Publications
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ResGNN: a residual GNN approach for leveraging general user preferences in session-based recommender systems
In recent years, session-based recommender systems (SBRSs) have emerged as pioneers for intelligent recommendation environments by capturing short-term user preferences without requiring direct access to user history. However, the challenge remains in effectively considering both short-term and long-term user preferences. Graph neural networks (GNNs) have shown promise in this field by leveraging the structural information […]
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Advanced PV-enabled heat generation system with precise thermal power regulation
This study presents an advanced PV-enabled heat generation system with precise thermal power regulation for resistive heating applications. Conventional solar thermal systems often rely on direct PV–resistor coupling, which leads to poor energy utilization, or MPPT-based operation, which maximizes electrical extraction but provides limited control over chamber temperature. To address these limitations, the proposed system […]
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LOOPER: A framework for synthetic dataset generation with configurable sensors and multi-view XR environments
Collecting multi-view datasets is essential for training and evaluating AI models in domains such as robotics and autonomous systems. However, generating such datasets remains challenging due to sensor synchronization issues and the labeling process, which is often timeconsuming, error-prone, and dependent on manual intervention. To address these limitations, a novel framework, LOOPER (Light Object and […]
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Toward Seamless Human-Robot Collaboration: VR-Enhanced Teleoperation with Transparent Shared Control
Shared control is widely used in robotic telemanipulation to combine human input with autonomous assistance. However, assis- tive behaviors are often embedded within the control loop and remain difficult for operators to interpret, leading to potential misalignment be- tween user intent and system response. To resolve this misalignment, we propose an immersive teleoperation system based […]
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Strength, repairability, and debonding of vitrimer structural adhesive joints
Introduction: Debondable and repairable epoxy-based vitrimer adhesives offer a sustainable solution to conventional epoxy adhesives for structural applications. This study evaluated the performance of a vitrimer adhesive in structural single-lap-bonded joints and investigated its repairability and debonding characteristics in comparison with those of a conventional epoxy adhesive. Materials and methods: Metal substrates, aluminum (Al6061) and […]
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Synergistic data-resource participant selection for efficient Federated Edge Learning in IoT ecosystems
The Internet of Things (IoT), as a concept, is becoming increasingly integral to our daily lives, enabling smart environments through sensing, communication, and computation. However, real-world edge devices exhibit pronounced heterogeneity and inherent limitations in both computational resources and data distributions, posing significant challenges for deploying robust, efficient, and adaptive edge intelligence. We propose FedCDRP, […]
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Decision-support analytics for material selection for production tooling: A systematic review and multi-objective optimisation of biocomposites
Biocomposites are increasingly promoted as sustainable substitutes for conventional composites, yet material selection remains uncertain because existing studies rely on heterogeneous datasets, inconsistent sustainability indicators, and divergent decision models. This paper addresses this gap by combining a systematic literature review with an auditable decision-support workflow for sustainable biocomposite selection, retaining 58 primary studies. The synthesis […]
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DRIFT: Distributed RPL Injection Flooding Threat in IoT Networks
The Routing Protocol for Low-Power and Lossy Networks (RPL) is widely adopted in IoT environments due to its efficiency in handling constrained devices and networks. However, RPL’s vulnerability to specific attacks presents significant security challenges. This paper introduces DRIFT, a novel distributed DIS attack targeting RPL-based IoT networks. DRIFT attack involves multiple malicious nodes flooding […]
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Fed-DCSRW: a privacy-preserving, dynamic client selection framework for heterogeneous federated learning via roulette wheel mechanism
We introduce Fed-DCSRW, a federated-learning framework that combines three pillars: (i) a decentralized, data-parallel client-clustering stage that computes centroids in parallel to scale and compute efficiently across heterogeneous clients; (ii) a centroid-based, noise-tolerant Roulette-Wheel client-selection strategy; and (iii) end-to-end differential privacy on both loss reports and gradient updates. The parallel clustering phase partitions the distance […]
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Modeling occupant air-conditioning behavior in Mediterranean offices with manual HVAC control: a case study conducted in Montpellier during summer 2025
Occupant behavior related to air conditioning (AC) has a considerable impact on the energy consumption of office buildings, particularly in southern France, which is characterized by a hot Mediterranean climate and frequent heat waves. Although several studies have developed behavioral models using logistic regression, relatively few have examined the application of machine learning methods to […]
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A Model Predictive Control Approach To Blending In Shared Control
Shared control aims at assisting human operators using robots in physically and cognitively demanding tasks which cannot be automated as they require human expertise and deliberative abilities. Sharing control for a given task typically involves blending algorithms that combine human control inputs and (pre)planned assistance trajectories. Conventional blending techniques, such as Linear Blending, compute a […]
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Predictive maintenance in cyber-physical systems: a comprehensive review of applications, approaches, and challenges
The rapid proliferation of predictive maintenance (PdM) techniques has led to a fragmented research landscape, limiting knowledge consolidation and solution reuse across industrial contexts. Motivated by this issue, this study presents a systematic review of PdM approaches for cyber-physical systems, covering data-driven, statistical, stochastic, AI-based, knowledge-based, and hybrid methodologies. In the context of Industry 4.0 […]