Reviewerly/exHarmony huggingface dataset:
Incentive Mechanism for Throughput Enhancement in Blockchain-based Energy Trading System

In a blockchain-based energy trading system, prosumers engage in energy trading and simultaneously participate in blockchain mining to process trading transactions. However, as blockchain mining consumes energy, prosumers may lack incentives to participate, resulting in a low blockchain throughput and negatively affecting the energy trading market. To address this issue, we propose a Two-Piece Linear Compensation (TPLC) mechanism to incentivize sufficient participation for blockchain mining. We model the mechanism design as a two-stage game, wherein the system designer proposes the mechanism in Stage I, and prosumers decide on their energy allocation strategies in Stage II. We analyze the complex and coupled interactions of prosumers and uncover the reason for the low throughput issue: a self-interested prosumer neglects the positive externality of energy allocation to the blockchain, as it does not benefit him. Despite the diverse objectives of prosumers and the large search space of the mechanism, we demonstrate that the distributed TPLC mechanism achieves the maximum social welfare of a centralized decision benchmark. The numerical analysis compares the TPLC mechanism with linear compensation and no compensation benchmarks. Our results show that the TPLC mechanism achieves the highest social welfare and average payoff for prosumers. Additionally, the TPLC mechanism supports a large energy market without sacrificing the user experience.
A Game Theoretic Approach for Data Asset Protection in Metaverse

The metaverse maintains a virtual space in which users can interact through their avatars. To facilitate the virtual activities, users could bring their real-world data to the virtual world, which constitutes their data assets; users (or avatars) therefore can purchase data from others to feed their metaverse applications, as well as sell their data assets to make profits in the metaverse. The sharing of data assets would proliferate the metaverse applications (e.g., AI training) and enrich the user experience, which however would also disclose the user privacy if inappropriately used. In this paper, we study the data asset protection in the metaverse to prohibit dishonest data asset distributions. Specifically, to make a profit, a user may purchase data assets and then resell the data to others; the resale behaviours violate the agreement of data asset trade and harm the original data owner. To prevent this behaviour, in this work, a user is dictated to deposit a certain amount of credits to a third authority when purchasing data. The deposit would be released and returned to the purchaser if no dishonest behaviours are observed; otherwise, the deposit would be paid to the data owner to compensate for its loss. This deposit amount should be carefully balanced as overwhelming deposits would increase the burden of data asset purchase, whereas a trivial amount would not be able to prohibit dishonest behaviours. We model the problem from a game theoretic perspective and develop a distributed solution to strike a balance. Using analysis and simulations, we prove the existence of the Nash Equilibrium, and show that our scheme can effectively reduce the risk of dishonest data asset distribution.
Efficient Kernel Design of Support Vector Machine for IoT Networks

A high-level goal of 6G cellular networks will be achieved by enhancing the current 5G technologies as well as adopting new techniques such as artificial intelligent (AI). The AI will be a new blood to improve 6G cellular networks. The AI techniques enable us to find the hidden patterns from cellular network data and optimize cellular networks. The Internet-of-Things (IoT) will be a key application of 6G systems and play an important role in 6G ecosystem by creating many new business models. As 6G systems have a higher requirement of networks, the IoT networks in 6G should be designed more efficiently. We expect to improve the performance of IoT networks significantly by adopting AI techniques. The support vector machines (SVMs) as one of key machine learning techniques solved many real-world problems in many different areas such as face recognition, imagine processing, and so on. Since the kernel method as a key component of SVMs is computationally cheaper than the explicit computation, it is widely used when implementing SVMs. Selection of a kernel function directly affects to the performance of SVMs. In this paper, we investigate kernel function design and propose a new design method using a linear combination and K-fold cross validation. We show that the proposed scheme reduces approximately 30% misclassification error rather than conventional single kernel.
Blockchain’s Role in Metaverse Trust and Transactions

The metaverse is a new technology which promises to enhance human-to-computer interaction and achieve an immersive experience in computing and communication. In parallel, blockchain is viewed by many as a pillar of trust in creating a secure and decentralized metaverse. In this paper, we first articulate the functionalities required by a simple metaverse use case, and then examine the challenges an strengths of a blockchain-based implementation in providing those functions. Our analysis shows that blockchain-based solutions may require centralized functions in name lookup, lacks assurance for name uniqueness and accessible data authentication as of now, and faces scalability challenges due to the high demand on computing and storage resources.
Utilizing Latent Codes for Minting AI-Generated Digital Assets into NFTs

