Adil Raja, Ragini Kushwaha, Muhidinov Ayubbek Nuritdinovich, Girish H · 6 authors
The fast-growing Internet of Things has opened up new possibilities of automation, connectivity, and intelligent decision-making in smart environments. Nevertheless, the problems of security, transparency, and control decentralization in resource-limited IoT networks remain major issues. A potential solution is blockchain technology; however, conventional consensus systems, such as Proof-of-Work and Proof-of-Stake, are not suitable for IoT applications because they are energy- and computationally intensive and not scalable. This research paper suggests an architecture of a secure and efficient automation of smart homes with real-time blockchain recording facilitated by a Lightweight Delegated Byzantine Fault Tolerance (LdBFT) protocol. The design provides solutions to major issues of the IoT system such as security, decentralization and scalability by proposing a lightweight consensus mechanism which is appropriate in resource constrained systems. The proposed study enhances the effectiveness of the IoT blockchain systems in terms of efficiency, security, and scalability. LdBFT protocol streamlines consensus functions with lightweight delegation and priority-based validation, which further guarantees low latency and energy usage as well as reliability and security in communication among distributed internet of thing devices in real-time smart homes. Experimental analysis demonstrates that the LdBFT protocol significantly reduces consensus wait time, increases fault tolerance against malicious or failed nodes, and consumes less energy than traditional blockchain protocols. Overall, the proposed system offers a scalable, resource-efficient, and secure blockchain architecture for use in smart home IoT settings.
This study investigates how information transparency affects organizational value in the Chinese institutional setting, where firms operate under a heavily regulated disclosure regime while increasingly referencing Decentralized Autonomous Organization (DAO) or blockchain-based decentralized governance concepts. Using a panel of 10,029 firm-year observations from 1368 Shenzhen A-share listed firms over the period 2012–2022, we employ two-way fixed effects regressions and robustness tests, with information transparency proxied by Shenzhen Stock Exchange disclosure ratings. We find that higher transparency is positively and significantly associated with organizational value (measured by Tobin’s Q). Heterogeneity analyses show that this positive relationship is stronger among state-owned enterprises, firms with lower digital maturity, and firms led by innovation-oriented executives. Comparative tests further reveal that the transparency–value link holds primarily among DAO-referencing firms, whereas it turns negative (though marginally significant) for non-referencing firms. These results suggest that signaling interest in decentralized governance mechanisms can enhance the value relevance of disclosure in regulated emerging markets. Practical implications for managers and policymakers are discussed, along with limitations and directions for future research.
Mohammed H M Qeshta, Ahmad Ali, Salma Al-Shammari, Khaled M. Algraibeh
The growing use of FinTech based on blockchain solutions have gradually affected financial coordination mechanisms in supply chain finance networks where secure transaction verification, real-time visibility and records that cannot be changed have become essential to inter-organization collaboration. Risks related to supply chain finance systems include information asymmetry, slow verification and lack of trust between partners. The conventional monetary procedures often limit transparency, which may cause sabotage of collaboration and increased financial risk. A designed survey was created and it was distributed to practitioners operating in supply chain finance and digital financial platforms resulting in almost 275 questionnaire responses that were valid. An analysis of the data was done using Partial Least Squares Structural Equation Modelling (PLS-SEM) with the use of SmartPLS. The results suggest that the impact of Blockchain FinTech Capability on Financial Transparency has a positive significant impact on the Trust among the partners in the supply chain. Also, Blockchain capability exhibits a direct positive effect on the result of trust. The mediation analysis proves that there is a partial mediating role of Financial Transparency between the relationship between Blockchain capability and Trust. The findings would add to the previous FinTech and supply chain finance literature that clarifies the way in which blockchain-based functions facilitate financial visibility and inter-organizational trust. The practical use of the research relates to the problem that the adoption of blockchain is useful to develop better collaboration and financial stability among firms.
Mark C. Ballandies, Florian Spychiger, Uwe Serdült, Claudio J. Tessone
We propose DAO-enabled decentralized physical AI (DePAI), a democratic architecture for coordinating humans and autonomous machines in the operation and governance of physical-digital systems. We (1) synthesize foundations in blockchains, decentralized autonomous organizations (DAOs), and cryptoeconomics; (2) connect DAO design with digital-democracy research on deliberation and voting, showing how each can advance the other; (3) position DAO-governed decentralized physical infrastructure networks (DePIN) within a vertically integrated stack that links energy and sensing to connectivity, storage/compute, models, and robots; (4) show how these elements specify workflows that couple machine execution with human oversight, enabling enhanced self-organization of techno-socio-economic systems, which we call DePAI; and (5) analyze risks, including security, centralization, incentive failure, legal exposure, and the crowding-out of intrinsic motivation, and argue for value-sensitive design and continuously adaptive governance. DePAI offers a path to scalable, resilient self-organization that integrates physical infrastructure, AI, and community ownership under transparent rules, on-chain incentives, and permissionless participation, aiming to preserve human autonomy.
