Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?

Memes can sway people's opinions over social media as they combine visual and textual information in an easy-to-consume manner. Since memes instantly turn viral, it becomes crucial to infer their intent and potentially associated harmfulness to take timely measures as needed. A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities. Here, we aim to understand whether the meme glorifies, vilifies, or victimizes each entity it refers to. To this end, we address the task of role identification of entities in harmful memes, i.e., detecting who is the 'hero', the 'villain', and the 'victim' in the meme, if any. We utilize HVVMemes - a memes dataset on US Politics and Covid-19 memes, released recently as part of the CONSTRAINT@ACL-2022 shared-task. It contains memes, entities referenced, and their associated roles: hero, villain, victim, and other. We further design VECTOR (Visual-semantic role dEteCToR), a robust multi-modal framework for the task, which integrates entity-based contextual information in the multi-modal representation and compare it to several standard unimodal (text-only or image-only) or multi-modal (image+text) models. Our experimental results show that our proposed model achieves an improvement of 4% over the best baseline and 1% over the best competing stand-alone submission from the shared-task. Besides divulging an extensive experimental setup with comparative analyses, we finally highlight the challenges encountered in addressing the complex task of semantic role labeling within memes. © 2023 Association for Computational Linguistics.

Large-Scale Urban Heating and Pollution Domes over the Indian Subcontinent

The unique geographical diversity and rapid urbanization across the Indian subcontinent give rise to large-scale spatiotemporal variations in urban heating and air emissions. The complex relationship between geophysical parameters and anthropogenic activity is vital in understanding the urban environment. This study analyses the characteristics of heating events using aerosol optical depth (AOD) level variability, across 43 urban agglomerations (UAs) with populations of a million or more, along with 13 industrial districts (IDs), and 14 biosphere reserves (BRs) in the Indian sub-continent. Pre-monsoon average surface heating was highest in the urban areas of the western (42 degrees C), central (41.9 degrees C), and southern parts (40 degrees C) of the Indian subcontinent. High concentration of AOD in the eastern part of the Indo-Gangetic Plain including the megacity: Kolkata (decadal average 0.708) was noted relative to other UAs over time. The statistically significant negative correlation (-0.51) between land surface temperature (LST) and AOD in urban areas during pre-monsoon time illustrates how aerosol loading impacts the surface radiation and has a net effect of reducing surface temperatures. Notable interannual variability was noted with, the pre-monsoon LST dropping in 2020 across most of the selected urban regions (approx. 89% urban clusters) while it was high in 2019 (for approx. 92% urban clusters) in the pre-monsoon season. The results indicate complex variability and correlations between LST and urban aerosol at large scales across the Indian subcontinent. These large-scale observations suggest a need for more in-depth analysis at city scales to understand the interplay and combined variability between physical and anthropogenic atmospheric parameters in mesoscale and microscale climates.

Machine Learning in ASD: An Intensive Study of Automated Disease Prediction System

Of late, due to drastic climate change and excessive pollution, people live in such an atmosphere where they have to combat continuously several deadly diseases. To get the proper treatment of such diseases, people must rely on appropriate diagnoses. There are a lot of signs or symptoms that bear the existence of a particular condition. Generally, almost all the people who suffer from viral infections, dengue, and COVID-19 get a common sign of high fever. Therefore, it is challenging for doctors to determine the exact disease with this particular symptom. Accordingly, a technically equipped medical system should be developed to get a more error-free diagnosis. In this context, a case study uses the Random Forest Algorithm to combine diagnostic prediction and technology, which will help medical practitioners detect diseases. Agile Software can be used here. One of the essential advantages of agile methodology is speed to market and risk reduction. This paper showcases a module developed with the help of Machine Learning. Here, Agile Software is designed to become very effective in detecting a particular disease more efficiently. In this specific system preventing errors and malfunctions has been proven to be 95% effective in the medical field. © 2023 Scrivener Publishing LLC.

Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter

The widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news. The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation. However, the rate of information dissemination is such that it vastly outpaces the fact-checkers' strength. Therefore, to aid manual fact-checkers in eliminating the superfluous content, it becomes imperative to automatically identify and extract the snippets of claim-worthy (mis)information present in a post. In this work, we introduce the novel task of Claim Span Identification (CSI). We propose CURT, a large-scale Twitter corpus with token-level claim spans on more than 7.5k tweets. Furthermore, along with the standard token classification baselines, we benchmark our dataset with DABERTa, an adapter-based variation of RoBERTa. The experimental results attest that DABERTa outperforms the baseline systems across several evaluation metrics, improving by about 1.5 points. We also report detailed error analysis to validate the model's performance along with the ablation studies. Lastly, we release our comprehensive span annotation guidelines for public use. © 2022 Association for Computational Linguistics.

Computational study of some potential inhibitors of COVID-19: A DFT analysis

Background: There is an urgent demand of drug or therapy to control the COVID-19. Until July 22, 2021 the worldwide total number of cases reported is more than 192 million and the total number of deaths reported is more than 4.12 million. Several countries have given emergency permission for use of repurposed drugs for the treatment of COVID-19 patients. This report presents a computational analysis on repurposing drugs-tenofovir, bepotastine, epirubicin, epoprostenol, tirazavirin, aprepitant and valrubicin, which can be potential inhibitors of the COVID-19.Method: Density functional theory (DFT) technique is applied for computation of these repurposed drug. For geometry optimization, functional B3LYP/6-311G (d, p) is selected within DFT framework.Results: DFT based descriptors-highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap, molecular hardness, softness, electronegativity, electrophilicity index, nucleophilicity index and dipole moment of these species are computed. IR and Raman activities are also analysed and studied. The result shows that the HOMO-LUMO gap of these species varies from 1.061 eV to 5.327 eV. Compound aprepitant with a HOMO-LUMO gap of 1.419 eV shows the maximum intensity of IR (786.176 km mol-1) and Raman spectra (15036.702 a.u.).Conclusion: Some potential inhibitors of COVID-19 are studied by using DFT technique. This study shows that epirubicin is the most reactive compound whereas tenofovir is found to be the most stable. Further analysis and clinical trials of these compounds will provide more insight.

Papain Like Protease: A Potential Target for Middle East Respiratory Syndrome Coronavirus (Mers-Cov): The Battle Continues

Middle-East Respiratory Syndrome coronavirus (MERS-CoV) can trigger severe acute pneumonia, renal, digestive failure and even death. Coronaviruses express papain-like proteases (PLpro), multipurpose enzymes which had protease activity and can lacerate nonstructural proteins to manipulate the viral polyprotein responsible for replication. They also have deubiquitinating function, which can modify the innate immune response. The reduction of the infection of MERS-CoV is by Inhibition of PLpro with a ligand will wedge the cleavage progression of nonstructural protein. As a result, papain-like protease may be considered as a candidate for antiviral drug production. This current study focuses on screening of extracts from Neem and Eucalyptus for MERS-CoV that could be potentially used as an inhibitor against the disease. Blind molecular docking study was conducted by using Auto Dock followed by visualization using PyMol, which is examined in this existing study. Deacetylgedunin (Neem) and eucalyptol (Eucalyptus) showed successful binding to MERS-CoV papain-like protease based on measured parameters such as root Mean Square Deviation (RMSD), binding capacity and inhibiting constant. The compound Deacetylgedunin found in neem exhibited the lowest RMSD value of 16.388 A and the highest binding energy of -8.28 kcal/mol. It also had the highest inhibition constant value of 851.36 nM and the lowest inhibition constant value of 851.36 nM. Since Deacetylgedunin gave a better result compared to Indinavir, hence it can be considered as a potential and safe alternative for the current medicine given for MERS-CoV disease. Copyright All © 2022 are reserved by International Journal of Pharmaceutical Sciences and Research.

Changing Landscape of Technical Education Pedagogy From Traditional to Practical e-Learning

With the COVID-19 pandemic, online university education assumed greater importance. We propose a practical e-learning model with suggestions to augment online education of all forms to support effective technical education. We also share our experiences of implementations of some of these suggestions in online teaching.

