YOLOv7 for Face Mask Identification Based on Deep Learning

The World Health Organization (WHO) has publicized a global public health emergency due to the COVID-19 coronavirus pandemic. Wearing a mask in public can provide protection against the spread of disease. Tremendous progress has been made in object detection in recent times, thanks in large part to deep learning models, which have shown encouraging results when it comes to recognizing objects in images. Recent technological developments have made this progress possible. Wearing a mask in public is one way to prevent the transmission of COVID-19 from others. Our study employs You Only Look Once (YOLO) v7 to determine whether a subject is wearing a mask, and then divides them into three groups depending on the degree to which they are wearing a mask correctly (none, bad, and good). In this study, we merged two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), to conduct our experiment. These models' evaluations and ratings include crucial criteria. According to our data, YOLOv7 achieves the highest mAP (98.5%) in the "Good"class. © 2023 IEEE.

Covid-19: Impact of the Lockdown on the Behavior and on Waste Management, Case Study Ajdir, Morocco

Since the containment measures placed in several countries to deal with Covid-19 pandemic, air and noise pollution has been significantly reduced, but what about soil pollution and greenhouse gas emissions from waste management? Covid-19 has given a break to the earth by immobilizing a very large part of the world economy, industrial activity, and transport and by having an important modification on the Moroccan consumer behaviour. This demographic evolution and change of consumption do not infect the resources, but are a source of degradation and pollution of the different environmental components. Among these sources, we find the production of household and similar waste. Our objective is to rate the impact of this pandemic on waste production, in Ajdir landfill, El Hoceima, which reflects the activity of the Moroccan citizen (between March 20 and April 27, 2020), and the initiatives that have been taken and launched to solve the problems at the level of each province through the establishment of a provincial master plan for the management of household and similar waste. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

User Experience Design for Automatic Credibility Assessment of News Content About COVID-19

The increasingly rapid spread of information about COVID-19 on the web calls for automatic measures of credibility assessment [18]. If large parts of the population are expected to act responsibly during a pandemic, they need information that can be trusted [20]. In that context, we model the credibility of texts using 25 linguistic phenomena, such as spelling, sentiment and lexical diversity. We integrate these measures in a graphical interface and present two empirical studies to evaluate its usability for credibility assessment on COVID-19 news. Raw data for the studies, including all questions and responses, has been made available to the public using an open license: https://github.com/konstantinschulz/credible-covid-ux. The user interface prominently features three sub-scores and an aggregation for a quick overview. Besides, metadata about the concept, authorship and infrastructure of the underlying algorithm is provided explicitly. Our working definition of credibility is operationalized through the terms of trustworthiness, understandability, transparency, and relevance. Each of them builds on well-established scientific notions [41, 65, 68] and is explained orally or through Likert scales. In a moderated qualitative interview with six participants, we introduce information transparency for news about COVID-19 as the general goal of a prototypical platform, accessible through an interface in the form of a wireframe [43]. The participants' answers are transcribed in excerpts. Then, we triangulate inductive and deductive coding methods [19] to analyze their content. As a result, we identify rating scale, sub-criteria and algorithm authorship as important predictors of the usability. In a subsequent quantitative online survey, we present a questionnaire with wireframes to 50 crowdworkers. The question formats include Likert scales, multiple choice and open-ended types. This way, we aim to strike a balance between the known strengths and weaknesses of open vs. closed questions [11]. The answers reveal a conflict between transparency and conciseness in the interface design: Users tend to ask for more information, but do not necessarily make explicit use of it when given. This discrepancy is influenced by capacity constraints of the human working memory [38]. Moreover, a perceived hierarchy of metadata becomes apparent: the authorship of a news text is more important than the authorship of the algorithm used to assess its credibility. From the first to the second study, we notice an improved usability of the aggregated credibility score's scale. That change is due to the conceptual introduction before seeing the actual interface, as well as the simplified binary indicators with direct visual support. Sub-scores need to be handled similarly if they are supposed to contribute meaningfully to the overall credibility assessment. By integrating detailed information about the employed algorithm, we are able to dissipate the users' doubts about its anonymity and possible hidden agendas. However, the overall transparency can only be increased if other more important factors, like the source of the news article, are provided as well. Knowledge about this interaction enables software designers to build useful prototypes with a strong focus on the most important elements of credibility: source of text and algorithm, as well as distribution and composition of algorithm. All in all, the understandability of our interface was rated as acceptable (78% of responses being neutral or positive), while transparency (70%) and relevance (72%) still lag behind. This discrepancy is closely related to the missing article metadata and more meaningful visually supported explanations of credibility sub-scores. The insights from our studies lead to a better understanding of the amount, sequence and relation of information that needs to be provided in interfaces for credibility assessment. In particular, our integration of software metadata contributes to the more holistic notion of credibility [47, 72] that has become popular in recent years Besides, it paves the way for a more thoroughly informed interaction between humans and machine-generated assessments, anticipating the users' doubts and concerns [39] in early stages of the software design process [37]. Finally, we make suggestions for future research, such as proactively documenting credibility-related metadata for Natural Language Processing and Language Technology services and establishing an explicit hierarchical taxonomy of usability predictors for automatic credibility assessment. © 2022, Springer Nature Switzerland AG.