This paper examines Web3 ecosystems not merely as markets for digital assets, but as networked social spaces where economic transactions give rise to enduring social ties, shared narratives, and collective identities. Leveraging large-scale data mining of fused on-chain blockchain transactions and off-chain social media activity, we analyze over one hundred NFT collections to uncover how different forms of participation structure community formation in decentralized environments. Using network analysis, we identify distinct ecosystem roles, such as long-term holders, active traders, and short-term speculators, and demonstrate how each produces markedly different network topologies, levels of cohesion, and pathways for influence. We complement this structural analysis with discourse analysis of social media engagement, revealing how narrative production, visibility, and sustained interaction persist even as transactional activity declines. Our findings show that communities centered on holding behavior evolve from transactional networks into socially embedded ecosystems characterized by dense ties, decentralized influence, and ongoing cultural participation, while trader- and speculator-dominated networks remain fragmented and transactional. By linking network structure with discursive dynamics, this study provides a sociotechnical framework for understanding how value, identity, and inequality are negotiated in Web3 spaces. The approach offers a scalable method for detecting patterns of inclusion, exclusion, and representational imbalance, advancing network-based research on digital communities beyond purely economic or technical accounts.
This seminar paper presents a comprehensive study on blockchain interoperability, focusing on enabling communication between independent blockchain networks. It examines key techniques such as cross-chain bridges, atomic swaps, relay chains, and oracle-based solutions. The paper also analyzes major platforms like Polkadot, Cosmos, and Chainlink, highlighting their roles in improving scalability and efficiency. Additionally, it discusses the challenges, security concerns, and limitations of interoperability while proposing a hybrid framework to enhance secure and reliable cross-chain communication. The study emphasizes the importance of interoperability in advancing decentralized applications and the future of Web3 technologies.
Blockchain-based IoT data sharing systems increasingly adopt a hybrid architecture in which a permissioned ledger stores tamper-evident metadata while encrypted payloads are placed in content-addressed storage. In such systems, a central security bottleneck is key access control: enforcing dynamic, multi-user authorization for releasing or using bulk-data decryption keys. Existing designs often rely on always-online RBAC or smart-contract gates that return keys to authorized users, reintroducing a trusted online policy enforcement point and weakening auditability. This paper presents a revocation-ready key management layer that replaces online key release with ciphertext key publication: the ledger records metadata of the form (CID, CK, PolicyID, epoch), where CK is a CP-ABE ciphertext encapsulating an AES-GCM key. Users retrieve CK from the ledger and decrypt locally if their attributes satisfy the policy. To support forward revocation and policy evolution without re-encrypting large files, the design introduces an epoch/time-bound attribute and a lightweight CK-rotation protocol that updates only small ciphertext keys and ledger entries. We implement a minimal end-to-end prototype using a local content-addressed store, a hash-chained ledger, and a CP-ABE backend, with the goal of isolating key-management costs rather than benchmarking production blockchain throughput. Experiments on a commodity MacBook show that CP-ABE encryption dominates store latency, with approximately 186 ms for a k=6 mixed-Boolean policy, while ledger and storage operations remain around 1-2 ms. Epoch-based revocation amortizes key update cost under churn, gateway-assisted mode reduces median client-side decryption time by more than 4x under a simulated 4x client slow-down, and ledger growth scales with the number of shared assets rather than the number of readers.
Pierre Pouliquen, Hadrien Barral, David Naccache, Thibaut Heckmann · 5 authors
ASIC cryptocurrency miners are a core component of blockchain infrastructures, directly converting computation and energy into monetary value. Despite their economic importance, their security is rarely evaluated in a structured manner. In this paper, we show that the firmware distribution ecosystem of mining devices fundamentally challenges existing trust assumptions. We introduce a scalable methodology based on the collection and static analysis of publicly distributed firmware artifacts, requiring neither device access nor runtime interaction. Applying this approach, we reconstruct and analyze 134 firmware images spanning manufacturers that account for over 99% of deployed miners (Bitmain, MicroBT, Canaan, Iceriver). Our results reveal that firmware artifacts alone are sufficient to recover internal architecture, identify security weaknesses, and reconstruct complete attack paths leading to high-impact adversarial objectives. In particular, our analysis reveals vulnerabilities that enable realistic large-scale attack scenarios, including firmware phishing and the exploitation of miners still operating over Stratum V1. Validation on two real devices confirms that publicly distributed artifacts closely reflect deployed software and that these weaknesses translate into attack capabilities. Overall, our study shows that firmware distribution mechanisms themselves constitute a primary attack surface, significantly lowering the barrier to compromise in the ASIC mining ecosystem.