Document Retrieval and Claim Verification to Mitigate COVID-19 Misinformation

During the COVID-19 pandemic, the spread of misinformation on online social media has grown exponentially. Unverified bogus claims on these platforms regularly mislead people, leading them to believe in half-baked truths. The current vogue is to employ manual fact-checkers to verify claims to combat this avalanche of misinformation. However, establishing such claims' veracity is becoming increasingly challenging, partly due to the plethora of information available, which is difficult to process manually. Thus, it becomes imperative to verify claims automatically without human interventions. To cope up with this issue, we propose an automated claim verification solution encompassing two steps - document retrieval and veracity prediction. For the retrieval module, we employ a hybrid search-based system with BM25 as a base retriever and experiment with recent state-of-the-art transformer-based models for re-ranking. Furthermore, we use a BART-based textual entailment architecture to authenticate the retrieved documents in the later step. We report experimental findings, demonstrating that our retrieval module outperforms the best baseline system by 10.32 NDCG@100 points. We escort a demonstration to assess the efficacy and impact of our suggested solution. As a byproduct of this study, we present an open-source, easily deployable, and user-friendly Python API that the community can adopt.

Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes

We present the findings of the shared task at the CONSTRAINT 2022 workshop on "Hero, Villain, and Victim: Dissecting Harmful Memes for Semantic Role Labeling of Entities." The task aims to delve deeper into meme comprehension by deciphering the connotations behind the entities present in a meme. In more nuanced terms, the shared task focuses on determining the victimizing, glorifying, and vilifying intentions embedded in meme entities to explicate their connotations. To this end, we curate HVVMemes, a novel meme dataset of about 7,000 memes spanning the domains of COVID-19 and US Politics, each containing entities and their associated roles: hero, villain, victim, or other. The shared task attracted 105 registered participants, but eventually only nine of them made official submissions. The most successful systems used ensembles combining textual and multimodal models, with the best system achieving an F1-score of 58.67.

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

Background: Unequal housing access resulted in more than 150 million homeless people worldwide, with mil-lions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research. Methods: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research. Results: Our findings indicated a geographic clustering of case study locations- 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters. Conclusion: Most studies were restricted in analyzing and visualizing the patterns and disease clusters;however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.

Speaker and time-aware joint contextual learning for dialogue-act classification in counselling conversations

The onset of the COVID-19 pandemic has brought the mental health of people under risk. Social counselling has gained remarkable significance in this environment. Unlike general goal-oriented dialogues, a conversation between a patient and a therapist is considerably implicit, though the objective of the conversation is quite apparent. In such a case, understanding the intent of the patient is imperative in providing effective counselling in therapy sessions, and the same applies to a dialogue system as well. In this work, we take forward a small but an important step in the development of an automated dialogue system for mental-health counselling. We develop a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations. We identify the requirement of such conversation and propose twelve domain-specific dialogue-act (DAC) labels. We collect ∼ 12.9K utterances from publicly-available counselling session videos on YouTube, extract their transcripts, clean, and annotate them with DAC labels. Further, we propose SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the dialogue-act classification. Our evaluation shows convincing performance over several baselines, achieving state-of-the-art on HOPE. We also supplement our experiments with extensive empirical and qualitative analyses of SPARTA. © 2022 ACM.

Theta Autoregressive Neural Network: A Hybrid Time Series Model for Pandemic Forecasting

Forecasting time series present a perpetual topic of research in statistical machine learning for the last five decades. Due to the unprecedented outbreak of the novel coronavirus (COVID-19), forecasting the COVID-19 pandemic became a key research interest for both epidemiologists and statisticians. These future predictions are useful for the effective allocation of health care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public-health policymakers. This paper develops an effective forecasting model that can generate real-time short-term (ten days) and long-term (fifty days) out-of-sample forecasts of COVID-19 outbreaks for eight profoundly affected countries, namely the United States of America, Brazil, India, Russia, South Africa, Mexico, Spain, and Iran. A novel hybrid approach based on the Theta method and Autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is proposed. The proposed method outperforms previously available single and hybrid forecasting models for COVID-19 predictions in most data sets. The ergodicity and asymptotic stationarity of the TARNN model are also studied.

