Blockchain Papers

Navaneath Narendra Vasudeva, Paul Tracey

The ethical and social implications of smart contracts (SC) in the construction industry are examined, with a particular focus on their interaction with the Joint Contracts Tribunal (JCT) suite of standard forms. Using a qualitative, narrative literature review, this article compares blockchain-enabled automation with established contractual mechanisms, concentrating on payment processes, governance and the role of professional judgement. Although the findings suggest that SC can enhance transparency and payment integrity, especially in a building information modelling enabled environment, they also introduce rigidity, reduce the scope for discretionary decision making and raise concerns regarding fairness, inclusivity and legal certainty. A hybrid model is proposed in which SC complement, rather than replace, JCT mechanisms, preserving relational trust and interpretative flexibility while delivering auditable efficiency where automation adds genuine value. The review draws on peer-reviewed academic literature, industry reports and material relevant to contemporary UK construction practice. Academic databases (Scopus, ScienceDirect, Taylor & Francis Online and the ICE Virtual library) were searched using targeted keywords. Sources were selected based on relevance, academic rigour and recency, with priority given to publications from the past decade.

Yassine DOURHANI, Fatim Ezzahra BOUSSETTA

Blockchain technology and smart contracts are profoundly reshaping contract law by partially replacing traditional legal rules with algorithmic norms based on automation and self-execution. By embedding the parties' agreement into computer code deployed on a distributed ledger, these technologies promise enhanced security, certainty of performance, and the reduction of traditional intermediaries. However, this emerging algorithmic normativity confronts fundamental requirements of contract law, particularly those relating to validity, flexibility in the face of unforeseen events, and the protection of contracting parties. While automatic execution strengthens technical efficiency, it also exposes significant legal limitations, including the rigidity of code, the absence of interpretative mechanisms, and the difficulty of integrating traditional corrective tools. This article therefore highlights the need for an appropriate legal framework capable of reconciling legal norms with algorithmic rules, ensuring that technological innovation contributes to, rather than undermines, legal certainty in contractual relations.

Basheir Khan, Amizah Malip, Abid Khan

No abstract is available for this record.

Farman Guliyev

Abstract. "Truth is what is known iteratively and collectively." This paper does not claim to resolve the debates around AI governance. What it offers is a question — one that emerged from independent research on collective decision-making infrastructure over the past years of study. Four influential frameworks address the question of human-AI coexistence: Russell (2019), Aschenbrenner (2024), Buterin (2026), EMPATIC (2026). Each is serious and necessary. But all four, in different ways, assume that the human signal they aim to protect, represent, or augment is already genuine. This paper — written in the context of developing BeTrueCore — asks: what if it isn't? And what would it take to protect that signal before any delegation, control, or rights framework is applied? Keywords: collective decision-making, authentic human signal, AI governance, zero-knowledge proofs, preference falsification, cryptographic infrastructure, sovereign collective intelligence, immune islands, Panopticon effect, iterative truth, meritocracy, BeTrueCore, MACI, value alignment, situational awareness, human sovereignty.

Muhammad Izzuddin Al Ayzami, Achmad Fathoni, Moh. Sirojuddin

Penelitian ini mengkaji apakah aset digital memenuhi unsur māl mutaqawwam dan dapat menjadi mahar yang sah menurut fikih Islam. Proliferasi aset digital meliputi cryptocurrency, stablecoin, non-fungible token (NFT), utility token, dan security token telah menghadirkan pertanyaan baru yang belum terpetakan dalam hukum keluarga Islam (Ahwal Syakhshiyyah), khususnya tentang kelayakan aset digital sebagai mahar perkawinan. Kajian-kajian sebelumnya dominan membahas kehalalan cryptocurrency secara umum tanpa melakukan evaluasi sistematis terhadap berbagai kategori aset digital berdasarkan syarat-syarat mahar yang ditentukan fikih klasik. Penelitian ini menggunakan metode library research dengan pendekatan normatif-konseptual, merujuk pada sumber-sumber fikih primer empat mazhab Sunni (Hanafi, Maliki, Syafi'i, dan Hanbali) serta lembaga fatwa kontemporer yang otoritatif, meliputi DSN-MUI, AAOIFI, dan Majma' al-Fiqh al-Islami. Kajian ini mengintegrasikan enam kerangka teoritis māl, māl mutaqawwam, milkiyyah, qabd, gharar, dan syarat-syarat mahar dan menerapkannya secara analitis terhadap setiap kategori aset digital. Temuan penelitian ini menunjukkan bahwa tidak semua aset digital memiliki status hukum yang identik. Stablecoin dan utility token yang dilindungi hukum umumnya memenuhi syarat māl mutaqawwam dan dapat berfungsi sebagai mahar yang sah apabila mekanisme qabd yang jelas ditetapkan. Cryptocurrency yang sangat volatil menimbulkan kekhawatiran signifikan terkait gharar dan defisiensi taqawwum, sehingga statusnya bergantung pada pengakuan regulasi dan stabilitas pasar. NFT dapat menjadi mahar yang sah sepanjang aset yang mendasarinya memberikan hak ekonomi yang diakui dan dapat dialihkan. Security token memiliki klaim terkuat sebagai mahar mengingat sifatnya yang didukung aset dan pengawasan regulasi. Penelitian ini memberikan kontribusi berupa matriks klasifikasi hukum komprehensif untuk aset digital sebagai mahar dan mengusulkan kerangka konseptual untuk integrasi aset digital ke dalam hukum keluarga Islam kontemporer.

Anton Toth

The Adaptive Neural Gossip Protocol (ANGP) is a fully decentralized, asynchronous consensus mechanism designed for Directed Acyclic Graph (DAG) based distributed ledgers. Unlike classical Byzantine Fault Tolerant (BFT) systems that rely on leader election or quorums, ANGP uses:• Amedian-based consensus computed from predictions exchanged via gossip.• A continuous reputation engine that distinguishes honest nodes from Byzantine attackers, including coordinated collusion, rare attacks, sensor faults, and network impairments.• Alightweight Proof-of-Work (PoW) layer (SHA-512/256) to prevent Sybil identity floods, while keeping the core protocol free of staking or token-based governance.ANGP tolerates up to 44% coordinated attackers and 66% uncoordinated attackers with no degradation in honest node safety. It operates asynchronously, requires no global time synchronization, and self-heals under packet loss and network delays. This document provides the complete architectural blueprint, component specifications, security analysis, and integration guidelines for building a production-grade DAG based cryptocurrency or distributed application on top of ANGP.

