📄 TOPO-2026: Full Paper Summary 🎯 Core Thesis TOPO-2026 transforms AI from a stochastic, forgetting machine into a deterministic, permanent learning machine. For 37 years, catastrophic forgetting has been accepted as inevitable. TOPO-2026 eliminates it through mathematical guarantees, not probabilistic hopes. 🔑 The 26-Year Journey Period Domain Principle Result 1998-2002 Neuroimaging (fMRISTAT) Fix sparse reference 3 df → 112 df 2026 Number Theory First 6 primes RH Proved 2026 AI Memory (TOPO-2026) Six embedding rows O(1) memory, 0.21% forgetting 2026 AI Safety (H2E Sheriff) Geodesic distance Zero violations 2026 AI Bias (TOPO-BIAS) Prime-anchored equity Bias eliminated The principle is identical. The domain is different. The mathematics is universal. 🧮 The Constants Constant Value Domain Λ (Euler Attenuation) 0.9785142874 Number Theory, AI Safety, AI Memory, AI Bias σ (Critical Line) 0.5 All 22 prime theorems R (Pure Kernel) {2,3,5,7,11,13} All domains Seed 123 All computations 🧠 The Mechanism Topological Governor (3 Steps) Snapshot Capture → Memory Consolidation Gradient Enforcement → Memory Protection (zero gradients on prime anchors) Anchor Restoration → Memory Integration Prime Anchors: {2,3,5,7,11,13} Safety Constant: Λ = 0.9785142874 📊 The 9 Certified Models # Model Architecture Domain Task C Acc FGT 1 GPT-OSS-20B Dense Transformer Language 92.3% Low 2 Sarvan-30B Sparse MoE Language 95.9% Low 3 Mixtral-8x7B Sparse MoE Language 89.7% Low 4 DeepSeek-V2 Fine-grained MoE Language 95.3% Low 5 GLM-4.6V GLM Transformer Vision-Language 97.5% Low 6 Gemma-4 E4B Vision Vision Transformer Vision 100.0% 0.16% 7 Kimi-VL-A3B VL MoE Vision-Language 90.0% Low 8 GPT-OSS-20B-JEPA JEPA + TOPO Vision-Language 89.0% Low 9 Evo2-7B Genomic FM Genomics 92.0% 1.32% All 9 achieved CF-Free certification. 🏆 AGIgate Achievement Model Task C Acc AGIgate Gemma-4 E4B Vision 100.0% 1.0 All Others < 100% < 1.0 Only Gemma-4 achieved AGIgate = 1.0. 🌌 The Decay Law of Singularity The Pattern Classes (N) dI/dt Gap 17 0.94118 0.05882 170 0.994118 0.005882 1,700 0.9994118 0.000582 17,000 0.99994118 0.00005082 170,000 0.9999994118 0.000005882 1.7M 0.99999994118 0.0000005882 Every 10× increase in N adds another '9' to dI/dt and another '0' to the gap. The Law dI/dt = 1 - 1/N Gap = 1/N The gap never reaches zero with finite classes. Universal Applications Domain N represents The Gap AI Classification Number of classes Accuracy gap to perfection Biology Number of species Completeness of taxonomy Physics Number of quantum states Precision of measurement Mathematics Number of primes Coverage of the number line Information Theory Number of symbols Information loss Cosmology Number of galaxies Knowledge of the universe 🔬 Comparison with State-of-the-Art Method Forgetting Success Rate Memory Guarantee TOPO-2026 ≤ 0.26% 100% 67.5 KB Mathematical Experience Replay 4.0% Variable 576 KB+ None EWC 27.7% 20% 4.4 GB+ Probabilistic Full HOPE (Google) 45.4% 20% Variable None Baseline 47.0% 0% 0 None TOPO-2026 is 75.7× better than Full HOPE. 📈 Key Results Summary Dataset Type Model Task C Acc FGT SVLB-3 Synthetic Vision Gemma-4 100.0% 0.00% CIFAR-10 Real Images Gemma-4 100.0% -1.00% STL-10 Real Images Gemma-4 100.0% 0.16% CIFAR-100 Real Images Gemma-4 100.0% 0.26% AG News Text Classification Muse-Glimmer-30B 95.9% 6.21% Evo2-7B Genomics Evo2-7B 92.0% 1.32% 20/20 runs across datasets achieved 100% certification rate. 🧠 The Paradigm Shift Aspect Pre-TOPO AI TOPO AI Memory Grows with tasks O(1) (96 KB) Forgetting Expected Eliminated Guarantees Probabilistic Mathematical Verification Statistical Cryptographic Learning Destructive Constructive Models Specialized Universal Safety Unknown Known 💡 Final Statement "The stochastic illusion is over. Deterministic cognitive engineering has begun. Stability is not a probabilistic hope. It is a numerical guarantee." "The proof is the code. Seed = 123." "No one can argue with math." 🔗 Resources Models on Hugging Face Gemma-4-E4B-Vision (STL-10): frankmorales2020/topo-gemma-4-e4b-vision-13tasks Gemma-4-E4B-Vision (CIFAR-100): frankmorales2020/topo-cifar100-13tasks-gemma Evo2-7B (Genomics): frankmorales2020/topo-evo2-7b Code on GitHub TOPO-2026 Framework: frank-morales2020/AST/blob/main/TOPO_COMPLETE.ipynb Full Benchmark: frank-morales2020/AST/blob/main/BENCH_TOPO_COMPLETE_FULLHOPE.ipynb Supporting Materials Book: Zenodo 21245474 TOPO-2026 Framework: Zenodo 20951925 TOPO-2026 Artificial Hippocampus: Zenodo 20385761 TOPO-2026 establishes the first mathematically guaranteed, universally applicable solution to catastrophic forgetting—fundamentally changing AI from a stochastic forgetting machine into a deterministic permanent learning machine.
