Mohammed Amine Merzoug

Many people suffer from cervical disc herniation, which significantly affects the lives of individuals, causing chronic pain and functional limitations. This paper presents the development and evaluation of an AI-powered inflatable neck collar designed to provide personalized and adaptive support for individuals experiencing neck pain, particularly those with disc herniations. The system seamlessly integrates motion sensors, a robust AI model trained on a dataset of MRI scans, and a custom-designed inflatable collar. The AI model accurately detects disc herniations and segments vertebral structures, enabling real-time, targeted inflation adjustments based on the user’s unique anatomy, posture, and movements. A user-centered design approach ensures a seamless and intuitive user experience, allowing for personalized profile management, control over inflation levels, and data logging for tracking progress. Extensive simulations using 3D models and real-time data flow systems validated the effectiveness of the AI-guided system. Results demonstrated accurate detection and segmentation of disc herniations, robust real-time response, and adaptability to user needs. The proposed system, reviewed and validated by a neurosurgeon, demonstrates significant potential as a novel and effective solution for personalized treatment of neck pain, particularly in cases of disc herniation. Further development and research will focus on expanding the dataset to improve fairness and accuracy for diverse demographics and increasing the robustness and generalizability of the system.

This paper focuses on limiting misinformation propagation in networks. Its first contribution is introducing the notion of vaccinated observers, which is a node enriched with additional power. Vaccination is adding, locally, a plugin or asking for the help of a trusted third party, called a trusted authority. The plugin or the authority is able to detect if the received information is misinformation or not. Vaccinated Observers must stop forwarding detected misinformation. Based on this notion, two algorithms for limiting misinformation are proposed. The second contribution of the paper is an algorithm based on Moving Observers for locating a strong adversary diffusion source. This algorithm selects a random subset of nodes as observers for a random period Δ. This means that the observer subset may change over time in a randomized manner. Consequently, the strong adversary diffusion source can’t have global knowledge about observers positions. Having these positions by the diffusion source will make its localization by the observers more complicated, even impossible. The third contribution is proposing an algorithm for stopping misinformation propagation based on a punishment strategy. This algorithm has a very simple principle design and it assumes that an authority or a mechanism A is available. The authority A has the ability to detect if the received information is misinformation or not. If a node n_i receives information m from its neighbor n_j and m is detected, by n_i via the authority A , as misinformation then n_j is punished for a period pp ( pp stands for punishment period). If the node n_j repeats this action for n time then the punishment period increases to n * pp . The punishment in this algorithm is stopping the forwarding of the information received from a punished node n_j . The simulation results show that the proposed techniques are both efficient and accurate while locating the diffusion source. Consequently, misinformation propagation is limited.

In the context of automotive applications, complex tasks such as automatic driving of electric vehicles are handled through the composition of several components, each offering a different service. Such component composition is not straightforward and is often subject to bugs that might stem mainly from the incompatibility of services. In other words, in this context, which includes critical services and in which people's life is at stake, detecting and eliminating bugs early at the design stage is crucial and even mandatory. To remedy this issue, we propose in this paper a formal approach for modeling and verifying the reliability of electric self-driving vehicles that are continuously communicating with off-road infrastructures. First, for the modeling phase, SysML language is used to model the system architecture and to specify the connections between its embedded components. Second, we present a formal verification approach based on the extended interface automata formalism to verify the compatibility between the interacting components, and to check whether this set of components achieve their required tasks. This formalism allows to specify component interfaces that exhibit component protocols and system non-functional constraints. The proposed approach permits an algorithmic verification to decide whether a set of components, when assembled together, fulfill compatibility conditions. Results in this paper show, on one hand, that SysML and extended interface automata formalism are relevant to model and capture component features in the context of automotive systems, on the other hand, that our methodology allows to develop autonomous electric vehicle systems correct-by-design, regarding to component compatibility.

Continuous data streams, generated by modern sensed cities, open many opportunities and perspectives in terms of developing new innovative services. To exploit this potential, flexible and scalable platforms are needed to ease the design, development, deployment, and operations of new city services. In recent years, several problem-specific platforms have been proposed in different application domains; however, to boost the evolution of smart cities, we claim the need for city-oriented platforms that can be easily customized to address different day-to-day life challenging problems. In this paper, we present the main architectural challenges and solutions proposed for the design of a novel open-source platform (named PROMENADE) characterized by: (i) a graph-based data-driven modeling support to ensure high generality for addressing disparate problems related to the networked nature of many city infrastructures and systems, (ii) the big dynamic nature of its data/graph entities which come in a real-time fashion from different sources (e.g., IoT/Edge networks, data providers, etc.), and (iii) its high efficiency, flexibility, and scalability to easily support new city services. The platform is designed around a core that provides a set of built-in functions for graph metrics computation exposed as a collection of containerized microservices. A specialization of the platform, called PROMENADE-v2.0, has been developed for road networks monitoring. It has been deployed in Openshift/Kubernetes and tested using realistic datasets collected from the city of Lyon, France. The analysis addresses an important problem of big data processing pipelines: the synchronization between data ingestion and processing in order to produce an accurate result in useful time. To this end, we study different approaches for synchronization and show how end-to-end latency is kept under control by leveraging the scalability of the platform.

Web service discovery is one of the most motivating issues of service-oriented computing field. Several approaches have been proposed to tackle this problem. In general, they leverage similarity measures or logic-based reasoning to perform this task, but they still present some limitations in terms of effectiveness. In this paper, we propose a probabilistic-based approach to merge a set of matching algorithms and boost the global performance. The key idea consists of learning a set of relevance probabilities; thereafter, we use them to produce a combined ranking. The conducted experiments on the real world dataset “OWL-S TC 2” demonstrate the effectiveness of our model in terms of mean averaged precision (MAP); more specifically, our solution, termed “probabilistic fusion”, outperforms all the state of the art matchmakers as well as the most prominent similarity measures.