I'm out of breath from laughing! I think? A dataset of COVID-19 Humor and its toxic variants

Humor is a cognitive construct that predominantly evokes the feeling of mirth. During the COVID-19 pandemic, the situations that arouse out of the pandemic were so incongruous to the world we knew that even factual statements often had a humorous reaction. In this paper, we present a dataset of 2510 samples hand-annotated with labels such as humor style, type, theme, target and stereotypes formed or exploited while creating the humor in addition to 909 memes. Our dataset comprises Reddit posts, comments, Onion news headlines, real news headlines, and tweets. We evaluate the task of humor detection and maladaptive humor detection on state-of-the-art models namely RoBERTa and GPT-3. The finetuned models trained on our dataset show significant gains over zero-shot models including GPT-3 when detecting humor. Even though GPT-3 is good at generating meaningful explanations, we observed that it fails to detect maladaptive humor due to the absence of overt targets and profanities. We believe that the presented dataset will be helpful in designing computational methods for topical humor processing as it provides a unique sample set to study the theory of incongruity in a post-pandemic world. The data is available to research community at https://github.com/smritae01/Covid19-Humor. © 2023 ACM.

Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter

While COVID-19 text misinformation has already been investigated by various scholars, fewer research efforts have been devoted to characterizing and understanding COVID-19 misinformation that is carried out through visuals like photographs and memes. In this paper, we present a mixed-method analysis of image-based COVID-19 misinformation in 2020 on Twitter. We deploy a computational pipeline to identify COVID-19 related tweets, download the images contained in them, and group together visually similar images. We then develop a codebook to characterize COVID-19 misinformation and manually label images as misinformation or not. Finally, we perform a quantitative analysis of tweets containing COVID-19 misinformation images. We identify five types of COVID-19 misinformation, from a wrong understanding of the threat severity of COVID-19 to the promotion of fake cures and conspiracy theories. We also find that tweets containing COVID-19 misinformation images do not receive more interactions than baseline tweets with random images posted by the same set of users. As for temporal properties, COVID-19 misinformation images are shared for longer periods of time than non-misinformation ones, as well as have longer burst times. we compare non-misinformation images instead of random images, and so it is not a direct comparison. When looking at the users sharing COVID-19 misinformation images on Twitter from the perspective of their political leanings, we find that pro-Democrat and pro-Republican users share a similar amount of tweets containing misleading or false COVID-19 images. However, the types of images that they share are different: while pro-Democrat users focus on misleading claims about the Trump administration's response to the pandemic, as well as often sharing manipulated images intended as satire, pro-Republican users often promote hydroxychloroquine, an ineffective medicine against COVID-19, as well as conspiracy theories about the origin of the virus. Our analysis sets a basis for better understanding COVID-19 misinformation images on social media and the nuances in effectively moderate them. © 2023 ACM.

Think About It: Promoting Physical Activity with a Mobile App with a Theory-based Approach

Currently, people’s highly busy lifestyles and sedentary behavior contribute negatively to multiple health factors. During the COVID-19 pandemic, the different sanitary measures, such as limited mobility and the closing of gyms and sports centers, have contributed to limited physical activity. In this context, there are several apps to enhance physical activity across all mobile stores with an emphasis on mobile sensing. However, the use of a formal theory incorporated into the app development and interventions is less evident. A theory-based approach contributes to understanding the reasons and situations in which an intervention strategy can have an impact. The present work considers the Elaboration Likelihood Model (ELM), which addresses persuasion and attitude change. Can we develop a persuasive app that promotes physical activity based on contemporary attitudes and behavioral change theories? We developed a mobile application for Android OS. Then, 63 participants tested it, and were encouraged to think of ideas or arguments in favor of doing physical activity in a high elaboration task. A mediation analysis was done, with results showing that attitudes partially mediate the association between thought and physical activity. Participants’thoughts were seen to be positively correlated with their attitudes;and, in turn, participants’attitudes were correlated with their behavioral intention (to do physical activity). This suggests that a theory-based approach for the active production of biased beliefs is effective when designing an app that encourages positive attitudes toward physical activity. Author

Exploration and Practice of Teacher-student Collaboration in Micro-lecture Construction under the Epidemic Situation

In the case of COVID-19 epidemic, online education supported by computing technology is playing an increasingly important role, while online education resources, especially micro-lectures, are seriously insufficient, which greatly hinders the development of online education. In this paper, a micro-lecture resource construction scheme for online courses with teacher-student collaboration was proposed based on the learning pyramid theory. The practice proved that this scheme can make full use of students' technical foundation in the Internet era to build micro-lectures, which can not only improve the quality of online courses, but also build curriculum resources quickly and with high quality, thus providing a strong resource guarantee for the follow-up online teaching. © 2022 Association for Computing Machinery.