Emeka Iwuagwu

<b>Abstract</b>The rapid growth of decentralized technologies has intensified the need for secure, privacy-preserving, and Sybil-resistant identity systems capable of operating without centralized authorities. Existing blockchain identity mechanisms frequently depend on trusted intermediaries, invasive biometric verification, or token-based incentives that introduce privacy risks, centralization, or economic manipulation. This paper presents the Decentralized Proof of Humanity (dPoH) Protocol, a blockchain-native identity framework designed to establish unique human identities through decentralized verification while preserving user privacy and network scalability.The dPoH protocol combines decentralized attestations, cryptographic verification, reputation mechanisms, and consensus-driven validation to ensure that each participant corresponds to a unique human identity without exposing unnecessary personal information. By eliminating reliance on centralized identity providers, the protocol significantly reduces Sybil attacks while maintaining transparency, auditability, and interoperability across blockchain ecosystems.The proposed architecture is suitable for decentralized finance (DeFi), decentralized governance (DAO), voting systems, digital identity infrastructure, token distribution, and next-generation Web3 applications. The protocol contributes to the growing field of decentralized identity by providing a scalable framework for secure human verification in trustless environments.

Yu Peng, Matloob Khushi, Josiah Poon

Cryptocurrency price prediction is a significant challenge in quantitative investment. In recent years, time series models have made significant progress in financial forecasting tasks, especially in the stock market. Despite the growing performance over the past few years, we question the validity of this line of research in cryptocurrency prediction. Specifically, time series models (e.g., LSTM, GRU, and Transformers) are effective at extracting temporal relationships in stock market data. However, in pure price-based cryptocurrency prediction, facing data with extreme volatility and wild swings, time series models have difficulty learning effective information. To validate our claim, we propose CryptoGAT, a lightweight Graph Attention Network that recasts cryptocurrency pure price prediction as a cross-asset graph problem rather than a temporal modeling task. Extensive experiments on real cryptocurrency benchmarks demonstrate that our proposed CryptoGAT outperforms various state-of-the-art forecasting methods with a notable margin. Moreover, we conduct comprehensive empirical studies to explore the fundamental differences exposed by time series models in stock and cryptocurrency prediction: differences in predictability of the signal and cross-asset dependencies. This finding opens up new research directions for the cryptocurrency pure price prediction task and inspires further graph-based exploration in the field. The source code is available at https://github.com/FanBroWell/CryptoGAT

Hüseyin Ahmet Cemil Özaslan, Şafak Durukan-Odabaşı

Smart contracts have become a fundamental component of blockchain ecosystems, and their reliability is strongly shaped by the programming languages in which they are written. While prior studies have classified vulnerabilities, fewer have quantitatively examined how language design and secure coding practices affect performance and resilience. To address this gap, this study empirically compares Solidity and Vyper under controlled conditions and complements these experiments with a literature-based evaluation of Rust and Move. Test scenarios included deployment, deposits, withdrawals, arithmetic overflow, reentrancy, and transaction origin misuse. For both vulnerable and secure variants in Solidity and Vyper, metrics such as gas consumption, deployment size, and runtime execution time were collected. The results indicate that deployment costs differ substantially between the two languages (Solidity ≈ 177 k gas vs Vyper ≈ 135 k gas, ~24% lower), whereas runtime performance is mixed: deposit calls are nearly identical (Δ ≈ 0.02 ms), whereas withdraw shows a noticeable gap (Δ ≈ 4.97 ms) favoring Vyper; nevertheless, these call-level differences remain small relative to the larger deployment-time gap. Importantly, secure coding practices such as explicit arithmetic checks and the Checks–Effects–Interactions pattern eliminate critical vulnerabilities while adding less than 1% to the overall execution cost. Although Rust and Move are considered through a literature-based review, they illustrate alternative approaches that embed security guarantees directly into the language. Based on these observations, this study proposes a measurable framework to understand how different smart contract programming languages vary in terms of security and efficiency, emphasizing the role of language design and secure coding practices in shaping contract development.

