Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity

Purpose Changes in the world show that the role, importance, and coherence of SSH (social sciences and the humanities) will increase significantly in the coming years. This paper aims to monitor and analyze the evolution (or overlapping) of the SSH thematic pattern through three funding instruments since 2007. Design/methodology/approach The goal of the paper is to check to what extent the EU Framework Program (FP) affects/does not affect research on national level, and to highlight hot topics from a given period with the help of text analysis. Funded project titles and abstracts derived from the EU FP, Slovenian, and Estonian RIS were used. The final analysis and comparisons between different datasets were made based on the 200 most frequent words. After removing punctuation marks, numeric values, articles, prepositions, conjunctions, and auxiliary verbs, 4,854 unique words in ETIS, 4,421 unique words in the Slovenian Research Information System (SICRIS), and 3,950 unique words in FP were identified. Findings Across all funding instruments, about a quarter of the top words constitute half of the word occurrences. The text analysis results show that in the majority of cases words do not overlap between FP and nationally funded projects. In some cases, it may be due to using different vocabulary. There is more overlapping between words in the case of Slovenia (SL) and Estonia (EE) and less in the case of Estonia and EU Framework Programmes (FP). At the same time, overlapping words indicate a wider reach (culture, education, social, history, human, innovation, etc.). In nationally funded projects (bottom-up), it was relatively difficult to observe the change in thematic trends over time. More specific results emerged from the comparison of the different programs throughout FP (top-down). Research limitations Only projects with English titles and abstracts were analyzed. Practical implications The specifics of SSH have to take into account—the one-to-one meaning of terms/words is not as important as, for example, in the exact sciences. Thus, even in co-word analysis, the final content may go unnoticed. Originality/value This was the first attempt to monitor the trends of SSH projects using text analysis. The text analysis of the SSH projects of the two new EU Member States used in the study showed that SSH's thematic coverage is not much affected by the EU Framework Program. Whether this result is field-specific or country-specific should be shown in the following study, which targets SSH projects in the so-called old Member States.

Computer networks and computer systems are experiencing attacks and threats from many areas. Threats are also extended to include the individual user's computer assets and resources. Information will be presented on the categories of security and privacy threats, integrity threats, vulnerabilities, delay and denial threats, and intellectual property threats that are being directed towards corporate, educational, governmental, and individual assets.

Sentiment analysis has emerged as a prominent research domain within the realm of natural language processing, garnering increasing attention and a growing body of literature. While numerous literature reviews have examined sentiment analysis techniques, methods, topics and applications, there remains a gap in the literature concerning thematic trends and research methodologies in sentiment analysis, particularly in the context of Chinese text. This study addresses this gap by presenting a comprehensive survey dedicated to the progression of research subjects, methods and trends in sentiment analysis of Chinese text. Employing a framework that combines keyword co-occurrence analysis with a sophisticated community detection algorithm, this survey offers a novel perspective on the landscape of Chinese sentiment analysis research. By tracing the interplay between research methodologies and emerging topics over the past two decades, our study not only facilitates a comparative analysis of their correlations but also illuminates evolving patterns, identifying significant hotspots and trends over time for Chinese language text analysis. This invaluable insight provides a roadmap for researchers seeking to navigate the intricate terrain of sentiment analysis within the context of Chinese language. Moreover, this paper extends beyond the academic realm, offering practical insights into sentiment analysis methodologies and themes while pinpointing avenues for future exploration, technical limitations, and directions for sentiment analysis of Chinese text.

Security and privacy in Internet of things: methods, architectures, and solutions