The use of Artificial Intelligence (AI) generators to create digital artwork as the content of Non-Fungible Tokens (NFTs) is prevalent. Typically, when minting AI-generated digital artwork into NFTs, the data of digital artwork is stored in the cloud or decentralized storage system, and a Uniform Resource Identifier (URI) or Content Identifier (CID) of the data is stored in the smart contract of NFTs to access the data. This makes AI art NFTs suffer from potential asset loss as conventional NFTs. Can AI be utilized to enhance the availability of AI-generated digital assets as NFT content? In this paper, we propose a new method for minting AI-generated digital assets into NFTs. The key idea of our approach is to store the latent codes of the generated assets on the blockchain instead of URI or CID in conventional NFTs. Here, the latent codes are intermediate variables in the process of generating digital assets by the generator and could restore the assets through the generator. Meanwhile, to be able to restore assets, the universal generator is stored on a distributed system, and its high popularity guarantees its availability. Experiments demonstrate the feasibility of our method. In addition, the integrity and the availability of assets minted by the proposed method and the existing ones are discussed, concluding that our approach has better availability while safeguarding integrity.
Fast and Atomic Cross-blockchain Asset Exchange for Metaverse Interoperability

This work concerns metaverse interoperability.It is a hot topic because more than 240 metaverses were launched in the past years but are merely interoperable.Existing work only focused on particular issues, e.g., asset exchange and object visualization in different metaverses, but neglected the overall picture.Moreover, the existing asset exchange protocols are time-consuming and can hardly afford high-frequency metaverse transactions.To address the issues, we first introduce a layered metaverse interoperability framework that comprehensively considers interoperable cyber worlds, compatible interaction mechanisms, and consistent physical infrastructures.Furthermore, we propose a novel cross-blockchain asset exchange protocol based on key exchange and smart contracts.The proposed protocol is secure and reduces the time overhead from linear to constant.
A Novel Metaverse-as-a-Service Architecture from an Operator View

In this research paper, the authors present a comprehensive overview of three key elements of Metaverse-as-a-service (MaaS) platforms: privacy and security, edge computing, and blockchain technology. The study begins by examining security concerns for wireless access to the Metaverse. It then delves into privacy and security issues within the Metaverse from data-centric, learning-centric, and human-centric perspectives. The authors propose novel and less-explored methods to aid mobile network operators and Metaverse service providers in creating secure and private MaaS platforms across various layers of the Metaverse, from access to social interactions. The article also explores the potential of edge computing to enhance various aspects of the Metaverse and the challenges associated with its implementation. Furthermore, the study examines the 10 main challenges of MaaS platforms and how blockchain technology can address them. Finally, the authors present future visions and recommendations, such as content-centric security and zero-trust Metaverse, as well as unresolved challenges in blockchain technology to provide insights for network designers in the Metaverse era.
A New Training Method for Photovoltaic Forecasting Based on ELM Auto-Encoder and Sparrow Search Algorithm

Forecasting technology is an effective way to mitigate the randomness and volatility of solar energy in the modern power system, considering the high proportion of solar energy in the power system and high degree of electricity electronic characteristics, To enhance the accuracy of photovoltaic prediction model, a two-stage training method is proposed based on extreme learning machine (ELM) and sparrow search algorithm (SSA). In the first stage, ELM combined with auto-encoder (AE) is utilized to estimate the weight matrix between the connection layers. In the second stage, the optimization of parameters is performed by using the SSA. Case studies show that the two-stage model delivers highly accurate predictions. After subjecting the SSA-ELM-AE model to the two-stage optimization process, significant improvement has been observed in the prediction results.
Visual Data Compression for Metaverse: Technology, Standard, and Challenges

There has been an increasing consensus that the emerging metaverse world relies on high efficiency visual data compression techniques for effective data storage, ultra-low delay interaction and ubiquitous communication. This paper provides a comprehensive survey on the recent advances of the visual data compression technology and standard, which could play essential roles in building the metaverse. In particular, we will review the visual data compression methodologies and standards, and present our vision on how the interactive coding could more efficiently support the metaverse. Finally, we discuss the distinguished requirements and fundamental challenges on the data compression for metaverse, and envision the future technology trend.
Revolutionizing Virtual Shopping Experiences: A Blockchain-Based Metaverse UAV Delivery Solution