How Reliable Is Smartphone-Based Electronic Contact Tracing for COVID-19?

WITH GLOBAL SURGES of the novel coronavirus SARS-CoV-2 in 2020 and 2021, electronic contact tracing has been adopted in different countries, the goal being to identify the most relevant contacts with a reasonable reliability. Owing to the need to quickly reduce the number of transmissions, contact-tracing solutions built on smartphones were developed because they could be mass-deployed on short notice. Their major advantage was that the hardware was already deployed and only the software remained to be developed.

Nowcasting of COVID-19 Confirmed Cases: Foundations, Trends, and Challenges

The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting more than 200 countries and territories worldwide. As of September 30, 2020, it has caused a pandemic outbreak with more than 33 million confirmed infections, and more than 1 million reported deaths worldwide. Several statistical, machine learning, and hybrid models have previously been applied to forecast COVID-19 confirmed cases for profoundly affected countries. Future predictions of daily COVID-19 cases are useful for the effective allocation of healthcare resources and will act as an early-warning system for government policymakers. However, due to the presence of extreme uncertainty in these time series datasets, forecasting of COVID-19 confirmed cases has become a very challenging job. For univariate time series forecasting, there are various statistical and machine learning models available in the literature. Still, nowcasting and forecasting of COVID-19 cases are difficult due to insufficient input data, flaw in modeling assumptions, lack of epidemiological features, inadequate past evidence on effects of available interventions, and lack of transparency. This chapter focuses on assessing different short-term forecasting models that are popularly used to forecast the daily COVID-19 cases for various countries. This chapter provides strong empirical evidence that there is no universal method available that can accurately forecast pandemic data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Combining Exogenous and Endogenous Signals with a Semi-supervised Co-attention Network for Early Detection of COVID-19 Fake Tweets

Fake tweets are observed to be ever-increasing, demanding immediate countermeasures to combat their spread. During COVID-19, tweets with misinformation should be flagged and neutralised in their early stages to mitigate the damages. Most of the existing methods for early detection of fake news assume to have enough propagation information for large labelled tweets – which may not be an ideal setting for cases like COVID-19 where both aspects are largely absent. In this work, we present ENDEMIC, a novel early detection model which leverages exogenous and endogenous signals related to tweets, while learning on limited labelled data. We first develop a novel dataset, called ECTF for early COVID-19 Twitter fake news, with additional behavioural test-sets to validate early detection. We build a heterogeneous graph with follower-followee, user-tweet, and tweet-retweet connections and train a graph embedding model to aggregate propagation information. Graph embeddings and contextual features constitute endogenous, while time-relative web-scraped information constitutes exogenous signals. ENDEMIC is trained in a semi-supervised fashion, overcoming the challenge of limited labelled data. We propose a co-attention mechanism to fuse signal representations optimally. Experimental results on ECTF, PolitiFact, and GossipCop show that ENDEMIC is highly reliable in detecting early fake tweets, outperforming nine state-of-the-art methods significantly. © 2021, Springer Nature Switzerland AG.

Recent development in the formulations of ginger for therapeutic applications and an overview towards the action on sars-cov-2

Ginger or Zingiber officinale, Roscoe of the Zingiberaceae family is a rhizome that is widely found and most consumed in South east Asian countries;also used as a traditional remedy to treat various ailments like nausea, vomiting, pain, arthritis, indigestion, gastro reflux, cardiovascular disease, diabetes, obesity, microbes, cancer, inflammation, oxidation, and wounds to name some of its activity which is based on the various chemical constituents present in ginger as it is found to contain more than 400 different compounds which include sugar, protein, and fats. The major phenolic active constituents of ginger are 6-gingerol, 6-shogaol and 6- paradol;which are safe and showing only a few insignificant adversarial effects. Here, this review aims to summarize and discuss the ideas on how ginger is formulated and improve from conventional to novel formulations using novel techniques;to improve the pharmacological, biopharmaceutical and chemical properties of ginger extract and its compounds. Different novel formulations of ginger like a tablet, capsule, powder, cream, gel, transdermal patch, nanoparticles, liposomes and phytosomes along with its therapeutic actions that were developed in recent years. Future aspects in research are suggested for the advances in the novel formulation of ginger using each isolated compound and improving bioavailability, therapeutic effect, and delivery. Also, this article discusses the in silico studies that have been carried out for ginger and its phytochemicals that may be considered as potential agents in the treatment of SARS-CoV-2.

