Land record management in developing nations suffers from corruption, fraud, and lack of transparency due to centralized government-controlled databases, where unauthorized modifications and deliberate deletion of transaction histories erode public trust and jeopardize citizens’ property rights. This paper presents a blockchain-based land registry system deployed on the Ethereum network, implementing a role-based access control model with three distinct roles (Admin, Registrar, and Citizen) enforced through Solidity smart contracts. A structured transfer workflow automates request submission, registrar approval, stamp duty calculation, and ownership finalization, while property documents are stored on IPFS with only content-addressed hash references recorded on-chain. We evaluate the system using a custom dataset of over 1,100 transactions on the Sepolia Testnet. Results demonstrate that gas consumption correlates directly with storage operation complexity, with the ownership transfer function consuming the highest average gas (309,104 units), while transaction confirmation times remain independent of computational complexity, averaging 13-16 seconds across all functions. The findings demonstrate blockchain as a viable infrastructure layer for tamper-proof, auditable, and publicly verifiable land record management in developing nations.

The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products.

Generative Pretrained Transformers (GPTs) represent a transformative leap in artificial intelligence, transitioning from analytical systems to those capable of creating new content across various media. This advancement, rooted in the Distributional Hypothesis of Natural Language Processing (NLP), enables AI to produce human-like text by learning the nuances of language. However, the reliance on large-scale datasets and advanced algorithms brings forth significant security and privacy concerns. This paper explores the evolution of GPT models, their applications, and the associated vulnerabilities. We address the issue of misplaced trust in AI-generated information, highlighting potential impacts on critical sectors such as healthcare and finance. Furthermore, we examine the ethical dilemmas and unforeseen repercussions of biased content generation. By conducting an extensive literature review and analyzing real-world case studies, we identify gaps in existing research and propose comprehensive mitigation strategies. Our comprehensive review categorizes various types of attacks on GPT models, offering practical recommendations to enhance the security and reliability of GPT-based systems in critical applications.

Federated learning systems face a critical vulnerability from edge-case backdoor attacks that target uncommon but legitimate inputs occurring in the tail of data distributions, yet appearing regularly in real deployments. These attacks are particularly insidious because they maintain normal performance on common data while introducing malicious behaviors on infrequent patterns, making them undetectable through standard validation. Existing defenses that analyze parameter statistics fail because sophisticated attackers can craft updates that appear statistically normal while embedding semantic backdoors. We propose RBBD, a representation-based framework that detects backdoors by analyzing semantic anomalies in learned feature representations. Our approach combines three novel components: multi-layer feature space analysis, distribution-aware filtering that applies heightened scrutiny to low-density data regions, and adaptive thresholding that evolves with threat patterns. Extensive evaluation across multiple datasets and attack scenarios demonstrates that RBBD achieves 78.9 % clean accuracy while reducing backdoor success rates to $\mathbf{3. 1 \%}$. This represents a significant improvement over state-of-the-art defenses by achieving superior performance through semantic understanding while preserving clean edge-case accuracy.

While comparisons between Apache Hadoop and Apache Spark are well-documented, there has been limited research comparing Apache Spark with Apache Airflow, especially in terms of speed and memory usage. With Apache Airflow's recent introduction of dynamic task mapping, which performs similar functions to Apache Spark's map operation, a detailed comparison between the two tools has become increasingly relevant. A comparison in these areas would provide valuable insights for the Big Data Science community, helping determine which methods are better suited for tasks requiring high speed and efficient memory usage. This study focuses on comparing the Apache Spark Map function and Apache Airflow Dynamic Task Mapping function on two key metrics: memory utilization and computation speed. Specifically, we evaluate their performance in sorting formatted electrocardiogram sensory data. We hypoth-esize that Apache Spark will demonstrate faster processing times due to its advanced in-memory processing and sorting algorithms. However, this speed advantage is expected to come with higher memory usage compared to Apache Airflow. Our findings provide actionable insights into the strengths and limitations of these tools, guiding data scientists and engineers in choosing the most suitable framework for specific big data processing tasks. These results are particularly relevant for large-scale data sorting and transformation operations, contributing to informed decision-making in the Big Data Science community.

