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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shang, Zongjiang Chen, Ling Wu, Binqing Cui, Dongliang State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University China

Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e.g., rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4.56%, 10.38%, and 4.97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https://***/shangzongjiang/Ada-MSHyper. © 2024 Neural information processing systems foundation. All rights reserved.

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Public auditing for real‑time medical sensor data in cloud‑assisted HealthIIoT system

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Frontiers of Optoelectronics 2022年第3期15卷 1-14页

作者： Weiping Ye Jia Wang Hui Tian Hanyu Quan College of Computer Science and Technology Huaqiao UniversityXiamen 361021China Wuhan National Laboratory for Optoelectronics Wuhan 430074China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen 361021China Fujian Key Laboratory of Big Data Intelligence and Security Xiamen 361021China

With the advancement of industrial internet of things(IIoT),wireless medical sensor networks(WMSNs)have been widely introduced in modern healthcare systems to collect real-time medical data from patients,which is known as *** the limited computing and storage capabilities of lightweight HealthIIoT devices,it is necessary to upload these data to remote cloud servers for storage and ***,there are still some serious security issues within outsourcing medical sensor data to the *** of the most signifcant challenges is how to ensure the integrity of these data,which is a prerequisite for providing precise medical diagnosis and *** meet this challenge,we propose a novel and efcient public auditing scheme,which is suitable for cloud-assisted HealthIIoT ***,to address the contradiction between the high real-time requirement of medical sensor data and the limited computing power of HealthIIoT devices,a new online/ofine tag generation algorithm is designed to improve preprocessing efciency;to protect medical data privacy,a secure hash function is employed to blind the data *** formally prove the security of the presented scheme,and evaluate the performance through detailed experimental comparisons with the state-of-the-art *** results show that the presented scheme can greatly improve the efciency of tag generation,while achieving better auditing performance than previous schemes.

关键词： Healthcare industrial internet of things(HealthIIoT) Medical sensor data Online/ofine signature Public auditing

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DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach 38

DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowl...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Qian Chen, Ling State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University China

Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot capture the temporal evolution of high-order correlations in TKGs. To this end, we propose a Deep Evolutionary Clustering jointed temporal knowledge graph Representation Learning approach (DECRL). Specifically, a deep evolutionary clustering module is proposed to capture the temporal evolution of high-order correlations among entities. Furthermore, a cluster-aware unsupervised alignment mechanism is introduced to ensure the precise one-to-one alignment of soft overlapping clusters across timestamps, thereby maintaining the temporal smoothness of clusters. In addition, an implicit correlation encoder is introduced to capture latent correlations between any pair of clusters under the guidance of a global graph. Extensive experiments on seven real-world datasets demonstrate that DECRL achieves the state-of-the-art performances, outperforming the best baseline by an average of 9.53%, 12.98%, 10.42%, and 14.68% in MRR, Hits@1, Hits@3, and Hits@10, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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Vision Mamba Mender 38

Vision Mamba Mender

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Cao, Anda Feng, Zunlei Zhang, Shengxuming Wang, Yi Jia, Lingxiang Song, Mingli College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. For more information, please visit https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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Non-stationary Online Convex Optimization with Arbitrary Delays 41

Non-stationary Online Convex Optimization with Arbitrary Del...

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41st International Conference on Machine Learning, ICML 2024

作者： Wan, Yuanyu Yao, Chang Song, Mingli Zhang, Lijun The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China Institute of Blockchain and Data Security Hangzhou China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Online convex optimization (OCO) with arbitrary delays, in which gradients or other information of functions could be arbitrarily delayed, has received increasing attention recently. Different from previous studies that focus on stationary environments, this paper investigates the delayed OCO in non-stationary environments, and aims to minimize the dynamic regret with respect to any sequence of comparators. To this end, we first propose a simple algorithm, namely DOGD, which performs a gradient descent step for each delayed gradient according to their arrival order. Despite its simplicity, our novel analysis shows that the dynamic regret of DOGD can be automatically bounded by (equation presented) under mild assumptions, and (equation presented) in the worst case, where d¯ and d denote the average and maximum delay respectively, T is the time horizon, and PT is the path-length of comparators. Furthermore, we develop an improved algorithm, which reduces those dynamic regret bounds achieved by DOGD to (equation presented), respectively. The key idea is to run multiple DOGD with different learning rates, and utilize a meta-algorithm to track the best one based on their delayed performance. Finally, we demonstrate that our improved algorithm is optimal in the worst case by deriving a matching lower bound. Copyright 2024 by the author(s)

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Vertical Federated Learning Based on Consortium blockchain for data Sharing in Mobile Edge Computing

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 345-361页

作者： Yonghao Zhang Yongtang Wu Tao Li Hui Zhou Yuling Chen State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and TechnologyWeifang262700China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin541004China

The data in Mobile Edge Computing(MEC)contains tremendousmarket value,and data sharing canmaximize the usefulness of the ***,certain data is quite sensitive,and sharing it directly may violate *** Federated Learning(VFL)is a secure distributed machine learning framework that completes joint model training by passing encryptedmodel parameters rather than raw data,so there is no data privacy leakage during the training ***,the VFL can build a bridge between data demander and owner to realize data sharing while protecting data ***,the VFL requires a third party for key distribution and decryption of training *** this article,we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in *** specifically,we propose a V-Raft consensus algorithm based on Verifiable Random Functions(VRFs),which is a variant of the *** VRaft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by ***,we apply secret sharing todistribute the private key to avoid the situationwhere the training result cannot be decrypted if the leader ***,we analyzed the performance of the V-Raft and carried out simulation experiments,and the results show that compared with Raft,the V-Raft has higher efficiency and better scalability.

