This dissertation examines the evolving market microstructure of digital assets, focusing on transaction costs, liquidity provision returns, and the development of innovative exchange mechanisms. In three essays, the research provides empirical evidence on digital asset trading in both traditional and emerging decentralized market architectures. Each essay addresses previously unresolved questions, offering valuable insights for researchers, practitioners, and regulators to better understand and manage the benefits, costs, and risks of trading in digital asset markets.The first essay examines the cost of trading across digital assets in traditional centralized limit-order-book exchanges and a nascent, decentralized market architecture: the Automated Market Maker. By employing a novel methodology the study extends prior research that relies on less detailed, low-frequency information. The findings reveal transaction cost advantages for Automated Market Makers with remarkable stability across varying levels of market volatility, trading volume, and market capitalization. These results offer practical insights into execution venue selection and market design considerations.The second essay explores the evolution of Automated Market Makers, using the introduction of a new generation of these exchange architectures as a case study. In addition to documenting their technical advancements, the research shows that asset pairs migrate to the new Automated-Market-Maker models based on asset-specific fundamentals. The study makes key contributions through two experimental setups, demonstrating that reductions in inventory costs and the introduction of flexible fee tiers deliver welfare benefits for both liquidity demanders and providers. These findings enrich the broader discussion on market design and highlight the potential for innovative mechanisms to enhance efficiency in both decentralized and traditional financial systems.The third essay sheds light on liquidity provision in Automated Market Makers. Leveraging granular profitability data, the study finds that a small subset of liquidity providers dominate liquidity provision. These sophisticated agents achieve significantly higher absolute and relative profits compared to retail participants, while demonstrating a high level of skill. The emergence of these de-facto intermediaries challenges the decentralized finance ethos of disintermediation, highlighting that liquidity provision, even in decentralized markets, remains dominated by specialists. Understanding the composition of participants in these nascent markets is not only crucial for practitioners but also regulators, enabling them to develop targeted and effective policies that promote fair and competitive market environments.
We investigate how transparency—crypto exchanges' verification of trader identities through Know-Your-Customer (KYC) and their transmission of trader and transaction data to tax authorities—shapes the effectiveness of tax policies in cryptocurrency markets. Using regulatory events and cross-exchange price variation, we provide initial global evidence that transparency amplifies the capitalization of statutory crypto-tax liabilities into prices. In the United States, Bitcoin prices on exchanges subject to new tax reporting obligations fall by an average of 0.34 % following announcements that raise expectations of information transmission, even without changes in statutory tax liabilities. Across jurisdictions, price declines are significantly larger where reporting systems are more transparent, and in cross-sectional analysis, exchanges that both enforce KYC and transmit information show the strongest price sensitivity to local tax liabilities, particularly where capital controls constrain arbitrage. These findings reveal a transparency–privacy trade-off unique to crypto markets and demonstrate how digital assets provide rare opportunities to test classic tax-capitalization theories under conditions of anonymity and regulatory heterogeneity, with implications for the design of effective tax policies.
We investigate a multi-class machine learning (ML) framework to generate daily Bitcoin trading signals—Buy, Sell, or Hold. Three algorithms—XGBoost, LightGBM, and Random Forest—are compared with a naive buy-and-hold strategy. Using BTC/USD daily data (2015–2024), we apply a range of technical indicators across trend, momentum, volatility, and volume, later pruned by correlation analysis. A ±1% threshold defines the "Hold" zone to avoid minor fluctuations. Empirical tests show that LightGBM outperforms other models and even surpasses buy-and-hold in final portfolio value. Our findings support the design of tri-class ML strategies tailored for high-volatility markets like cryptocurrency.
Hongzhi Li, Dun Li, Gang Lv, Noël Crespi · 8 authors
The widespread application of medical information systems has promoted the growth of personal electronic medical records (EMRs), which are typically produced in different medical institutions and stored in data centers. Consequently, data owners no longer retain control over their medical data, nor can they establish access control rules for their EMRs. Therefore, this study designs a patient-centered EMR access control system that integrates decentralized smart contracts and role-based access control (RBAC) to provide fine-grained data access control. In this system, we integrate a role-based access control model to achieve user-permission definition and adopt a personalized data access policy definition mechanism to achieve patient-centered data access control. The proposed system allows data owners to define a series of data access policies through smart contracts, achieving decentralized management of data access control permissions. In addition, we analyze the security features of this scheme and design a series of comparative experiments to evaluate the performance. The experimental results show that this system can efficiently achieve access control of personal electronic medical records and has higher reliability compared to traditional cloud-based EMR sharing systems.
