In the tourism industry, big data has emerged as a revolutionizing the way businesses operate and travelers experience destinations. With the vast amount of data generated from online bookings, social media interactions, and mobile applications, tourism companies can gain valuable insights into traveler preferences, behavior patterns, and market trends. This paper proposes a development strategy for the tourism industry in the post-epidemic situation, leveraging network big data analysis with Multi-Factor Hashing Ethereum Classification (MFH-EC). By harnessing the power of network big data, this strategy aims to provide insights into changing traveler preferences, market dynamics, and risk factors in the wake of the pandemic. The MFH-EC model facilitates the classification and analysis of diverse factors influencing tourism development, including economic indicators, health and safety measures, environmental conditions, and traveler sentiment. Through simulated experiments and empirical validations, the effectiveness of the proposed strategy is assessed, demonstrating significant improvements in predictive accuracy and decision-making capabilities. For instance, the MFH-EC model achieved an 80% accuracy rate in predicting tourism demand shifts and a 30% reduction in forecasting errors compared to traditional methods. These results underscore the potential of network big data analysis with MFH-EC in guiding strategic decision-making and fostering sustainable recovery and growth in the tourism industry post-epidemic.
This research aims to develop a deployable blockchain smart contract and oracle model for automated and transparent customs compliance checking in cross-border construction logistics. A scene analysis is conducted to extract requirements, and then a blockchain-enabled model is prototyped and tested in two case studies. We find that the prototype is feasible, and the performance is satisfactory in terms of compliance checking time (t = 1523–1530 s), accuracy (a = 55.9% to 100%), private data collection (p = 100%), and information transparency (l = 0.787–1). The innovation of this research is to combine blockchain smart contracts and oracles to achieve automation and transparency in cross-border construction logistics.
In recent years, with the continuous development and growth of the Non-Fungible Token market, it is of great significance to conduct clustering research on its users in order to improve marketing efficiency and enhance customer service. In the field of user clustering research, the classic unsupervised algorithm K-Means has been widely used. However, the K-Means algorithm has some deficiencies, such as need to pre-select the value of K, lack utilization of prior knowledge, limited interpretability of clustering results. Therefore, this paper proposes an improved K-Means algorithm named PKK-Means. Firstly, the algorithm uses the key users identified by prior knowledge to analyze the distribution of key users and other users in each feature item, thereby obtaining the initial cluster centers and the initial number of K. Then, based on the attribute items of the user sample distribution matrix, the algorithm calculates the sum of squared errors within clusters after K-partitioning, and adjusts the similarity measurement of users accordingly. Finally, this paper improves the user value measurement model RFM named RFMCO by introducing the digital currency market value and the on-chain index. Experiments show that compared to traditional algorithms, the K-Means algorithm based on prior knowledge does not require the pre-selection of K value and achieves significant improvement in clustering effectiveness, especially when the dataset is large, the interpretability of the algorithm is significantly improved.
Abstract Smart devices are widely used in every application area and they serve specific application purposes. While some devices have their own large memory and processing unit, others are limited to data collection without processing capabilities. The land registry, one of the oldest processes for economic growth and governance, traditionally involves mapping the land in the field and collecting data on-site. Despite the shift to online processes, measurement and verification still rely on the traditional system. Blockchain technology, known for its transparency, immutability, speed, security, and decentralized storage, processes data on an immutable distributed ledger. Each node in the network maintains an updated copy of the ledger after each block addition. In the context of land registry, blockchain records every detail while addressing security, privacy, and smart network challenges. In the proposed system, a geolocation-based land registry using blockchain technology is utilized to enhance security, transparency, privacy, and speed compared to existing blockchain-based land registry systems. The proposed scheme follows a three-step registry process. The first step involves handling previous owner records, while the second step focuses on creating new records using smart devices and blockchain. The third step includes record verification and finalization of the registry process with the new owner. This process ensures personal data minimization, secure transactions, and payment of registry fees using blockchain technology. Smart devices play a crucial role in the proposed scheme by verifying the correct land, taking land measurements, and directly recording entries on the blockchain. Based on geolocations and areas, all required parameters for the registry are calculated. The implementation of the proposed system can prevent fraud and document forgeries in the land registry. A security analysis is conducted, and the system is implemented using multichain blockchain. Smart filters facilitate communication between sellers and buyers. Overall, the proposed system offers a hassle-free, geolocation-based, privacy-preserving land registry using blockchain technology.
