Weixu Zhang, Ye Yuan, Changjiang Han, Yuxing Tian, Zipeng Sun, Linfeng Du, Jikun Kang, Hong Kang, Xue Liu, Haolun Wu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

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.

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.

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 (Bézier Slicing for 3Ds), a middleware-enabled method that slices 3D meshes and fits the contours using Bézier 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 progressions in blockchain technologies have propelled the advancement of the Internet of Things (IoT), particularly the Industrial IoT (IIoT) system, toward a decentralized structure. This shift is driven by the need to address the privacy and security vulnerabilities inherent in centralized organizations. In a blockchain-based IoT system, the IoT nodes are tasked with both mining and data collection activities concurrently. However, due to the limited resources of these nodes, a rational resource allocation mechanism is imperative to maximize social welfare. Insufficient allocation of resources to mining can significantly reduce the overall Hashrate of the blockchain, thereby jeopardizing its security. Conversely, prioritizing resource allocation to mining can diminish the value of collected data, resulting in a decrease in overall social welfare. To tackle these challenges, this article proposes a transaction fee mechanism that levies charges on data consumers based on the resources utilized by the nodes for data collection. The objective is to achieve a balanced allocation of resources between mining and data collection while ensuring the security of the blockchain. Specifically, we model the interactions among the system designer, data consumer, and IoT nodes as a three-stage Stackelberg game. By characterizing the equilibrium of the game, we derive optimal strategies that lead to the maximization of social welfare. The effectiveness of the proposed approach is validated through extensive experimental results.

Non-Fungible Tokens (NFTs) have emerged as a pivotal digital asset, offering authenticated ownership of unique digital content. Despite it has gained remarkable traction, yet face pressing storage and verification challenges stemming from blockchain's permanent data costs. Existing off-chain or centralized storage solutions, while being alternatives, also introduce notable security vulnerabilities. We present SemNFT, an innovative decentralized framework integrated with blockchain oracle middleware services, addressing these persistent NFT dilemmas. Our approach compresses NFT source data into compact embeddings encapsulating semantic essence. These arrays are stored on-chain, while facilitating reliable decentralized image reconstruction and ownership verification. We implemented ERC721-compliant smart contracts with supplementary functionalities, demonstrating SemNFT's seamless integrative capabilities within the ecosystem. Extensive evaluations evidence marked storage optimizations and preservation of requisite visual fidelity by comparison with existing solutions. The proposed SemNFT framework marks a significant advancement in holistically confronting rising NFT storage and verification challenges without compromising decentralization. It substantively propels the meaningful evolution of NFT infrastructure to achieve digital asset immortality.

As mobile applications grow increasingly computation-intensive, the challenges arising from the limitations of mobile devices in terms of computing resources and battery life become more pronounced. Mobile Edge Computing (MEC) provides a promising avenue to address these challenges and enhance user experience. While existing studies have extensively explored resource allocation and task scheduling in MEC, most treat tasks as monolithic entities, overlooking the nuanced subtasks/components that often make up mobile applications. This paper endeavors to bridge the gap between the need for incentive mechanisms and the offloading of dependent computation tasks in MEC. Drawing inspiration from auction theory, we introduce a novel Multi-stage Iterative Combinatorial Double Auction (MICDA) mechanism, specifically tailored for dependent tasks in a cloud-edge-end cooperative computing scenario. Through theoretical analysis, the MICDA mechanisms demonstrate truthfulness, individual rationality, budget balance, and computational efficiency. Comprehensive experiment results further confirm its superior performance in improving application makespan and social welfare compared to other existing offloading strategies. This work validates the effective integration of dependency-aware computation offloading and auction mechanisms in overcoming economic and computational challenges in MEC systems, thereby paving the way for their potential application in broader real-world scenarios.

Mobile Edge Computing (MEC) enables computation offloading from resource-constrained mobile devices to edge servers in close vicinity, effectively promoting the user experience on emerging interactive multimedia applications such as virtual/augmented reality, mobile gaming, and mobile video editing. However, most contemporary MEC offloading research disregards the interdependencies between partitioned subtasks of application. Also, few studies focused on application topologies have neglected to design effective incentives to encourage edge servers to provide offloading services. In this paper, we propose a dependency-aware offloading algorithm based on a multi-round truthful combinatorial reverse auction (MTCRA) to address the social welfare maximization problem in the paradigm of MEC. Building on the topology of directed acyclic graphs (DAGs) modeled from applications, we discuss the complementarity and substitutability of subtasks in the context of combinatorial auction. Theoretical analysis shows that the presented auction mechanism achieves computing efficiency while maintaining desirable economic features like truthfulness, individual rationality, and budget balance. Simulation results demonstrate that the proposed algorithm achieves high social welfare regarding reduced execution time and good economic benefits for MEC servers.

Breast cancer has become the leading cause of women cancer death worldwide. Despite the consensus that breast cancer early detection can significantly reduce treatment difficulty and cancer mortality, people still are reluctant to go to hospital for regular checkups due to the high costs incurred. A timely, private, affordable, and effective household breast cancer early detection solution is badly needed. In this paper, we propose a household solution that utilizes pairs of sensors embedded in the bra to measure the thermal and moisture time series data (BTMTSD) of the breast surface and conduct time series classification (TSC) to diagnose breast cancer. Three main challenges are encountered when doing BTMTSD classification, (1) small supervised dataset, which is a common limitation of medical research, (2) noisy time series with unique noise patterns, and (3) complex interplay patterns across multiple time series dimensions. To mitigate these problems, we incorporate multiple data augmentation and transformation techniques with various deep learning TSC approaches and compare their performances for the BTMTSD classification task. Experimental results validate the effectiveness of our framework in providing reliable breast cancer early detection.

Effectively integrating multi-scale information is of considerable significance for the challenging multi-class segmentation of fundus lesions because different lesions vary significantly in scales and shapes. Several methods have been proposed to successfully handle the multi-scale object segmentation. However, two issues are not considered in previous studies. The first is the lack of interaction between adjacent feature levels, and this will lead to the deviation of high-level features from low-level features and the loss of detailed cues. The second is the conflict between the low-level and high-level features, this occurs because they learn different scales of features, thereby confusing the model and decreasing the accuracy of the final prediction. In this paper, we propose a progressive multi-scale consistent network (PMCNet) that integrates the proposed progressive feature fusion (PFF) block and dynamic attention block (DAB) to address the aforementioned issues. Specifically, PFF block progressively integrates multi-scale features from adjacent encoding layers, facilitating feature learning of each layer by aggregating fine-grained details and high-level semantics. As features at different scales should be consistent, DAB is designed to dynamically learn the attentive cues from the fused features at different scales, thus aiming to smooth the essential conflicts existing in multi-scale features. The two proposed PFF and DAB blocks can be integrated with the off-the-shelf backbone networks to address the two issues of multi-scale and feature inconsistency in the multi-class segmentation of fundus lesions, which will produce better feature representation in the feature space. Experimental results on three public datasets indicate that the proposed method is more effective than recent state-of-the-art methods.