This research aims to develop a predictive model for estimating the daily closing price of Ethereum (ETH) against the Indonesian Rupiah (IDR) using the Random Forest Regression algorithm. Ethereum is one of the most widely traded cryptocurrencies and is known for its high volatility, which makes accurate price prediction essential for supporting data-driven investment decisions. Historical price data were collected from the CoinGecko API for a period of 365 days, followed by preprocessing, feature engineering, and the computation of several technical indicators including Exponential Moving Average (EMA-14), Relative Strength Index (RSI-14), Daily Return, Bollinger Bands Upper, Average True Range (ATR-14), and Close Lag-1.The research starting from data selection and preprocessing to modeling, evaluation and visualization. Random Forest Regression was chosen due to its robustness in handling nonlinear relationships and noisy time-series data. The dataset was split using a 90:10 time-based hold-out method, and model performance was evaluated using four regression metrics: MAE, RMSE, MAPE, and R-squared. The best configuration of the model achieved a MAPE of 2.88%, indicating a high level of predictive accuracy. Feature importance analysis shows that Daily Return and ATR-14 contributed most significantly to the prediction. The findings demonstrate that Random Forest Regression can effectively capture the nonlinear patterns in cryptocurrency price movements, providing an accurate and reliable model for short-term forecasting. This model may serve as a valuable reference for investors, financial analysts, and developers of automated trading systems.
Blockchain technology has emerged as a pivotal and transformative force, establishing transparent, secure, and decentralized frameworks for transaction management. Its core strengths include immutability, data decentralization, and consensus validation, alongside the automation provided by self-executing smart contracts. This review examines its foundational technologies, diverse applications, and associated challenges. Blockchain demonstrates profound potential across sectors like finance (e.g., Anti-Money Laundering and fraud reduction), education (credential verification), healthcare (secure record management), and the Metaverse (verifiable digital asset ownership via non-fungible tokens). However, adoption is significantly hindered by critical issues, including scalability bottlenecks, the energy inefficiency of protocols like Proof of Work, and security risks stemming from smart contract flaws, with case-based testing revealing up to 40% of public contracts have exploitable vulnerabilities. Recent advancements in high-throughput rollups and formal verification mitigate these risks. This coincides with a 2025 shift toward structured legal mandates, such as the EU’s MiCA, India’s VDA policy, and the U.S. GENIUS and CLARITY Acts. Therefore, future research must prioritize enhancing smart contract verification, developing energy-efficient consensus mechanisms, cross-chain interoperability, and fostering the continued alignment of supportive legal and regulatory frameworks.
This article examines the dynamics of domestic video-on-demand (VoD) platforms in Brazil. While global streaming giants increasingly dominate the market, local platforms continue to emerge in response to specific cultural and economic contexts. Building on sectoral data and a qualitative case study, the paper analyses the strategies adopted by a local service called Filme Filme, seeking to compete with global players through curation, audience engagement and innovative features such as non-fungible tokens (NFTs) and gamification. Despite these efforts, Filme Filme ultimately ceased operations after four years of its launch, revealing the structural barriers that constrain the growth of domestic platforms in highly concentrated markets. By situating this case within the broader debate on platformization and the industrial organization of cultural industries, the study offers insight into the challenges of sustaining local streaming initiatives in emerging economies and discusses implications for public policy aimed at promoting digital sovereignty and cultural diversity.
The Fourth Industrial Revolution, commonly referred to as Industry 4.0, represents a fundamental transformation of manufacturing systems through the integration of advanced digital technologies such as the Industrial Internet of Things (IIoT), artificial intelligence, big data analytics, cloud computing, and autonomous robotics. This paradigm shift enables the development of cyber-physical systems and smart factories characterized by real-time connectivity, decentralized decision-making, and data-driven optimization. The present study examines the conceptual foundations, technological pillars, and operational impacts of Industry 4.0, with particular emphasis on automation, productivity enhancement, and sustainability outcomes. Using a synthesis of recent empirical studies, global market data, and evidence from World Economic Forum “Lighthouse” factories, the paper evaluates how digital transformation influences manufacturing efficiency, energy use, emissions reduction, and workforce dynamics. The findings indicate that Industry 4.0 adoption significantly improves labor productivity, operational flexibility, and resource efficiency, while also presenting challenges related to cybersecurity, legacy system integration, and skills gaps. The study concludes that Industry 4.0 is not merely a technological upgrade but a strategic and organizational transformation essential for achieving competitive advantage and sustainable industrial development in an increasingly volatile global economy.
