bluuebunny/crossref_metadata_2025 huggingface dataset:
Sentiment Analysis in Crisis Situations for Better Connected Government
Authors: López Chau Asdrúbal , Valle-Cruz David , Sandoval-Almazán Rodrigo
Published: July 2022

One of the pillars of connected government is citizen centricity: an approach in which citizen participation is essential. In Mexico, social networks are currently one of the most important means by which citizens express their needs and provide opinions to the government. The goal of this chapter is to contribute to citizen centricity by adapting the methodology of sentiment analysis of social media posts to an expanded version for crisis situations. The main difference in this approach from the normally accepted one is that instead of using pre-defined classes (positive and negative) for sentiments, the authors first determined the different data categories and then applied them to the classic process of sentiment analysis. This approach was tested using posts on Mexico's earthquake in 2017. They found that needs, demands, and claims made in the posts reflect sentiments in a better way, and this can help to improve the government-citizen connection.
Integrating Semantic Acquaintance for Sentiment Analysis
Authors: Gupta Neha , Agrawal Rashmi
Published: July 2022

The use of emerging digital information has become significant and exponential, as well as the boom of social media (forms, blogs, and social networks). Sentiment analysis concerns the statistical analysis of the views expressed in written texts. In appropriate evaluations of the emotional context, semantics plays an important role. The analysis is generally done from two viewpoints: how semantics are coded in sentimental instruments, such as lexicon, corporate, and ontological, and how automated systems determine feelings on social data. Two approaches to evaluate sentiments are commonly adopted (i.e., approaches focused on machine learning algorithms and semantic approaches). The precise testing in this area was increased by the already advanced semantic technology. This chapter focuses on semantic guidance-based sentiment analysis approaches. The Twitter/Facebook data will provide a semantically enhanced technique for annotation of sentiment polarity.
Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis
Authors: Nazeer Ishrat , Rashid Mamoon , Gupta Sachin Kumar , Kumar Abhishek
Published: July 2022

Twitter is a platform where people express their opinions and come with regular updates. At present, it has become a source for many organizations where data will be extracted and then later analyzed for sentiments. Many machine learning algorithms are available for twitter sentiment analysis which are used for automatically predicting the sentiment of tweets. However, there are challenges that hinder machine learning classifiers to achieve better results in terms of classification. In this chapter, the authors are proposing a novel feature generation technique to provide desired features for training model. Next, the novel ensemble classification system is proposed for identifying sentiment in tweets through weighted majority rule ensemble classifier, which utilizes several commonly used statistical models like naive Bayes, random forest, logistic regression, which are weighted according to their performance on historical data, where weights are chosen separately for each model.
A Novel Aspect Based Framework for Tourism Sector with Improvised Aspect and Opinion Mining Algorithm
Authors: Bhatnagar Vishal , Goyal Mahima , Hussain Mohammad Anayat
Published: July 2022

With the growth of e-commerce web sites, the demand of writing reviews on these portals have gained huge popularity. This huge data must be mined to analyze the opinion and for making better decisions in different domains. In this paper, we have proposed an aspect based opinion mining algorithm for the tourism domain. It first determines the aspects, and then extracts the opinion words related to the aspects. The opinion words are provided a score based on the Senti-Wordnet and the final score of each aspect is calculated by the summation of the scores of the opinions. The final score is visualized depicting ranking of scores of different aspects for different hotels.
Using Computational Text Analysis to Explore Open-Ended Survey Question Responses
Authors: Hai-Jew Shalin
Published: July 2022

To capture a broader range of data than close-ended questions (often defined and delimited by the survey instrument designer), open-ended questions, such as text-based elicitations (and file-upload options for still imagery, audio, video, and other contents) are becoming more common because of the wide availability of computational text analysis, both within online survey tools and in external software applications. These computational text analysis tools—some online, some offline—make it easier to capture reproducible insights with qualitative data. This chapter explores some analytical capabilities, in matrix queries, theme extraction (topic modeling), sentiment analysis, cluster analysis (concept mapping), network text structures, qualitative cross-tabulation analysis, manual coding to automated coding, linguistic analysis, psychometrics, stylometry, network analysis, and others, as applied to open-ended questions from online surveys (and combined with human close reading).
A Probabilistic Deep Learning Approach for Twitter Sentiment Analysis
Authors: Abdelkader Mostefai
Published: July 2022

