Due to the COVID-19 pandemic and the development of educational technology, e-learning has become essential in the educational process. However, the adoption of e-learning in sectors such as engineering, science, and technology faces a particular challenge as it needs a special Laboratory Learning Management System (LLMS) capable of supporting online lab activities through virtual and controlled remote labs. One of the most challenging tasks in designing such LLMS is how to assess a student’s performance while an experiment is being conducted and how stuttering students can be automatically detected while experimenting and providing the appropriate assistance. For this, a generic technique based on Artificial Intelligence (AI) is proposed in this paper for assessing student performance while conducting online labs and implemented as a performance evaluation module in the LLMS. The performance evaluation module is designed to automatically detect the student performance during the experiment run time and triggers the LLMS virtual assistant service to provide struggling students with the appropriate help when they need it. Also, the proposed performance assessment technique is used during the lab exam sessions to support the automatic grading process conducted by the LLMS Auto-Grading Module. The proposed performance evaluation technique has been developed based on analyzing the student’s mouse dynamics to work generally with any type of simulation or control software used by virtual or remote controlled laboratories; without the need for special interfacing. The study has been applied to a novel dataset built by the course instructors and students simulating a circuit on TinkerCad. Using mouse dynamics fetching, the system extracts features and evaluates them to determine if the student has built the experiment steps in the right way or not. A comparison study has been developed between different Machine Learning (ML) models and a number of performance metrics are calculated. The study confirmed that Artificial Neural Network (ANN) and Support Vector Machine (SVM) are the best models to be used for automatically evaluating student performance while conducting the online labs with a precision reaching up to 91%.

Nowadays, the issue of scheduling multi-core real-time systems has become the focus of such research in industrial, biomedical, military, and other fields. As a consequence, a new semi-partitioning algorithm that uses a static Rate-Monotonic criterion to schedule real-time tasks on multi-core platforms is proposed. The improvement in the performance of real-time systems is achieved by exploitingthe fact that the utilization boundary of a task set increases to fully utilize the processors if the periods of tasks have harmonic nature among each other. Experimental results on randomly generated datasets and real-world datasets show that the proposed algorithm inevitably outperforms other competitive algorithms.

Owing to the fast advancements of wireless communication, the telehealthcare platform makes it possible for patients to access healthcare services online. However, creating a secure and efficient authentication scheme for healthcare systems still presents a challenge. Several solutions have been introduced, but the majority are shortly found to be unable to meet some essential security standards. In this paper, we first revisit Dharminder et al.'s scheme, and prove its failure to provide mutual authentication, and pacient’s untraceability, and its vulnerability to impersonation attacks. Furthermore, we suggest an improved RSA-based authentication scheme to mitigate the deficiencies observed in Dharminder et al.'s schema. The proposed scheme can provide mutual authentication, patients’ anonymity and untraceability, and resist various types of attacks. Extensive evaluation on AVISPA proves the safeness of the proposed scheme against both passive and active attacks. Additionally, the proposed scheme is computationally and communicationally more efficient in comparison to existing schemes.

In this paper, a new semi-partitioning scheduling algorithm for muli-core real time system is proposed. The proposed algorithm is called Harmonic Semi-Partitioning and Task Splitting (HSPTS) which uses the Rate-Monotonic approach to address the problem of scheduling periodic tasks with implicit deadlines on multi-core systems. Two challenges have been addressed and resolved by the proposed algorithm, the first is to find the set of tasks which have harmonic relations with each other. While the second challenge is to assign and split the appropriate tasks among different processors without overrun. In this context, the overall utilization for multi-core systems will be improved and exceed the Liu&Layland's boundary for N tasks. To this extent, the suitable task pairs are assigned to processors using hyperbolic boundary. Experimental results show that the developed algorithm enhances not only the overall system utilization but also the number of processors needed to schedule multi-core real-time systems compared with other competitive algorithms.