Analysis of SARS-CoV-2 Temporal Molecular Networks Using Global and Local Topological Characteristics

The global COVID-19 pandemic continues to have a devastating impact on human population health. In an effort to fully characterize the virus, a significant volume of SARS-CoV-2 genomes have been collected from infected individuals and sequenced. Comprehensive application of this molecular data toward epidemiological analysis in large parts has employed methods arising from phylogenetics. While undeniably valuable, phylogenetic methods have their limitations. For instance, due to their rooted structure, outgroup samples are often needed to contextualize genetic relationships inferred by branching. In this paper we describe an alternative: global and local topological characterization of neighborhood graphs relating viral genomes collected from samples in longitudinal studies. The applicability of our approach is demonstrated by constructing and analyzing such graphs using two distinct datasets from Israel and France, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

CHALLENGES OF SAFETY – AND CRISIS MANAGMENET TRAINING DURING COVID19

Simulation based training is a common way of training for effective learning for high-risk contexts. COVID19 changed large parts of the education and training of safety-and risk management at Nord University in Norway. The training and education have been based on theoretical lectures prior to simulated practical exercises at the university’s emergency preparedness laboratory, NORDLAB. Here, academic staff, mentors, facilitators, and the students cooperated prior to, during and after exercises in order to provide an optimal learning context. However, this cooperation required close contact which suddenly ended when COVID19 hit, due to infection control. This resulted in challenges including how to uphold the learning outcome for the students based on the theoretical foundation of Kolb’s (2014) experiential learning, while changing the form of training on a short notice. Kolb’s theory is the foundation NORDLAB is based on. The learning context was changed to net-based training and exercises using the software zoom, with all participants geographically spread all over Norway. Thus, our research question was: Which challenges in use of simulation and lab exercises in safety management education during COVID19 are central, and how could they be solved? The challenges identified were 1) student’s lack of ability to actively participate in face to face learning activities 2) mentors’ lack of technological flexibility 3) inability to share the lab’s simulation technology with zoom 4) novice students difficulties of forming and interacting in digital teams 5) zoom fatigue 6) the need for increased administrative and technological support 7) low body language feedback 8) lack of visualization of injects. Solving the challenges were defiant and elements we used in this case were 1) on-boarding 2) table top exercises 3) video recorded lectures 4) flipped classroom 5) gaming simulated exercises 6) podcasts 7) shorter training sessions We would like to discuss how and if the solutions matched the challenges for safety training in regard to the expected learning outcome for students who were to enter practical emergency preparedness and safety management. © ESREL 2021. Published by Research Publishing, Singapore.

GAN-based Face Reconstruction for Masked-Face

Facial recognition and identification which play an important role in human-computer interaction, secure authentication and criminal face recognition, are impeded by the advent of face masks due to COVID-19 pandemic. This is a challenging problem due to the following reasons: (i) masks cover quite a large part of the face even below the chin, (ii) it is not possible to collect and prepare a real paired-face images with and without mask object, (iii) face alterations and the presence of different masks is even more challenging. In this work, we propose a general framework that can be used to reconstruct the hidden part of face concealed by mask. We have employed GAN-based unpaired domain translation technique to translate masked face images from the source to the unmasked images in the destination domain. To this end, we also create a paired datasets of real face images and synthesized correspondence's with face-masks and use it towards training of our proposed GAN-based facial reconstruction system which can be used for facial identification and secure authentication in human-computer interaction. The obtained results demonstrate that our model outperforms other representative state-of-the-art face completion approaches both qualitatively and quantitatively. © 2022 Owner/Author.