Privacy preserving via multi-key homomorphic encryption in cloud computing

As the world grapples with the COVID-19 and its variants, multi-user collaboration by means of cloud computing is ubiquitous. How to make better use of cloud resources while preventing user privacy leakage has become particularly important. Multi-key homomorphic encryption(MKHE) can effectively deal with the privacy disclosure issue during the multi-user collaboration in the cloud computing setting. Firstly, we improve the DGHV homomorphic scheme by modifying the selection of key and the coefficients in encryption, so as to eliminate the restriction on the parity of the ciphertext modulus in the public key. On this basis, we further propose a DGHV-type MKHE scheme based on the number theory. In our scheme, an extended key is introduced for ciphertext extension, and we prove that it is efficient in performance analysis. The semantic security of our schemes is proved under the assumption of error-free approximate greatest common divisor and the difficulty of large integer factorization. Furthermore, the simulation experiments show the availability and computational efficiency of our MKHE scheme. Therefore, our scheme is suitable for the multi-user scenario in cloud environment. © 2023 Elsevier Ltd

Effects of trigger events on innovation behaviour: insights from the data collected from construction professionals during COVID-19

During the COVID-19 pandemic, several instances of innovation were reported in construction and other sectors, consistent with previously noted spikes in innovation activities during crises and environmental perturbations. Yet the behavioural mechanisms and factors leading to changes in the innovation behaviour of actors under environmental perturbation are not adequately understood. This paper studies such behavioural mechanisms and factors, building on the Excitable Innovation Behaviour Model (EIBM), which explains the voluntary or coercive change in the innovation behaviour of actors in terms of their stable state needs and excited stated needs. The findings build on data collected through an online survey (N = 266) and interviews (N = 14) during the COVID situation. The results show that environmental perturbations can trigger both an increase and decrease in innovation activities. Actors' network dependencies, motivation, and years of experience influence their innovation behaviour. Environmental perturbation triggers accelerated alignment and shared prioritization of the needs of the different stakeholders, resulting in commitment and timely actions towards innovation from each stakeholder. Actors' ability and financial stability at the time of the excitation trigger mediate their innovation behaviour, revealing similarities and differences between EIBM and Fogg's Behavioural Model of persuasion. The grounding of EIBM in behavioural theories makes it potentially generalizable and compatible with other behavioural models and theories on innovation. The underlying state-change mechanisms in EIBM also make it amenable to developing a parametric and computational model of innovation adoption and diffusion. The research insights will inform innovation management strategies, including technology adoption roadmaps in the construction sector. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Quantum computing led innovation for achieving a more sustainable Covid-19 healthcare industry

Involvement of multiple stakeholders in healthcare industry, even the simple healthcare problems become complex due to classical approach to treatment. In the Covid-19 era where quick and accurate solutions in healthcare are needed along with quick collaboration of stakeholders such as patients, insurance agents, healthcare providers and medicine supplier etc., a classical computing approach is not enough. Therefore, this study aims to identify the role of quantum computing in disrupting the healthcare sector with the lens of organizational information processing theory (OIPT), creating a more sustainable (less strained) healthcare system. A semi-structured interview approach is adopted to gauge the expectations of professionals from healthcare industry regarding quantum computing. A structured approach of coding, using open, axial and selective approach is adopted to map the themes under quantum computing for healthcare industry. The findings indicate the potential applications of quantum computing for pharmaceutical, hospital, health insurance organizations along with patients to have precise and quick solutions to the problems, where greater accuracy and speed can be achieved. Existing research focuses on the technological background of quantum computing, whereas this study makes an effort to mark the beginning of quantum computing research with respect to organizational management theory. © 2022

