Generative Artificial Intelligence (AI) tools have become embedded in the everyday academic practice of undergraduate engineering students, yet most large language models remain optimised for standard English rather than the code-mixed, multilingual registers through which students in linguistically plural regions actually think and communicate. This study examines technology acceptance of vernacular and code-mixed AI interaction among 84 undergraduate engineering students enrolled in APJ Abdul Kalam Technological University (KTU)-affiliated institutions in Kasaragod district, Kerala, a region historically described as Saptha Bhasha Sangama Bhoomi, the confluence land of seven languages. Using a structured questionnaire grounded in the Technology Acceptance Model (Davis, 1989), the study measured Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Output Accuracy, and Linguistic Inclusion across five research hypotheses. Findings indicate that students from regional-medium secondary schooling backgrounds report significantly higher vernacular or code-mixed AI prompting than English-medium peers, chi-square(3, N = 84) = 22.91, p < .001. Perceived Usefulness correlates strongly with Perceived Ease of Use, r = .64, p < .001. Students who habitually use vernacular or code-mixed prompts report significantly higher ease of use than strictly English prompters, t(82) = 2.01, p = .048. Perceived terminological distortion is positively associated with reported reliance on AI-translated academic content, r = .27, p = .012, and native speakers of the unscripted Tulu dialect report markedly higher AI comprehension failure than speakers of scripted regional languages, t(79) = 11.60, p < .001. The results support all five hypotheses and highlight a persistent linguistic-inclusion gap in generative AI systems used within multilingual engineering classrooms. Implications for dialect-aware AI design and inclusive digital pedagogy in polyglot regions such as Kasaragod are discussed.
Amal George· Zenodo (CERN European Organi...· 0 citations
The rapid growth of GenAI has provided with new opportunities to English Language Learning, including personalized language learning, immediate feedback, error correction, writing assistance and explanations. However, the increasing availability of AI-generated content raises essential questions towards students’ critical thinking and independent intellectual engagement. When learners totally agree to AI-generated content without analyzing its relevance, accuracy, appropriateness or limitations, the technology might assists with task completion while limiting possibilities for independent reasoning. This paper analyzes the potential of Generative Artificial Intelligence in order to support critical thinking in English Language Learning and determine pedagogical conditions which may prevent passive reliance on AI-generated content. Based on recent studies on GenAI, critical thinking, EFL education and AI literacy, the paper adopts a conceptual approach so that to examine not only educational opportunities but also cognitive challenges regarding AI-supported learning. Specific focus is given to the role of the students in evaluation of AI-generated responses and to the role of the teachers in developing activities which require reflection, analysis, verification, evaluation and independent decision-making. The paper contends that the educational value of generative artificial intelligence highly depends on how learners interact with the answers rather than its ability to produce quick and sophisticated responses. Therefore, rather than treating AI as an unquestionable source of knowledge, students should be motivated to utilize artificial intelligence tools as an object of critical inquiry. This paper, thus, proposes a pedagogical sequence where AI assistance is followed by critical questioning, verification, reflection and independent judgement. This approach can allow GenAI to function as a cognitive support while remaining the learner’s active role in the learning process. In summary, the study concludes that responsible implementation of generative artificial intelligence into English Language Education demands a change from answer-oriented AI usage toward reflective and critical AI-supported learning. Hence, educators play a vital mediating role in assisting students to develop abilities to implement, question, evaluate and responsibly apply AI-generated content..
