Benjamin Kenwright

Benjamin Kenwright

In recent years, the fields of artificial intelligence and web-based programming have seen tremendous advancements, enabling developers to create dynamic and interactive websites and applications. At the forefront of these advancements, creative AI tools and game-based methodologies have emerged as potent instruments, promising enhanced user experiences and increased engagement in educational environments. This chapter explores the potential of these tools and methodologies for interactive web-based programming, examining their benefits, limitations, and real-world applications. We examine the challenges and ethical considerations that arise when integrating these technologies into web development, such as privacy concerns and the potential for bias in AI-generated content. Through this exploration, we aim to provide insights into the exciting possibilities that creative AI tools and game-based methodologies offer for the future of web-based programming.

Benjamin Kenwright

University cybersecurity protocols have intensified dramatically in response to rising threats of data breaches, ransomware, and credential theft. While necessary, these measures have created a parallel crisis of accessibility - even for students physically on campus. This paper argues that domestic, on-campus students already face significant barriers: mandatory multi-factor authentication (MFA), device compliance rules, browser and operating system restrictions, and administrative remote-management permissions on personal phones and laptops. However, these difficulties are magnified to near-breaking point in the context of international partnerships, such as the increasingly common UK-China transnational education programmes. For a student in China accessing a UK university's virtual learning environment (VLE) from an 8-hour time difference, with no on-hand IT support during their active hours, the same security architecture becomes functionally disabling. Drawing on testimonies from public forums (Reddit's r/college, r/UniUK, r/Professors), higher education IT help boards, and student accounts from UK-China partnership programmes, this paper documents how over-engineering digital security disproportionately harms remote international learners. We show that while on-campus students can at least visit an IT desk or borrow a library terminal, their counterparts in partner institutions abroad face authentication failures, device lockouts, and unsupported browsers with no real-time remedy. The paper concludes that current university security models assume a co-located, 9-to-5, English-time-zone user - an assumption that fails both domestic students and, catastrophically, international partnership cohorts.

Jose Cerqueira Fernandes, Benjamin Kenwright

Real-world images used for training machine learning algorithms are often unstructured and inconsistent. The process of analysing and tagging these images can be costly and error prone (also availability, gaps and legal conundrums). However, as we demonstrate in this article, the potential to generate accurate graphical images that are indistinguishable from real-world sources has a multitude of benefits in machine learning paradigms. One such example of this is football data from broadcast services (television and other streaming media sources). The football games are usually recorded from multiple sources (cameras and phones) and resolutions, not to mention, occlusion of visual details and other artefacts (like blurring, weathering and lighting conditions) which make it difficult to accurately identify features. We demonstrate an approach which is able to overcome these limitations using generated tagged and structured images. The generated images are able to simulate a variety views and conditions (including noise and blurring) which may only occur sporadically in real-world data and make it difficult for machine learning algorithm to 'cope' with these unforeseen problems in real-data. This approach enables us to rapidly train and prepare a robust solution that accurately extracts features (e.g., spacial locations, markers on the pitch, player positions, ball location and camera FOV) from real-world football match sources for analytical purposes.