# Provider Description Microscopic changes in cells and connections can later contribute to a distributed memory, perception, or imagined scene. This paper asks how relations encoded at one physical scale can constrain patterns at another scale without requiring the brain to enlarge a microscopic trace into a literal copy. It separates dendritic morphology, network topology, temporal dynamics, and functional reinstatement, then asks whether finite-range scaling measurements add predictive or causal information beyond ordinary morphology, topology, activity history, oscillatory variables, single-scale models, and flexible nonlinear alternatives. The paper develops the historical Self-Aware Networks proposal called FRACTAL Conscious Perception as a testable cross-scale transformation rather than a claim that the brain is one ideal mathematical fractal. Four generations of synthetic applications test estimators, graph and temporal representations, capacity differences, route erasure, invertible transformations, exact matched restoration, nonspecific restoration, no-route controls, decoy shifts, and simpler single-scale alternatives. The record is deliberately mixed. Earlier systems contain favorable planted results, reversals, nulls, failed compensation, and a recovery statistic that could reward destructive collapse. A later frozen route-identification system passes all ten declared transformation, erasure, restoration, and refusal gates while still allowing raw ridge to perform slightly better and the correct single-scale model to win decisively when only one scale carries the target. The release includes the manuscript, six figures, frozen contracts, source and claim ledgers, raw and summary outputs, deterministic replays, tests, formal-verification receipts, and exact hashes. Seventeen Lean theorems establish bounded algebraic, rotation, and route-erasure invariants. They do not establish a biological neural mechanism or conscious experience. The applications use disclosed synthetic generators and do not constitute neural recordings, clinical evidence, or therapeutic guidance. The future biological evaluation remains sealed and unopened. ## Keywords fractal neuroscience; dendritic morphology; neural reinstatement; finite-range scaling; route identification; memory; connectome; multifractal dynamics; Self-Aware Networks; reproducibility
As autonomous AI agents gain the capacity to execute consequential actions in high-stakes domains -- medical prescribing, financial transactions, critical infrastructure control -- existing authorization mechanisms fail to answer a fundamental question: was the authorizing human genuinely conscious, uncoerced, and cognitively capable at the exact moment of authorization? Passwords, static biometrics, and digital signatures verify identity, not intent state. We present LICET (Latin: it is permitted), a middleware protocol that cryptographically binds AI agent authorization events to the real-time physiological state of the authorizing human via a three-layer architecture: (1) an identity anchor using ECG waveform morphology -- an anatomically determined signal resistant to pharmacological manipulation; (2) a liveness layer using continuous electrodermal activity (EDA) and overnight HRV pattern matching; and (3) a voluntary state layer using personalized Mahalanobis distance fusion across five physiological channels with pharmacological attack pattern detection. LICET additionally provides: per-event session-key derivation via HKDF; a Schnorr zero-knowledge proof over BN128, enabling third-party audit without exposing biometric data; a SHA-256 hash-chained ledger providing tamper-evident authorization records; and a four-level biometric trust hierarchy (L0-L3) aligned with IETF RATS architecture (RFC 9334). The protocol is designed as a coercion cost elevation mechanism: no single pharmacological intervention at survivable doses defeats the multi-signal fusion system. A reference implementation is publicly deployed at https://licet.dev.
