Ensemble learning techniques continue to show greater interest in forecasting the volatility of cryptocurrency assets. In particular, XGBoost, an ensemble learning technique, has been shown in recent studies to provide the most accurate forecast of Bitcoin volatility. However, the performance of XGBoost largely depends on the tuning of its hyperparameters. In this study, we examine the effectiveness of the Bayesian optimization method for tuning the XGBoost hyperparameters for Bitcoin volatility forecasting. We chose to explore this method rather than the most commonly used manual, grid, and random hyperparameter choices due to its ability to predict the most promising areas of hyperparameter spaces through exploitation and exploration using acquisition functions, as well as its ability to minimize error with a reduced amount of time and resources required to find an optimal configuration. The obtained XGBoost configuration improves the forecast accuracy of Bitcoin volatility. Our empirical results, based on letting the data speak for itself, could be used for a comparative study on Bitcoin volatility forecasting. This would also be important for volatility trading, option pricing, and managing portfolios related to Bitcoin.
Hakam Dzakwan Diash, Vannesa Nathania, Mohammad Idhom, Trimono Trimono
The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself. This study aims to develop an ETH price prediction model using a combined architecture of Convolutional Neural Network (CNN) and also Bidirectional Long Short-Term Memory (BiLSTM). CNN is used to extract local features from historical ETH closing price data, while BiLSTM models bidirectional temporal patterns. The dataset used includes ETH daily price from January 2020 to January 2025, which are obtained from Yahoo Finance and have gone through a normalization process and transformation into sequential form. The model is trained for 100 epochs with an early stopping mechanism to prevent overfitting and evaluated using the MAPE and coefficient of determination (R²) metrics. The evaluation results show that the CNN-BiLSTM model is able to predict ETH prices with a MAPE value of 2.8546% and an R² of 0.9415, indicating high performance in capturing actual data trends. This study shows that the hybrid CNN-BiLSTM approach is effective for Ethereum price prediction.
Hope Leticia Nakayiza, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae‐Min Lee
Increased automation, connectivity, and data sharing enabled by 6G technology have heightened the vulnerability of Internet of Vehicles (IoV) networks. Addressing this challenge requires an intrusion detection system (IDS) capable of accurately identifying data falsification within IoV while adhering to real-time constraints. This paper presents a Blockchain-enhanced feature-engineered IDS to ensure precise attack detection and classification with minimal computational overhead in in-vehicle networks (IVNs). The proposed lightweight IDS utilizes a hybrid Pearson’s Correlation Coefficient (PCC) feature selection technique designed for deployment on the Telematics Control Unit (TCU). Furthermore, we propose a custom private blockchain network utilizing the proof of authority and association (PoA) consensus mechanism, deployable on the Roadside Unit (RSU), for the secure logging of vehicle Electronic Control Unit (ECU) information, detection results, and the automatic isolation of malicious ECUs via smart contracts. Experimentation analysis demonstrates that the proposed approach achieves notable performance, with a 99.9% detection accuracy and minimal computation times of 0.24s and 1.32s on the CICIoV2024 and CAN-Intrusion datasets. Furthermore, the system achieves high blockchain scalability, maintaining stable throughput of 16 tx/s and low transaction latency of 0.062s under increasing ECU density and RSU coverage.
Xander Pottier, Thomas De Ruijter, Jonas Bertels, Wouter Legiest · 6 authors
The Multi-Scalar Multiplication (MSM) is the main barrier to accelerating Zero-Knowledge applications. In recent years, hardware acceleration of this algorithm on both FPGA and GPU has become a popular research topic and the subject of a multi-million dollar prize competition (ZPrize). This work presents OPTIMSM: Optimized Processing Through Iterative Multi-Scalar Multiplication. This novel accelerator focuses on the acceleration of the MSM algorithm for any Elliptic Curve (EC) by improving upon the Pippenger algorithm. A new iteration technique is introduced to decouple the required buckets from the window size, resulting in fewer EC computations for the same on-chip memory resources. Furthermore, we combine known optimizations from the literature for the first time to achieve additional latency improvements. Our enhanced MSM implementation significantly reduces computation time, achieving a speedup of up to x12.77 compared to recent FPGA implementations. Specifically, for the BLS12-381 curve, we reduce the computation time for an MSM of size 224 to 914 ms using a single compute unit on the U55C FPGA or to 231 ms using four U55C devices. These results indicate a substantial improvement in efficiency, paving the way for more scalable and efficient Zero-Knowledge proof systems.
