Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

Virtual-reality-based digital twin of office spaces with social distance measurement feature

Background Social distancing is an effective way to reduce the spread of the SARS-CoV-2 virus.Many students and researchers have already attempted to use computer vision technology to automatically detect human beings in the field of view of a camera and help enforce social distancing.However,because of the present lockdown measures in several countries,the validation of computer vision systems using large-scale datasets is a challenge.Methods In this paper,a new method is proposed for generating customized datasets and validating deep-learning-based computer vision models using virtual reality(VR)technology.Using VR,we modeled a digital twin(DT)of an existing office space and used it to create a dataset of individuals in different postures,dresses,and locations.To test the proposed solution,we implemented a convolutional neural network(CNN)model for detecting people in a limited-sized dataset of real humans and a simulated dataset of humanoid figures.Results We detected the number of persons in both the real and synthetic datasets with more than 90%accuracy,and the actual and measured distances were significantly correlated(r=0.99).Finally,we used intermittent-layer-and heatmap-based data visualization techniques to explain the failure modes of a CNN.Conclusions A new application of DTs is proposed to enhance workplace safety by measuring the social distance between individuals.The use of our proposed pipeline along with a DT of the shared space for visualizing both environmental and human behavior aspects preserves the privacy of individuals and improves the latency of such monitoring systems because only the extracted information is streamed.

Alert Device for Social Distancing

Social distancing among people is vital in minimizing spread of COVID-19 within community and can be effective in flattening the outbreak. This research work focuses on developing a close contact proximity detection system among smartphone users, particularly of COVID-19 patient, using Bluetooth signal to identify and analyze close contact proximity and social distancing from other anonymous smartphone users in their surroundings. Covering a physical space of six feet, a mandatory safety measure in shopping centers, schools, and other crowded areas, is a social solution advocated by World Health Organization (WHO) officials in this COVID-19. Everyone is concerned about their safety in the COVID-19 environment, so we came up with the concept of producing this new equipment. Most of the time, our attention is drawn to those in front of us and to our sides, but we are unable to keep track of those behind us. The major goal of this project is to keep individuals at a safe distance from one another. PIR sensor is used in this proposed work. Why did the World Health Organization (WHO) put 6 feet as a social distancing? When someone coughs or sneezes, small droplets are spread from the cough or sneeze. If you are in close proximity, you can breathe in those droplets, which may contain the COVID-19 virus, according to the World Health Organization. Vanderbilt University infectious disease expert Dr. William Schaffner said the “6 feet distance” rule comes from studies of respiratory physiology. Schaffner explains that even “without a cough or sneeze, the exhaled air mixes with the surrounding air within a distance of 3 to 6 feet, which is known as the breathing zone.” Schaffner continues: “If you are standing 3 to 6 feet away from me, you may inhale droplets that spread through coughing or sneezing. Of course, if I am infected with the virus, these droplets will contain the virus.”

Social distance integrated gravity model for evacuation destination choice

Evacuation is an effective and commonly taken strategy to minimize death and injuries from an incoming hurricane.For decades,interdisciplinary research has contributed to a better understanding of evacuation behavior.Evacuation destination choice modeling is an essential step for hurricane evacuation transportation planning.Multiple factors are identified associated with evacuation destination choices,in which long-term social factors have been found essential,yet neglected,in most studies due to difficulty in data collection.This study utilized long-term human movement records retrieved from Twitter to(1)reinforce the importance of social factors in evacuation destination choices,(2)quantify individual-level familiarity measurement and its relationship with an individual’s destination choice,(3)develop a big data approach for aggregated county-level social distance measurement,and(4)demonstrate how gravity models can be improved by including both social distance and physical distance for evacuation destination choice modeling.