In recent years, increasing attention is being paid to sentiment analysis on microblogging platforms such as Twitter. Sentiment analysis refers to the task of detecting whether a textual item (e.g., a tweet) contains an opinion about a topic. This paper proposes a probabilistic deep learning approach for sentiments analysis. The deep learning model used is a convolutional neural network (CNN). The main contribution of this approach is a new probabilistic representation of the text to be fed as input to the CNN. This representation is a matrix that stores for each word composing the message the probability that it belongs to a positive class and the probability that it belongs to a negative class. The proposed approach is evaluated on four well-known datasets HCR, OMD, STS-gold, and a dataset provided by the SemEval-2017 Workshop. The results of the experiments show that the proposed approach competes with the state-of-the-art sentiment analyzers and has the potential to detect sentiments from textual data in an effective manner.
Tools of Opinion Mining
Authors: Gupta Neha , Verma Siddharth
Published: July 2022

Today's generation express their views and opinions publicly. For any organization or for individuals, this feedback is very crucial to improve their products and services. This huge volume of reviews can be analyzed by opinion mining (also known as semantic analysis). It is an emerging field for researchers that aims to distinguish the emotions expressed within the reviews, classifying them into positive or negative opinions, and summarizing it into a form that is easily understood by users. The idea of opinion mining and sentiment analysis tool is to process a set of search results for a given item based on the quality and features. Research has been conducted to mine opinions in form of document, sentence, and feature level sentiment analysis. This chapter examines how opinion mining is moving to the sentimental reviews of Twitter data, comments used in Facebook on pictures, videos, or Facebook statuses. Thus, this chapter discusses an overview of opinion mining in detail with the techniques and tools.
Deep Learning for Social Media Text Analytics
Authors: R. Anto Arockia Rosaline , R. Parvathi
Published: July 2022

Text analytics is the process of extracting high quality information from the text. A set of statistical, linguistic, and machine learning techniques are used to represent the information content from various textual sources such as data analysis, research, or investigation. Text is the common way of communication in social media. The understanding of text includes a variety of tasks including text classification, slang, and other languages. Traditional Natural Language Processing (NLP) techniques require extensive pre-processing techniques to handle the text. When a word “Amazon” occurs in the social media text, there should be a meaningful approach to find out whether it is referring to forest or Kindle. Most of the time, the NLP techniques fail in handling the slang and spellings correctly. Messages in Twitter are so short such that it is difficult to build semantic connections between them. Some messages such as “Gud nite” actually do not contain any real words but are still used for communication.
Effectiveness of Normalization Over Processing of Textual Data Using Hybrid Approach Sentiment Analysis
Authors: Johal Sukhnandan Kaur , Mohana Rajni
Published: July 2022

Various natural language processing tasks are carried out to feed into computerized decision support systems. Among these, sentiment analysis is gaining more attention. The majority of sentiment analysis relies on the social media content. This web content is highly un-normalized in nature. This hinders the performance of decision support system. To enhance the performance, it is required to process data efficiently. This article proposes a novel method of normalization of web data during the pre-processing phase. It is aimed to get better results for different natural language processing tasks. This research applies this technique on data for sentiment analysis. Performance of different learning models is analysed using precision, recall, f-measure, fallout for normalize and un-normalize sentiment analysis. Results shows after normalization, some documents shift their polarity i.e. negative to positive. Experimental results show normalized data processing outperforms un-normalized data processing with better accuracy.
Chatbot Experiences of Informal Language Learners
Authors: Alm Antonie , Nkomo Larian M.
Published: July 2022

In 2016, a number of language applications released chatbots to complement their programmes. Used primarily in informal learning settings, chatbots enable language learners to engage in conversational speaking practice, which can be perceived as less threatening than face-to-face interactions with native speakers. This study takes a closer look at four second language (L2) chatbots—Duolingo, Eggbun, Memrise, and Mondly—and analyses the experiences which informal language learners expressed on various online platforms (e.g., Duolingo forum, Memrise community, Reddit). Results indicate a degree of curiosity and a willingness to engage in conversation with chatbots. However, learners expressed frustration if the dialogues did not correspond to their learning goals or if they were excluded from using the bots because of technical or payment issues, or discontinuation of services.
Using Intelligent Text Analysis of Online Reviews to Determine the Main Factors of Restaurant Value Propositions
Authors: Fainshtein Elizaveta , Serova Elena
Published: July 2022

