Angry, Sad, or Scared? Within-valence Mapping of Emotion Words to Facial and Body Cues in 2 to 4-Year-Old Children

The acquisition of emotion words is critical to children's socio-emotional development. Previous studies report that children acquire emotion words gradually during ages 3-5 and beyond. The majority of this work, however, has used demanding tasks for young children (e.g., asking children to label emotion-related facial configurations) and has predominantly relied on facial configurations. Here we designed a child-friendly, word-comprehension task incorporating both facial configurations and body language. In two preregistered online experiments, we asked two to four-year-olds (N = 96) to connect emotion words-happy, sad, angry, and scared-to either facial configurations (Experiment 1) or combined facial and body cues (Experiment 2). We found relatively early competence in understanding emotion words, especially those of the same-valence. All age groups, including 2-year-olds, successfully linked emotion words to corresponding facial configurations (Experiment 1). Experiment 2 replicated this pattern and further showed that children performed equally well (though not substantially better) when given additional body cues. Parental reports of children's exposure to and use of masks during the COVID-19 pandemic did not correlate with children's performance in either experiment. Even before children can produce emotion words in an adult-like manner, they possess at least a partial understanding of those words and can map them to emotion cues within valence domains. © 2023 University of California Press. All rights reserved.

Visual analysis of mask sales data based on crawler

The outbreak of COVID-19 makes people feel distant from each other, and masks have become one of the indispensable articles in People's Daily life. At present, there are many brands of masks with various types and uneven quality. In order to understand the current market of masks and the sales of different brands, users can choose masks with perfect quality. This paper uses Python web crawler technology, based on the input of the word "mask", crawl JD website sales data, through data visualization technology drawing histogram, pie chart, the word cloud, etc., for goods compared with the relationship between price, average price of all brands, brands, average distribution of analysis and evaluation of user information, In this way, the sales situation, price distribution and quality evaluation of each store of the product can be visually displayed. At the same time, it also provides some reference for other users who need to buy the product. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

Multi-Joint Unit Network for COVID-19 Diagnosis using X-Ray and CT Images

The global COVID-19 pandemic has caused a health crisis globally. Automated diagnostic methods can control the spread of the pandemic, as well as assists physicians to tackle high workload conditions through the quick treatment of affected patients. Owing to the scarcity of medical images and from different resources, the present image heterogeneity has raised challenges for achieving effective approaches to network training and effectively learning robust features. We propose a multi-joint unit network for the diagnosis of COVID-19 using the joint unit module, which leverages the receptive fields from multiple resolutions for learning rich representations. Existing approaches usually employ a large number of layers to learn the features, which consequently requires more computational power and increases the network complexity. To compensate, our joint unit module extracts low-, same-, and high-resolution feature maps simultaneously using different phases. Later, these learned feature maps are fused and utilized for classification layers. We observed that our model helps to learn sufficient information for classification without a performance loss and with faster convergence. We used three public benchmark datasets to demonstrate the performance of our network. Our proposed network consistently outperforms existing state-of-the-art approaches by demonstrating better accuracy, sensitivity, and specificity and F1-score across all datasets. © 2022 ACM.

Scada Web-Based Instrumentation and Control Laboratory with Real-Time Remote Monitoring and Control System for Columban College

This paper provides a remote access control experiment for students who can't go to the campus because of the COVID-19 pandemic. This paper utilizes the SCADA (supervisory control and data acquisition) using LabView with the Internet of things technology to control the laboratory remotely in real-time. Remote access experiments of a Linear actuator, PID algorithm, Dynamics and Control of Second-order system response, and survey questionnaires were applied and used as an example to show how effective the research study is. The safety of the SCADA system was also considered by using the Virtual Private Network as the primary connection between the student and the server. The remote access laboratory will give a solution to the current problem of the academe for not providing a real-time laboratory equipment experiment. © 2022 IEEE.