SPECIAL ISSUE： MIGRANT WORKERS IN THE COURSE OF URBANIZATOIN The social distance between urban residents and migrant workers： an institutional analysis

Statistical analysis reveals that urban residents＇ preferred occupations are mainly those in which they themselves are relatively concentrated, such as government officials, teachers, and scientific researchers. Their least preferred occupations are mainly those in which migrant workers are relatively concentrated, such as the construction industry and the hospitality industry. The category of social distance between the two groups is mostly medium or remote. There is a certain level of asymmetry in their evaluations of social distance. Social distance is greatly influenced by differences in social status, cultural differences, spatial segregation, and informal institutions. Social distance between urban residents and migrant workers has been diminishing continuously, and a degree of social integration between them has occurred.

Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

Pandemics have always been a nightmare for humanity,especially in developing countries.Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics.Still,developing countries cannot afford such solutions because these may severely damage the country’s econ-omy.Therefore,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly.The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled technologies.Compared to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is proposed.The use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding solutions.Therefore,this study also emphasizes information security,end-to-end solution,and experimental results.Firstly,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing data.Thirdly,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism.Lastly,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine management.The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management.Unlike existing research studies,the security aspect is also considered in the realm of blockchain.The practical implementation of the proposed solution also obtains the results.The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques,which could help battle pandemic situations.Researchers can also consider the 5G-enabled narrowband internet of things(NB-IoT)technologies to implement contact tracing solutions.

A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

The COVID-19 virus is usually spread by small droplets when talking,coughing and sneezing,so maintaining physical distance between people is necessary to slow the spread of the virus.The World Health Organization(WHO)recommends maintaining a social distance of at least six feet.In this paper,we developed a real-time pedestrian social distance risk alert system for COVID-19,whichmonitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge,thus avoiding the problem of too close social distance between pedestrians in public places.We design a lightweight convolutional neural network architecture to detect the distance between people more accurately.In addition,due to the limitation of camera placement,the previous algorithm based on flat view is not applicable to the social distance calculation for cameras,so we designed and developed a perspective conversion module to reduce the image in the video to a bird’s eye view,which can avoid the error caused by the elevation view and thus provide accurate risk indication to the user.We selected images containing only person labels in theCOCO2017 dataset to train our networkmodel.The experimental results show that our network model achieves 82.3%detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall,Precision and mAP,proving the effectiveness of our system and the efficiency of our technology.

Social Distancing and Isolation Management Using Machine-to-Machine Technologies to Prevent Pandemics

Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish u,swine u,and coronavirus disease 2019(COVID-19).This study describes the comprehensive and effective implementation of the Industrial Internet of Things and machine-to-machine technologies for social distancing and smart self-isolation management.These technologies can help prevent outbreaks of any disease that can disperse widely and develop into a pandemic.Initially,a smart wristband is proposed that incorporates Bluetooth beacon technology to facilitate the tracing and tracking of Bluetooth Low Energy beacon packets for smart contact tracing.Second,the connectivity of the device with Android or iOS applications using long-term evolution technology is realized to achieve mobility.Finally,mathematical formulations are proposed to measure the distance between coordinates in order to detect geo-fencing violations.These formulations are specically designed for the virtual circular and polygonal boundaries used to restrict suspected or infected persons from trespassing in predetermined areas,e.g.,at home,in a hospital,or in an isolation ward.The proposed framework outperforms existing solutions,since it is implemented on a wider scale,provides a range of functionalities,and is cost-effective.

A Hybrid Heuristic Algorithm for Solving COVID-19’s Social Distancing at Universities Campus

Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main constraints.First, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.