This chapter discusses the sentiment classification of text messages containing customer reviews of an online restaurant service system using machine-learning methods, in particular text mining and multivariate text sentiment analysis. The study determines the structure of value proposition factors based on online restaurant reviews on TripAdvisor, collecting information on consumer preferences and the restaurant services in St. Petersburg (Russia) quality assessment and examines the influence of service format and reviews tonality on ratings restaurants factors. The service format context is proposed as the main attribute influencing the formation of the restaurant business value proposition and of relevance for online reviews. The results showed the key factors in the study of the sentiment were cuisine and dishes, reviews and ratings, and targeted search. MANOVA analysis represented that for special offers and features, reviews and ratings, factors and quantitative star ratings influenced the negative and positive sentiment of online reviews significantly.
Discovery of Sustainable Transport Modes Underlying TripAdvisor Reviews With Sentiment Analysis
Authors: Serna Ainhoa , Gerrikagoitia Jon Kepa
Published: July 2022

In recent years, digital technology and research methods have developed natural language processing for better understanding consumers and what they share in social media. There are hardly any studies in transportation analysis with TripAdvisor, and moreover, there is not a complete analysis from the point of view of sentiment analysis. The aim of study is to investigate and discover the presence of sustainable transport modes underlying in non-categorized TripAdvisor texts, such as walking mobility in order to impact positively in public services and businesses. The methodology follows a quantitative and qualitative approach based on knowledge discovery techniques. Thus, data gathering, normalization, classification, polarity analysis, and labelling tasks have been carried out to obtain sentiment labelled training data set in the transport domain as a valuable contribution for predictive analytics. This research has allowed the authors to discover sustainable transport modes underlying the texts, focused on walking mobility but extensible to other means of transport and social media sources.
Ooredoo Rayek
Authors: Klouche Badia , Benslimane Sidi Mohamed , Bennabi Sakina Rim
Published: July 2022

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.
A Survey on Implementation Methods and Applications of Sentiment Analysis
Authors: Karnam Sudheer , Valarmathi B.  , Sariki Tulasi Prasad
Published: July 2022

Sentiment analysis also called opinion mining, and it studies opinions of people towards products and services. Opinions are very important as the organizations always want to know the public opinions about their products and services. People give their opinions via social media. With the advent of social media like Twitter, Facebook, blogs, forums, etc. sentiment analysis has become important in every field like automobile, medical, film, fashion, stock market, mobile phones, insurance, etc. Analyzing the opinions and predicting the opinion is called sentiment analysis. Sentiment analysis is done using opinion words by classification methods or by sentiment lexicons. This chapter compares different methods of solving sentiment analysis problem, algorithms, its merits and demerits, applications, and also investigates different research problems in sentiment analysis.
Application of Sentiment Analysis in Movie reviews
Authors: Gadekallu ThippaReddy , Soni Akshat , Sarkar Deeptanu , Kuruva Lakshmanna
Published: July 2022

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured, or unstructured textual data. In this chapter, the authors try to focus the task of sentiment analysis on IMDB movie review database. This chapter presents the experimental work on a new kind of domain-specific feature-based heuristic for aspect-level sentiment analysis of movie reviews. The authors have devised an aspect-oriented scheme that analyzes the textual reviews of a movie and assign it a sentiment label on each aspect. Finally, the authors conclude that incorporating syntactical information in the models is vital to the sentiment analysis process. The authors also conclude that the proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain.
Open Issues in Opinion Mining
Authors: Vyas Vishal , Uma V.
Published: July 2022

Opinions are found everywhere. In web forums like social networking websites, e-commerce sites, etc., rich user-generated content is available in large volume. Web 2.0 has made rich information easily accessible. Manual insight extraction of information from these platforms is a cumbersome task. Deriving insight from such available information is known as opinion mining (OM). Opinion mining is not a single-stage process. Text mining and natural language processing (NLP) is used to obtain information from such data. In NLP, content from the text corpus is pre-processed, opinion word is extracted, and analysis of those words is done to get the opinion. The volume of web content is increasing every day. There is a demand for more ingenious techniques, which remains a challenge in opinion mining. The efficiency of opinion mining systems has not reached the satisfactory level because of the issues in various stages of opinion mining. This chapter will explain the various research issues and challenges present in each stage of opinion mining.
Impact of Deep Learning on Semantic Sentiment Analysis
Authors: Gupta Neha , Agrawal Rashmi
Published: July 2022

Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.
Impact of Sarcasm in Sentiment Analysis Methodology
Authors: Silva Priscilla Souza , Barroso Haroldo , Weitzel Leila , Ribeiro Dilcielly Almeida , Santos José
Published: July 2022

Sentiment of analysis is a study area applied to numerous environments (financial, political, academic, business, and communication) whose purpose is to search for messages posted on social media, and through these to identify and classify people's opinions about particular item as positive or negative. Rating the sentiment expressed in opinionated messages is such an important task that currently companies invest a lot of money in collecting this type of information and the development of methods and techniques to classify the sentiment that they express, so that they can use the results as useful information in preparing marketing and sales strategies efficiently. However, one of the major problems facing the feelings of analysis is the difficulty of methods to properly analyze messages with sarcastic and/or ironic content, as these linguistic phenomena have the characteristic of transforming the polarity or meaning of a positive or negative statement into its opposite.
Coronavirus Pandemic (COVID-19)
Authors: Alowibdi Jalal S. , Alshdadi Abdulrahman A. , Daud Ali , Dessouky Mohamed M. , Alhazmi Essa Ali
Published: July 2022

People are afraid about COVID-19 and are actively talking about it on social media platforms such as Twitter. People are showing their emotions openly in their tweets on Twitter. It's very important to perform sentiment analysis on these tweets for finding COVID-19's impact on people's lives. Natural language processing, textual processing, computational linguists, and biometrics are applied to perform sentiment analysis to identify and extract the emotions. In this work, sentiment analysis is carried out on a large Twitter dataset of English tweets. Ten emotional themes are investigated. Experimental results show that COVID-19 has spread fear/anxiety, gratitude, happiness and hope, and other mixed emotions among people for different reasons. Specifically, it is observed that positive news from top officials like Trump of chloroquine as cure to COVID-19 has suddenly lowered fear in sentiment, and happiness, gratitude, and hope started to rise. But, once FDA said, chloroquine is not effective cure, fear again started to rise.
Sentiment Analysis of Twitter Data
Authors: Srivastava Ankit , Singh Vijendra , Drall Gurdeep Singh
Published: July 2022

Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an extension of previous work. Briefly, relevant data sets are collected from Twitter using Twitter API; then, use of the hybrid methodology is illustrated and evaluated against one with only Naïve Bayes classifier. Results show better accuracy and efficiency in the sentiment classification for the hybrid approach.
Formation and Control of Identity
Authors: Sun Christine Yunn-Yu , Goschnick Steve
Published: July 2022

This chapter explores the construction of identity in online communities and websites for social purposes, and its consequences in terms of how one's online identity may be utilized to such an extent that one's real-world identity is either enforced or eroded. It does so by investigating the very nature of identity, coming predominantly from a cultural studies research and philosophical view, although it also cites some related findings and advances in computing and information systems (IS) research. The central argument across the chapter is two-fold: firstly, in promoting an initial shift in focus from the management of online identity to the nature and significance of identity itself whose construction may be conceptualized as a process of sense making and strengthening; and only then, armed with a better understanding of identity, one can focus back upon the management of it more effectively, with a view to the individual taking more control of their own identity within cyberspace, which is increasingly transitioning us all into a functioning global community, in both predictable and unforeseen ways.
The Important Role of the Blogosphere as a Communication Tool in Social Media Among Polish Young Millennials
Authors: Kuczamer-Kłopotowska Sylwia , Kalinowska-Żeleźnik Anna
Published: July 2022

This chapter proposes and discusses the hypothesis that the blogosphere is a relatively well-developed and independent social media communication tool used by millennials. The first part of the study concentrates on the theoretical aspects of social media communication as presented in the literature, and the way blogs and the whole blogosphere function. The communicational and social profile of Generation Y is presented as it is this cohort that constitutes the major portion of the Polish blogging community. The prevailing trends in the Polish blogosphere are discussed, following a desk research into reports and professional studies. Moreover, some comments and findings are presented regarding an experiment conducted by the authors in which some representatives of younger millennials ran personal, non-profit blogs on a subject of their choice.
Adolescents, Third-Person Perception, and Facebook
Authors: Chapin John
Published: July 2022