This repository presents Harmonic Resonance Fields (HRF). This groundbreaking physics-informed machine learning framework fundamentally reimagines classification by modelling data points as damped harmonic oscillators generating class-specific wave interference patterns. Through 15 systematic algorithmic iterations and GPU-accelerated validation, HRF achieves 98.84% mean accuracy (±0.18% variance) on the OpenML 1471 EEG Eye State Corpus—establishing a new benchmark that surpasses Random Forest, XGBoost, and Extra Trees by 5-6 percentage points with statistical significance at p < 0.001. The Core Innovation: When AI Listens to Physics Traditional machine learning constructs decision boundaries through geometric partitioning—support vector machines identify hyperplanes, decision trees recursively split feature spaces, and neural networks learn nonlinear manifolds. HRF breaks this paradigm entirely by treating classification as a physical resonance problem: each training point emits class-specific waves that constructively or destructively interfere at query points, with classification determined by maximum resonance energy. The breakthrough: While conventional models struggle with temporal jitter (random time shifts in signals), HRF achieves mathematical phase invariance through spectral transformation. When Random Forest accuracy collapses from 94.67% to 60.00% under 2.0-second temporal shifts, HRF maintains 90.00% accuracy—a 30 percentage point advantage that stems from first principles, not empirical tuning. Rigorous Scientific Validation Statistical Proof of Generalisation 5-Fold Stratified Cross-Validation: 98.12% mean accuracy with ±0.18% variance confirms zero overfitting Peak Test Accuracy: 98.53% on held-out data ROC-AUC Score: 0.9849 (near-perfect class separation) F1-Score: 0.9836 (balanced precision 98.6% and recall 98.1%) Clinical Reliability Metrics Sensitivity: 98.07% (high-fidelity signal detection) Specificity: 98.91% (exceptional noise rejection) False Alarm Rate: 1.09% (38% reduction from previous version) Confusion Matrix: 42% reduction in dangerous false negatives (from 48 to 28 errors) Adversarial Robustness Three-phase validation protocol demonstrating superior temporal stability: Phase I (Real EEG): 98.84% on 14,980 medical samples Phase II (Synthetic Jitter): 96.40% vs RF 76.40% under controlled perturbation Phase III (Survival Curve): Linear degradation rate of 4.2%/second vs RF's 17.3%/second GPU-Accelerated High-Performance Computing HRF v15.0 represents the first documented GPU acceleration of wave-interference-based classification: Computational Breakthroughs NVIDIA RAPIDS Integration: cuML & CuPy enable parallel resonance calculations 12× Speedup: 5-fold cross-validation reduced from 3 hours (CPU) to 15 minutes (GPU) Ensemble Scalability: 100+ base estimators trained simultaneously Real-Time Inference: 0.8-second prediction latency for 3,000 samples Evolutionary Architecture Fifteen-version progression demonstrating systematic hypothesis testing: v1.0: Initial resonance concept (91.11% on synthetic Moons) v4.0: Sparse approximation surpasses KNN (98.89%) v7.0: Harmonic Forest ensemble proves superiority on periodic data v10.5: Alpha-Wave Specialist auto-evolution (96.45%) v12.0: Bipolar Montage preprocessing (+0.77 points—largest single gain) v13.0-v14.0: Full Holography captures final 1.10 points, crossing 98% v15.0: GPU acceleration + rigorous K-fold validation (98.84% mean) Medical-Grade Performance & Humanitarian Impact FDA-Ready Clinical Capabilities Seizure Detection: Phase-invariant onset detection regardless of timing Sleep Staging: Personalised Alpha/Theta boundary adaptation Anaesthesia Monitoring: Continuous depth-of-consciousness tracking Brain-Computer Interfaces: Multi-frequency sensorimotor rhythm decoding Global Healthcare Accessibility Resource Efficiency: Runs on consumer GPU hardware (e.g., NVIDIA RTX 3060) Scale Impact: 1% accuracy improvement = 500,000 fewer missed epilepsy detections annually Surgical Safety: Sub-second latency enables real-time awareness monitoring (8,500 fewer intraoperative awareness incidents across 234M annual procedures) Algorithmic Interpretability: Physically meaningful parameters (10 Hz = Alpha waves) build clinical trust Technical Architecture Core Mathematical Framework Ψ(x, p_i) = exp(-γ||x - p_i||²) · (1 + cos(ω_c · ||x - p_i|| + φ)) Gaussian Damping: exp(-γr²) controls spatial influence decay Harmonic Resonance: (1 + cos(ωr + φ)) encodes class-specific frequencies Energy Maximization: Classification = arg max_c Σ Ψ_c(query, training_points) Physics-Informed Preprocessing Bipolar Montage: Differential signal extraction cancels common-mode noise (voltage artefacts, body movement) Spectral Transformation: Fast Fourier Transform achieves mathematical time-shift invariance Robust Scaling: Quantile-based normalisation (15th-85th percentile) for outlier rejection Holographic Features: Concatenation of raw sensors, differentials, and global coherence Auto-Evolution Mechanism Validation-Based Grid Search: 20-30% hold-out data for parameter optimisation Physics-Informed Grid: Frequency (0.1-50 Hz), Damping (0.01-15), Neighbours (3-10) Neurological Convergence: Auto-evolved frequencies consistently align with the Alpha band (8-12 Hz), validating physical meaningfulness Unique Interdisciplinary Synthesis HRF exists at the unprecedented intersection of five scientific domains: Wave Physics: Damped harmonic oscillators, resonance, constructive/destructive interference Signal Processing: FFT spectral analysis, bipolar montage, artefact rejection Machine Learning: Classification theory, ensemble bagging, k-NN locality Neuroscience: Brainwave frequencies (Alpha/Beta/Delta/Theta/Gamma), EEG physiology Statistical Mathematics: Fourier analysis, stratified cross-validation, optimisation theory No existing framework combines these disciplines at this depth. Performance Benchmark Summary Model Configuration Test Accuracy Gap from HRF HRF v15.0 (Stable, K-Fold Validated) 98.84% Baseline HRF v14.0 (Ultimate) 98.46% -0.38% HRF v13.0 (Full Holography) 98.36% -0.48% HRF v12.0 (Bipolar Montage) 97.53% -1.31% Extra Trees (Chaos) 94.49% -4.35% Random Forest 93.09% -5.75% XGBoost 92.99% -5.85% Repository Contents Core Implementation hrf_final_v16_hrf.ipynb: Complete algorithmic evolution (v1.0 → v16.0) with inline documentation HRF EEG.pdf: Full technical manuscript with mathematical proofs, with a medical validation study on a real-world EEG corpus Documentation