The quality control of investment casting remains a critical challenge due to defect detection, real-time processing, and data traceability inefficiencies. This study presents an innovative Blockchain-integrated IoT system for advanced inspection of casting defects, combining a ResNet-based deep learning model for defect detection and dimensional measurement with Blockchain technology to ensure data integrity and traceability. The system demonstrated a significant improvement in defect detection accuracy, achieving an F1-score of 0.94, alongside high data integrity (0.99) and traceability (0.98) metrics. Additionally, it processes each casting in an average of 2.3 s, supporting a throughput of 26 castings per minute. By addressing critical challenges in smart manufacturing, this approach enhances operational efficiency, regulatory compliance, and user confidence. While scalability and energy efficiency remain areas for improvement, the proposed method provides a transformative solution for Industry 4.0, fostering transparency and reliability in manufacturing processes.
The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse.
Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework. Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point. Our code and NFT datasets are publicly available at https://github.com/seferlab/deep_nft .
As a key enabler of Web3, Ethereum has long faced scalability challenges. The recent EIP-4844 upgrade aims to alleviate the scalability issue by introducing the ''blob'', a new data structure for Layer-2 rollups that enables off-chain storage with much reduced costs. Yet, this new mechanism's impact on Ethereum, and the wider Web3 ecosystem, remains largely underexplored. In this paper, we conduct the first large-scale empirical analysis of the post-EIP-4844 ecosystem, leveraging a dataset of 319.5 million transactions, out of which 1.3 million are blob-carrying. Our analysis reveals two major trends: (1) average block size has increased 2.5 times, from 150 KB to 400 KB, while the share of conventional transactions has shrunk from over $150$ KB to around 80 KB; (2) rollups are rapidly migrating from expensive calldata, falling from approximately 7,500 to nearly zero, toward cheap blobs, rising from zero to about 10,000. These shifts introduce a new economic game between block builders and rollups. Thus, we develop a game-theoretic model to characterize their equilibrium strategies: a profit-maximizing inclusion rule for builders, and a cost-minimizing blob batching strategy for rollups. Empirically, however, we find notable economic inefficiencies: for example, 29.48% of blob-containing blocks are built sub-optimally, yielding less revenue than available alternatives. These findings highlight the intricacies of the blob marketplace, and our work has established both methodological and empirical foundations to understand the evolving post-EIP4844 Ethereum ecosystem.
Artists are digitally disenfranchised, rampant online sharing and ease of copying make protecting their work from unauthorized use an uphill battle. Digital art is effortlessly duplicated, shared, and manipulated without credit or compensation, even leading to individuals profiting off stolen work. This widespread issue, particularly plaguing social media, demands a solution. We propose a blockchain-based platform utilizing Non-Fungible Tokens (NFTs) to empower artists. By creating NFTs for their art, they claim irrefutable ownership, authenticity, and copyright, enabling secure sales via cryptocurrencies and combating unauthorized use. This innovative approach empowers artists to thrive in the digital age
Open access
Visual Attention and Saliency Detection
Generative Adversarial Networks and Image Synthesis
In the context of blockchain technology and cryptocurrencies, computer graphics integration seeks to improve data visualization and user interfaces, offering intuitive and aesthetically pleasing experiences. This entails developing immersive user interfaces for decentralized apps, strengthening security using visual cryptography, and increasing accessibility to complicated blockchain data. Visualizations help users comprehend transaction patterns and system efficiency by making blockchain data easier to analyze. The ultimate goal of this strategy is to improve the usability and engagement of interactions with digital assets and blockchain technology. Index Terms: Blockchain technology, cryptocurrency, data visualization, Computer Graphics, Supply chain, finance.