A smart factory is an advanced manufacturing system that utilizes various cutting-edge technologies such as IIoT (Industrial Internet of Things), big data, AI(Artificial Intelligence), blockchain to automate and optimize production processes. While there is a growing demand for smart factories in recent times, the adoption and proliferation of these facilities have been delayed due to concerns about security, the reliability of collected information, and challenges in management and control. Moreover, traditional centralized smart factory systems pose a risk of operational downtime in the event of failures or attacks since a central server controls the entire system. Therefore, we propose the design of a secure, transparent, and reliable blockchain-based IIoT framework for smart factories. The framework consists of three layers: the blockchain core layer, the blockchain operation layer, and the IIoT service layer. The IIoT service layer plays a crucial role in providing various services essential for the advancement of smart factories, utilizing blockchain technology. Our proposed framework combines IIoT and blockchain technologies to leverage the advantages of decentralization, trust, security, transparency, data management, and traceability.
Exploring mechanisms for internal data sharing within government departments is important in advancing digital and intelligent society. This paper is based on the establishment of decentralized nodes on the external network of government departments, constructing a decentralized node, and establishing a government internal data sharing model based on blockchain. Subsequently, integrating attribute fields from government data into the shared model, accompanied by the formulation of data-sharing rules through smart contracts, serves to streamline the implementation of efficient and secure cross-validation mechanisms across diverse departments. Finally, this article concludes by conducting a model performance testing experiment, evaluating the model from three perspectives: storage cost, blockchain performance, and security analysis. The test results show that our model enhances the efficiency of querying and retrieving data within the government's internal data-sharing system, effectively addressing challenges such as low efficiency, high costs, and issues related to the security and real-time aspects of data sharing within the government. Overall, our article provides a new way of thinking about government data sharing.
Latifa Albshaier, Seetah Almarri, M. M. Hafizur Rahman
The Internet’s expansion has changed how the services accessed and businesses operate. Blockchain is an innovative technology that emerged after the rise of the Internet. In addition, it maintains transactions on encrypted databases that are distributed among many computer networks, much like digital ledgers for online transactions. This technology has the potential to establish a decentralized marketplace for Internet retailers. Sensitive information, like customer data and financial statements, should be routinely transferred via e-commerce. As a result, the system becomes a prime target for cybercriminals seeking illegal access to data. As e-commerce increases, so does the frequency of hacker attacks that raise concerns about the safety of e-commerce platforms’ databases. Owing to the sensitivity of customer data, employee records, and customer records, organizations must ensure their protection. A data breach not only affects an enterprise’s financial performance but also erodes clients’ confidence in the platform. Currently, e-commerce businesses face numerous challenges, including the security of the e-commerce system, transparency and trust in its effectiveness. A solution to these issues is the application of blockchain technology in the e-commerce industry. Blockchain technology simplifies fraud detection and investigation by recording transactions and accompanying data. Blockchain technology enables transaction tracking by creating a detailed record of all the related data, which can assist in identifying and preventing fraud in the future. Using blockchain cryptocurrency will record the sender’s address, recipient’s address, amount transferred, and timestamp, which creates an immutable and transparent ledger of all transaction data.
