Darine Ameyed

Abir Trabelsi, Imen Benzarti, Hafedh Mili et al.

Translating business problems into well-specified machine learning solutions is a prerequisite for successful AI systems, yet this upstream translation is still one of the least supported steps in existing methodologies. We conduct a structured narrative literature review of 18 approaches spanning requirements engineering (RE), machine learning (ML) project management, and automation. We organize these approaches into a taxonomy of four families and compare them across six input artifact categories, six output artifact categories, and a transformation framework of seven stages, grounded in RE refinement theory and ML lifecycle process. Our study shows that most approaches list ML task or algorithm specification among their expected outputs, yet only four provide partial guidance for deriving it, and none provides systematic guidance. We characterize this gap as the Analytics Translation Problem (ATP) and derive five research recommendations addressing multi-formulation exploration, task derivation guidance, constraint-algorithm filtering, probabilistic traceability, and data-triggered revision.

Wissam Salhab, Darine Ameyed, Hamid Mcheick et al.

Robustness in AI systems refers to their ability to maintain reliable and accurate performance under various conditions, including out-of-distribution (OOD) samples, adversarial attacks, and environmental changes. This is crucial in safety-critical systems, such as autonomous vehicles, transportation, or healthcare, where malfunctions could have severe consequences. This paper proposes an approach to improve OOD detection without the need of labeled data, thereby increasing the AI systems' robustness. The proposed approach leverages the principles of self-supervised learning, allowing the model to learn useful representations from unlabeled data. Combined with graph-theoretical techniques, this enables the more efficient identification and categorization of OOD samples. Compared to existing state-of-the-art methods, this approach achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) = 0.99.

Marcela G. dos Santos, Darine Ameyed, Fabio Petrillo et al.

Over the past two decades, the Internet of Things (IoT) has become an underlying concept to a variety of solutions and technologies that it is now hardly possible to enumerate and describe all of them. The concept behind the Internet of Things is as powerful as it is complex, and for the components in the IoT solution tomesh together perfectly, they all have to be part of a well-thought-out structure. That is where understanding the IoT architecture becomes paramount. Because of the vast domain of IoT, there is no single consensus on IoT architecture. Different researchers and organizations proposed different architectures under a variety of classifications, mainly: conceptual, standard and, industrial or commercial adoption. It is indispensable to make a systematic analysis of IoT architecture to be able to compare the industrial proposals and identify their similarities and their differences. In this work, we summarize information about seven IoT industrial architectures in order to propose an approach that makes possible a comparative analysis between different IoT architectures. This work presents two main contributions: (i) an approach for analyzing and comparing IoTarchitectures using Layer-Model; (ii) a comparative study of seven industrial IoT architectures.

Darine Ameyed, Fehmi Jaafar, Jaouhar Fattahi

Cloud computing relies on sharing computing resources rather than having local servers or personal devices to handle applications. Nowadays, cloud computing has become one of the fastest growing fields in information technology. However, several new security issues of cloud computing have emerged due to its service delivery models. In this paper, we discuss the case of distributed denial-of-service (DDoS) attack using Cloud resources. First, we show how such attack using a cloud platform could not be detected by previous techniques. Then we present a tricky solution based on the cloud as well.

Darine Ameyed, Moeiz Miraoui, Chakib Tadj

Pervasive systems refers to context-aware systems that can sense their context, and adapt their behavior accordingly to provide adaptable services. Proactive adaptation of such systems allows changing the service and the context based on prediction. However, the definition of the context is still vague and not suitable to prediction. In this paper we discuss and classify previous definitions of context. Then, we present a new definition which allows pervasive systems to understand and predict their contexts. We analyze the essential lines that fall within the context definition, and propose some scenarios to make it clear our approach.