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  • Alternant Chargé d Etudes H/F

    Ingénieur de recherche/post doctorant LINEACT/Thèses LINEACT – CDD à temps plein – Dijon (Quetigny)

  • Prospective ergonomics in the anthropocene era: Reconsidering human needs

    This position paper discusses the roles of Prospective Ergonomics to face the challenges of Anthropocene. In particular, we question the nature of human needs to distinguish between fundamental needs essential to human development and artificial needs partly responsible for overconsumption and detrimental effects on Earth system. An overview of theories of human needs across Psychology, […]

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  • Net-zero futures cities and transportation systems: estimation and analyzing of vehicle’s carbon dioxide production by knowledge transferring

    The limited energy resources, critical climate change conditions, and globalwarming, coupled with today’s enormous industrial development, necessitate innovative approaches to control the situation. The automotive industry and its pollution emissions remain among the top environmental concerns. In this article, we present a progressive plan that leverages deep neural networks and inductive transfer learning methods to […]

  • Graph-based Learning for Multimodal Route Recommendation

    Transportation recommendations are a vital feature of map services in navigation applications. Earlier transportation recommendation systems have struggled to deliver a satisfactory user experience because they focus exclusively on single-mode routes, such as cycling, taxis, or buses. In this paper, we represent the transportation network as a complex network (or graph). Modeling transportation as a […]

  • Adaptive Compression of Supervised and Self-Supervised Models for Green Speech Recognition

    Computational power is crucial for the development and deployment of artificial intelligence capabilities, as the large size of deep learning models often requires significant resources. Compression methods aim to reduce model size making artificial intelligence more sustainable and accessible. Compression techniques are often applied uniformly across model layers, without considering their individual characteristics. In this […]

  • Enhancing IoT Network Intrusion Detection with a new GraphSAGE embedding algorithm using Centrality measures

    The rapid expansion of the Internet of Things (IoT) has led to many opportunities in addition to introducing complex security challenges, necessitating more powerful Network Intrusion Detection Systems (NIDS). This study addresses this challenge by enhancing Graph Neural Networks (GNNs) with centrality measures to improve intrusion detection performance in IoT environments. We propose the so-called […]

  • Generating Realistic Cyber Security Datasets for IoT networks with Diverse Complex Network Properties

    In the cybersecurity community, finding suitable datasets for evaluating Intrusion Detection Systems (IDS) is a challenge, particularly due to limited diversity in complex network properties. This paper proposes a dualpurpose approach that generates diverse datasets while producing efficient, compact versions that maintain detection accuracy. Our approach employs three techniques – community mixing modification, centralitybased modification, […]

  • Ingénieur développement et intégration logiciel (H/F)

    Ingénieur de recherche/post doctorant LINEACT/Enseignant chercheur LINEACT – CDD à temps plein – Strasbourg (Lingolsheim)

  • Ingénieur génie industriel

    Ingénieur de recherche/post doctorant LINEACT – CDD à temps plein – Angoulême (La Couronne)

  • Chercheur Doctorant H/F

    Ingénieur de recherche/post doctorant LINEACT/Thèses LINEACT – CDD à temps plein – Strasbourg (Lingolsheim)

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