Investigation of Coronavirus Deposition in Realistic Human Nasal Cavity and Impact of Social Distancing to Contain COVID-19:A Computational Fluid Dynamic Approach

The novel coronavirus responsible for COVID-19 has spread to several countries within a considerably short period.The virus gets deposited in the human nasal cavity and moves to the lungs that might be fatal.As per safety guidelines by theWorld Health Organization(WHO),social distancing has emerged as one of the major factors to avoid the spread of infection.However,different guidelines are being followed across the countries with regards to what should be the safe distance.Thus,the current work is an attempt to understand the virus deposition pattern in the realistic human nasal cavity and also to find the impact of distance that could be termed as a safety measure.This study is performed usingComputationalFluid Dynamics as a solution tool to investigate the impact of COVID-19 deposition(i)On a realistic 3D human upper airway model and(ii)2D social distancing protocol for a distance of 0.6,1.2,1.8,and 2.4 m.The results revealed that the regional deposition flux within the nasal cavity was predominantly observed in the external nasal cavity and nasopharyngeal section.Frequent flushing of these regions with saltwater substitutes can limit contamination in healthy individuals.The safe distancing limit estimated with 1 m/s airflow was about 1.8 m.The extensive deposition was observed for distances less than 1.8 m in this study,emphasizing the fact that social distancing advisories are not useful and do not take into account the external dynamics associated with airflow.

Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location analytics.Based on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19.Given the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.More specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor trajectories.The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users.The network of users is used to model an optimization problem to manage the human mobility on a site.The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools.The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering trajectories.By adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.

The Local Wisdom of Balala Tamakng Custom in Social Distancing During the Covid-19 Pandemic

This study aims to describe the local wisdom of balala tamakng custom in social distancing during the Covid-19 pandemic.The research approach used was descriptive qualitative with an ethnographic method.The informants participated were Dayak customary officials.The data collection used was deep interview,and data were validated using sources triangulation,and were analyzed by data reduction,data display,and conclusion drawing.The general conclusion of this research:Balala as a local wisdom in maintaining the balance between humans and the nature that has been passed down from generation to generation is still carried out today.The specific conclusions of this study:(1)Balala is the custom of abstinence in the Kanayatn Dayak community;(2)balala consists of regular balala and balala tamakng;(3)balala tamakng has the meaning as a means of creating a balance between human relations and the universe;(4)there is no direct relationship between the custom of balala tamakng with social and physical distancing against Covid-19.

Henriksen and Flora （1999） Revisited A Literature Review on Third-Person Effects and Children/Adolescents＊

In this literature review on TPE （third-person effects） and the behavioral consequences on children, the research questions posed are how the body of knowledge has evolved since the first empirical evidence of TPE among children and what knowledge gaps that remain. The traceable developments are two： （1） Compared to the vast amount of articles on TPE in general, the 5 9 identified on the topic of children are few and two thirds actually focus on adolescents/young adults rather than children. The reason put forward for studying younger children is the urge to prevent risky behavior through media literacy programs or pro-social advertisements; and （2） The studies have not primarily addressed results to support occurrence of TPE among children. Rather they support parental TPE or among the adolescents that TPE and reverse TPE occur due to certain kind of media content. The discussion on knowledge gaps that remain follow three themes： （1） Differentiations between self and others are in psychological studies implied to occur among children between the ages of 3-4 years old, yet no study address how children develop TPE; （2） There is a tendency to follow the more general development within TPE research with the renewed interest in behavioral consequences. But the primary behavioral consequence studied in TPE in general and within studies of TPE and children is support for censorship. Few studies address ＂real＂ behavioral consequences like parental mediation; and （3） There is also a need for more theoretically coherent research on the importance of social distance.

How EFL Learners from China and ASEAN Countries Politely Express Disagreement in an English Context:A Comparative Study

This paper aims to make a comparative study of cross-cultural communication upon a special speech act-"disagreement".38 undergraduates from China and 30 undergraduates from ASEAN countries were involved.They responded to the DCT(discourse completion test).Five contexts were selected and detailed descriptions of the scenarios were given.Social distance and gender were selected as the main variants in this study.From the results,we found that both groups of undergraduates generally ten d to use the same politeness strategies according to the same social distance,but gender was a more significant factor in politeness strategies adoption among EFL learners from ASEAN countries.Females tend to use negative strategies more than males do.We can conclude from the results that EFL learners from China and ASEAN countries incline to adopt the same politeness strategies in English context,but females from ASEAN countries are less likely to say“no”directly to express their disagreement compared to their counterpart.Those findings may offer reference to both sides during the pragmatic occasions of communicating.

