Financial Sentiment Analysis (FSA) traditionally relies on human-annotated sentiment labels to infer investor sentiment and forecast market movements. However, inferring the potential market impact of words based on their human-perceived intentions is inherently challenging. We hypothesize that the historical market reactions to words, offer a more reliable indicator of their potential impact on markets than subjective sentiment interpretations by human annotators. To test this hypothesis, a market-derived labeling approach is proposed to assign tweet labels based on ensuing short-term price trends, enabling the language model to capture the relationship between textual signals and market dynamics directly. A domain-specific language model was fine-tuned on these labels, achieving up to an 11% improvement in short-term trend prediction accuracy over traditional sentiment-based benchmarks. Moreover, by incorporating market and temporal context through prompt-tuning, the proposed context-aware language model demonstrated an accuracy of 89.6% on a curated dataset of 227 impactful Bitcoin-related news events with significant market impacts. Aggregating daily tweet predictions into trading signals, our method outperformed traditional fusion models (which combine sentiment-based and price-based predictions). It challenged the assumption that sentiment-based signals are inferior to price-based predictions in forecasting market movements. Backtesting these signals across three distinct market regimes yielded robust Sharpe ratios of up to 5.07 in trending markets and 3.73 in neutral markets. Our findings demonstrate that language models can serve as effective short-term market predictors. This paradigm shift underscores the untapped capabilities of language models in financial decision-making and opens new avenues for market prediction applications.
T. Q. D. Pham, K. D. Tran, Khanh T. P. Nguyen, X. V. Tran · 6 authors
As global industries transition towards Industry 5.0 predictive maintenance PM remains crucial for cost effective operations resilience and minimizing downtime in increasingly smart manufacturing environments In this chapter we explore how the integration of Federated Learning FL and blockchain BC technologies enhances the prediction of machinerys Remaining Useful Life RUL within decentralized and human centric industrial ecosystems Traditional centralized data approaches raise concerns over privacy security and scalability especially as Artificial intelligence AI driven smart manufacturing becomes more prevalent This chapter leverages FL to enable localized model training across multiple sites while utilizing BC to ensure trust transparency and data integrity across the network This BC integrated FL framework optimizes RUL predictions enhances data privacy and security establishes transparency and promotes collaboration in decentralized manufacturing It addresses key challenges such as maintaining privacy and security ensuring transparency and fairness and incentivizing participation in decentralized networks Experimental validation using the NASA CMAPSS dataset demonstrates the model effectiveness in real world scenarios and we extend our findings to the broader research community through open source code on GitHub inviting collaborative development to drive innovation in Industry 5.0
We study the impact of the order flow auction (OFA) in the context of the proposer-builder separation (PBS) mechanism in blockchains through a game-theoretic perspective. The OFA is designed to improve user welfare by redistributing maximal extractable value (MEV) to the users, in which two sequential auctions take place: the order flow auction and the block-building auction. We formulate the OFA as a multiplayer game, and establish the existence of a Nash equilibrium, and in the two-player case derive a closed-form solution (and prove its uniqueness) via a quartic equation. Our result shows that the builder with a competitive advantage pays a lower cost, leading to a higher revenue, and adding to centralization in the builder space. In contrast, the proposer's shares evolve as a martingale process, which implies decentralization in the proposer/validator space. Our analyses rely on various tools from stochastic processes, convex optimization, and polynomial equations. We also conduct numerical studies to corroborate our findings, and to bring out other features of the OFA under the PBS mechanism.
Bahar Acilan, Andrei Constantinescu, Lioba Heimbach, Roger Wattenhofer
Given the low throughput of blockchains like Bitcoin and Ethereum, scalability - the ability to process an increasing number of transactions - has become a central focus of blockchain research. One promising approach is the parallelization of transaction execution across multiple threads. However, achieving efficient parallelization requires a redesign of the incentive structure within the fee market. Currently, the fee market does not differentiate between transactions that access multiple high-demand storage keys (i.e., unique identifiers for individual data entries) versus a single low-demand one, as long as they require the same computational effort. Addressing this discrepancy is crucial for enabling more effective parallel execution. In this work, we aim to bridge the gap between the current fee market and the need for parallel execution by exploring alternative fee market designs. To this end, we propose a framework consisting of two key components: a Gas Computation Mechanism (GCM), which quantifies the load a transaction places on the network in terms of parallelization and computation, measured in units of gas, and a Transaction Fee Mechanism (TFM), which assigns a price to each unit of gas. We additionally introduce a set of desirable properties for a GCM, propose several candidate mechanisms, and evaluate them against these criteria. Our analysis highlights two strong candidates: the weighted area GCM, which integrates smoothly with existing TFMs such as EIP-1559 and satisfies a broad subset of the outlined properties, and the time-proportional makespan GCM, which assigns gas costs based on the context of the entire block's schedule and, through this dependence on the overall execution outcome, captures the dynamics of parallel execution more accurately.