The IOTA Tangle, a Directed Acyclic Graph (DAG)-based distributed ledger, is popular for its scalability and suitability for IoT applications, offering fee-less transactions. A critical component of IOTA’s architecture is the Cumulative Weight Calculation (CWC), essential for its tip selection mechanism. This paper introduces an optimization of the IOTA Reference Implementation (IRI) CWC process originally implemented using Breadth-First Search (BFS) by employing Depth-First Search (DFS) and Iterative Deepening Search (IDS) algorithms. We present a comparative analysis of these methods, demonstrating that DFS and IDS provide significant improvements in computational efficiency, particularly beneficial for IoT devices with limited processing capabilities. Our findings are substantiated through a series of experiments on a Tangle snapshot, highlighting the enhanced performance and reduced resource utilization of the proposed methods. This study contributes to the ongoing development of DAG-based distributed ledgers, offering insights into more efficient algorithmic solutions for large-scale, decentralized networks.
991013340354003412 HKUST Electronic Theses Defect detection for smart contracts and blockchain applications by Wuqi Zhang thesis 2024 1 online resource (xix, 129 pages) : illustrations Blockchain technology has attracted significant interest with the advent of Turing-complete smart contracts, which enable…Read more ›
In this research, we analyzed three different models for Ethereum price prediction: a custom Hidden Markov Models (HMM), GHMM Pretrained, Deep Reinforcement Learning and LSTM. Our results demonstrate the distinct strengths and weaknesses of every model. Although HMM and HMM Pretrained excel in capturing volatility and short-term price fluctuations, the custom model demonstrates remarkable predictive capabilities for long-term trends. The present study provides significant contributions to the field of cryptocurrency price prediction, hence assisting traders, investors, and scholars in maneuvering through the complex Ethereum market.
In the rapidly advancing domain of smart manufacturing, securing data integrity and preventing unauthorized access are critical challenges. This study introduces a novel approach that synergizes anomaly detection techniques with Zero-Knowledge Proofs (ZKPs) to fortify the security framework of smart manufacturing systems. Our methodology employs a combination of data preprocessing, including statistical imputation and data smoothing, alongside advanced anomaly detection using classification methods and neural networks, particularly focusing on deep learning architectures. The detected anomalies undergo verification through zk-SNARKs, a specialized ZKP scheme, ensuring a robust validation process without compromising data confidentiality. Our findings reveal a notable enhancement in the accuracy of anomaly detection, achieving detection rates of approximately 95% for temperature fluctuations and 90% for pressure irregularities, with a significant reduction in false positives. This performance is markedly superior to traditional methods and aligns closely with the highest efficacy rates reported in contemporary studies. Moreover, the utilization of ZKPs for anomaly verification demonstrated a 98% success rate, ensuring the secure and private verification of anomalies. The integration of anomaly detection with ZKPs presents a significant leap forward in addressing the security vulnerabilities inherent in smart manufacturing. This study not only showcases the effectiveness of our approach in enhancing data security and integrity but also sets a benchmark for future research in creating more resilient and trustworthy industrial operations.
Non-fungible tokens (NFTs) have recently gained widespread popularity as an alternative investment. However, the lack of assessment criteria has caused intense volatility in NFT marketplaces. Identifying attributes impacting the market performance of NFT collectibles is crucial but challenging due to the massive amount of heterogeneous and multi-modal data in NFT transactions, e.g., social media texts, numerical trading data, and images. To address this challenge, we introduce an interactive dual-centric visual analytics system, NFTeller, to facilitate users’ analysis. First, we collaborate with five domain experts to distill static and dynamic impact attributes and collect relevant data. Next, we derive six analysis tasks and develop NFTeller to present the evolution of NFT transactions and correlate NFTs’ market performance with impact attributes. Notably, we create an augmented chord diagram with a radial stacked bar chart to explore intersections between NFT collection projects and whale accounts. Finally, we conduct three case studies and interview domain experts to evaluate the effectiveness and usability of this system. As such, we gain in-depth insights into assessing NFT collectibles and detecting opportune moments for investment.
Decentralized Finance (DeFi) is revolutionizing the traditional financial system by offering trustless financial transactions and services worldwide through blockchain technology.Yield farming is a well-known DeFi application enabling users to earn passive income in cryptocurrency through providing liquidity to decentralized protocols.This research paper focuses on yield farming, its working mechanisms, the benefits and risks involved, the various strategies deployed, and its future in the DeFi ecosystem.