Mathematical Morphology View of Topological Rough Sets and Its Applications

This article focuses on the relationship between mathematical morphology operations and rough sets, mainly based on the context of image retrieval and the basic image correspondence problem. Mathematical morphological procedures and set approximations in rough set theory have some clear parallels. Numerous initiatives have been made to connect rough sets with mathematical morphology. Numerous significant publications have been written in this field. Others attempt to show a direct connection between mathematical morphology and rough sets through relations, a pair of dual operations, and neighborhood systems. Rough sets are used to suggest a strategy to approximatemathematicalmorphology within the general paradigm of soft computing. A single framework is defined using a different technique that incorporates the key ideas of both rough sets and mathematical morphology. This paper examines rough set theory from the viewpoint of mathematical morphology to derive rough forms of themorphological structures of dilation, erosion, opening, and closing. These newly defined structures are applied to develop algorithm for the differential analysis of chest X-ray images from a COVID-19 patient with acute pneumonia and a health subject. The algorithm and rough morphological operations show promise for the delineation of lung occlusion in COVID-19 patients from chest X-rays. The foundations of mathematical morphology are covered in this article. After that, rough set theory ideas are taken into account, and their connections are examined. Finally, a suggested image retrieval application of the concepts from these two fields is provided. © 2023 Tech Science Press. All rights reserved.

Bridging the Gap Between Domain Models and Computational Models: A Case Study of COVID-19

Novel coronavirus pneumonia (COVID-19) has broken out and spread rapidly in many countries and regions around the world. Since the outbreak, many researchers have proposed propagation models of COVID-19, among which the mainstream computational epidemiology model requires the establishment of a corresponding artificial society model for computational experiments. However, such models tightly coupled domain knowledge about epidemics with computational models and have low reusability. On this basis, we take COVID-19 as our research object and propose a hierarchical modeling framework for epidemic transmission, which describes how to decouple and dock domain models and computational models. This framework consists of three levels: individual capability model and virus model at the individual level, organizational structure and interaction mechanisms between individuals at the organizational level, and intervention model and environmental model design at the social level. The experimental results show that this is an effective hierarchical framework modeling approach for studying transmission mechanisms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Investigating Student Learning about Disease Spread and Prevention in the Context of Agent-Based Computational Modeling

COVID-19 has brought increased attention to the importance of health literacy, including understanding of the transmission and prevention of disease. This study presents data from a project aimed at developing a computational modeling microworld to help middle school students learn about these topics. Specifically, the microworld is meant to help students model and test their ideas about how a disease spreads through a population and how an epidemic can be prevented. The paper analyzes one student's knowledge refinement through the building, testing, and debugging of a disease spread and prevention model. We model student refinement of thinking through steps of building initial models and predicting results, testing initial models and making sense of the results, debugging and retesting models, observing final models, and explaining results. Our findings suggest adolescents can learn about strategies for disease prevention through computational modeling. © ISLS.

Outbreak Prediction of COVID-19 3rd Wave in Asia

Coronavirus is a pandemic for whole world and infected more than 200 countries of the world. Spreading of coronavirus started from china at the end of December and within three months, it infected whole world. Coronavirus is belonging to beta coronavirus family. Common symptoms of coronavirus are fever, dry cough, fatigue, and respiratory-related problem. This paper tries to study the infection rate of coronavirus in Asian countries, rest of world countries, and overall world countries. Asia is largest continent of the world which contains approximate 50 countries and highest contributes in GDP. Population of Asian countries 446.27 crore, that is 60 percentage of whole world population and covers 30 percent geographical area of world. China and India are the most populated countries in Asia. Total confirmed cases 2,056,051, recovered cases 502,045, and death cases 134,177 in the world. Scholar also divides the Asia continent into six regions such East, South, Central, North, Southeast and Western Asia for better understanding infection of coronavirus. This paper analyzes the confirmed cases, recovered cases, and death cases in Asia and understands the infection pattern date wise and countries wise. Machine learning algorithm is used for prediction infection in the world and also predicts future infection rate and death rate in Asian countries and world. Prophet is used for future prediction of confirmed and death cases in the Asian countries. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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.

Numerical simulation of air distribution in the consultation room of patients with novel coronavirus

The hospital room is the first line of assistance to patients, to ensure the comfort of doctors and patients at the same time, but also to ensure the protection of the personal safety of doctors and patients. For this reason, a good airflow organization helps to reduce the concentration of respiratory particles in the whole space and also creates a comfortable environment. Based on the CFD theory of computational fluid dynamics, ANSYS Fluent software is used to simulate the clinic environment, and four airflow organizations are used as the research objects, and the temperature cloud map, air age, and draft rate (DR) are used as the evaluation indexes, while the particulate matter concentration in the clinic is analyzed, and the 10 main indexes for evaluating the clinic environment are subjected to the principal component analysis algorithm (PCA), the four airflow organizations are comprehensive ranking. Since the traditional questionnaire has a lot of human subjectivity, using the algorithm can effectively compensate for the shortcomings of the questionnaire, and comparing the conclusions derived from the PCA algorithm with the results of the questionnaire can make the conclusions more scientific. The final conclusion is that the airflow organization of the replacement air supply can meet human comfort and air freshness while reducing the concentration of respiratory particulate matter in the clinic environment under the evaluation of various indexes, for which the replacement air supply scheme can provide a theoretical basis and reference for future construction implementation. © 2022 SPIE.