Objectives: To construct a comprehensive weighted multi-dimensional model to assess the impact of influence score of Twitter users, considering the credibility based on user profile, their tweets and social interactions aiming to empower users in distinguishing fake news or misinformation. Methods: The credibility evaluation is formulated based on text analysis, user account attributes, and user social engagement. We've gathered around 100,000 tweets from 100 users using Tweepy API over a six-month duration for the purpose of evaluating credibility. The collected tweets spanned diverse professions namely politics, entertainment, business, science, sports, and trending topics. We chose to utilize a self-devised deep active learning model to classify and label the unlabelled data instead of engaging in time-consuming human annotation for the tweets we gathered. Findings: The obtained accuracy for influence score evaluation for Recurrent Neural Network, Random Forest, Naïve Bayes, Decision Tree, and Support Vector Machine are 89.03%, 79.10%, 81.59%, 73.06% and 79.45% respectively. Upon reviewing and analysing the outcomes, RNN surpassed all other models achieving an exceptional accuracy of 89.03%. Novelty: Employing a weighted multi-dimensional framework, it systematically evaluates the influence score by considering the credibility of both users and tweets within the context of Twitter. Weighted features are instrumental in capturing the relative importance of different elements, leading to a more refined and context-aware decision-making process. In contrast to earlier research, which predominantly centred on the credibility of individual tweets, our research work shifts the focus to a broader perspective, encompassing the credibility of users, their tweets and their overall social influence. By incorporating user influence score, the framework not only empower users in discerning fake news or mis-information but also elevates their ability to gauge the reliability of information, offering a nuanced approach to news credibility analysis. Keywords: Active Learning, Credibility Score, User Influence, Twitter, Machine Learning, Recurrent Neural Network

The motivation for this contingency-type study is based on the notion that prior studies on management accounting systems (MAS) has almost entirely focused on large business organizations and neglected consideration of the effects of contextual variables and MAS on organizational effectiveness or performance of small business. Using a sample of small business (SB) managers/owners as subjects, this study therefore examined the moderating effects of perceived environmental uncertainty (PEU) and computer usage on die management accounting system (MAS)/performance relationship. Performance in this study was measured using the compound growth rate of market value of the firm (GMV) and growth rate of fixed assets (GFA). SB was defined as companies with between 10 and 100 employees. Results using multiple regression analyses and partial derivatives of the regression equations showed that PEU moderated the effects of MAS on performance with MAS having a positive effect on performance under high levels of PEU. In addition, the results also showed that levels of computer usage moderated the effects of MAS on performance with MAS also having a positive effect on performance under conditions where there was a high level of computer usage. Under low levels of PEU and computer usage MAS had a negative relationship with performance.

Background: Ergonomic training is the most widely used intervention in addressing the issue of musculoskeletal disorder (MSD). However, its effectiveness is uncertain. This systematic review provides a narrative synthesis of up-to-date randomised controlled trials (RCTs) of already conducted ergonomic training interventions at workplaces to reduce the risk of MSD among computer users with a view to provide evidence on its effectiveness and thus inform policy on improvement of work practises. Methods: Searches for RCTs were conducted on 15 databases, journals and grey literatures between March and September 2014 and retrieved studies were exported to Refworks. After application of the inclusion and exclusion criteria, out of 33 studies appraised, 13 studies ranging from average to high quality based on the methodology and results were included in the review. Included studies were critically screened and extracted data were narratively synthesized. Results: The synthesis of included studies showed statistically significant reduction in MSDs in the neck, shoulder, fingers, wrist, elbow/forearm, lower back, upper back, foot and trunk. One study recorded a reduction in symptoms such as cervicothoracic myalgia, numbness, weakness and nocturnal exacerbation, though not statistically significant. While reduced effects were widely reported across studies, it was not maintained at long-term follow-up in some studies. Findings suggest that the method of training delivery influenced the duration of effects across the studies. Conclusion: Findings suggest that ergonomic training interventions are effective in reducing the risk of MSDs. However, across the studies, the method of training delivery influenced the long-term and short-term effects. Computer users who experience MSDs may benefit on a long-term basis from ergonomic training interventions designed to visually engage, instigate participation and offer application assistance/supervision. However, this review identified areas for further research into the impact of ergonomic training interventions on absence from work and productivity.

