliver food supplies to customers. Because Covid-19 is still so contagious, it is safer to minimize human-to-human contact and use more technologies. However, due to their specific nature, UAVs present some limitations. Solutions to address these limitations are also discussed in this article. © 2021 IEEE.