Save the Trip to the Store: Sustainable Shopping, Electronic Word of Mouth on Instagram and the Impact on Cosmetic Purchase Intentions

The COVID-19 pandemic has forced many brands to stop using cosmetic testers to avoid the risk of spreading the infection, jeopardising the future of cosmetic testing. Consequently, consumers must find alternative methods to conduct their information searches and, more importantly, the prospects of shopping online without going to the store to test the product. With the enormous prospects of social media cosmetic electronic word of mouth (eWOM), it is imperative to examine the influence of cosmetic eWOM on social media and for cosmetic marketers to understand the antecedents that result in cosmetic consumers making a purchase. The adapted information adoption model was validated through structural equation modelling based on 341 eligible surveys. The results confirmed that information quality, source credibility, information usefulness, and information adoption are the key antecedents in eWOM on Instagram when investigating purchase intentions in the colour cosmetic industry. This study is one of the pioneers in empirically testing the relationship between information quality and source credibility on information usefulness and, subsequently, the relationship between information usefulness, information adoption, and purchase intentions in a western market based on the cosmetic industry. These new insights provide practical implications for a cosmetic marketer, suggesting the key variables leading to purchase intentions in cosmetic eWOM, which can be utilised in marketing techniques.

How Do People React to COVID-19 Vaccination? A Corpus-Based Study of Macau Netizens' Online Comments

This study draws on corpus methodology to investigate people's reactions to COVID-19 vaccination using the data of Macau netizens' comments on a YouTube channel. Four main topics under discussion were identified based on the word lists. Meanwhile, people were concerned about the activity of vaccines and were also engaged in heated debates on both domestic and foreign vaccines according to the collocation of "疫苗” yìmiáo (vaccine). The discussion topics and concerns varied along with time, evidenced by the results of word lists and collocates of each month. It is also noticeable that some misinformation on vaccines burgeoned and faded before and after the mass vaccination of Macau residents. The supportive voices for the (Chinese) vaccines were building up their momentum over time. This phenomenon lends support to the effective persuasion of gain-framed messages in advocating safe behaviour based on Prospect Theory. Our research has revealed that the corpus-based study of online comments can be leveraged to uncover people's social behaviour in the pandemic context. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Stacked Bi-directional Long Short Term Memory model for Multi-Class Arabic Sentiment Analysis on Covid-19

The world has been affected by the Covid-19 epidemic during the last three years. During that period, most people tended to use social networks, where by searching for topics related to Covid-19, information could be provided to manage decisions by organizations or governments about public health. With the importance of the Arabic language, despite the lack of research targeting it, using Arabic language as a source of data and analyzing it due to the large number of users on social networks gives an impetus to understand people's feelings about the Covid-19 pandemic. One of the challenges facing sentiment analysis in Arabic is the use of dialects. The most common and existing methods used have been quite ineffective as they are oblivious to contextual information and cannot handle long-distance word dependencies. The Iraqi Arabic dialect is one of the Arabic dialects that still suffers from a lack of research in sentiment analysis. In this study, the official page of the Iraqi Ministry of Health on Facebook was used to collect and analysis comments. Word2vec model is incorporated to extract words semantic characteristics. To capture contextual features, Stacked Bi-directional Long Short Term Memory model (Stacked Bi-LSTM) utilizes sequential word vectors derived from the Continuous Bag of Words model. When compared to most common and existing approaches, the proposed method performed well. © 2022 IEEE.

Smart human resource analytics for happiness management

PurposeThe coronavirus disease 2019 pandemic changed the lives of people around the world. In a post pandemic era, leaders have a role to enable the changes needed to make workplaces smart and happier. The aim of this study is to look at human resource management (HRM) from new perspectives: being smart and happy in the workplace. Some research questions are proposed: What do we know about smart human resources (smart HR)? What do we know about human resource analytics (HRA)? and how can future research on smart and happy HRM be oriented?Design/methodology/approachA bibliometric technique is used to identify the main topics studied in smart HR and HRA. A logical reasoning is applied to propose future research models.FindingsFor smart HR, the roadmap considers the approaches, practices and purposes. For HRA, the roadmap shows what are the perspectives HR processes, tools and its usefulness. Considering the context of Industry 5.0 and post pandemic era, a future research line for studying smart HRA for happy management is proposed.Originality/valueThis study has developed a proposed model to guide future research on the application of HRA to manage smart and happy workers.

A Semi-automated Approach for Bengali Neologism.