Joel Mark P. Rodriguez, Genesis S. Austria, Glen B. Millar
This study investigates how ABCD technologies can improve learning assessments in higher education. The objective is to research how students perceive things, plan their behavior, and how ABCD technologies affect individual learning, academic integrity, co-learning, and trust in the assessment. Through a quantitative research design, survey responses were gathered from university students, and statistical tests, such as correlation and regression, were used to establish relationships between Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Behavioral Intention (BI) towards ABCD adoption. The results showed that there was no significant relationship between PU, PEU, and BI, which suggests that students' attitudes, institutional policies, faculty support, and infrastructure matter more in adoption than institutional policies, faculty support, and infrastructure. While students recognize ABCD's efficiency and security benefits, fairness, ease of use, and engagement issues limit their adoption of these technologies. The research adds to Technology Acceptance Model (TAM) and Constructivist Learning Theory (CLT) by emphasizing external drivers of technology adoption. The limitations are based on self-reported data and one institutional sample. It is suggested that universities invest in faculty development, infrastructure, and policy-making to facilitate effective and ethical use of ABCD technologies in higher education.
Estimation of mean shift in a temporally ordered sequence of random variables with a possible existence of change-point is an important problem in many disciplines. In the available literature of more than fifty years the estimation methods of the mean shift is usually dealt as a two-step problem. A test for the existence of a change-point is followed by an estimation process of the mean shift, which is known as testimator. The problem suffers from over parametrization. When viewed as an estimation problem, we establish that the maximum likelihood estimator (MLE) always gives a false alarm indicting an existence of a change-point in the given sequence even though there is no change-point at all. After modelling the parameter space as a modified horn torus. We introduce a new method of estimation of the parameters. The newly introduced estimation method of the mean shift is assessed with a proper Riemannian metric on that conic manifold. It is seen that its performance is superior compared to that of the MLE. The proposed method is implemented on Bitcoin data and compared its performance with the performance of the MLE.
Multi-scalar multiplication (MSM) is crucial in cryptographic applications and computationally intensive in zero-knowledge proofs. MSM involves accumulating the products of scalars and points on an elliptic curve over a 377-bit modulus, and the Pippenger algorithm converts MSM into a series of elliptic curve point additions (PADDs) with high parallelism. This study investigates accelerating MSM on the Versal ACAP platform, an emerging hardware that employs a spatial architecture integrating 400 AI Engines (AIEs) with programmable logic and a processing system. AIEs are SIMD-based VLIW processors capable of performing vector multiply-accumulate operations, making them well-suited for multiplication-heavy workloads in PADD. Unlike simpler multiplication tasks in previous studies, cryptographic computations also require complex operations such as carry propagation. These operations necessitate architecture-aware optimizations, including intra-core dedicated coding style to fully exploit VLIW capabilities and inter-core strategy for spatial task mapping. We propose various optimizations to accelerate PADDs, including (1) algorithmic optimizations for carry propagation employing a carry-save-like technique to exploit VLIW and SIMD capabilities and (2) a comparison of four distinct spatial mappings to enhance intra- and inter-task parallelism. Our approach achieves a computational efficiency that utilizes 50.2% of the theoretical memory bandwidth and provides 568 speedup over the integrated CPU on the AIE evaluation board.
