The distributed nature of Internet of Things (IoT) and its rapid increase on a large scale raises many security and privacy issues. Access control is one of the major challenges currently addressed through centralized approaches that may rely on a third party and they are constrained by availability and scalability, which may result in a performance bottleneck. Therefore, this paper proposes a novel solution to manage the delivery of lightweight and decentralized secure access control of an IoT system based on a multi-agent system and a blockchain. The main objective of the proposed solution is to build Blockchain Managers (BCMs) for securing IoT access control, as well as allowing for secure communication between local IoT devices. Moreover, the solution also enables secure communication between IoT devices, fog nodes and cloud computing.

Business rules extraction is an important activity in situations in which a software system becomes obsolete and needs to be replaced by a newer system, since the replacing system needs to satisfy the business rules embedded in the legacy software sy...

Code comments play an essential role in software development by providing documentation, explanations, and clarifications for program logic and functionality. It is crucial to effectively classify code comments to improve software maintainability and collaboration in the face of a growing amount of code. Developers can easily identify and comprehend different code sections' purpose, behavior, and requirements by accurately classifying code comments. This paper presents a novel approach utilizing multi-label classification to enhance code comment classification in three programming languages: Python, Pharo, and Java. We employ BERT, a widely used language model, and achieve an F1 score of 0.64 through experimentation. Our proposed approach facilitates the understanding and managing code comments, making software development more efficient and productive. Additionally, our approach can be extended to other programming languages and serve as a foundation for further research in code comment classification.