Back to the Future of Work: Old Questions for New Technologies

In the past decade or so the Future of Work question has emerged as a major policy concern at national and international level. This is in large part due to opportunities and challenges created by the development of data and AI driven automation technologies, and in the past two years by the Covid pandemic, which has led many employees and employers to rethink the ways in which they work, as individuals and organisations. In the USA, there is now talk of a great resignation, as many employees reconsider the value and quality of their working lives. If there is one lesson that we have already learned it is that the future of work question resists easy formulations and answers, nor is it primarily a matter of jobs being replaced by automation. As work touches nearly every aspect of our lives the future of work is bound to be a complex question in need to careful investigation. In this talk I won't offer predictions, but try to unpack the problem, asking not so much what is the future of work, but rather how should we ask good question(s) about it in the first place. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Trends of Employee Layoff in Russian Post During the Pandemic Period

Transfer to telecommuting, part-time salary and unpaid leave have become unavoidable occurrences during the COVID-19 pandemic. Restrictions, caused by the spread of infection, forced employers to lay off a fairly large part of their workforce, which was a consequence of the economic decline in the country as a whole, and had affected income of businesses and population both. This caused an increase in anxiety among workers and employers, leading to an upshot in turnover rates of organizations. Regardless, people would still occasionally resign of their own accord during the pandemic, something that represents scientific interest in regards to studying causes for turnover and working conditions. The main subject of this study is the response of employees to the work conditions during COVID-19, which reflects in the causes of resignations. The article analyzes changes in staff turnover during a pandemic using the Russian Post JSC of the Volga Region as an example. The causal analysis is carried out among the resigning employees, and used as foundation for conclusion on main causes of resignation. The main trends in changes of employee behavior during the period of coronavirus restrictions and their influence on staff turnover have been identified. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Blockchain and FOT (Fog of Things) based Framework and Technique for Anticipating an Infectious Illness Sent by a Harmful Respiratory Infection

The current exploration is by and large identified with a framework and technique for anticipating an irresistible sickness, for example, COVID-19 communicated by a harmful respiratory infection. Revealed are a framework and technique for anticipating an irresistible infection communicated by a harmful respiratory infectionThe system joins a larger part of Internet of Things (IoT) sensors, a larger part of fog center devices, and a greater part of handling contraptions in cloud server ranches. The IoT sensors are organized to be joined to a larger part of individuals to deliver a prosperity dataset. The Fog registering devices are connected with a haze lay-er to get the prosperity dataset from the IoT sensors to quantify and store the prosperity dataset over a square chain organization. The contraptions measure the prosperity da-taset at the mist layer by playing out haze handling. The figuring contraptions and cloud server ranches get the taken care of prosperity dataset from the haze center point devices over the blockchain network. This assessment is in like manner requested of for the patent in Indian Patent Office with application number - 202011021969. © 2021 IEEE.

WICO Text: A Labeled Dataset of Conspiracy Theory and 5G-Corona Misinformation Tweets

The COVID-19 pandemic has been accompanied by a flood of misinformation on social media, which has been labeled an "infodemic". While a large part of such fake news is ultimately inconsequential, some of it has the potential to real-world harm, but due to the massive amount of social media contents, it is impossible to find this misinformation manually. Thus, conventional fact-checking can typically only counteract misinformation narratives after they have gained significant traction. Only automated systems can provide warnings in advance. However, the automatic detection of misinformation narratives is very challenging since the texts that spread misinformation may be short messages on Twitter. They may also transmit misinformation by implication rather than by stating counterfactual information outright, and satirical messages complicate the issue further. Thus, there is a need for highly sophisticated detection systems. In order to support their development, we created substantial ground truth data by human annotation. In this paper, we present a dataset that deals with a specific piece of misinformation: the idea that the COVID-19 pandemic is causally connected to the 5G wireless network. We selected more than 10,000 tweets that deal with COVID-19 and 5G and labeled them manually, distinguishing between tweets that propagate the specific 5G misinformation, those that spread other conspiracy theories, and tweets that do neither. We provide the human-annotated dataset along with an additional large-scale automatically (by using the human-annotated dataset as the training set) labelled dataset consist of more than 100,000 tweets. © 2021 ACM.