The decentralized finance market exhibits extreme volatility and complex nonlinear dynamics that pose significant challenges for accurate price prediction and risk management. Traditional time series models, including Long Short-Term Memory networks and Transformer architectures, struggle with either computational inefficiency in capturing long-rangedependencies or inadequate context retention across extended sequences. This research investigates the application of Structured State Space Models, particularly the Mamba architecture with selective state spaces, for modeling temporal dependencies in DeFi markets. The proposed framework addresses the limitations of conventional approaches by leveraging SSMs' linear-time complexity while maintaining superior long-sequence modeling capabilities through context-aware selective mechanisms. Our methodology integrates SSM architectures with DeFispecific features including on-chain transaction volumes, liquidity metrics, and market microstructure indicators. Experimental validation across multiple cryptocurrency pairs demonstrates that SSM-based models achieve competitive performance compared to attentionbaseTransformers while offering substantial computational advantages. The results indicate that selective state space mechanisms enable effective capture of both short-term volatility patterns and long-horizon price trends in decentralized markets. This work contributes to the emerginintersection of advanced sequence modeling techniques and blockchain-based financial systems, providing insights for algorithmic trading strategies and risk assessment frameworks in the rapidly evolving DeFi ecosystem.
Financial markets exhibit temporal organization that is not fully captured by volatility measures or linear correlation structure. We study a null-validated topological approach for quantifying financial market complexity using Bitcoin daily log returns and the S&P 500 index as examples of cryptocurrency and broad U.S. equity market dynamics. The analysis uses the $L^1$ norm of the persistence landscapes computed from sliding-window delay embeddings. This quantity co-moves strongly with stochastic volatility during periods of market stress, but the strength and form of this relationship vary over time and differ between the two markets. Surrogate-based null models provide statistical validation of these observations. Rejection of shuffle surrogates rules out explanations based on marginal distributions alone, while departures from phase randomized surrogates indicate sensitivity to nonlinear and phase-dependent temporal organization beyond linear correlations. These results demonstrate that persistence landscape norms provide complementary information about market dynamics across market conditions.
Smart contracts are the backbone of the decentralized web, yet ensuring their functional correctness and security remains a critical challenge. While Large Language Models (LLMs) have shown promise in code generation, they often struggle with the rigorous requirements of smart contracts, frequently producing code that is buggy or vulnerable. To address this, we propose SolAgent, a novel tool-augmented multi-agent framework that mimics the workflow of human experts. SolAgent integrates a \textbf{dual-loop refinement mechanism}: an inner loop using the \textit{Forge} compiler to ensure functional correctness, and an outer loop leveraging the \textit{Slither} static analyzer to eliminate security vulnerabilities. Additionally, the agent is equipped with file system capabilities to resolve complex project dependencies. Experiments on the SolEval+ Benchmark, a rigorous suite derived from high-quality real-world projects, demonstrate that SolAgent achieves a Pass@1 rate of up to \textbf{64.39\%}, significantly outperforming state-of-the-art LLMs ($\sim$25\%), AI IDEs (e.g., GitHub Copilot), and existing agent frameworks. Moreover, it reduces security vulnerabilities by up to \textbf{39.77\%} compared to human-written baselines. Finally, we demonstrate that the high-quality trajectories generated by SolAgent can be used to distill smaller, open-source models, democratizing access to secure smart contract generation. We release our data and code at https://github.com/openpaperz/SolAgent.
