Reviewerly/exHarmony huggingface dataset:
Ethereum DeFi Apps in the Wild: Profiling and Implications

Decentralized Finance (DeFi) has been gaining rising importance due to advanced blockchain technology and web3.0. Consequently, many decentralized finance applications (DeFi apps) have emerged, which aim to reconstruct traditional finance and provide financial services with decentralization, transparency, and autonomy. To better understand the characteristics of DeFi apps in this rapidly evolving era, in this paper, we propose to profile DeFi apps based on 666 rigorously classified DeFi apps from a multi-layer perspective, which includes the transaction layer, token layer, and value layer. Our research provides insight into the multiple layers of DeFi apps, including both the breadth and the depth of current transactions, the degree to which apps are interconnected at the level of value flow, and an initial investigation into the relationship between whale trading and market crashes, which better help DeFi app developers with app design, performance evaluation, and optimization.
Study on Micrometeorological On-line Monitoring Method for Transmission and Transformation Power Station

In order to meet the monitoring and warning of medium-small scale disastrous weather in the power grid and solve the problem of inefficient monitoring of transmission line meteorological monitoring devices, this paper proposes the overall architecture and software and hardware construction scheme of the micro-meteorological online monitoring system for transmission and transformation power stations. Focus on the analysis of the system architecture and the functions of each module of the system, study the comprehensive assessment model of flood control risk and the flood control risk prediction model, and use the micro-meteorological monitoring data to assist decision-making in the whole process of flood control. Taking a provincial construction scheme as an example, the effective application of the method is verified.
Inducing Trust in Blockchain-enabled IoT Marketplaces Through Reputation and Dispute Resolution

In order for the metaverse to achieve its full potential, access to data from the physical world is required. The Internet of Things is a potential data provider, but a widely adopted marketplace to enable data monetization and trade with the metaverse does not yet exist. One of the reasons is the level of trust between the participants regarding the integrity of the exchanged data. In this paper, a reputation system is proposed that aims to alleviate this problem by providing a mechanism to ensure the validity of the traded data. Under this system, every seller is assigned a score, which buyers can use later to make informed decisions while ensuring it is economically disadvantageous for illicit actors to manipulate the scores. A dispute resolution scheme is also presented that acts as a fail-safe mechanism to further establish trust in this system, along with a proof-of-concept prototype of the proposed reputation system. Empirical results in this paper show the feasibility of the proposed system and provide insight in its behaviour with respect to its parameters.
Non Fungible Mutable Tokens: dynamic assets traceability for the Metaverse

In this paper we focus on the mutability of Non Fungible Tokens associated to digital assets in the metaverse. We argue that mutability is a key property of digital assets if we want to provide users with a virtual experience in the metaverse as close as possible to the real world. We discuss the shortcomings of current dynamic Non Fungible Token proposals, and present the advantages of a new mutable Non Fungible Token approach based on decoupling the asset ownership and descriptor in separate smart contracts. To show a possible usage of mutable Non Fungible Tokens in a metaverse, we provide a reference example about a university gadget. Finally, we present a proof of concept implementation of our reference example on the Decentraland metaverse and the Goerli Ethereum testnet to showcase its feasibility.
Holographic Multi-Channel QR Code based Copyright Distribution Management System

In this paper, we address the growing challenges arising from the rapid expansion of the global digital content market, fueled by COVID-19-induced digital transformation and increased reliance on online streaming services. While this trend has many positive implications, it has also led to persistent concerns surrounding copyright infringement and unfair distribution of copyright fees, negatively impacting copyright holders. To tackle these issues, we propose a new copyright distribution management system utilizing holographic multi-channel QR codes, which aims to safeguard the rights of copyright holders and ensure a fair distribution of royalties.
CD-PBFT: Incentive-based Efficient Blockchain Consensus Mechanism for Web 3.0

