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The authorship authentication (AA) problem is concerned with correctly attributing a text document to its corresponding author. Historically, this problem has been the focus of various studies focusing on the intuitive idea that each author has a unique style that can be captured using stylometric features (SF). Another approach to this problem, known as the bag-of-words (BOW) approach, uses keywords occurrences/frequencies in each document to identify its author. Unlike the first one, this approach is more language-independent. This paper aims to study and compare both approaches focusing on the Arabic language which is still largely understudied despite its importance.

Being a supervised learning problem, the authors start by collecting a very large data set of Arabic documents to be used for training and testing purposes. For the SF approach, they compute hundreds of SF, whereas, for the BOW approach, the popular term frequency-inverse document frequency technique is used. Both approaches are compared under various settings.

The results show that the SF approach, which is much cheaper to train, can generate more accurate results under most settings.

Numerous advantages of efficiently solving the AA problem are obtained in different fields of academia as well as the industry including literature, security, forensics, electronic markets and trading, etc. Another practical implication of this work is the public release of its sources. Specifically, some of the SF can be very useful for other problems such as sentiment analysis.

This is the first study of its kind to compare the SF and BOW approaches for authorship analysis of Arabic articles. Moreover, many of the computed SF are novel, while other features are inspired by the literature. As SF are language-dependent and most existing papers focus on English, extra effort must be invested to adapt such features to Arabic text.

Presently the whole world has been experiencing a pandemic threat of coronavirus diesease 2019 (COVID-19) and at the same time facing unprecedented changes in everything including education. E-learning has evolved as the only alternative of knowledge transmission even in third world nations, and e-assessment has been playing an increasingly important role in this digital transformation of education. But how far and of what depth it has made its place among students' minds need to be studied to leverage its full potential to transform students' learning needs. This study reports an investigation made in this direction.

An online survey consisting of 40 questions in Google Forms was conducted to collect primary data on students' perception of e-assessment among 200 Indian students pursuing higher education from several geographical locations. The quantitative methodological approach was followed. The data were analyzed descriptively and inferentially.

The results were analyzed based on the model of acceptance and usage of e-assessment (MAUE), and findings revealed that students' overall perception toward e-assessment was of moderate level and this perception varies depending on their gender, academic level, nature of the stream of study and their economic condition. Of the eight domains investigated, students showed better perception in the perceived usefulness, perceived ease of use, compatibility, subjective norms and self-efficacy domains, while they cut a sorry figure in domains like awareness, resource facilitation and information technology (IT) support. It became evident from their responses that COVID was instrumental in enhancing their interest in e-assessment.

The implication of this study lies in strengthening e-assessment by attending to the factors as noted in the MAUE in India and alike developing nations having huge space left for e-learning to reach a boom.

This is an empirical investigation conducted in India on the state of students' perception of the e-assessment in the backdrop of the COVID-19 outbreak. To do this work, the authors conducted online surveys, and the write-up of the findings focus on the survey data only.

To improve the academic integrity of online examinations, digital proctoring systems have recently been implemented in higher education institutions (HEIs). The paper aims to understand how digital proctoring has been practised in higher education (HE) and proposes future research directions for studying digital proctoring in HE.

A systematic literature review was conducted. The PRISMA procedure was adapted for the literature search. The topics were identified by topic modelling techniques from 154 relevant publications in seven databases.

Seven widely discussed topics in literature were identified, including solutions for detecting cheating and student authentication, challenges/issues of uptakes and students' performance in different proctoring environments.

This paper provides insights for academics, policymakers, practitioners and students to understand the implementation of digital proctoring in academia, its adoption by HEIs, impacts on students' and educators' performance and the rapid increase in its use for digital exams in HEIs, with particular emphasis on the impacts of the systems on digitalising examinations in HE.

This review paper has systematically and critically described the state-of-the-art literature on digital proctoring in HE and provides useful insights and implications for future research on digital proctoring, and how academic integrity in online examinations can be enhanced, along with digitalising HE.

The purpose of this paper is to demonstrate how changes in K-12 educational delivery methods in the USA impacts students as 1:1 device programs become a required tool for learning. This change produces gaps in knowledge and understanding of the digital environment and exposes minors to risk. Mandatory technology integration by school districts places the ethical responsibility on school districts to prepare students to use the digital environment to mitigate risk.

The author’s literature review focused on the impact of personal device integration in education on students. The author surveyed teachers in the district on what they perceived as risk to students accessing the digital environment and what they believe creates value in digital citizenship instructional content. The author also gathered information while serving on the school district technology steering committee and digital citizenship working group.

Mandatory 1:1 device programs used for learning provide unlimited access to the digital environment. This technology integration creates digital knowledge gaps in understanding among students and exposes them to risk or dangers such as loss of privacy, psychological harms and engaging in or being a victim of illegal online activities. School districts are responsible for providing a remedy to close this gap and mitigate risk by developing learning content resources for teachers.

