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During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration. [ABSTRACT FROM AUTHOR]

Managing passenger information during disruption (PIDD) is a significant factor in running effective and quick-to-recover rail operations. Disruptions are unpredictable, and their timely resolution is ultimately dependent on the expert knowledge of experienced frontline staff. The development of frontline employees by their employers usually takes the form of practice reviews and 'on-the-job' learning, while academic education majors on theoretical approaches and classroom-based teaching. This paper reports on a novel industry-funded project that has developed a serious game (the 'Rail Disruption Game') that combines theory and practice to better manage PIDD for frontline staff in a UK train operating company (TOC). It defines challenges and the development method for the Rail Disruption Game; it also incorporates developer and user feedback. This paper provides insight into how to design, make and deploy a serious game as part of a gamified management process. [ABSTRACT FROM AUTHOR]

Right from the beginning, attendance has played an important role in the education systems, not only in student success but in the overall interest of the matter. Although all schools try to accentuate good attendance, still some schools find it hard to achieve the required level (96% in UK) of average attendance. The most productive way of increasing the pupils′ attendance rate is to predict when it is going to go down, understand the reasons—why it happened—and act on the affecting factors so as to prevent it. Artificial intelligence (AI) is an automated machine learning solution for different types of problems. Several machine learning (ML) models like logistic regression, decision trees, etc. are easy to understand; however, complicated (Neural Network, BART etc.) ML models are not transparent but are black-boxes for humans. It is not always evident how machine intelligence arrived at a decision. However, not always, but in critical applications it is important that humans can understand the reasons for such decisions. In this paper, we present a methodology on the application example of pupil attendance for constructing explanations for AI classification algorithms. The methodology includes building a model of the application in the Isabelle Insider and Infrastructure framework (IIIf) and an algorithm (PCR) that helps us to obtain a detailed logical rule to specify the performance of the black-box algorithm, hence allowing us to explain it. The explanation is provided within the logical model of the IIIf, thus is suitable for human audiences. It has been shown that the RR-cycle of IIIf can be adapted to provide a method for iteratively extracting an explanation by interleaving attack tree analysis with precondition refinement, which finally yields a general rule that describes the decision taken by a black-box algorithm produced by Artificial intelligence. [ABSTRACT FROM AUTHOR]

Toxic online content has become a major issue in recent years due to the exponential increase in the use of the internet. In France, there has been a significant increase in hate speech against migrant and Muslim communities following events such as Great Britain's exit from the EU, the Charlie Hebdo attacks, and the Bataclan attacks. Therefore, the automated detection of offensive language and racism is in high demand, and it is a serious challenge. Unfortunately, there are fewer datasets annotated for racist speech than for general hate speech available, especially for French. This paper attempts to breach this gap by (1) proposing and evaluating a new dataset intended for automated racist speech detection in French; (2) performing a case study with multiple supervised models and text representations for the task of racist language detection in French; and (3) performing cross-lingual experiments. [ABSTRACT FROM AUTHOR]

Citizen engagement and participation are a key focus for government and government agencies, and with the advent of Internet technologies questions arise about the role and impact of technology on citizen participation. This paper aims to explore the role of technology in citizen participation within schools. This research used in-depth comparative case studies using examples from two different schools and school systems, one in the United Kingdom and one in Indonesia. The wider school systems are complex and dynamic environments with multiple stakeholders, media, and supporting systems, and the schools operate under geopolitical and social influences. This paper provides a framework, based on Actor-Network Theory (ANT), for capturing e-participation in schools, particularly identifying the influence of technology as a conduit for enabling, engaging, and empowering stakeholders. [ABSTRACT FROM AUTHOR]

Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions. Finally, the probability density curve of electricity consumption is obtained by combining kernel density estimation (KDE). The model was tested using historical demand-side data from five UK households to achieve energy consumption predictions 5 min in advance. It is demonstrated that the model can achieve both reliable probabilistic prediction and accurate deterministic prediction. [ABSTRACT FROM AUTHOR]

Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML's role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry. [ABSTRACT FROM AUTHOR]

Most aircraft in the world are tracked by various surveillance radar systems. Currently there is no legal requirement for light aircraft to be fitted with a transponder; however, this does not mean light aircraft should not be tracked. By adding a cheap, live tracking solution for light aircraft, the safety of low-flying aircraft pilots can be greatly increased. The radio operators who coordinate the aircraft can have an improved understanding of the air traffic and in the event of an emergency, the position of the aircraft can be relayed to emergency services. This paper proposes an approach to use a smartphone as an aircraft transponder to improve the radar tracking capabilities of low-flying aircraft. This study presents a practical and effective approach as well as a prototype implementation. The study includes the development of the three main components: (1) A mobile application that transforms a smartphone into an aircraft transponder; exploiting the GPS functionalities, (2) a desktop application that visualizes the aircraft data in real time on a map, and (3) a backend that bridges the mobile and the desktop application. To evaluate the study, flight tests were performed in a real aircraft over the Isle of Wight in the UK. [ABSTRACT FROM AUTHOR]

The ubiquity of the Internet and its technology and the increasing aging of the world's population are ever more evident. Older users have different demands and capabilities when using the services offered in the digital environment. As a service provider to its population, the government has sought to optimize the provision of services and access to information through information and communication technology. Older adults are a relevant group of users of public services and have significant demands in some specific public services. To identify questions about the factors that promote the use, perception and barriers to the older population regarding the use of the Internet and government websites, this study was inspired by a study carried out in the United Kingdom to identify these factors. The study reports on a survey with 143 participants recruited from different geographical regions of the countryside of Brazil. The research showed that although government websites are strongly inclined to offer quality content and maintain the satisfaction of older adult users, there is still a more significant number of users who, due to low technology skills, do not use these sites or the Internet. Older citizens also had high Internet penetration and mobile device use. Lower computer literacy in the countryside of Brazil was related to factors such as gender, education level, race and sociocultural factors. A partial comparison with a study in the United Kingdom showed a lag in the use of e-government services by older adults in the countryside of Brazil. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases. [ABSTRACT FROM AUTHOR]

This research investigated the current maturity levels of cybersafety in South African schools. The maturity level indicates if schools are prepared to assist relevant role players (teachers and learners) in establishing a cybersafety culture within the school environment. The research study measured the maturity levels of cybersafety in 24 South African schools by evaluating the four main elements that are needed to improve cybersafety within schools. These elements are (1) leadership and policies, (2) infrastructure, (3) education, and (4) standards and inspection. The study used a UK-approved measurement tool (360safe) to measure the cybersafety maturity of schools within South Africa, using five levels of compliance (Level 1: full compliance, to Level 5: no compliance). The data analysis clearly indicated that all the schools that participated in the study had a significantly low level of cybersafety maturity and compliance. Schools are starting to adopt technology as part of their educational and social approach to prepare learners for the future, but there is a clear lack of supporting cybersafety awareness, policies, practices and procedures within South African schools. The research proposed a step-by-step approach involving a ten-phase cybersafety plan to empower schools to create and grow their own cybersafety culture. [ABSTRACT FROM AUTHOR]

Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with 'smart' becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How can data be used in making this vision a reality? What can we learn from smart campuses that can be scaled up to smart cities? A short research study was conducted over a three-month period at a public university in the United Kingdom, employing stakeholder interviews and user surveys, which aimed to gain insight into these questions. Based on the study, the authors suggest that making data publicly available could bring many benefits to different groups of stakeholders and campus users. These benefits come with risks and challenges, such as data privacy and protection and infrastructure hurdles. However, if these challenges can be overcome, then open data could contribute significantly to improving campuses and user experiences, and potentially set an example for smart cities. [ABSTRACT FROM AUTHOR]