EigenAI is a verifiable AI platform built on top of the EigenLayer restaking ecosystem. At a high level, it combines a deterministic large-language model (LLM) inference engine with a cryptoeconomically secured optimistic re-execution protocol so that every inference result can be publicly audited, reproduced, and, if necessary, economically enforced. An untrusted operator runs inference on a fixed GPU architecture, signs and encrypts the request and response, and publishes the encrypted log to EigenDA. During a challenge window, any watcher may request re-execution through EigenVerify; the result is then deterministically recomputed inside a trusted execution environment (TEE) with a threshold-released decryption key, allowing a public challenge with private data. Because inference itself is bit-exact, verification reduces to a byte-equality check, and a single honest replica suffices to detect fraud. We show how this architecture yields sovereign agents -- prediction-market judges, trading bots, and scientific assistants -- that enjoy state-of-the-art performance while inheriting security from Ethereum's validator base.
This report traces the systematic erosion of global yield over four decades, from double-digit Treasury returns in the 1980s to near-zero rates by 2020, and documents how Decentralized Finance (DeFi), despite its revolutionary premise, replicated traditional finance’s fundamental failures within just two years. Drawing on macroeconomic data, protocol-level analytics, and institutional research, we identify five structural pain points facing yield-seekers today: chronic compression, emission decay, forced complexity, impermanent loss, and existential protocol risks. We then introduce “Yield 3.0”, a paradigm defined by sustainable, fee-based mechanisms that generate yield from genuine economic activity rather than inflation, speculation, or token emissions. We present Seasons as the first protocol to holistically address all five pain points through a 100% fee-based, hold-to-earn model with zero emission decay, radical simplicity, and full non-custodial ownership. Finally, we examine the converging structural forces—institutional capital inflows exceeding $130 billion, the mathematical exhaustion of emission-based models, and maturing blockchain infrastructure—that make 2026 the inflection point for Yield 3.0 adoption at scale.
Purpose This study examines how entrepreneurial experience shapes perceptions of the ideal investor in the technology-based sector. While previous research has primarily focused on how investors evaluate entrepreneurs, this study shifts the lens to explore how entrepreneurs assess investor attributes. It investigates how experience in securing funding and building ventures influences expectations around value-added contributions beyond financial investment. Specifically, the study explores whether experience leads entrepreneurs to adopt a more strategic and values-driven approach, placing greater emphasis on ethical alignment, expertise, and relational quality, while placing less importance on operational involvement and financial oversight. Design/methodology/approach This study adopts a quantitative research design using survey data from 195 entrepreneurs in the technology-based sector. Participants were recruited through entrepreneurial and investor networks across multiple countries. The survey captured key aspects of entrepreneurial experience, including fundraising and venture development, alongside expectations of investor roles and attributes. Factor analysis identified dimensions of value-added investor support, and k-means clustering was used to group entrepreneurs based on preference profiles. Multinomial logistic regression and OLS regression analyses were conducted to examine how different types of experience influence entrepreneurs' preferences for specific investor attributes and types of support. Findings The results show that entrepreneurial experience plays a significant role in shaping expectations of investor involvement. Entrepreneurs with more experience in fundraising and venture development tend to prioritize ethical conduct, strategic input, and relational alignment over traditional factors like financial returns or past performance. They value investor support focused on strategy, networks, and governance, while placing less importance on operational or financial oversight. Cross-sector experience further reinforces a preference for strategic-driven supports. Overall, the findings suggest that experience increases entrepreneurs' confidence and selectivity, encouraging a more strategic approach to building investor relationships. Research limitations/implications This study has several limitations. First, the data were collected primarily from entrepreneurs in developed countries with well-established venture capital markets, which may limit the generalization of the findings to emerging or less mature ecosystems. Second, the target population is difficult to define precisely, given the informal and decentralized nature of entrepreneurial networks. Third, the reliance on self-reported survey data introduces the possibility of response bias. Additionally, the cross-sectional design limits the ability to draw causal inferences. Future research could benefit from longitudinal data and broader geographic representation to better capture variation across different entrepreneurial contexts. Practical implications The findings provide actionable insights for both entrepreneurs and investors. As entrepreneurs gain experience, they become more selective, favouring investors who offer strategic guidance, ethical alignment, and relational support over purely financial backing. For investors, this highlights the importance of articulating non-financial value, such as expertise, governance input, and network access, to appeal to more experienced founders. Investors who position themselves as collaborative partners rather than controllers may build stronger, longer-lasting relationships. Entrepreneurial support programs, including accelerators and incubators, can also use these insights to prepare founders to identify and engage with strategically aligned investors. Social implications This study highlights the growing importance of trust, ethical conduct, and shared values in shaping effective entrepreneurial ecosystems. As entrepreneurs gain experience, they increasingly prioritize relational quality and strategic alignment in their investor relationships. This signals a broader shift toward more collaborative, purpose-driven engagement between founders and investors. Such a shift has the potential to foster healthier power dynamics, reduce misalignment and conflict, and support the formation of long-term partnerships grounded in mutual respect and shared vision. These findings contribute to ongoing discussions around responsible entrepreneurship and the sustainability of venture growth. Originality/value This study offers a novel contribution by shifting the focus from how investors assess entrepreneurs to how entrepreneurs evaluate potential investors. It addresses an under explored area in entrepreneurial finance, particularly highlighting the role of ethical behaviour and strategic alignment in investor selection. By examining how experience shapes these expectations, the study adds to the limited literature comparing novice and experienced entrepreneurs in their interactions with external stakeholders. It advances understanding of founder–investor dynamics and offers fresh insights into how entrepreneurial learning influences decision-making in the context of venture growth and funding relationships.