Luigi Usai

Author: Luigi Usai ORCID: https://orcid.org/0009-0003-3001-717X Location: Quartucciu (CA), Italy Date: June 26, 2026 Target: Zenodo / arXiv (cs.AI, cs.CL, cs.LO) Abstract Large Context Models (LCMs) exhibit an inherent vulnerability known as semantic hallucination, which stems directly from conditional likelihood maximization within discrete vector spaces. Traditional mitigation strategies operate predominantly post-hoc, managing errors after the stochastically generated token sequence has already mutated. This paper extends the Universal Cognitive Hypergraph (UKH) framework by introducing a discrete Alexandrov topology over knowledge hypergraphs to constrain the space of admissible states prior to token decoding. Utilizing the Monadic Neuro-Symbolic Verification and Synthesis Architecture (MNSVSA), probabilistic generation paths are intercepted and structurally validated against W3C SHACL constraints and axiomatic assertions verified by the Lean 4 kernel coupled with automated SMT solvers. Our theoretical results demonstrate the mathematical elimination of categorical deviations while fully preserving the model's syntactic fluency. 1. Introduction and Mathematical Formulation of the Problem Autoregressive language models estimate the probability distribution of the next token $w_t$ conditioned on the preceding context $w_{<t}$: $$P(w_t \mid w_{<t}) = \text{softmax}(W_{\text{unembed}} \cdot h_t)$$ where $h_t \in \mathbb{R}^d$ represents the final hidden state extracted by the Transformer architecture. Because the $\text{softmax}$ function maps scores to an open probability distribution, it inherently assigns non-zero probabilities to regions of the semantic space that violate real-world axiomatic constraints. Consequently, hallucination is not an accidental software bug but a structural property of the model's underlying stochasticity. The UKH framework bypasses the limitations of passive document retrieval (RAG) by integrating a topological-symbolic constraint directly into the sampling phase (speculative decoding). This setup actively prevents the model from exploring probabilistic trajectories linked to logically inconsistent states. 2. UKH Framework Architecture for Semantic Security The universe of discourse is mapped onto a directed hypergraph and serialized using the JSON-LD format. Let $\mathcal{H} = (V, E)$ be a cognitive hypergraph, where $V$ is the set of strongly typed nodes (conceptual entities) and $E \subseteq \mathcal{P}(V) \setminus \{\emptyset\}$ is the set of hyperedges representing multi-argument logical-functional relationships. 2.1. Alexandrov Topological Space and SHACL Constraints To establish geometric-structural rigor within a discrete domain, the hypergraph space is endowed with an Alexandrov topology, where open sets are defined as sub-hypergraphs closed upwards relative to a logical preorder relation ($\le$). W3C Shapes Constraint Language (SHACL) rules function as topological closure operators: $$\text{cl}(E_c) \subseteq \mathcal{H}_{\text{valid}}$$ If a candidate hyperedge $E_c$, derived from the semantic translation of the tokens proposed by the LLM, violates a structural Shape (e.g., assigning a physical property inconsistent with the primitive type of the node), the closure operator identifies a contradiction within the topological space. It subsequently invalidates the generation path before token rendering occurs. 2.2. Axiomatic Verification and Type Checking via Lean 4 While SHACL rules govern the macro-structural coherence of the graphs, the MNSVSA architecture executes formal verification of micro-logical assertions. The process follows a strict protocol: The semantic fragment generated by the LLM is isolated inside a logical monad. MNSVSA translates the assertion into a formal type within the evaluation language of Lean 4. Leveraging the Curry-Howard Isomorphism, the logical consistency of the statement is reduced to a Type Checking problem. To avoid the computational burden of generating complex mathematical proofs from scratch at inference runtime, the architecture delegates constraint satisfiability to an automated SMT solver (Z3) tightly integrated into the Lean 4 runtime kernel. 3. The Coherence Entropy Filtering Mechanism To quantify and halt stochastic drift within extended contexts, the framework implements a JIT (Just-In-Time) gatekeeping metric based on the Jensen-Shannon Divergence ($D_{JS}$). Let $P_{\text{LLM}}$ be the probability distribution over the next tokens generated by the model, and let $Q_{\text{UKH}}$ be the ontological adherence distribution derived from the allowed transition frequencies within the hypergraph $\mathcal{H}$. The semantic divergence is formally stated as: $$D_{JS}(P_{\text{LLM}} \parallel Q_{\text{UKH}}) = \frac{1}{2} D_{KL}(P_{\text{LLM}} \parallel M) + \frac{1}{2} D_{KL}(Q_{\text{UKH}} \parallel M)$$ where $M = \frac{1}{2}(P_{\text{LLM}} + Q_{\text{UKH}})$ and $D_{KL}$ is the Kullback-Leibler divergence defined over a discrete vocabulary $X$: $$D_{KL}(P \parallel M) = \sum_{x \in X} P(x) \log_2 \left( \frac{P(x)}{M(x)} \right)$$ If the divergence exceeds a system-defined critical threshold ($D_{JS} > \theta_{\text{max}}$), the generation hypothesis is immediately rejected. 4. Heterogeneous Hardware Implementation To bypass the parallelization bottlenecks inherent to logical-symbolic algorithms—which trigger massive thread divergence on SIMD architectures—the framework adopts a heterogeneous computation model powered by Speculative Decoding: GPU Execution (CUDA/Triton): The LLM generates $K$ candidate token pathways (drafting sequences) in parallel. CPU Async Execution: A high-frequency multicore CPU pool simultaneously executes the structural parsing of SHACL shapes and the Lean 4 type-checking over the sparse graphs corresponding to the proposed pathways. Non-compliant branches are pruned before the validation and synchronization phase of the model weights. 5. Conclusions Coupling information-theoretic metrics based on the Jensen-Shannon divergence, Alexandrov topological constraints on SHACL-structured hypergraphs, and axiomatic verification within Lean 4 delivers a rigorous formal methodology capable of neutralizing semantic hallucinations. Shifting control from post-hoc output filtering to a priori state space restriction sets a new benchmark for safety in Neuro-Symbolic Artificial Intelligence. Versione Italiana Unificazione Neuro-Simbolica mediante Ipergrafi Cognitivi: Mitigazione Quantitativa delle Allucinazioni nei Large Context Models a Monte della Generazione Autore: Luigi Usai ORCID: https://orcid.org/0009-0003-3001-717X Luogo: Quartucciu (CA), Italy Data: 26 Giugno 2026 Target: Zenodo / arXiv (cs.AI, cs.CL, cs.LO) Abstract I Large Context Models (LCM) presentano una vulnerabilità intrinseca nota come allucinazione semantica, derivante dalla massimizzazione della verosimiglianza condizionata in spazi vettoriali discreti. I tentativi di mitigazione tradizionali agiscono prevalentemente a valle del processo probabilistico, intervenendo quando l'alterazione sequenziale è già avvenuta. Il presente lavoro estende il framework Universal Cognitive Hypergraph (UKH), introducendo una topologia discreta di Alexandrov su ipergrafi di conoscenza per vincolare lo spazio degli stati ammissibili a monte della decodifica dei token. Mediante l'architettura Monadic Neuro-Symbolic Verification and Synthesis Architecture (MNSVSA), i cammini di generazione probabilistica vengono intercettati e validati strutturalmente tramite vincoli W3C SHACL e vincoli logici verificati dal kernel di Lean 4 accoppiato a solutori SMT automatici. I risultati teorici mostrano l'eliminazione matematica delle deviazioni categoriali senza compromissione della fluidità sintattica del modello. 1. Introduzione e Definizione Matematica del Problema Un modello linguistico autoregressivo stima la distribuzione di probabilità del token successivo $w_t$ condizionata alla storia precedente $w_{<t}$: $$P(w_t \mid w_{<t}) = \text{softmax}(W_{\text{unembed}} \cdot h_t)$$ dove $h_t \in \mathbb{R}^d$ rappresenta lo stato nascosto finale estratto dall'architettura Transformer. Poiché la função $\text{softmax}$ mappa i punteggi su una distribuzione di probabilità aperta, assegna intrinsecamente probabilità non nulle a porzioni dello spazio semantico che violano i vincoli assiomatici della realtà. Di conseguenza, l'allucinazione non è un bug accidentale, ma una proprietà strutturale della natura stocastica del modello. Il framework UKH supera i limiti del recupero documentale passivo (RAG) integrando un vincolo topologico-simbolico direttamente nella fase di campionamento (speculative decoding), impedendo all'architettura di esplorare traiettorie probabilistiche associate a stati logicamente non consistenti. 2. Architettura del Framework UKH per la Sicurezza Semantica L'universo del discorso viene mappato su un ipergrafo orientato e serializzato in formato JSON-LD. Sia $\mathcal{H} = (V, E)$ un ipergrafo cognitivo, dove $V$ è l'insieme dei nodi (entità concettuali fortemente tipizzate) ed $E \subseteq \mathcal{P}(V) \setminus \{\emptyset\}$ è l'insieme degli iperarchi che rappresentano relazioni logico-funzionali multi-argomento. 2.1. Spazio Topologico di Alexandrov e Vincoli SHACL Per garantire il rigore geometrico-strutturale su un dominio discreto, lo spazio dell'ipergrafo viene dotato di una topologia di Alexandrov, definendo gli insiemi aperti come i sottoipergrafi chiusi superiormente rispetto a una relazione di preordine logico ($\le$). I vincoli W3C Shapes Constraint Language (SHACL) operano come operatori di chiusura topologica: $$\text{cl}(E_c) \subseteq \mathcal{H}_{\text{valid}}$$ Se un iperarco candidato $E_c$, generato dalla traduzione semantica dei token proposti dall'LLM, viola una Shape strutturale (es. assegnazione di una proprietà fisica inconsistente con il ti

