This editorial introduces the first part of the Special Issue on "Advanced Technologies in the Decentralized Web." As the Internet evolves toward more user-centric and resilient architectures, concepts like Web3 and Web 3.0 have gained significant prominence. This issue explores key advancements including decentralized machine learning, AI-blockchain integration, identity management, and data sovereignty. We provide an overview of the selected papers, highlighting their contributions to creating a more transparent and secure decentralized digital future.

Autonomous AI agents, increasingly empowered by large language models, are becoming important components of human-machine systems for high-stakes decision support in digital twin ecosystems. However, existing multi-agent systems often lack robust verification for identity, capability, and policy compliance, especially in decentralized environments spanning multiple institutions. This paper proposes a neuro-symbolic decentralized governance framework for verifiable agents in collaborative digital twin environments. By representing agents through multi-layer semantic profiles, the framework bridges probabilistic neural reasoning with deterministic institutional governance, thereby supporting trustworthy human-AI collaboration and meaningful human oversight. Capabilities are grounded in formal domain ontologies to enable machine-interpretable, policy-aware, and context-sensitive participation. These credentials, issued by organizational authorities, are validated via blockchain-based smart contracts, ensuring auditable participation without exposing sensitive data. We demonstrate the framework using a decision-support prototype with clinic, digital twin, and wearable provider agents effectively prevents unauthorized interaction and enforces institutional policies with manageable overhead. Our findings suggest that neuro-symbolic decentralized governance provides a scalable and trustworthy pathway for safe human-machine collaboration across institutional boundaries.

Recently, AI agents are able to understand multi-modal data, allowing machines to execute various tasks, especially bypassing CAPTCHAs which poses significant security threats to applications. We observe that humans are sensitive to unreal timing in videos, while current AI still struggles to comprehend and respond to such situations effectively. Thus, we design UTCAPTCHA, a mechanism that leverages human perception of timing in video unreal changes. By utilizing generative AI's capability to extend original videos, we introduce unreal changes to create a pipeline for generating guided short videos for CAPTCHA purposes. We develop a prototype and conduct human-centered experiments to collect data on time biases in timing identification by participants. This data serves as a basis for distinguishing between human users and bots. Additionally, we perform a detailed security analysis and user study of UTCAPTCHA. We hope that UTCAPTCHA will act as a robust defense, safeguarding applications in the AI-driven era.

The quest for carbon neutrality in the 21st century has led to the rise of decentralized low-carbon energy systems as a promising solution. Blockchain technology has played a pivotal role in catalyzing this transition, with various Web3 projects exploring decentralized operational models and carbon credit markets. However, there is a notable gap in harnessing blockchain's potential to integrate electric vehicles (EVs) into low-carbon energy systems effectively. This article addresses this gap by proposing a decentralized low-carbon EV charging system that enables transactions between individual low-carbon energy producers and EV owners. Leveraging blockchain and smart contracts, the proposed system issues low-carbon tokens to certify and incentivize environmentally conscious charging behaviors, while enabling token circulation to further promote low carbon participation. A blockchain-based double auction mechanism is designed to ensure fair and efficient energy allocation, achieving individual rationality, incentive compatibility, and social welfare maximization. By incentivizing user engagement and ensuring fair transactions, this model paves the way for sustainable EV integration within low-carbon energy systems.

Cryptocurrency airdrops power the growth and governance of the cryptocurrency ecosystem, yet attract airdrop hunters, who coordinate wallets, script interactions, and cash out quickly, distorting metrics and fairness. Prior detection strands (heuristics/clustering, light-supervised community partitioning, and graph learning) face three fundamentals: inconsistent definitions, weak explainability, and poor cross-context generalization. We distill expert knowledge into a computable, interpretable baseline: open/axial coding of expert narratives followed by two Delphi rounds to (1) formalize a consensus, operational definition with six contrasts to regular users; (2) derive 15 measurable indicators spanning operations and fund-flow, tempered by human-ness counter-evidence; and (3) report thresholds as reference distributions (medians, quartiles). The baseline supplies shared semantics and computation for labeling/evaluation, yields inspectable why-flagged rationales for audit and governance, and offers context-aware guidance across chains, campaign designs, and market phases, thereby strengthening on-chain security while informing the design of socio-technical systems perceived as fair, trustworthy, and resistant to strategic misuse.

This study explores the role of governance tokens in shaping player communities in blockchain games, focusing on Axie Infinity. Blockchain games introduced governance tokens to empower players with decision-making capabilities, enhancing decentralization. However, their impact on community dynamics remains insufficiently understood. This research adopts a data-driven approach, collecting AXS transaction data, constructing monthly token transfer networks, and analyzing community evolution using a detection algorithm. The results reveal an increasing trend towards decentralization over time. While larger communities generally have longer lifespans, surpassing a certain size threshold can accelerate their decline due to decentralization. Maintaining stability within community membership is essential for longevity, highlighting the roles played by experienced players and established communities in governance.