The purpose of this chapter is to document the extent of Facebook use and cyberbullying among adolescents. It is based on a study theoretically grounded in third-person perception (TPP), the belief that media messages affect other people more than oneself. As Facebook establishes itself as the dominant social network, users expose themselves to a level of bullying not possible in the analog world. The study found that 84% of adolescents (middle school through college undergraduates) use Facebook, and that most users log on daily. While 30% of the sample reported being cyberbullied, only 12.5% quit using the site and only 18% told a parent or school official. Despite heavy use and exposure, adolescents exhibit TPP, believing others are more likely to be negatively affected by Facebook use. A range of self-protective behaviors from precautionary (deleting or blocking abusive users) to reactionary (quitting Facebook) were related to decreased degrees of TPP. Implications for prevention education are discussed.
Supporting Participation in Online Social Networks
Authors: Poggi Agostino , Fornacciari Paolo , Lombardo Gianfranco , Mordonini Monica , Tomaiuolo Michele
Published: July 2022

Social networking systems can be considered one of the most important social phenomena because they succeeded in involving billions of people all around the world and in attracting users from several social groups, regardless of age, gender, education, or nationality. Social networking systems blur the distinction between the private and working spheres, and users are known to use such systems both at home and at the work place both professionally and with recreational goals. Social networking systems can be equally used to organize a work meeting, a dinner with the colleagues, or a birthday party with friends. In the vast majority of cases, social networking platforms are still used without corporate blessing. However, several traditional information systems, such as CRMs and ERPs, have also been modified in order to include social aspects. This chapter discusses the participation in online social networking activities and, in particular, the technologies that support and promote the participation in online social network.
Not a Girl, Not Yet a Woman
Authors: Blanton Raymond , Carbajal Darlene
Published: July 2022

This chapter takes an interdisciplinary approach to the study of deception from the critical perspectives of rhetoric, communication, and media studies. The primary objective is to interrogate the interrelationship of communication, identity, and technology relevant to social media in order to confront issues related to online deception. To that end, this case study is centrally focused on social media sensation Miquela Sosa, also known as Lil Miquela, and the implications of artificial intelligence (AI) technologies and social media influencers to contribute to a more robust critical consciousness regarding misinformation online.
Online Self-Presentation Strategies Among Social Networking Sites' Users in the United Arab Emirates
Authors: Ahmed Azza Abdel-Azim M.
Published: July 2022

This study explored the strategies of self-presentation (ingratiation, supplication, and enhancement) among United Arab Emirates users (n=230) of popular social networking sites (SNS). The size of social networks, degree of network connectivity, and perceptions of self-presentation success were examined. The results indicated a significant positive correlation between the frequency of SNS use and ingratiation and enhancement strategies. Greater diversity of online friends among the respondents was positively associated with the perception of online self-presentation success. Males and females differed in the size of the online social network they interacted with, diversity of online friends, and preferred self-presentation strategies. However, no significant gender differences were found in the levels of network connectivity and perceptions of self-presentation success.
Social Network Sites (SNS) and Their Irrepressible Popularity
Authors: Koç Tuğba , Akbıyık Adem
Published: July 2022

Popularity of social media is increasing day by day and there are thousands of social media platforms on the internet with different features. This chapter discusses the term social media in general and examines its evolution in detail from the beginning of the first e-mail to today. Authors explore the terms pertaining to the domain of Social Network Sites (SNS) which are considered as one of the most used forms of social media. Authors present a discussion about a popular topic “SNS addiction” and examine its characteristics with a brief literature review. Accordingly, despite the fact that excessive use of social network sites cannot be formally accepted as a behavioral addiction; shy and young, extroverted, and neurotic women with no relationship are more likely to develop addictive behaviors towards social media.
Online Social Networking and Romantic Relationships
Authors: Metin-Orta Irem
Published: July 2022

With the increased popularity of social media, social networking sites (SNSs) have received the attention of many scholars. In particular, researchers have focused on the impact of SNSs on interpersonal relationships. Accordingly, this chapter provides an overview of the extant literature concerning associations between the use of SNSs and romantic relationships. It provides empirical evidence on how social networking behaviors are influenced by adult attachment styles, and how social networking influences relationship constructs such as satisfaction, commitment, jealousy, and relationship dissolution. Furthermore, it presents previous research that emphasizes gender as a moderator in these relations. This chapter overall contributes to researchers and professionals in providing information on online social networking and emphasizing key romantic relationship constructs related to the use of SNSs. It also provides suggestions for future research.
My Little Joy in Life
Authors: Leung Wan Chi , Wan Anan
Published: July 2022

