December 30-31, 2026, Virtual Conference
Nabila Zine and Cherkaoui Leghris, Hassan II University of Casablanca, Morocco
The Internet of Things (IoT) has become a critical infrastructure exposed to an increasing diversity of cyberattacks, amplified by protocol heterogeneity, the resource constraints of embedded devices and the decentralisation of deployment architectures. Traditional reactive mechanisms, based on a posteriori detection and static signatures, no longer suffice against sophisticated, multi-stage and evolving threats. This paper reviews twenty contributions published between 2019 and 2026 on IoT security architectures and compares them along seven transversal criteria: target layers, main approach, proactivity, adaptivity, decentralisation, edge integration and compatibility with embedded constraints. The analysis shows a clear shift from static layered taxonomies towards intelligent architectures that combine deep learning, Zero Trust governance, decentralised trust mechanisms and, more recently, digital twins and large language models for threat anticipation. No existing architecture, however, simultaneously satisfies proactivity, adaptivity without full retraining, and deployability under embedded resource constraints.
Internet of Things, Security architecture, Intrusion detection, Zero Trust, Federated learning, Blockchain, Digital twin, Large language models, Edge computing.
Davit Kapanadze, Georgian National University SEU, Georgia and Institute of Mathematics and its Applications (IMA), United Kingdom
This paper proposes a cognitive-constructivist model for teaching the half-derivative, motivated by a student’s question: if we can speak of first, second and higher-order derivatives, why should there not be a derivative of order one half? The question is interpreted as an attempt to generalize a familiar operation; no empirical claims are made about its neural origins. The instructional model links, in a coherent sequence, prior knowledge, an operator hypothesis, the choice of a monomial space, a recurrence for the coefficients, the discovery of a free normalization parameter, and the derivation of the Gamma-function formula. The author’s previously developed mathematical results erve as the foundation; the independent contribution of the present paper is the instructional sequence built upon them, together with a set of diagnostic questions and a comparative system of problems. The Riemann–Liouville and Caputo definitions are compared on monomials: their agreement for positive exponents, their disagreement on constants, and the role of the domain of operator composition are demonstrated. An Abel equation, an initial value problem, a normalized heat-flux model and a derivative of order 3/2 are examined. The applied part is deliberately restricted to monomials and their finite linear combinations: a polynomial Abel equation and a Caputo initial value problem with a polynomial righthand side are considered, in which the constructive route reduces to finitely many algebraic operations. Finally, a dialogic AI protocol and a finite example in a Gamma-normalized basis are proposed. We claim neither a universal computational advantage nor an experimentally confirmed effectiveness of the model.
Half-Derivative; Graded Monomial Space; Fractional Differentiation; Constructivist Teaching; Apos; Cognitive Load; Artificial Intelligence
Virgil Ganescu and David Kaplan, USA
The rapid integration of Artificial Intelligence (AI) into higher education has generated both enthusiasm and skepticism among stakeholders. This study evaluates the perceived usefulness of six categories of AI-assisted learning tools through a comparative survey of students and faculty. Using a five-point Likert scale, respondents assessed AI functionalities ranging from theoretical summarization to step-by-step problem-solving and exam preparation. Results indicate a clear divergence between student and faculty perceptions, with students demonstrating moderate acceptance of AI tools—particularly those supporting procedural learning—while faculty responses reflect more cautious attitudes. The findings suggest that AI is most effective when deployed as a supplementary tool for applied learning rather than conceptual instruction. This study contributes to the growing body of research on AI in education by offering a structured comparison of specific AI functionalities and their perceived pedagogical value.
AI, Higher Education, Learning, Tools