Neologisms refer to newly coined words or phrases adopted by a language, and it is a slow but ongoing process that occurs in all languages. Sometimes, rarely used or obsolete words are also considered neologisms. Certain events, such as wars, the emergence of new diseases, or advancements like computers and the internet, can trigger the creation of new words or neologisms. The COVID-19 pandemic is one such event that has rapidly led to an explosion of neologisms in the context of the disease and several other social contexts. Even the term COVID-19 itself is a newly coined term. Studying such adaptation or change and quantifying it is essential from a linguistic perspective. However, identifying newly coined terms or extracting neologisms computationally is a challenging task. The standard tools and techniques for finding newly coined terms in English-like languages may not be suitable for Bengali and other Indic languages. This study aims to use a semi-automated approach to investigate the emergence or modification of new words in the Bengali language amidst the COVID-19 pandemic. To conduct this study, a Bengali web corpus was compiled consisting of COVID-19 related articles sourced from various web sources in Bengali. The current experiment focuses solely on COVID-19-related neologisms, but the method can be adapted for general purposes and extended to other languages as well.

Brazilian Portuguese-Russian (BraPoRus) corpus: automatic transcription and acoustic quality of elderly speech during the COVID-19 pandemic

This article presents the Brazilian Portuguese-Russian (BraPoRus) corpus, whose goal is to collect, analyze, and preserve for posterity the spoken heritage Russian still used today in Brazil by approximately 1,500 elderly bilingual heritage Russian-Brazilian Portuguese speakers. Their unique 100-year-old variety of moribund Russian is disappearing because it has not been passed to their descendants born in Brazil. During the COVID-19 pandemic, we remotely collected 170 h of speech samples in heritage Russian from 26 participants (M (age) = 75.7 years) in naturalistic settings using Zoom or a phone call. To estimate the quality of collected data, we focus on two methodological challenges, automatic transcription and acoustic quality of remote recordings. First, we find that among commercially available transcription programs, Sonix far outperforms Google Transcribe and Vocalmatic on the measure of word error rate (WER). Second, we also establish that the acoustic quality of the remote recordings was adequate for intonational and speech rate analysis. Moreover, this remote method of collecting and analyzing speech samples works successfully with elderly bilingual participants who speak a heritage language different from their dominant societal language, and it can become a new norm when face-to-face communication with elderly participants is not possible.

How do hotel star ratings affect the relationship between environmental CSR and green word-of-mouth?

Hotels have increasingly engaged in environmentally responsible initiatives to demonstrate their commitment to environmental concerns and sustainable hospitality and tourism. These initiatives are expected to become even more popular in the context that the COVID-19 crisis has driven people to further acknowledge the importance of the ecosystem. This study aims to examine how hotels' environmental corporate social responsibility (CSR) affects customers' green word-of-mouth (WOM). Structural equation modeling was employed to analyze data from an online survey of 749 Chinese respondents. The findings reveal that hotels' environmental CSR indirectly enhances customers' green WOM intention via green perceived value (i.e., cognitive route) and green hotel pride (i.e., emotional route). Furthermore, the indirect effects of hotels' environmental CSR on customers' green WOM are more substantial for hotels with higher star ratings. These findings offer valuable insights for hoteliers to develop genuine environmentally responsible initiatives that can generate positive customer responses.

Factors Predicting Hotel Recommendations: A Comparison of Guest Feedback Before and After the Hotel Closures During the COVID-19 Pandemic

The COVID-19 pandemic adversely impacted the hospitality industry. The current study explored potential factors (i.e. cleanliness, location, room, service, and value) influencing guests' hotel recommendations before the shutdown of hotels due to COVID-19 and after the reopening of hotels. This study employed secondary data and random forest analysis to test the hypotheses. The results indicated that before COVID-19, the value factor had the most significant effect on a guest's willingness to recommend, followed by service, cleanliness, room, and location. A year after the shutdown period, the value factor still had the most significant effect, followed by service, room, and cleanliness, while location had an insignificant effect. Hotel managers can utilize the findings to create new strategies to attract guests and allocate resources to better address guests' evolving expectations. [ FROM AUTHOR] Copyright of International Journal of Hospitality & Tourism Administration is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Homefluencers' endorsement of millennial consumers' purchase intention in new normal