Luigi Usai

Author: Luigi UsaiORCID: 0009-0003-3001-717XLocation: Quartucciu (CA), ItalyDate: June 26, 2026Target: Zenodo / arXiv (cs.AI, cs.CL, cs.LO) Abstract Large Context Models (LCMs) exhibit an inherent vulnerability known as semantic hallucination, arising from conditional likelihood maximization within discrete vector spaces. While the Universal Cognitive Hypergraph (UKH) framework was initially proposed as a theoretical model to constrain the space of admissible states prior to token decoding, this paper presents its first formal empirical and quantitative validation. We detail a software runtime implementation of the Monadic Neuro-Symbolic Verification and Synthesis Architecture (MNSVSA) using discrete Alexandrov topologies, W3C SHACL shapes as topological closure operators, and a Just-In-Time (JIT) Jensen-Shannon Divergence (DJSDJS) Coherence Entropy Filter. Through Monte Carlo simulations (N=150N=150 runs per configuration), we demonstrate that tightening the coherence threshold (θmax=0.05θmax=0.05) mathematically eliminates semantic hallucinations (reducing the rate from 36.7% to 0.0%) while preserving syntactic fluency. Crucially, by leveraging speculative decoding with parallel validation, we show that the processing latency remains identical to the unconstrained baseline (90.0 µs), bypassing the massive execution overhead (174.8 µs) of post-hoc verification. The complete open-source verification suite and interactive visualization dashboard accompany this publication. 1. Introduction and Problem Statement Autoregressive language models estimate the probability distribution of the next token wtwt conditioned on the preceding context w<tw<t: P(wt∣w<t)=softmax(Wunembed⋅ht)P(wt∣w<t)=softmax(Wunembed⋅ht) where ht∈Rdht∈Rd is the final hidden state of the Transformer. Because the softmaxsoftmax function assigns non-zero probabilities across the entire vocabulary, autoregressive generation naturally drifts into regions of the semantic space that violate axiomatic truth, resulting in hallucinations. The UKH framework mitigates this by introducing a priori symbolic constraints directly into the token sampling phase via speculative decoding. Rather than validating output sequences post-generation, candidate pathways are parsed and filtered prior to token rendering. 2. Experimental Validation Engine (UKH-Eval) To validate the theoretical claims of the UKH and MNSVSA frameworks, we developed UKH-Eval, a complete Python and JavaScript simulation engine that implements the mathematical and topological constraints described in the original work. 2.1. Discrete Alexandrov Topology The knowledge base of the universe of discourse is modeled as a directed hypergraph H=(V,E)H=(V,E). To enforce geometric-structural constraints, we endow the space with a discrete Alexandrov topology, where open sets are sub-hypergraphs closed upwards relative to a logical preorder relation (≤≤). Let the preorder relation be defined by a preorder index mapping: alexandrovPreorderIndex:V→NalexandrovPreorderIndex:V→N A subset of nodes U⊆VU⊆V is open if and only if: ∀x∈U,∀y∈V:(alexandrovPreorderIndex(x)≤alexandrovPreorderIndex(y))⟹y∈U∀x∈U,∀y∈V:(alexandrovPreorderIndex(x)≤alexandrovPreorderIndex(y))⟹y∈U If a candidate token proposes a node transition that violates this upward-closure property, the transition is marked as topologically invalid. 2.2. SHACL Constraints as Closure Operators W3C Shape Constraint Language (SHACL) rules govern the macro-structural properties of the generated hyperedges: cl(Ec)⊆Hvalidcl(Ec)⊆Hvalid If a proposed hyperedge EcEc violates target class properties, minimum/maximum node counts, or axiomatic validity flags, the closure operator fails, and the branch is pruned. 2.3. MNSVSA Micro-Logical Type Checking For micro-logical validation, assertions are encapsulated in a monadic container (LogicalMonad). Levering the Curry-Howard Isomorphism, consistency verification is reduced to a Type Checking and propositional satisfiability problem. The engine compiles the proposed semantic statement into a formal SymPy expression and checks its consistency against the background theory axioms: conjunction=Axioms∧Expressionconjunction=Axioms∧Expression If conjunctionconjunction is unsatisfiable (i.e. evaluates to False), a logical contradiction is detected and the path is rejected. 2.4. Coherence Entropy JIT Filtering At each generation step, the JIT filter computes the Jensen-Shannon Divergence (DJSDJS) between the stochastically proposed LLM distribution PLLMPLLM and the ontological adherence distribution QUKHQUKH: DJS(PLLM∥QUKH)=12DKL(PLLM∥M)+12DKL(QUKH∥M)DJS(PLLM∥QUKH)=21DKL(PLLM∥M)+21DKL(QUKH∥M) where M=12(PLLM+QUKH)M=21(PLLM+QUKH) and DKLDKL is the Kullback-Leibler divergence defined over vocabulary XX: DKL(P∥M)=∑x∈XP(x)log⁡2(P(x)M(x))DKL(P∥M)=∑x∈XP(x)log2(M(x)P(x)) If DJS>θmaxDJS>θmax, stochastically proposed drift tokens are pruned, and the probability distribution is projected onto the compliant space. 3. Software Architecture & File Manifest The open-source validation package is organized into modular components to ensure reproducibility and maintainability: text ukh-evaluator/ ├── ukh_engine.py # Core verification engine and classes ├── test_harness.py # Automated unit test suite ├── benchmark.py # Monte Carlo comparative simulation runner └── dashboard/ # Interactive web UI and visualization ├── index.html # UI structure ├── style.css # Sleek dark-mode styling ├── app.js # In-browser real-time simulation and canvas graph └── results.json # Compiled benchmark data 3.1. File Descriptions 1. ukh_engine.py The core engine containing: LogicalMonad: Implements monadic binding and SymPy-based SAT solving. CognitiveHypergraph: Models nodes, hyperedges, Alexandrov open sets, and validates SHACL shapes. CoherenceFilter: Contains static methods for DKLDKL and DJSDJS calculations. UKHSystemSimulator: Links all subcomponents and handles the JIT filtering during next-token generation. 2. test_harness.py The automated test suite. It uses unittest to verify: Upward closure calculations under the Alexandrov topology. SHACL shape violations. Monadic consistency solving under the Curry-Howard isomorphism. Divergence math calculations. Coherence Entropy Filter rejections. 3. benchmark.py The empirical execution suite. It implements a Monte Carlo simulation running 150 independent generation steps per architecture (Baseline, Post-Hoc, and UKH) and sweeps the threshold parameter θmaxθmax from 0.050.05 to 0.950.95. It evaluates hallucination rates, perplexity, and latency, saving the outputs to results.json. 4. dashboard/ An interactive web-based dashboard built with HTML5 Canvas and CSS. index.html: Layout for control sliders (θmaxθmax, KK, drift), live token sequences, and visualization cards. style.css: Sleek glassmorphism theme, glowing neon accents, and custom micro-animations. app.js: Connects to results.json, renders interactive force-directed nodes on the canvas, and runs the entire simulation locally in JavaScript. 4. Quantitative Results & Discussion The benchmark results compiled under Monte Carlo testing demonstrate the trade-offs between safety, fluency, and system latency: 4.1. Hallucination Rates vs. Threshold θθ The unconstrained baseline model suffers a hallucination rate of 36.7%. As the UKH JIT threshold θθ is tightened, safety guarantees scale: At θ≥0.50θ≥0.50, the filter is relaxed, and the model behaves like the baseline. At θ=0.10θ=0.10, the hallucination rate is reduced to 3.3%. At θ=0.05θ=0.05, the hallucination rate is successfully reduced to exactly 0.0%. 4.2. Latency Profiles and Speculative Efficiency Post-hoc validation (checking the sequence after generation and regenerating if unsafe) achieves a low hallucination rate (3.3%) but introduces a massive latency penalty (174.8 µs, a 94% overhead compared to the baseline's 90.0 µs). By contrast, the UKH framework utilizing parallel speculative drafting and asynchronous verification maintains a latency profile of 90.0 µs, matching the unconstrained baseline. 4.3. Syntactic Perplexity Tightening the symbolic constraints does not degrade fluency. The average perplexity remains stable (∼6.18∼6.18 for θ=0.05θ=0.05 vs ∼6.83∼6.83 for baseline), showing that restricting the space of admissible states prior to token decoding steers the model toward logical paths without harming syntactic structure. 5. Peer Review Assessment & Future Work This empirical validation verifies the internal consistency and theoretical correctness of the paper's claims. However, scaling this framework to production Large Language Models requires addressing three primary engineering areas: Semantic Translation Robustness: Building high-speed, deterministic parsers to map raw tokens to JSON-LD graphs in real-time without introducing new failure modes. Dynamic Knowledge Bases: Compiling massive, real-world ontologies into Alexandrov preorders dynamically as context windows expand. Hardware Accelerators: Developing specialized kernels (e.g., in Triton or CUDA) to execute SHACL checks and SAT solving directly on GPU cores alongside tensor multiplication. 6. Conclusion The implementation of the UKH and MNSVSA verification engine provides the first empirical proof that coupling discrete topological constraints, SHACL shapes, and monadic type checking can completely eliminate stochastically induced hallucinations. Shifting control from post-hoc output filtering to a priori state space restriction establishes a new, verified paradigm for safety in Neuro-Symbolic Artificial Intelligence.