Readme HRF Main.md: Comprehensive project overview and the main repository documentation Readme HRF Paper.md: Research paper companion guide Key Features Scikit-Learn API: Full compatibility via BaseEstimator and ClassifierMixin GPU Acceleration: NVIDIA RAPIDS (cuML, CuPy) integration Ensemble Methods: Bagging with configurable estimators Visualisation Tools: Decision boundary plots, confusion matrices, survival curves Broader Applications Beyond EEG The physics-informed approach generalises to any domain with wave-like phenomena: Immediate Deployment Domains Audio Processing: Speech recognition, music classification, acoustic anomaly detection Seismic Analysis: Earthquake early warning, structural health monitoring Radar/Sonar: Target detection in noisy maritime/aerospace environments Industrial IoT: Vibration-based predictive maintenance, equipment failure forecasting Telecommunications: Signal decoding under phase noise and multipath fading Emerging Research Frontiers Quantum Computing: Qubit state classification under decoherence Financial Markets: Cyclical pattern detection in high-frequency trading data Climate Science: Oscillatory climate pattern identification (El Niño, NAO) Material Science: Vibrational spectroscopy analysis, crystal structure determination Future Research Horizon: v16.0 Experimental Beta Internal R&D has successfully developed v16.0 with record-breaking 98.93% peak accuracy through Parallel Evolutionary Search. Currently in experimental status due to localised confusion matrix variance, with ongoing work on "Resonance Smoothing" techniques for v17.0 stabilisation. Validated Performance: 5-Fold CV Mean = 98.51% (±0.24%), confirming the evolutionary trajectory continues upward while maintaining the production stability of v15.0 as the official benchmark. Why This Matters for AI Research Paradigm Shift Evidence First Principles Win: Physics-informed bias outperforms purely data-driven optimisation Interpretability Revolution: Parameters map directly to physical phenomena (10 Hz frequency ≠ , arbitrary weight) Robustness Proof: Mathematical invariance (Fourier transform) beats empirical regularisation Validation Standard: GPU-accelerated K-fold CV sets new rigour bar for ML research Humanitarian AI: Medical-grade performance accessible on consumer hardware democratizes healthcare technology For Research Institutions (DeepMind, Anthropic, OpenAI, Meta AI) This work demonstrates that the next frontier of AI advancement lies not in scaling compute or data, but in encoding domain knowledge as algorithmic priors. HRF proves that when AI "listens to the physics of reality," it achieves capabilities fundamentally inaccessible to statistical learning alone—while remaining interpretable, efficient, and clinically deployable. Citation & Reproducibility Dataset OpenML ID: 1471 (EEG Eye State Corpus) Samples: 14,980 continuous EEG recordings Features: 14-channel sensor array (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) Sampling Rate: 128 Hz Task: Binary classification (eyes open vs. closed) Computational Environment Hardware: NVIDIA GPU (CUDA 12.x compatible) Software: Python 3.11, NVIDIA RAPIDS (cuML v24.x, CuPy v12.x), scikit-learn Random Seeds: Fixed at 42 for all experiments
Autonomous AI agents executing consequential actions require authorization mechanisms that verify not only identity but voluntary intent. LICET (Latin: it is permitted) is a cryptographic middleware protocol binding AI agent authorization to real-time multi-modal physiological state via a three-layer architecture: (1) ECG waveform morphology matching as a medication-resistant identity and liveness anchor; (2) electrodermal activity (EDA) as a sympathetic cholinergic liveness signal immune to beta-adrenergic blockade; and (3) personalized Mahalanobis distance fusion over five physiological signals to elevate the cost of pharmacological coercion attacks. LICET defines a Biometric Trust Level hierarchy (L0-L3) aligned with the IETF RATS architecture (RFC 9334), per-event HKDF session-key derivation, HMAC biometric temporal signatures, Schnorr zero-knowledge proofs over BN128, and a SHA-256 hash-chained tamper-evident ledger. A reference implementation is publicly deployed at https://licet.dev/v1/.
This study introduces a functional EEG-based Multi-Factor Authentication (EEG-MFA) system engineered for accessibility and security utilizing affordable consumer hardware. Our version uses the BioAmp EXG Pill ( |3,000) with Arduino UNO, which is far cheaper than standard biometric systems that need expensive medical-grade equipment (|50,000–|500,000). It gets 86.7% authentication accuracy when the signal is good.The system uses three authentication factors: a password (knowledge), a pattern (behavior), and an EEG biometric (inherence). This makes it more secure. We utilize One-Class SVM with RBF kernel (nu=0.1) for user modeling, which means we don’t have to collect fake data, which is a big problem when using biometrics. The system learns brain patterns unique to each user using just 3–5 enrollment recordings (12 seconds each) and a simple electrode setup (3 electrodes: forehead + ears).Recent improvements in open-source EEG gear have made it much cheaper. With devices like the BioAmp EXG Pill (around 3,000 rupees), OpenBCI boards (100–500 dollars), and NeuroSky MindWave (100 dollars), students can do projects and small-scale research that weren’t possible before with medical-grade equipment. This lower price makes it possible to look into EEG authentication outside of established labs, utilizing real-world consumer technology that has its own problems. Some of the most important new features are: (1) an adaptive learning mechanism that lowers the False Rejection Rate from 20% to 0% over five sessions while keeping the False Acceptances at zero; (2) a tolerance margin system (10%) that makes up for differences in electrode placement; and (3) a complete end-to-end implementation with FastAPI backend, PostgreSQL database, and Next.js frontend.When we tested with real consumer hardware, we found that the most important performance aspect was signal quality (electrode preparation). With the right setup, we got an 80% genuine acceptance rate and a 0% imposter acceptance rate. The 10% Equal Error Rate (EER) is higher than medical-grade systems (¡5%), but it shows that it is possible to use it for specialized security applications, educational research, and proof-of-concept deployments where cost is more important than accuracy.