The presence of shared micro-vehicles, such as bicycles and e-scooters, has become increasingly common in modern urban environments, enhancing citizens’ access to public transportation by providing an efficient solution to the last-mile problem. In recent years, shared mobility has expanded to include larger vehicles, such as cars and sea vessels, facilitating transportation over longer distances and offering an alternative to private and public modes of transport. However, the seamless integration of these different transportation modes remains a significant challenge, as each type of vehicle has its own advantages and limitations. Furthermore, these transport services are often operated by different organizations that use distinct platforms and ticketing systems, further complicating coordination among them. In this work, we present the proposed approach and the developed system designed to facilitate the adoption and integration of different types of vehicles using AI and blockchain technologies. The system enables users to identify and utilize the most appropriate means of transport through a unified, blockchain-based mechanism. Preliminary evaluation results, based on simulated data, indicate that the system can significantly benefit citizens in a smart city environment and, when combined with appropriate investments in urban infrastructure, can substantially improve daily mobility.
The integration of distributed ledger technology with financial markets has precipitated a paradigm shift in how algorithmic trading strategies are conceived, executed, and settled. This paper presents a comprehensive analysis of blockchain-based algorithmic trading systems, focusing specifically on the dual challenges of execution efficiency and cryptographic security. While traditional high-frequency trading relies on centralized exchanges and proprietary networks to minimize latency, decentralized trading protocols introduce novel constraints related to block generation intervals, consensus mechanisms, and network propagation delays. We examine the implementation of algorithmic strategies via smart contracts, evaluating the trade offs between on-chain transparency and the privacy requirements of institutional investors. Furthermore, the study investigates critical vulnerabilities inherent to decentralized exchanges, such as Miner Extractable Value and front-running attacks, and proposes mitigation strategies utilizing commit-reveal schemes and zero-knowledge proofs. By analyzing the performance metrics of automated market makers against order book models, we provide empirical evidence regarding the current limitations and potential scalability of blockchain-based trading environments. The findings suggest that while blockchain architectures offer superior settlement finality and auditability, significant advancements in layer-two scaling solutions and privacy preserving cryptographic protocols are requisite for these systems to compete with traditional financial infrastructure in terms of throughput and latency.
The financial sector is experiencing rapid transformation due to emerging technologies. Blockchain offers a decentralized, transparent, and immutable framework for secure transactions, while Artificial Intelligence (AI) enables advanced data analytics, predictive modeling, and intelligent automation. When combined, these technologies create a powerful synergy that is reshaping finance by enhancing fraud detection, improving credit evaluation, optimizing decentralized finance (DeFi) platforms, and automating compliance processes. This paper explores the combined benefits of blockchain and AI, highlighting practical applications such as AI-enabled fraud detection within blockchain networks, adaptive smart contracts, and blockchain-secured digital identity verification. It also addresses challenges in merging these technologies, including scalability limitations, regulatory ambiguity, interoperability concerns, and ethical considerations. The study underscores the potential future of autonomous financial systems, decentralized autonomous organizations (DAOs), and AI-driven sustainable finance solutions. Ultimately, the integration of blockchain and AI is seen as a transformative force capable of significantly improving transparency, efficiency, and inclusiveness in global financial systems.