The rapid advancements in technology have led to the emergence of innovative concepts like Unmanned Aerial Vehicle (UAV) delivery systems, the Metaverse, and Blockchain. As these technologies gain traction, their integration is expected to revolutionize various industries. In this paper, we propose a Blockchain-based Metaverse UAV delivery solution and a secure consensus protocol for UAV deliveries in Metaverse. Also, we conduct throughput tests using Ethereum permissioned chain. The results are promising, demonstrating that drone delivery will be an important element in future Metaverse shopping experiences. This research investigates the convergence of UAV delivery systems, the Metaverse, and Blockchain, and their implications for the future of virtual shopping experiences. The study investigates the opportunities and challenges arising from the integration of these technologies and assesses their potential synergies through the literature review and real-world case studies. Also, the experimental results demonstrate the effectiveness of our proposed architecture and consensus algorithm for drone deliveries, highlighting its ability to handle increasing network loads and maintain optimal performance.
Trial of Risk Assessment for Business Application of Metaverse

The metaverse has gained popularity in recent years, being utilized in gaming and businesses due to its many benefits. However, security risks have become a major concern, making appropriate risk assessment essential. While the authors have previously conducted risk assessments for gaming using the metaverse, applying conventional risk assessment methods to metaverse business applications, which require higher security and safety, has proven difficult. Therefore, the authors developed the SVR (System - Virtual - Real) risk factor extraction method, which not only visualizes system processing and data flows but also visualizes the state of the virtual world and the surrounding environment in the real world as a diagram, plotting risk factors in parallel. Results from applying the method to VR meetings have shown that it is effective, extracting four times more risk factors than conventional methods and ten times more if focused on metaverse or VR-specific factors.
A Distributed Asset Trading Mechanism Based on Automated Negotiation

Asset digitization is an important part of digital society. However, there are many problems in existing centralized asset trading platforms, for example, the platform has the dominant power in the transaction process and holds a large amount of user data, which is detrimental to users’ interests. To solve these problems, we propose a distributed asset trading mechanism based on automated negotiation. Firstly, we propose a distributed asset trading framework based on the consortium blockchain, which provides trust guarantees for trading through the access control of the consortium blockchain, represents the ownership of assets through non-fungible tokens (NFT), and reduces the blockchain burden by moving the negotiation process off the blockchain through the state channel technology. Then, to reduce the negotiation cost and improve the negotiation results, we propose a bilateral automated multi-issue negotiation model for asset trading. The experimental results show that the negotiation model performs well in terms of success rate and negotiation time, and can achieve win-win agreements.
Avatar Fusion Karaoke: Research and development on multi-user music play VR experience in the metaverse

This paper contributes to building a standard process of research and development (R&D) for new user experiences (UX) in metaverse services. We tested this R&D process on a new UX proof of concept (PoC) for Meta Quest head-mounted display (HMDs) consisting of a school-life karaoke experience with the hypothesis that it is possible to design the avatars with only the necessary functions and rendering costs. The school life metaverse is a relevant subject for discovering issues and problems in this type of simultaneous connection. To qualitatively evaluate the potential of a multi-person metaverse experience, this study investigated subjects where each avatar requires expressive skills. While avatar play experiences feature artistic expressions, such as dancing, playing musical instruments, and drawing, and these can be used to evaluate their operability and expressive capabilities qualitatively, the Quest’s tracking capabilities are insufficient for full-body performance and graphical art expression. Considering such hardware limitations, this study evaluated the Quest, focusing primarily on UX simplicity using AI Fusion techniques and expressiveness in instrumental scenes played by approximately four avatars. To achieve our PoC, we first researched the limitations and challenges of such applications. In Meta Quest 1 and Meta Quest 2, the benchmark for simultaneous avatar rendering was 10 for Quest 1 and 22 for the Quest 2. The appropriate interpersonal distance (IPD) should be designed with culture in mind. We assume that various avatars communicate at a maximum distance of 1–2 meters while generating collisions for individual avatars and avoiding overlap. Considering the interference in stereophonic sound, we consider that 5–11 avatars can be displayed simultaneously while maintaining quality communication. Through our standard R&D process, we assessed the production needs for our PoC and obtained multiple ways to retrieve feedback. A notable effect was observed when other users entered the same session. When users waved and called out to one another (saluted), their participation time increased by 3 minutes (i.e., they played more than one piece of music). Most users did not perceive the AI-only piano-playing avatar as human (i.e., they perceived it as a non-player character [NPC] in the game). This research reported methods for multi-user metaverse communication and its supporting technologies, such as head-mounted devices and their graphics performance, special interaction techniques, and complementary tools and the importance of PoC development, its evaluation, and its iterations. The result is remarkable for further research; these expressive technologies in a multi-user context are directly related to the quality of communication within the metaverse and the value of the user-generated content (UGC) produced there.
XVRS: Extended Vulnerability Risk Scoring based on Threat Intelligence