Himanshu Udupi, Ashutosh Sahoo, Akshay S. P., Gurukiran S. · 6 authors
Modern society functions on trust. The onchain economy, however, is built on the founding principles of trustless peer-to-peer interactions in an adversarial environment without a centralised body of trust and needs a verifiable system to quantify credibility to minimise bad economic activity. We provide a robust framework titled zScore, a core primitive for reputation derived from a wallet's onchain behaviour using state-of-the-art AI neural network models combined with real-world credentials ported onchain through zkTLS. The initial results tested on retroactive data from lending protocols establish a strong correlation between a good zScore and healthy borrowing and repayment behaviour, making it a robust and decentralised alibi for creditworthiness; we highlight significant improvements from previous attempts by protocols like Cred showcasing its robustness. We also present a list of possible applications of our system in Section 5, thereby establishing its utility in rewarding actual value creation while filtering noise and suspicious activity and flagging malicious behaviour by bad actors.
This work proposes a dual-functional blockchain framework named BagChain for bagging-based decentralized learning. BagChain integrates blockchain with distributed machine learning by replacing the computationally costly hash operations in proof-of-work with machine-learning model training. BagChain utilizes individual miners' private data samples and limited computing resources to train potentially weak base models, which may be very weak, and further aggregates them into strong ensemble models. Specifically, we design a three-layer blockchain structure associated with the corresponding generation and validation mechanisms to enable distributed machine learning among uncoordinated miners in a permissionless and open setting. To reduce computational waste due to blockchain forking, we further propose the cross fork sharing mechanism for practical networks with lengthy delays. Extensive experiments illustrate the superiority and efficacy of BagChain when handling various machine learning tasks on both independently and identically distributed (IID) and non-IID datasets. BagChain remains robust and effective even when facing constrained local computing capability, heterogeneous private user data, and sparse network connectivity.
Mohammed Shalan, Md Rakibul Hasan, Yan Bai, Juan Li
The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed approach combines knowledge distillation and transfer learning to support heterogeneous IoT devices with varying computational capacities, ensuring efficient local training without compromising privacy. Blockchain technology is integrated to provide decentralized, tamper-resistant access control through Role-Based Access Control (RBAC), allowing only authenticated devices to participate in the federated learning process. This combination ensures data confidentiality, system integrity, and trust among devices. This framework’s performance was evaluated using the N-BaIoT dataset, showcasing its ability to detect anomalies caused by botnets such as Mirai and BASHLITE across diverse IoT devices. Results demonstrate significant improvements in intrusion detection accuracy, particularly for resource-constrained devices, while maintaining privacy and adaptability in dynamic smart home environments. These findings highlight the potential of this blockchain-enhanced federated learning system to offer a scalable, robust, and privacy-preserving solution for securing smart homes against evolving threats.
Mátyás Lukács, Fruzsina Toth, Roland Horvath, Gyula Solymos · 13 authors
The rapid growth of the human population, the increase in consumer needs regarding food authenticity, and the sub-par synchronization between agricultural and food industry production necessitate the development of reliable track and tracing solutions for food commodities. The present research proposes a simple and affordable digital system that could be implemented in most production processes to improve transparency and productivity. The system combines non-destructive, rapid quality assessment methods, such as near infrared spectroscopy (NIRS) and computer/machine vision (CV/MV), with track and tracing functionalities revolving around the Internet of Things (IoT) and radio frequency identification (RFID). Meanwhile, authenticity is provided by a self-developed blockchain-based solution that validates all data and documentation “from farm to fork”. The system is introduced by taking certified Hungarian sweet potato production as a model scenario. Each element of the proposed system is discussed in detail individually and as a part of an integrated system, capable of automatizing most production flows while maintaining complete transparency and compliance with authority requirements. The results include the data and trust model of the system with sequence diagrams simulating the interactions between participants. The study lays the groundwork for future research and industrial applications combining digital tools to improve the productivity and authenticity of the agri-food industry, potentially increasing the level of trust between participants, most importantly for the consumers.
Utilizing information technology (IT) in human resource management (HRM) platforms is a prerequisite for every business to successfully adopt and implement the Fourth Industrial Revolution (Industry 4.0). These methods are necessary to provide a fair, efficient, transparent, and safe environment. Successful implementation of these requirements may be facilitated by blockchain technology, which is based on a decentralized distributed ledger. The purpose of this study is to ascertain how blockchain technology is currently being applied in human resource management. Along with anticipated adoption barriers that can restrict its use, it also outlines possible opportunities associated with the implementation of blockchain technology in the field of human resource management. There are definite benefits when comparing the proposed system to the existing hiring practices. Thus, blockchain technology has also been widely used in human resources management. This essay will look more closely at and explore blockchain technology's application potential in HRM. To determine the possible opportunities associated with the use of blockchain technology in the HRM domain as well as the expected adoption challenges that may impede its utilization, this paper analyses the findings of an empirical study that conducted one semi-structured interview with HRM experts. Both blockchain and HRM researchers can benefit from the study by using the potential suggested as a basis for future research and attempting to address the expected adoption issues. KEY WORDS: Blockchain Technology, Human Resource Management (HRM), Industry 4.0, Decentralized Ledger, Recruitment and Hiring, Adoption Challenges.
