This paper presents the Smart Local Search (SLS) algorithm, a hybrid metaheuristic framework for the NP-hard examination timetabling problem. SLS distinctively integrates graph-based construction heuristics with a guided local search framework, enhanced by an aspiration criterion to recover promising solutions overlooked by penalty terms and a strategic random move mechanism to escape local optima. Extensive experiments on all 11 of Carter’s widely used benchmark datasets show that SLS achieves competitive results, with an average penalty of 28.03 and a standard deviation of 42.30 across the benchmarks. Statistical significance testing (Wilcoxon signed-rank test, α = 0.05) confirms that SLS’s performance is significantly better than most baseline methods (p-value−10). The algorithm demonstrates particular strength in stability and consistency, making it a robust and efficient solution for complex educational scheduling environments.

The rise of Industry 4.0 technologies, such as the Internet of things (IoT), Cyber-Physical Systems (CPS), cloud computing, and Artificial Intelligence (AI). Has transformed traditional manufacturing into smart, data-driven systems. This shift has increased the complexity of production scheduling, especially in the Flexible Job-Shop Scheduling Problem (FJSP), which is already a complex problem by itself and further complicated by the integration of robotic job transfers. To address these challenges, we present the Dual Attention Network for MultiObjective Proximal Policy Optimization (DANMO-PPO) model, a Deep Reinforcement Learning (DRL) framework that formulates the FJSP as a Markov Decision Process (MDP). Our model employs actor-critic networks with a dual-attention mechanism to prioritize scheduling actions based on operational and machinelevel features. It is trained using the Proximal Policy Optimization (PPO) algorithm and guided by a weighted reward function balancing makespan and energy consumption. Experimental results show that DANMO-PPO effectively learns adaptive scheduling policies suited for complex, real-time industrial environments.

The Internet of Things (IoT) has become an integral part of our daily lives. While modern interactions have become more convenient due to the myriad of IoT devices, this diversity also makes IoT devices fertile targets for cyber attacks. However, due to the resource constraints of IoT deployment devices, intrusion detection schemes must be customized to meet the specific requirements of the IoT environment, especially in terms of power consumption and computing performance. In this paper, we benchmark multiple lightweight CNN-based models using public IoT network traffic datasets due to their wide popularity in network traffic classification. We evaluated also 1D and 2D variants of an optimized CNN model. Empirical results reveal that 1D models tend to perform better than 2D variants and other evaluated popular lightweight models. On the other hand, 2D-CNN offers less computation time and less memory footprint when compared with 1D-CNN indicating better efficiency. We further subject the competing methods to an early intrusion detection experiment. Results indicate that intrusions are successfully detected using as few as 6 initial packets of a session.

Achieving an alignment of the components within an Enterprise Architecture (EA) is challenging since it reflects both the business and IT views, and it must be regularly updated in response to the changes of the firm. Blockchain technology, and more specifically the means by which the logic of smart contracts may be extended to the business, is one avenue that has been explored and that may still be fruitful in addressing the issue. Through the use of smart contracts, we offer a new form of activity for operational processes that may identify when the process in which they are engaged exhibits unexpected behavior and so provide early warning of the need to update the EA. This research goes in depth as well as proposes a model to allow for a continuous alignment between the IT and business operations. Not only during normal circumstances would this model be able to be upheld but the research focuses on instances where both operations might experience unfavorable situations due to unforeseen circumstances. In these instances by implementing a blockchain solution both IT and business operations can stay intact without the inclusion of a third party allowing for the blockchain to make autonomous decisions as well as providing a middle ware between both entities to continue their operations.

Digital technologies such as 3D imaging, data analytics and computer vision opened the door to a large set of applications in cultural heritage. Digital acquisition of a cultural assets takes nowadays a couple of seconds thanks to the achievements in 2D and 3D acquisition technologies. However, enriching these cultural assets with labels and relevant metadata is still not fully automatized especially due to their nature and specificities. With the recent publication of several cultural heritage datasets, many researchers are tackling the challenge of effectively classifying and annotating digital heritage. The challenges that are often addressed are related to visual recognition and image classification. In this paper, we present a novel approach of hierarchical classification for cultural heritage assets. The metadata structural differences that exist between cultural assets motivated us to design a classification framework that can efficiently perform the classification of multiple types of assets. Our approach relies on several deep learning classifiers, each of them is assigned the task of classifying a certain type of assets. The classification framework starts the labeling process by first determining the asset type. The asset is then assigned to a specific classifier in order to be annotated with data fields related to its type. As a preliminary step, we successfully designed a general cultural type classifier and a specific type classifier for paintings. Our approach is currently achieving interesting results and is set to be improved by the integration of more asset types.