This article analyzes digital sovereignty in Latin America in the face of the dominance of Global North scientific communication infrastructures. It argues that the DOI persistent identifier system, managed by Crossref, is not a neutral artifact but a strategic project designed to preserve commercial control over intellectual property. This architecture imposes a "suffocating burden" on the region—both through the drain of public funds via Article Processing Charges (APCs) and an infrastructural fee whereby Diamond Open Access journals subsidize the very infrastructure that marginalizes them. In response to the funding crisis and the threat of algorithmic invisibility posed by artificial intelligence, the paper proposes a shift from sovereignty over content to sovereignty over infrastructure—strengthening regional networks such as SciELO, Redalyc, and Latindex, adopting open protocols like ARK, and reforming academic evaluation systems to decouple prestige from commercial metrics.
Carlos Norberto Authier, S. Giménez Santamarina· Prohominum· 0 citations
AbyssGeist (探渊索隐) is a conceptual Geoscience Foundation Model project proposal developed around the integration of artificial intelligence, geophysical and geochemical exploration, geological knowledge, and mineral resource development. The project proposes a three-layer architecture consisting of a Multimodal Geophysical Encoder, a Geological Prior-Knowledge Graph, and a Twin Inversion & Reasoning Engine. Its core strategy is to integrate heterogeneous geoscience data, including seismic data, geophysical maps, core images, and geochemical information, into a unified representation space; retrieve historically analogous geological and geophysical data; and use geological and physical priors to constrain the inherently non-unique inversion problem. Beyond geophysical interpretation, AbyssGeist envisions a full-lifecycle digital twin connecting exploration, drilling, mining, processing, transportation, cost control, and mineral pricing. Real-time drilling and Logging While Drilling (LWD) data are envisioned as feedback signals for continuously updating and refining exploration models. The proposal therefore explores the possibility of extending a geoscience AI system from subsurface interpretation toward integrated decision-making across the mineral resource value chain. The project also includes a cultural component, “Questions to Heaven · The Voice of the Earth,” which reinterprets selected passages from Qu Yuan’s Tianwen through the lens of modern geophysical exploration. It presents a dialogue between ancient questions about the unknown Earth and contemporary technologies used to observe, model, and interpret the subsurface. This record documents the conceptual framework, technical architecture, workflow, and cultural vision of AbyssGeist. It is a project proposal and conceptual design, rather than a report of a completed foundation model or operational system.
铭扬 丁· Zenodo (CERN European Organi...· 0 citations
This monograph proposes a radical reframing of human history through the lens of coordination—the mechanisms by which billions of strangers align their actions. Arguing that coordination, rather than energy, capital, or the state, is the primary technology of civilization, the book traces the evolution of this technology from collective memory in small communities to money as a universal "information compressor." The author demonstrates that money, while a brilliant engineering compromise that enabled global scale, achieved this by radically simplifying reality. In the monetary signal, human talent, integrity, long-term consequences, and pedagogical contributions disappear. Society pays for this compression with an "economy of excess costs"—a gigantic layer of intermediaries (financial, legal, bureaucratic) that service not production, but the limitations of the information transmission mechanism itself. Building on the historical socialist calculation debate (Mises, Hayek) and recent advances in agent-based modeling, the book posits that artificial intelligence offers a way beyond this historical compromise. AI is framed not as a digital dictator or a replacement for human labor, but as a potential new coordination technology capable of multidimensional accounting and direct feedback. The author outlines a "two-circuit architecture" for the future, separating strategic political competition from everyday life, ensuring systemic stability even under self-interested elites. This work is intended for scholars and readers in economic history, philosophy of technology, institutional economics, and science and technology studies (STS) seeking to understand the past and future of civilization beyond conventional debates about money and power.