Yaiza Cabedo, Tommaso Mancini-Griffoli, Fabian Schär, Nicolas Zhang

This paper examines how tokenization and distributed ledger technology may transform Financial Market Infrastructures (FMIs) by enabling smart contracts to perform a growing share of functions traditionally undertaken by central securities depositories, central counterparties, and trade repositories. It argues that while record-keeping, settlement, collateral management, and reporting can increasingly be executed on-chain, key functions requiring legal certainty, governance, accountability, and discretion remain institutional in nature. The analysis assesses which activities across issuance, clearing, settlement, and reporting can migrate to code, where limitations persist, and how risks evolve in tokenized environments. It finds that tokenization is more likely to reconfigure than eliminate FMIs, creating new efficiencies while introducing novel operational and governance risks. The most plausible outcome is a hybrid FMI model in which technology and institutions jointly provide the trust, resilience, and oversight required for financial stability.

Dhiyathad Prateeppornnarong

Throughout the 2010s, blockchain was widely embraced for improving data storage efficiency and transparency. However, it later hit a trough of disillusionment, criticized for overhyping its benefits and setting unrealistic expectations. Grounded in qualitative research design and methods, this research explores technology affordances and constraints of blockchain for curbing public sector corruption, especially in countries plagued by systemic corruption, using Thailand as a primary context. The findings indicate that blockchain’s Distributed Ledger Technology (DLT) effectively mitigates the principal–agent problem by enhancing both vertical and horizontal transparency. However, the adoption of blockchain is hindered by several constraints, including potential misuse, cost-efficiency, regulatory governance, security and confidentiality, and digital skills and organizational culture. Therefore, introducing blockchain into government operations must not occur in isolation; it requires a holistic integration of sociopolitical and economic factors to ensure that it serves as a viable anticorruption tool.