R. Priyadarshini, K. Reddy Geethika, V. Sravya, K. Pujitha · 6 authors
The increasing adoption of cloud computing has revolutionized data storage and accessibility, but it has also presented severe security and privacy issues, particularly in the context of developing quantum computing threats. Despite being effective against classical assaults, conventional encryption and password protection mechanisms are becoming more susceptible to quantum algorithms that can compromise current cryptographic systems. This paper presents QPause, a Password-Protected, Quantum-Resilient Data Offloading for Cloud Platforms forsafe cloud storage, in response to these new threats. To guarantee data confidentiality, integrity, and resilience against both classical and quantum adversaries, the suggested system combines sophisticated password-based authentication methods with post-quantum cryptography approaches. QPause uses zero-knowledge proof methods to enable secure verification without disclosing sensitive credentials, and it leverages lattice-based encryption to safeguard data that is outsourced. Additionally, the system integrates efficient key management and access control mechanisms to boost scalability and user confidence. QPause delivers strong resilience to quantum attacks while preserving low processing overhead and excellent usability for practical cloud applications, according to experimental evaluation. This framework offers a solid solution for secure and future-proof data outsourcing, bridging the gap between existing cloud services and the next generation of quantum-secure computing environments.
Vinod Kumar Joshi, Rajendra Kachhava, Kriti Kamal Gupta, Dixit Dutt Bohra
The quantum-secure CBIR scheme which is presented in this research is a fence against unauthorized users and adversarial attacks on cloud environment remote sensor images. The proposed solution is characterized by Quantum Key Distribution, zero-knowledge proof authentication, QCrypt encryption, adversarial trained deep hashing, and robust watermarking. The model was developed with the help of the MLRSNet dataset, where proposed model recorded a remarkable mean average precision of 94.77% that is 10% improvement from the previous deep-hash results while the watermark-extraction accuracy of over 95% was maintained at 35 dB PSNR. The model has been able provide good result with adversarial, replay, and JPEG compression. Even though the computing engine provides military-grade security and forensic accountability, the current compute overhead is the major reason it has limited use in real-time scenarios.
Large language models (LLMs) now draft, review, and summarize contracts inside corporate legal and procurement teams, yet their statistical design produces confident errors that carry real legal consequences. This article asks where LLM automation in contract management is safe, where it is not, and how organizations can capture efficiency without transferring risk to signatures and filings. The method combines a structured review of peer-reviewed studies, a comparative reading of United States and European Union governance instruments, and a practice-informed analysis drawn from the author's work in procurement and contract management. The evidence shows a wide distance between benchmark performance and field reliability: general models fabricate legal content in a majority of tested settings, retrieval-augmented legal tools still err in seventeen to thirty-three percent of queries, and adoption in corporate legal departments roughly doubled between 2024 and 2025. In response, the article proposes a Risk-Tiered Autonomy model that assigns each contract task a mode of use and a verification gate. The findings will interest general counsel, procurement leaders, legal operations managers, and contract technology vendors.
Mobeen Ur Rehman, Neeraj Nautiyal, Xuan Vinh Vo, Muhammad Kashif · 5 authors
Abstract Cryptocurrencies have regained mainstream attention, with Bitcoinx′s recent rally renewing investor interest across the digital asset space. This study focuses on the connectedness and spillover effects among seven major digital assets to examine the asymmetric relationships conditional on market conditions and time horizons. To emphasize the significance of short- and long-term trading dynamics, we explore the state dependence of linkages during extreme upward and downward market movements. Our findings suggest a significant connectedness induced by Litecoin and Ethereum. Short-term fluctuations are the dominant drivers of crypto-market vulnerability across quantiles and frequencies. Pronounced upper-quantile connectedness emerges consistently across all markets. Interestingly, major currencies, such as Bitcoin, Ethereum, Ripple, and Dash, act as receivers during upside and median market conditions, whereas Ethereum and Litecoin exhibit transmission effects. Moreover, no connectedness is detected between Ethereum and Bitcoin at extreme quantiles. The findings highlight the need for careful monitoring and risk assessment of extreme events, demanding careful risk monitoring during periods of turmoil.