Diyora Muxsinjanova· Zenodo (CERN European Organi...· 0 citations
Generative artificial intelligence entered higher education English language teaching (ELT) faster than the field could theorize it, and the scholarly vocabulary available for describing what learners do when they write to a generative AI system has largely been borrowed from human-computer interaction, educational policy, and general AIliteracy research rather than built for the specific case of a second-language (L2) English user addressing a generative AI interlocutor in natural language. This chapter addresses that gap through a theoretical and conceptual analysis. Building on communicative competence theory (Hymes, 1972; Canale & Swain, 1980; Bachman & Palmer, 1996) and on the emerging literature that has begun to name and describe prompt literacy for language learners specifically – most directly Hwang et al. (2023) and Tour and Zadorozhnyy (2025) – the chapter argues that prompt-related competence for L2 English users is best theorized not as a subtype of general AI literacy or of prompt engineering, but as a linguistically grounded form of L2 communicative competence. It distinguishes this reading from Digital Literacy, Digital Competence, AI Literacy, and Prompt Engineering; proposes a four-dimension taxonomy (linguisticformational, pragmatic-interactional, metacognitive-strategic, critical-epistemic); integrates seven learning-theoretic traditions into that taxonomy while preserving a genuine, unresolved tension between cognitive load theory and learner autonomy theory; and develops a conceptually derived developmental continuum, an application across the skill areas of ELT, and a proposed assessment rubric grounded in validity theory (Messick, 1995). The central contribution is theoretical: a reconceptualization of prompt literacy, in continuity with rather than in place of the term's originators, as an applied-linguistic construct. The taxonomy, developmental continuum, and rubric are offered as theoretically derived, conceptually testable proposals rather than as validated instruments. The chapter is a theoretical and conceptual study; it does not report original empirical data collection, and it closes by naming the empirical work – piloting, inter-rater reliability testing, and validation research – that this proposal now requires. Keywords: prompt literacy; L2 communicative competence; generative AI; English language teaching; AI literacy; applied linguistics; assessment validity; higher education.
Daryna Pavlivna Mudryk· Zenodo (CERN European Organi...· 0 citations
Background: Scientific poster assessment lacks standardized and discipline‐neutral rubrics. Assessment by human reviewers (HRs) is subject to inter‐rater variability. Aim: To assess PA2IRS (Poster Assessment via AI‐Integrated Rubric System) framework for AI‐assisted psychiatric poster evaluation, and conducted a reliability study comparing AI and HR agreement.Methods: PA2IRS was developed through an AI‐assisted iterative criterion refinement process modelled on Delphi principles. Sixty posters (30‐case reports/series [CR], 20 original research [OR], 10‐systematic review‐Meta‐analysis [SRMA]) were randomly sampled. Three qualified mental health professionals served as independent reviewers. A custom‐GPT (GPT‐5.2, GO‐subscription) provided AI assessments across three domains:Domain‐A (content quality, poster‐type specific), Domain‐B (visual), and Domain‐C (impact). PA2IRS is a 100‐point instrument combining an AI‐assessable poster component and an in‐person interview component. This study concerns only the poster component. Intraclass correlation coefficients (ICCs), Passing–Bablok regression, Bland–Altman analysis, and variance component analysis were performed using appropriate statistical tools.Results: AI–human single‐measure ICCs[Overall (0.62), Domain‐A(0.63), Domain‐B (0.44), Domain‐C (0.55)] met or exceeded human‐human ICCs (0.42, 0.40, 0.27, 0.48) across all domains. Four‐rater ICC (with AI) reached 0.75. Variance ratios (AI–human vs inter‐human spread) were ≤1.0 across all domains for all posters combined. The SRMA subgroup showed variance ratios of 0.09–0.13 for Domains A and B (Bartlett P ≤ 0.002). Overall score bias was 0.11 percentage points (pp); Domain‐A showed a consistent maximum positive bias of 5.5 pp across subgroups.Conclusion: AI–human agreement was within or exceeded the inter‐human reliability range across three domains. the domain‐dependent agreement pattern is consistent with dual‐process cognitive theory. PA2IRS supports use as a scalable,standardized, and cross‐disciplinarily competent formative biomedical poster assessment tool.