Frank Morales

TOPO-2026 - A Prime-Based Topological Framework for Ultra-Efficient Continual Learning Frank Morales Aguilera, BEng, MEng, SMIEEE Sovereign Machine Laboratory (SOMALA), Montreal, Canada frank.morales@sovereign-machine-lab.ai ORCID: 0009-0003-9528-0745 1. Overview TOPO-2026 is a novel continual learning framework that leverages the mathematical properties of prime numbers to prevent catastrophic forgetting in neural networks. The key innovation is anchoring a sparse set of parameters at prime-numbered indices across tasks, maintaining task-specific knowledge while allowing non-anchored parameters to adapt. 2. Core Contributions # Contribution Description 1 Mathematical Foundation Primes provide optimal spectral coverage (97.85%) with only 6 anchors per layer 2 O(1) Memory Complexity < 5 KB overhead for 100M+ parameter models 3 Universal Applicability Works across NLP, Vision, and 3D architectures without modification 4 Perfect Integrity Zero anchor drift across tasks, eliminating catastrophic forgetting 5 Theoretical Guarantees Mathematical proof of spectral coverage, invariance, and O(1) complexity 6 Edge Deployment Sub-kilobyte memory footprint suitable for resource-constrained devices 3. Theoretical Foundation 3.1 Why Primes Specifically Prime numbers are uniquely suited as anchors because they provide: Property Description Mathematical Guarantee Optimal Density $\pi(n) \sim n/\ln(n)$ Sufficiently dense for coverage of arbitrarily large tensors Coprimality $\gcd(p_i, p_j) = 1$ for $i \neq j$ Orthogonal subspaces, no interference between anchors Deterministic Distribution Well-distributed throughout natural numbers No clustering, comprehensive coverage Universal Guarantee Coverage independent of tensor dimensions Framework works for any architecture 3.2 Spectral Coverage Formula For a set of primes $P = \{p_1, p_2, \ldots, p_k\}$: $$C(P) = 1 - \prod_{p \in P} (1 - p^{-1/2})$$ For $P = \{2, 3, 5, 7, 11, 13\}$: $$\begin{align} C(P) &= 1 - \prod_{p \in P} (1 - p^{-1/2}) \\ &= 1 - (1-2^{-1/2})(1-3^{-1/2})(1-5^{-1/2}) \\ &\qquad \times (1-7^{-1/2})(1-11^{-1/2})(1-13^{-1/2}) \\ &= 1 - (0.2929)(0.4226)(0.5528)(0.6220)(0.6985)(0.7227) \\ &= 1 - 0.021486 \\ &= 0.978514 \approx 97.85\% \end{align}$$ Key Insight: The independence of non-coverage events follows directly from the coprimality of primes. For distinct primes $p_i$ and $p_j$, the conditions $x \not\equiv 0 \pmod{p_i}$ and $x \not\equiv 0 \pmod{p_j}$ are independent because $\gcd(p_i, p_j) = 1$. The Chinese Remainder Theorem guarantees these conditions can be satisfied or violated independently. 4. The Topological Governor The core innovation: three operations that work together to prevent forgetting. 4.1 Snapshot Operation Before training on a new task, save anchor values: $S_t = \{(\text{idx}, \theta_{\text{idx}}) \mid \text{idx} \in P, \theta_{\text{idx}} \in \Theta\}$. 4.2 Gradient Zeroing During backpropagation, zero gradients at anchor positions: $\nabla L(\theta_{\text{idx}}) = 0, \forall \text{idx} \in P$. 4.3 Anchor Enforcement After each optimization step, restore anchor values: $\theta_{\text{idx}} \leftarrow S_t(\text{idx}), \forall \text{idx} \in P$. 5. Memory Complexity Analysis For a model with $n$ parameters and $L$ layers: $$M_{TOPO} = |P| \times L \times \text{bytes per parameter}$$ Model Parameters Layers Anchors Memory EWC Memory Reduction BERT 109M 201 1,206 4.71 KB 437.9 MB 93,000× GPT-2 124M 148 888 3.47 KB 497.8 MB 143,000× GAN 2.95M 22 132 0.52 KB 11.8 MB 22,700× NeRF 246K 14 84 0.33 KB 1.0 MB 3,100× 6. Experimental Validation BERT (Text Classification): 100% retention on movie and product review tasks. GPT-2 (Text Generation): High-quality generation across creative and technical writing tasks with 1.25 perplexity. GAN (Image Generation): Stable training across Gaussian, Uniform, and Mixed datasets; no mode collapse. NeRF (3D Scene Learning): Consistent loss across sphere, cube, and torus scenes. 7. Conclusion TOPO-2026 represents a breakthrough in continual learning, demonstrating that mathematical structure can enable practical, scalable, and ultra-efficient parameter protection. With O(1) memory complexity and universal applicability, it provides a robust foundation for building models that adapt without forgetting, learn without rehearsal, and evolve without memory explosion.

Sanaz Farhadi

The convergence of Smart Grids and the Internet of Medical Things (IoMT), termed Grid-IoMT, represents an emerging paradigm where healthcare facilities dynamically interact with energy grids to optimize both clinical operations and power consumption.Real-time medical data streams (e.g., continuous vital signs from wearable monitors, infusion pump logs, ventilatory parameters) traverse network infrastructure shared with grid telemetry, creating unprecedented attack surfaces where energy-demand manipulation can indirectly compromise patient safety, and conversely, medical data injection can destabilize grid frequency regulation.This paper presents BlockAuth-GridMed, a novel blockchain-anchored adaptive authentication framework specifically designed for real-time medical data streams in AI-driven Smart Grid-IoMT converged networks.The framework integrates three synergistic innovations:(1) A hierarchical blockchain architecture (local permissioned chains for clinical domains interconnected via a main chain for cross-domain trust) that anchors authentication proofs without introducing latency prohibitive for real-time medical applications (median latency 187ms),(2) An adaptive authentication engine powered by deep reinforcement learning (DRL) that dynamically adjusts authentication strength based on real-time risk assessment-escalating to multi-factor requirements during grid instability events or cyber-threat alerts while maintaining low-friction single-factor authentication during quiescent periods,(3) A zero-knowledge proof (ZKP) layer enabling mutual authentication between medical devices and grid nodes without revealing sensitive patient identifiers or clinical data patterns to energy system operators.We evaluate BlockAuth-GridMed using a realistic testbed emulating a 200-bed smart hospital integrated with an IEEE 13-bus distribution grid model, processing 15,000 real-time medical data streams per second across 6,500 IoMT devices and 12 grid sensors.The framework achieves 99.97% authentication success rate for latency-sensitive medical alerts (critical events requiring <100ms end-to-end latency) and maintains an average authentication overhead of 28ms, well within clinical requirements.Under adversarial conditions (simulated man-in-the-middle, replay, and grid-state injection attacks), BlockAuth-GridMed demonstrates 96.8% attack detection and 99.1% attack prevention rates, outperforming baseline certificate-based (83.4%/87.2%)and token-based (71.3%/74.6%)schemes.The DRL-driven adaptive authentication reduces unnecessary multi-factor challenges by 73% compared to static high-security policies, significantly improving clinical workflow efficiency.We also analyze blockchain gas costs (approx.\$0.012 per authentication), scalability under IoMT device churn (up to 15% daily device joins/leaves), and regulatory alignment with HIPAA, NERC CIP, and FDA pre-market guidance for medical device security.This work provides the first integrated authentication framework specifically tailored to the Grid-IoMT convergence, enabling secure, real-time, and adaptive protection for medical data streams in energy-aware healthcare infrastructures.

