ABSTRACT
The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model, and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward. © 2023 SPIE.
ABSTRACT
During the COVID-19 coronavirus epidemic, people usually wear masks to prevent the spread of the virus, which has become a major obstacle when we use face-based computer vision techniques such as face recognition and face detection. So masked face inpainting technique is desired. Actually, the distribution of face features is strongly correlated with each other, but existing inpainting methods typically ignore the relationship between face feature distributions. To address this issue, in this paper, we first show that the face image inpainting task can be seen as a distribution alignment between face features in damaged and valid regions, and style transfer is a distribution alignment process. Based on this theory, we propose a novel face inpainting model considering the probability distribution between face features, namely Face Style Self-Transfer Network (FaST-Net). Through the proposed style self-transfer mechanism, FaST-Net can align the style distribution of features in the inpainting region with the style distribution of features in the valid region of a face. Ablation studies have validated the effectiveness of FaST-Net, and experimental results on two popular human face datasets (CelebA and VGGFace) exhibit its superior performance compared with existing state-of-the-art methods. © 2023 SPIE.
ABSTRACT
Amid concerns over airflow-induced transmission of the COVID-19 virus in buildings frequented by large numbers of people, such as offices, the necessity for radiant ceiling heating panels has increased. This is due to the concern that the airflows emitted from the convection heating systems installed near the ceiling or windows for winter heating may be a major cause of COVID-19 transmission. In this study, we aim to evaluate thermal comfort under various indoor and outdoor environmental conditions of a building and present the thermal output conditions of the radiant ceiling heating panel that can replace the convection heating system while ensuring comfort in the perimeter zone and handling the heating load. As a result, we were able to present, in a chart format, the thermal output conditions that can secure thermal comfort by analyzing the indoor airflow distribution depending on the surface temperature of the radiant ceiling heating panel, the interior surface temperature of the window, and the influence of internal heat generation. Moreover, through derived empirical formulas, we were able to determine the heating conditions of the panel that can secure the necessary heat dissipation while minimizing discomfort, such as downdrafts, even for indoor and outdoor conditions that were not evaluated in this study.
ABSTRACT
The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.
ABSTRACT
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.
ABSTRACT
Contact tracing is one of the methods used by the government and organizations for controlling viral diseases like COVID-19, which claimed many human lives. Social distancing is advised to everyone to minimize the virus from spreading. This study aims to build a contact tracing tool that monitors social distancing individually using computer vision in real-time. Object tracking by detection is used for individual monitoring with YOLOv4 (You Only Look Once) as the object detector and SORT (Simple Online and Real-time Tracking) as the object tracker. The combination gained an average streaming and detection frame rate of 26 FPS and 10 FPS on NVIDIA's GTX 1650, respectively. It is expected to have more frame rate when used in a more powerful device. Moreover, the system obtained 98.2% accuracy in measuring the distance between individuals. Furthermore, the performance of the QR scanner used in the study attains a 100% success rate and a 98% accuracy in allocating the QR code to the correct owner from the video stream. © 2022 IEEE.
ABSTRACT
In 2022, the COVID-19 pandemic is still occurring. One of the optimal prevention efforts is to wear a mask properly. Several previous studies have classified the use of masks incorrectly. However, the accuracy resulting from the classification process is not optimal. This research aims to use the transfer learning method to achieve optimal accuracy. In this research, we used three classes, namely without a mask, incorrect mask, and with a mask. The use of these three classes is expected to be more detailed in detecting violations of the use of masks on the face. The classification method used in this research uses transfer learning as feature extraction and Global Average Pooling and Dense layers as classification layers. The transfer learning models used in this research are MobileNetV2, InceptionV3, and DenseNet201. We evaluate the three models' accuracy and processing time when using video data. The experimental results show that the DenseNet201 model achieves an accuracy of 93%, but the processing time per video frame is 0.291 s. In contrast to the MobileNetV2 model, which produces an accuracy of 89% and the processing speed of each video frame is 0.106 s. This result is inversely proportional to accuracy and speed. The DenseNet201 model produces high accuracy but slow processing time, while the MobileNetV2 model is less accurate but has faster processing. This research can be applied in the crowd center to monitor health protocols in the use of masks in the hope of inhibiting the transmission of the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Recently, with the normalization of non-face-to-face online environments in response to the COVID-19 pandemic, the possibility of cyberattacks through endpoints has increased. Numerous endpoint devices are managed meticulously to prevent cyberattacks and ensure timely responses to potential security threats. In particular, because telecommuting, telemedicine, and tele-education are implemented in uncontrolled environments, attackers typically target vulnerable endpoints to acquire administrator rights or steal authentication information, and reports of endpoint attacks have been increasing considerably. Advanced persistent threats (APTs) using various novel variant malicious codes are a form of a sophisticated attack. However, conventional commercial antivirus and anti-malware systems that use signature-based attack detection methods cannot satisfactorily respond to such attacks. In this paper, we propose a method that expands the detection coverage in APT attack environments. In this model, an open-source threat detector and log collector are used synergistically to improve threat detection performance. Extending the scope of attack log collection through interworking between highly accessible open-source tools can efficiently increase the detection coverage of tactics and techniques used to deal with APT attacks, as defined by MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK). We implemented an attack environment using an APT attack scenario emulator called Carbanak and analyzed the detection coverage of Google Rapid Response (GRR), an open-source threat detection tool, and Graylog, an open-source log collector. The proposed method expanded the detection coverage against MITRE ATT&CK by approximately 11% compared with that conventional methods. © 2023 Tech Science Press. All rights reserved.