We classify regular full-dimensional stochastic containment among binary standard semi-directed strongly tree-child level-2 phylogenetic networks under the Kimura two-parameter (K2P) model. On the principal positive Fourier domain D₊ = {(s,g): 02s−1}, a directed containment germ exists if and only if the two labelled networks are isomorphic after independently redirecting ordinary three-cycle factors. The same condition is equivalent to a common full-dimensional regular germ; in particular, no proper one-sided containment occurs. It follows that the semi-directed topology is generically identifiable modulo ordinary triangle redirection, and that its structural triangle class is exactly reconstructible away from a proper algebraic exceptional set. The proof combines displayed-quartet inequalities and exact whole-map identities, an exact two-sector bridge-fibre theorem, physical marginal submersions, localization, and a bounded graph-to-algebra classification of cycle and theta factors. The bounded classification is computer-assisted: every directed primitive relation, rank exclusion, restoration parent, transport, and one-/two-port probe is represented by an exact certificate with independent replay and mutation evidence. The classification transfers to the strict continuous-time domain 0<s<1, s²<g<1. For every n≥3, two weakly but not strongly tree-child level-2 networks have continuous-time K2P images sharing a regular germ of dimension 4n−3, proving sharpness of strong tree-childness. This record is the complete v1.0.5-r1 priority and reproducibility package: the 26-page article, 24-page reader supplement, compile-complete five-file source archive, deterministic 495-member referee/verifier archive, external archive-qualification report, checksum sidecars, and dual-license notice. The manuscript source is v1.0.5; revision r1 repairs only an auxiliary probe-current semantic binding and changes neither the theorem, manuscript, PDFs, nor frozen classification. The clean verifier replay passed 41/41 layers, and the focused semantic mutation suite rejected 20/20 attacks. Exact source bindings: package tag k2p-same-referee-package-v1.0.5-r1; annotated tag object 6c9c89d38f4f4cdc9c328d8bb1237458c617136d; commit e2f6e32e6fe885e90c8e83a8c5b00785e663a4ae; referee archive SHA-256 4564cd1f8cd95f670a2e0d9619babaf3c343762cfd8ceeb190cd17df72802889. Article, supplement, and certificate data are licensed under CC BY 4.0; verifier and build code are licensed under MIT. No specific funding supported this work. The author declares no competing interests. Generative-AI assistance and its verification workflow are disclosed in the article. No mixed-sign K2P classification is claimed.r
Alec Kriebel· Zenodo (CERN European Organi...· 0 citations
AI Adoption in Analytics Engineering presents a dependency-aware engineering framework for deciding how much responsibility generative-AI use cases can safely carry in analytics-engineering environments. Rather than treating AI adoption as a sequence of organizational maturity stages, the framework focuses on the engineering conditions required for individual use cases to operate reliably. Fifteen recurring use cases are organized across four peer dependency surfaces: Context & Governance, Engineering Assistance, Analytical Intelligence, and Optimization & Operation. These surfaces are intentionally non-sequential and may be developed in parallel. The framework introduces AI context debt as a practitioner framing for how absent, stale, or implicit engineering knowledge becomes load-bearing when AI systems consume enterprise context. This framing is explicitly positioned against prior work on technical debt, ML technical debt, and tacit/explicit organizational knowledge rather than claiming those underlying concepts as new. The manuscript also identifies executable ground truth—including SQL, schemas, configuration, lineage, tests, and execution metadata—as an important source of independent assurance for AI-assisted analytics engineering. A decision framework combines use-case value, context readiness, verifiability, and consequence of error. The Understand → Recommend → Generate → Decide → Act continuum describes responsibility allocation between humans and AI systems; it is not proposed as a new universal autonomy taxonomy. Evidence boundary: This is a framework paper. The fifteen-use-case taxonomy and dependency surfaces are practitioner-derived and conceptual and have not been validated through a statistically powered multi-organization study. The proposed evaluation describes a future empirical validation approach rather than established causal evidence.