Web 3.0 is the promising technology which enables users to read, write, and own user-generated data. It is attracting attentions from academia and industries to based on Web 3.0 construct infrastructures to provide services for participants. Blockchain is a key technology to implement Web 3.0, which can record data and prevent data mutations in a secure and decentralized way. Since Web 3.0 is an integration of multiple technologies, blockchain is required to frequently interact with them, which challenges the throughput of consensus. An efficient consensus algorithm plays an important role to support Web 3.0 infrastructures and services. However, current researches on consensus algorithms require large-scale nodes to participate and switch view frequently, which causes high communication overheads and long consensus delays. To solve this problem, in this paper, we propose an incentive-based blockchain consensus mechanism for Web 3.0, called CD-PBFT, to improve efficiency and record consensus quality to regulate behaviors of nodes. Since nodes with higher incentives and qualities are selected as the primary nodes frequently to reduce view switching, a cooling mechanism is introduced to prevent these nodes dominating the consensus process. Moreover, a K-medoids-based clustering mechanism is elaborated to form a multi-cluster network to reduce the amount of nodes required for consensus. Simulation results prove that the proposed consensus mechanism can prevent centralization on the primary nodes, and reduce communication overheads and consensus delays.
Joining Edge-Enabled Metaverse Services with Network Externality: A Stackelberg Game Approach

The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking, visual or auditory rendering and virtual scene dynamic change at the edge of networks. Users enjoy the immersive experience provided by the Metaverse Service Provider (MSP), and interact through digital avatars in the virtual world by requesting metaverse services. In this paper, we formulate the edge computing resources pricing, metaverse service rendering bitrate pricing and request problem in the metaverse. Especially, we model the interactions among edge servers, the MSP and Metaverse Service Users (MSUs) as a hierarchical three-stage Stackelberg game. The problem of joining edge-enabled metaverse services is also studied with the consideration of network externality, including the network effect and congestion effect. Based on the backward induction method, we derive the optimal computing resources pricing of edge servers, rendering bitrate pricing of the MSP, and rendering bitrate requests of MSUs. We also prove the existence and uniqueness of the Stackelberg game equilibrium. Simulation results show that the network effect can bring more positive utilities to entities when the social tie coefficient among MSUs is higher. Besides, all entities in the edge-enabled metaverse are affected negatively by the congestion effect.
Development of a Metaverse Platform for Tourism Promotion in Apulia

Metaverse is an engaging way to recreate in a digital environment the real world. It allows people to connect not by just browsing a website, but by using headsets and virtual reality techniques. The metaverse is actually in a rapid development phase, thanks to the advances in different topics. This paper proposes a smart tourism platform in which tourists can interact with guides and different kinds of suppliers, without the need to phisically visit the city they are in. We propose some techniques to scan the real world and transpose it in a metaverse platform, using the recreation of an Italian city, Bari, as a real life scenario.
AR Assembly Navigation with Local 5G to Improve Industrial Production Efficiency

To improve the production efficiency of industrial manufacturing, we proposed AR Assembly Navigation System. The system utilizes local 5G and MEC connectivity, so that it can proceed digitalization on the industrial field with few devices allocation. Also, the container orchestration mechanism to MEC helps developers to deliver new features to industrial field. We implemented and evaluated the system and concluded that the system responds quickly with Local 5G to be expected to help workers assembly process.
An Implementation and Analysis of Zero Knowledge Based E-Voting Solution With Proof of Vote on Public Ethereum Blockchain

Transparent e-voting is one of the applications that the turing complete public blockchain aspires to deliver by assuring verifiable votes. Public blockchains are much transparent, secure and have better auditing. In Blockchain based e-voting a voter identity is established with his account, whereas one account can cast a vote which leaves scope for user identity binding based on other activities on the blockchain. In this work we propose a privacy preserving and anonymous e-voting approach on the public blockchain. We introduce the concept of Proof of Vote.
An Analysis of Zero-knowledge Proof-based Privacy-preserving Techniques for Non-fungible Tokens in the Metaverse

Non-fungible tokens (NFTs) have huge potential to be included in metaverse-related applications such as digital ownership management and asset trading. However, existing research identified that privacy-preserving techniques and methods are essential for NFTs for large-scale adoption in the metaverse. This paper conducted an analysis of several existing research works that mainly use zero-knowledge proofs (ZKPs) and/or commitments to protect privacy for blockchain applications. Based on the results of this comparative analysis, we deducted several assumptions. This paper identifies the potential next steps to design new privacy-preserving techniques that will enable privacy-aware metaverse users to leverage the maximal benefits of the NFTs.
Ownership Tokenization and Incentive Design for Learning-based User-Generated Content