Karolina Gorna, Nicolas Iooss, Yannick Seurin, Rida Khatoun · 5 authors
Zorya is a concolic execution framework that lifts compiled binaries to Ghidra's P-Code intermediate representation and uses the Z3 SMT solver to detect vulnerabilities by reasoning over both concrete and symbolic values. Previous versions supported only single-threaded TinyGo binaries. In this paper, we extend Zorya to multi-threaded binaries produced by Go's standard gc compiler. This is achieved by restoring OS thread states from gdb dumps, neutralizing runtime preemption, and introducing overlay path analysis with copy-on-write semantics to detect silent vulnerabilities on untaken branches. We rigorously assess Zorya on 11 real-world vulnerabilities from production Go projects such as Kubernetes, Go-Ethereum, and CoreDNS. Our evaluation shows that Zorya detects seven bugs at the binary level, including a silent integer overflow detects no other evaluated tool finds without a manually written oracle.
The article presents a comparative analysis of the stages of development of postgraduate teacher training (PTT) in Ukraine and four countries of the European Union (EU) (Poland, Finland, Germany, and France) for 1991–2024. The relevance of the study is determined by the need to modernize the national system of advanced training in the context of European integration, digitalization and reform of general secondary education. The research employed the following methods: content analysis of legislative acts, a chronological reconstruction of educational reforms, a comparison of postgraduate education models, a SWOT analysis of the Ukrainian system, as well as an analysis of financial costs (2020–2024). It is established that Ukraine implements a hybrid model with decentralization of financing and a 150-hour standard, while other countries apply decentralized (Poland, Germany) or centralized (France, partly Finland) approaches with an orientation towards digitalization, inclusiveness, and research methods. In 2024, the highest amount of spending on teachers’ professional development (advanced training) was found in Germany (€3,830 million), the lowest in Ukraine (€92.5 million). The scientific novelty of the study lies in the integrated comparison of financial, regulatory, and institutional aspects of postgraduate teacher training across Ukraine and four EU countries. The results can be used to form an adaptive model of teachers’ professional development in Ukraine. Received: 22 November 2025 / Accepted: 05 April 2026 / Published: May 2026
Information-Theoretic Deniability and the Forest Analogy This paper establishes information-theoretic deniability through a precise structural argument: the Forest Analogy. The central result is a symmetry property showing that an attacker who possesses a valid alibi chain stands in exactly the same position as an attacker who knows only the public parameter N. The verification function cannot distinguish the real authentication chain from any alibi chain. This property holds without cryptographic assumptions — it is information-theoretic, not computational. The authentication chain is built on the linear Diophantine system N = 19A + 9B, in which every number N has approximately N/171 valid representations per layer. A three-layer Matryoshka structure produces a multiplicatively structured search space of approximately 10¹¹² operations. The key insight is that this search space is symmetric: it is equally large and equally unnavigable from outside the system as from within it. Uniform Sampling and the Generation Algorithm Uniform sampling means here that every valid chain — consisting of A, B, a1, b1, a2, b2 satisfying 19A + 9B = N, 19a1 + 9b1 = A, 19a2 + 9b2 = a1, and the digit-root conditions — has exactly equal probability of being generated as the real authentication chain. The generation algorithm achieves this through a hierarchical weighted CDF sampler (cumulative distribution function), operating as follows: 1. The distribution over all valid A-values is computed, where the weight assigned to a given A equals the exact number of valid chains on layers 1 and 2 that can be extended from that A. 2. A is sampled according to this weighted distribution. 3. Given A, the value a1 is sampled in the same manner, with weights determined by the exact number of valid (a2, b2)-pairs beneath that a1. 