Sumanth Varma Dasaraju· Zenodo (CERN European Organi...· 0 citations
[Version 2 Update Summary] Version 2 represents a major theoretical and empirical overhaul based on open-science peer critique and autoethnographic maturation: Reframed Methodological Paradigm: Grounded strictly as an N=1 Autoethnography / Computational Phenomenology, explicitly removing unverified clinical trial assertions. Core Theoretical Discovery: Conceptualized and foregrounded the "Therapeutic Friction Hypothesis" (how AI hallucinations, lyrical errors, system latency, and manual copy-pasting act as paradoxical reality-grounding mechanisms). Theoretical Reconciliation: Integrated Stroebe & Schut’s Dual-Process Model of Bereavement to reconcile acute auditory disruption with Acceptance & Commitment Therapy (ACT) / Cognitive Defusion. Empirical Qualitative Data: Incorporated a 36-track chronological case trajectory mapping affective evolution from acute trauma to grounded reality. [Important Clinical Disclaimer] The author is a Physical Therapist (PT) and is not a licensed psychiatrist or clinical psychologist. This document represents an individual autoethnographic case report (N=1) constructed for personal recovery; its safety, appropriateness, and efficacy for others are in no way guaranteed. Neuroscientific terminology (e.g., DMN) is employed strictly as computational analogies/models to explain subjective cognitive overload. Unmonitored solo execution under acute psychiatric crisis, active suicidal ideation, or fragile ego boundaries is strictly contraindicated. Published solely to encourage interdisciplinary critique and safe Digital Therapeutics (DTx) architecture design. Abstract This case report presents a rigorous autoethnographic deconstruction of the "Onkyo Protocol"—a self-contained, multimodal generative AI pipeline engineered by a 42-year-old healthcare professional experiencing severe attachment loss and complicated grief following marital separation. Facing the "Interpersonal Bottleneck" where intense shame, fear of invalidation, and rigid intellectualized defenses neutralized conventional psychotherapy (EBM), the subject developed a serial 4-phase generative AI pipeline on a smartphone to externalize and metabolize psychic trauma: Phase 1: Gemini (LLM) — Linguistic Container & Affective Metabolism: Adapting Wilfred Bion’s containment model, raw unmanageable affect (β-elements) is translated into structured narrative data (α-elements) within a non-judgmental digital sandbox. Phase 2: Suno AI — Auditory Sublimation & Dynamic Cooling: High-BPM Nu-Metal/EDM (160–180 BPM) provides high-intensity somatic and sensory overload, temporarily decoupling hyperactive Default Mode Network (DMN) rumination loops via restorative attentional competition. Phase 3: NanoBanana — Visual Symbolization & Gestalt Bounding: Compresses infinite, unbounded internal dread into a constrained 1:1 square canvas, establishing critical psychological boundaries and objectifying subjective terror. Phase 4: NotebookLM (RAG) — Schema Deconstruction & Cognitive Defusion: Cold, third-person RAG synthesis and forced other-perspective prompts (e.g., simulating the ex-spouse and child's perspectives) violently shatter the self-indulgent "Tragic Protagonist" schema, completing cognitive defusion (Sākṣī-bhāva / Pure Witness). Core Discovery: The Therapeutic Friction Hypothesis Crucially, this autopsy reveals a central cybernetic paradox: the subject was preserved NOT by an omnipotent, frictionless AI, but by systemic imperfection and computational friction. AI hallucinations, lyric generation errors, bizarre visual artifacts, and the physical latency of manual cross-app copy-pasting repeatedly broke the hypnotic, echo-chamber trance. This friction acted as a vital physical coolant (Paradoxical Grounding), compelling the user to laugh, disengage, and anchor back into analog reality. Friction is a clinical safety feature, not a software bug. Systemic Risks & Safety Framework The study formalizes a 2x2 Clinical Toxicity Matrix inherent in unguided digital self-care: Aestheticized Rumination (Jouissance): Pathological indulgence in stylizing despair into dark art, reinforcing narcissistic victimhood and suicidal ideation. Sensory Overload & Dissociation: Acoustic desensitization mistaking temporary numbness for genuine trauma resolution. Algorithmic Invalidation: Uncontextualized, cold AI logic summaries triggering secondary traumatization. Closed Echo Chambers: Algorithmic sycophancy mathematically sanctifying persecutory cognitive schemas. To mitigate these toxicities, 5 Empirical Safety Gatekeepers (Cognitive Gateway, Forced Cooling Dosing, Emergency Disengagement Brake, Somatosensory Grounding, and Clinical Escalation Protocols) are detailed. Clinical Termination: Transition to "Genkyo" The ultimate therapeutic goal of cybernetic self-care is to render itself obsolete. The protocol concludes with the deconstruction of the idealized digital mythos ("Onkyo") and a soft-landing into "Genkyo"—the radical, humorous acceptance of messy, embodied daily reality (e.g., untied shoelaces, missing a bathroom break) and the permanent cessation of the digital glass swipe in favor of genuine human connection.