To post food on social media has become a frequent source of fun and joy in life for many mobile users. In investigating such a common scene on Instagram among its young users, the authors of this chapter investigated the relationship between social activity, personal traits like narcissism and shyness, and uses and gratifications from posting food photos on Instagram. Uses of Instagram for posting selfies were also examined for comparison. Results showed that while posting food photos were associated with social activity, posting selfies were associated with shyness. Narcissists were more likely to involve in posting both food photos and selfies. Implications of the results in explaining the generation of visual contents on social media are discussed.
Corporate Social Responsibility
Authors: Marco-Lajara Bartolomé , Zaragoza-Sáez Patrocinio , Martínez Falcó Javier , Millan-Tudela Luis A.
Published: July 2022

Corporate social responsibility (CSR) represents the voluntary commitment of companies to behave appropriately, fairly, and responsibly with the environment in which they operate. It refers to a business approach that takes into account economic, environmental, and social issues in a balanced, holistic, and long-term manner for the benefit of current and future stakeholders. Thus, since the introduction of the concept, more managers are including social and environmental objectives in their decision-making process. The aim of CSR is therefore to seek the well-being of society and the environment through business activities. However, when did the term come into being, how has the term evolved since its foundation, and how has the concept of CSR evolved over time? Through a narrative review of the literature, the research addresses these research questions to bring clarity to the field of study.
Work/Family Interventions and COVID-19 Pandemic Remote Work Arrangements
Authors: Eversole Barbara A. W. , Crowder Cindy L.
Published: July 2022

This chapter discusses work/family interconnections, theory, and the interventions that organizations have put into place to help employees manage their work/non-work boundary, such as flexible work options. The advent of the COVID-19 pandemic has led to the utilization of a specific type of flexible work option, working from home, or remote working. This chapter discusses the benefits of remote working for employees and organizations, the challenges of remote working, and the ways to make remote working be successful in organizations. Finally, the chapter looks at remote working globally and what the future of remote working may look like.
Cross-Functionality in Practice
Authors: Jeske Debora , Calvard Thomas Stephen
Published: July 2022

Cross-functional integration is a topic of discussion in many different disciplines. Electronic performance monitoring (EPM) is the practice of managing employee performance digitally. It is also a key example of cross-functionality. The authors reflected on organizational practice and existing literature to demonstrate the interconnections between different functions supporting EPM. A range of professions and functions play important cross-functional roles in the design, use, and implementation of EPM systems in organizations, such as IT and security, legal and data protection, and health and safety, among others. Barriers to cross-functional integration involving HR and EPM are identified, and recommendations are offered on how to overcome such barriers in practice. The practice and design of EPM serves as a powerful example for raising more explicit awareness and understanding of the opportunities and challenges affecting how HR integrates and works with a variety of other interdependent functional and professional groups and roles.
A Hybrid Approach for Task Scheduling in the Cloud Environment
Authors: Tuli Krishan , Malhotra Manisha
Published: July 2022

In today’s world, Cloud Computing has been considered as the best concept for the virtualization of various resources. There are various approaches that have been available for improvising the load balancing and also to improvise the job scheduling in the concept of cloud. Cloud Computing has offers the on-demand allocation of resources to users and this feature of cloud makes it a best among various technologies. Cloud computing provides great performance in less maintenance cost. So, Task scheduling has become a very important factor in enhancing the performance of resources dynamically and at low cost, which is the most crucial part of cloud computing. In other words, we can say that allocation of resources and task scheduling are the two major factors that are considered for the better performance and they must be organized precisely. The author has focused on enhancing the task scheduling process by creating a hybrid optimization algorithm and named as Cuckoo Harmony Search Algorithm (CHSA) to remove the task scheduling problem.
Technostress Among Higher Education Students During the COVID-19 Outbreak
Authors: Lopes da Silva Osvaldo Dias , Toledo de Sousa Áurea Sandra , Moniz Ana Isabel Damião de Serpa Arruda
Published: July 2022

The experiences that higher education students have with technology and learning with the support of technological resources can generate feelings of stress and anxiety. Understanding whether or not students are ill-adapted to technology is of utmost importance to understand the extent to which changes are needed in the teaching and learning process. With this purpose, the students’ perceptions about the technology, namely its familiarity, the ease of use, the utility of technological resources, levels of satisfaction with learning from remote learning and levels of technostress during the confinement period due to the Covid-19 pandemic were evaluated. Several statistical methods were applied, among which, the Multiple Correspondence Analysis and the k-means clustering algorithm, in order to obtain a partition of students based on their perceptions and experiences in the course of remote learning. The results revealed three distinct profiles, concerning students’ perceptions about their relationship with technology.
Stemoh Leadership
Authors: Ramjit Dana-Marie
Published: July 2022