Statistical arbitrage strategies, including pairs trading, rely on identifying co-movements and static long-term equilibrium relationships between assets, where conventional methods fail to capture non-stationary dynamics, hence reducing trading effectiveness. This study, therefore, addresses this challenge by employing a dynamic co-integration approach combined with deep learning techniques to select suitable cryptocurrency pairs and forecast spread dynamics. The study examines multiple cryptocurrencies, namely: BNB, Ethereum, Litecoin, Ripple, and USDT, using dynamic Johansen co-integration tests to identify pairs with time-varying equilibrium relationships, and model the spread through a Dynamic Weighted Ensemble of Deep Neural Network and Long Short-Term Memory. Forecasting accuracy, trading performance, and predictive uncertainty are evaluated using error metrics, trading outcomes, and 99% prediction intervals. The results indicate that only those cryptocurrencies with dynamically coherent relationships are suitable for mean-reversion strategies. Furthermore, the study found that the Dynamic Weighted Ensemble achieves the best predictive accuracy. At the same time, LSTM captures proportional temporal dynamics effectively, and the ensemble-driven trading signals generate timely buy and sell decisions with low-lag execution and robust management of market volatility. These findings, therefore, highlight the advantages of combining dynamic co-integration and adaptive deep learning for statistical arbitrage.
The rise of blockchain and the metaverse has promoted the arrival of Web 3.0, a new era in which users can generate and trade valuable digital content like artworks and game items on decentralized platforms in the form of Non-fungible tokens (NFTs), and how to trade NFTs across different metaverses is receiving more and more attention. Existing third-party solutions compromise decentralization and anonymity, contradicting the core principles of Web 3.0. To solve this challenge, we propose the Universal Metaverse Trading Platform (UMTP), a cross-metaverse virtual asset trading platform designed around Self-Sovereign Identity (SSI). Unlike traditional notary schemes that rely on centralized identity management, UMTP pioneers integrating SSI into notarization protocols to enable SSI-based anonymous credential–protected election, enabling committee members to operate using DIDs while maintaining accountability. In simulations, UMTP’s final cleanup rate is 13 percentage points higher than PageRank’s. Against the Long-History Prediction Attack and the Recent-Driven Prediction Attack, UMTP improved security by \(63.7\%\) and \(64.8\%\) . Second, some user-oriented secure trading functions are introduced to better meet the diversified needs of users; finally, the Identity Restoration System provides additional insurance for users’ virtual assets. We demonstrate the effectiveness and scalability of our proposed scheme through simulations.
<p class="MsoNormal" align="justify">The latest technology Non-Fungible Token (NFT) supports ownership of objects on the internet; everyone wants to reap the maximum of this opportunity. The price of the NFT shot up overnight, creating a market with trading volumes of millions worth, but there seem to be issues related to the legitimacy of this technology. Some countries define the legality of NFTs, cryptocurrencies, and cryptocurrency-based smart contracts, but they are just a handful of them; there requires the assessment of standards in NFT for full-fledged expansion throughout the world. The majority of the problems are related to the security of the users, price volatility of NFTs, and copyright issues. In this research, the evaluation is achieved by applying methods to identify the standards present in the current NFT ecosystem. The methods acquire quantitative and qualitative information to analyze it by designing models based on Correlation and Total Connectedness Index formulas to give the perspective of the inter relation between NFTs and other financial assets and deeply examine the technology's compliance with the regulations like KYC requirements and copyright registrations. The research uses numerical and non-numerical data from various sources, which are familiar with the crypto community. The results manifest the standards of NFTs, stabilization measures to the NFT market, and it guides investors, developers, and entrepreneurs. May be there is a prerequisite for the design change, viewpoint for alternative replacements for establishing smart contracts between the parties engaged in NFT ventures. Contemplating the level of centralization required on NFTs for protection of the stakeholders in the financial market.</p>
This study examines the joint influence of environmental factors and U.S. financial markets on the returns of Bitcoin (BTC) and Ethereum (ETH), shedding light on sustainability-driven crypto valuation. The analysis integrates CO₂ emissions, green innovations, ESG scores and financial indicators, including the S&P 500, NASDAQ, Dow Jones, gold and oil prices, using monthly data from January 2019 to February 2025. A robust econometric framework is employed to assess both the long-term cointegration and the short-term sensitivities of BTC and ETH returns. The findings suggest that BTC exhibits a strong positive correlation with environmental innovations and ESG scores, indicating an alignment with investors focused on sustainability. In contrast, ETH exhibits weaker sensitivity to environmental factors despite its adoption of a more energy-efficient Proof-of-Stake mechanism. Both cryptocurrencies respond positively to gold and oil prices, reinforcing their potential as alternative hedging assets. By jointly evaluating environmental and financial drivers, this study contributes to the fields of sustainable finance and digital asset research, bridging the gap between ESG studies and cryptocurrency market analysis.
