School of Business

Academic integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond

This paper explores the academic integrity considerations of students’ use of Artificial Intelligence (AI) tools using Large Language Models (LLMs) such as ChatGPT in formal assessments. We examine the evolution of these tools, and highlight the potential ways that LLMs can support in the education of students in digital writing and beyond, including the teaching of writing and composition, the possibilities of co-creation between humans and AI, supporting EFL learners, and improving Automated Writing Evaluations (AWE). We describe and demonstrate the potential that these tools have in creating original, coherent text that can avoid detection by existing technological methods of detection and trained academic staff alike, demonstrating a major academic integrity concern related to the use of these tools by students. Analysing the various issues related to academic integrity that LLMs raise for both Higher Education Institutions (HEIs) and students, we conclude that it is not the student use of any AI tools that defines whether plagiarism or a breach of academic integrity has occurred, but whether any use is made clear by the student. Deciding whether any particular use of LLMs by students can be defined as academic misconduct is determined by the academic integrity policies of any given HEI, which must be updated to consider how these tools will be used in future educational environments.

School of Computing and Innovative Technologies

Acceleration of Deep Neural Network Training Using Field Programmable Gate Arrays

Convolutional neural network (CNN) training often necessitates a considerable amount of computational resources. In recent years, several studies have proposed for CNN inference and training accelerators in which the FPGAs have previously demonstrated good performance and energy efficiency. To speed up the processing, CNN requires additional computational resources such as memory bandwidth, a FPGA platform resource usage, time, power consumption, and large datasets for training. They are constrained by the requirement for improved hardware acceleration to support scalability beyond existing data and model sizes. This paper proposes a procedure for energy efficient CNN training in collaboration with an FPGA-based accelerator. We employed optimizations such as quantization, which is a common model compression technique, to speed up the CNN training process. Additionally, a gradient accumulation buffer is used to ensure maximum operating efficiency while maintaining gradient descent of the learning algorithm. To validate the design, we implemented the AlexNet and VGG-16 models on an FPGA board and laptop CPU along side GPU. It achieves 203.75 GOPS on Terasic DE1 SoC with the AlexNet model and 196.50 GOPS with the VGG-16 model on Terasic DE-SoC. Our result also exhibits that the FPGA accelerators are more energy efficient than other platforms.

School of Computing and Innovative Technologies

Multivariate Analysis for Overcoming Complexities of Corporate Governance and Managerial Dilemma using Data Mining Technique

"The increased number of corporate dirty pools raised serious concerns about the interest of the shareholders. The board room politics, conflict of interest, and bully pulpit proclivity gave birth to the “agency complexities.” The complexities of the corporate world made a buzz for the serious thoughts on corporate governance. is analysis aims at an objective and scientific inquiry about the relationship between corporate governance complexities and from performance by utilizing data mining tools. It aims at overcoming the corporate dilemma over profitability vs. good governance and presenting a scientific model to eradicate the complexities in the corporate governance system and aims at providing a scientific basis to overcome the complex issues of governance faced by the corporate. the multivariate analysis in this paper utilizes a data mining tool for regression analysis and ANOVA. this paper also proposes a mathematical model that supports the study outcomes. the investigation outcomes are not only backed by the mathematical model and scientific tools but also by a comprehensive comparative analysis. e outcome of the investigation clearly mentions the significance and the primacy of each variable in the corporate decisions making process, which will facilitate the organizations in framing their corporate governance policies and will also be helpful to the managers in overcoming the corporate dilemma faced by them."