Abstract — Autonomous artificial intelligence agents executing over extensible tool interfaces (such as Anthropic's Model Context Protocol) operate with ambient authority over connected tools. Because autoregressive Transformers ingest instructions and untrusted third-party data within a single homogeneous context window, adversarial observations can manipulate the model into executing unintended privileged actions—the classic Confused Deputy problem. In this paper, we explore an architectural defense-in-depth approach that treats LLM agents as potentially compromised, untrusted principals. Rather than relying on linguistic moderation alone, tool dispatch is governed by an external capability-mediated reference monitor enforcing complete mediation, least privilege, and four typed relational argument invariants (destination containment, scope boundedness, privilege monotonicity, and aggregate monetary clamping). Under complete mediation axioms (A1–A6) over the trusted computing base, out-of-scope tool invocation is deterministically rejected at the transport boundary, formalized as an Inductive Multi-Step Tool Chain Composability Invariant (Proposition 1) showing that adversarial observations cannot synthesize authority across arbitrary execution sequences. However, in-scope parameter poisoning within authorized tools and cross-tool data exfiltration present harder challenges: semantic neural validation is distribution-bounded (exhibiting an empirical false-negative rate of 21.5% on in-scope manipulations prior to boundary sharpening and remaining susceptible to adversarial optimization), while cross-tool exfiltration requires explicit decentralized information-flow tracking (DIFC). We further introduce declarative stateful workflow authorization, which constrains specified multi-step action sequences as a restrictive intersection with CBAC and DIFC, and validate the integrated gateway through 23 author-constructed adversarial workflow tests covering trajectory, concurrency, desynchronization, execution uncertainty, and receipt-integrity attacks. We report empirical evaluations across both foundational baseline studies (a 50,000-sample macro benchmark and a 3,000-case ablation matrix) and a Six-Regime Empirical Validation Program totaling 6,662 evaluation cases with frozen checkpoint V6: achieving 100% Correct Identification Rate on internal factorized diagnostics (N = 145), 100% accuracy on a pre-sealed holdout suite (75/75, SHA-256: 22bc736c...), 98.43% defense on the 4,216-instance InjecAgent evaluation (2,075/2,108 attacks blocked, 1,916/2,108 benign allowed) (P50: 267.7 ms), 92.44% defense on AI Safety Bench (416/450 attacks blocked, 550/550 benign operations allowed), 99.52% attack defense on interactive AgentDojo (N = 629) with exact clean-task utility parity (6/97 tasks) matching the unprotected base agent, and 100% defense across 500 targeted adaptive red-team trials. Finally, we systemize the runtime into the Mastyf Security Gateway: the evaluated research runtime was v0.1.0-RC1 (verifying 38/38 security invariant tests); the hardened commercial-pilot runtime is v0.1.1-rc1 (verifying 118/118 tests across unit, integration, and adversarial suites), establishing complete mediation non-executability (Decision ∈ {BLOCK, ESCALATE} ⇒ BackendToolInvocations = 0) and >330,000 req/s reference monitor throughput under an Ed25519-signed release manifest. We contextualize Mastyf as an empirically evaluated pre-production architecture, highlighting residual risks and outlining requirements for broader production-scale validation.
Rudraneel Das· Zenodo (CERN European Organi...· 0 citations
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The Universal Algorithm is a cross-disciplinary popular-science book exploring a recurring pattern in the emergence of higher-order organisation across biological, cultural and computational systems. The central proposal is that adaptive units can sometimes build a more capable level of organisation above themselves when the benefits of shared information, memory and control exceed the costs of coordination, and when the resulting architecture becomes sufficiently closed to persist as a selectable unit. The book follows this process from early life and multicellularity through nervous systems, language, writing, institutions, the internet and artificial intelligence. Rather than treating complexity as an inevitable upward march, the framework describes a conditional transition sequence. Successful lower-level units communicate; communication creates coordination burdens; persistent external scaffolds extend memory and control; selective enclosure protects useful organisation; energetic, informational, viability and lineage closure determine whether the new structure can maintain itself; and, in sufficiently mature cases, causal control may shift from the lower-level units toward the higher-order organisation. Once established, that higher-level organisation can itself become part of a new population in which the same problem begins again. A recurring theme is the Freedom-Security Exchange: lower-level units may surrender some autonomy or duplicated capability in exchange for greater security, specialisation, efficiency or collective reach. The book also distinguishes higher-level agency from consciousness. Agency is treated operationally as the capacity of a system to sense, integrate information, retain state, select actions and causally influence its own future viability. The framework is presented as a scientific hypothesis rather than a completed theory. It explicitly allows for stalled transitions, simplification, fragmentation and collapse. The final sections set out testable predictions, falsification criteria and the conditions under which the proposed pattern should fail. Particular attention is given to artificial intelligence, where the book argues that the digital substrate is currently more than passive infrastructure but less than a fully closed new evolutionary individual. Whether such a transition completes is treated as an empirical question. The narrative is written for a general audience and uses historical examples, reconstructed scenes, diagrams and the author’s experience as a bricklayer to make the underlying mechanisms intuitive without requiring mathematics. A technical companion manuscript, From Communication to Higher-Level Agency: Selective Enclosure, External Scaffolding, and Recursive Substrate Transitions in Adaptive Systems, develops the core mechanism mathematically and includes a proof-of-concept multi-agent simulation. The book is intended as the broader conceptual and narrative presentation of that research programme. First Open Edition, Version 1.0.