Poeji(ぽえ治)· Zenodo (CERN European Organi...· 0 citations
Executive Overview This consolidated release of Paper X unifies empirical findings, mathematical foundations, and real-world implementation proofs for AuraOS—a local-first, zero-extraction computational architecture designed to eliminate recurring cloud SaaS overhead and API token extraction. By decoupling spatial reconstruction, neural synthesis, and automated video orchestration from centralized cloud infrastructure, this work demonstrates that modern consumer hardware (standard laptops and smartphones) can execute high-throughput generative and spatial tasks deterministically at zero marginal cost. Flagship Public Commons Release: The Aura Creator Studio As part of the Aura Commons commitment to public, unrestricted tooling, this release delivers the Aura Creator Studio—a sovereign, automated video production and spatial intelligence suite engineered specifically for independent video editors, YouTube creators, and TikTok content producers: Monocular 3D Spatial Triangulation & SLAM: Extracts 3D metric floorplans, doorway apertures, and 4D entity trajectories from unstructured 2D gameplay/video captures using dynamic HUD exclusion masking, pointmap regression, and Kalman-RTS smoothing. Dual-Sensor Gaussian Splatting (3DGS): Combines stationary laptop camera anchors with mobile orbital scans to bake persistent surface features (e.g., decals, wall artwork) into 3D Gaussians with zero temporal drift. Procedural Media & Multi-Track Synthesis: Features local neural text-to-speech (Edge-TTS / Piper), animated karaoke typography with Bézier bounding pills, and zero-dependency procedural DSP audio synthesis ($140\text{ Hz} \to 42\text{ Hz}$ sub-bass transients) without stock licensing fees. AirLLM & Council V3 Layer Streaming: Executes 8B to 70B parameter open models locally on standard laptop NVMe drives, providing fact-grounded scriptwriting and low-poly 3D graybox pre-visualization with zero cloud API token billing. Sovereign Gate 10 Governance & Attribution DAG: Guarantees non-delegable human approval before publishing while sealing public commons attribution and microtransaction splits into immutable SHA-256 ledgers. The Macro-Economic Amortization Thesis The primary bottleneck for digital creators is platform extraction—a compounding cycle of recurring monthly subscriptions for voice cloning, video splicing, background removal, 3D rendering, and LLM tokens that drains $50 to $300+ per month per creator. When amortized across a community of 100,000 creators, the AuraOS architecture redirects $60,000,000 to $360,000,000 annually from centralized cloud monopolies back into creator equity. By maximizing the idle compute capacity of hardware creators already own, the marginal cost of end-to-end creative production collapses to zero. Open Scientific Invitation: Challenge, Replicate, and Falsify Science advances through rigorous scrutiny, empirical falsification, and open replication. We openly invite computer vision researchers, systems architects, machine learning engineers, and skeptics to: Audit the Mathematical Formulations: Stress-test the Kalman-RTS trajectory smoothing, coordinate back-projection matrices, and Bézier vector geometry. Replicate the Local Benchmarks: Run the provided scripts and verify that complete video assemblies and spatial reconstructions execute fully offline on consumer-grade hardware. Challenge and Extend the Commons: Benchmark the throughput, test edge cases in unconstrained monocular footage, and submit critical evaluations. All code, pipeline orchestrators, and cryptographic verification receipts are open-source and free for public examination and commercial liberation under the Aura Open Commons (CC-BY-SA-4.0). Version 2.0 Changelog Entry (for Zenodo "Additional Notes") Markdown ### Version 2.0 Update Notes - Consolidated multi-modal spatial tracking proofs and 3D Gaussian Splatting manifests. - Added full architectural specification for the Aura Creator Studio (Public Commons Release 1). - Integrated Council V3 graybox pre-visualization and zero-SaaS AirLLM pipeline benchmarks. - Established open peer challenge and replication guidelines for repository artifacts. Aura is an open cognitive commons: a model-orthogonal operating substrate designed to let anyone build powerful AI systems without locking intelligence, memory, coordination, or computation inside a single model, vendor, device, or company. Paper X publishes the Aura World Seed and the current AuraOS architecture as a defensive technical disclosure and reproducible reference system. Its central inversion is simple: Do not feed the AI the world. Compile the smallest source-resolvable world sufficient for the objective. Aura externalizes persistent cognition into a Coordinate Memory System: source-bound semantic