PurposeThe purpose of this study is to examine how "homefluencers" sponsored posts on millennial consumers' purchase intention in the international marketing sphere can be impacted in the new normal by drawing on source credibility, parasocial interaction (PSI) and persuasion knowledge model (PKM) theory.Design/methodology/approachThis research applies structural equation modeling (SEM) and mediation analysis as the data analysis method using non-probability purposive sampling of a total of 217 local millennial Instagram and Facebook users, who have followed homefluencers sponsored posts in fashion-beauty, yoga-fitness and food sectors.FindingsBased on hypothesis testing, advertising recognition strongly mediates purchase intention with the indirect effects of expertise and trustworthiness than attractiveness.Research limitations/implicationsThis research extends the international marketing literature on source credibility, PSI, PKM and purchase intention theory in the new normal by proposing "Homefluencer's Endorsement Model for Purchase Intention" (HEMPI). Specifically, the mediating role of ad recognition of homefluencers sponsorship disclosure (#paidad, #sponsored), positively affects "change-of-persuasion meaning" on Instagram and Facebook, where research is rare.Practical implicationsThis research provides valuable suggestions for global brand owners, consumers and authorities of Instagram and Facebook to consider post-COVID consumer behavior highlighting homefluencers sponsored collaboration.Originality/valueThe authors have contributed to the use of the source credibility model and PSI to identify the antecedents in determining how the homefluencer's effective sponsorship disclosure can positively activate ad recognition on millennial consumers' purchase intention in a crisis period from an international standpoint with the practical implications in post-COVID.

Polarized stance on the COVID-19 pandemic as reflected in discursive naming strategies. The case of Polish nominal covidianin

The article explores the ways in which discursive naming strategies reflect polarized stance on the COVID-19 pandemic, and serve as means of discrediting ideological opponents. The data for the analysis, excerpted from Monco PL and Google search engines, exemplify the uses of the nominal derivative covidianin in Internet discourse. The material exhibits a two-fold function of this derivative: 1) it is embedded within the conceptual category of RELIGION in order to exploit the FAITH–REASON dichotomy;2) it is used to portray ideological opponents as representing the category of OTHER. The analysis employs the cognitive linguistics framework, thus broadening the traditional formal and semantic description to include conceptual content underlying the newly coined structure and emergent meanings, as well as the cognitive critical discourse analysis model. © 2023, Society of Friends of the Polish Language. All rights reserved.

Local Newspaper Coverage of the COVID-19 Pandemic in Two Major Texas Cities: A Visual Comparison of Houston and El Paso

In the US, the absence of a coordinated national response to the COVID-19 pandemic left decision-making to state and local leaders. In Texas, debate over how best to decrease the virus' spread highlighted political tensions between the Republican state leadership and the predominantly Democratic county- and city-leaders. We analyze the daily newspapers of two major cities, Houston and El Paso, to understand similarities and differences in local pandemic-related concerns. We focus specifically on three periods: the days immediately following the first case of COVID-19 in Texas in March 2020, the days surrounding the peak of the first major spike in July 2020, and the days surrounding the second, more deadly spike in January 2021. We trace the progression of the pandemic in Houston and El Paso, analyzing the prevalent newspaper themes and illustrating regional differences through word clouds, which provide a visual analysis of the COVID-19 related coverage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Context Aware Automatic Subjective and Objective Question Generation using Fast Text to Text Transfer Learning

Online learning has gained a tremendous popularity in the last decade due to the facility to learn anytime, anything, anywhere from the ocean of web resources available. Especially the lockdown all over the world due to the Covid-19 pandemic has brought an enormous attention towards the online learning for value addition and skills development not only for the school/college students, but also to the working professionals. This massive growth in online learning has made the task of assessment very tedious and demands training, experience and resources. Automatic Question generation (AQG) techniques have been introduced to resolve this problem by deriving a question bank from the text documents. However, the performance of conventional AQG techniques is subject to the availability of large labelled training dataset. The requirement of deep linguistic knowledge for the generation of heuristic and hand-crafted rules to transform declarative sentence into interrogative sentence makes the problem further complicated. This paper presents a transfer learning-based text to text transformation model to generate the subjective and objective questions automatically from the text document. The proposed AQG model utilizes the Text-to-Text-Transfer-Transformer (T5) which reframes natural language processing tasks into a unified text-to-text-format and augments it with word sense disambiguation (WSD), ConceptNet and domain adaptation framework to improve the meaningfulness of the questions. Fast T5 library with beam-search decoding algorithm has been used here to reduce the model size and increase the speed of the model through quantization of the whole model by Open Neural Network Exchange (ONNX) framework. The keywords extraction in the proposed framework is performed using the Multipartite graphs to enhance the context awareness. The qualitative and quantitative performance of the proposed AQG model is evaluated through a comprehensive experimental analysis over the publicly available Squad dataset. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Managing students' attitude, learning engagement, and stickiness towards e-learning post-COVID-19 in Australian universities: a perceived qualities perspective