Yongsheng Guo, Ezaddin Yousef, Mirza Muhammad Naseer
This study investigates the relationship between cryptocurrency adoption rates (CARs) and the development of central bank digital currencies (CBDCs) using a global panel of 109 countries from 2020 to 2024. The analysis employs pooled OLS, fixed effects, ordered logistic regression and GMM models with robust controls for macroeconomic indicators, institutional quality, and technological readiness. CBDC status is measured as an ordinal variable representing five development stages, while CAR is derived from the Chainalysis Crypto Adoption Index. The empirical results show that higher CAR significantly increases the probability of a country progressing to more advanced CBDC stages. Margins analysis further indicates that increases in CAR substantially reduce the likelihood of remaining in early CBDC phases and raise the probability of reaching the pilot or launched stages. Heterogeneity analysis reveals that this relationship is strongest in low- and middle-income economies and in countries with low levels of financial inclusion, where cryptocurrencies present greater competition to traditional financial systems. The study contributes new large-sample evidence to the debate on digital currencies and provides policy-relevant insights: central banks in financially constrained economies appear to adopt CBDCs as developmental tools to enhance financial access and preserve monetary sovereignty in the face of growing cryptocurrency adoption.
Rene Casanova, Fernán A Villa-Garzón, John W. Branch
Background: Health information systems (HIS) are critical for digital health transformation, yet fragmentation and poor interoperability adoption remains a major challenge. Objectives: This study systematically reviews architectural patterns used in HIS and evaluates their alignment with ecosystem-level requirements. Methods: Following PRISMA 2020 guidelines, a systematic literature review was conducted across Scopus, IEEE Xplore, PubMed, and Web of Science (2020-2025). Eligible studies described, evaluated, or proposed HIS solutions. Results: From an initial set of 304 records, 89 met the inclusion criteria. Service-based and decentralized/distributed ledger architectures were predominant, with emerging models integrating edge computing and modular design. FHIR-based contracts are found as stabilizers of interfaces, enabling validation and reducing integration costs. However, gaps persist in cross-border care, sustainability, and artificial intelligence integration. Conclusion: While microservices dominate current HIS architectures, achieving resilient, interoperable ecosystems requires greater architectural diversity and intersectoral collaboration.
Smart contracts are commonly audited through static analysis to explore vulnerabilities. However, static approaches typically produce heterogeneous findings rather than reproducible, executable proof-of-concept (PoC) test cases, leading to costly and ad hoc manual validation. Large language models (LLMs) offer a promising way to translate audit reports into PoC test cases, but face three major challenges: noisy inputs, lack of execution grounding, and missing runtime oracles. We present SmartPoC, an end-to-end approach for validating reported vulnerabilities in audit reports by generating and executing PoC test cases with automated exploitability verification. SmartPoC first extracts a focused function-level slice from each report to reduce noise, centering on the key functions referenced in a finding and augmenting them with execution-relevant neighbors. To improve executability, we wrap LLM-based PoC synthesis in a generate-repair-execute loop, combining deterministic pre-execution sanitization with feedback-driven post-execution debugging. We further use differential verification as an oracle to confirm the exploitability of generated test cases. On the SmartBugs-Vul and FORGE-Vul benchmarks, SmartPoC achieves confirmation precision of 98.32% and 98.65%, with recall of 84.17% and 85.28%, respectively. On a recent Etherscan verified-source corpus, SmartPoC confirms 64 bugs from 545 audit findings at an average cost of $0.03.
Zhuo Chen, Gaoqiang Ji, He Yun, Lei Wu · 5 authors
Decentralized finance (DeFi) is experiencing rapid expansion. However, prevalent code reuse and limited open-source contributions have introduced significant challenges to the blockchain ecosystem, including plagiarism and the propagation of vulnerable code. Consequently, an effective and accurate similarity detection method for EVM bytecode is urgently needed to identify similar contracts. Traditional binary similarity detection methods are typically based on instruction stream or control flow graph (CFG), which have limitations on EVM bytecode due to specific features like low-level EVM bytecode and heavily-reused basic blocks. Moreover, the highly-diverse Solidity Compiler (Solc) versions further complicate accurate similarity detection. Motivated by these challenges, we propose a novel EVM bytecode representation called Stable-Semantic Graph (SSG), which captures relationships between 'stable instructions' (special instructions identified by our study). Moreover, we implement a prototype, Esim, which embeds SSG into matrices for similarity detection using a heterogeneous graph neural network. Esim demonstrates high accuracy in SSG construction, achieving F1-scores of 100% for control flow and 95.16% for data flow, and its similarity detection performance reaches 96.3% AUC, surpassing traditional approaches. Our large-scale study, analyzing 2,675,573 smart contracts on six EVM-compatible chains over a one-year period, also demonstrates that Esim outperforms the SOTA tool Etherscan in vulnerability search.