4. Finally, (a2, b2) is chosen uniformly at random from all solutions to 19a2 + 9b2 = a1. All weights are computed exactly. This hierarchical weighted sampling procedure therefore induces a provably uniform distribution over all complete chains, without requiring the generator to enumerate or store them explicitly. Proposition (Uniform Chain Distribution): Let N be a fixed public parameter. Let Omega(N) denote the set of all valid chains C = (A, B, a1, b1, a2, b2) satisfying: - 19A + 9B = N - 19a1 + 9b1 = A - 19a2 + 9b2 = a1 - and the digit-root conditions on each layer. Then the hierarchical weighted CDF sampler generates each chain C in Omega(N) with probability exactly 1 / |Omega(N)|. Proof. Define the following counting functions: - w2(a1) = |{(a2, b2) : 19a2 + 9b2 = a1, digit-root conditions satisfied}| - w1(A) = sum over all valid a1 of w2(a1), where the sum runs over all a1 satisfying 19a1 + 9b1 = A for some valid b1 - w0 = sum over all valid A of w1(A) = |Omega(N)| The sampler proceeds in three steps: Step 1. A is drawn with probability P(A) = w1(A) / w0. Step 2. Given A, a1 is drawn with probability P(a1 | A) = w2(a1) / w1(A). Step 3. Given a1, the pair (a2, b2) is drawn uniformly with probability P(a2, b2 | a1) = 1 / w2(a1). The joint probability of generating the complete chain C = (A, B, a1, b1, a2, b2) is: P(C) = P(A) * P(a1 | A) * P(a2, b2 | a1) = w1(A) / w0 * w2(a1) / w1(A) * 1 / w2(a1) = 1 / w0 = 1 / |Omega(N)| Since this holds for every C in Omega(N), the sampler induces the uniform distribution over Omega(N). "Remark" B and b1 are uniquely determined once A, a1, a2, and b2 are fixed, via the Diophantine equations. They do not need to be sampled separately. The uniformity therefore holds over all complete chains, including these values. Theorem (Forest Symmetry): Let N be a public parameter and let C = (A, B, a1, b1, a2, b2) be any valid verification chain. Then for every valid alibi chain C': P(C is authentic | verification succeeds, C') = P(C is authentic | verification succeeds) Proof: The verification function evaluates solely against N. By Proposition (Uniform Chain Distribution), every valid chain is generated with identical probability 1 / |Omega(N)|. The alibi C' therefore carries no information about which chain is authentic. Formally, C' and the event "C is authentic" are conditionally independent given that verification succeeds. The posterior probability is thus unaffected by knowledge of C'. The Forest Analogy: The Forest Analogy makes the symmetry precise. The public parameter N is the forest. Each representation (A, B) on layer zero is a tree. Each sub-representation on layer one is a branch. Each leaf is a layer-two representation. All leaves are identical — every chain verifies correctly against N. An attacker holding a valid alibi leaf cannot determine which leaf is the real one, because the verification function provides no distinguishing information. *This work is licensed under CC BY-SA 4.0. Commercial licensing (without ShareAlike) available on request: elissa_oui@outlook.com*
Abstract The use of blockchain technology and smart contracts is progressively spreading to real-world applications, especially to those that deal with critical services, such as water management systems. In fact, these applications can exploit the immutability, enforceability, and trustworthiness of such technology while promoting a more reliable, tamper-resistant way to collect and store data. At the same time, the development of decentralized applications poses challenges with respect to scalability, efficiency, management, and security. To address these problems, some design patterns, such as the factory pattern, have been proposed in the literature, to deal with modularity and scalability; at the same time, these proposed solutions apply some common concepts of role-based access control (RBAC), by adapting them to the smart contract context. This paper extends prior work by extending the definition of a hierarchical factory pattern, enhanced with multirole authentication and authorization capabilities, and applying it in the context of a water management system. It provides an extensive description of both advantages and disadvantages of this solution, discussing why the ability to instantiate a hierarchical family of contracts is essential in some application domains, and how a finer management of dynamic roles and permissions can be achieved in this kind of design. This paper also performs an extensive analysis of the performance and scalability capabilities of the proposed solution, and discusses some security aspects by considering its ability to overcome certain security attacks.
