Generative Artificial Intelligence (GAI) is emerging as a transformative force that empowers transnational education (TNE) in engineering. Recent trends indicate a significant shift in the application of generative AI in engineering policies, academic research, business practices, and educational settings throughout TNE. Governments and organizations are transitioning from restrictive stances to developing guiding frameworks for its application, enabling cross-border collaboration in TNE. Numerous universities have permitted and even promoted the utilization of GAI. Furthermore, academic research around the world is looking into the pros and cons of GAI in engineering education, focusing on how it can help teachers and keep students interested. Industrial applications are diversifying, extending across disciplines, and TNE is occurring in engineering contexts, including cross-border programs. GAI possesses the capacity to transform TNE by revolutionizing talent development, reformulating engineering models, and facilitating scientific assessment across multinational frameworks. However, problems like the generative illusion, ethical and ideological risks, lack of trust between teachers and students, and new threats to TNE in engineering equity in global settings require substantial focus. This study examines these concerns and outlines potential strategies to leverage GAI for transnational education in engineering, offering stakeholders the opportunity to prioritize AI literacy among educators and learners. This work emphasizes that cross-disciplinary and collaborative R&D, following national and international standards, should tackle application hurdles while guaranteeing safety and inclusion. This study also addresses several future directions that can contribute to creating a unified framework and cost-effective solutions. These solutions, integrated with platforms like the National Smart Education Platform, can bridge digital divides, ensuring equitable access and enabling global TNE stakeholders to capitalize on the GAI revolution. We also provide several statistics and case studies to show the effectiveness of GAI over TNE in engineering and provide practical solutions for the incorporation of GAI into TNE within engineering frameworks, guaranteeing inclusivity and equity.

In order to address the issue of insufficient task offloading decisions in vehicle networks of transportation cyber-physical systems (TCPS) because of multitasking and resource constraints, this study presents a quasi-Newton deep reinforcement learning-based two-stage online offloading (QNRLO) algorithm. Computer simulation experiments show that the approach performs exceptionally well in terms of convergence under various conditions and parameter configurations. Most of the trials are carried out in a simulated setting, and further real-world scenarios may be required to confirm the algorithm’s efficacy. This methodology initially implements batch normalization techniques to enhance the training process of the deep neural network, subsequently utilizing the quasi-Newton method for optimization to successfully approximate the ideal answer. According to the experimental results, the QNRLO algorithm’s loss function and normalized computation rate have converged after 2,000 iterations, demonstrating the algorithm’s excellent stability and dependability. The findings demonstrate that the computational load and training time can be further optimized by appropriately adjusting certain parameters without compromising convergence performance. Furthermore, the technique incorporates system transmission time allocation into the TCPS model, hence augmenting the model’s practicality. The proposed approach markedly enhances the efficiency and stability of job offloading compared to previous algorithms, effectively addressing task offloading challenges in TCPS and exhibiting considerable applicability and reliability.

Abstract Context Organizations constantly strive to protect their assets from outsider attacks by implementing various security controls, such as data encryption algorithms, intrusion detection software, firewalls, and antivirus programs. Unfortunately, attackers strike not only from outside the organization but also from within. Such internal attacks are called insider attacks or threats, and the people responsible for them are insider attackers or insider threat agents. Insider attacks pose more significant risks and can result in greater organizational losses than outsider attacks. Thus, every organization should be vigilant regarding such attackers to protect its valuable resources from harm. Finding solutions to protect organizations from such attacks is critical. Despite the importance of this topic, little research has been conducted on providing solutions to mitigate insider attacks. Objective This study aims to develop an organizational readiness model to assess an organization's readiness for insider attacks. Method We conducted a multivocal literature review to identify practices that can be used to assess organizations' readiness against insider attacks. These practices were grouped into different knowledge areas of insider attacks for organizations. The insider attack readiness model was developed using identified best practices and knowledge areas: compliance, top management, human resources, and technical. Results This model was evaluated at two levels—academic and real‐world environments. The evaluation results show that the proposed model can identify organizations' readiness against insider attacks. Conclusion The proposed model can guide organizations through a secure environment against insider attacks.

Software deployment is the last stage of the software development life cycle (SDLC). It includes the execution of software in a customer environment. Nowadays, security has been integrated with the SDLC stages to produce secure software, improve software quality, and increase customer satisfaction. However, the software has become complex in recent execution environments, putting more pressure on securely deploying the software in these environments. This work extends our previous study published in [11], in which we have identified a list of best practices to address the secure software deployment challenges.

research-article Share on Challenges of Secure Software Deployment: An Empirical Study Authors: Azzah A. Alghamdi King Fahd University of Petroleum and Minerals, SA and Imam Abdulrahman Bin Faisal University, Saudi Arabia King Fahd University of Petroleum and Minerals, SA and Imam Abdulrahman Bin Faisal University, Saudi ArabiaView Profile , Mahmood Niazi King Fahd University of Petroleum and Minerals, SA King Fahd University of Petroleum and Minerals, SAView Profile Authors Info & Claims EASE '22: Proceedings of the International Conference on Evaluation and Assessment in Software Engineering 2022June 2022 Pages 440–445https://doi.org/10.1145/3530019.3531337Online:13 June 2022Publication History 0citation39DownloadsMetricsTotal Citations0Total Downloads39Last 12 Months39Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access