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On the Minimal Memory Set of Cellular Automata
No full textPart 2: Contributed PapersInternational audienceFor a group G and a finite set A, a cellular automaton (CA) is a transformation τ:AG→AG defined via a finite memory set S⊆G and a local map μ:AS→A. Although memory sets are not unique, every CA admits a unique minimal memory set, which consists on all the essential elements of S that affect the behavior of the local map. In this paper, we study the links between the minimal memory set and the generating patterns P of μ; these are the patterns in AS that are not fixed when the cellular automaton is applied. In particular, we show that when |S|≥2 and |P| is not a multiple of |A|, then the minimal memory set must be S itself. Moreover, when |P|=|A|, |S|≥3, and the restriction of μ to these patterns is well-behaved, then the minimal memory set must be S or S\{s}, for some s∈S\{e}. These are some of the first general theoretical results on the minimal memory set of a cellular automaton
Fuzzy Maturity Model for Transformative Procurement Readiness: Procurement 4.0 Perspective
No full textPart 1: Digital Transformation Approaches in Production and ManagementInternational audienceIn today’s dynamic business environment, transformative procurement is essential for organizations to adapt to changing market conditions and maintain competitiveness. While the Procurement 4.0 notion is still in its early stages, it is essential for large, medium, and small-sized enterprises, particularly in the energy sector, to possess a certain level of maturity in order to initiate the deployment of innovative and sophisticated procurement operations. Therefore, this work proposes the development of a fuzzy-rule-based model to assess the maturity level of procurement. This is accomplished through the creation of a fuzzy expert system that relies on criteria derived from four primary Procurement 4.0 components: modularity, resilience, agility, and human-centricity. Finally, the fuzzy maturity model is validated using a case study involving a company in the energy industry. By leveraging fuzzy logic modeling, the framework accommodates the inherent uncertainty and ambiguity in procurement operations, enabling organizations to make more informed decisions and optimize their procurement strategies. Through the case study and empirical validation, this work demonstrates the effectiveness of the proposed framework in enhancing procurement functions and driving digital transformation in all industrial sectors
Unlocking the Potential of Blockchain in Road Freight Transportation Operations
No full textPart 1: Digital Transformation Approaches in Production and ManagementInternational audienceRoad freight transportation operations are impact companies and countries economically and socially, making it critical to utilize technologies that increase its efficiency. Blockchain is one of the increasingly key technologies in the supply chain context, including freight operations. Although several studies have been conducted to investigate blockchain’s role in supply chains, there is still a lack of research on freight transportation, specifically related to blockchain-enabled capabilities. Our study identified a blockchain-enabled capabilities framework that enables an understanding of blockchain’s role in road freight transportation operations. This framework will support operators in determining which capabilities should be prioritized for higher visibility and efficiency in road freight transportation operations
Enhancing Traceability in the Norwegian Fish Supply Chain: Blockchain Adoption
No full textPart 3: Healthcare, Social Well-Being, and EthicsInternational audienceThis study investigates the adoption of blockchain technology in the Norwegian fish supply chain to address the critical issue of illegal fishing, a context where existing traceability systems face significant challenges. Focusing on the Norwegian Directorate of Fisheries’ CatchID Program, this research examines the factors influencing stakeholder perceptions and adoption of BCT within the Norwegian fish supply chain. Through observational and survey data, the study leverages affordance theory to reveal that while stakeholders recognize blockchain technology’s potential to increase transparency, data integrity, and security, significant barriers hinder its adoption, including challenges with integrating it with existing systems and navigating regulatory hurdles. The research underscores the need for strategic alignment between technological capabilities and socio-cultural contexts to facilitate successful BCT integration
Lost in Translation: Managing Digital Projects with Virtual Teams – Challenges, Opportunities, and Required Digital Intelligence Skills
No full textPart 2: Digital Transformation and Organizational InnovationInternational audienceThe digitization of project management has accelerated in recent years, driven by remote work and the adoption of digital tools. This evolution of work includes new challenges, such as virtual collaboration, work coordination, and trust building. Addressing these challenges requires more than mere technological adaptation; it necessitates the development of digital intelligence (DQ). DQ encompasses the ability to acquire and apply knowledge in digital technologies, fostering skills to navigate the digital world responsibly and effectively. Despite growing interest in DQ skills within management studies, there is limited research on their application in digital project management. This study, employing an exploratory research design and semi-structured interviews, examines ten challenges and five opportunities faced by project managers (PMs) in managing digital projects with virtual teams. Furthermore, it identifies the DQ skills necessary for effective management in this context, thereby contributing to both research and practice