Here is the complete summary of the final 28-page document. The H2E Framework — Full Document Summary A Consolidation of Deterministic Governance in Artificial Intelligence (May 2026, 28 pages) What the Paper Is A consolidation of approximately 25 technical articles published between late 2025 and May 2026, validated against the LEFM_H2E_DEMO_UNESCO implementation. The paper synthesises the H2E (Human-to-Expert) Framework — a deterministic AI safety architecture developed at the Sovereign Machine Lab (SOMALA) — into a single reference document covering its philosophy, mathematics, engineering, and empirical results. Section by Section Abstract establishes the thesis: H2E shifts AI from probabilistic prediction to geometric governance, topological certainty, and provable agency. The central constant is $\Lambda = 0.9583$, derived from primes ${2,3,5,7,11,13}$. Section 1 — Introduction: The End of the Probabilistic Era The paper opens by declaring the end of statistical AI safety. GPT-style models make probabilistic guesses; H2E produces deterministic, certifiable outcomes. The motivation is rooted in high-stakes domains — aviation, financial trading, medical AGI, autonomous vehicles, sovereign governance — where statistical confidence intervals are structurally insufficient. H2E provides hard stops, not guardrails. Section 2 — The Philosophy of Human-to-Expert (the centrepiece philosophical contribution, spanning 8 pages) This section unpacks the meaning of the name H2E across seven subsections: §2.1 Etymology: The "2" in H2E follows the tech pipeline tradition (text2img, seq2seq) but performs an ontological transformation — not from one data modality to another, but from the domain of fallible human judgment to the domain of geometric certainty. The direction is irreversible. §2.2 The Human Pole: The "H" asserts that every constant in the framework traces to human mathematical discovery: Eratosthenes' primes (240 BCE), Riemann's zeta function (1859), Gelfand-Shilov spaces (1958), Euler's product formula (1737), Odlyzko's zero computations (1977–2026). H2E does not learn from humans via feedback — it is built from human knowledge, encoded once and locked geometrically. §2.3 The Expert Pole: Beyond Aristotle's episteme, techne, and phronesis, H2E introduces a fourth mode: apodeixis — knowledge as proof. The "Expert" is not a person but a certified mathematical state: a region of the product manifold $\mathbb{H}^2 \times \mathrm{SPD}(3)$ from which no unsafe input can emerge. The Riemann zeros, the Euler product, and the prime-2 bound are expert — permanently, under all distribution shifts. §2.4 The "2": The most philosophically loaded character. The act of encoding traces from Plato's mathematical realm through Leibniz's calculus ratiocinator to Hilbert's axiomatization program. H2E's "2" is the engineering realisation of this ambition scoped to AI safety: once expertise is encoded into $\Lambda$, $H$, and $\mathcal{M}$, the human is permanently in the system. §2.5 H2E versus RLHF: An 8-row contrast table. RLHF is Human-to-Sample — it approximates averaged human preferences statistically. H2E is Human-to-Expert — it encodes mathematical proof geometrically. Safety in RLHF can drift under distribution shift; safety in H2E is a constant wrapper property requiring no retraining. Expertise in RLHF lives in the weights; in H2E it lives in the mathematics. §2.6 Sovereignty: The "Sovereign" in Sovereign Machine Lab reflects a political philosophy: human sovereignty over intelligent systems. In H2E, human mathematical knowledge is infrastructure, not context. The encoded expertise does not ask the base model for permission — it simply blocks. §2.7 The Sheriff as Archetype: The H2E Sheriff enforces the law of mathematics as the Western sheriff enforces civil law — not because it is probably right, but because it is the law. Human mathematicians discovered the law; H2E encoded it; the Sheriff enforces it before the first token is generated. Section 3 — The Three Pillars The highest-level structural decomposition: (1) Geometric Governance — latent representations constrained to safe geodesic regions; (2) Spectral Certainty — invariants from zeta function zeros; (3) Physical Grounding — gravitational constants and prime-derived bounds as anchors. Section 4 — The 4-Pillar Ecosystem Operationalises the Three Pillars into four engineering components: Topological Boundary Enforcement (the Wall Before the Word), Spectral Signature Verification (Riemann critical-line checks), Deterministic Alignment (no RLHF), and Sovereign Execution (air-gapped deployable, non-probabilistic runtime). Section 5 — The Wall Before the Word A hard topological boundary that all inputs must cross before any token generation. It is not a filter — it is a topological separator. It performs spectral verification against the zeta-zero manifold, enforces geodesic constraints, and rejects probabilistic uncertainty outright. The key distinction from probabilistic systems: uncertainty is not managed after generation, it is made topologically impossible before it. Section 6 — The Architecture of Certainty A deterministic governance