Decentralized machine learning (DML) is a new paradigm in artificial intelligence (AI) that combines the power of distributed computing and blockchain technology to enable secure and privacy-preserving machine learning.In DML, multiple devices or nodes collaborate to train a machine learning model without sharing their data, thereby enhancing data privacy and security.This research paper provides a comprehensive review of recent developments in DML on the blockchain, including its applications, challenges, and potential solutions.The paper analyzes relevant literature and case studies to highlight the advantages and limitations of DML on the blockchain.The study looks at the many consensus techniques used in DML and how they affect system performance, including proof-of-work, proof-of-stake, and proof-of-authority.The function of smart contracts in DML and how they might improve the system's security and transparency are also discussed in the paper.The paper also covers DML on the blockchain's difficulties and potential solutions, including scalability, interoperability, and privacy issues.According to the study's results, DML on the blockchain has the power to change the AI industry by providing safe and private machine learning.To address the technological and nontechnical problems, however, it also requires additional research and development.To fully realize the potential of DML on the blockchain, the study emphasizes the necessity of a coordinated effort by researchers, developers, and policymakers.
Real-time and vision-based quality control for industrial processes has drawn great interest from both scientists and practitioners, particularly following the transition to Zero Defect Manufacturing (ZDM) and Industry 4.0. Despite considerable progress, most ZDM approaches focus on the accuracy of the inspection process, often neglecting critical factors for application in the shop floor. On one hand, near real-time methods are needed for early defect detection and containment. On the other hand, data scarcity is an issue causing AI methods to overfit. Another concern is the accountability of AI results, since even if an AI pipeline is successfully deployed, its predictions are not verifiable in the long term. In this work, we explore a real-time solution based on lightweight Deep Residual Networks and Blockchain technology to address these issues. Concretely, we propose a two-phase training strategy to boost the performance of baseline classifiers while maintaining low inference times. The performance of the proposed methodology is presented in two different industrial use cases with strict timing requirements, one concerning battery assembly line and the other antenna manufacturing. We validate the proposed method for defect detection and compare the results with common training strategies demonstrating an improvement of 3% and 10% in F1-score and accuracy on the two cases respectively, while lowering inference time by 2.2× compared to existing light architectures. Contributing to the accountability of AI results, we present an IoT framework using Blockchain deployed in Private Ethereum.
<p>In order to use the fabric blockchain to store ERC 20 tokens, each one must be issued by the owner of the asset. In the case of Fabric, the owner issues the tokens in an "unspent" state on the ledger. The owner can then open a channel on the blockchain to allow others to receive the tokens. Once the token is transferred to a recipient, it is considered "spent" and removed from the channel. When the user redeems the token, it is stored on the Hyperledger Fabric blockchain ledger. This ensures that the token will not be transferred to another owner after the end of the term.</p>\n\n<p>The infrastructure component of Hyperledger Fabric is built on the Ethereum blockchain. It has the same benefits as Ethereum, but it is not as widely adopted as Ethereum. The ERC20 standard has been around for a while, so it can be used to store ERC 20 tokens. The fabric is modular and supports customization to suit different requirements. The use of Hyperledger Fabric 2.0 as a token platform means that the asset can be stored on the blockchain and used by the users.</p>\n\n<p>Unlike Ethereum, Hyperledger is a permissioned network where participants are known and have known identities. Companies with strict data protection laws and regulations will likely be comfortable with this kind of system. However, the network doesn't offer its own token system, so if a company wants to transfer funds between accounts, it should use Ethereum. This is also the case for companies that want to implement a decentralized payment system, such as an ICO.</p>\n\n<p>Aside from its scalability and security, Hyperledger Fabric has many advantages. In the case of ERC 20 tokens, Fabric allows the developers to code "chaincodes", which are smart contracts in Hyperledger parlance. These "chaincodes" are a type of cryptographic data hash. The Fabric consensus algorithm mirrors the workflow of organizations. It also offers secure access to data and can be used to create and execute smart contracts.</p>\n\n<p>As a result of this robust security, EVM chaincode can interact with Ethereum smart contracts and Fabric. By downloading the GitHub repository, developers can customize the chaincode to map to a folder of their choice. The instructions are very detailed. Once you have the appropriate software installed, you can build the multi-container apps and test them. Additionally, you can use "Go" language for Ethereum smart contracts on Fabric.