ABSTRACT
The spread of the Corona Virus pandemic on a global scale had a great impact on the trend towards e-learning. In the virtual exams the student can take his exams online without any papers, in addition to the correction and electronic monitoring of the exams. Tests are supervised and controlled by a camera and proven cheat-checking tools. This technology has opened the doors of academic institutions for distance learning to be wide spread without any problems at all. In this paper, a proposed model was built by linking a computer network using a server/client model because it is a system that distributes tasks between the two. The main computer that acts as a server (exam observer) is connected to a group of sub-computers (students) who are being tested and these devices are considered the set of clients. The proposed student face recognition system is run on each computer (client) in order to identify and verify the identity of the student. When another face is detected, the program sends a warning signal to the server. Thus, the concerned student is alerted. This mechanism helps examinees reduce cheating cases in early time. The results obtained from the face recognition showed high accuracy despite the large number of students' faces. The performance speed was in line with the test performance requirements, handling 1,081 real photos and adding 960 photos. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic and movement control order that started in 2020 has changed the shopping behavior to online shopping. This also increases in-home delivery services by the shipping providers. However, since the virus can be transmitted through surface transmission, the buyer is advised to avoid touching surfaces and clean or sanitize surfaces regularly with standard disinfectants to prevent the spread. In addition, with an increase in the parcel delivery process, missing parcels also will be one of the main problems that the buyer will be facing. This study has developed a smart parcel box with sanitizer to overcome the issues stated above. Smart in this context refers to the notification that the buyer will get once the parcel is placed inside the parcel box, the box itself will be locked once the parcel is in and only can be opened by the authorized user. This study utilizes the Arduino IDE software to control the operation of the locks and notifications. The notifications are linked to the Blynk Application that needs to be installed on the buyer's smartphone. The software is also coded to run the motor that controls the standard disinfectants that will be sprayed on the parcel for a few seconds. The results indicate one solution for the online shopping addicts to shop while avoiding the spread of COVID-19 viruses. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The purpose of this study was to measure the number and concentration of airborne particulates occurring in a dental clinic while performing dental procedures, with and without the simultaneous use of air purifier systems and a central ventilation system. The initial background concentrations of airborne particulates recorded during dental procedures, i.e., grinding of natural teeth and metals, without the use of air purifier systems, and with closed windows, reduced by 68% for ΡΜ10, 77% for ΡΜ2.5, and 81% for ΡΜ1 when the same procedures were carried out with the simultaneous use of air purifying systems. In addition, measurements taken during patient treatment showed that an operating central ventilation system contributes to the reduction of airborne particles by a significant 94% for ΡΜ10, 94% for ΡΜ2.5, and 88% for ΡΜ1 compared to dental procedures performed without the simultaneous use of air purifiers. Air purifying systems were also observed to contribute to the further reduction of airborne particulates when dental procedures were performed in combination with an operating central ventilation system. The majority of particles captured had diameters of 0.25-0.30 μm, 0.5 μm, and 1.0-4.0 μm, while particles with diameters of >5.0 μm were the least commonly observed in all experiments. Finally, a statistically significant difference between concentrations of particulate matter was recorded during dental procedures carried out with and without the simultaneous operation of air purifiers and central ventilation system increasing the risk of SARS-CoV-2 virus contamination in dental clinics due to the aerosols emitted by the use of common dental instruments during standard treatments.