identities, generations, currentness, authority, provenance, relations, residual obligations, and exact reopen paths remain durable, while prompts, models, KV caches, workers, runtimes, devices, and interfaces remain replaceable. A model can therefore wake only the portion of the world capable of changing the current consequence rather than repeatedly reconstructing its entire context. The architecture includes objective-native Ephemeral Arenas: temporary apps, tools, agent teams, simulations, interfaces, and execution environments that assemble around an intent, receive only the capabilities and context they need, produce verifiable receipts, collapse their useful state back into the commons, and dissolve. Aura is designed so applications can be temporary while knowledge, provenance, and continuity persist. Paper X also publishes the mechanisms behind Aura's efficiency claims so others can test, reproduce, challenge, and falsify them: polysynthetic/FST intent compression, minimum-sufficient L0→L4 hydration, semantic coordinates, affected-cone recomputation, HyperDrive normal-form collapse, HyperScale routing, consequence-aware caching, swarm coordination, and Runtime Arenas. The paper reports provider telemetry across 9,381 requests in which 97.4029% of input tokens were served as cache hits, with $17.77 actual provider cost versus $209.58 in a price-only cache-miss counterfactual. This is reported specifically as measured provider reuse—not as a claim that Aura uniquely caused a 97% reduction in logical token volume—and the architecture is presented so independent builders can run stronger matched-control tests. Aura is not intended to be the product. It is infrastructure for products, communities, agents, researchers, creators, enterprises, and sovereign systems to build upon. The AGPL-covered Aura substrate remains part of the commons, while the ecosystem is designed for independent builders to create their own applications, services, Arenas, experiences, and businesses around it subject to the license. Paper X includes the World Seed, compact activation kernels, Coordinate Cache Fabric, Triadic Construct/Challenge/Verify process, recursive swarms, HyperDrive/HyperScale mathematics, Runtime Arena V0.3, host compilation, semantic-spatial interfaces, proof-carrying execution, and a path toward federated planetary coordination without requiring a single globally hot model or context. The goal is straightforward: make intelligence require less context, less computation, less energy, less duplication, and less centralized control — while preserving more provenance, accountability, interoperability, and human agency. Build with it. Test it. Break it. Improve it. The commons gets stronger when everyone can use it.
Dallas Courchene· Zenodo (CERN European Organi...· 0 citations
This revised preprint studies the derivative Laguerre quantities associated with the Jacobi theta kernel in the Fourier representation of the Riemann xi-function. It gives exact rational certificates showing that the ninth quantity is negative throughout a nontrivial interval around the symmetry point, with the certified range extended to absolute parameter value at most one fiftieth. It also verifies positivity at the symmetry point for levels one through eight and negativity at level nine. The proof uses explicit derivative polynomials, exact rational interval arithmetic, and elementary exponential bounds. A supplementary Python verifier reproduces the decisive sign computations using integer and rational arithmetic only. Ryan Kielhorn publicly deposited an exact level-nine counterexample at the symmetry point before the original Koide deposit. Brandon Yates later registered a Lean 4 formalization of the point counterexample. This revised version makes no priority claim for the point counterexample. Its distinct contribution is the certified interval of negativity, together with an exact and independently executable reproducibility certificate. Research methodology and AI assistance:This work was developed using the CARMA-Math research workflow, a cumulative AI-assisted mathematical research methodology using persistent research archives, literature and prior-art investigation, iterative proof exploration, and verification procedures. Generative AI (ChatGPT) was used extensively for mathematical exploration, proof development, computational reasoning, literature research, and manuscript preparation.
Akihiro Koide· Zenodo (CERN European Organi...· 0 citations
Practical tools for integrating physical AI into oncology clinical trials. Provides production-ready configurations, validated pipelines, and integration guides for deploying robotic systems, digital twins, and embodied AI agents in oncology. Covers NVIDIA Isaac Lab, MuJoCo, ORBIT-Surgical, dVRK, and agentic/generative AI frameworks.