With the rapid development of the digital economy, the application of blockchain technology in the field of e-commerce finance is becoming increasingly widespread, but its potential risks are also increasing. This article aims to use blockchain algorithms to conduct risk assessment on the development of e-commerce finance. Firstly, build a smart contract based on Ethereum to monitor abnormal behaviour in real-time during the transaction process; Secondly, utilising the pluggable consensus mechanism of the super ledger, evaluate the efficiency and security of different consensus algorithms in processing transactions, and analyse their impact on compliance risks. The found risk components are quantitatively investigated to build a risk assessment model by using real instances and merging fuzzy comprehensive evaluation approach. The findings of the research show that the suggested approach can support pertinent judgments, clearly identify and measure any hazards in e-commerce finance, and encourage its sustainable development.
With the popularity of blockchain technology, decentralized applications (Dapp) have gradually become the focus of attention. The integration of smart contracts and front-end technologies is of great significance in Dapp development. This paper first introduces the development background of Dapp and the basic concept of smart contracts, and then discusses several ways for the integration of smart contracts and front-end technologies, including the direct use of smart contracts as the back end, the use of smart contracts to generate API, and the integration of smart contracts with the front-end framework. In addition, the article also analyzes the advantages of this fusion, such as reducing development costs, improving development efficiency, enhancing data security and user privacy protection. Finally, this paper looks forward to the future integration trend of smart contract and front-end technologies, including cross-chain technology, scalability solutions, and richer application scenarios.
Supply chain management involves multiple participants and links.Smart contract technology improves traceability, transparency and efficiency of supply chain through programmable and automated contract execution mechanism.By solving the security and privacy protection problems of smart contract technology, this paper puts forward the corresponding solutions.The results show that smart contract technology provides enterprises with more efficient, reliable, and secure supply chain management solutions that enhance competitive advantage and performance.The results of this paper will provide a useful reference for researchers and enterprise managers, and inspire the future development of smart contract technology in supply chain management 3 .
As an emerging interest in beauty and fashion, it is clear that many of the brands have been considering adopting Artificial Intelligence to promote their brands by utilizing different technologies.Consumers are provided with more options to interact with brands, products, and services as being a member in the online and offline community.This paper aims to study the Artificial Intelligence (AI) applications and its challenges in the beauty and fashion industry.From virtual try-on to generative AI, to Non-Fungible Token (NFT) collections, brands execute different technologies to engage with their clients.Clearly, it is a trend that more and more brands will take into consideration to promote their brands in the future.However, even the AI applications has been utilized but it is not fully matured in other aspects, such as data privacy, regulations and ethical concerns.Therefore, more actions are needed to implement AI-powered technology in real practice.
Digital transformation is increasingly seen as a dynamic and enduring trend, offering unprecedented opportunities for growth and vitality within the sports industry. It brings new dimensions to engaging fans, sponsors, sports organizers, and enterprises by enhancing experiences in a participative sports paradigm. This research explores how digital transformation can revolutionize the sports sector, focusing on successful cases from developed countries such as Australia and Japan. These examples shed light on how digital tools like Artificial Intelligence (AI) and Non-Fungible Tokens (NFTs) are leveraged to enhance fan engagement, operational efficiency, and new revenue streams. By drawing insights from these global leaders, this study aims to identify both the opportunities and challenges faced by China’s sports market, which currently stands at a critical juncture of digital reform and policy modernization. The goal is to unlock the potential of China’s sports market, invigorate its landscape, and introduce greater flexibility in governance through digital transformation. The research objectives are twofold: firstly, to review successful cases of digital transformation in developed countries and the current challenges China faces in adopting these digital innovations, and secondly, to optimize and stimulate market vitality, fostering new paradigms within China’s sports industry. This study aims to offer actionable insights and strategies to drive China’s sports industry toward a more dynamic and sustainable future.