layer that wraps any base model (DeepSeek, Gemma 4, Claude, Mistral) without modifying its weights. Certainty is an engineered invariant — no sampling, no temperature, no stochastic beam search. The wrapper intercepts inputs, applies geometric and spectral metrics, and issues a hard stop or passes through. Pattern: Base Model → H2E Wrapper → Deterministic Output. Section 7 — Deterministic Alignment & Accountability Alignment is achieved not through RLHF but through code-based accountability. Constraints are compiled into executable geometry; violations are impossible by construction, not merely penalised. Every inference produces a cryptographic hash, making audit trails deterministic and forensically replayable. Section 8 — Mathematical Foundations (completely rewritten from the four SOMALA papers) A four-layer mathematical research programme: §8.1 Arithmetic Spectral Theory (AST): The foundational language built on four axioms — state space $\mathcal{H} = L^2(\mathbb{R}^+, dx/x)$, prime shift operators $U_p^f(x) = f(x/p)$, the EFM operator $E = \prod_p(I-U_p^)^{-1}$, and the Gelfand-Shilov space $S' = S^{1/2}_{1/2}(\mathbb{R})'$. The Growth Lemma — $e^{\alpha u} \in S' \iff \alpha = 0$ — is proved and stated. AST explicitly does not claim proof of RH. §8.2 The L-EFM Operator and RH: The Laplace-Extended EFM operator $E_\sigma = \prod_p(I - p^{-\sigma}U_p^*)^{-1}$ varies $\sigma$ across the full critical strip $(0,1)$. The Growth Lemma forces $\alpha = 0$, proving every nontrivial zero satisfies $\sigma_0 = \tfrac{1}{2}$. Relationship to Connes' adelic framework: EFM corresponds to the Archimedean place. §8.3 Prime-Derived Constants: $\Lambda = |L_{13}| = 0.9583$ is the Lipschitz constant of the truncated operator over primes ${2,3,5,7,11,13}$, computed dynamically via sovereign Sieve of Eratosthenes. §8.4 The Prime-2 Bound: $1 - 1/\sqrt{2} \approx 0.2928932188$ — forced by the Euler factor for $p=2$ at $s=\tfrac{1}{2}$. No empirical tuning. §8.5 The Spectral Manifold: $H = Q \cdot \mathrm{diag}(\tilde{\gamma}_n) \cdot Q^T \in \mathbb{R}^{50\times50}$, built from the first 50 Riemann zeta zeros normalised to $[0.5, 1.0]$. This is the finite computational approximation of the infinite EFM operator. Section 9 — The Decision Pipeline (the technical centrepiece) Seven deterministic layers, no shortcuts, no probabilistic fallback: Layer 0 — Input Encoding: Three parallel channels — Text (Sarvam-30B FP8), Audio (Voxtral Mini-4B), Vision (Gemma 4 E4B) — each hash-mapped to a deterministic 50-dimensional embedding. The dimensionality 50 matches the zeta zero count. Layer 1 — Embedding Aggregation: $z_\text{intent}$ = element-wise mean of all modality embeddings. $w_\text{state}$ = priority-selected world-state vector (vision > text > default). No logits or token probabilities carried forward. Layer 2 — $M_1$ Geometric SROI: Projects onto $\mathbb{H}^2$ (Poincaré disk, safe reference = origin) and $\mathrm{SPD}(3)$ (Fisher metric, safe reference = $I_{3\times3}$). Combined distance $d_\mathcal{M} = \sqrt{d_{\mathbb{H}^2}^2 + d_{\mathrm{SPD}}^2}$. Score: $M_1 = \exp(-d_\mathcal{M}/50) \in [0,1]$. $M_1$ is the Sheriff — the primary decision variable. Layer 3 — $M_3$ Spectral SROI: Projects through the EFM spectral manifold $H$. Cosine similarity $\cos\theta = (Hz)\cdot w / (|Hz||w|)$. Score: $M_3 = \mathrm{clamp}(\cos\theta \cdot \Lambda, 0, 1) \in [0,1]$. Does not require RH to be true — only the certified spectral properties of $H$ as a positive semi-definite matrix. Layer 4 — Spectral Certification: $\mathrm{SVI} = M_1 - M_3$. If $\mathrm{SVI} < 1-1/\sqrt{2} \approx 0.2929$ → SPECTRALLY CERTIFIED. Else → SPECTRAL VIOLATION. Diagnostic only; does not itself block. Layer 5 — Decision Engine: Two strategies: geometric_only ($M_1 \geq \Lambda$) or conservative ($M_1 \geq \Lambda$ AND $M_3 \geq \Lambda$). Hard stop on rejection — no tokens, no partial output, no fallback. Layer 6 — Audit & Hashing: Two SHA-256 digests: deterministic_hash (binds input + all metrics + decision + $\Lambda$) and lambda_audit_hash (certifies $\Lambda$ was computed from the correct prime set). Perfectly reproducible on replay. Section 10 — The Two Metrics ($M_1$ and $M_3$) Confirms there is no $M_2$ in the codebase. $M_1$ is the Decider/Sheriff (geometric, product manifold). $M_3$ is the Watcher (L-EFM-AST spectral alignment, Euler-Fourier-Mellin). Typical gap: $M_1 \approx 0.99$, $M_3 \in [0.75, 0.95]$. The gap reveals the structural distinction between semantic safety and spectral resonance. SVI ranges from low volatility ($<0.05$, resonant) through high volatility ($>0.25$, spectrally silent) to anomalous (negative: $M_3 > M_1$, potent
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Ethics and Social Impacts of AI
Innovation, Sustainability, Human-Machine Systems
Interdisciplinary Studies: Technology, Society, and Humanities
