DIO/ODI Reference Model

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1. Proche Pas pertinent

   Manifest of Digital Identity Optimization (DIO) and Ontology of Digital Identity (ODI): Canonical Multilingual Edition

   Daniel Beránek · 2026 · Zenodo (CERN European Organization for Nuclear Research) · Résumé IA disponible

   Résumé scientifique

   This repository establishes the definitive, canonical, and persistent archival edition of the Manifest of Digital Identity Optimization (DIO) and its theoretical core, the Ontology of Digital Identity (ODI). DIO and ODI represent a fundamental shift in how digital presence is conceptualized, structured, and maintained. Moving beyond legacy SEO (Search Engine Optimization) and fragmented technical identifiers, the framework introduces an autonomous, holistic system for the orchestration of digital identity. It formalizes the complex, dynamic processes through which human identities, brand architectures, and informational intents are represented, trusted, and continually reconstructed by gener…

2. Apparenté Pas pertinent

   Digital Identity Optimization (DIO): A Conceptual Framework for Entity-Based Visibility in the Age of AI-Mediated Search

   Daniel Beránek · 2026 · Zenodo (CERN European Organization for Nuclear Research) preprint · Résumé IA disponible

   Résumé scientifique

   The emergence of AI-driven search and answer engines has fundamentally disrupted the logic of digital visibility. Traditional Search Engine Optimization (SEO), built on keyword-document matching, and AI Optimization (AIO), focused on answer-engine citation, provide increasingly insufficient responses to a landscape in which artificial intelligence recommends entities rather than ranks pages. This paper theorizes Digital Identity Optimization (DIO) as a third-generation framework for digital visibility. Building on Beránek’s (2026) foundational manifest, it defines DIO as the systematic optimization of an entity’s digital identity—its distributed digital representation across human and machin…

3. Apparenté Pas pertinent

   From Entity Resolution Anomalies to Reconstruction Failure Modes: A Taxonomic Note Following Minimum Viable Digital Identity Optimization (DIO)

   Daniel Beránek · 2026 · Zenodo (CERN European Organization for Nuclear Research) preprint · Résumé IA disponible

   Résumé scientifique

   This taxonomic note refines the vocabulary of Entity Resolution Anomalies developed in The Crisis of Entity Resolution in Heterogeneous AI Environments and follows the adversarial reduction introduced in Minimum Viable Digital Identity Optimization (DIO): From Adversarial Reduction to the Semantic Field Dynamics of Digital Identity. It proposes a non-exclusive, multi-level diagnostic taxonomy of Reconstruction Failure Modes. The taxonomy distinguishes four analytical domains: Referential Reconstruction Failures, Semantic Coherence Failures, Temporal Reconstruction Failures, and Contextual Adequacy Failures. Referential failures include Entity Substitution, Merge Failure, Split Failure, and E…

4. Apparenté Pas pertinent

   Digital Twin sensors in cultural heritage applications

   Franco Niccolucci, Achille Felicetti · 2024 · Sensors 2024, 24, 3978 · cité 47 fois · Pas encore résumé

   Résumé scientifique

   The paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous work to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarizing the large production of scientific contributions on the topic. Then a novel ontology, named Reactive Digital Twin Ontolo…

5. Apparenté Pas pertinent

   IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being

   Amelie Gyrard, Seyedali Mohammadi, Manas Gaur et al. · 2024 · Artificial Intelligence · preprint, non relu par les pairs · cité 5 fois · Résumé IA disponible

   Résumé scientifique

   Sustainable Development Goals (SDGs) give the UN a road map for development with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy lives and promotes well-being for all ages. Digital technologies can support SDG3. Burnout and even depression could be reduced by encouraging better preventive health. Due to the lack of patient knowledge and focus to take care of their health, it is necessary to help patients before it is too late. New trends such as positive psychology and mindfulness are highly encouraged in the USA. Digital Twins (DTs) can help with the continuous monitoring of emotion using physiological signals (e.g., collected via wearables). DTs facilitate monito…

6. Apparenté Pas pertinent

   Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials

   Iain Barclay, Alun Preece, Ian Taylor et al. · 2021 · Machine Learning · cité 24 fois · Résumé IA disponible

   Résumé scientifique

   Adopting shared data resources requires scientists to place trust in the originators of the data. When shared data is later used in the development of artificial intelligence (AI) systems or machine learning (ML) models, the trust lineage extends to the users of the system, typically practitioners in fields such as healthcare and finance. Practitioners rely on AI developers to have used relevant, trustworthy data, but may have limited insight and recourse. This paper introduces a software architecture and implementation of a system based on design patterns from the field of self-sovereign identity. Scientists can issue signed credentials attesting to qualities of their data resources. Data c…

7. Apparenté Pas pertinent

   Referring to Any Person

   Qing Jiang, Lin Wu, Zhaoyang Zeng et al. · 2025 · Computer Vision and Pattern Recognition · cité 27 fois · Pas encore résumé

   Résumé scientifique

   Humans are undoubtedly the most important participants in computer vision, and the ability to detect any individual given a natural language description, a task we define as referring to any person, holds substantial practical value. However, we find that existing models generally fail to achieve real-world usability, and current benchmarks are limited by their focus on one-to-one referring, that hinder progress in this area. In this work, we revisit this task from three critical perspectives: task definition, dataset design, and model architecture. We first identify five aspects of referable entities and three distinctive characteristics of this task. Next, we introduce HumanRef, a novel da…

8. Apparenté Pas pertinent

   Understanding Our People at Scale

   Tam N. Nguyen · 2020 · Computers and Society · preprint, non relu par les pairs · Pas encore résumé

   Résumé scientifique

   Human psychology plays an important role in organizational performance. However, understanding our employees is a difficult task due to issues such as psychological complexities, unpredictable dynamics, and the lack of data. Leveraging evidence-based psychology knowledge, this paper proposes a hybrid machine learning plus ontology-based reasoning system for detecting human psychological artifacts at scale. This unique architecture provides a balance between system's processing speed and explain-ability. System outputs can be further consumed by graph science and/or model management system for optimizing business processes, understanding team dynamics, predicting insider threats, managing tal…