CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets Using Deep Learning Models

Online news and information sources are convenient and accessible ways to learn about current issues. For instance, more than 300 million people engage with posts on Twitter globally, which provides the possibility to disseminate misleading information. There are numerous cases where violent crimes have been committed due to fake news. This research presents the CovidMis20 dataset (COVID-19 Misinformation 2020 dataset), which consists of 1,375,592 tweets collected from February to July 2020. CovidMis20 can be automatically updated to fetch the latest news and is publicly available at: https://github.com/everythingguy/CovidMis20. This research was conducted using Bi-LSTM deep learning and an ensemble CNN+Bi-GRU for fake news detection. The results showed that, with testing accuracy of 92.23% and 90.56%, respectively, the ensemble CNN+Bi-GRU model consistently provided higher accuracy than the Bi-LSTM model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy

The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users' intents, due to updates in guidelines or in response to new information, we highlight the important task of intent discovery in this use-case. We introduce a novel automatic evaluation framework for intent discovery, leveraging the existing intent classifier of VIRA. We use this framework to report baseline intent-discovery results over VIRADialogs, that highlight the difficulty of this task. © 2023 Association for Computational Linguistics.

Suggesting Assess Queries for Interactive Analysis of Multidimensional Data

Assessment is the process of comparing the actual to the expected behavior of a business phenomenon and judging the outcome of the comparison. The ${{\sf assess}}$assess querying operator has been recently proposed to support assessment based on the results of a query on a data cube. This operator requires (i) the specification of an OLAP query to determine a target cube;(ii) the specification of a reference cube of comparison (benchmark), which represents the expected performance;(iii) the specification of how to perform the comparison, and (iv) a labeling function that classifies the result of this comparison. Despite the adoption of a SQL-like syntax that hides the complexity of the assessment process, writing a complete assess statement is not easy. In this paper we focus on making the user experience more comfortable by letting the system suggest suitable completions for partially-specified statements. To this end we propose two interaction modes: progressive refinement and auto-completion, both starting from an assess statement partially declared by the user. These two modes are evaluated both in terms of scalability and user experience, with the support of two experiments made with real users.

On the energy performance of the Mediterranean school building stock: the case of the Apulia Region

The refurbishment opportunities provided by climate policies require an adequate knowledge of the school building stock, characterised by an urgent need of maintenance. Nevertheless, empirical evidence on energy performance of school samples appears limited due to the difficulty in retrieving data, although field data analysis is crucial in the built environment management. This study aims to explore existing energy conditions of an educational building sample hosting pre-schools, primary and lower secondary schools, located in southern Italy (Apulia Region). Firstly, an overview of the schools based on data retrieved from the regional dataset was performed. Then, more than 1000 buildings were clustered based on two predictors (construction year and surface-to-volume ratio), identifying five clusters representing the majority Apulian schools. In addition, billed gas and electricity data collected for 47 schools over a five-year period (2017-2021) were analysed, identifying annual and monthly trends, benchmarks, and mean values, which account for 46.5 (gas consumption), 15.59 kWh/m2 (electricity consumption). On average, source total consumption in 2020 experienced a reduction of 20%, partly due to Covid-19 restrictive measures. Finally, factors affecting heating consumptions were explored, and a regression analysis was performed, identifying heating degree days, construction year and boiler power to be the most significant.

Incidence of alpine skiing and snowboarding injuries.

BACKGROUND: The incidence of injuries on alpine ski slopes have been assessed using various methods. A decline in injury rate has been observed throughout the literature; however, the actual incidence remains unclear. The purpose of this study was therefore to evaluate the incidence of skiing and snowboarding injuries using large-sample data from an entire geographic state. METHODS: Data on alpine injuries over the course of five winter seasons between 2017 and 2022 were prospectively collected from the emergency service dispatch center of Tyrol (Austria). The incidence of injuries was assessed in relation to the number of skier days, which was obtained from the chamber of commerce. RESULTS: A total of 43,283 cases were identified, and a total of 98.1 Mio skier days were registered during the inclusion period of our study, resulting in an overall incidence of 0.44 injuries per 1,000 skier days. This is significantly less than reported from previous studies. From 2017/18 to 2021/22 there was a slight increase in injuries per 1000 skier days with an exception only for the COVID-19 related season 2020/21. CONCLUSION: Our study showed a significant reduction in the incidence of alpine skiing and snowboarding injuries in comparison with previous studies and should be considered a benchmark for future studies. Long-term studies on the efficacy of safety gear, as well as the influence of ski patrol and air-borne rescues on patient outcome are warranted.