<p>Blockchain technology is the foundation of cryptocurrencies, which are virtual currencies. The decentralized nature of cryptocurrencies has resulted in a significant reduction of central authority over them, which has implications for global trade and relations. The need for an effective model to anticipate the price of cryptocurrencies is essential due to their wide variations in value. Due to the shortcomings of conventional production forecasting, in this work, four distinct models were used. The deep learning models are the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), and both the Facebook-Prophet and Silverkite support the exponential smoothing technique. Silverkite is designed to handle a wide range of time series forecasting tasks. Considering past bitcoin information from January 2012 to March 2021, a period of nine years, we looked at the models. The Bi-LSTM model yields a 7.073 mean absolute error (MAE) and a 3.639 root mean squared error (RMSE). The Bi-LSTM model identifies the deviations that might draw attention and avert any problems.</p>
Ensuring scalability in cryptocurrency systems is significant in guaranteeing real-world utility along with the remarkable increment of cryptographic currency. As an alternative in solving scalability issue, payment channel allows users to deliver extensive offline transactions without uploading massive transaction details to the blockchain, such that increasing efficiency can be achieved. However, the implementation of payment channel still encounters privacy concerns when considering the publicly available transaction amounts and the potentials in mining associations between transaction parties. In this paper, we propose a novel payment channel scheme, entitledCommitment-basedAnonymousPayment ChannEl (CAPE), to facilitate unlimited off-chain bidirectional payments while guaranteeing participants’ privacy. The proposed scheme adopts zero-knowledge proof (zk-SNARKs) and verifiable timed (VTD) commitments to ensure the anonymity of the relationship between on-chain and off-chain transactions, privacy of transaction amounts, and security of balances. We comprehensively formalize security definitions and present rigorous proofs for each security attribute. Experiment results further demonstrate the practical viability of CAPE.
The Internet of Behaviors (IoB) is an emerging concept that utilizes devices to collect human behavior and provide intelligent services. Although some research has focused on human behavior analysis and data collection within IoB, the associated security and privacy challenges remain insufficiently explored. This article analyzes the security and privacy risks at different stages of behavioral data generating, uploading, and use while also considering the dynamic characteristics of user activity areas. Then, we propose a blockchain-based distributed IoB data storage and sharing framework, which is categorized into sensing, processing, and management layers based on these stages. To accommodate both identity authentication and behavioral privacy, zero-knowledge proofs are used in the sensing layer to separate the correlation between behavior and identity, which is further extended to a distributed architecture for cross-domain authentication. In the processing layer, an improved consensus protocol is proposed to enhance the decision-making efficiency of distributed IoB by analyzing the geographical and computational capability of the servers. In the management layer, user permission differences and the privacy of access targets are considered. Different types of behavior are modeled as corresponding relationships between keys, and fine-grained secure access is achieved through function secret sharing. Simulation results demonstrate the effectiveness of the proposed framework in multi-scenario IoB, with average consensus and authentication times reduced by 74 percent and 56 percent, respectively.
DeFi (Decentralized Finance) is one of the most important applications of today's cryptocurrencies and smart contracts. It manages hundreds of billions in Total Value Locked (TVL) on-chain, yet it remains susceptible to common DeFi price manipulation attacks. Despite state-of-the-art (SOTA) systems like DeFiRanger and DeFort, we found that they are less effective to non-standard price models in custom DeFi protocols, which account for 44.2% of the 95 DeFi price manipulation attacks reported over the past three years. In this paper, we introduce the first LLM-based approach, DeFiScope, for detecting DeFi price manipulation attacks in both standard and custom price models. Our insight is that large language models (LLMs) have certain intelligence to abstract price calculation from smart contract source code and infer the trend of token price changes based on the extracted price models. To further strengthen LLMs in this aspect, we leverage Foundry to synthesize on-chain data and use it to fine-tune a DeFi price-specific LLM. Together with the high-level DeFi operations recovered from low-level transaction data, DeFiScope detects various DeFi price manipulations according to systematically mined patterns. Experimental results show that DeFiScope achieves a high recall of 80% on real-world attacks, a precision of 96% on suspicious transactions, and zero false alarms on benign transactions, significantly outperforming SOTA approaches. Moreover, we evaluate DeFiScope's cost-effectiveness and demonstrate its practicality by helping our industry partner confirm 147 real-world price manipulation attacks, including discovering 81 previously unknown historical incidents.