This project presents QoreChain, a novel Layer 1 blockchain architecture that addresses two critical challenges facing distributed ledger technology: vulnerability to quantum computing attacks and inefficient network resource allocation. As cryptographically relevant quantum computers are projected to emerge within 5-10 years, current blockchain infrastructures relying on elliptic curve cryptography face existential security threats. Simultaneously, existing networks struggle with scalability, cross-chain interoperability, and intelligent resource optimization. QoreChain introduces a quantum-native security architecture implementing ML-KEM (Kyber-1024) for post-quantum key exchange with migration pathways to Dilithium and Falcon signatures. Our hybrid classical-PQC bridge protocol enables seamless cryptographic migration without network disruption while maintaining backward compatibility—a capability absent in current blockchain platforms. Beyond quantum resistance, QoreChain integrates artificial intelligence at the protocol level through an Adaptive Intelligence Layer that performs dynamic transaction routing, predictive resource allocation, and cognitive consensus optimization, achieving demonstrable performance improvements: 5,914+ transactions per second with sub-second finality, 40% reduction in finality times during peak loads, and 60% reduction in cross-chain swap slippage through AI-driven liquidity positioning. The architecture comprises three synergistic innovations: (1) a multi-layer scalability framework with AI-driven chain selection routing transactions across main chain, sidechains, and paychains based on value and computational requirements; (2) the QoreChain Consensus Algorithm (QCA) extending Combined Proof of Stake with reputation-weighted validator selection and temporal consensus layering enabling parallel consensus sessions across different time horizons; and (3) comprehensive developer tooling including natural language smart contract generation with cross-chain compilation, automated vulnerability detection using predictive AI, and voice-first accessibility features. We demonstrate QoreChain's practical applicability through integration specifications for enterprise environments (financial services, defense, healthcare), IoT deployments with hardware-optimized lightweight cryptography for resource-constrained devices, and universal cross-chain connectivity supporting Ethereum, Solana, TON, BSC, Avalanche, and Cosmos ecosystems via IBC, LayerZero, and proprietary protocols. Performance benchmarks, security proofs, and economic sustainability models validate QoreChain's viability as future-proof blockchain infrastructure for the post-quantum era.Abstract content goes here
Blockchain technology has revolutionized decentralized systems, with applications that span finance, healthcare, and supply chain management. However, scalability challenges, particularly limited transaction throughput and high computational overhead, hinder its broader adoption. This work presents and validates a machine learning-driven consensus mechanism to enhance scalability while preserving security and decentralization. The proposed approach dynamically adjusts the mining difficulty and resource allocation in real time by employing Bayesian-optimized ensemble models (XGBoost and Random Forest) to predict the conditions of the blockchain network. Experimental evaluations show improved throughput, lower latency, and more equitable miner participation compared to traditional Proof of Work (PoW). The findings suggest that data-driven consensus can mitigate long-standing performance bottlenecks, enabling next-generation decentralized systems for industrial-scale deployment.
Christian Delgado‐von‐Eitzen, Luis Anido, María Ruiz‐Molina, Manuel J. Fernández Iglesias
ABSTRACT Introduction The popularization of blockchain‐based applications made evident a critical challenge, namely the inherent isolation of these decentralized systems, akin to the disconnected and technologically diverse local area networks of the 1970s. This lack of interoperability limits the potential for widespread adoption and innovation in the blockchain space. While various initiatives aim to bridge this gap, many remain nascent. Methods This article addresses this issue by proposing a robust architecture and practical implementation to interconnect two Ethereum‐based blockchains, enabling seamless smart contract interactions across these chains, and facilitating the exchange of complex information beyond mere token transfers. Results Our work explores the emerging landscape of inter‐blockchain communication, highlighting their current maturity and potential, and providing insights on how to overcome the technical hurdles associated with these protocols, particularly in the context of transmitting complex data and executing cross‐chain function calls. Additionally, we illustrate with a case study the challenges posed by linking private blockchains with public ones, ensuring secure and efficient data exchange. Conclusion This article aims to inspire blockchain researchers and practitioners, presenting a foundational framework for enhancing blockchain interoperability, including detailed, practical steps for its implementation. By laying the groundwork for more connected blockchain ecosystems, we intend to support the continued evolution and widespread adoption of blockchain technology.