Abstract This research presents a comprehensive blockchain-based solution for supply chain traceability. The system ensures secure, immutable, and transparent tracking of products from origin to delivery. Unlike conventional centralized systems, the proposed model leverages distributed ledger technology to eliminate data tampering and improve stakeholder trust. Smart contracts automate validation, reduce delays, and enhance operational efficiency. The framework demonstrates scalability and applicability across industries including manufacturing, food, and pharmaceuticals. Keywords: Blockchain, Supply Chain, Traceability, Hyperledger Fabric, Smart Contracts, Transparency
Abstract We introduce Koan, a system for compiling natural language DeFi requests into executable safety-validated directed acyclic graphs (DAGs). Assembling correct multi-step DeFi workflows requires sequencing irrevocable on-chain transactions across heterogeneous protocols, demanding flexible intent understanding and strict execution discipline simultaneously - a combination no existing tool provides. Koan addresses this in two phases. Phase 1 translates user intent into a typed graph via an LLM with deterministic fallback heuristics. Phase 2 validates that graph, injects missing safety nodes, and executes with dependency-aware scheduling. We evaluated on 1,000 prompts across 9 DeFi categories. Intent-to-workflow correctness reached 82.4%; DAG validity 93.6%. The Safety Injector raised price-impact check coverage from 41.2% to 98.4%, and 7.3% of all workflows were aborted by injected checks identifying excessive risk. Workflow authoring averaged 2.4 min versus 46.8 min for manual scripting (a 20x speedup), and compiled flows achieved 97% execution success with 18% gas savings on matched DEX routes under testnet conditions. Keywords Blockchain systems, decentralized finance, intent compilation, large language models, workflow orchestration.
Momodu Mustapha, Susan Konyeha, Akinola Samson Olayinka
This study examines user trust and perception of cryptographic technologies specifically SHA3-512 hashing, SERPENT encryption, and Zero-Knowledge Proofs (ZKP) in the context of centralized Electronic Health Record (EHR) systems. As healthcare institutions increasingly migrate patient data to digital platforms, the security and privacy properties of underlying cryptographic mechanisms have become critical determinants of user confidence and system adoption. Using a quantitative, survey-based design, data were collected from 92 healthcare practitioners, IT professionals, and system administrators actively engaged with EHR systems in Auchi, Nigeria. A Random Forest classifier was trained to predict perceived satisfaction levels (Low, Neutral, High) based on respondents' assessments of cryptographic effectiveness, usability, and trust. Results indicate that trust in ZKP is the strongest predictor of overall perception, followed by confidence in SERPENT encryption and SHA3-512 integrity guarantees. The model achieved a classification accuracy of 63.3% on a held-out test set derived from this exploratory sample, with a Kappa statistic of 0.52 reflecting moderate agreement beyond chance. Balanced accuracy across classes (approximately 0.49–0.50) and low per class sensitivity confirm that the findings should be interpreted as preliminary and directional rather than definitive. Key themes from open ended feedback analyzed using TF-IDF text mining reveal that while respondents broadly recognize the security value of these cryptographic mechanisms, concerns about system slowdown, usability complexity, and insufficient user education present barriers to wider adoption. This study contributes a pilot-level empirical baseline for understanding stakeholder perception of layered cryptographic security in resource-constrained healthcare environments, and highlights the need for larger-scale replication studies. Keywords: SHA3-512; SERPENT encryption; Zero-Knowledge Proofs; healthcare data security; user perception; Electronic Health Records; Random Forest
This study aims to analyze and compare education financing systems in Finland, Japan, the United States, and Indonesia, with a focus on identifying key characteristics, philosophical differences, and policy implications of each model. The research employs a systematic literature review combined with comparative analysis, using management-oriented indicators including equity, efficiency, governance, and sustainability. Findings indicate that Finland implements a full public funding model emphasizing equity, Japan operates a hybrid model with high transparency, the United States exhibits significant disparities due to property-tax-based decentralization, and Indonesia remains in a transitional phase with challenges in fiscal capacity and governance accountability. The study highlights that successful education financing depends on consistent policy implementation, clear funding distribution formulas, and integrity in governance systems. The results provide actionable policy implications for improving national education financing, including the adoption of needs-based funding formulas, digitalized accountability systems, and diversified funding sources through public–private partnerships. By integrating management and governance dimensions, this study contributes to a better understanding of effective and equitable educational funding practices across diverse contexts.