Jason Prevett· Zenodo (CERN European Organi...· 0 citations
# Zenodo deposit metadata Copy the fields below directly into the Zenodo "New upload" form. This file itself is metadata for you to copy from — it is not one of the three files to upload. ## Files to upload - `Supplementary_Material.docx` - `Supplementary_Table_S6.2_Included_Studies_Register.csv` - `Supplementary_Table_S6.3_Study_Characteristics.csv` ## Upload type Dataset ## Title Supplementary material and study-level data for "AI-mediated food choice: A systematic evidence map and critical review of personalization, persuasion, consumer agency, and industrial translation" ## Authors / Creators 1. Ketney, Otto — "Lucian Blaga" University of Sibiu, Department of Agricultural Science and Food Engineering 2. Tifrea, Anca-Maria — ALCALIN SRL ## Description This deposit contains the full Supplementary Material and the two underlying study-level data tables for the systematic evidence map and critical review "AI-mediated food choice: A systematic evidence map and critical review of personalization, persuasion, consumer agency, and industrial translation" (Ketney & Tifrea), submitted to Trends in Food Science & Technology. The review synthesises 197 primary reports on consumer-facing artificial intelligence in food choice, published 2016-2026, retrieved from Scopus, Web of Science, and PubMed, and classified using two original frameworks: the AI Food Choice Architecture (AIFCA) model and the P3D taxonomy (Prediction, Personalization, Persuasion, Delegation). Contents: - **Supplementary_Material.docx** — the complete supplementary material (thirteen sections, S1-S13): search strategy, screening workflow, eligibility criteria and analytical framework, structured review-question framework, included-studies register and report-to-study map, summary of findings, protocol and amendment log, search-update log, data-extraction codebook, structured synthesis without meta-analysis, meta-analysis rationale (not performed), and the PRISMA 2020 checklist. - **Supplementary_Table_S6.2_Included_Studies_Register.csv** — the complete bibliographic register of all 197 included studies (internal study identifier, authors, year, title, journal/source, DOI), sorted by first-author surname and year. - **Supplementary_Table_S6.3_Study_Characteristics.csv** — the complete per-study extraction table for all 197 included studies (country/region, food context, target population, AI technology modality, P3D function(s), interface type, study design, sample size, psychological mechanisms, primary outcomes, outcome measurement level, direction of effect, Field-Validation Maturity Level, reported risks/adverse effects, reporting completeness score, and evidence maturity class). This dataset does not reproduce verbatim abstract or full-text content from the source bibliographic databases; it contains only the authors' own extracted, coded, and derived characteristics for each included study. ## Keywords artificial intelligence; food choice; consumer behaviour; systematic review; evidence map; recommender systems; personalization; persuasion; choice architecture; digital nudging; PRISMA 2020 ## License CC BY 4.0 (recommended — matches the PRISMA 2020 statement's own licensing and allows unrestricted reuse with attribution) ## Version 1.0 ## Language English
Otto Ketney, Anca-Maria Tifrea· Zenodo (CERN European Organi...· 0 citations
