Abstract Conventional methodologies employed in detecting distributed denial‐of‐service attacks have frequently struggled to adapt to the dynamic and multi‐faceted evolution of such threats. Furthermore, many of the contemporary detection and prevention solutions, while innovative, remain anchored to dedicated workstations, lacking the flexibility and scalability required in today's digital landscape. To bridge this technological chasm, this research introduces a state‐of‐the‐art intrusion detection system firmly rooted in advanced Deep Learning techniques. By leveraging the expansive and adaptable nature of cloud‐centric, service‐oriented architectures, we not only bolster detection precision but also offer a solution designed for modern infrastructures. This system provides enterprises with a robust, easily deployable tool that is both versatile in its application and proactive in its defence approach, ensuring that networks remain resilient against the continuously evolving spectrum of cyber threats.

Abstract In recent years, deep learning techniques had a revolutionary impact on several domains, including computer vision and image processing. This research paper focuses on exploring deep learning methods to achieve precise illuminance estimation, which holds significant importance in applications such as augmented reality, virtual reality, and photography. However, accurately estimating illuminance in complex scenes continues to pose challenges due to the intricate interplay between light sources, objects, and surfaces. The results of extensive experimentation demonstrate the immense potential of deep learning techniques in illuminance estimation. These techniques exhibit promising accuracy and robustness, enabling them to handle diverse scenarios effectively. The valuable insights derived from this study can serve as a guiding framework for future research endeavours and contribute to the development of efficient and precise methodologies for illuminance estimation across a wide range of practical applications.

Abstract Surface defects in industrial refrigerator manufacturing processes can cause significant production losses and compromise product quality. This area is underexplored and currently, visual quality inspection is a subjective process that requires expert intervention, which limits process efficiency and can lead to errors in defect detection. This paper presents a novel approach for automatic surface defect detection using a combination of convolutional neural networks (CNN) and deflectometry. The proposed method takes advantage of the high accuracy and robustness of CNNs in image classification tasks and the sensitivity of deflectometry to detect subtle surface variations. First, a prototype was built to get the images from the refrigerator. Second, using video recordings, we captured surface topographic data using deflectometry, which we then use to generate surface images. Next, we train a CNN to classify the surface images as defective or normal. The proposed method offers a promising solution for automatic detection and quality control of surface defects in refrigerator manufacturing processes. However, this method could also improve the production of vehicles, household appliances in general, and any product that can suffer scratches and dents.

Abstract Pervasive computing has become a key element to build applications that use fun as a motivating component because it allows exploring new interaction schemes by making the concept of space and time ambiguous and confusing. The present research describes a pervasive social gaming experience, using as a reference SocialPG, which is a model that describes social expansion as a strategy to improve gaming experiences supported by pervasive computing. In this article a description of the model is offered, software architecture is proposed to support it and a case study related to the process of error resolution and detection of improvement opportunities in software products is developed, finally, a general idea about the final software product that will support the game experience is offered together with an evaluation performed by a set of users, where some important findings are highlighted, such as the importance of the missions as a unit of cooperative work and the spectator's participation.