This work presents the CTC–E₈ Integrated Architecture, a unified thermodynamic–informational control system designed for large-scale, autonomous off-world industrial civilizations. The framework combines the CTC nuclear architecture—a passive-safe, thorium–carbon high-temperature power system capable of tritium generation—with the QSOL-IMC E₈ Qutrit Power Module, an informational control layer that uses qutrit-based node governance and E₈ symmetry for global synchronization. The architecture establishes a dual-layer system: 1. Thermodynamic Power (CTC Architecture):Provides meltdown-proof, long-cycle thorium–carbon reactor output, high-temperature industrial heat, tritium production, and in-situ manufacturability suitable for distributed lunar, Martian, or orbital industry. 2. Informational Control (E₈ Qutrit Module):Implements computational-resonance control using qutrit-state vectors for local process management and an E₈ Cartan torus for global phase, load balancing, and industrial rhythm coordination. E₈ root-vector coupling provides decentralized, symmetry-enforced coherence across hundreds of reactors without a central controller. Together, these layers create a resonance-governed industrial ecosystem that delivers predictable harmonics, smooth power distribution, self-organizing energy networks, and fractal scalability from a single reactor outpost to a full city-state colony. The system minimizes computational overhead while maximizing stability, autonomy, and expansion capability. This submission includes the full technical manuscript and accompanying architecture documentation for researchers, engineers, and organizations exploring next-generation off-world industrial infrastructure.
This paper presents a comprehensive comparative analysis of two dominant blockchain consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), evaluated across seven critical metrics: energy use, security, transaction speed, scalability, centralization risk, environmental impact, and transaction fees. Utilizing recent academic research and real-world blockchain data, the study highlights that PoW offers robust, time-tested security but suffers from high energy consumption, slower throughput, and centralization through mining pools. In contrast, PoS demonstrates improved scalability and efficiency, significantly reduced environmental impact, and more stable transaction fees, however it raises concerns over validator centralization and long-term security maturity. The findings underscore the trade-offs inherent in each mechanism and suggest hybrid designs may combine PoW's security with PoS's efficiency and sustainability. The study aims to inform future blockchain infrastructure development by striking a balance between decentralization, performance, and ecological responsibility.
Audio piracy detection is increasingly complex in decentralised distribution settings, where mainstream approaches fail to ensure robustness, verifiability, or computational efficiency. Conventional Digital Rights Management (DRM) systems mainly enforce licensed access, but once content is copied or redistributed outside their control they offer little protection. Classical fingerprinting approaches such as MFCC based hashes can detect near-exact duplicates, yet they often fail under signal edits like pitch shifting, time stretching or equalisation. Deep learning embeddings improve robustness but demand heavy computation and centralised resources, making them less suitable for edge or decentralised deployments. These limitations call for a solution that is both edit resilient and verifiable. We propose HashWave, a blockchain-integrated perceptual hashing framework that combines robust audio fingerprinting with tamper-proof verification. The system fuses MFCC, chroma and chroma CENS, CQT, spectral contrast, and lightweight tempo/energy cues, applying operation-aware weighting via [Formula: see text] and constrained DTW for time-scale edits. Evaluated across GTZAN, FMA-A Dataset for Music Analysis, and MUSAN (SLR17) with over twenty signal-processing transformations, HashWave achieves AUC 0.957 and TPR@1%FPR 0.952, outperforming MFCC-only baselines and approaching deep embeddings at lower CPU cost. The blockchain layer, built on Ethereum and IPFS, ensures decentralised hash storage, duplication control, and verifiable authorship with average upload and contract execution times of 0.017 s and 0.044 s. Together, these results establish HashWave as a practical, scalable, and secure framework for piracy detection across streaming, podcasting, and Web3 ecosystems.