The Metaverse is established by twining a practical world in a virtual form, where users could become creators of learning-based user-generated content (UGC). However, the digital assets are not owned by the creators, which deviates from the decentralized Metaverse based on blockchain in Web 3.0. The contribution incentive design for UGC still faces the challenges of ownership verification and privacy protection. To this end, we propose minting the learning model into the non-fungible token (NFT) with federated learning (FL) assistance (referred to as FL-NFT), such that users as stakeholders can control the ownership and share the economic value of UGC. Specifically, the users are encouraged to establish a decentralized autonomous organization (DAO) to aggregate local models and mint FL-NFT. We formulate an auction interaction of FL-NFT as imperfect information Stackelberg game (IISG) to optimize the bidding strategies to realize individual rationality. Finally, we conduct simulations to show the effectiveness of the proposed scheme.
Achieving Distributed and Privacy-Preserving Cross-Chain Transactions in Account-Model Blockchain Systems

Cross-Chain is an emerging technology to enable transaction circulation across different blockchain systems, breaking down transaction isolation among decentralized ecosystems. Existing cross-chain solutions are often constructed based on centralized trusted third parties, which introduce additional threats to users’ identities due to potentially compromised risks on the centralized third parties. To exclude the single-point compromised risk in centralized systems, this paper focuses on a fully decentralized scenario and develops a distributed cross-chain transaction scheme, which enables cross-chain transaction exchanges while ensuring the unlinkability between payees and payers. Specifically, we first propose a cross-chain cryptocurrency exchange scheme by leveraging Ethereum’s smart contracts, which achieves transaction exchanges in decentralized settings. Then, we leverage zero-knowledge succinct non-interactive argument of knowledge (i.e., zk-SNARKs) to explore a privacy-preserving method for cross-chain transactions in account-model blockchain systems, such that the unlinkability between payees and payers and the privacy of cross-chain participants can be guaranteed. Security analysis and experimental evaluations demonstrate that the proposed scheme achieves the security goal at acceptable costs.
Exploring Domain Randomization’s Effect on Synthetic Data for Activity Detection*

The construction and operation of Metaverse virtual environments, e.g., 3D reconstruction and activity detection is an important supporting technology of computer vision. Recently, synthetic data has seen a surge in adoption for model training in computer vision. Prior research generally show a positive correlation between the volume of synthetic training data and inference accuracy. This paper focuses on the domain of activity detection, and explores how to improve the performance of such algorithms using synthetic data. In particular, we present an overview of the state-of-the-art in using domain randomization approaches for synthetic data generation. This paper presents initial inference accuracies of a model trained on initial attempts at domain randomized synthetic data (7.2%), compared to a model trained on real-world data (9.2%). The synthetic data, although performed worse, indicated promising trajectories for future work, approximately 2% away from the real-world result.
Metaverse Key Technologies and Blockchains: Impacts &amp; Considerations

As of October 2021, Facebook officially renamed itself Meta, and social networks and three-dimensional (3D) virtual worlds have adopted Metaverse as a new model. The metaverse offers consumers 3D immersive and individualized experiences through various innovative technologies. Although the metaverse is widely popular and beneficial, protecting user data and digital material is a natural concern where the transparency, decentralization, and immutability of blockchain make it a conclusive answer in this aspect. We intend to present the impact of blockchains on different metaverse key technologies and their applications. The work proves the technical challenges of the metaverse in each perspective and emphasizes the importance of blockchains. The article investigates blockchain's role in different metaverse enablers, such as immersive apps, digital twins (DTs), the Internet of Things (IoT), big data, and artificial intelligence (AI). This article shows the blockchain's role in metaverse applications and services. Finally, we describe promising directions for future study, innovation, and development that will enable blockchain to be applied to the metaverse.
Instantaneous Account Settlement in Roll-Up based Layer-2 Blockchain Framework for Metaverse Applications

Increasing popularity of the metaverse and its emergence as a platform for online social interaction tend to disrupt the current notion of business process execution. It is predicted that blockchain technology will act as a driving force in the development of the metaverse, specifically in trust building among different stakeholders and dealing with the financial aspect of the business organization working inside the metaverse ecosystem. This paper focuses on the scalability issues of blockchain, specifically on reducing the delay in finalizing the micro-transactions within the metaverse ecosystem. The paper proposes an instant account settlement process for roll-up based layer-2 blockchain framework that can be applied by business service providers in metaverse ecosystem for performing micro-transactions. The proposed mechanism is a blend of optimistic roll-up and sidechains protocols addressing the challenges of long delays in account settlement, cost of verification of transactions and must be resilient to common adversarial attacks. For this, the paper introduces the concept of Layer-2 Sphere (L2S), in which the business organization (BO) can deploy consortium blockchain (L2B). The entry, exit and account settlement is facilitate by gateway smart contract (GSC) deployed on layer-1 blockchain (L1B). The experimental evaluation shows that the proposed methodology is efficient in terms of associated cost and storage and it can be easily applied in the upcoming metaverse ecosystem.
Blockchain Enabled Architecture for Secure Authentication in the Metaverse Environment: A Student Training Use Case

