Exploring the Power of Creative AI Tools and Game-Based Methodologies for Interactive Web-Based Programming

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.

A Survey on Dual-Quaternions

Over the past few years, the applications of dual-quaternions have not only developed in many different directions but has also evolved in exciting ways in several areas. As dual-quaternions offer an efficient and compact symbolic form with unique mathematical properties. While dual-quaternions are now common place in many aspects of research and implementation, such as, robotics and engineering through to computer graphics and animation, there are still a large number of avenues for exploration with huge potential benefits. This article is the first to provide a comprehensive review of the dual-quaternion landscape. In this survey, we present a review of dual-quaternion techniques and applications developed over the years while providing insights into current and future directions. The article starts with the definition of dual-quaternions, their mathematical formulation, while explaining key aspects of importance (e.g., compression and ambiguities). The literature review in this article is divided into categories to help manage and visualize the application of dual-quaternions for solving specific problems. A timeline illustrating key methods is presented, explaining how dual-quaternion approaches have progressed over the years. The most popular dual-quaternion methods are discussed with regard to their impact in the literature, performance, computational cost and their real-world results (compared to associated models). Finally, we indicate the limitations of dual-quaternion methodologies and propose future research directions.

Dual-Quaternions: Theory and Applications in Sound

Sound is a fundamental and rich source of information; playing a key role in many areas from humanities and social sciences through to engineering and mathematics. Sound is more than just data 'signals'. It encapsulates physical, sensorial and emotional, as well as social, cultural and environmental factors. Sound contributes to the transformation of our experiences, environments and beliefs. Sound is all around us and everywhere. Hence, it should come as no surprise that sound is a complex multicomponent entity with a vast assortment of characteristics and applications. Of course, an important question is, what is the best way to store and represent sound digitally to capture these characteristics? What model or method is best for manipulating, extracting and filtering sounds? There are a large number of representations and models, however, one approach that has yet to be used with sound is dual-quaternions. While dual-quaternions have established themselves in many fields of science and computing as an efficient mathematical model for providing an unambiguous, un-cumbersome, computationally effective means of representing multi-component data. Sound is one area that has yet to explore and reap the benefits of dual-quaternions (using sound and audio-related dual-quaternion models). This article aims to explore the exciting potential and possibilities dual-quaternions offer when applied and combined with sound-based models (including but not limited to the applications, tools, machine-learning, statistical and computational sound-related algorithms).

Identifying and Extracting Football Features from Real-World Media Sources using Only Synthetic Training Data

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.