Corona Larona: A 2.5D Mobile Game Advocating COVID-19 Safety Protocols and Mitigation Strategies

Governments and health agencies around the world have been at the forefront of managing the COVID-19 pandemic. To control the spread of the outbreak, mandatory safety protocols have been put into effect. Despite the continuous development and strict enforcement of these preventive guidelines, non-compliance with these mandatory safety protocols has been reported. Getting the message to the public is one of the key challenges in convincing people to follow mitigation policies. In this study, we employed the media of video games to advocate for COVID-19 safety protocols. We developed a video game called "Corona Larona"that features microgames with action gameplay playable on a mobile platform. Our video game concentrated on several preventive measures such as physical distancing, hand washing, wearing face masks as well as basic knowledge about the virus using in-game multiple choice questions. To our knowledge, this is the first video game dedicated to the COVID-19 outbreak and the mandatory safety protocols. In a time when many people play video games to survive their current situation, the Corona Larona game is a strategic example of using and maximizing this form of media for a more noble purpose. © 2022 IEEE.

An Improved Computer Vision based Face Mask Detection and Alerting System

A global healthcare crisis has been declared as a result of the covid-19 nandemic's extensive snread. The coronavirus spreads mostly by the release of droplets from an infected person's irritated nose and throat. The risk of spreading disease is highest in public gathering places. Wearing a facial mask in public is one of the greatest ways, according to the World Health Organization, to avoid getting an infectious disease. This research work proposes an approach to human face mask detection using TensorFlow and OpenCV. Whether or not a character is wearing a mask is indicated by an enclosing field drawn around their head. An alert email will be sent to a person whose face is in the database if they make a call without a mask worn. © 2023 IEEE.

Node Research on the Involvement of China's Carbon Tax Policy in the Context of COVID-19

The outbreak of COVID-19 provides a rare opportunity for the implementation of the carbon tax. To determine which stage is the most appropriate for introducing the policy, a simulation model based on China's panel data is established to analyze the impact of the carbon tax on government revenue and residents' income from five scenarios. A new GM-SD modeling method is proposed to ensure the accuracy of the model. The results show that the impact of the carbon tax on the government and the public is significantly different at different stages, and even the implementation of the carbon tax in the early stage of COVID-19 will reduce the government's tax revenue. The score analysis of government tax revenue, residents' surplus disposable income, residents' emotional value, and government administrative power finds that the middle period of COVID-19 is the best time to implement the policy. In addition, a more detailed analysis of five aspects, including total population, energy consumption, and national income, shows that the best time to implement the carbon tax policy is when the damage degree of COVID-19 is moderate. The analysis results can provide a reference and basis for China to introduce the carbon tax in the event of similar events as COVID-19, and have reference significance for other countries that have not implemented a carbon tax. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Using Gamified Interactive Experience to Relieve Psychological Pressure in the Post-pandemic Era

The COVID-19 Pandemic brought the whole society to a standstill, which has more significant psychological pressure on children and adolescents. Governments, companies, and social groups are trying to confront COVID-19 and social distancing in a gamified way. However, due to fear of the virus and uncertainty about the future, even after the Pandemic is well controlled in physical space, people are still reluctant to stop and play in public areas and are afraid to engage with others because of their internal sense of alienation. From the perspective of urban renewal and environmental design, creating a series of micro-scale design interventions in public spaces to relieve psychological pressure has urgency and relevant significance. This paper analyzes the symbiotic relationship between public art installations and communities. Then discovers the characteristics of public installations based on emotional healing. Furthermore, create two design prototypes to demonstrate more vividly how gamified interactive experience could relieve the mental pressure of the surrounding residents and help them gradually adapt to the new normal life. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Smart Entry System Following COVID-19 Protocols

COVID-19 is an ongoing global pandemic and is continuing to be a fast-spreading virus all over the world. It transmits when people breathe in air contaminated by droplets and small airborne particles containing the virus. The risk becomes highest when people are nearby, but they can be over longer distances, while indoors. It is necessary to isolate such infected persons in public places with large gatherings. In addition to screening them, individual protection measures can also be taken.Two primary requirements that fulfil the entry to the public are by scanning of temperature to prevent person suspected and also to ensure one has masked face properly to permit entry. At the moment, all places use this system. But they are manual and depend on the person inspecting temperature and mask. There are few automated processes, but they do not have automatic entry control. Thus, there are risks of false entry of people inside the public place.In this paper, an integrated approach in mask detection and temperature scanning, indicated visually by LED and LCD Display, and further control entry with the operation of boom barrier has been presented. The information is also recorded to identify each entry. Open CV is used to detect masks and obtain better accuracy. An infrared temperature sensor and a proper guide to scan the temperature are used. Each step of the process is implemented on one Raspberry PI-based board. The system is suitably packaged and demonstrated. © 2022 IEEE.