The syndrome decoding problem is one of the NP-complete problems lying at the foundation of code-based cryptography. The variant thereof where the distance between vectors is measured with respect to the Lee metric, rather than the more commonly used Hamming metric, has been analyzed recently in several works due to its potential relevance for building more efficient code-based cryptosystems. The purpose of this article is to present a zero-knowledge proof of knowledge for this variant of the problem.
As a typical instance of human–computer interaction, the notion of decentralized autonomous organization (DAO) represents an organization constructed by automatically executed rules, such as via smart contracts, incorporating features of the permissionless committee, transparent proposals, and fair contributions by stakeholders. As of May 2023, DAO has impacted over $24.3B market caps. However, there are limited studies focused on this emerging field. To fill the gap, we start from the ground truth by empirically studying the breadth and depth of the DAO markets in mainstream public chain ecosystems in this article. We dive into the most widely adoptable DAO launchpad,Snapshot, which covers 95% of the wild DAO projects for data collection and analysis. By integrating extensively enrolled DAOs and corresponding data measurements, we explore statistical resources from Snapshot and analyze data from 581 DAO projects, encompassing 16 246 proposals over the course of 3+ years. Our empirical research has uncovered a multitude of previously unknown facts about DAOs, spanning topics such as their status, features, performance, threats, and ways of improvement. We have distilled these findings into a series of key insights and takeaway messages, emphasizing their significance. Notably, our study is the first of its kind to comprehensively examine the DAO ecosystem with a focus on scale and scope of data, real-time relevance, practical implementations, and comprehensive metrics, addressing critical gaps in the current literature.
Decentralization is a core principle of blockchain technology and Decentralized Autonomous Organizations (DAOs), enhancing security and resilience by distributing control across a network. Traditional metrics like the Gini coefficient and Nakamoto coefficient often fall short in capturing the complex dynamics of decentralization. This paper introduces the Apokedro decentralization index, a metric that evaluates decentralization by considering the probabilities of all possible subsets of nodes that could collectively centralize control. These concepts from game theory, such as the Nash equilibrium, and the Apokedro index, when incorporated, provide a nuanced assessment of centralization risks. Key contributions include the mathematical formulation of the index, an efficient computational algorithm utilizing pruning techniques, and benchmarking experiments that compare the index performance against traditional metrics across various statistical distributions. The Apokedro index offers a comprehensive tool for measuring decentralization in blockchain networks and DAOs.
Grassroots platforms aim to offer an egalitarian alternative to global platforms. Whereas global platforms can have only a single instance, grassroots platforms can have multiple instances that emerge and operate independently of each other and of any global resource except the network, and can interoperate and coalesce into ever-larger instances once interconnected. Key grassroots platforms include grassroots social networks, grassroots cryptocurrencies, and grassroots democratic federations. Previously, grassroots platforms were defined formally and proven grassroots using unary distributed transition systems, in which each transition is carried out by a single agent. However, grassroots platforms cater for a more abstract specification using transactions carried out atomically by multiple agents, something that cannot be expressed by unary transition systems. As a result, their original specifications and proofs were unnecessarily cumbersome and opaque. We enhance the notion of a distributed transition system to include atomic transactions and revisit the notion of grassroots platforms within this new foundation; present crisp specifications of key grassroots platforms using atomic transactions: befriending and defriending for grassroots social networks, coin swaps for grassroots cryptocurrencies, and communities forming, joining, and leaving a federation for grassroots democratic federations; prove a general theorem that a platform specified by atomic transactions that are so-called interactive is grassroots; show that the atomic transactions used to specify all three platforms are interactive; and conclude that the platforms thus specified are indeed grassroots. We thus provide a crisp mathematical foundation for grassroots platforms and a solid and clear starting point from which their implementation can commence.
