System logs are a valuable source of information for monitoring and maintaining the security and stability of computer systems. Techniques based on Deep Learning and Natural Language Processing have demonstrated effectiveness in detecting abnormal behaviour from these system logs. However, existing anomaly detection approaches have limitations in terms of flexibility and practicality. Techniques that rely on log templates such as DeepLog and LogBERT fail to capture semantic information and are unable to handle variability in log content. On the other hand, classification-based approaches such as LogSy, LogRobust and HitAnomaly require time-consuming data labelling for supervised training. In this paper, a novel log anomaly detection model named LogFiT is proposed. The LogFiT model doesn’t make use of a vocabulary of log templates and it doesn’t require any labeled data as the model only requires self-supervised training. The LogFiT model uses a pretrained Bidirectional Encoder Representations from Transformers (BERT)-based language model fine-tuned to recognise the linguistic patterns of the normal log data. The LogFiT model is trained using masked sentence prediction on the normal log data only. Consequently, when presented with the new log data, the model’s top-k token prediction accuracy serves as a threshold for determining whether the new log data deviates from the normal log data. Experimental results show that LogFiT’s F1 score exceeds that of baselines on the HDFS, BGL, and Thunderbird datasets. Critically, when variability is introduced in the log data during evaluation, LogFiT retains its effectiveness compared to that of baselines.

“You are imparting human qualities to it because it looks human—but I assure you: it is not. If it were a box on wheels, I would not be facing this opposition.” Commander Bruce Maddox spoke these words during a Star Trek, The Next Generation episode as Lieutenant Commander Data faced an enquiry into his sentience. Similarly, this literature review explores the role of generative artificial intelligence (GenAI) in education, specifically examining its function as a learning assistant within the Information and Communication Technology (ICT) discipline in higher education. Using AI‐powered tools such as Litmaps and Google Scholar, we conducted an iterative search and reviewed 26 peer‐reviewed articles, focusing on the use of GenAI tools as learning assistants in ICT education. We adhered to predefined inclusion and exclusion criteria to ensure methodological rigour, excluding non‐ICT populations, secondary sources, and non‐peer‐reviewed literature. The findings highlight the manifold advantages of integrating GenAI into ICT education, including its ability to diagnose programming code, provide tailored feedback, and serve as a valuable learning aid. However, significant challenges were also identified, particularly the tendency of novice programmers to uncritically accept GenAI‐generated output, concerns over academic integrity violations, and issues surrounding the privacy of personal information. We pinpoint that the integration of GenAI influences four key components: Andragogy, Assessment, Integrity, and Privacy. As GenAI becomes more entrenched in educational practices, this research provides a foundation for further exploration and refinement to maximise its transformative potential in higher education.

The potential of fifth‐generation (5G) and beyond wireless connectivity is to deliver higher data rates, much lower latency, more frequent increase in channel capacity, and significant improvements in Quality of Service (QoS), compared to the current Long Term Evolution (LTE) networks. The continued growth of smart devices, the introduction of trending multimedia applications, and the dramatic increase in demand and use of wireless data communication are already causing a significant load on current mobile networks. 5G wireless networks, with enhanced user data rates, delays, capacity and QoS, are anticipated to solve many of the existing problems of mobile networks. In this study, we review the emergent wireless LTE‐A and 5G networks to provide an overview of the challenges and the solutions suggested in recent literature towards forthcoming 6G networks. We first discuss the new structural changes associated with the Radio Access Network (RAN) design, heterogeneous network and LTE or LTE‐Advanced (LTE‐A) network. To understand the challenges and research gaps in 5G and beyond networks, the paper reviews the outstanding features of the new QoS and Self‐Organization Networks (SON) related to the emerging 5G networks. Data rates, bandwidth and coverage have been given significance throughout this review because these are some major challenges towards 6G networks. Since understanding the present state of 5G usage is critical to its adoption, the paper also discusses related field tests, trials, and simulation tests. Finally, we identify existing major research problems and outline potential research areas.

During the recent COVID-19 outbreak, educational institutions have transitioned to online teaching for all students for most of the programs. Due to lack of in-person interactions and monitoring, assessments in online courses may be more susceptible to contract cheating, collusion, fabrication and other types of academic misconduct than the assessments in face-to-face courses. This situation has raised several research questions that need immediate attention, such as what are the best possible options for online assessments and how to administer online assessments so that academic integrity could be preserved. The authors have conducted a scoping study and carried out an extensive literature review on i) different types of assessments that are suitable for online courses, ii) strategies for ensuring academic integrity, and iii) methods, tools and technologies available for preventing academic misconduct in online assessments. It is evident from the literature review that there are a range of options available for designing assessment tasks to detect and prevent violations of academic integrity. However, no single method or design is enough to eliminate all sorts of academic integrity violations. After thorough research and analysis of existing literature, the authors have provided a comprehensive set of recommendations that could be adopted for ensuring academic integrity in online assessments.

“The intelligence that was formed on the Enterprise didn’t just come out of the ship’s systems. It came from us. From our mission records , personal logs , holodeck programs , our fantasies.” This fictitious quote from Captain Jean-Luc Picard of the Starship Enterprise in the 1990s, though predating the advent of generative AI (GenAI) technologies, reflects a key truth: the efficacy of artificial intelligence is fundamentally tied to the quality of human input and interaction. GenAI tools are most valuable when they augment rather than replace human cognition. Guided by this principle, our study investigates the potential of GenAI tools as an adjunct or assistant to student learning in ICT education at universities. Using structured focus groups conducted across three institutions, we explored student perspectives on GenAI’s utility, challenges, learning outcomes and skills development. Participants generally expressed positive attitudes towards GenAI, recognising its time-saving and problem-solving capabilities, but also highlighted concerns about accuracy, ethical usage, and the necessity for guidance on effective utilisation. The findings of the thematic analysis informed the development of the GROW-AI framework, a holistic strategy for integrating GenAI tools into educational practices, addressing components that included guidelines, resources, oversight, workforce preparation, and awareness. This framework provides actionable insights for institutions seeking to harness the potential of GenAI while mitigating its risks, fostering a balanced approach to AI in education.

Blockchain technology is increasingly recognised for its potential to enhance the security, reliability, and resilience of distributed systems. By ensuring data integrity and protecting against unauthorised access, blockchain creates immutable and transparent records, making it an appealing option for various applications. In the current healthcare landscape, secure data sharing is essential for improving patient outcomes, streamlining healthcare operations, and ensuring compliance with legal standards. This paper conducts a systematic literature review to explore how blockchain can be utilised as a security solution for health data sharing. We assess the strengths and weaknesses of blockchain-based solutions in areas such as secure data management, data sharing, access control, trust and transparency, and authentication and authorisation. Our findings indicate that blockchain has significant potential to secure health data sharing, although we identify certain limitations in blockchain-based access control systems and propose potential solutions. Additionally, this paper provides detailed insights into future research directions in this domain.