Metaverse technology comprises of a virtual world and physical world running in parallel. Metaverse is evolving day by day and has many applications across domains. In this paper, we focus on the Education domain and especially student training related use cases such as inter-disciplinary research, field visits, campus exchange programs, remote learning, virtual trainings, etc. In such user scenarios, multiple virtual and physical worlds come together in metaverse using technologies like Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR). In such Metaverse environments, avatars, users, and devices interact with one another. While interacting, they use the conventional centralized architecture for authentication and identity management. Due to its centralized nature, such architectures are susceptible to various authentication security issues such as impersonation, identity interoperability, mutual authentication, replay, server spoofing, etc. For the student training related use cases, Avatars are moving across multiple Edu-verses. There are certain decentralized solutions proposed in the literature for the authentication and identity management for Avatars. But they come with their own limitations. Also, none of them talk about the authentication interoperability across verses. In this paper, Blockchain is proposed as a solution for the secure and interoperable authentication and identity management for the authentication of Avatars and users for the student training use cases.
A Cloud-Edge-Terminal Collaborative System for Image-Based Crowd Counting

Crowd counting is of great importance for urban management and disaster relief. However, monitoring the whole situation of crowd density in a wide area such as a city will involve processing a great number of images or videos, which requires intensive computation. Transmission of images and videos also requires large bandwidth of network, which might not be satisfied under some circumstances. This work proposed a cloud-edge-terminal collaborative system to shift the computing load to edge devices and enlarge the monitor range via visual sensors installed on portable devices like unmanned aerial vehicles (UAVs). Also, we proposed a light-weight crowd counting neural network to satisfy the resource constraint scenarios. Experiments on realistic and challenging datasets and comparisons with contemporary approaches show that our method achieves competitive performance with less computation and storage costs.
Multi-Features Fusion based Viewport Prediction with GNN for 360-Degree Video Streaming

360-degree video streaming is one of the essential VR applications which provides viewers with immersive experiences. However, traditional viewport prediction methods for tile-based 360-degree video streaming that aim to maximize prediction accuracy cannot guarantee the Quality of Experience (QoE) for viewers because they do not fully exploit the correlation of video content and users’ viewing behavior. In this paper, we propose a multi-features fusion based viewport prediction method for 360-degree video streaming using graph neural network (GNN) to improve both prediction accuracy and users’ QoE. First, we extract three tile-level prediction features from the current user’s viewing information, video content, and cross-user information. Then, we create a sparse directed graph to represent the relationship between video content and viewing behavior by using the three tile-level features to characterize nodes and the viewed traces to describe edges. Following that, the GNN is used to receive the graph as input and output prediction viewing probability for each tile. The multi-features fusion based prediction model is obtained after data training. In addition, a tile-oriented quality adaptation is proposed to help with 360-degree video streaming. Extensive experiments using public datasets show that the proposed approach outperforms state-of-the-art solutions in terms of prediction accuracy and users’ QoE.
Inferring Private Data from AI Models in Metaverse through Black-box Model Inversion Attacks

The widespread application of artificial intelligence technologies in metaverse introduces significant privacy concerns. It is critical to study the training information leakage of AI models during the interaction of metaverse. Model inversion attacks have revealed the privacy vulnerability of deep learning models through reconstructing their training data during predictions. In this paper, we reconstruct the training samples of AI models (target model) in metaverse under a more practical threat model, where the adversary only has black-box access to the target model and no side information besides auxiliary dataset. We propose a contrastive supervised model inversion attack (CSMI). Specifically, we modify contrastive learning to train a neural network (projector) for inferring semantical knowledge contained in target model’s outputs. Afterwards, we design a supervised inversion model similar to the architecture of conditional GAN, where the projected outputs of the target model are involved as conditional inputs to supervise the training process. Finally, to generate inversion samples, we propose a bi-level random search strategy to search proper inputs of the trained inversion model through an objective function, which consists of the attacking success rate and the qualities of the reconstructed image. We conduct extensive experiments to evaluate the performance of the proposed CSMI. The experimental results show that samples reconstructed by CSMI are more visually plausible and reveal more features of the target than the state-of-the-art methods under a black-box setting.