ABSTRACT
Airborne transmission via aerosol particles without close human contact is a possible source of infection with airborne viruses such as SARS-CoV-2 or influenza. Reducing this indirect infection risk, which is mostly present indoors, requires wearing adequate respiratory masks, the inactivation of the viruses with radiation or electric charges, filtering of the room air, or supplying ambient air by means of ventilation systems or open windows. For rooms without heating, ventilation, and air conditioning (HVAC) systems, mobile air cleaners are a possibility for filtering out aerosol particles and therefore lowering the probability of indirect infections. The main questions are as follows: (1) How effectively do mobile air cleaners filter the air in a room? (2) What are the parameters that influence this efficiency? (3) Are there room situations that completely prevent the air cleaner from filtering the air? (4) Does the air cleaner flow make the stay in the room uncomfortable? To answer these questions, particle imaging methods were employed. Particle image velocimetry (PIV) was used to determine the flow field in the proximity of the air cleaner inlet and outlet to assess regions of unpleasant air movements. The filtering efficiency was quantified by means of particle image counting as a measure for the particle concentration at multiple locations in the room simultaneously. Moreover, different room occupancies and room geometries were investigated. Our results confirm that mobile air cleaners are suitable devices for reducing the viral load indoors. Elongated room geometries, e.g., hallways, lead to a reduced filtering efficiency, which needs to be compensated by increasing the volume flow rate of the device or by deploying multiple smaller devices. As compared to an empty room, a room occupied with desks, desk separation walls, and people does not change the filtering efficiency significantly, i.e., the change was less than 10%. Finally, the flow induced by the investigated mobile air cleaner does not reach uncomfortable levels, as by defined room comfort standards under these conditions, while at the same time reaching air exchange rates above 6, a value which is recommended for potentially infectious environments.
ABSTRACT
With the recent global spread of the COVID-19 (also known as the corona virus) pandemic, several governments have attempted to control its transmission through preventive and precautionary measures. Education is one of the factors that has been impacted by the pandemic. As a result, to limit the spread of the virus, many countries adopted distance education instead of traditional education to ensure the continuity of the educational process. Cloud computing is a technology that offers numerous advantages in the field of education. The Kingdom of Saudi Arabia was one of the countries that had decided and continues to use various cloud platforms for distance education. In this study, we look at how effective cloud computing platforms are in the learning process in Saudi Arabian schools. The primary goal of this research was to investigate the teacher's ability to access different cloud computing services, as well as their ease of use and utility, by evaluating the effectiveness of these platforms as a mode of teaching before and after the pandemic. A total of 559 male and female schoolteachers' data was collected using self-administered questionnaires in Al-Bahah region and was analyzed using the IBM SPSS Statistics software. The analysis of this study expanded our understanding on the possibility of using educational platforms across schools in the kingdom. The findings also revealed that the use of cloud platforms during the pandemic increased by 28% in the region which have now become integral part of education. Furthermore, the findings revealed that teachers frequently encountered difficulties in implementing cloud-based educational processes, particularly in rural and mountainous areas. © 2023 IEEE.
ABSTRACT
The forced closure of cultural realities, dictated by the Coronavirus emergency, generated new needs for urgent solutions. Italy has heavily suffered as everything has been interrupted: exhibitions, trade fairs and museums, between more or less hard lockdowns and #iorestoacasa. From the spread of the virus, that thanks to vaccines sooner or later will begin to wane, to the spread of beauty that will help us revive the world. This is the path to follow and Italy a generator of beauty since ancient times, will have to be at the forefront of this mission. The reason is soon to be said. The aesthetic emotion brings man closer: the first sensation we experience in front of something beautiful - whatever it is - is that of not being alone. To encourage the meeting of beauty and people, many have done their utmost, let's think for example of the campaigns #museichiusimuseiaperti, #laculturanonsiferma, #MuseumFromHome, so well organized as to successfully substitute real life visits to museums and galleries, at least for the moment. Thanks to digital technology that allows activities to be carried out in places of art and culture and the distribution of content by all the main cultural institutions through social media. For the first time, the doors of the Web open wide and present to the public, through viewing rooms and virtual exhibition spaces, shows of all kinds. Broadening the horizon, the same choice is made by art galleries around the globe. Changing only the exhibition spaces, no longer physical but digital. Thanks to immersive 3D technology, one can enjoy the sensations and atmospheres of traditional visits. Therefore, the cultural mission of museums is not interrupted, rather it is strengthened and new projects are born exclusively for the Web that follow the themes of the moment. The @Re-Art project fits into this scenario which aims to combine the dynamism of multimedia and digital means of communication (webinar, podcast, social network, virtual tour, and exhibitions intended for the Web) with the uniqueness of the carefully archived heritage from Milanese exhibitions of the past two decades. A way to make them accessible, galvanize them and bring them into circulation, making them live again. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The recent outbreak of COVID-19 poses a serious threat to people's lives. Epidemic control strategies have also caused damage to the economy by cutting off humans' daily commute. In this article, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention. IDRLECA first hires an infection probability model to calculate the current infection probability of each individual. Then, the infection probabilities together with individuals' health status and movement information are fed to a novel GNN to estimate the spread of the virus through human contacts. The estimated risks are used to further support an RL agent to select individual-level epidemic-control actions. The training of IDRLECA is guided by a specially designed reward function considering both the cost of mobility intervention and the effectiveness of epidemic control. Moreover, we design a constraint for control-action selection that eases its difficulty and further improve exploring efficiency. Extensive experimental results demonstrate that IDRLECA can suppress infections at a very low level and retain more than 95% of human mobility. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ABSTRACT
The main goal of this paper is to assess the impact of covid-19 on marketing practices and airlines' adaptation to the new business and economic environment. In the first part, we introduce a marketing mix framework and look at the impact of covid-19 on seat capacity and airline operating revenues. In the second part, we determine current marketing strategies using the marketing framework of the 4Ps and its impact on operational changes among full-service network carriers and low-cost carriers. The central theme in advertising and campaigns for full-service network carriers is high hygiene standards and cleanliness of flights, as passengers are concerned with not contracting covid-19 or other viruses during the longer flight routes. Low-cost carriers still focus primarily on the price and convenience to potential customers. Both types of carriers do improve and push out technology changes in terms of mobile apps, improved online check-ins and contactless, touchless kiosks used for airport check-ins. These slight changes increase passengers' comfort, speed up the check-in process and minimize the spread of any viruses. © 2022 IEEE.