Design and Development of Virtual Lab Environment for Embedded System and IoT Applications

In recent years, the education officials have been forced to cancel classes and close the doors of the campus across the world in response to the growth of coronavirus outbreak. Due to the development of e-learning, a significant transformation is happening in education. Digital platforms are used in e-learning for giving instructions remotely. The faculty chose to take video conference classes using one platform and the resources are uploaded in another platform which are accessed by students. But while coming to laboratory classes, the hands-on experience of students on the equipment and components are totally missed. Resources are in the colleges and students are at home. This separation creates a long gap in the education. At KPR Institution of Engineering and Technology (KPRIET), a virtual lab setup with controllers and essential hardware modules was implemented in the Internet of Things (IoT) laboratory of Electronics and Communication Engineering (ECE) Department, where students can access and control it from their current location using secured login credentials.Virtual setup provides an easy access for the students to get hands-on experience with the academic laboratory sessions. These sessions are very useful for the students to gain more relevant and keen knowledge of their laboratories. This project provides a greater number of students to engage with their academic theory and practical laboratory sessions. We use server software and addons along with Remote Desktop Protocol (RDP) as well as Virtual Network Computing (VNC). © 2022 IEEE.

Flowmeter based on capillary tubes trough 3D stereolithography fabrication for test mechanical ventilation equipment

Mechanical ventilators and high-flow machines are medical devices with important measuring instruments for monitoring patients with respiratory failure. The most common monitoring parameters are lung proximal pressure, inspiratory flow, expiratory flow, inspiratory oxygen fraction, etc. The present work delves into the design, fabrication, and experimental measurement of a proximal flow sensor based on the theory of capillary tubes and stereolithography. The design was carried out in Inventor Professional 2020 software and then the computational study by CFD ANSYS to compare the dynamic pressure states of the geometric measurement points. The manufacturing was carried out using SLA 3D printing technology on an ANYCUBIC FHOTON MONO X.The fabricated FM SLA prototype has radially positioned latex tubing lines to achieve differential pressure measurement at two points separated by capillary tubes. These hoses are connected to a developmental embedded system based on a HONEYWELL 001PG7A5 differential pressure sensor and Arduino Uno Microcontroller. Finally, experimental tests of the Flow Meter Stereolithography (FM SLA) protype measurements were performed with flow rates from 0 to 44.5 lpm in 1 lpm increments. From the collected data we have an R2: 0.9983 in quadratic polynomial approximation with the actual measurement data. © 2022 IEEE.

EPROACH: A Population Vaccination Game for Strategic Information Design to Enable Responsible COVID Reopening

The COVID-19 lockdowns have created a significant socioeconomic impact on our society. In this paper, we propose a population vaccination game framework, called EPROACH, to design policies for reopenings that guarantee post-opening public health safety. In our framework, a population of players decides whether to vaccinate based on the public and private information they receive. The reopening is captured by the switching of the game state. The insights obtained from our framework include the appropriate vaccination coverage threshold for safe-reopening and information-based methods to incentivize individual vaccination decisions. In particular, our framework bridges the modeling of the strategic behaviors of the populations and the spreading of infectious diseases. This integration enables finding the threshold which guarantees a disease-free epidemic steady state under the population's Nash equilibrium vaccination decisions. The equilibrium vaccination decisions depend on the information received by the agents. It makes the steady-state epidemic severity controllable through information. We find that the externalities created by reopening lead to the coordination of the players in the population and result in a unique Nash equilibrium. We use numerical experiments to corroborate the results and illustrate the design of public information for responsible reopening. © 2022 American Automatic Control Council.

City-Level Structured Prediction and Simulation Model of COVID-19

It is important for social public security and urban management to explore the spread of infectious diseases. A city-level structured prediction and simulation model for COVID-19 is proposed. This model is consisted of SEIR and social network model on the basis of latest infectious disease dynamics theory and real geographic networks. The prediction region is divided into multiple levels. Specifically, a bipartite network is applied to simulate the relationship between public facilities and community nodes at the macro level, and a modified SEIR is applied to simulate the infection within nodes at the micro level. Besides, intelligent agent is applied to track the individual transmission process. The contrast experimental results based on the confirmed and cursed cases of Wuhan and Beijing in 2020 published by National Health Commission, show that the proposed model has better flexibility and higher accuracy, and reflects the distribution and movement of people more directly. © 2022 Institute of Computing Technology. All rights reserved.