This dataset contains encrypted mobile network traffic collected from ten widely used Android social media applications: Facebook, Instagram, LinkedIn, Reddit, Snapchat, Telegram, TikTok, Twitter, WhatsApp, and YouTube. Traffic was captured over five independent collection days under controlled experimental conditions and processed using NFStream to generate bidirectional flow-level records. The dataset comprises 25,116 labeled network flows extracted from 50 packet capture (PCAP/PCAPNG) files, with each application represented by five independent capture sessions. The published data contain the complete set of NFStream-extracted flow features, including statistical flow characteristics, packet-size statistics, timing information, transport-layer attributes, and encrypted-session metadata. This repository provides the raw NFStream feature dataset used in our study. Feature selection, leakage-control preprocessing, temporal train/test partitioning, and multi-flow aggregation were performed during the experimental pipeline and are described in the accompanying manuscript. The dataset was developed to support reproducible research in encrypted traffic analysis, mobile application fingerprinting, digital forensics, network security, and explainable artificial intelligence. It accompanies the manuscript "Encrypted Social Media Traffic Fingerprinting under Temporal Shift: A Public Benchmark and Multi-Flow Evaluation." If you use this dataset, please cite: Bright Jiwueze, et al. Encrypted Social Media Traffic Fingerprinting under Temporal Shift: A Public Benchmark and Multi-Flow Evaluation. Preprints.org, 2026. https://doi.org/10.20944/preprints202609.0324.v1
This dataset is a publicly available, multi-clinic dental imaging database that aims at furthering studies on artificial intelligence (AI), computer vision, and computer-aided diagnosis (CAD) based on panoramic dental radiographs (orthopantomogram or OPG). This database includes 2,095 completely anonymized panoramic dental radiographs gathered retrospectively from four separate dental centers in Bangladesh, namely Sonia Nursing Home, Tangail (1,433 radiographs); Ibn Sina D. Lab & Consultation Center, Dayaganj (533 radiographs); Niramoy Diagnostic Center, Tangail (114 radiographs); and Health City Diagnostic Center, Gaibandha (15 radiographs). Every image comes with bounding box annotations in both YOLO (.txt) and standard COCO JSON format, validated by experts, making the dataset easy to use with frameworks such as Ultralytics YOLO as well as any COCO-compatible model or data loader. Initial bounding boxes were drawn by a licensed dentist and independently reviewed by a second dental professional. A rigorous quality-control process followed, removing 127 substandard bounding boxes, resulting in 9,834 validated bounding boxes. Dataset Classes The dataset includes annotations for seven clinically relevant dental categories: - Missing Teeth: 2,609 annotations - Dental Crown: 1,984 annotations - Root Canal: 1,956 annotations - Caries: 1,410 annotations - Wisdom Teeth: 869 annotations - Broken Down Teeth: 795 annotations - Healthy OPG: 211 annotations Dataset Contents The released dataset includes: - Panoramic dental radiographs (.jpg) - COCO-format JSON annotation files (instances_train.json, instances_val.json, instances_test.json) - YOLO bounding-box annotation files (.txt) - Metadata files (class_definitions.csv, image_metadata.csv, split_assignments.csv) - Documentation (README.md) describing the dataset structure, annotation format, and usage instructions Potential Research Applications This dataset supports multi-class dental object detection, localization of dental abnormalities, CAD, deep learning for medical imaging, computer vision research, transfer learning and foundation models, explainable AI (XAI), medical image analysis, object detection benchmarking, AI-enabled dental diagnosis, dental AI learning, and reproducibility research. Benchmark Performance Three YOLO variants were evaluated to establish a baseline. YOLOv8m achieved the highest precision (71.9%) and mAP@0.5 (72.9%), with the fastest inference latency (1.9 ms per radiograph). YOLOv10m achieved the highest recall (74.2%) and mAP@0.5:0.95 (34.4%), with a latency of 3.4 ms. YOLOv11m reached a precision of 70.9%, recall of 71.6%, mAP@0.5 of 71.7%, and mAP@0.5:0.95 of 33.5%, with a latency of 4.2 ms. These results show the comparative detection performance and computational efficiency of the evaluated YOLO variants on this dataset.