Digital Nomadism an Opportunity for Low-Density Territories: Trás-os-Montes Lands Case

Digital nomadism is a relatively recent tourism segment associated with the generalization of information and communication technologies (ICTs), having increased notoriety and relevance with the COVID-19 pandemic. This public is characterized by professionals who exclusively work online, while having an independent lifestyle, balancing work and leisure. This research aims to understand if the Trás-os-Montes Lands (a small region in the northeast of Portugal) hold the necessary conditions to position itself as an attractive destination for digital nomads. To this end, a macro analysis of the characteristics of this territory and the tourist accommodation in the region was carried out. In view of the results obtained it was found that although Trás-os-Montes Lands have touristic potential ability to meet the particular needs of the digital nomads segment, it is necessary an action plan to enhance the attractiveness of the destination for this audience. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Quarantine Perimeter Breach Monitoring System on Smartphone for COVID-19 Self Quarantine Patient

Quarantine place perimeter breach COVID-19 patients that required to do self-isolate and self-quarantine from public is usually occur when the patient is not following the SOP and regulations drawn by government. This will lead to possibly spread of the COVID-19 virus into the community. These persons need to be closely monitored their movement or location online in order to prevent further spread of COVID-19 to public within their surrounding area. In view of this situation, the research project proposed to develop Quarantine Perimeter Breach Monitoring System (QPBMS) that can detect and alert the authority when the COVID-19 patient commit the offence of perimeter breach from their isolation place. A smartphone GPS and HERE Tracker apps are used in the system for the detection and notification alert. The impact of this monitoring system will enable authority or health official to locate and closely monitored the COVID-19 adhered to their self-isolation period. With the accurate and stable tracing of the HERE tracker, the health officer law enforcer can be notified with a precise info about any quarantine period violation. © 2022 IEEE.

Machine Learning based Face Mask Classifier

The COVID19 pandemic has affected almost entire world. The virus is spreading at a rapid rate through droplets generated while sneezing or coughing. All the governments in the world have made it mandatory to wear face masks. But almost 30 to 40% of public is not following the rules. Some people wear masks but they do not wear them in a proper way i.e., they wear masks below their nose. This paper proposes a model based on InceptionV3 algorithm which classifies people into 3 categories namely: face fully covered, face not covered and face partially covered with mask. This paper will help the police to detect people without masks or people not wearing masks properly at a crowded place. The police can then keep record of such people and fine them. © 2022 IEEE.

Development of Bilingual Sentiment and Emotion Text Classification Models from COVID-19 Vaccination Tweets in the Philippines

Social media can be used to understand how the public is responding to the ongoing nationwide COVID-19 vaccination campaign, allowing policymakers to respond effectively through informed decisions. However, conducting social media analysis in the Philippine-context presents a challenge because natural informal conversations make use of a combination of English and local language. This study addresses this challenge by including part-of-speech tags, frequency of code switching and language dominance features to represent bilingualism in training machine learning models with COVID-19 vaccination-related Tweets for sentiment and emotion analysis. Results showed that the English-Tagalog Logistic Regression sentiment classification model performed better than Textblob, VADER and Polyglot with an accuracy of 70.36%. Similarly, the English-Tagalog SVM emotion classification model performed better than Text2emotion, NRC Affect Intensity Lexicon and EmoTFIDF with an average mean-squared error of 0.049. The added bilingual features only improved these performance metrics by a small margin. Nevertheless, SHAP analysis still revealed that sentiment and emotion classes exhibit varying levels of these bilingual features, which shows the potential in exploring similar linguistic features to distinguish between classes better during text classification for future studies. Finally, Tweets from September 2021 to January 2022 shows negative, mainly anger and sadness, perceptions towards COVID-19 vaccinations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A COVID-19 Safety Monitoring System: Personal Protective Equipment (PPE) Detection using Deep Learning

Medical personal protective equipment (PPE) identification has acquired attention in the computer vision and deep learning sectors as a result of COVID-19's recent breakthrough and quickly spread. The need for people to wear face masks in public is growing. COVID-19 transmission can be considerably reduced with the use of face masks and PPE, according to research. The goal of this research is to construct a medical PPE detection system using deep learning. The goal of this study is to utilize the YOLOv3 object detection method to conduct object detection, detecting whether a health worker is wearing complete medical PPE or not, upon entering wards or environments prone to the virus. In the study's findings, the detection model got a mean average precision score of 96.59%, detected complete and incomplete PPE varied with accuracies ranging from 40% to 80% which is to be expected given that there is a lot of variations of medical PPE with different colors and types. © 2022 IEEE.