Social media has emerged as a prominent tool in the digital era, showcasing substantial growth, utilization, and impact within the realm of digital innovation. It epitomizes humanity's deep-seated desires for connectivity, accessibility, visibility, and engagement. Yet, despite its apparent success, it has also brought to light significant drawbacks that have left users in a quandary, such as privacy breaches, centralized control, automated accounts, and unwelcome intrusions. This proposed solution introduces a novel blockchain-based social media platform designed to address these challenges, both real and perceived. By leveraging the unique attributes of blockchain technology-decentralization, security, and transparency-the platform aims to establish a fully decentralized user-controlled system. The implementation of the Lens Protocol, which manages decentralized social networks using zero-knowledge proofs to thwart bot interference, in conjunction with the Polygon Mumbai Blockchain, offers a solution to mitigate transaction fees and combat bot-related activities. Through these innovative technological advancements and user-centric approaches, users will have ownership of their data, social networks, and content, enabling direct monetization opportunities. Additionally, the incorporation of an advanced token gating mechanism will incentivize creators to produce high-quality content. This paper elucidates the detailed methodology, spanning from the technological infrastructure to its practical implementation, laying the groundwork for our contribution to a new paradigm of social media where users are empowered as the primary architects of their content and communities.

Concerns regarding loot box fairness, along with increased exploitations of video game players and a lack of trustworthiness in video games and their merchandise, continue to rise, yet these issues have received limited attention and research. In response, this study introduces an Ethereum blockchain-based solution aimed at addressing the lack of transparency and fairness in loot box markets. Emphasizing transparency, fairness, and player trust, our model employs smart contracts to disclose reward probabilities to players, promoting informed decision-making. Central to our approach is the integration of a Random Number Generator (RNG) within the smart contract to ensure impartial outcomes. Deployed on the Sepolia testnet, designed to closely replicate the real Ethereum mainnet, our evaluation highlights improved transaction transparency and fairness. Analysis of Sepolia testnet's performance provides valuable and positive insights into blockchain operational dynamics. This paper contributes to ethical gaming practices by proposing a framework that could reshape how virtual rewards are managed, advocating for blockchain adoption to enhance gaming equity.

Many people find the legal system to be convoluted and resource-intensive with all of the complications, which often derive from the vagueness and ambiguity of legal texts, that arise in its processes. Oftentimes, legal processes become inconvenient or even inaccessible to parts of the general population. The integration of artificial intelligence (AI) and blockchain technology into legal processes, particularly in the interpretation of contract transactions, promises to revolutionize the legal field by enhancing efficiency and reducing human intervention. The conversion of legal contracts into smart contracts specifically has the power to make legal transactions auto-executable and instantaneous. However, this convergence introduces a significant challenge in how to automate the process of converting a legal contract, written in text, into a smart contract, written in code. This paper presents a comprehensive evaluation of how artificial intelligence can achieve complete automation of this conversion by generating legal contract interpretations and employing a dual-framework analysis based on the metrics of Enforceability and Acceptability. Through an in-depth examination of the opinions of legal professionals and AI-generated contract interpretations, we explore the extent to which AI can reliably interpret the vagueness and ambiguity in legal texts through interpretations and translate legal language into smart contract code while adhering to legal standards and ethical considerations. This paper contributes to the ongoing discourse on the role of AI and smart contracts in law, proposing a strategy to make the legal system more accessible and offering insights for legal practitioners, AI developers, and policymakers on navigating the complexities of AI-driven legal interpretations.

The study and preservation of ancient texts presents unique challenges due to the degradation and damage these manuscripts often exhibit. Papyrology, specifically, relies on meticulous study and reconstruction of fragmented texts by experts. Current digital platforms for editing and publishing papyrological texts lack the ability to manage the complex components of critical editions while facilitating peer review. In our work we propose a novel framework utilizing blockchain and smart contracts to automate the storage and retrieval of multiple related editions of a text while ensuring contributions by multiple authors are recorded. The system architecture consists of a user interface for submitting editions, a smart contract that manages storage, the blockchain ledger which stores data locations, and the use of the decentralized storage platform, the Interplanetary Files System (IPFS). Experiments demonstrated the feasibility of the framework by storing 501 synthetic editions on IPFS and recording metrics for encoding, transmission time, blockchain transaction time, and transaction costs. We believe this novel framework could enable the advancement of digital papyrology through distributed peer review and allow for the integration of AI agents into papyrology. Future development of the proposed platform could significantly modernize digital edition management for papyrology and the humanities in general.

In recent years, Smart Contracts have gained in popularity, facilitating billions of US Dollars in daily transactions. However, the recent increase in smart contract vulnerabilities threatens to undermine trust in the technology. The study aims to detect and address potential vulnerabilities in smart contracts in blockchain technology through a comprehensive analysis of four principal modalities: Solidity source code, bytecode, opcode, and intermediate representations. This proactive identification of vulnerabilities can contribute to bolstering the security and dependability of blockchain-based systems. In this paper, we propose a novel multimodal Transformer architecture named QuadraCode AI, utilizing these four distinct modalities. Unlike traditional unimodal analysis, multimodal analysis can provide a more holistic understanding of both the semantic and syntactical contexts of smart contracts to identify underlying vulnerabilities. By employing advanced data fusion techniques such as cross-attention and concatenations across 12 different multimodal frameworks, our approach enhances the detection capabilities beyond traditional unimodal approach. Notably, the framework that integrates opcode with bytecode achieves an impressive average F score of 86%, demonstrating the effectiveness of our method.