In the context of the complex and changing international situation and the uncertain global political and economic situation, cross-border trade faces many new challenges. In the form of "barter", cross-border barter avoids the credit creation needs of financial intermediaries, but puts forward higher requirements for the information coordination and resource integration of each subject in the transaction chain. The construction of a new type of cross-border barter biological system emphasizes the cooperation and interaction among the participants, and effectively integrates the barter link by means of financial technology, which is of great significance to promote the development of new cross-border barter trade. In this context, this paper proposes an ecosystem framework combining fintech and cross-border barter, and deeply studies the settlement model based on cross-border barter. The results show that the new cross-border barter ecosystem consists of platform leaders, barter trading entities, logistics warehousing supporters and environmental parasitism groups. The application of fintech can promote the collaboration of all parties in the ecosystem and realize the complementary advantages of system members. Through evolutionary game analysis, this paper finds that promoting cooperation among various groups, optimizing incentive policies of leaders and establishing fair profit distribution mechanism are conducive to the stable development of cross-border barter ecosystem. This paper designs a blockchain-based cross-border barter settlement model by referring to the Trade Swap Support Instrument (INSTEX) and the Bank of Kunlun's settlement model for Iran. Blockchain technology is used to achieve property rights allocation and value transfer of barter assets, and the barter settlement model of cross-border circulation of goods and domestic settlement of funds is realized through the "closed loop" of debt repayment, further exploring the deep-level settlement problems of cross-border barter, and providing theoretical support and practical guidance for the development of new cross-border barter trade.
Jan 1, 2024·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Ola Henfridsson, Robert Wayne Gregory, Youngjin Yoo
This minitrack seeks to foster interdisciplinary discussions and innovative research that elucidate the evolving landscape of Web3 technologies, digital innovation, and organizational transformation.This year's minitrack features papers related to decentralized autonomous organizations, collaboration within development communities, resale royalties for digital goods, as well as the impact of grants on user engagement and innovation on blockchain networks.
Sentiment classification for marketing block chain-based digital assets through Twitter data is a pivotal component of crypto currency marketing strategies. Twitter serves as a real-time crucible of opinions, discussions, and news within the crypto space, making it a treasure trove of sentiment insights. Employing advanced natural language processing and machine learning techniques, marketers can discern prevailing sentiment - whether it's bullish, bearish, or neutral - toward their block chain projects. Harnessing positive sentiment can bolster trust, generate enthusiasm, and attract potential investors and users while identifying and addressing negative sentiment enables proactive reputation management. Moreover, tracking sentiment trends over time aids in assessing the impact of marketing efforts, news events, or product launches on the digital asset's perception and market performance, allowing marketing teams to adapt strategies effectively and maintain a favorable brand image in this dynamic and highly competitive landscape. Present research would consider the textual and graphical sentiment of the user for classification. This would help in sales increment or understanding the taste of users. Present research considers the Digital assets of NFTMANIA that are present on the core chain. The market marketplace for the selling of such digital assets is Young Parrot which allows the sale and purchase of such assets. Thus present research would play a significant role in marketing digital assets that are managed on blockchain.
Aiming at the existing problems of insufficient security and poor scalability of existing blockchain data management solutions for the Internet of Vehicles, we use IPFS distributed technology to design a data privacy protection method for the Internet of Vehicles based on zero-knowledge proof. First, we use the Schnorr identity authentication protocol, a classic non-interactive zero-knowledge proof scheme, to complete the authentication and authorization of vehicles by RSU under the premise of protecting vehicle privacy. Secondly, we store encrypted data on IPFS-based edge distributed servers, and use Hyperledger Fabric to store authentication records and data indexes, which solves the problem of insufficient blockchain scalability. Finally, in order to improve the controllability and security of the data, we use the proxy re-encryption mechanism to manage the Symmetric-key of the data, and let RSU act as a proxy to perform re-encryption. Compared with the existing schemes, the proposed scheme reduces the communication overhead of the vehicle identity authentication phase, which is only 1728 bits. And while improving the scalability of the blockchain, it also enhances the privacy protection of the data of the Internet of Vehicles.