Real-Time Face Mask Detection Using CNN in Covid-19 Aspect

The COVID-19 pandemic has reshaped everyone’s life as we know it. Many measures and proposals have been suggested to prevent the virus’s rapid spread since scientists detected it. In the midst of many of these, one major piece of advice was to “wear a mask!”. The mask or face coverings can protect the wearer from contracting the virus or spreading it to others. Hence, to ensure that the public is not wearing a mask, one of the applications we have developed is face mask detection, which uses biometrics to map the features of a human face, analyse the data, and determine whether the detected face is a positive image or a non-face or negative image by placing a box around it. The main ideology of studying and researching face mask detection is to use computer technology to directly mimic the action and manner of human facial features and develop a system that reduces the amount of effort required to identify whether or not the person is wearing a mask and notifying them with the assistance of the proposed model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition

The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases. © 2021 Gesellschaft fur Informatik (GI). All rights reserved.

The Day-Ahead Forecasting of the Passenger Occupancy in Public Transportation by Using Machine Learning

Public transport is one of the main infrastructures of a sustainable city. For this reason, there are many studies on public transportation which mostly answer the question of “when my next bus will arrive?”. However now when the public is under the restrictions of the Covid-19 pandemic and learning to live with new social rules such as “social distance” a new yet crucial question arise on public transportation: “how crowded my next bus will be?” To prevent the crowdedness in public transportation the traffic regulators need to forecast the number of passengers the day ahead. In this study, in cooperation with Synteda, we suggest a machine learning algorithm that forecasts the occupancy in a bus or tram the day ahead for each stop for a route. The input data is past passenger travel data provided by the Västtrafik AB which is the public transportation company in Gothenburg, Sweden. The hourly data for the precipitation and temperature also has been added to the forecasting method;the database of precipitation and temperature is obtained by the SMHI, Swedish Meteorological and Hydrological Institute. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Personal Protective Equipment Detection with Live Camera

With the recent outbreak and rapid transmission of COVID-19, medical personal protective equipment (PPE) detection has seen significant importance in the domain of computer vision and deep learning. The need for the public to wear face masks in public is ever increasing. Research has shown that proper usage of face masks and PPE can significantly reduce transmission of COVID-19. In this paper, a computer vision with a deep-learning approach is proposed to develop a medical PPE detection algorithm with real-time video feed capability. This paper aims to use the YOLO object detection algorithm to perform one-stage object detection and classification to identify the three different states of face mask usage and detect the presence of medical PPE. At present, there is no publicly available PPE dataset for object detection. Thus, this paper aims to establish a medical PPE dataset for future applications and development. The YOLO model achieved 84.5% accuracy on our established PPE dataset comprising seven classes in more than 1300 images, the largest dataset for evaluating medical PPE detection in the wild. © 2021 IEEE

Factors Affecting Data-Privacy Protection and Promotion of Safe Digital Usage

India is facing the problem of the digital divide. Being developing countries and with low literacy rates, digital knowledge among the public is weak. Those who know a bit about digital operations on smartphones and computers are not having complete knowledge of data security and its peculiarities. Therefore, this study aimed to find determinants of data-privacy anxiety among Indians and to understand their stress and anxiety during the use of digital applications in their daily routines, especially amid the COVID-19 scenario. The current study adopted an inductive qualitative exploratory approach to delve into the above issues. This study employed a reflexive thematic analysis method to analyse interview data of 10 participants across young-adult to middle-adult age groups of male and female gender. Participants belonged to middle socio-economic status having urban background. The study found 6 themes and 26 subordinate themes as determinants of data-privacy anxiety. Emerging themes from the data indicated at the systemic determinants of data-security anxiety, the paradox of learned helplessness and convenience preference among participants. This paper employed the Foucauldian lens of bio-power to discuss the circumscribing function of ill-structured knowledge dissemination approaches. This paper argues in favor of a critical pedagogy approach in educating people about digital security, dealing with data-privacy anxiety, and promoting safe digital usage among all generations of Indians. It also suggests measures of modifications in policies and documentation processes of major online platforms and apps to curb uncertainty and sense of insecurity among users. © 2022 Copyright for this paper by its authors.

A VR Game for Obsessive-Compulsive Disorders Therapy

Virtual Environments (VE) are used more and more for the treatment of phobias. The therapy using VE is starting to be a way of treatment of several types of phobias for people with obsessive-compulsive disorders (OCD). People with OCD experience intrusive, unwanted thoughts that cause an increased amount of anxiety and intentional repetitive behaviors that decrease anxiety. The advantage of using a VE to experience and challenge compulsions is that it allows the user to imagine taking the risk without taking a risk. This is also true for the games, where players can face challenges without consequences in real life. This paper presents a virtual reality serious game for OCD therapy. The main purpose of this game is to serve as a tool to expose the patients to stimuli that can trigger OCD symptoms, for example, cleaning, checking and tidying. The game is more oriented for adolescents and younger adults with OCD because this public is more related to new technologies. In addition, OCD affects younger subjects, accompanying them throughout adulthood, generating a very accentuated degree of disability in their routines. A preliminary evaluation of the game was realised with a group of specialists and the results were positive. An evaluation with patients was not yet possible due to Covid-19 restrictions. © 2021 IEEE.