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data.

The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarized and incorporated into patient outcome prediction models in several ways; however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integrate approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalization when using multiple datasets as the model input.

Sentiment Analysis of Bengali Texts on Online Tech Gadget Reviews using Machine Learning

Online marketing and e-commerce companies are booming in Bangladesh in this age of internet technology. As more people were afflicted with the COVID-19 epidemic, internet purchasing became the primary channel for closure shopping and was considered the safest method. The enterprises were pushed to appear online. There are many online service providers, such beneficial for individuals, but it also calls into question the quality of the products with services. Therefore, it is simple for new clients to be deceived, when doing internet purchasing. The enormous volume of tech gadget review data that is generated online every day can be examined for the purpose of assessing public sentiment and assisting in market intelligence. While the study of sentiment classification has advanced greatly in languages with abundant resources, it is still in the preliminary stage for languages with limited resources, such as Bengali. This work proposes a model for classifying the sentiment on online Bengali tech gadget reviews into three basic categories- positive, negative, and neutral. For this purpose, around 6015 Bengali tech review data is collected. Various Machine Learning techniques are then applied along with different feature extraction techniques. After evaluating the performance, the Random Forest outperforms the rest of other techniques, having a maximum accuracy of 86.28%. © 2022 IEEE.

Masked Face Recognition Dataset and Application

During COVID-19 coronavirus epidemic, almost everyone wears a mask to prevent the spread of virus. It raises a problem that the traditional face recognition model basically fails in the scene of face-based identity verification, such as security check, community visit check-in, etc. Therefore, it is imminent to boost the performance of masked face recognition. Most recent advanced face recognition methods are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets, especially real ones. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). Besides, we conduct benchmark experiments on these three datasets for reference. As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset.. IEEE

Indicator-based assessment of sustainable energy performance in the European Union

The COVID-19 and the resulting global energy crises highlighted the importance of decarbonization and the necessity of shifting the economy from fossil fuels towards renewable energy sources. Sustainable energy transition is also a key element of circular economy, social welfare and justice. In this paper we developed an indicator set and we compiled a composite indicator to measure the performance of the EU Member States regarding the sustainable energy transition between 2007 and 2019. Our results show significant differences which do not follow the usual East-West division of the integration. Both convergence and divergence can be revealed.

A comparison of five Illumina, Ion Torrent, and nanopore sequencing technology-based approaches for whole genome sequencing of SARS-CoV-2.

Rapid identification of the rise and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern remains critical for monitoring of the efficacy of diagnostics, therapeutics, vaccines, and control strategies. A wide range of SARS-CoV-2 next-generation sequencing (NGS) methods have been developed over the last years, but cross-sequence technology benchmarking studies have been scarce. In the current study, 26 clinical samples were sequenced using five protocols: AmpliSeq SARS-CoV-2 (Illumina), EasySeq RC-PCR SARS-CoV-2 (Illumina/NimaGen), Ion AmpliSeq SARS-CoV-2 (Thermo Fisher), custom primer sets (Oxford Nanopore Technologies (ONT)), and capture probe-based viral metagenomics (Roche/Illumina). Studied parameters included genome coverage, depth of coverage, amplicon distribution, and variant calling. The median SARS-CoV-2 genome coverage of samples with cycle threshold (Ct) values of 30 and lower ranged from 81.6 to 99.8% for, respectively, the ONT protocol and Illumina AmpliSeq protocol. Correlation of coverage with PCR Ct values varied per protocol. Amplicon distribution signatures differed across the methods, with peak differences of up to 4 log10 at disbalanced positions in samples with high viral loads (Ct values ≤ 23). Phylogenetic analyses of consensus sequences showed clustering independent of the workflow used. The proportion of SARS-CoV-2 reads in relation to background sequences, as a (cost-)efficiency metric, was the highest for the EasySeq protocol. The hands-on time was the lowest when using EasySeq and ONT protocols, with the latter additionally having the shortest sequence runtime. In conclusion, the studied protocols differed on a variety of the studied metrics. This study provides data that assist laboratories when selecting protocols for their specific setting.