Memecoins, driven by social media engagement and cultural narratives, have rapidly grown within the Web3 ecosystem. Unlike traditional cryptocurrencies, they are shaped by humor, memes, and community sentiment. This paper introduces the Coin-Meme dataset, an open-source collection of visual, textual, community, and financial data from the Pump.fun platform on the Solana blockchain. We also propose a multimodal framework to analyze memecoins, uncovering patterns in cultural themes, community interaction, and financial behavior. Through clustering, sentiment analysis, and word cloud visualizations, we identify distinct thematic groups centered on humor, animals, and political satire. Additionally, we provide financial insights by analyzing metrics such as Market Entry Time and Market Capitalization, offering a comprehensive view of memecoins as both cultural artifacts and financial instruments within Web3. The Coin-Meme dataset is publicly available at https://github.com/hwlongCUHK/Coin-Meme.git.

In recent years, the rapid development of artificial intelligence (AI) especially multi-modal Large Language Models (MLLMs), has enabled it to understand text, images, videos, and other multimedia data, allowing AI systems to execute various tasks based on human-provided prompts. However, AI-powered bots have increasingly been able to bypass most existing CAPTCHA systems, posing significant security threats to web applications. This makes the design of new CAPTCHA mechanisms an urgent priority. We observe that humans are highly sensitive to shifts and abrupt changes in videos, while current AI systems still struggle to comprehend and respond to such situations effectively. Based on this observation, we design and implement BounTCHA, a CAPTCHA mechanism that leverages human perception of boundaries in video transitions and disruptions. By utilizing generative AI's capability to extend original videos with prompts, we introduce unexpected twists and changes to create a pipeline for generating guided short videos for CAPTCHA purposes. We develop a prototype and conduct experiments to collect data on humans' time biases in boundary identification. This data serves as a basis for distinguishing between human users and bots. Additionally, we perform a detailed security analysis of BounTCHA, demonstrating its resilience against various types of attacks. We hope that BounTCHA will act as a robust defense, safeguarding millions of web applications in the AI-driven era.

In the era of Big Data, large-scale machine learning models have revolutionized various fields, driving significant advancements. However, large-scale model training demands high financial and computational resources, which are only affordable by a few technological giants and well-funded institutions. In this case, common users like mobile users, the real creators of valuable data, are often excluded from fully benefiting due to the barriers, while the current methods for accessing largescale models either limit user ownership or lack sustainability. This growing gap highlights the urgent need for a collaborative model training approach, allowing common users to train and share models. However, existing collaborative model training paradigms, especially federated learning (FL), primarily focus on data privacy and group-based model aggregation. To this end, this paper intends to address this issue by proposing a novel training paradigm named decentralized relay learning (DeRelayL), a sustainable learning system where permissionless participants can contribute to model training in a relay-like manner and share the model. In detail, this paper presents the architecture and workflow of DeRelayL, designs incentive mechanisms to ensure sustainability, and conducts theoretical analysis and numerical simulations to demonstrate its effectiveness

Mesh simplification of 3D models can accelerate rendering, reduce storage space, and improve performance. However, for high-poly 3D models, there are ongoing concerns about potentially compromising the Quality of Experience (QoE), the need to set simplification ratios or parameters, and the time-consuming nature of the simplification process. To address these issues, we proposed a new mesh simplification for the preprocessing step. Based on the Quadratic Error Metric (QEM) simplification algorithm, we conducted human-centered 3D model comparison experiments to determine the optimal simplification ratio for high-poly 3D models in full body shots. From experimental data, we proposed and implemented ASimp, an automatic 3D mesh simplification scheme. In evaluation experiments, ASimp demonstrated rapid preprocessing speeds while ensuring QoE and the effectiveness of its simplification products. We hope that ASimp will contribute to the optimization of 3D models and find applications in fields such as cultural heritage, archaeology, visual effects, video games, medicine, metaverse, and beyond.

The emergence of Web3 introduces decentralization to online spaces and applications. In particular, Web3 participatory art extends this concept to artistic creation. This late-breaking work provides an initial exploration of the impact ofWeb3 decentralization on the collective artistic creation of participatory art, and potential strategies that can help artists adapt to this new technological environment. Grounded in smart contract technology, we examine prominent Web3 participatory art projects and discuss existing implementation for achieving decentralization. Building upon this, we conduct a fully decentralized participatory art project with a broad scope of decentralized creative functionality. By analyzing participants' on-chain behavior, off-chain communication, and motivation data, we unveil potential shifts in artists' responsibilities and provide design considerations for decentralized participatory art.

In mobile edge computing (MEC), one optimization strategy for mobile applications is to offload heavy computing tasks to cloud and edge servers. Constructing partitioning algorithms involves modeling individual methods through static code profilers, but exploiting dynamic user-driven execution patterns is also crucial. This paper introduces PartFlow, an interactive visualization system that supports comprehensive analysis of mobile application components and aids researchers in developing partitioning and offloading algorithms using real human behavioral data. PartFlow collects application component data remotely through binary instrumentation of mobile applications. Interactive diagrams are designed to evaluate component performance and illustrate transition patterns using the collected data. Additionally, PartFlow integrates a deep learning (DL)-based approach for multi-step forecasting of component states to improve accuracy and user experience in algorithm design. A case study and user feedback demonstrate PartFlow’s effectiveness in assisting researchers and engineers in creating offloading strategies.