As 1:1 device programs continue to grow in school districts in the USA, it is essential for students to learn to apply protocols and understand norms of the digital world. Providing a digital citizenship curriculum in a format such as a Google Site will offer educators access to instructional content that teaches students to apply protocols, understand norms of the internet and social media and foster critical thinking to analyze power structures, biases and recognize manipulation online. Student must learn how to apply rules that challenge assumptions behind the digital content they see, and they must be able to identify and resolve digital practices and behaviors that are problematic, so they are prepared to participate in a digital society.

This perspective may be relevant to school districts contemplating personal device integration, providing insight into how 1:1 device use impacts students and develops an ethical position for creating digital citizenship resources for teachers.

In the context of information retrieval, text genre is as important as its content, and knowledge of the text genre enhances the search engine features by providing customized retrieval. The purpose of this study is to explore and evaluate the use of stylometric analysis, a quantitative analysis for the linguistics features of text, to support the task of automated text genre detection for Classical Arabic text.

Unsupervised clustering and supervised classification were applied on the King Saud University Corpus of Classical Arabic texts (KSUCCA) using the most frequent words in the corpus (MFWs) as stylometric features. Four popular distance measures established in stylometric research are evaluated for the genre detection task.

The results of the experiments show that stylometry-based genre clustering and classification align well with human-defined genre. The evidence suggests that genre style signals exist for Classical Arabic and can be used to support the task of automated genre detection.

This work targets the task of genre detection in Classical Arabic text using stylometric features, an approach that has only been previously applied to Arabic authorship attribution. The study also provides a comparison of four distance measures used in stylomtreic analysis on the KSUCCA, a corpus with over 50 million words of Classical Arabic using clustering and classification.

With the outbreak of the COVID-19 pandemic, online assessment has become the dominant mode of examination in higher education institutions. However, there are contradictory findings on how students perceive online assessment and its impact on their academic performance. Thus, the purpose of this study is to evaluate the potential impact of online assessment on students' academic performance.

This study proposes a research model based on the task–technology fit theory and empirically validates the model using a survey from students in the UK. In addition, the study conducted four experiments based on paper-based and online assessments and analysed the data using paired sample t test and structural equation modelling.

The findings show that the use of online assessment has a positive impact on students' academic performance. Similarly, the results from the experiment also indicate that students perform better using online assessments than paper-based assessments.

The findings provide crucial evidence needed to shape policy towards institutionalising online assessment. In addition, the findings provide assurance to students, academics, administrators and policymakers that carefully designed online assessments can improve students' academic performance. Moreover, the study also provides important insights for curriculum redesign towards transitioning to online assessment in higher education institutions.

This study advances research by offering a more nuanced understanding of online assessment on students' academic performance since the majority of previous studies have offered contradictory findings. In addition, the study moves beyond existing research by complementing assessment results with the views of students in evaluating the impact of online assessment on their academic performance. Second, the study develops and validates a research model that explains how the fits between technology and assessment tasks influence students' academic performance. Lastly, the study provides evidence to support the wide use of online assessment in higher education.

Multi-label Text Classification (MTC) is one of the most recent research trends in data mining and information retrieval domains because of many reasons such as the rapid growth of online data and the increasing tendency of internet users to be more comfortable with assigning multiple labels/tags to describe documents, emails, posts, etc. The dimensionality of labels makes MTC more difficult and challenging compared with traditional single-labeled text classification (TC). Because it is a natural extension of TC, several ways are proposed to benefit from the rich literature of TC through what is called problem transformation (PT) methods. Basically, PT methods transform the multi-label data into a single-label one that is suitable for traditional single-label classification algorithms. Another approach is to design novel classification algorithms customized for MTC. Over the past decade, several works have appeared on both approaches focusing mainly on the English language. This work aims to present an elaborate study of MTC of Arabic articles.

This paper presents a novel lexicon-based method for MTC, where the keywords that are most associated with each label are extracted from the training data along with a threshold that can later be used to determine whether each test document belongs to a certain label.

The experiments show that the presented approach outperforms the currently available approaches. Specifically, the results of our experiments show that the best accuracy obtained from existing approaches is only 18 per cent, whereas the accuracy of the presented lexicon-based approach can reach an accuracy level of 31 per cent.

Although there exist some tools that can be customized to address the MTC problem for Arabic text, their accuracies are very low when applied to Arabic articles. This paper presents a novel method for MTC. The experiments show that the presented approach outperforms the currently available approaches.

To analyze the market reception of multi-authored works of art through the lens of collaborative old master paintings (“formal/prestige collaboration”). This paper tests whether multi-authored attribution strategies (i.e. naming two artists as brand names) affect buyers' willingness to pay differently from single-authored works in the auction market.