With the rapid development of blockchain technology, it is expected to be combined with Web3.0, leading to increased global interest in a new internet system based on blockchain. As the e-commerce market has grown rapidly, the popularity of cryptocurrency and the opportunities for e-commerce utilizing them are also increasing. However, there’s limited direction on integrating cryptocurrencies into e-commerce, with few studies addressing Web3-based e-commerce includes them. Therefore, this paper proposes a Web3-based e-commerce cryptocurrency payment system. We designed and implemented a DApp that can create packs to sell online and offline contents and transact securely through an escrow account implemented through a smart contract. By presenting a Web3-based e-commerce cryptocurrency payment system, it is expected to promote the revitalization and growth of the e-commerce market and create new business opportunities as it can be used in various industries.
In the current task of text classification for smart contracts, the role of labels in the final classification performance is relatively small, and label information has not been better utilized. In order to make effective use of label information, this paper proposes a smart contract classification method based on label embedding and collaborative attention mechanism. This method introduces a collaborative attention mechanism to generate semantic representations of smart contracts related to labels and smart contract semantic vectors. This approach enables the classification model to focus on the relevant parts of both, thereby effectively improving the accuracy of classification.
The issue of quality traceability has been a persistent challenge in the current cotton supply chain, impeding the industry’s development. The lack of transparent and timely information transmission hampers effective regulation of cotton quality, thereby significantly impacting both the quality of cotton products and enterprises’ brand image. To address this problem, this paper proposes an Ethernet blockchain and smart contract-based platform for quality traceability in the cotton supply chain, enabling efficient management with complete transparency. We have developed five smart contracts and eight algorithms, providing comprehensive implementation, testing, and validation details for their integration into the cotton supply chain system. This approach ensures secure and authentic dissemination of quality information throughout the cotton supply chain while mitigating issues related to isolated product information.
Blockchain technology can create a shared platform for English translation and reserve a large number of practical corpus resources, thus improving the quality of machine translation. This paper first introduces the research status of foreign language corpus and blockchain English translation in China. Then it introduces the basic principles of BPNN and particle swarm optimization and constructs the PSO-BP model. Experiments show that the prediction accuracy of BPNN optimized by particle swarm optimization algorithm is greatly improved, the convergence speed is faster, and it will not fall into the local optimal trap. Finally, this paper proposes the implementation path of blockchain in corpus translation application: (1) build “blockchain+ AI” English translation corpus and (2) improve the machine English translation software of the “blockchain+ AI” English translation training platform.
Excelcius Ferdian Roni, Calandra Alencia Haryani, Arnold Aribowo, Aditya Mitra
Since the internet's emergence, numerous facets of daily life have evolved, including online investment opportunities. Cryptocurrency investment has gained popularity in this digital era. Prospective investors now have various cryptocurrencies to choose from, tailored to their preferences. Due to the volatility of cryptocurrency market, investors need to have careful consideration of which cryptocurrency to be chosen including its related decision to be made during their investment. One crucial consideration in this selection process is assessing fellow investors' opinions, often found on social media. This study employs text mining, using the k-means clustering algorithm to explore prevailing sentiments among cryptocurrency investors. The data source consists of Twitter comments on three prominent cryptocurrencies, Bitcoin, Ethereum, and Binance totaling 55,651 tweets. The findings predominantly reveal neutral sentiments towards these cryptocurrencies. Notable positive sentiment topics for Bitcoin include “worth” and “new,” while negative sentiments revolve around “firm” and “bank,” and neutral sentiments are linked to “digital” and “year.” Ethereum exhibits positive sentiments like “good” and “defi,” negative sentiments such as “time” and “long,” and neutral sentiments around “price” and “ethic”. Binance's positive sentiments include “live” and “kind,” a negative sentiment related to “case,” and neutral sentiments encompassing “learn” and “check”. Moreover, the Davies Bouldin Index evaluation for the coin clusters yielded scores of 0.7996 for Bitcoin, 0.7820 for Ethereum, and 0.7149 for Binance. These indices, falling between 0 and 1, signify well-structured clustering.
Information Retrieval and Data Mining
E-commerce and Technology Innovations
Advanced Steganography and Watermarking Techniques