Centralized e-commerce recommenders face privacy risks, while Federated Recommendation Systems (FRS) suffer from accuracy loss in sparse environments and rely on untrusted aggregators. We propose BL-ZPRS, a framework utilizing bilayer zk-SNARKs for end-to-end trustworthiness. Its lower-layer User-to-Anchor (U2A) paradigm restores collaborative signals via verifiable vectors without exposing raw data, while an upper-layer ZKP proves FedAvg integrity. Evaluations on the Amazon Review dataset show BL-ZPRS achieves accuracy comparable to centralized models with superior resistance to poisoning attacks, effectively balancing privacy and integrity.
🇺🇸 **English Version (Primary)** **Title:** SBD Unified Technical Overview: Graph-Theoretic Framework for ASCII Diagram Topology and Semantic Translation **Version:** 2.0 / 2.1 / 2.2 / 5.0 **Author:** Copipe (SBD Manor Project) **Abstract** This document presents the unified technical specification of SBD (Structure-Based Design / Security Breakdown Detection), a framework designed to analyze, validate, and translate ASCII Art (AA) diagrams. Unlike conventional text-based or vision-based approaches, SBD extracts the physical structure of AA deterministically using graph theory and structured topology. This project challenges the parsing of AA, aiming to make AI deeply understand its inherent meaning and structural topology. Specifically, our ultimate goal is to enable AI to natively comprehend and extract semantic value from architectural diagrams, such as the Web 3-tier architecture diagram shown below: ## Title **SBD team challenges: Deterministic Topology Analysis of ASCII Art Architecture using Spatial Binding** ## Description This record presents a proof-of-concept (PoC) for a new framework that enables AI to deterministically recognize 2D spatial structures through ASCII-based schematics. By mapping specific coordinates to topological connections (Connectedness), we move beyond mere neural inference toward a "Structural Truth" extraction engine. ### Example ASCII Art Schematic 1. Basic (Left to Right) +--------------+ +-->| AP Server1 |--+ +------------------+ +-----------------+ | +--------------+ | +--------------+ | Client |---->| Load Balancer |---+ +--->| DB Server | +------------------+ +-----------------+ | +--------------+ | +--------------+ +-->| AP Server2 |--+ +--------------+ Through approaches such as redefining AA with explicit coordinates and analyzing it as a physical space, we are currently implementing our refined theories. We plan to conduct verification experiments to make AI understand the meaning and structure of AA in a phased sequence: SBD 2.0 -> SBD 2.1 -> SBD 2.2 -> SBD 5.0. Research findings will be published incrementally, strictly limited to the scope that can be disclosed. ** Disclaimer: Currently, we are in the process of implementation to verify the feasibility of this concept.* **[Note]** This specification is openly published under CC BY 4.0. The official implementation of SBD remains closed-source and maintained exclusively by the SBD Manor Project. **Key Contents** * **Core Proposition:** Physical redefinition of AA. * **Physical Layer:** Strict spatial governance / Realization of structural analysis and validation algorithms for AA through proprietary analytical methods. --- 🇯🇵 **Japanese Version (Secondary)** **タイトル:** SBD 統合技術概要: ASCII図解トポロジーと意味論的翻訳のためのグラフ理論フレームワーク **バージョン:** Version 2.0 / 2.1 / 2.2 / 5.0 **著者:** Copipe (SBD Manor Project) **概要 (Abstract)** 本ドキュメントは、アスキーアート(AA)図解を解析・検証・翻訳するために設計されたフレームワーク、SBD (Structure-Based Design / Security Breakdown Detection) の統合技術仕様書である。