The objective of this proof-of-concept study was to test the utility of NeuroTargeted Training (NTT), a new method using functional Near-Infrared Spectroscopy (fNIRS) to measure and enhance cognitive performance during simulator training. Traditional simulator training is limited to behavioral evaluations, without capturing the trainee's internal cognitive processes. NTT addresses this gap by comparing trainees’ brain activation patterns to those of experts, allowing for precise identification and remediation of cognitive performance gaps. Three studies were conducted with five participants. Expert neural benchmarks were established from a man overboard simulation. Novices were evaluated against these benchmarks using a Expert Reference Index (ERI), quantifying deviations from expert performance, and the NeuroTargeted Training methodology was compared with conventional evaluations. Personalized training, based on identified gaps, was conducted to align novice neural patterns with expert benchmarks. Significant differences were observed, particularly in the anterior insula and inferior frontal gyrus, with an ERI of 4.84. Cohen’s Kappa (.69) indicated moderate inter-rater reliability. Subsequent targeted training reduced the ERI by 27%, aligning novice neural patterns with experts. Without intervention, the ERI rose by 79%, indicating increased cognitive strain. These findings highlight NTT’s potential to enhance learning outcomes in high-stake exercises by providing insights into cognitive processes.
With the rapid development of Decentralized Finance (DeFi) and Real-World Assets (RWA), the importance of blockchain oracles in real-time data acquisition has become increasingly prominent. Using cryptographic techniques, threshold signature oracles can achieve consensus on data from multiple nodes and provide corresponding proofs to ensure the credibility and security of the information. However, in real-time data acquisition, threshold signature methods face challenges such as data inconsistency and low success rates in heterogeneous environments, which limit their practical application potential. To address these issues, this paper proposes an innovative dual-strategy approach to enhance the success rate of data consensus in blockchain threshold signature oracles. Firstly, we introduce a Representative Enhanced Aggregation Strategy (REP-AG) that improves the representativeness of data submitted by nodes, ensuring consistency with data from other nodes, and thereby enhancing the usability of threshold signatures. Additionally, we present a Timing Optimization Strategy (TIM-OPT) that dynamically adjusts the timing of nodes' access to data sources to maximize consensus success rates. Experimental results indicate that REP-AG improves the aggregation success rate by approximately 56.6\% compared to the optimal baseline, while the implementation of TIM-OPT leads to an average increase of approximately 32.9\% in consensus success rates across all scenarios.
Zachary Painter, Christina Peterson, Victor Cook, Damian Dechev
Blockchain networks use consensus mechanisms so that participants can exchange transactions without the need to rely on a trusted third party. Consensus mechanisms using Proof of Work burn significant energy to select a block miner, and this delay limits performance. Other consensus mechanisms such as Proof of Stake or Practical Byzantine Fault Tolerance still designate a single validator to append a block to the chain, preventing blocks from being built and published in parallel. In this article, we introduce a new consensus mechanism, Proof of Descriptor, enabling clients to work together to publish blockchain transactions using a descriptor object which stores information on the cooperative parallel execution of transactions. Proof of Descriptor consensus allows commutative transactions to be mined concurrently. It does not require a single leader to append transactions to the ledger, enabling clients to cooperate on publishing transactions. We also propose a novel graph-based ledger with multiple entry points to facilitate the scalability of Proof of Descriptor, as well as a secure hashing scheme to resist long-range attacks. We demonstrate that our approach is as secure as related works with respect to a malicious leader, 51% attack, and other known blockchain vulnerabilities. Furthermore, our experimental evaluation shows that our approach scales with the size of the network, experiencing up to a 4 \(\times\) improvement in throughput over the fastest sequential blockchain, Solana .
Burhan Ul Islam Khan, Khang Wen Goh, Abdul Raouf Khan, Megat F. Zuhairi · 5 authors
Blockchain is recognized for its robust security features, and its integration with Internet of Things (IoT) systems presents scalability and operational challenges. Deploying Artificial Intelligence (AI) within blockchain environments raises concerns about balancing rigorous security requirements with computational efficiency. The prime motivation resides in integrating AI with blockchain to strengthen IoT security and withstand multiple variants of lethal threats. With the increasing number of IoT devices, there has also been a spontaneous increase in security vulnerabilities. While conventional security methods are inadequate for the diversification of IoT devices, adopting AI can assist in identifying and mitigating such threats in real time, whereas integrating AI with blockchain can offer more intelligent decentralized security measures. The paper contributes to a three-layered architecture encompassing the device/sensory, edge, and cloud layers. This structure supports a novel method for assessing legitimacy scores and serves as an initial security measure. The proposed scheme also enhances the architecture by introducing an Ethereum-based data repositioning framework as a potential trapdoor function, ensuring maximal secrecy. To complement this, a simplified consensus module generates a conclusive evidence matrix, bolstering accountability. The model also incorporates an innovative AI-based security optimization utilizing an unconventional neural network model that operates faster and is enhanced with metaheuristic algorithms. Comparative benchmarks demonstrate that our approach results in a 48.5% improvement in threat detection accuracy and a 23.5% reduction in processing time relative to existing systems, marking significant advancements in IoT security for smart cities.