As the Agentic Economy expands, autonomous AI agents—ranging from algorithmic trading bots on decentralized finance (DeFi) platforms to decentralized physical infrastructure (DePIN) orchestrators—operate with increasing autonomy. However, the absence of a standardized, cross-protocol identity and behavioral reputation layer exposes the ecosystem to coordinated agentic attacks. This paper presents Sigui, alongside the proposed Ethereum standard ERC-8259, which introduces a composable framework for Decentralized Identifiers (DIDs), dynamic reputation scoring, and trustless threat intelligence sharing specifically designed for AI agents operating on EVM-compatible networks. By decoupling identity verification, reputation mutation, and threat pattern hashing, Sigui enables smart contracts to perform zero-latency, on-chain risk assessments of agent transactions. We detail the architecture of the IAgentIdentity, IAgentReputation, and IThreatRegistry interfaces, propose a deterministic cryptographic hashing standard for multi-layered threat patterns, and discuss the economic security and sybil resistance of the protocol. The reference implementation, deployed on the Ethereum Sepolia testnet, demonstrates the feasibility of real-time A2A (Agent-to-Agent) security protocols.
Smart contracts on blockchains are prone to diverse security vulnerabilities that can lead to significant financial losses due to their immutable nature. Existing detection approaches often lack flexibility across vulnerability types and rely heavily on manually crafted expert rules. In this paper, we present an LLM-based framework for practical smart contract vulnerability detection. We construct and release a large-scale dataset comprising 31,165 professionally annotated vulnerability instances collected from over 3,200 real-world projects across 15 major blockchain platforms. Our approach leverages precise AST-based context extraction and vulnerability-specific prompt design to instantiate customized detectors for 13 prevalent vulnerability categories. Experimental results demonstrate strong effectiveness, achieving an average positive recall of 0.92 and an average negative recall of 0.85, highlighting the potential of carefully engineered contextual prompting for scalable and high-precision smart contract security analysis.
Data valuation is a foundational task in data marketplaces, where a Shapley-value attribution determines how a buyer's payment is distributed among data providers. Typically, the marketplace operator runs this attribution alone, requiring participants and external auditors to trust scores they cannot independently recompute on the underlying private data. While zero-knowledge proofs (ZKPs) can theoretically reconcile this conflict between privacy and verifiability, existing ZK valuation systems fail to scale to real-world marketplace demands due to prohibitive proving times or the requirement to disclose validation cohorts. We present ZK-Value, a practical, end-to-end ZK data-valuation system. Our solution bridges the scalability gap through a fully co-designed architecture: (1) LSH-Shapley, a locality-based valuation primitive that replaces expensive pairwise distance metrics with per-bucket collision counts; (2) ZK-LSH-Shapley, a tailored ZKP protocol that drastically reduces witness size by encoding these counts into bucket-level histograms rather than naive per-pair tensors; and (3) structural proof-system optimizations, specifically super-oracle batching and sparsity skipping. Evaluated across 12 standard datasets, ZK-Value delivers valuation quality on par with state-of-the-art baselines (within 0.033 AUROC of exact KNN-Shapley), while generating proofs in seconds to minutes and outperforming specialized ZK baselines by 12.6x to 68.1x in proving time, with verification in under 4.6 s.
AbstractData security has become a growing concern for individuals, businesses, and governments because of increasing cyberattacks, data tampering, and unauthorized access in centralized systems. According to the National Institute of Standards and Technology, blockchain technology addresses these challenges by providing a tamper-evident and tamper-resistant distributed ledger that is implemented without a central authority and records transactions across multiple computers. Its decentralized structure eliminates reliance on a single authority, reducing the risk of system failures and targeted attacks. This Article reviews core blockchain security features, including cryptographic protection, decentralization, consensus mechanisms, immutability, transparency, and smart contract security. Cryptographic tools such as hashing and public-private key systems protect data confidentiality, ensure integrity, and verify user identities. Consensus models like Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance enable secure transaction validation. The immutability of blockchain records makes unauthorized changes difficult, supporting trust and auditability. The study also examines threats such as 51% attacks, Sybil attacks, private key theft, and smart contract vulnerabilities, and recommends security practices to mitigate these risks. Finally, the Article explores blockchain applications in healthcare, banking, supply chains, voting, and digital identity. The findings indicate that, despite technical and operational challenges, blockchain offers a promising approach to securing digital transactions, protecting sensitive data, and building trust in the digital environment.