Open access
Advanced Steganography and Watermarking Techniques
Blockchain technology has emerged as a revolutionary paradigm for secure, transparent, and tamper-resistant data management. It offers a decentralized ledger where transactions are validated and recorded across a distributed network of nodes, eliminating the need for centralized authorities. Despite its widespread adoption across diverse domains—such as finance, supply chain, healthcare, and digital identity—blockchain still faces significant challenges in ensuring complete security and privacy. This paper addresses these challenges by proposing a novel security and privacy algorithm designed specifically to enhance blockchain resilience against evolving threats. The proposed approach integrates hybrid cryptography, pseudonymous identifiers, and an optimized consensus mechanism to achieve a balanced trade-off between security, privacy, and computational efficiency. The hybrid cryptographic model combines symmetric and asymmetric encryption techniques to safeguard transaction data at multiple layers. Symmetric encryption ensures fast and secure data exchange, while asymmetric keys are used for identity verification and secure key distribution. To further strengthen user anonymity, the algorithm incorporates pseudonymous identity management, which replaces permanent public keys with dynamically generated pseudonyms. These pseudonyms are refreshed periodically to prevent link ability between consecutive transactions, ensuring that individual identities remain hidden even if certain nodes or data patterns are compromised. Additionally, the optimized consensus protocol enhances transaction validation efficiency by reducing redundant computations and improving synchronization among nodes. This approach minimizes latency and energy consumption while maintaining strong resistance against consensus-based attacks such as 51% or Sybil attacks. Extensive simulations and experimental evaluations were conducted to measure the algorithm’s performance under various network conditions and adversarial scenarios. The results demonstrate that the proposed model significantly improves transaction validation speed and reduces cryptographic overhead compared to traditional Proof-of-Work and Proof-of-Stake systems.
Paradigm shifts in digital media, specifically the World Wide Web, directly influence paradigm shifts in brand models and the branding practices attached to them. This paper explains the transformation in brand models that is being driven by the move from Web 2.0 to web3 and the implications of the transition from a participatory Internet to an ownership Internet. The full implications are significant and still evolving, but one of the biggest challenges for brand owners and managers is the continued shift from centrally managed and owned brands to distributed collaboration, contribution and ownership for brands. This paper proposes that this may require moving from conceptualising brands as monolithic entities and instead viewing them as modular collections of attributes and assets that can operate and evolve independently yet are still able to resolve into a unified idea or set of ideas. Looking at emerging ideas in art and creativity, this paper presents a set of potential implications for the future of the brand. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/
Consumer Behavior in Brand Consumption and Identification
Recent advances in large language models (LLMs) have enabled the emergence of intelligent agents capable of performing complex multi-step tasks across various domains. In parallel, the growth of Web3 has introduced a decentralized web infrastructure, yet remains largely inaccessible to non-technical users due to operational complexity, fragmented information, and security risks. In this article, we present Web3Agent , an AI agent system that integrates LLM-based interaction with blockchain environments to enable language-driven on-chain operations. Web3Agent automatically decomposes user instructions into structured workflows, dynamically queries blockchain data and APIs, and performs multi-step operations such as asset transfers, token swaps, and smart contract execution. Web3Agent incorporates real-time inspection, error handling, and interaction transparency across its operation log, and flow visualization components. We evaluate the system and perform ablation study with customized dataset in a simulated environment, demonstrating its feasibility in orchestrating complex Web3 tasks and highlighting implications for agent-based abstraction in decentralized systems.
The rapid ascent of Non-Fungible Tokens (NFTs) has fundamentally disrupted the art market and the broader visual culture landscape. While much scholarly and popular attention has been focused on their economic impact and speculative nature, this article explores a less examined dimension: the potential of blockchain technology, as manifested in NFTs, to redefine the paradigms of digital heritage preservation. Digital art and born-digital cultural artifacts face an existential threat from technological obsolescence, format degradation, and the inherent fragility of digital media. Traditional preservation institutions, such as museums and archives, have struggled to develop scalable, sustainable models for conserving these ephemeral works. This article argues that NFTs, through their core properties of decentralized ownership verification, immutability of provenance, and programmable permanence, offer a novel, albeit complex, framework for safeguarding our collective digital visual heritage. By analyzing the technical architecture of NFTs, the challenges of preserving the digital asset separate from its token, and the emergent models of decentralized autonomous organizations (DAOs) and community-led preservation, this paper posits that we are witnessing the nascent stages of a new preservation ecology. This study synthesizes literature from digital humanities, media studies, conservation science, and computer science to critically assess both the promises and perils of this convergence. It concludes that while NFTs are not a panacea, they introduce powerful tools that, if ethically and thoughtfully integrated, can significantly bolster the resilience and longevity of digital visual culture for future generations.