Vulnerability scoring is a powerful tool for managing vulnerabilities and the associated risk. It is used to uncover and assess security vulnerabilities in various systems, networks, and applications. Vulnerability scoring has become an integral part of vulnerability management, which is, in turn, an essential component of any cybersecurity program. By implementing threat intelligence (e.g., vulnerability scoring), security professionals can identify and address potential threats more efficiently. Threat intelligence provides key recommendations to security teams that use a risk-based approach to decide how to respond to incidents. In addition, incorporating a prioritization process—with guidance from a framework—helps professionals make informed decisions about the best mitigation strategies. Moreover, threat intelligence provides real-time context, equipping security teams to respond quickly to new threats as they emerge. Threat intelligence comes in a number of different flavors, each of which leverages different strategies to facilitate vulnerability management and help security teams prioritize vulnerabilities and respond to incidents faster.However, in most cases, vulnerability scores are calculated based on the perceived severity of the vulnerability—but not the estimated risk. This work highlights the importance of vulnerability intelligence (i.e., threat intelligence that focuses on vulnerabilities) and how it can be used to help security professionals make better decisions about mitigating vulnerabilities and the associated risk. In particular, we introduce a new algorithm, dubbed XVRS (Extended Vulnerability Risk Scoring), to calculate vulnerability scores by incorporating the element of risk into the calculation.
Integration of MPC into Besu through an extended private transaction model

In the last few years we have seen many different approaches to incorporate privacy features to blockchains. In the area of cryptocurrencies that would normally mean protecting the identity of the owner of some funds, but there are other applications where privacy is even more important, especially in permissioned blockchains. Permissioned blockchain platforms, such as Hyperledger Besu or Hyperledger Fabric, already include the concept of private transactions, which essentially defines a sub-group of the blockchain where their participants share some private data. We want to go one step ahead and propose an extended model for private transactions where the different participants can have a separated view of the same transaction, allowing the integration of Multi-party Computation protocols in the blockchain. Our work extends Hyperledger Besu’s design for private transactions, offering better security properties and a finer grain customization. We cover two specific MPC examples, Private Set Intersection and Byzantine Fault-Tolerant Random Number Generation, and propose a mechanism to run them using smart contract interfaces.
A Survey on the Security of the Metaverse

With the rise of the metaverse, its security risks are of great concern. We have researched the literature related to metaverse security in recent years and summarised the security risks of metaverse at the technological level. First, we introduce the concept of the metaverse and the role of the five underlying technologies (blockchain, Internet of Things, cloud services, artificial intelligence, and digital twins) in the metaverse. Then, based on the security risks and countermeasures of these underlying technologies themselves, we summarise the security issues faced by the metaverse. Finally, we summarise the opportunities and challenges of metaverse security from the perspectives of the five underlying technologies and the overall perspective of the metaverse.
Production Capacity Negotiation for Blockchain-enabled Collaborative Manufacturing: A Stackelberg Game Approach

Blockchain, as a decentralized but trustworthy network technology, facilitates efficient and secure peer-to-peer collaboration and has promoted decentralization reforms in various industrial fields. Recently, industrial manufacturing calls upon mass personalization through extensive collaborative manufacturing among enterprises. To adapt to the new trend, a blockchain-enabled collaborative manufacturing (BCM) framework is proposed in this work. First, a digital model that describes the industrial production capacities is established, which enables the credible sharing of production capacities over the blockchain network. Second, a production capacity negotiation problem is constructed for manufacturing collaboration with diverse production needs and different timeliness requirements. Third, the dynamic negotiation process is formulated into a two-stage multileader and multi-follower Stackelberg game. The unique existence of Stackelberg equilibrium is proven. The equilibrium point indicating optimal price and quantity of capacity negotiation can be solved by the developed distributed ADMM-based algorithm with low complexity. Finally, a Fabric-based prototype system is implemented to verify the feasibility of the proposed BCM framework. Simulation results show that the proposed distributed ADMM-based production capacity negotiation algorithm guarantees production timeliness and outperforms baseline methods regarding stable social welfare and negotiation fairness.
Blockchain for Decentralized Know Your Customer (KYC) and Customer Due Diligence (CDD) Pipelines in the Metaverse

Transactions conducted in the Metaverse must be validated in an interoperable and decentralized manner in order to prevent money laundering risks. Some processes to ensure that transactions are legal include the Know Your Customer (KYC) and Customer Due Diligence (CDD) processes. However, there are many problems that currently exist with traditionalKYC and CDD pipelines that include lengthy and repetitive processes, as well as the bundling problem that compromises the privacy of customers’ information. As more transactions happen in the Metaverse domain, these problems can be exacerbated. In this paper, we present a system using permissioned blockchain technology, which makes use of public and private smart contracts to efficiently validate the legitimacy of transactions executed in the Metaverse.