While blockchain technology is viewed to revolutionize the energy sector by its cryptography-based, open, and direct peer-to-peer energy trading (P2PET) from producer to consumer, the current paper focused on the blockchain framework developed that allows for P2PET in the retail electricity market. The platform makes sure that there is proper supply-demand matching, transaction streamlining, and increased need for direct interaction, hence reducing the need for brokers on the platform. Its design monitors the entire energy trading process, with smart contracts automating payments and transactions to ensure security and fairness. Tests in a private Ethereum environment demonstrate benefits like accurate market pricing, fair profit distribution, and better renewable energy integration. It also incentivizes the participation of stakeholders in the P2PET through high-value information on gas usage, introducing computational efficiency. Besides, this proposed model adopted a consensus mechanism that would guarantee the permanence, scalability, and robustness of transactions across ...
ABSTRACT This paper investigates the current landscape of option trading platforms for cryptocurrencies, encompassing both centralized and decentralized exchanges. Option contracts in cryptocurrency markets offer functionalities akin to traditional markets, providing investors with tools to mitigate risks, particularly those arising from price volatility, while also allowing them to capitalize on future volatility trends. The paper discusses these applications of option contracts in the context of decentralized finance (DeFi), emphasizing their utility in managing market uncertainties. Despite a recent surge in the trading volume of options contracts on cryptocurrencies, decentralized platforms account for less than 1 % of this total volume. Hence, this paper takes a closer look by examining the design choices of these platforms to understand the challenges hindering their growth and adoption. It identifies technical, financial, and adoption‐related challenges that decentralized exchanges face and provides commentary on existing platform responses. Subsequently, the paper analyzes the impact of absent options markets on the inefficiencies of automated market maker liquidity. It examines historical on‐chain data for 14 ERC20 token pairs on Ethereum. The analysis shows 1143 instances in which deeper liquidity levels, as high as more, could have been achieved by establishing an options market.
Smart contracts are at the core of blockchain technology, but the cost of fixing their security vulnerabilities is high, making pre-deployment vulnerability detection crucial. Existing methods rely on fixed rules, which have limitations in accuracy and scalability, and their efficiency decreases with the complexity of the rules. Neural-network-based methods can identify some vulnerabilities but are inefficient in multi-vulnerability scenarios and depend on source code. To address these issues, we propose a multi-vulnerability-based smart contract detection method called RTMS. RTMS takes bytecode as input, disassembles it into opcodes, uses the gas consumed by the contract for data slicing, and extends the length of input opcodes through a layered structure. It employs a weighted binary cross-entropy (BCE) function to handle data imbalance and combines channel-sequence attention mechanisms to extract vulnerability correlation features. By using transfer learning, it reduces training parameters and computational costs. Our RTMS model can detect multiple vulnerabilities simultaneously, enhancing detection accuracy and efficiency. In experiments with 100,000 real contract samples, the model achieved a Jaccard coefficient of 0.9312, a Hamming loss of 0.0211, and an F1 score that improved by about 11 percentage points compared to existing models, demonstrating its superiority and stability.
Smart contract as a new form of contract is recognized to provide speedy and efficient transactions. Eliminating textual ambiguities, cumbersome contractual terms, enables negotiations, verify terms and automatically enforce tempered-free contractual terms without the need for intermediaries. In traditional contract, contractual terms are written in formal language which are quite cumbersome, the process of concluding transactions is slow and requires the intervention of lawyers, banks, registry departments and the courts. Many jurisdictions including Nigeria are still carrying out contractual transactions relying solely on traditional contract despite advancement in technologies including Blockchain technology, Ethereum and use of cryptocurrencies like Bitcoin and others as medium of exchange in online transactions. Using a qualitative doctrinal legal research method, this research gathered online sources and examined the legality of smart contract and Blockchain technology. While highlighting the importance of using smart contract in business transaction in Nigeria. The research adds to ongoing discuss on smart contracts and Blockchain technology. Suggesting the need for a shift from purely traditional contracts in Nigeria, to adoption of smart contracts to ease both domestic and trans-jurisdictional transactions mostly concluded online. However, there is a need for a robust framework for smart contract in Nigeria just like the E-SIGN Act and the UETA in the United States and other similar legislations that have been developed in other countries across the world.