A consensus across industry, academics, and policy-makers holds that a circular fashion system can reduce pollution and waste while producing continued economic growth. Gray literature, understood here as reports published outside of academic journals, has been foundational to describing and promoting circular economy and circular fashion. Yet, this literature is rarely subjected to critical scrutiny, allowing evidence, claims and methods to go unexamined. This review aims to understand how value and the market are conceptualized in a circular fashion system, and what are the implications of this. The study employs a problematizing review of 20 gray literature documents, including a key text of circular fashion: the Ellen MacArthur Foundation’s 2017 report A New Textiles Economy: Redesigning Fashion’s Future. Textual analysis is used to identify assumptions, inferences and problem framings in the texts. These are evaluated against academic understandings, demonstrating that core concepts of the circular fashion proposal are poorly defined and unconnected to existing theory and knowledge. Existing retail practices and consumer marketing messages are central to circular fashion, while the role of the market in setting prices is ignored. A $460 billion error is revealed, refuting the claim that a circular fashion system provides an opportunity for growth. We offer a critical perspective on the role of gray literature and gray publishers in shaping policy-making and, subsequently, academic research programs. We argue that new proposals for sustainable fashion are needed, based on established knowledge and validated models.

Grey literature encompasses documents not published in academic journals or books. Some grey literature has substantial societal importance, such as medical guidelines, government analyses and pressure group reports. Academic research cited in such documents may therefore have had indirect societal impact, such as in policy making, clinical practice or legislation. Identifying citations to academic research from grey literature may therefore help assess its societal impacts. This is difficult, however, due to the variety of document and referencing formats used in grey literature, even from a single organisation. In response, this study introduces and tests a semi-automatic method to match academic journal articles with unstandardised grey literature cited references. For this, the metadata (lead author last name, title, year) of 2.45 million UK Russell Group university outputs was matched against a 100-document sample of UK government grey literature to assess the accuracy of 21 matching heuristics. The optimal method (lead author last name and title in either order, maximum of 200 characters apart) is sufficiently accurate and scalable to make the task of matching research outputs to grey literature references feasible. The method was then applied to 3347 government publications, showing approximately 23% of UK government grey literature in this study contained at least one reference to UK Russell Group university output, and of this grey literature, an average of 3.79 references were present per document. The applied method also shows that economics and environmental science academic research is most cited between 2010 and 2018.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11192-022-04408-4.

Research interest in COVID-19 in the transportation field has increased sporadically since its outbreak in 2019. This has led to an unprecedented increase in the number of publications in academic journals, rendering it difficult to clearly capture and understand the themes being discussed in the entire literature. This study employs a Structural Topic Model, a robust probabilistic topic model that incorporates document-level metadata to extract hidden topics in unstructured textual big data that focuses on COVID-19 and transportation. To understand the topics identified, the study examined the topical trends over time and compared them to provide insights into authors’ perspectives based on their country’s economic status. In total, abstracts from 421 research articles published in top transportation/transportation science journals were collected and analysed. The results reveal that the major academic concerns in the area of COVID-19 and transportation are related to the changing travel behaviour, airport financial performance, and supply chain optimisation. Overall, research trends seem to be shifting towards shipping emissions, air transport recovery, travel behaviour, and the performance of airports. In addition, authors from both high-income and middle-and low-income countries were found to have different perspectives regarding the topics identified. The findings from this study contribute to understanding topical trends and perspectives in the literature on COVID-19 and transportation and can be used by researchers, policymakers, and fund providers to recognise current research issues to guide future research direction and for making more informed policy decisions.

Simulation-Based Mastery Learning With Virtual Coaching: Experience in Training Standardized Upper Endoscopy to Novice Endoscopists

Is there a connection between HDL and atrial fibrillation?

The Team Science Toolkit

342 A Brief Survey of Karachis Emergency Medical Services

The COVID-19 pandemic forced lockdowns in several countries, and many organisations had to introduce teleworking for their employees. While remote working is not a new thing, and was already permitted by laws, the extent to which enterprises had to redefine their process is unprecedented. Therefore, teleworking was widely discussed in national media. Newspapers are a relevant outlet for the diffusion and legitimation of schemata of interpretation, and we explored of teleworking was framed in the Italian discursive space during the first two months of the pandemic. We analysed seven national newspapers, and adopted a semi-automatic text analysis, which we performed through topic modelling. In this paper, we describe the topics that are used by newspapers to frame teleworking, the different use of these topics performed by different newspapers, the trend of topics over time, and we discuss the institutionalisation of the issue of teleworking.