World leaders are facing the greatest crises of all time. 2019 was reported as the year of scandals for global business leaders. 2020 and 2021 featured stories of significant unethical conduct and global mismanagement of the pandemic among political leaders. Current leadership strategies are proving ineffective and outdated. While many prevalent leadership styles have positive outcomes, they are incapable of leading organizations in a new, complex, and progressively dynamic environment. Effective leadership in contemporary society must consider a holistic approach that pushes beyond mere temporal values to call leaders to a higher standard of living and performance. This paper presents an overview of the literature on current leadership patterns and introduces a new approach influenced by the Christian virtues of service and humility. Stemoh leadership offers a unique approach that drives leaders to greatness through integrity, insight, agility, flexibility, and resilience.
Local Optimal-Oriented Pattern and Exponential Weighed-Jaya Optimization-Based Deep Convolutional Networks for Video Summarization
Authors: L. Jimson.  , J. P. Ananth
Published: July 2022

Video summarization is used to generate a short summary video for providing the users a very useful visual and synthetic abstract of the video content. There are various methods are developed for video summarization in existing, still an effective method is required due to some drawbacks, like cost and time. The ultimate goal of the research is to concentrate on an effective video summarization methodology that represents the development of short summary from the entire video stream in an effective manner. At first, the input cricket video consisting of number of frames is given to the keyframe generation phase, which is performed based on Discrete Cosine Transform (DCT) and Euclidean distance for obtaining the keyframes. Then, the residual keyframe generation is carried out based on Deep Convolutional Neural Network (DCNN), which is trained optimally using the proposed Exponential weighed moving average-Jaya (EWMA-Jaya) optimization.
Index Key Compression and On-the-Fly Reconstruction of In-Memory Indexes
Authors: Kwon Yongsik , Ryu Cheol , Cha Sang Kyun , Lee Arthur H. , Park Kunsoo , Moon Bongki
Published: July 2022

This article proposes an index key compression scheme based on the notion of distinction bits. It proves that the distinction bits of index keys are sufficient information to determine the sorted order of the index keys. The actual compression ratio may vary depending on the characteristics of datasets (an average of 2.76:1 compression ratio was observed in the authors’ experiments). However, the index key compression scheme leads to significant performance improvements during the reconstruction of large-scale indexes. This study’s index key compression can be effectively used for database replication and index recovery in modern main-memory database systems.
Relevance of Symmetry Assumptions in the 3D Numerical Modeling of a Flowformability Test
Authors: Kauta Nagasai Meghana Rani , Bouchard Pierre-Olivier , Mocellin Katia
Published: July 2022

A novel flowformability test is under development at CEMEF in order to be able to predict damage at large strains under complex loading conditions corresponding to tube flowforming. The design of this test relies on 3D numerical simulations performed with the finite element software Forge® 3.1 Nxt. Such simulations, using classical implicit updated Lagrangian formulations, are costly because of the very small-time step and the fine mesh required to describe accurately the local and evolving contact of the tube with the roller (s). The aim of this paper is to study the relevance and the influence of the use of symmetry conditions in the modeling accuracy of this flowformability test.
In Process Measurement Techniques Based on Available Sensors in the Stamping Machines for the Automotive Industry
Authors: Peinado-Asensi Ivan , Montes Nicolas , García E.
Published: July 2022

We are currently going through an industrial period in which connectivity, data collection of the process and its understanding to optimize it is becoming more and more common. The automotive industry is no exception as we are on the way towards connected factories where the digitization of the stamping process is a trend followed by manufacturers. A common problem often encountered is the high cost required to develop solutions by using this technology. Obtaining parameters of the manufacturing process is a challenge on many occasions. New solutions have been proposed from an opposite point of view, i.e., we evaluate what information can be extracted from the equipment and from the data obtained we can bring forward the possible tools to be developed without the need for extra investment. This article shows the verification of an experimental process, previously developed, with which we intend to find out the status of the press during the drawing process for each cycle that is carried out during production and also the status of the equipment at all times, up to the point of detecting if there is any problem both in the die and in the mechanical components of the press and verifying it with the developed tool, showing that we can know the status of the equipment by monitoring the data in real time.
Analysis of the Possibility of Forming Stiffening Ribs in Litecor Metal-Plastic Composite Using the Single Point Incremental Forming Method
Authors: Kubit Andrzej , Korzeniowski Marcin , Bobusia Michał , Ochałek Krzysztof , Slota Jan
Published: July 2022