Fake News in Health and Medicine

With the rise of social media, the world is faced with the challenge of increasing health-related fake news more than ever before. We are constantly flooded with health-related information through various online platforms, many of which turn out to be inaccurate and misleading. This chapter provides an overview of various health fake news and related studies which have been reported in various news articles and scientific journals. Some of the studies conducted on health misinformation identified a prominence of vaccine- and cancer-related fake news. The popularity of so-called unproven natural cures for cancer and other diseases is alarming. The chapter also highlights the importance of maintaining accurate and effective scientific communication in this COVID-19 pandemic-hit world to safeguard public health. The current pandemic has also proved fertile ground for spreading misinformation. The chapter brings the audience’s attention to the consequences of health misinformation, ranging from giving false hope to patients to the hurdles it poses to effective medical care. Finally, the chapter addresses some of the possible strategies to keep health misinformation in check. © 2021, Springer Nature Switzerland AG.

Integration of psychology and yoga for positive personification during covid-19 pandemic period

The Yoga has been recognized as an efficient intervention for various range of mental and psychological conditions. Yoga incorporates the breath, body and mind along with ethical, spiritual and life-style factors. The paper concentrated the impact of Yoga especially during the COVID-19 pandemic period. Mindfulness played the vital role in the psychological benefits of Yoga. The survey paper illustrated and analyzed the research of Yoga practice on how far the state of mindfulness have impact on post yoga intervention .The YOMI analysis report for the remedy of psychological disorders, self-regulatory potential yoga mechanisms for the wellness of psychological health and in the exploration of different types of yoga mechanisms would alter the psychological resources. In this survey, the analysis of yoga intervention having impact on the perceived effects on the students in terms of improvise emotional and physical health, steadiness and ease, increased wellness, and interpersonal integration has been described. The survey paper revealed the examination analysis of the impacts of short term yoga Nidra meditation on the levels of sleep, wellbeing, mindfulness and the stress disorders for the first time. The survey also reviewed the acute consequences of the yoga intervention (Bikram Yoga) on the practitioners to have positive and negative psychological impacts of the participants.Comparison of the individuals who have undergone the Yoga intervention to that of the individuals who possess major depression disorders has also been discussed in this paper. © 2021 Universitatea de Vest Vasile Goldis din Arad. All rights reserved.

Reduction in human activity can enhance the urban heat island: insights from the COVID-19 lockdown

The COVID-19 lockdowns drastically reduced human activity, emulating a controlled experiment on human–land–atmosphere coupling. Here, using a fusion of satellite and reanalysis products, we examine this coupling through changes in the surface energy budget during the lockdown (1 April to 15 May 2020) in the Indo-Gangetic Basin, one of the world’s most populated and polluted regions. During the lockdown, the reduction (>10%) in columnar air pollution compared to a five year baseline, expected to increase incoming solar radiation, was counteracted by a ∼30% enhancement in cloud cover, causing little change in available energy at the surface. More importantly, the delay in winter crop harvesting during the lockdown increased surface vegetation cover, causing almost half the regional cooling via evapotranspiration. Since this cooling was higher for rural areas, the daytime surface urban heat island (SUHI) intensity increased (by 0.20–0.41 K) during a period of reduced human activity. Our study provides strong observational evidence of the influence of agricultural activity on rural climate in this region and its indirect impact on the SUHI intensity.

Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts

Fake news, hostility, defamation are some of the biggest problems faced in social media. We present the findings of the shared tasks (https://constraint-shared-task-2021.github.io/ ) conducted at the CONSTRAINT Workshop at AAAI 2021. The shared tasks are ‘COVID19 Fake News Detection in English’ and ‘Hostile Post Detection in Hindi’. The tasks attracted 166 and 44 team submissions respectively. The most successful models were BERT or its variations. © 2021, Springer Nature Switzerland AG.