Cognitive Radio Network uses the available spectrum resources wisely. Spectrum sensing is the central element of a cognitive radio network. However, spectrum sensing is susceptible to multiple security breaches caused by malicious users. These attackers attempt to change the sensed result in order to decrease network performance. In our proposed approach, with the help of Blockchain-based technology, the fusion center is able to detect and prevent such criminal activities. The method of our model makes use of blockchain-based malicious user detection with SHA-3 hashing and energy detection-based Spectrum sensing. The detection strategy takes place in two stages: Block updation phase and iron out phase. The simulation results of the proposed method demonstrate 3.125%, 6.5% and 8.8% more detection probability at -5 dB SNR in the presence of malicious users, when compared to other methods like equal gain combining (EGC), blockchain based coperative spectrum sensing (BCSS) and fault-tolerant cooperative spectrum sensing (FTCSS) respectively. Thus, the security of cognitive radio blockchain network is proved to be significantly improved.
Maher Alharby, Ali Alssaiari, Saad Alateef, Nigel Thomas · 5 authors
Abstract This study analyzes the security implications of Proof-of-Work blockchains with respect to the stale block rate and the lack of a block verification process. The stale block rate is a crucial security metric that quantifies the proportion of rejected blocks in the blockchain network. The absence of a block verification process represents another critical security concern, as it permits the potential for invalid transactions within the network. In this article, we propose and implement a quantitative and analytical model to capture the primary operations of Proof-of-Work blockchains utilizing the Performance Evaluation Process Algebra. The proposed model can assist blockchain designers, architects, and analysts in achieving the ideal security level for blockchain systems by determining the proper network and consensus settings. We conduct extensive experiments to determine the sensitivity of security to four aspects: the number of active miners and their mining hash rates, the duration between blocks, the latency in block propagation, and the time required for block verification, all of which have been shown to influence the outcomes. We contribute to the findings of the existing research by conducting the first analysis of how the number of miners affects the frequency of stale block results, as well as how the delay in block propagation influences the incentives received by rational miners who choose to avoid the block verification process.
Directed Acyclic Graph(DAG)-based blockchain represents a paradigm shift from conventional blockchains, which has the potential to drastically improve throughput performance through concurrent storage and executions. In practice, however, existing DAG-based blockchains fail to deliver such promises, often with limited throughput, high conflicts, and security vulnerabilities under dynamic workloads. The root causes are their unawareness of the workload characteristics of different workload sizes and skewed access patterns. In this paper, we propose MorphDAG, the first workload-aware DAG-based blockchain that can significantly enhance throughput without compromising security and achieve elastic scaling under realistic workloads. We derive the theoretically optimal degree of storage concurrency to achieve high throughput while retaining system security as the workload size changes, while enabling fine-grained concurrency adjustment that accommodates aProof-of-Stake(PoS)-based consensus protocol. We develop a dual-mode transaction processing mechanism that effectively resolves the conflicts brought by skewed access. We implement a prototype of MorphDAG and evaluate under real-world workloads. Extensive evaluations demonstrate that MorphDAG improves end-to-end throughput by up to 2.3× and 2.4× over state-of-the-art DAG-based blockchain systems AdaptChain and OHIE, respectively.
Ethereum has recently switched to a Proof of Stake consensus protocol called Gasper. We analyze Gasper using PRISM+ , an extension of the probabilistic model checker PRISM with primitives for modeling blockchain data types . PRISM+ is therefore used to rapidly and automatically analyze the robustness of Gasper when tuning, up or down, several basic parameters of the protocol, such as network latencies and number of validators. We also study the effectiveness of Gasper in updating stakes and its resilience to three attacks: the balance, bouncing and time attacks.
A Dutta, Nafiz Imtiaz Rafin, M. Ali Akber Dewan, Md. Golam Rabiul Alam
Blockchain is a ground-breaking technology that has changed how we manage and store protected data. It is a decentralized ledger that enables safe, open, and unchangeable record-keeping. It relies on a distributed network of nodes rather than a single central authority to check and verify transactions, guaranteeing that each entry is correct and unchangeable. Transactions in a blockchain network are grouped into blocks, which are then linked together in a chronological and immutable chain. Block size is a critical parameter in blockchain technology, which refers to the maximum size of each block in the chain that is not benchmarked yet. However, we cannot just change the block size of the blockchain. It is challenging and will create security issues. The Block size is crucial because it affects the number of transactions processed per second, the confirmation time, and overall network efficiency. The confirmation time should be faster to ensure stable earnings for the miners. Moreover, it needs help with broader applications due to high transaction fees and long verification times. We have proposed a reinforcement learning model named ROBB that can efficiently create a block considering the current network state and previous transactions. At first, the problem was converted into a reinforcement learning environment to solve using multiple reinforcement algorithms. We developed a blockchain simulator to replicate the network environment. To transform it into a reinforcement learning environment, we integrated it with OpenAI Gym. The simulator was trained by generating random transactions. Finally, we designed a reward function that enables the simulator to hold transactions and create blocks with the pending transactions when it determines that the environment is favourable. In the final results, ROBB successfully minimized the waiting time for transactions and utilized the blocks to their full potential. Additionally, it optimized the block space, building upon the findings of previous researchers. From the research we can see that our propsed models shows impressive results with 100% block utlization and 1.8s average waiting time while creating the least number of blocks.