Comparing Interpretable AI Approaches for the Clinical Environment: an Application to COVID-19

Machine Learning (ML) models play an important role in healthcare thanks to their remarkable performance in predicting complex phenomena. During the COVID-19 pandemic, different ML models were implemented to support decisions in the medical settings. However, clinical experts need to ensure that these models are valid, provide clinically useful information, and are implemented and used correctly. In this vein, they need to understand the logic behind the models to be able to trust them. Hence, developing transparent and interpretable models has increasing relevance. In this work, we applied four interpretable ML models including logistic regression, decision tree, pyFUME, and RIPPER to classify suspected COVID-19 patients based on clinical data collected from blood samples. After preprocessing the data set and training the models, we evaluate the models based on their predictive performance. Then, we illustrate that interpretability can be achieved in different ways. First, SHAP explanations are built from logistic regression and decision trees to obtain the features' importance. Then, the potential of pyFUME and RIPPER in providing inherent interpretability are reflected. Finally, potential ways to achieve trust in future studies are briefly discussed. © 2022 IEEE.

RESEARCH ON THE WILLINGNESS OF USERS' PRIVACY CONCERNS TO USE EPIDEMIC PREVENTION TECHNOLOGY UNDER THE BACKGROUND OF THE NEW CORONAVIRUS EPIDEMIC

Nowadays, governments worldwide use artificial intelligence (AI), big data, cloud computing, and other technologies to control the spread of the epidemic. These measures significantly improve the efficiency of virus tracking. Nevertheless, such digital defences have also raised concerns about privacy leaks. Privacy concerns and use intention have been studied in several areas in the existing literature, but few have been explored in-depth based on epidemic prevention. Therefore, this paper focuses on the background of the novel coronavirus epidemic and constructs a structural equation model based on the theory of privacy concern and technology acceptance model. The research studies the influence of privacy concerns, perceived risk, and other factors on users’ willingness to use new technologies. Based on 132 samples, the results show that privacy concerns significantly impact perceived risk. Perceived trust has significant positive impacts on self-disclosure intention. This study discusses individual self-disclosure intention in the field of public security from multiple perspectives. The research results extend the relevant theories on adopting and using emerging technologies. This study provides ideas on how to alleviate residents' privacy concerns in practice and helps government departments to carry out better prevention work. © 2022 CURRAN-CONFERENCE. All rights reserved.

A Triangle Framework among Subgraph Isomorphism, Pharmacophore and Structure-function Relationship

Coronavirus disease 2019 (COVID-19) has gained utmost attention in the current time from academic research and industrial practices because it continues to rage in many countries. Pharmacophore models exploit molecule topological similarity as well as functional compound similarity so that they can be reliable via the application of the concept of bioisosterism. In this work, we analyze the targets for coronavirus protein and the structure of RNA virus variation, thereby complete the safety and pharmacodynamic action evaluation of small-molecule anti-coronavirus oral drugs. Common pharmacophore identifications could be converted into subgraph querying problems, due to chemical structures can also be converted to graphs, which is a knotty problem pressing for a solution. We adopt simplified representation pharmacophore graphs by reducing complete molecular structures to s to detect isomorphic topological patterns and further to improve the substructure retrieval efficiency. Our threefold architecture subgraph isomorphism-based method retrieves query subgraphs over large graphs. First, by means of extracting a sequence of subgraphs to be matched and then comparing the number of vertex and edge between the potential isomorphic subgraphs and the query graph, we lower the computational scaling markedly. Afterwards, the directed vertex and edge matrix recording vertex and edge positional relation, directional relation and distance relation has been created. Then, on the basis of permutation theorem, we calculate the row sum of vertex and edge adjacency matrix of query graph and potential sample. Finally, according to equinumerosity theorem, we check the eigenvalues of the vertex and edge adjacency matrices of the two graphs are equinumerous. The topological distance could be calculated based on the graph isomorphism and the subgraph isomorphism can be implemented after the combination of the subgraph. The proposed quantitative structure-function relationships (QSFR) approach can be effectively applied for pharmacophoric patterns identification. The framework of new drug development for covid-19 has been established based on this triangle. © 2022 ACM.