Assessing the informational content of card transactions for nowcasting retail trade: Evidence for Latvia

We use a novel card transaction data maintained at the Central Bank of Latvia to assess their informational content for nowcasting retail trade in Latvia. During the COVID-19 pandemic in Latvia, the retail trade turnover dynamics underwent drastic changes reflecting the various virus containment measures introduced during three separate waves of the pandemic. We show that the nowcasting model augmented with card transaction data successfully captures the turbulence in retail trade turnover induced by the COVID-19 pandemic. The model with card transaction data outperforms all benchmark models in the out-of-sample nowcasting exercise and yields a notable improvement in forecasting metrics. We conduct our nowcasting exercise in forecast-as-you-go manner or in real-time squared;that is, we use real-time data vintages, and we make our nowcasts in real time as soon as card transaction data become available for the target month. © 2023 The Authors. Journal of Forecasting published by John Wiley & Sons Ltd.

Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19.

Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm's explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis.

Price discovery in China's crude oil futures markets: An emerging Asian benchmark?

We examine the price discovery performance of China's crude oil futures traded on the Shanghai International Energy Exchange (INE) for the spot prices of 19 types of deliverable and nondeliverable Asian crude oil. We find evidence for the INE crude oil futures price discovery function even at the early stage for almost all the deliverable crudes and some nondeliverable crudes. Both the INE crude oil futures price and the spot price significantly contribute to the price discovery process, with substantially time‐varying informational roles. While the price discovery performance was severely damaged around the period of COVID‐19 pandemic shock intensification in China with the temporary cancellation of nighttime trading, it improved to some extent after China started the recovery from the shock. But such improvement deteriorated drastically and disappeared since early 2021. Further analysis reveals that both economic fundamentals (e.g., the warehouse inventory) and trading‐related characteristics of the futures market are significant determinants of the price discovery performance. The overall findings imply that the INE crude oil futures market has evolved into a useful and important information source in pricing Asian crudes, and is on the path to emerge as an Asian benchmark.

Questing benchmarks for the current ratio: An analysis of the Warsaw Stock Exchange firms

Objective: The first objective of the article is to assess the benchmarks for the current ratio commonly provided in the accounting and financial analysis literature. The minor objective is to arrange a research methodology permitting the accomplishment of the first objective. Research Design & Methods: The paper, apart from the literature review and its critique, presents the results of descriptive, quantitative research. The research sample consists of 5 148 firm-years. Data were retrieved from Worldscope Database via Refinitiv Eikon for domestic, going concern, non-financial companies listed on the Warsaw Stock Exchange from 2005 to 2021. For comparison, the U.S.-related data was retrieved from Internet resources as ready-made ratios. Methods used in the analysis include descriptive statistics, parametric and nonparametric ANOVA, confidence intervals, and linear and parabolic trend analysis. Findings: The tests show that identifying a benchmark for the current ratio is problematic. Benchmarks vary between countries and industries;universal standards do not exist. The benchmark for the current ratio commonly suggested, 1.2-2.0, may be used only as a rough evaluation of the desired value. The findings indicate that the acceptable range for CR is much broader, from 1.1 (the first quartile) to 2.3 (the third quartile) for the total sample. Moreover, the SIC division's quartiles vary from 0.7 to 1.2 (the first) and from 1.6 to 3.6 (the third). Statistical tests indicate that benchmarks do not vary annually. However, the distribution's median and the third quartile change slowly over time toward higher values. The Covid-19 pandemic resulted in a substantial increase. On the contrary, the first quartile of about 1 remains relatively stable over time, indicating the reasonable lower bound for CR. The mean of the distribution is useless as a benchmark because of its sensitivity to outliers. Various techniques must be used to assess the benchmark. Implications & Recommendations: Since there are material between-industry and between-country differences in benchmarks, analysts and investors should be very sensitive to the standards suggested in the literature and interpret them cautiously. Variability over time exists but is low unless a shock in the economy appears. Contribution & Value Added: The main contribution of the paper is empirical verification of the benchmarks for the current ratio provided in the literature. Tests show that suggested benchmarks are not universal. Seeking a benchmark, an analyst must compare the firm's financial standing with other firms from the same country, industry, and period.