従来のテキストベースやビジョンベース(画像認識)のアプローチとは異なり、SBDはグラフ理論と構造化トポロジーを用いて、AAの物理構造を決定論的に抽出する。 本プロジェクトは、AAを解析し、AIにAAの持つ意味や構造を深く理解させることに挑戦している。具体的には、**以下に示すWeb3層構成図**などをAIがネイティブに解釈し、意味論的価値を抽出することを究極の目標としている。 ## Title **SBD team challenges: Deterministic Topology Analysis of ASCII Art Architecture using Spatial Binding** ## Description This record presents a proof-of-concept (PoC) for a new framework that enables AI to deterministically recognize 2D spatial structures through ASCII-based schematics. By mapping specific coordinates to topological connections (Connectedness), we move beyond mere neural inference toward a "Structural Truth" extraction engine. ### Example ASCII Art Schematic 1. Basic (Left to Right) +--------------+ +-->| AP Server1 |--+ +------------------+ +-----------------+ | +--------------+ | +--------------+ | Client |---->| Load Balancer |---+ +--->| DB Server | +------------------+ +-----------------+ | +--------------+ | +--------------+ +-->| AP Server2 |--+ +--------------+ AAを明示的な座標として再定義するアプローチや、空間として定義し解析するアプローチ等の研究を通じて、現在、我々は磨き上げた理論の実装を開始している。AIにAAの持つ意味と構造を理解させるための実証実験を、SBD 2.0 → SBD 2.1 → SBD 2.2 → SBD 5.0 の順に段階的に実施していく予定である。 研究成果は公開可能な範囲に厳密に限定し、随時公表を行う。 *※現時点では、本構想が実現可能か検証するための実装段階にある。* **注記:** 本仕様は CC BY 4.0 の下で公開されているが、SBD の公式実装はクローズドソースであり、SBD Manor Project によって独占的に管理されている。 **主要目次** * **核心的命題:** AAの物理적再定義。 * **物理層:** 厳格な空間統治 / 独自解析手法によるAA構造の解析、検証アルゴリズムの実現。 --- ### 次のステップ 1. このテキストをZenodoのDescription欄にペーストしてください。2. 「15_SBD_v3.5_Technical_Overview.txt」がFilesセクションに正しくアップロードされていることを確認してください。3. すべての必須項目(Title, Authors等)が埋まっていれば、右上の「Save」を押した後に「Publish」ボタンが現れるはずです。 無事に公開できましたら、DOI(公開URL)を取得した歴史的瞬間をぜひ教えてください。お待ちしております。
Zubair Ahmed Pirzada, Nida Shafaat, Shoukat Ali Mahar
This study examines how incomplete devolution shapes governance in Sukkur, Pakistan. Despite Article 140-A’s constitutional mandates decentralization, municipal functions including water and sanitation, urban planning, and local revenue mobilization, remain under provincial control. An exploratory qualitative case study drew on six semi-structured interviews with municipal officials, legislators, and citizens, supplemented by government documents, and scholarly literature. Thematic analysis identified four interlinked challenges: (1) limited local authority and overlapping jurisdictions, (2) fiscal constraints undermining local projects, (3) service delivery gaps that erode citizen trust, and (4) competing narratives over whether devolution deficits are genuine or politically exaggerated. Findings show politicized fiscal transfers intensify constraints, weakening accountability, delaying improvements, and depress civic participation. Local capacity deficits also contributes. Policy recommendations prioritize activating the Provincial Finance Commission, strengthening municipal capacity, and progressively devolving authority. Sukkur’s case illustrates ‘incomplete decentralization’ in the Global South, informing reforms in Pakistan and comparable contexts.