In the fast-evolving domain of blockchain technology, smart contracts have become instrumental, facilitating billions of dollars in transactions daily. However, the increasing prevalence of smart contract vulnerabilities poses significant risks, potentially undermining trust in this innovative technology. To address these security challenges, our research introduces a novel approach for detecting and mitigating vulnerabilities through a comprehensive analysis of four principal modalities: Solidity source code, bytecode, opcode, and intermediate representations. This multimodal analysis employs a Transformer architecture, VulnFusion, which uniquely integrates these modalities to provide a deeper understanding of both the semantic and syntactical nuances of smart contracts. We propose an interleaving-based data fusion technique that outperforms traditional fusion methods such as concatenation and cross-attention. Experimental evaluations demonstrate that our interleavingbased fusion technique achieves an impressive average F1 score of 89% across source code, opcode, and intermediate representations, demonstrating the effectiveness of our approach.

This work explores the integration of blockchain technology into Electronic Health Record (EHR) systems, presenting a novel approach to overcome centralization, standardization, and security challenges in healthcare data management. By leveraging a patient-centric design, we ensure privacy, control, and trust in healthcare data, introducing blockchain’s potential to enhance trust and mitigate bias across healthcare systems. Our research highlights the importance of blockchain in fostering trust and ensuring data integrity, contributing insights into overcoming inherent limitations such as slower operational speeds while maintaining quality of service. We identify challenges related to patient access and transaction times, emphasizing the need for further research to optimize blockchain’s use in healthcare. We believe our novel approach offers practical steps towards realizing blockchain’s potential in creating a more equitable, secure, and trusted healthcare ecosystem.

The voluntary gathering and pooling of personal data by individuals via legal fiduciaries called data cooperatives is gaining a lot of attention as an approach to secure data management. Data cooperatives and blockchain are an excellent combination since they share fundamental features like decentralization and democratic design. In this paper, we leverage the power of blockchain to design a trusted news-sharing system for social media. We prove our concept by implementing a consumer news coop network on the Ethereum blockchain where members can voluntarily pool news information about their neighborhood for their benefit and also receive incentives in the form of an improved reputation for sharing credible news stories. We enforce honest behavior among the participants by implementing a trust and reputation scheme based on EigenTrust. Our results show that the blockchain approach to implementing a data cooperative is efficient with respect to memory consumption, scalability, and cost while also providing improved trust among participants. Furthermore, the reputation mechanism is effective in ensuring that malicious participants are severely penalized and removed from the system, while honest participants are rewarded. This approach can be used in a much bigger setup like Twitter so that the credibility of a shared post can be verified by a consensus before being shared on the network, thereby mitigating the spread of misinformation.

Data cooperatives allow their members—the data owners—to pool their digital assets together for processing and access management. In this context, reputation is an important measure of trust, which can effectively complement financial assets in the decentralized scenario, also providing incentives for users’ honest behavior. We present a decentralized data cooperative system based on the Proof-of-Reputation and Proof-of-Stake blockchains. In order to provide inclusivity for low-reputation (newly joined) users, which is required in our community-based scenario, we use the tier-based committee selection introduced by Kleinrock et al. at Indocrypt 2020. As the underlying Proof-of-Stake system, we use Snow White due to its convenient properties such as flexible committee selection and user participation.

Metaverse creates a fully immersive, self-sustaining, and hyper spatiotemporal virtual world where people from the physical world interact virtually irrespective of distances, communication protocols, regulations, and policies. The digital version of the real world should facilitate seamless communication, borderless transactions, and scalable infrastructure, encouraging participation and inclusivity. However, interoperability has become a significant issue that restricts the growth of the metaverse in reality due to independent standards, lone platforms, and unresolved communications challenges across the metaverse. This work discusses the necessity of metaverse interoperability from a socio-economic angle, emphasizing the need for connecting metaverses for distributed exchange, business interoperability, universal mobility, multi-application integration, and platform-agnostic experiences. We identified the components of interoperability that facilitate cross-metaverse communication to enable unbounded transactions and inter-metaverse user experiences. In addition, we proposed a four-layer architecture to achieve our goal of interoperable metaverse communication. Furthermore, we discuss how blockchain technology aids the expansion of interoperable metaverse through cross-platform transactions. Finally, we discussed key interoperability issues including dissimilar data standardization, different governance model, dissimilar consensus mechanisms, incompatible APIs, and heterogeneous technologies which make inter-metaverse communications challenging.