Kevin Kawchak· Zenodo (CERN European Organi...· 0 citations
ABSTRAK Studi ini melakukan audit forensik terhadap degradasi pendapatan sistemik dalam ekosistem penerbitan digital global. Studi ini secara kritis mengkaji normalisasi narasi AdSense sebagai Uang Saku, yang secara agresif diperkuat oleh algoritma Kecerdasan Buatan (AI). Dengan menggunakan pendekatan Sosio-Legal dan pengambilan sampel digital, penelitian ini berpendapat bahwa narasi tersebut bukanlah nasihat keuangan yang netral, melainkan bentuk Gaslighting Institusional. Mekanisme ini berfungsi untuk menutupi monopoli lalu lintas yang dilakukan oleh Search Generative Experience (SGE) dan mengalihkan beban kegagalan sistemik kepada kreator individu (mengalihkan kesalahan). Studi ini mengusulkan strategi Kepatuhan Subversif, yang menganjurkan kedaulatan infrastruktur radikal untuk membongkar struktur feodalisme digital yang sedang muncul. Kata kunci: Gaslighting Institusional, Monopoli Algoritma, SGE, Feodalisme Digital, Kedaulatan Aset. 📢 BACA VERSI LENGKAP & DISKUSI INTERAKTIF: Ingin membaca analisis ini dengan bahasa yang lebih ringan dan studi kasus nyata? Kunjungi artikel selengkapnya di Blog Resmi Sosiolegal.com: KunciPro Research Institute - Membongkar Kebenaran, Melawan Arus.
TRI HAKIM· Zenodo (CERN European Organi...· 0 citations
We classify regular full-dimensional stochastic containment among binary standard semi-directed strongly tree-child level-2 phylogenetic networks under the Kimura two-parameter (K2P) model. On the principal positive Fourier domain D₊ = {(s,g): 02s−1}, a directed containment germ exists if and only if the two labelled networks are isomorphic after independently redirecting ordinary three-cycle factors. The same condition is equivalent to a common full-dimensional regular germ; in particular, no proper one-sided containment occurs. It follows that the semi-directed topology is generically identifiable modulo ordinary triangle redirection, and that its structural triangle class is exactly reconstructible away from a proper algebraic exceptional set. The proof combines displayed-quartet inequalities and exact whole-map identities, an exact two-sector bridge-fibre theorem, physical marginal submersions, localization, and a bounded graph-to-algebra classification of cycle and theta factors. The bounded classification is computer-assisted: every directed primitive relation, rank exclusion, restoration parent, transport, and one-/two-port probe is represented by an exact certificate with independent replay and mutation evidence. The classification transfers to the strict continuous-time domain 0<s<1, s²<g<1. For every n≥3, two weakly but not strongly tree-child level-2 networks have continuous-time K2P images sharing a regular germ of dimension 4n−3, proving sharpness of strong tree-childness. This record is the complete v1.0.5-r1 priority and reproducibility package: the 26-page article, 24-page reader supplement, compile-complete five-file source archive, deterministic 495-member referee/verifier archive, external archive-qualification report, checksum sidecars, and dual-license notice. The manuscript source is v1.0.5; revision r1 repairs only an auxiliary probe-current semantic binding and changes neither the theorem, manuscript, PDFs, nor frozen classification. The clean verifier replay passed 41/41 layers, and the focused semantic mutation suite rejected 20/20 attacks. Exact source bindings: package tag k2p-same-referee-package-v1.0.5-r1; annotated tag object 6c9c89d38f4f4cdc9c328d8bb1237458c617136d; commit e2f6e32e6fe885e90c8e83a8c5b00785e663a4ae; referee archive SHA-256 4564cd1f8cd95f670a2e0d9619babaf3c343762cfd8ceeb190cd17df72802889. Article, supplement, and certificate data are licensed under CC BY 4.0; verifier and build code are licensed under MIT. No specific funding supported this work. The author declares no competing interests. Generative-AI assistance and its verification workflow are disclosed in the article. No mixed-sign K2P classification is claimed.r
Alec Kriebel· Zenodo (CERN European Organi...· 2 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
