Prof. Farid Shirazi

Toronto Metropolitan University, Canada

Dr. Shirazi is a Senior Researcher at Institute for Innovation and Technology Management at Toronto Metropolitan University, Canada. He is a Professor of the Ted Rogers school of Information Technology Management. Dr. Shirazi's research focuses mainly on the impact of ICTs on the social and economic development. His main research interests are IT-enabled sustainability and development, Cloud Computing, AI & Machine Learning, Big Data Analytics, Enterprise Architecture & Governance, Green IS management as well as the ethical and security perspectives associated with the introduction and use of ICTs.

He has published in several journals including: International Journal of Information Management (IJIM), Information & Management (I&M), Industrial Marketing Management, International Journal of Production Economics, British Journal of Management, Journal of Information Technology & People (ITP), Technological Forecasting and Social Change, Journal of Global Information Management (JGIM), Telematics and Informatics, International Journal of Information Systems in the Service Sector (IJISSS), Journal of E-Business Development, Electronic Journal of Information Systems in Developing Countries, Journal of Information Communication & Ethics in Society, International Journal of Computer Application, Journal of Law & Development Review, Journal of Systems & Software (JSS), British Food Journal, Journal of Leadership, Accountability and Ethics and Communications of the Association for Information Systems (CAIS).

Title:The Paradigm of Software Engineering in Quantum Computing Hybrid Cloud, A Blockchain Perspective

Abtract: The transformation of digital technology to Quantum Computing (QC) is underway. Despite differences in physical platforms, quantum computing, including superconducting electronic circuits, trapped ion or trapped atom systems, and light fields, all strive for maximum efficiency, offering the potential for significant improvements in the communication and processing of information. However, deploying quantum computing at full scale requires considerable time to achieve an effective implementation scheme. For this reason, the current quantum computing technology of the NISQ (noisy intermediate-scale quantum) era urgently requires a hybrid deployment to integrate with classical computing systems, emphasizing the need for immediate action. With a cloud computing platform as the interface, businesses can bring their problems to be solved by a QC with the assistance of software development kits. One of the biggest challenges for organizations is anticipated to be the migration from modern cryptographic systems to the adoption of Post-Quantum Computing (PQC) standards. This study presents a Blockchain Case study that integrates NIST algorithms with blockchain technology.

Assoc. Prof. Koen Smit

HU University of Applied Sciences Utrecht, Netherlands

Koen Smit is an associate professor at the HU University of Applied Sciences Utrecht, in the Netherlands. He obtained his PhD in Computer Science in 2018 at the Open Universiteit. He is responsible for the bachelor software development and supervises a research group focused on Digital Innovations for Public Organizations, part of the Digital Ethics research chair. His research primarily focuses on the combination of Business Process Management, Business Rules Management, Decision Management, Process Mining, Decision Mining, Digital Twin technology, and Social Robotics. His interest also leans towards how said technological innovations can be designed and implemented in such a way that human and public values are explicitly and adequately considered. He regularly reviews and/or publishes and presents his research contributions at conferences and journals (e.g., HICSS, ICIS, PACIS, AMCIS, PJAIS, JITTA, and BPM).

Title: Digital Accessibility in Higher Education: A Dutch case study

Abstract: This case study investigates the integration of digital accessibility within the IT portfolio of a Dutch higher education institution (HEX). Utilizing both quantitative conformance testing and qualitative interviews, the study identifies significant trends and challenges in implementing digital accessibility. Findings reveal 13 trends in non-compliance with WCAG 2.2 standards across 35 critical systems, highlighting issues such as poor contrast and inadequate text alternatives. The study underscores the need for a comprehensive digital accessibility policy, enhanced collaboration among departments, and the structural embedding of WCAG conformance-checking capabilities to achieve and maintain proper digital accessibility, fostering an inclusive educational environment.

Dr. Keitaro Nakasai

Osaka Metropolitan University College of Technology, Japan

Keitaro Nakasai is a lecturer in the Department of Technological Systems, Intelligent Informatics Course, at Osaka Metropolitan University College of Technology in Japan. His research interests include Mining Software Repositories (MSR) and human factors in software development, especially program comprehension using biometrics. Recently, he has been engaged in program comprehension and programming education utilizing large language models (LLM). He received a Doctor of Engineering in Information Science from Nara Institute of Science and Technology.

Title: An Optimal Approach to LLM Model Selection in Code Generation AI

Abstract: Generative AI using large language models (LLMs) such as ChatGPT excels not only in answering general-purpose questions but also in processing programming languages. To effectively make use of the programming language processing capabilities of LLMs in software development, code generation AIs, for example GitHub Copilot, have been released and are being adopted by many software development companies. Recently, GitHub Copilot announced support for multiple LLM models, enabling software developers to select an LLM model that best suits their software. By choosing the optimal LLM model, developers can potentially gain a competitive edge in software development. However, with the rapid advancement of LLM models, determining which model is optimal for one's software development is a costly endeavor. In this presentation, we introduce an approach to selecting the optimal LLM model for software development using the bandit algorithm. By utilizing the bandit algorithm, it becomes possible to dynamically select high-performance LLM models without prior evaluation. Additionally, we will discuss the effects of introducing GitHub Copilot at the speaker's institution and how to use benchmarks to evaluate the code generation capabilities of LLM models.

Senior Lecturer YIP, Kim-fung (Frankie)

The Chinese University of Hong Kong, China

YIP, Kim-fung (Frankie) received his M.Phil. and B.Eng. degrees from The Chinese University of Hong Kong, where he currently serves as a senior lecturer. Over the years, he has been recognized with six consecutive Departmental Teaching Awards from the Electronic Engineering Department, as well as two Dean's Exemplary Teaching Awards from the Faculty of Engineering at CUHK.
As a co-founder of two startups, Frankie has successfully secured funding from the Hong Kong Science & Technology Parks Corporation and the Pi Centre at CUHK. His research interests focus on leveraging regenerative AI to enhance teaching quality for both educators and students. Additionally, he aims to improve customer service efficiency through innovative software solutions and develop e-business strategies that streamline workflows across various sectors and industries.

Title: Cost-Effective Knowledge Delivery Strategies: Leveraging the Topic Guidance Enquiry Framework for Organizational Success in Paid Course Offerings and Training

Abstract: In the competitive landscape of offering paid courses or training programs, optimizing knowledge delivery systems is crucial for enhancing user engagement and operational efficiency. This research introduces the Topic Guidance Enquiry Framework (TGEF), which utilizes Natural Language Processing (NLP) to autonomously generate relevant questions from both public and internal knowledge sources.

By continuously engaging users with targeted inquiries, our framework not only assesses understanding but also corrects misconceptions in real time. This adaptive framework ensures that learners receive personalized feedback after their answers are evaluated, providing them with a knowledge level score that reflects their comprehension of the course content. For business centers offering paid courses, implementing the TGEF can significantly increase the value provided to users, leading to improved satisfaction and retention rates.

Moreover, the system's efficiency in delivering tailored training content allows organizations to reduce costs associated with traditional in-house training methods. By minimizing the need for extensive instructional resources and enabling self-directed learning, organizations can allocate financial resources more effectively. This not only enhances the quality of training but also generates potential revenue growth through expanded course offerings and improved learner outcomes.

We will present the design and implementation of the TGEF, supported by case studies that demonstrate its effectiveness in increasing user value and driving cost-saving measures in internal training programs. This research aims to elevate the standards of knowledge delivery in the paid course and training sector, ultimately fostering a more skilled and adaptable workforce.