Metaverse interoperability immerses users in the virtual world to interact among diverse digital environments, allowing communication, asset exchanges, asset management, and financial transactions across virtual platforms. Moreover, interoperability mandates fast, transparent, decentralized, and secure transactions to foster trusted immersive experiences that encourage more participation of users in metaverse platforms. Blockchain, a decentralized technology, supports interoperability with a cross-chain communication paradigm. However, cross-chain communication techniques such as atomic swap employ off-chain transactions, intermediaries (e.g. Vault) to lock assets or witnesses exchange, and asynchronous state updates for blockchain-to-blockchain exchanges that limit the trust, security, and faster convergence of transactions. This work proposed a novel on-chain atomic swap protocol using state synchronization-based smart contracts to provide cross-metaverse communication by transferring digital assets (cryptocurrency/Token/NFT) that outweigh the existing atomic swap limitations. In addition, the mutex-lock property of our experiment ensures security by synchronizing the states. We measure the performance with transaction cost and latency using local Ganache test networks representing two blockchain-based metaverses. Moreover, we tested ERC20 (Token) and ERC721 (NFT) based digital assets in the atomic swap to prove our hypothesis. In our test setup, the approximate overall cost of a complete exchange round is 0.013 ether, and the transaction latency is around 6 minutes, demonstrating a cheaper and faster protocol for realtime cross-metaverse communication. Finally, we discuss the stability of our work concerning race conditions, deadlock situations, and atomicity.

The metaverse is a new 3-D digital space that uses virtual reality, augmented reality, and other advanced internet and semiconductor technology to provide people with lifelike personal and business experiences online. Metaverse has these features of having a sense of immersion with realtime interactivity and user agency. By incorporating cutting-edge technologies like blockchain, artificial intelligence, extended reality, and edge computing, the metaverse is transitioning from science fiction to impending reality. Nevertheless, the metaverse has become increasingly common, but it still does not provide advancement in the real scenario. Users will inherently interact with the virtual world mainly through the use of realtime metaverse applications. We looked into the availability of active metaverse applications and discovered that there is an inadequate number of applications due to various challenges that developers are currently facing. We built the requirements in two phases, focusing on user and developer perspectives, for creating immersive metaverse applications. Therefore, this vision paper takes on to address the challenges that are acting as a barrier currently for developing the metaverse applications. We conclude the paper by demonstrating the impact of the derived challenges on metaverse in education.

Linear regression algorithms capture information from previous experiences and build a cognitive model to forecast the future. The information and the cognitive model representing the history of predicting future outputs must be reliable so that expected results are trusted. Furthermore, the algorithms must be explainable and traceable, making the learning process meaningful and trackable. Blockchain smart contracts boost information integrity, providing trust and the provenance of distributed ledger transactions that support such requirements. Smart contracts are traditionally developed to perform simple transactions with integer operations. However, developing learning algorithms such as linear regression with smart contracts mandates complex computation involving floating-point operations, which are not supported by smart contracts. Moreover, smart contract transactions are expensive and time-consuming. In this work, we propose a novel implementation of smart contracts for linear regression algorithms with fraction-based computation that can train and predict on the Ethereum blockchain. Our smart contract-based training and prediction technique with Solidity programming language produced a similar mean square error to the scikit-learn-based prediction model. Moreover, our design strategy saves training costs for linear regression algorithms through off-chain computations with an optimistic roll-up solution. The off-chain training and on-chain prediction strategy demonstrated in our work will help academic and industry researchers to develop cost-effective distributed AI applications in the future.

The digital experience emerging in the virtual world is a reality with the advent of the metaverse. Augmented reality(AR), virtual reality(VR), extended reality(XR), and artificial intelligence(AI) algorithms would pave the way for an immersive experience for the users in the virtual space. However, the explosion of these technologies broaches new challenges to threaten the success of metaverse due to security risks. The blockchain technology augmented with AI promises to deliver a trusted metaverse for everyone. Nevertheless, smart contracts fail to produce a cognitive prediction, dissuading users from confiding in the metaverse. We arm smart contracts with intelligence to predict using AI algorithms. Moreover, we deploy the smart contracts on the Ethereum blockchain platform and produce a prediction accuracy of 95% compared to Python scikit-learn-based predictions. Our results show that the prediction delay can obstruct the growth of metaverse applications to accept blockchain technologies. Furthermore, the limitation of blockchain technology can make integration unreasonable. Therefore, we discuss possible scalability solutions that can be part of our future work to help more metaverse applications adopt blockchain solutions.