The Internet of Behaviors (IoB) provides powerful tools for collecting, analyzing, and interpreting data on human actions and decision-making patterns. However, its application in education remains limited, despite the critical importance of being able to monitor and assess student focus, engagement, and participation in real time to improve learning outcomes and provide timely interventions. This study proposes an IoB-based system that employs deep learning and computer vision to assess and enhance student engagement in real-time. The system's architecture integrates two YOLOv8-based models: a multiclassifier for recognizing six classroom behaviors, and a binary classifier for distinguishing between focused and unfocused states. Anonymized identifiers are used to ensure a consistent and ethical tracking of individual behavior over time. These classifications trigger adaptive interventions designed to maintain or restore students' attention. Given the lack of standard benchmarks for assessing focus in classroom settings, especially under realistic conditions that include ambient noise and peer influence, a dedicated dataset was collected and used to train and evaluate the proposed system's performance in a university classroom context. The experimental results showed that the multi-class model achieved 97% accuracy, while the binary classification model reached 99% accuracy, both outperforming three state-of-the-art baseline methods. In a real-world classroom deployment, 75% of the students who received interventions subsequently re-engaged, underscoring the potential of the framework to improve learning outcomes through timely data-driven behavioral feedback.

Over the last decade, graphs have gained in popularity and have been widely used to model complex real world structures and data in various application domains. This leads to a rapid proliferation of large graph databases with hundreds of millions of nodes and edges. Thus, graph querying becomes a major problem that emerges from these collections of graphs. However, the current approaches for graph matching fail if no single graph satisfies all requirements of a query. In this paper, we present a novel approach for graph matching, where a new graph is constructed from fragments of other graphs. As a result, several relevant graph fragments are assembled and presented as solution. The proposed approach returns the best aggregated graph that fulfils as much as possible the query requirements, instead of returning a ranked list of graphs. We analyse the execution time of the proposed solution and conduct experimental evaluations to measure its effectiveness compared to several graph matching algorithms. The results show the good performance of the proposed approach.

Controlling access to data is one of the primary purposes of security, especially when it comes to dealing with safety critical systems. In such systems, it is of paramount importance to rigorously define access control models. In this article, a correct-by-construction specification of RBAC using the Event-B formal method is proposed. The specification defines closely the model properties with the behavior aspect of RBAC as guards of events, which allows applying a priori verifications. Accordingly, the resulted specification is correct-by-construction and avoids the combinatorial explosion problem. As well, a number of refinement operations are performed leading to a specification with several abstraction levels, where each level implements selected RBAC entities. The approach is illustrated by an instantiation of a healthcare system.

Summary Current innovative distributed architectures, proposing on‐line services, involve more and more computing resources. From a provider point of view, the platform management leads to challenging problematic relating to resource allocation, which involve different kind of quality of service parameters, the provider has to focus on to keep his platform reliable and efficient. MFHS is a modular generic framework, which can be adapted to any distributed computing environment. Structured in modules, MFHS allows to discover the existing computing resources in terms of computing performance, network throughput and disk I/O speeds (Resources Discovery module) and to predict how the experiment should behave (Pi value). As the setting up of real experiments is often complex, MFHS allows: to make theoretical experimentation (based on models), to use any kind of distributed emulators, or to deploy experiments on real‐experimental platforms. In this article, these three environments are used to highlight the reliability of MFHS (measured Pi=90% against 94% for the predicted Pi). Deployment and scheduling studies have also been achieved using an experimental Cloud based on OpenStack while Emulab test‐bed has been used as emulator. During experiments, four QoS parameters are taken into account (Resources Monitoring module): energy consumption, cost, resource utilization, and makespan. These studies also includes a new heuristic called MMin, based on Max‐Min and Min‐Min algorithms. Experimentation section, proposes a detailed comparative analysis of these algorithms in terms of QoS results, while the abilities of the proposed heuristic MMin regarding the makespan metric is shown.

Views materialization is a powerful technique for optimization of query evaluation. However, when data sources are updated, these materialized views (MVs) should be maintained in order to get appropriate queries’ answers. This work provides an overview of the research field of view maintenance, focusing on view maintenance issues and dimensions that cover the most important ones proposed in the literature review. We provide a classification and review the state-of-the-art of view maintenance approaches according to several parameters characterizing their algorithms, including information considered during the view maintenance process, principle used, and the execution mechanism. We provide the most important performance evaluation metrics of view maintenance process while performing a comparative study of some important approaches. Lastly, we give comprehensive perspectives on view maintenance in the different promising environments of MV applications.

Today, the design of modern Transport protocols follows modular architecture models. This approach transposes the loose coupling pattern used in software design to protocols allowing them to benefit from the high configurability, composability, flexibility and maintainability. Taking into account the “only TCP” policy applied by systems and Internet providers leading to the non-deployment of new protocols including modular ones, we propose in this works to apply this design approach to TCP and study the impact of the new design on its performances. This may allow TCP to benefit of all advantages of modularity while guaranteeing its deployment.

Some companies are willing to execute their business processes (BP) in the cloud for enjoying its benefits. However, they are also reluctant because of the new security risks that using cloud resources introduces. Security risk includes many dimensions, but this work focus on preserving the privacy of the logic of a BP deployed in a multi-cloud context by preventing a coalition of malicious clouds to re-construct important information from this logic. More precisely, the paper presents a BP logic privacy metric directly supporting the evaluation of the risk a company has its logic hacked in a particular multi-cloud configuration.

Companies are ready to outsource their business process to the cloud to enjoy its benefits. However they must be sure that their business know-how is preserved. Although some solutions already exist and consist in splitting the model into a collaboration of BP fragments for a deployment in a muti-cloud context, none of them takes into account the possibility of collusion between the clouds executing the different fragments. To address this issue, we propose in this paper a solution consisting in adding fake BP fragments at specific locations in the process, so as to complicate its structure and hide the direct interaction between clouds executing sensitive fragments. Therefore, the discovery of the process by malicious cloud providers is delayed. The approach is validated against an introduced metric. It demonstrates that our approach is better in the worst case than previous approaches in best cases.

Wireless sensor network (WSN) is often deployed in hostile environments, which make it vulnerable to attacks. WSN comprises a large number of sensor nodes with different hardware abilities and functions. Due to the limited memory resources and energy constraints, complex security algorithms cannot be used in WSN. Hence, it is necessary to balance between security requirements and energy consumption. In this paper, we propose a lightweight public-key-based authentication protocol for WSNs. Our protocol uses identity-based encryption in order to lighten the energy consumption from the public-key certificate management. We have developed two variations of the proposed solution depending on the manner and when an attack should be detected. We perform an overall evaluation of our approach through simulations. The results indicate out performance of our approach in terms of energy consumption while providing effective security.