A Comparative Study of Question Answering over Knowledge Bases

Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at https://github.com/tamlhp/kbqa. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Augmentation of rPPG Benchmark Datasets: Learning to Remove and Embed rPPG Signals via Double Cycle Consistent Learning from Unpaired Facial Videos

Remote estimation of human physiological condition has attracted urgent attention during the pandemic of COVID-19. In this paper, we focus on the estimation of remote photoplethysmography (rPPG) from facial videos and address the deficiency issues of large-scale benchmarking datasets. We propose an end-to-end RErPPG-Net, including a Removal-Net and an Embedding-Net, to augment existing rPPG benchmark datasets. In the proposed augmentation scenario, the Removal-Net will first erase any inherent rPPG signals in the input video and then the Embedding-Net will embed another PPG signal into the video to generate an augmented video carrying the specified PPG signal. To train the model from unpaired videos, we propose a novel double-cycle consistent constraint to enforce the RErPPG-Net to learn to robustly and accurately remove and embed the delicate rPPG signals. The new benchmark “Aug-rPPG dataset” is augmented from UBFC-rPPG and PURE datasets and includes 5776 videos from 42 subjects with 76 different rPPG signals. Our experimental results show that existing rPPG estimators indeed benefit from the augmented dataset and achieve significant improvement when fine-tuned on the new benchmark. The code and dataset are available at https://github.com/nthumplab/RErPPGNet. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Collaborative and AI-aided Exam Question Generation using Wikidata in Education

Since the COVID-19 outbreak, the use of digital learning or education platforms has substantially increased. Teachers now digitally distribute homework and provide exercise questions. In both cases, teachers need to develop novel and individual questions continuously. This process can be very time-consuming and should be facilitated and accelerated both through exchange with other teachers and by using Artificial Intelligence (AI) capabilities. To address this need, we propose a multilingual Wikimedia framework that allows for collaborative worldwide teacher knowledge engineering and subsequent AI-aided question generation, test, and correction. As a proof of concept, we present»PhysWikiQuiz«, a physics question generation and test engine. Our system (hosted by Wikimedia at https://physwikiquiz.wmflabs.org) retrieves physics knowledge from the open community-curated database Wikidata. It can generate questions in different variations and verify answer values and units using a Computer Algebra System (CAS). We evaluate the performance on a public benchmark dataset at each stage of the system workflow. For an average formula with three variables, the system can generate and correct up to 300 questions for individual students, based on a single formula concept name as input by the teacher. © 2022 Copyright for this paper by its authors.

Prerequisites for Building an Innovative Online Platform Supporting Bim Implementation in Higher Education

The aim of the paper is to present outcomes of the first phase of the ongoing EU-funded Project BIMaHEAD focused on building digital readiness in higher education institutions as well as supporting students in AEC related degrees to adjust to the new online education environment caused by the COVID-19 pandemic through integrating digital technologies with teaching and learning practices. An in-depth comparative analysis of 132 case studies focused on Building Information Modelling education in a Higher Education sector in Europe was completed and conclusions were drawn. A great amount of data was collected, studied, and analysed. The benchmarking analyses were fundamental for understanding the state of the art in the area, defining gaps and deficiencies, and rethinking teaching and learning methodologies. The findings also revealed evident differences in curricula as well as in the roles and responsibilities of main actors in the AEC sector in European countries. Therefore, they allowed to specify prerequisites and outline a vision of an open-access online platform to be developed within the second and third stages of the BIMaHEAD Project.

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm.

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

Price discovery in China's crude oil futures markets: An emerging Asian benchmark?

We examine the price discovery performance of China's crude oil futures traded on the Shanghai International Energy Exchange (INE) for the spot prices of 19 types of deliverable and nondeliverable Asian crude oil. We find evidence for the INE crude oil futures price discovery function even at the early stage for almost all the deliverable crudes and some nondeliverable crudes. Both the INE crude oil futures price and the spot price significantly contribute to the price discovery process, with substantially time-varying informational roles. While the price discovery performance was severely damaged around the period of COVID-19 pandemic shock intensification in China with the temporary cancellation of nighttime trading, it improved to some extent after China started the recovery from the shock. But such improvement deteriorated drastically and disappeared since early 2021. Further analysis reveals that both economic fundamentals (e.g., the warehouse inventory) and trading-related characteristics of the futures market are significant determinants of the price discovery performance. The overall findings imply that the INE crude oil futures market has evolved into a useful and important information source in pricing Asian crudes, and is on the path to emerge as an Asian benchmark.