</p>\n\n<p>Tokens on Ethereum are stored on the Ethereum network, which requires a transaction fee of $400. This fee varies depending on the size of the smart contract. However, this fee can be waived when generating tokens for customers. It's also possible to purchase a service that generates tokens for customers. You'll want to take the time to learn all you can about <a href="https://cryptominnie.com/">Crypto Minnie</a> before deciding. For $20, you can get the service of CoinManufactory and create your own custom ERC 20 tokens.</p>\n\n<p>Tokenization is a significant part of blockchain networks, and it is the process of converting physical assets into digital ones. Once digital, tokens are easily exchanged, purchased, or redeemed for other digital assets. Moreover, they can be categorized as either fungible or non-fungible assets. The latter has a broader utility than the former. You can make a list of different ERC 20 tokens in a Hyperledger Fabric instance and check out the corresponding information for Hyperledger Fabric.</p>\n\n<p> Overledger is a protocol that facilitates secure access to all DLTs. It configures each DLT to accept payments and send cryptocurrency to recipients. Then it provides a secure interface for the recipients to send and receive cryptocurrency. This is a highly effective way to manage distributed assets and tokens. Ultimately, it allows an organization to secure a blockchain while enabling a wide range of applications.</p>
This paper presents a complete solution consisting of sustainable IoT-based Reliable Industrial Data Services (RIDS) able to manage the huge amount of industrial data coming from cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework guarantees data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis and optimisation. With the i4Q RIDS, factories will be able to handle large amounts of data, achieving adequate levels of data accuracy, precision and traceability, using it for analysis and prediction as well as to optimise the process quality and product quality in manufacturing, leading to an integrated approach to zero-defect manufacturing. The i4Q Solutions efficiently collect the raw industrial data using cost-effective instruments and state-of-the-art communication protocols, guaranteeing data accuracy and precision, reliable traceability and time stamped data integrity through distributed ledger technology and provide simulation and optimisation tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency and optimal manufacturing quality.
Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system's quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system's quality control approach.
In the Architecture, Engineering, construction and Operations (AEcO) there is a growing interest in the use of the building Information modelling (bIm). Through integration of information and processes in a digital model, bIm can optimise resources along the lifecycle of a physical asset. Despite the potential savings are much higher in the operational phase, bIm is nowadays mostly used in design and construction stages and there are still many barriers hindering its implementation in Facility management (Fm). Its scarce integration with live data, i.e. data that changes at high frequency, can be considered one of its major limitations in Fm. The aim of this research is to overcome this limit and prove that buildings or infrastructures operations can benefit from a digital model updated with live data. The scope of the research concerns the optimisation of Fm operations. The optimisation of operations can be further enhanced by the use of maintenance smart contracts allowing a better integration between users' behaviour and maintenance implementation. In this case study research, the Image recognition (Imr), a type of Artificial Intelligence (AI), has been used to detect users' movements in an office building, providing real time occupancy data. This data has been stored in a bIm model, employed as single reliable source of information for Fm. This integration can enhance maintenance management contracts if the bIm model is coupled with a smart contract. Far from being a comprehensive case study, this research demonstrates how the transition from bIm to the Asset Information model (AIm) and, finally, to the Digital Twin (i.e. a near-real-time digital clone of a physical asset, of its conditions and processes) is desirable because of the outstanding benefits that have already been measured in other industrial sectors by applying the principles of Industry 4.0.
Supply chain traceability is one of the most promising use cases to benefit from characteristics of blockchain, such as decentralization, immutability and transparency, not required to build prior trust relationships among entities. A plethora of supply chain traceability solutions based on blockchain has been proposed recently. However, current systems are limited to tracing simple goods that have not been part of the manufacturing process. We recommend a method that allows for the traceability of manufactured goods, including their components. Products are represented using non-fungible digital tokens that are created on a blockchain for each batch of manufactured products. To create a link between a product and the components that are needed to produce it, we propose “token recipes” that define the amount of tokenized goods required for minting a new token. As input tokens are automatically and transparently consumed when creating a product token, the physical process of producing a new item out of existing components is projected onto the ledger. This ultimately leads to the complete traceability of goods, including the origin of inputs. Evaluating the performance of the system, we show that a prototypical implementation for the Ethereum Virtual Machine (EVM) scales linearly with the amount of the input and goods tracked.