Governing an Unexpected Potential Ending of a Strategic IT Sourcing Partnership: Picking up the Right Signals
No full textPart 2: Digital Transformation and Organizational InnovationInternational audienceWhile IT sourcing partnerships are an appropriate instrument for developing effective IT “solutions” in an efficient way, they also face high uncertainty. This uncertainty could unexpectedly lead to situations in which the legitimacy of an IT-sourcing partnership may be questioned and therefore make a premature ending of the partnership a serious option. However, properly coping with an unexpected potential ending of a partnership is often not easy. A decision to end may be taken too early or too late, and partners who are unprepared for an ending may be caught off guard and be confronted with high costs in legal fees, time and effort. Remarkably enough, this topic has not received a lot of attention in both practice and in literature. Therefore, the aim of this paper is to review the literature about this underexplored topic of governance of unexpected potential endings of IT sourcing partnerships and to use the findings of six explorative case studies to identify the boundaries of the knowledge of this topic and to acquire ideas on how to further our understanding on this topic. We argue that signaling theory provides a useful ontology to discover the mechanics in which partnerships evolve. By rigorously and systematically studying ending signals in numerous actual partnerships probably patterns could be identified which “announce” a potential ending. Currently, we are in the process of setting up a specific signaling logging tool and are actively searching for actual partnerships willing to log and analyse their detected signals in quasi real time. We believe this research co-operation between researchers and practitioners is beneficial to both parties and may narrow the gap between them and will create a faster adoption of new insights as soon as they become available
Key Factors Affecting the Implementation of Immersive Technologies in Ophthalmology Education
No full textPart 3: Healthcare, Social Well-Being, and EthicsInternational audienceThe rapid evolution of immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), present a transformative potential for various sectors, especially education. Within the specialized field of ophthalmology education, understanding the integration and effectiveness of these technologies is key. A systematic literature review (SLR) was executed to identify the factors influencing the adoption of immersive technologies in ophthalmology education. Thematic analysis was applied to 62 research papers, resulting in the identification of 77 key factors. These factors, encompassing both success and hindering elements, were categorized into 12 sub-categories and six main areas: immersive technology feature factors, user acceptance factors, learning outcome factors, active learning and control factors, student factors, and inhibiting factors. The categorization presented serves as a guideline for medical practitioners and technology developers, guiding them in optimizing the development, deployment, and enhancement of immersive tools in ophthalmology education. This research highlights the significance of each identified factor in the successful integration of immersive technologies in ophthalmology education
A Similarity Approach for the Classification of Mitigations in Public Cybersecurity Repositories into NIST-SP 800-53 Catalog
No full textInternational audienceBy 2025, it is projected that cybercrimes will escalate to an alarming annual figure of 10.5 trillion USD. To counter this growing threat, cybersecurity repositories such as CVE, CWE, CAPEC, and Mitre Att &ck serve as crucial platforms for the exchange of threat intelligence and mitigations. These repositories play a pivotal role in the prevention of cyber threats. Yet, mitigations in these repositories are manually described by various experts, lacking standardized rules and often failing to reference widely accepted catalogs like NIST SP800-53.To enhance the effectiveness and usability of mitigations within security repositories, this paper proposes an automatic classification method for repository mitigations, categorizing them into NIST SP800-53 classes. This classification relies on similarity approaches and introduces a novel algorithm aimed at refining and optimizing the accuracy of the classification results
Enhancing Weakly Supervised Medical Segmentation via Heterogeneous Co-training with Box-Wise Augmentation and Pseudo-Label Filtering
No full textPart 7: Medical Artificial IntelligenceInternational audienceIn this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods
Driver Fatigue Recognition Based on EEG Signal and Semi-supervised Learning
No full textPart 5: Perceptual IntelligenceInternational audienceDriving fatigue has become a serious hidden danger to road traffic safety. Drivers in a fatigued state often have problems such as delayed reactions and lack of concentration, which increases the risk of traffic accidents. In fatigued driving, the brain activity of the driver undergoes a series of changes, such as a decrease in the frequency of brain waves and a decrease in the amplitude of electroencephalogram (EEG) signals. Therefore, we propose a novel Semi-supervised Label Propagation with Optimal Graph Learning (SOGL) model that for identifying the fatigue state of drivers. This model uses class information from a small amount of labeled EEG data to assist the learning of unlabeled data and uses soft projection matrix learning to handle non-linear data structures. In addition, we also introduce a partially labeled graph learning method that extracts potential data structure information through graph structure learning techniques to improve the robustness and generalization ability of the model. Experimental results show that the model has good performance on a driving fatigue dataset