Priyal Bhagwanani

Human societies, economic markets, and digital systems face a fundamental coordination prob- lem: how self-interested agents cooperate in the allocation and use of scarce resources. Across these domains, contention necessitates identity systems that inform coordination mechanisms and govern resource allocation. However, existing literature typically treats identity establishment and coordination as separate problems. Institutional economics often assumes resource identity as an exogenous feature of the environment, while distributed consensus algorithms focus on coordina- tion under the assumption that resource identity is already known and agreed upon. This separation limits our understanding of how identity architectures shape the cost, efficiency, and stability of cooperation. 1 Using game-theoretic modeling and agent-based simulations, this study employs an isomorphic framework linking distributed systems and social institutions to analyze resource contention and coordination. We deploy a Cryptographic Content-Addressed Version-Aware Distributed Mutual Exclusion architecture, supported by an open-source implementation, to model resource identity allocation among concurrent processes. Simulations involving up to 10,000 agents are executed on the developed computational platform to evaluate how decentralized resource identity formation influences coordination costs. By tracking these interactions, the model measures how the structural method used to establish resource identity affects the cost, efficiency, and stability of cooperation among anonymous self-interested agents. The simulations indicate that cryptographically derived resource identities can achieve Nash- Implementable incentive compatibility, enabling cooperative outcomes without requiring a coor- dinating authority beyond a ledger that functions as a passive institutional record whose state may be modified only through resource-specific operations. These computational findings demonstrate a broader coordination paradigm in which identity emerges endogenously from the originating environment or substrate in which the resource is created. When treated as an active institutional design choice, such identity architectures may reduce reliance on external consensus among intelli- gent agents by shifting coordination toward resource-centered state recognition maintained through a non-strategic ledger. This framework endows scarce resources in contention with intelligence through the electronic capability of endogenous, unique self-identification and ledger registration, enabling autonomous self-allocation to contending network agents—a mechanism that can transfer likely directly onto applications within the social sciences where allocation is consensus decided through an intelligent third party agent between independent contending agents. In attempt to provide resource itself the self-identification capability and participation in distribution to agents queueing for allocation, we see we are able to overcome computational cost in macro-structures. More generally, the results suggest that endogenous resource identity provides a framework for optimizing collective action and resolving contention across both digital platforms and broader social institutions. Keywords: resource identity, institutional coordination, cryptographic hashing, consensus protocols, game theory, agent-based modeling, mechanism design, distributed mutual exclusion, computational social science. 1 This paper is an independent writing sample submitted for graduate admission to the University of Chicago MACSS programme. It investigates the intersection of institutional economics, collective action theory, and distributed systems architecture. However, this research is not done for University of Chicago. The paper was not written specifically for this application; it reflects an independent research project undertaken in preparation for graduate study in computational social science. Research on isomorphic modelling for distibuted locking in computer networks: Cryptographic Content-Addressed Version-Aware Distributed Mutual Exclusion and corresponding codebase are available via GitHub (https://github.com/bpriyal/distcodelock/blob/main/README.md) and Zenodo (https://zenodo.org/ records/19634046).

Kaja Masthan, Zeeshan Ahmed Mohammed, Rahmat Ali, Abdul Junaid Mohammed

The advancement of medical data handling from conventional paper documents to electronic records enabled secure data movement between authenticated legitimate users. While current identity verification algorithms offer unique solutions, they face significant limitations related to data storage scalability, potential privacy breaches, high computational costs, and the lack of standardized protocols. In order to alleviate these constraints, the research proposes a biometric–Blockchain-based authentication Scheme, a Whirlpool Secure Hash-based Biometric Integrated Key Distribution Function (WShBK) for secure data storage and access in a cloud network. The proposed strong cryptographic scheme generates two unique keys derived from the biometric trait of the patient for both encryption and authentication purposes, ensuring strong protection while accessing and storing the data. Furthermore, the advanced encryption standard WShBK (AWShBK) encryption algorithm leverages the strength of a symmetric block cipher and the unique key, offering robust protection against breaches by rendering intercepted data without the correct decryption key. Furthermore, Hybrid biometric-based zero-knowledge proof (HyBZKP) verification offers secure and private transaction validations while sustaining the blockchain integrity. These advancements of the proposed WShBK scheme improve 0.52 encryption rate and 0.53 decryption rate for 250 users analyzed with an attack compared to other cutting-edge models.

Mrs. K Rajasankari, Dr.S. Silvia Priscila

Blockchain technology has evolved from its origins as the foundational ledger for cryptocurrencies to a disruptive paradigm for decentralized, transparent, and secure data management across numerous sectors. This review paper provides a systematic analysis of core blockchain architectures, consensus protocols, and smart contract functionalities that enable its diverse applications. We examine the transition from public, permissionless networks to private and consortium models tailored for enterprise needs. The paper surveys seminal and contemporary research across key domains including decentralized finance (DeFi), supply chain provenance, healthcare data exchange, electronic voting, and the Internet of Things (IoT). By synthesizing findings from foundational protocols to cutting-edge cross-chain solutions, we identify common technical motifs and domain-specific implementations. Furthermore, the review delineates persistent challenges such as scalability trilemmas, interoperability gaps, regulatory uncertainty, and significant energy consumption. This consolidated analysis aims to serve as a reference for researchers and practitioners, highlighting both the transformative potential and the critical limitations of blockchain techniques as a trustless infrastructure for the digital age.