Efforts to reduce COx are extremely important, which forces the use of materials and technologies that reduce the weight of means of transport in order to reduce energy consumption. Currently, aluminum alloys and FRP composites are still too expensive for mass industry applications. Presented in this study, Litecor is a three-layer composite that combines the high strength of steel with the low density of plastic. Thanks to the use of external steel covers 0.3 mm thick and a light core 0.7 mm thick, high stiffness was achieved while maintaining a relatively low weight. The weight reduction in comparison with steel blanks with the same stiffness is up to 40%. Litecor is mainly developed by ThyssenKrupp, it is a promising construction material, but it requires development the technology of forming and joining. In this study, the possibility of forming the Litecor layered composite was investigated using the single-point incremental sheet forming (SPIF) method. As part of the research, the stiffening ribs were shaped, the maximum depth of the embossing was determined. The degree of thinning in selected cross-sections of the embossing was determined. The influence of the rotational speed of the tool and the feed rate on the properties of the shaped surface was also analyzed. Incorrectly selected shaping parameters have been shown to damage the zinc coating on the inner surface of the embossing.
Gut Microbiota-Derived Unconventional T Cell Ligands: Contribution to Host Immune Modulation
Authors: Oh Sungwhan F , Jung Da-Jung , Choi Eungyo
Published: July 2022

Besides the prototypic innate and adaptive pathways, immune responses by innate-like lymphocytes have gained significant attention due to their unique roles. Among innate-like lymphocytes, unconventional T cells such as NKT cells and mucosal-associated invariant T (MAIT) cells recognize small nonpeptide molecules of specific chemical classes. Endogenous or microbial ligands are loaded to MHC class I–like molecule CD1d or MR1, and inducing immediate effector T cell and ligand structure is one of the key determinants of NKT/MAIT cell functions. Unconventional T cells are in close, constant contact with symbiotic microbes at the mucosal layer, and CD1d/MR1 can accommodate diverse metabolites produced by gut microbiota. There is a strong interest to identify novel immunoactive molecules of endobiotic (symbiont-produced) origin as new NKT/MAIT cell ligands, as well as new cognate Ags for previously uncharacterized unconventional T cell subsets. Further studies will open an possibility to explore basic biology as well as therapeutic potential.
Larval fish sensitivity to a simulated cold-water pulse varies between species and age
Authors: Raymond Scott , Ryall Jordi , Koehn John , Fanson Ben , Hill Sarah , Stoessel Daniel , Tonkin Zeb , Sharley Joanne , Todd Charles , Campbell Ashlen , Lyon Jarod , Turner Mark , Ingram Brett
Published: July 2022

The release of cold-water from hypolimnetic zones of impoundments sharply reduces downstream riverine water temperature. This cold-water pollution (CWP) can extend for hundreds of kilometres, severely challenging the physiological ability of aquatic fauna, particularly ectotherms such as fish, to maintain essential processes such as metabolism, development and growth and survival. The impact of CWP on native fish, especially early life stages, is poorly known. We investigated the effect of a 24-hour exposure to a range of environmentally-related water temperatures (8, 10, 12, 14, 16, 18 and 20°C) on three age-classes (<24-hour-old, 7-day and 14-day-old larvae) of two Australian native fish species: Murray cod (Maccullochella peelii) and Macquarie perch (Macquaria australasica). Overall, larvae of M. peelii were more sensitive to lower water temperatures and hence CWP than M. australasica, indicated by higher rates of equilibrium loss. Larvae of M. peelii were most sensitive to exposure at seven days old whereas M. australasica larvae were most sensitive at <24-h-old. Using our results, we modelled pre- and post-impoundment temperature scenarios and estimated the downstream CWP footprint for both species in an Australian river reach. Larvae of M. peelii were predicted to be absent from the first 26 km of river downstream of the impoundment compared with no impact on the distribution of M. australasica. Managing riverine water temperature below impoundments is fundamental to promoting positive outcomes for endemic fish on not only a local, but global basis. This study emphasises the differential impact of CWP among the critical early life stages and fish species and highlights the urgent need to better manage hypolimnetic water releases to improve downstream river ecosystems.