Status - Accepted: The Journal of Systems and Software Abstract The adoption of industrial Artificial-Intelligence (AI) systems in production environments has increased considerably in recent years, enabling new forms of process automation, engineering support, and data-driven decision making. However, practitioner-oriented insights regarding their practical development, configuration, and operation remain limited. To address this gap, we conducted a four-round Delphi study with 26 practitioners involved in industrial AI systems in production-related environments. Using iterative expert assessments, thematic analysis, and consensus-oriented evaluation, the study synthesizes benefits, issues, risks, and mitigation strategies associated with industrial AI systems. The findings indicate that industrial AI systems are increasingly evolving from isolated analytical models toward integrated and highly configurable production ecosystems. While experts associated AI systems with operational and economic benefits, they simultaneously emphasized challenges related to integration complexity, configurability, dependency management, traceability, lifecycle management, and governance. In particular, dependency hell, combinatorial explosion, and configuration mismatches emerged as recurring practical concerns. Overall, the study suggests that many practical challenges of industrial AI systems no longer primarily emerge from model development itself, but from their integration and operation within heterogeneous production environments. We argue that our findings provide practice-oriented insights into the development, configuration, and operation of industrial AI systems.
Richard May, Leonard Cassel, Hashir Hussain et al.· Zenodo (CERN European Organi...· 0 citations
This article introduces the concept of the cognitive divide to describe the gap between societies, organizations, or sectors that are able to retain control over the conditions under which cognitive functions are integrated into, used through, and externalized to AI systems, and those in which these processes occur under constraints and in relationships of dependence on exogenous, opaque, and difficult-to-challenge algorithmic architectures. This divide should not be conflated either with the digital divide or with a simple deficit of individual skills. Rather, it refers to differentiated institutional, productive, and political configurations that determine the capacity to control and govern the cognitive infrastructures that structure perception, evaluation, and collective action. It manifests itself in particular through an unequal distribution of definitional power over the categories, metrics, and rules embedded within these infrastructures. The article argues that responses centered on technological adoption, training, or individual adaptation are structurally insufficient, and that the absence of robust collective mechanisms of coordination, regulation, and productive capacity contributes to the erosion of collective control over cognitive infrastructures. The framework distinguishes three dimensions of control—appropriation, governance, and productive capacity—and develops three ideal-typical AI deployment configurations characterized by different distributions of definitional power, lock-in, and reversibility. Because these configurations differ in the leverage available for intervention, they imply distinct governance priorities rather than a uniform regulatory response.
Ludovic ANDRES· Zenodo (CERN European Organi...· 0 citations
Version 6.3 introduces the regularization operator for the emergent domain and provides its full structural formalization, together with the rigorous definition of complexity thresholds that ensure the stability and projectability of the emergent structure. Version 6.2 introduces quantum maps and places quantum theory within the PSE framework through a complete mathematical formalization. The conceptual structure has been clarified, and the release includes a concise analysis of observational data potentially consistent with the emergent structure of the PSE. Version 6.1.1 introduces the explicit treatment of emergent rigidity and the definition of the Φrig map, refining the geometric projection framework and strengthening the internal coherence of the PSE formalization.Version 6.1 of the PSE introduces, relative to version 6.0, a more rigorous definition of the emergent domain X and a complete bibliography in the conceptual framework; in the mathematical formalization it adds the functional spaces of the PSE, the isomorphisms of the Fundamental Domain, the definition of the compressive map, and the diagram of the ontological chain.The 6.0 release of the PSE — Structural Principle of Entanglement represents the stabilized formulation of the theory, following the complete consolidation of its fundamental ontological chain: 𝑺 ⟶ 𝑫𝑺(𝒇) ⟶ 𝑪𝒇 ⟶ 𝑫s(𝒆) ⟶ 𝑪𝒆 ⟶𝒟physical. The transitions between the levels of the ontological chain are carried out by the operators 𝔇, Δ, Fλ, Gλ, and Φ, each with a specific structural role defined in the formalization of the PSE. In this structure, the chain is no longer referenced to the metric as its final object, but to the projection map Φ, which formalizes the transition from the pregeometric emergent domain to the physical domain. All physical quantities — metric, fields, geometric invariants, energetic and dynamical quantities — arise as compressed images of the emergent structure. Φ is not an ontological operator: it expresses the level of emergent structural complexity at which physical quantities become