This case study focuses on collaborative paintings by Flemish masters, based on a data set comprising 11,630 single-authored and collaborative paintings auctioned between 1946 and 2015. Hedonic regressions have been employed to test whether or not co-branded artworks are differently valued by buyers and how the reputation of each artist might influence valuation.

Despite the opportunity for buyers to purchase one artwork with two brand names, this study reveals that the average value of collaborative paintings is statistically lower than that of single-authored paintings. This is especially true when a reputed master was involved in the collaboration. The present findings suggest that the valuable characteristics of formal collaborations (i.e. double brand name, dual authorship and reputation, high-quality standards) are no longer perceived and valued as such by buyers, and that co-branding can affect the artist brand equity because of a contagion effect. We argue that integral authorship is more valued than partial authorship, suggesting that the myth of the artist as a lone genius is still well-anchored in purchasing habits.

Prestige collaborations are a very particular form of early co-branding in the art world, with limited data available. Further research should consider larger samples to reiterate the analysis on other collaboration forms in order to challenge the current findings.

Researchers and living artists should be aware that brand building and co-branding are marketing strategies that may generate negative effects on prices in the art market. The perceived and market value of co-branded works are time-varying, and depends on both the context of reception of these works and the reputation of the artists at time t.

This market segment has never been considered in art market studies, although formal collaboration is one of the earliest documented forms of co-branding in the art world. This paper provides new empirical evidence from the auction market, based on buyers' willingness to pay, and it further highlights the reception of multi-authored art objects in Western art markets that particularly value individual creators.

Ever since Mark Weiser coined the term “ubiquitous computing” (ubicomp) in 1988, there has been a general interest in proposing various solutions that would support his vision. However, attacks targeting devices and services of a ubicomp environment have demonstrated not only different privacy issues, but also a risk of endangering user’s life (e.g. by modifying medical sensor readings). Thus, the aim of this paper is to provide a comprehensive overview of security challenges of ubicomp environments and the corresponding countermeasures proposed over the past decade.

The results of this paper are based on a literature review method originally used in evidence-based medicine called systematic literature review (SLR), which identifies, filters, classifies and summarizes the findings.

Starting from the bibliometric results that clearly show an increasing interest in the topic of ubicomp security worldwide, the findings reveal specific types of attacks and vulnerabilities that have motivated the research over the past decade. This review describes most commonly proposed countermeasures – context-aware access control and authentication mechanisms, cryptographic protocols that account for device’s resource constraints, privacy-preserving mechanisms, and trust mechanisms for wireless ad hoc and sensor networks.

To the best of our knowledge, this is the first SLR on security challenges in ubicomp. The findings should serve as a reference to an extensive list of scientific contributions, as well as a guiding point for the researchers’ novel to the security research in ubicomp.

The purpose of the study is to conduct a scientometric analysis of cybersecurity literature indexed in the core collection of the Web of Science for a period of ten years (2011–2020).

Cybersecurity is a focused topic of research across the globe. To identify the global research productivity in the field, the terms “cybersecurity, cyber-security, web security, information security, computer security, etc.” were used for retrieving the publications in the advanced search mode of the database “Web of Science”, limiting the time frame for 2011– 2020. The results retrieved were downloaded in the Excel file for further analysis and interpretation. The harvested data was analysed by using scientometric techniques to measure the progress such as growth rate, doubling time and author collaborations. Besides, the Biblioshiny and VOSviewer software were used for mapping networks.

The research output in the field of cybersecurity has shown an increasing trend during 2011–2020, and the maximum number of scholarly publications was published in 2020 (1,581), i.e. more than 715% of 2011 (221). A good number of countries (93) have contributed globally in cybersecurity research, and the highest share in research publications was reported by the USA (23.55%), followed by China (23.24%), South Korea (5.31%), UK (5.28%) and India (4.25%). The authorship patterns in cybersecurity publications show a collaborative trend, as most articles have been published by multiple authors. Total 5,532 (90.14%) articles have been published in co-authorship, whereas only 605 (9.86%) articles have been published by single authors. Keyword analysis shows that the most common keyword research by the authors is cybersecurity and its variants such as “cyber security” and “cyber-security” (1,698) followed by security (782), computer security (680) and information security (329).

The database studied for the work does not represent the total literary output available on the theme. There are plenty of other databases, such as Scopus, Compendex, INSPEC, IEEE Xplore, arXiv, contributing to the same theme as well.

The findings of the study may help researchers, information technologists, library professionals and information specialists to identify the research progress, authorship patterns, collaborative networks and hot topics of research in the field of cybersecurity. Besides, it will assess the global response to the cybersecurity issue.

The study is the scientometric analysis of the cybersecurity based on current literature and will highlight the progress and development of global research in the said field.