In the crypto industry, venture capital investors provide funding to drive the domain towards its decentralized vision, while many individual investors follow online investment news to aid their decision-making. By scraping investment event data from crypto data analytics websites, we construct and analyze the joint investment network of venture capital investors in the crypto industry. Our study reveals the centralized nature of the investor network in this supposedly decentralized domain, by identifying central nodes such as Coinbase Venture and disclosing their persistence of dominance, despite Coinbase's relatively small market share as an exchange. Based on node features, we divide the network into different communities, hinting at investor clustering. To measure the robustness of this network, we simulate various attack strategies to model bankruptcy and risk propagation, confirming its vulnerability as seen in historical events. Additionally, using graph neural network approaches, we fill in unknown structural information of investors, mitigating information asymmetry in investment disclosures and achieving classification accuracy above 70% for tier ratings and over 65% for investor types. Results are validated using data from another platform, exhibiting consistency. This study sheds light on the dynamics of joint investment networks in this emerging tech domain, offering insights for both potential and existing investors.

Current 3D model Level of Detail (LOD) methods require multiple models with varying detail levels to reduce client computational load, transmitting different models based on the user's distance to the object. However, this process consumes excessive network bandwidth and strains the client's memory and storage. To address this, we propose BS3 (Bezier Slicing for 3Ds), a middleware-enabled method that slices 3D meshes and fits the contours using Bezier curves. Acting as an intermediate layer, the BS3 middleware handles slicing, vectorization, sampling and reconstruction, allowing.bs3 files to be streamed only once and adjusted dynamically at different sampling rates. Our experiments demonstrate the efficiency and performance analysis of BS3, which shows that it can reduce network and storage burdens while keeping the display effect. We believe that BS3 will enhance 3D multimedia in the game, exhibition, digital museum, cultural heritage, metaverse, etc.

Empowered by blockchain technology, smart contracts have attracted considerable interest from Web3 users due to their distinct advantages. Nevertheless, it is challenging to address problems caused by the dramatic expansion of the Web3 ecosystem. This article introduces the smart contract-as-a-service (SCaaS) paradigm to mitigate smart contracts' redundant deployment via their composability and reusability. Moreover, we design trust and incentive schemes to ensure project security and developer engagement in SCaaS. Specifically, we first introduce a reputation filter by leveraging the authentic on-chain data, aiming to eliminate high-risk contracts. We then design a contract-based incentive mechanism to help the foundation attract heterogeneous developers with multidimensional private information, and maximize the foundation's utility by inducing developers to undertake projects of differing complexities based on their ability. We further differentiate between veteran and newcome developers and examine their influences on foundational strategies. Finally, extensive experimental results demonstrate that our proposed contracts can efficiently remove high-risk smart contracts, maximize the foundation's utility, and ensure that developers select contracts honestly and participate in the SCaaS ecosystem actively.

Blockchain technologies, particularly blockchain-based digital twins (DTs), have gained widespread interest in academia and industry. Despite this, existing literature frequently overlooks the unique characteristics of location information for consensus nodes in DTs, often merging them with conventional blockchain frameworks. This article introduces LocPoS, a novel location-based proof-of-stake mechanism tailored for industrial DTs, bridging this gap. Our proposed model strives to enhance security via broader consensus node distribution. Despite this enhanced security, potential for dishonest behavior by users for gain maximization persists. We perform an in-depth analysis of user strategies and devise a mechanism to inhibit malicious behavior, fostering truthfulness among all users. Our theoretical and simulation results demonstrate that LocPoS satisfies truthfulness and individual rationality, while reducing the proportion of malicious nodes in the consensus group by over 50% compared to PoS and DPoS, thereby significantly improving consensus integrity. The proposed mechanism’s effectiveness in ensuring truthful behavior and secure node selection is validated through both analytical proofs and extensive simulations.

The popularity of artificial intelligence (AI)-generated content (AIGC) has experienced significant growth recently. Despite AIGC’s potential to transform content creation in various industries, its dependence on extensive computational resources poses a challenge for widespread adoption. To address this challenge, AIGC as a service has been proposed. However, concerns related to dataset compliance have emerged as a source of apprehension among stakeholders. The complexities associated with manual supervision, apprehensions regarding data leakage, and the potential for malicious behavior by third-party supervisors collectively present formidable challenges in the regulation of datasets within the domain of AIGC service. To tackle these challenges, this article presents a blockchain-based system for regulating AIGC datasets. Our proposed system employs AI, zero-knowledge proofs, and smart contracts as integral components for overseeing dataset compliance. To assess the feasibility and effectiveness of the proposed system, a comprehensive analysis and a series of simulations have been conducted. These evaluations offer valuable insights into the system’s security, privacy, and performance.