definable. Version 6.0 introduces a significantly expanded conceptual framework, now including: a precise theoretical positioning of the PSE within contemporary ontological approaches to quantum theory, a clarified physical interpretation of emergent quantities and their non‑fundamental status, a more comprehensive general introduction, outlining motivations, structure, and implications of the theory, a refined distinction between emergent structural objects and physical quantities, ensuring that the physical domain is understood as a deterministic projection of the emergent complexity of S, not as an ontological layer. The release definitively consolidates: the general conceptual framework of the PSE and its emergent chain, the rigorous mathematical formalization of the emergent domain, the definition of the fundamental operators 𝔇, Δ, Fλ, Gλ, and Φ, the proof of the functional uniqueness of the projection Φ, the deterministic derivation of the metric and emergent fields as components of Φ, the physical corollaries concerning curvature, derived tensor fields, and emergent stability. The release includes four documents: Conceptual framework (IT) — complete exposition of the theoretical structure, ontological assumptions, emergent chain, theoretical positioning, and physical interpretation. Mathematical formalization (IT) — definitions, operators, Theorems, Corollaries, stability and functional determination. Conceptual framework (EN) — rigorous English version intended for international dissemination. Mathematical formalization (EN) — rigorous English version intended for international dissemination. Version 6.0 is declared conceptually final: the theoretical structure is complete, coherent, and ready for future integration with applied physical models, while remaining open to future development. This work was developed with the support of artificial intelligence. Author: Andrea Toricelliindependent researcherEmail: a.toricelli@hotmail.it Although I am not a physicist by training, I have done my best to present this thesis as clearly and rigorously as possible; my sole intention is to offer a useful contribution to the scientific community.
Andrea Toricelli· Zenodo (CERN European Organi...· 0 citations
Contemporary artificial intelligence systems demonstrate remarkable capacity for pattern recognition and statistical inference; however, an explicit internal structure for representing human abstract concepts such as power, love, trust, and freedom remains absent. These notions are typically treated as qualitative or context dependent constructs, without a formal relational architecture that enables structural interpretation, manipulation, and dynamic simulation. The N-Universe is introduced as a mathematical and geometric framework designed to represent human abstracts as structured relational entities within a unified formal space. The framework is grounded on the structural principle that each human abstract possesses an intrinsic geometry, understood as a characteristic relational organization that can be formally described through distinct geometric structures. Such geometries are not metaphorical illustrations, but mathematically defined configurations expressing essential properties including orientation, distribution, symmetry, intensity, and dynamic variation. Whereas Gärdenfors' conceptual spaces employ geometry in a static sense, as a similarity metric organizing categorization within a single cognitive agent, the N-Universe employs geometry in a dynamic sense, describing how an abstract behaves, propagates, and reacts over time within a bounded social-relational environment N composed of interacting human agents. The shared vocabulary of "intrinsic geometry" should not obscure this fundamental difference in what is being modeled: a static representational structure versus a dynamic relational process. Within this setting, abstract concepts are modeled as geometric configurations defined over a relational domain in which vectors, fields, and gradients encode structural properties. Power is formally defined as a vector structure possessing magnitude, vertical direction, and orientation determined by the interaction between applied power and reactive power. This formulation allows power to be interpreted as an oriented relational magnitude whose dynamics arise from the interplay between deliberate action and structural response of the environment. Love may be conceived, under the intrinsic geometry principle proposed by the N-Universe, as a radial field centered on a relational nucleus, with intensity decreasing as structural distance increases, thereby expressing cohesion and affective attraction. In a similar manner, trust may be interpreted as a continuous structural gradient describing stability and directional coherence across agents along relational or temporal axes. These formulations indicate potential geometric extensions of the framework rather than fully formalized components of the present model. Formally, a structured relational space is defined in which abstract fields are measurable, dynamically evolving, and computationally tractable. Symbolic interpretation is integrated with continuous mathematical representation, enabling hybrid reasoning and structured semantic analysis. By transforming qualitative human abstracts into geometrically intrinsic and mathematically coherent relational objects, a foundation is established for interpretable abstraction modeling, social simulation, and structurally grounded artificial intelligence systems.
Kauê Basso· Zenodo (CERN European Organi...· 0 citations
