Antonis M. Messinis

Vasileios Alevizos, Ilias Georgousis, Akebu Simasiku et al.

The rise of advanced technology in project management (PM) highlights a crucial need for inclusiveness. This work examines the enhancement of both inclusivity and efficiency in PM through technological integration, focusing on defining and measuring inclusiveness. This approach illuminates how inclusivity-centered technology can significantly elevate project outcomes. The research navigates through the challenges of achieving inclusivity, mainly biases in learning databases and the design process of these technologies, assessment of transformative potential of these technologies, particularly in automating tasks like data collection and analysis, thus enabling managers to prioritize human-centric aspects of projects. However, the integration of such technology transcends efficiency, indicating a paradigm shift in understanding their societal roles. This shift necessitates a new approach in the development of these systems to prevent perpetuating social inequalities. We proposed a methodology involving criteria development for evaluating the inclusiveness and effectiveness of these technologies. This methodical approach is vital to comprehensively address the challenges and limitations inherent in these systems. Emphasizing the importance of inclusivity, the study advocates for a balance between technological advancement and ethical considerations, calling for a holistic understanding and regulation. In conclusion, the paper underscores that while these technologies can significantly improve outcomes, their mindful integration, ensuring inclusivity, is paramount. This exploration into the ethical and practical aspects of technology in PM contributes to a more informed and balanced approach within the field.

Vasileios Alevizos, George A Papakostas, Akebu Simasiku et al.

While new technologies emerge, human errors always looming. Software supply chain is increasingly complex and intertwined, the security of a service has become paramount to ensuring the integrity of products, safeguarding data privacy, and maintaining operational continuity. In this work, we conducted experiments on the promising open Large Language Models (LLMs) into two main software security challenges: source code language errors and deprecated code, with a focus on their potential to replace conventional static and dynamic security scanners that rely on predefined rules and patterns. Our findings suggest that while LLMs present some unexpected results, they also encounter significant limitations, particularly in memory complexity and the management of new and unfamiliar data patterns. Despite these challenges, the proactive application of LLMs, coupled with extensive security databases and continuous updates, holds the potential to fortify Software Supply Chain (SSC) processes against emerging threats.