School of Communications & Creative Industries

Bird Sounds

School of Computing and Innovative Technologies

Predictive Modeling 0f Length 0f Stay in General Surgery Patients Using Artificial Intelligence

For effective resource allocation, patient management, and discharge planning, it is crucial to accurately forecast the length of stay (LOS) for patients undergoing general surgery. In this study, we suggest a predictive modeling strategy utilizing Artificial Intelligence (AI) methods to calculate the LOS for patients with adult spinal deformity (ASD). LOS following ASD surgery denoted a crucial phase to enable the best possible recovery. The categorization of high-risk patients is made possible by predictive algorithms that estimate LOS. Patients with ASD were found in a multicenter database that was prospectively gathered. Patients who had staged surgery or a LOS for more than 30 days were not included. Redundancy and collinearity tests, as well as univariable predictor importance of 0.90, were used to choose the variables for the model. Using a dataset created from a bootstrap sample, the Gradient Ascent Decision Tree Model (GADTM) was suggested for prediction; patients who were not by chance chosen for the bootstrap sample were selected for the dataset. To determine an accuracy percentage, LOS forecasts, and actual LOS were compared. 653 patients complied with the inclusion requirements. 893 patients were modeled using bootstrapping. Accuracy of the prediction within two days of the actual LOS. Our approach accurately predicted LOS after ASD surgery within two days. Rehab accommodation and social assistance services are not included in large projected databases. Predictive analytics will become more important in ASD surgery as future models improve accuracy

School of Computing and Innovative Technologies

Multi-modal Retrieval Augmented Generation for Product Query

Product Query, when an e-commerce website needs to return a product for a user’s query, is essential for any e-commerce system. Traditional search systems only consider the user query in the text and then try to match the search queries with the products’ descriptions in the database. Some recent image search systems utilize deep learning methods to match the image query with the products’ images in the database. However, none combine text and images in a single query. This kind of search is common in modern daily life as a user can take a photo easily with their smartphone and provide a short text description then try to search for a product. In this paper we consider a multi-modal retrieval augmented generation (RAG) to provide a product query system that allows users to search simultaneously by image and text. Our system will provide a better experience and improve the performance of e-commerce websites.

School of Hospitality and Tourism

Tourism and Easternisation

This book critically discusses on epoch of “Asian century” through Asia’s dominance in economic, political and demographic dividend. The major themes include Asianisation, Asianism, Asia’s eventful rise, Asian culture and philosophy, Asia – The global host, Decolonial Asia and tourism. Furthermore, it discusses Asian tourism and regional integration and geopolitics, Arabian peninsula’s economic boom beyond petrodollar, the BTS and K Pop wave in South Korean tourism and the Asian multimodal infrastructure developments in the Asian century. It makes a profound academic contribution by offering a comprehensive and interdisciplinary exploration of the evolving relationship between Easternisation and the tourism industry. By delving into in-depth case studies and empirical analyses of various Asian destinations, the book presents concrete evidence of how Easternisation transforms tourism practices and experiences across the continent. Overall, this book is a groundbreaking work that enhances the academic discourse in tourism studies and provides valuable insights for policymakers, industry practitioners, and researchers.

School of Business

Chinese voluntourist gaze: a self-reflection perspective

Voluntourism, a contested form of alternative tourism, has received limited scholarly attention regarding the agency of voluntourists, particularly in non-Western contexts. This study investigates the self-reflections of young Chinese international volunteers through netography and thematic analysis of online narratives. Utilizing Urry's gaze theory as a ‘lens’, it examines how these voluntourists reflect not only on the destinations but also on themselves during their pre-trip, onsite and post-trip experiences. Three phases of the voluntourist gaze are identified: an initial ‘othering’ gaze, an evolving onsite gaze that challenges romanticization, and a post-trip reflective moral gaze. While similarities exist between the gazes of Chinese and Western voluntourists, the Chinese voluntourist gaze is uniquely nurtured by an inquiring intratourist gaze and by traditional Chinese philosophies such as Confucianism and Taoism. Notably, Chinese voluntourists exhibit a greater capacity for self-reflection compared to conventional Chinese tourists, positioning them as ‘moral pioneers’ in promoting Chinese ethical tourism by demonstrating more empathetic and humanitarian gazes.