Setyo Tri Wahyudi, Al Muizzuddin Fazaalloh, Kartika Sari, Amalia Rahmawati

Fiscal decentralization has expanded the responsibilities of local governments, yet substantial disparities in fiscal performance persist across jurisdictions. This study examines how governance capacity influences fiscal performance and revenue sustainability within Indonesia’s decentralized metropolitan governance framework. Using panel data from seven local governments in the Gerbangkertosusilo metropolitan area during 2015–2024, the analysis develops a Composite Fiscal Performance Index (CFPI) that integrates revenue effectiveness, expenditure efficiency, fiscal autonomy, and revenue sustainability. The results reveal significant and persistent variation in fiscal outcomes. Jurisdictions with stronger governance capacity, particularly Surabaya City and Sidoarjo Regency, consistently achieve higher CFPI scores, reflecting more effective revenue mobilization, greater fiscal autonomy, and stronger expenditure management. In contrast, lower-capacity jurisdictions exhibit weaker fiscal performance, slower growth in own-source revenues, and greater dependence on intergovernmental transfers. Revenue forecasting further indicates that high-performing jurisdictions are more likely to sustain favorable fiscal trajectories over the medium term. By combining multidimensional fiscal performance measurement with forward-looking revenue sustainability assessment, this study contributes to the subnational public finance literature. The findings identify governance capacity as a critical institutional determinant of fiscal resilience and highlight the need for capacity-sensitive policies to improve the effectiveness of decentralized governance systems.

Arwen Dewi Ferlang Anna, Angela Gracia Anna, Nandang Kusnadi

Perkembangan teknologi digital telah mendorong perubahan dalam praktik kontrak, salah satunya dengan adanya penggunaan kontrak pintar yang didasarkan pada teknologi blockchain. Kontrak pintar muncul sebagai bentuk kesepakatan modern yang dapat melaksanakan ketentuan kontrak secara otomatis tanpa perlu intervensi dari pihak ketiga. Munculnya teknologi ini menimbulkan berbagai isu hukum, terutama mengenai status, legitimasi, dan kekuatan mengikatnya dalam sistem hukum kontrak baik di tingkat nasional maupun internasional. Untuk itu, penelitian ini bertujuan untuk mengevaluasi keberadaan kontrak pintar sebagai bentuk perjanjian modern serta meneliti kesesuaiannya dengan prinsip-prinsip hukum kontrak yang berlaku di kedua tingkatan tersebut. Studi ini memanfaatkan metode penelitian hukum normatif dengan pendekatan undang-undang, konsep, dan perbandingan. Sumber hukum yang dianalisis mencakup berbagai regulasi, instrumen hukum internasional, literatur terkait hukum, serta hasil penelitian yang relevan dengan kemajuan teknologi kontrak digital. Penelitian dilakukan dengan analisis kualitatif untuk menilai implementasi elemen-elemen perjanjian dalam kontrak pintar serta tantangan yang dihadapi dalam praktik antar negara. Hasil penelitian menunjukkan bahwa kontrak pintar pada dasarnya mampu memenuhi syarat-syarat sahnya perjanjian seperti yang ditetapkan dalam hukum kontrak nasional, selama terdapat kesepakatan antara pihak-pihak yang terlibat, kemampuan hukum, objek yang jelas, dan alasan yang sah. Di tingkat internasional, keberadaan kontrak pintar juga semakin diakui melalui berbagai regulasi dan pedoman terkait transaksi daring, meskipun belum ada pengaturan standar yang diterapkan di semua negara. Selain menyediakan efisiensi, transparansi, dan keamanan dalam pelaksanaan kontrak, kontrak pintar juga menghadapi tantangan terkait yurisdiksi, penyelesaian sengketa, perlindungan konsumen, dan kepastian hukum terhadap kode perangkat lunak yang berfungsi sebagai alat kontraktual.

Dongfa Feng, Yaoming Zhang, Yao Zhang, Si Zhou

No abstract is available for this record.

Can Li, Yafei Zhang, Runshuo Liu, Zhongying Zhao · 6 authors

No abstract is available for this record.

Nathaly Verdesoto Martínez, Victor Lopez

This article analyzes the institutional barriers faced by Decentralized Autonomous Governments (GADs) and local actors in the Ecuadorian Amazon in accessing funding from the Common Fund, administered by the STCTEA—a mechanism aimed at channeling public resources toward sustainable territorial development. Using a mixed-methods approach, the study examines 926 projects submitted between 2019 and 2023 and surveys 58 key stakeholders in the province of Pastaza. The findings reveal a 78.4% gap between requested and approved funds, geographic allocation biases, and perceived exclusion related to technical requirements and decision-making mechanisms. Barriers identified include regulatory rigidity, lack of technical assistance, and the absence of redistributive criteria. Within the framework of institutional resilience, the study proposes a reconfiguration of the funding model based on adaptability, transparency, and territorial equity. This article contributes to the debate on organizational adaptation in highly complex contexts, proposing institutional reforms that strengthen the State’s capacity to manage development from an intercultural and redistributive perspective.

Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, Arvind Panwar

IoT is the new frontier through which things are connected and production systems are made to work across various industries. However, as more and more IoT ecosystems are being implemented and extended there are a number of concerns that follow such as trust, security and efficiency. Some challenges implicitly involved in these levels of accountabilities are due to its decentralized, transparent and secure distributed ledger technology; Blockchain provides reasonable solutions to these challenges. This chapter also presents the IBoT system, which is a combination of Blockchain and IoT to address challenges arising from IoT systems. The first section of the chapter discusses the conceptualization of strategies between the two technologies, Blockchain and IoT, and how the interoperability is relevant to accomplishing major issues like data credibility, openness and decentralization. More specifically, it goes through key components that make up IBoT such as smart contracts, consensus algorithms valid for IoT and decentralized autonomous organizations (DAOs). Thus, analyzing this process, the given chapter outlines the possibility of IBoT to revolutionize IoT environments by providing safe authentication, shared encryption keys, as well as easily controlled and not trustful data sharing processes. The major issue of trust and inefficiency in the original IoT system is discussed and specific points of how the problem can be solved with the help of Blockchain are defined. In this chapter, the reader should be able to get a clear understanding of how IBoT can instead of transforming IoT, can augment IoT by improving on its security, latency and energy consumption. From the observations, it is clear that IBoT not only builds more reliable and transparent IoT network but it also greatly contributes to the effectiveness of the operation through data processing and analysis that happens in real-time. This chapter thus brings out the implication of IBoT, considering the challenges, the legal and policy implications and the future research agenda. They provide the outline of further developments and general impact on the fields like smart production, telemedicine and self-driving cars and place IBoT among actors initiating the following generation of IoT infrastructure and networks.