History,epidemiology,vaccine and transmission of coronavirus disease 2019 (COVID-19):a quick review

Coronavirus belonging to family Coronaviridae is a large group of viruses causing respiratory and gastrointestinal diseases.The outbreak of 2019-novel coronavirus has created panic in the whole world after being transmitted from Wuhan,China in December 2019.A large number of people are affected throughout the world that forced a solitary situation in the affected areas confining common people to their homes to prevent its spread.Cellular receptors in humans for 2019-novel coronavirus are found to be same as for severe acute respiratory syndrome coronavirus,the first human pandemic of 21st century,however,2019-novel coronavirus is highly contagious,virulent and mutable than the severe acute respiratory syndrome coronavirus.The most attractive site for vaccine development against 2019-novel coronavirus is the spike glycoprotein which is crucial for receptor binding,membrane fusion via conformational changes,virus internalization,and host tissue tropism or spike inactivation through antibody induced destabilization.First outbreak was in December 2019,later on at the end of 2020,the mortality ratio decreased that created a sigh of temporary relief in the globe.Summer season was considered as one of the sources combating infection and preventing spread of virus.However,for the last few months,suddenly a huge outrage of new variants of coronavirus disease 2019 are capturing attention of the entire population of earth endangering lives of people irrespective of their ages and communities.Researchers must focus on the inventions and discoveries of either preventive or therapeutic medicines that precisely target the spike proteins that enable the severe acute respiratory syndrome coronavirus-2’s entry into a host cell.In this article,we have highlighted various vaccines being approved for emergency use by Federal Investigating Agency to combat the disastrous effects of pandemic on humanity.Mutated variants have created an alarming situation for the health care workers,researchers and scientists to consider the possible mutants and their threat to humanity in the coming years.Further,we have tried to create awareness for developing a global internet community to promote a barrier-free connectivity among all corners of the world to facilitate humanity in case of a category 5 coronavirus disease-hurricane,a term used by researchers for future threats of pandemic.

汉维大学生道歉策略跨文化社会语用对比探析

本文探讨汉维大学生使用道歉策略的差异。经问卷调查在汉维大学生中收集数据,用卡方检验来分析道歉频率,以均数来描写数据。结果发现:受试的汉维大学生使用五种策略有显著性差异;五种策略在三种权力类型和三种距离类型中分布都不同。这可能反映了汉维社会在社会权势与社会距离上的认识。

An Intelligent Cluster Verification Model Using WSN to Avoid Close Proximity and Control Outbreak of Pandemic in a Massive Crowd

Assemblage at public places for religious or sports events has become an integral part of our lives.These gatherings pose a challenge at places where fast crowd verification with social distancing(SD)is required,especially during a pandemic.Presently,verification of crowds is carried out in the form of a queue that increases waiting time resulting in congestion,stampede,and the spread of diseases.This article proposes a cluster verification model(CVM)using a wireless sensor network(WSN),single cluster approach(SCA),and split cluster approach(SpCA)to solve the aforementioned problem for pandemic cases.We show that SD,cluster approaches,and verification by WSN can overcome the management issues by optimizing the cluster size and verification time.Hence,our proposed method minimizes the chances of spreading diseases and stampedes in large events such as a pilgrimage.We consider the assembly points in the annual pilgrimage to Makkah Al-Mukarmah and Umrah for verification using Contiki/Cooja tool.We compute results such as verified cluster members(CMs)to define cluster size,success rate to determine the best success rate,and verification time to determine the optimal verification time for various scenarios.We validate ourmodel by comparing the results of each approach with the existing model.Our results showthat the SpCAwith SD is the best approach with a 96% success rate and optimization of verification time as compared to SCA with SD and the existing model.