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Among the different approaches to implementing Agility, the Scrum, created in the late 1980s, has stood out as the most used tool by the software development industry. Understand how the concepts underlying this approach, such as ceremonies and time boxes, have been adapted to meet the situation of telework imposed by social distancing to prevent the spread of the COVID-19 virus, experienced by the elements of the software development teams, presents an opportunity to learn what are the most efficient ways to use its process. It was observed how Scrum was applied in practice by software development teams during the mandatory telework period and from there, it will be look for better ways to do it, either by developing new technological tools, or using existing tools, to support the ceremonies, or, by creating new processes to support such evolution on methodology's process. © 2022 IEEE.
ABSTRACT
When pandemic rose in 2020, people were fighting against COVID-19 virus and organizations had accelerated their digitization and cloud adoption rapidly (De et al. in Int J Inf Manag 55:102171, 2020 [1]) to meet the online based business during the lockdown. This chaos helped fraudsters and attackers taking advantage of the momentary lack of security controls and oversight. Federal Investigation Bureau (FBI) Internet Crime Compliant Center (IC3) 2020 reported highest number of complaints in 2020 (791 k + ) compared to prior five years (298 k + in 2016), with peak losses reported ($4.2 Billion in 2020 compared to $1.5 Billion in 2016) (Internet Crime Complaint Center in Internet crime report. Federal Bureau of Investigation, Washington, D.C., 2020 [2]). Majority of these incidents were connected to financial fraud, identity fraud, and phishing for personally identifiable information (PII). Considering the severity and impact of personal data exposure over cloud and hybrid environment, this paper provides a brief overview of prior research and discuss technical solutions to protect data across heterogeneous environments and ensure privacy regulations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
As we all know fingerprint recognition is one of the secure and accurate Biometric Technologies. If think about it in deep even with the Biometric system the virus can be spread during these situations. To overcome this, we need to come up with a secure and contactless way of authentication. So, let's update to some contactless remedies like Iris authentication which are unique for every individual and they don't need to have any physical contact. So, we can use this Iris detection for a secure and contactless authentication system. The main aim of this research is to provide contactless remedies for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. Coming to the outline of the attendance management system, we will first collect the data from the Kaggle repository. Next, we split the data into training and testing, then we will train the data using transfer learning techniques and test the model by using test data. Finally, we integrated the trained model with the flask. If the Iris matches then the attendance of a particular person will be posted. If not matched then we train the model by adding new person's data. For the construction of modern electronic security systems, real-time face recognition is crucial. Face detection, feature extraction, and face recognition are the three procedures involved. After recognizing the face, it will check whether the person's face matches the collected database. If it matches it will show the person's name, the number of books he took, and what those books are for Library transactions and in the same way the locker will be open if the person's data is matched. The proposed methods are secure and unique contactless ways of authentication for every individual. So, we can use these detection and authentication systems for secure and contactless applications. It can be successfully used for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. The Covid-19 infection in society will undoubtedly decline if the proposed argument is implemented. © 2023 The authors and IOS Press.
ABSTRACT
In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set S, the problem is to minimize the influence spread from S by deleting a limited number of nodes and edges. This problem reflects many application scenarios, such as blocking virus (e.g. COVID-19) propagation in social networks by quarantine and vaccination, blocking rumor spread by freezing fake accounts, or attacking competitor's influence by incentivizing some users to ignore the information from the competitor. In this paper, under the linear threshold model, we adapt the reverse influence sampling approach and provide efficient algorithms of sampling valid reverse reachable paths to solve the problem. We present three different design choices on reverse sampling, which all guarantee 1/2 - ϵ approximation (for any small ϵ >0) and an efficient running time. © 2023 ACM.