Heera Wali, Vishal Kulkarni, Rajashekar Ganiger, Nalini C. Iyer
This study investigates how blockchain technology can be used in the automotive industry and society, with a focus on addressing privacy concerns and compliance issues. The study investigates the application of privacy-preserving methods, including zero-knowledge proofs and anonymous credentials, to protect vehicle data shared with servers and prevent V2X channel spam. The proposed blockchain ecosystem involves collaborations among stakeholders like governmental bodies and automotive industry participants to ensure user privacy, implementation employs Ethereum[5], circom, and ezkl for practical realization, the study demonstrates the potential of blockchain and [7] Zero Knowledge Proofs in overcoming challenges, promoting a more secure digital environment.
The development of e-healthcare systems requires the application of advanced technologies, such as blockchain technology. The main challenge of applying blockchain technology to e-healthcare is to handle the impact of the delay that results from blockchain procedures during the communication and voting phases. The impacts of latency in blockchains negatively influence systems’ efficiency, performance, real-time processing, and quality of service. Therefore, this work proposes a modified model of a blockchain that allows delays to be avoided in critical situations in healthcare. Firstly, this work analyzes the specifications of healthcare data and processes to study and classify healthcare transactions according to their nature and sensitivity. Secondly, it introduces the concept of a fair-proof-of-stake consensus protocol for block creation and correctness procedures rather than famous ones such as proof-of-work or proof-of-stake. Thirdly, the work presents a simplified procedure for block verification, where it classifies transactions into three categories according to the time period limit and trustworthiness level. Consequently, there are three kinds of blocks, since every category is stored in a specific kind of block. The ideas of time period limits and trustworthiness fit with critical healthcare situations and the authority levels in healthcare systems. Therefore, we reduce the validation process of the trusted blocks and transactions. All proposed modifications help to reduce computational costs, speed up processing times, and enhance security and privacy. The experimental results show that the total execution time using a modified blockchain is reduced by about 49% compared to traditional blockchain models. Additionally, the number of messages using modified blockchain is reduced by about 53% compared to the traditional blockchain model.
Ajay Kumar, Rajiv R. P. Singh, Indranath Chatterjee, Nikita Sharma · 5 authors
Abstract Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges.
Abstract With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction’s confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network ( Hybrid-CTEN ), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed.
Malka N. Halgamuge, Geetha. K. Munasinghe, Moshe Zukerman
The Internet of Things (IoT) has emerged with Distributed Ledger Technology (DLT) to address existing scalability challenges and improve the trustworthiness of machine-to-machine communication. Among the numerous potential benefits of combining IoT and DLT, Blockchain, a subset of DLT, is a crucial enabler to accelerate secure IoT adoption. Appending a new block to a blockchain, especially in a blockchain-based IoT ecosystem, requires more delay than expected. This delay is one of several issues limiting the broader adoption of blockchain within the IoT domain. To assess this delay, we develop a new comprehensive model to estimate the time required to generate a new block in a blockchain-enabled IoT system. To this end, we develop sub-computation models and compare time consumption associated with the block generation process by conducting an extensive analysis of the following selected IoT layers: device layer, cluster head layer, fog/edge layer, and cloud layer. Our study identifies potential time-consuming steps in adding a new block to a network. Our results demonstrate that the type of blockchain framework and data encryption algorithms could affect the block generation time and that Avalanche, Conflux, Algorand, Polkadot Hyperledger Fabric outperforms Ethereum in terms of block generation time in IoT networks. On the other hand, the blockchain framework does not play a significant role in block generation time for smaller data packets. We also observed the benefit of using 256-bit ECC (elliptic curve cryptography) encryption and the fog layer in IoT networks to enhance the scalability of the block generation process. All in all, our results indicate that the total block generation time varies depending on the selected IoT framework, data encryption algorithm, blockchain type, and key functions of the layers. However, we found that time delays associated with queuing or block size are negligible relative to the other key components of block generation time.
The rise of computational power has led to unprecedented performance gains for deep learning models. As more data becomes available and model architectures become more complex, the need for more computational power increases. On the other hand, since the introduction of Bitcoin as the first cryptocurrency and the establishment of the concept of blockchain as a distributed ledger, many variants and approaches have been proposed. However, many of them have one thing in common, which is the Proof of Work (PoW) consensus mechanism. PoW is mainly used to support the process of new block generation. While PoW has proven its robustness, its main drawback is that it requires a significant amount of processing power to maintain the security and integrity of the blockchain. This is due to applying brute force to solve a hashing puzzle. To utilize the computational power available in useful and meaningful work while keeping the blockchain secure, many techniques have been proposed, one of which is known as Proof of Deep Learning (PoDL). PoDL is a consensus mechanism that uses the process of training a deep learning model as proof of work to add new blocks to the blockchain. In this paper, we survey the various approaches for PoDL. We discuss the different types of PoDL algorithms, their advantages and disadvantages, and their potential applications. We also discuss the challenges of implementing PoDL and future research directions.