Primary Contacts Identification for COVID-19 Carriers from Surveillance Videos

COVID-19 (Coronavirus disease of 2019) is caused by SARS-CoV2(Severe Acute Respiratory Syndrome Coronavirus 2) and it was first diagnosedin December 2019 in China. As of 25th Aug 2021, there are 165 million con-firmed COVID-19 positive cases and 4.4 million deaths globally. As of today,though there are approved COVID-19 vaccine candidates only 4 billion doseshave been administered. Until 100% of the population is safe, no one is safe. Eventhough these vaccines can provide protection against getting seriously ill anddying from the disease, it does not provide 100% protection from getting infectedand passing it on to others. The more the virus spreads;it has more opportunity tomutate. So, it is mandatory to follow all precautions like maintaining social distance, wearing mask, washing hands frequently irrespective of whether a person isvaccinated or not. To prevent spread of the virus, contact tracing based on socialdistance also becomes equally important. The work proposes a solution that canhelp with contact tracing/identification, knowing the infected persons recent travelhistory (even within the city) for few days before being assessed positive. Whilethe person would be able to give the known contacts with whom he/she has interacted with, he/she will not be aware of who all were in proximity if he/she hadbeen in public places. The proposed solution is to get the CCTV (Closed-CircuitTelevision) video clips from those public places for the specific date and time andidentify the people who were in proximity—i.e., not followed the safe distance tothe infected person. The approach uses YOLO V3 (You Only Look Once) whichuses darknet framework for people detection. Once the infected person is locatedfrom the video frames, the distance from that person to the other people in theframe is found, to check if there is a violation of social distance guideline. If thereis, then the people violating the distance are extracted and identified using Facialdetection and recognition algorithms. Two different solutions for Face detectionand Recognition are implemented and results compared—Dlib based modelsand OpenCV (Open Source Computer Vision Library) based models. The solutions were studied for two different CCTV footages and the results for Dlib basedmodels were better than OpenCV based models for the studied videos.

Remote Teaching of Experimental Chemistry in Developing Countries

During the ongoing 2020 CoVid-19 crisis,the use of remote meeting technologies such as Zoom™,Microsoft Teams™and Google Meetings™has been paramount to theoretical teaching in a safe socially distanced environment.However,several problems arise when there is a need for an experimental approach.This paper looks at one of the possible solutions,including how to best separate the students,how to minimize close interactions and how a mixed environment of remote/presential teaching is required,minimizing the amount of extra materials,resources and protection equipment required,such that developing countries can quickly adopt this method,without the purchase of any external equipment.

Partial Unlock for COVID-19-Like Epidemics Can Save 1 - 3 Million Lives Worldwide

Background: A large percentage of deaths in an epidemic or pandemic can be due to overshoot of population (herd) immunity, either from the initial peak or from planned or unplanned exit from lockdown or social distancing conditions. Objectives: We study partial unlock or reopening interaction with seasonal effects in a managed epidemic to quantify overshoot effects on small and large unlock steps and discover robust strategies for reducing overshoot. Methods: We simulate partial unlock of social distancing for epidemics over a range of replication factor, immunity duration and seasonality factor for strategies targeting immunity thresholds using overshoot optimization. Results: Seasonality change must be taken into account as one of the steps in an easing sequence, and a two-step unlock, including seasonal effects, minimizes overshoot and deaths. It may cause undershoot, which causes rebounds and assists survival of the pathogen. Conclusions: Partial easing levels, even low levels for economic relief while waiting on a vaccine, have population immunity thresholds based on the reduced replication rates and may experience overshoot as well. We further find a two-step strategy remains highly sensitive to variations in case ratio, replication factor, seasonality and timing. We demonstrate a three or more step strategy is more robust, and conclude that the best possible approach minimizes deaths under a range of likely actual conditions which include public response.