Studies have been conducted on university students' acceptance of e-learning systems during COVID-19. However, less attention has been paid to students' use of e-learning post-pandemic. This research provides a more comprehensive framework to investigate the effects of e-learning students' various quality perceptions on attitude, learning engagement, and stickiness toward e-learning platforms. A survey-based quantitative method is adopted by this study in which sample data are collected from students in Australian universities. A total of 403 valid samples were analysed using covariance-based structural equation modelling. This study found that students' perceived educational quality, service quality, information quality, and technical system quality play different roles in their attitudes and behaviours towards e-learning. It expands the information system success model by comparing the effects of students' various perceived qualities on their ongoing commitment to e-learning. It provides insights to e-learning providers in pursuing better designs and more sustainable development of educational information systems.

Development of online system checkable for Japanese writing tasks

Online learning environments have attracted attention of many educators especially in recent years since COVID-19 is still ongoing situation. Meanwhile, the various resources are becoming more and more available in online. In this study, some available online resources were used to create the system checkable for some writing abilities and the depth of understanding for Japanese writing tasks. The system was also made to provide some evaluation scores without depending the number of characters. The demonstration of system were given after the integration and implementation of some modules customized using online resources. The data sheet in the system finally saved the written content for 67 students. The writing task was given as the writing of summarization for what a student understand in a class. The following features were demonstrated from the analytical findings of online system developed in this study. The effectiveness of some available online resources was indicated through the demonstration of system checkable for some writing abilities and the depth of understanding for Japanese writing tasks. It was definite that the system was also made to provide some evaluation scores without depending the number of characters.

Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study.

Background: Social media plays a pivotal role in disseminating news globally and acts as a platform for people to express their opinions on various topics. A wide variety of views accompany COVID-19 vaccination drives across the globe, often colored by emotions that change along with rising cases, approval of vaccines, and multiple factors discussed online. Objective: This study aims to analyze the temporal evolution of different emotions and the related influencing factors in tweets belonging to 5 countries with vital vaccine rollout programs, namely India, the United States, Brazil, the United Kingdom, and Australia. Methods: We extracted a corpus of nearly 1.8 million Twitter posts related to COVID-19 vaccination and created 2 classes of lexical categories-emotions and influencing factors. Using cosine distance from selected seed words' embeddings, we expanded the vocabulary of each category and tracked the longitudinal change in their strength from June 2020 to April 2021 in each country. Community detection algorithms were used to find modules in positive correlation networks. Results: Our findings indicated the varying relationship among emotions and influencing factors across countries. Tweets expressing hesitancy toward vaccines represented the highest mentions of health-related effects in all countries, which reduced from 41% to 39% in India. We also observed a significant change (P<.001) in the linear trends of categories like hesitation and contentment before and after approval of vaccines. After the vaccine approval, 42% of tweets coming from India and 45% of tweets from the United States represented the "vaccine_rollout" category. Negative emotions like rage and sorrow gained the highest importance in the alluvial diagram and formed a significant module with all the influencing factors in April 2021, when India observed the second wave of COVID-19 cases. Conclusions: By extracting and visualizing these tweets, we propose that such a framework may help guide the design of effective vaccine campaigns and be used by policy makers to model vaccine uptake and targeted interventions.

Food and Beverage (F&B) Consumption Behavior Changes During COVID-19 in the Taiwan's Aging Society

This study examines the relationships among perceived value, trust, electronic word of mouth (eWOM), and online purchasing intention to determine Food and Beverage (F&B) consumption behavior changes in Taiwan's aging society since the onset of COVID-19. The 305 valid online questionnaires received from people above the age of 55 who had prior online purchasing experience of F&B products in the past year in Taiwan have been obtained to implement data analysis through confirmatory factor analysis and structural equation model. The results reveal that eWOM positively affects trust, both eWOM and trust positively affect perceived value, and perceived value positively affects online purchasing intention. While eWOM and trust do not have a direct positive effect on online purchasing intention, perceived value has a mediation effect on the relationship between eWOM and online purchasing intention and the relationship between trust and online purchasing intention. Lastly, this study proposes pragmatic suggestions to merchants to better adapt to ever-changing consumer behavior. © 2023,International Journal of Business. All Rights Reserved.