Reddit is in the minority of mainstream social platforms that permit posting content that may be considered to be at the edge of what is permissible, including so-called Not Safe For Work (NSFW) content. However, NSFW is becoming more common on mainstream platforms, with X now allowing such material. We examine the top 15 NSFW-restricted subreddits by size to explore the complexities of responsibly sharing adult content, aiming to balance ethical and legal considerations with monetization opportunities. We find that users often use NSFW subreddits as a social springboard, redirecting readers to private or specialized adult social platforms such as Telegram, Kik or OnlyFans for further interactions. They also directly negotiate image "trades" through credit cards or payment platforms such as PayPal, Bitcoin or Venmo. Disturbingly, we also find linguistic cues linked to non-consensual content sharing. To help platforms moderate such behavior, we trained a RoBERTa-based classification model, which outperforms GPT-4 and traditional classifiers such as logistic regression and random forest in identifying non-consensual content sharing, showing better performance in this specific task. The source code and model weights are publicly available at https://github.com/socsys/15NSFWsubreddits.
Partha S. Dey, Aditya S. Gopalan, Vijay G. Subramanian
We investigate the time to consensus in Nakamoto blockchains. Specifically, we consider two competing growth processes, labeled \emph{honest} and \emph{adversarial}, and determine the time after which the honest process permananetly exceeds the adversarial process. This is done via queueing techniques. The predominant difficulty is that the honest growth process is subject to \emph{random delays}. In a stylized Bitcoin model, we compute the Laplace transform for the time to consensus and verify it via simulation.
The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication protocols that expose vast attack surfaces around both individual and network-wide safety. Existing security schemes are unable to provide sub-10 ms (milliseconds) anomaly detection and distributed coordination of large-scale networks of vehicles within an acceptable safety/privacy framework. This paper introduces a three-tier hybrid security architecture HAVEN (Hierarchical Autonomous Vehicle Enhanced Network), which decouples real-time local threat detection and distributed coordination operations. It incorporates a light ensemble anomaly detection model on the edge (first layer), Byzantine-fault-tolerant federated learning to aggregate threat intelligence at a regional scale (middle layer), and selected blockchain mechanisms (top layer) to ensure critical security coordination. Extensive experimentation is done on a real-world autonomous driving dataset. Large-scale simulations with the number of vehicles ranging between 100 and 1000 and different attack types, such as sensor spoofing, jamming, and adversarial model poisoning, are conducted to test the scalability and resiliency of HAVEN. Experimental findings show sub-10 ms detection latency with an accuracy of 94% and F1-score of 92% across multimodal sensor data, Byzantine fault tolerance validated with 20\% compromised nodes, and a reduced blockchain storage overhead, guaranteeing sufficient differential privacy. The proposed framework overcomes the important trade-off between real-time safety obligation and distributed security coordination with novel three-tiered processing. The scalable architecture of HAVEN is shown to provide great improvement in detection accuracy as well as network resilience over other methods.
Smart contracts, the stateful programs running on blockchains, often rely on reports. Publishers are paid to publish these reports on the blockchain. Designing protocols that incentivize timely reporting is the prevalent reporting problem. But existing solutions face a security-performance trade-off: Relying on a small set of trusted publishers introduces centralization risks, while allowing open publication results in an excessive number of reports on the blockchain. We identify the root cause of this trade-off to be the standard symmetric reward design, which treats all reports equally. We prove that no symmetric-reward mechanism can overcome the trade-off. We present Personal Random Rewards for Reporting (Prrr), a protocol that assigns random heterogeneous values to reports. We call this novel mechanism-design concept Ex-Ante Synthetic Asymmetry. To the best of our knowledge, Prrr is the first game-theoretic mechanism (in any context) that deliberately forms participant asymmetry. Prrr employs a second-price-style settlement to allocate rewards, ensuring incentive compatibility and achieving both security and efficiency. Following the protocol constitutes a Subgame-Perfect Nash Equilibrium, robust against collusion and Sybil attacks. Prrr is applicable to numerous smart contracts that rely on timely reports.