关键词： Mobile edge computing vertical federated learning consortium blockchain consensus algorithm

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Dual-Perspective Activation: Efficient Channel Denoising via Joint Forward-Backward Criterion for Artificial Neural Networks 38

Dual-Perspective Activation: Efficient Channel Denoising via...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qiu, Tian Gao, Chenchao Feng, Zunlei Lei, Jie Hu, Bingde Wang, Xingen Gao, Yi Song, Mingli State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Dalian University of Technology China Zhejiang University of Technology China Bangsheng Technology Co. Ltd. China

The design of Artificial Neural Network (ANN) is inspired by the working patterns of the human brain. Connections in biological neural networks are sparse, as they only exist between few neurons. Meanwhile, the sparse representation in ANNs has been shown to possess significant advantages. Activation responses of ANNs are typically expected to promote sparse representations, where key signals get activated while irrelevant/redundant signals are suppressed. It can be observed that samples of each category are only correlated with sparse and specific channels in ANNs. However, existing activation mechanisms often struggle to suppress signals from other irrelevant channels entirely, and these signals have been verified to be detrimental to the network's final decision. To address the issue of channel noise interference in ANNs, a novel end-to-end trainable Dual-Perspective Activation (DPA) mechanism is proposed. DPA efficiently identifies irrelevant channels and applies channel denoising under the guidance of a joint criterion established online from both forward and backward propagation perspectives while preserving activation responses from relevant channels. Extensive experiments demonstrate that DPA successfully denoises channels and facilitates sparser neural representations. Moreover, DPA is parameter-free, fast, applicable to many mainstream ANN architectures, and achieves remarkable performance compared to other existing activation counterparts across multiple tasks and domains. Code is available at https://***/horrible-dong/DPA. © 2024 Neural information processing systems foundation. All rights reserved.

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Improved Regret for Bandit Convex Optimization with Delayed Feedback 38

Improved Regret for Bandit Convex Optimization with Delayed ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wan, Yuanyu Yao, Chang Song, Mingli Zhang, Lijun School of Software Technology Zhejiang University Ningbo China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

We investigate bandit convex optimization (BCO) with delayed feedback, where only the loss value of the action is revealed under an arbitrary delay. Let n, T, d¯ denote the dimensionality, time horizon, and average delay, respectively. Previous studies have achieved an O(√nT3/4 +(nd¯)1/3T2/3) regret bound for this problem, whose delay-independent part matches the regret of the classical non-delayed bandit gradient descent algorithm. However, there is a large gap between its delay-dependent part, i.e., O((nd¯)1/3T2/3), and an existing Ω(√d¯T) lower bound. In this paper, we illustrate that this gap can be filled in the worst case, where d¯is very close to the maximum delay d. Specifically, we first develop a novel algorithm, and prove that it enjoys a regret bound of O(√nT3/4 + √dT) in general. Compared with the previous result, our regret bound is better for d = O((nd¯)2/3T1/3), and the delay-dependent part is tight in the worst case. The primary idea is to decouple the joint effect of the delays and the bandit feedback on the regret by carefully incorporating the delayed bandit feedback with a blocking update mechanism. Furthermore, we show that the proposed algorithm can improve the regret bound to O((nT)2/3 log1/3 T + dlog T) for strongly convex functions. Finally, if the action sets are unconstrained, we demonstrate that it can be simply extended to achieve an O(n√T log T + dlog T) regret bound for strongly convex and smooth functions. © 2024 Neural information processing systems foundation. All rights reserved.

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International Standardization of blockchain and Distributed Ledger technology:Overlaps,Gaps and Challenges

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1491-1523页

作者： Xiangjuan Jia Jing Xu Mengwei Han Qing Zhang Lu Zhang Xiaofeng Chen Department of Standardization and Research Project Management Hangzhou Qulian Technology Co.Ltd.Hangzhou310000China State Key Laboratory of Blockchain and Data Security Zhejiang UniversityHangzhou310000China

As one of the most concerned digital technologies in recent years,blockchain and distributed ledger technology are an important driving force for a new round of technological *** is currently in the process of accelerating its evolution and maturity,and has gradually integrated with other digital *** has been applied in many industries,providing decentralized solutions for various industries,realizing innovative storage models,and building a new trust *** blockchain technology is officially incorporated into China’s new information infrastructure category,the application fields of blockchain have expanded rapidly,gradually extending from the financial field and government affairs to other fields in the real *** the same time,with the continuous development of the globalized economy,blockchain technology will also have a profound impact on international technological and economic ***,for the healthy and orderly development and real implementation of the blockchain industry,standardize the application of blockchain,effectively break through the cognitive and technical barriers between different countries,industries and systems on a global scale,prevent application risks,the development of the global blockchain industry needs standardization basis,which is particularly important and urgent.A sound standard system is an important key to the successful development of technology,and formulating the right standard at the right time for technology development helps ensure the ease of use and interoperability of the *** the perspective of international standardization,this article first introduces the general situation of ISO,ITU-T,IEEE,W3C and other international standardization organizations,and sorts out the status quo of the blockchain standardization working groups of mainstream international standardization *** blockchain-related standards under development have been analyzed for the charac

关键词： ISO ITU-T IEEE blockchain distributed ledger technology international standard standardization status overlaps gaps challenges

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When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?

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ACM Transactions on Software Engineering and Methodology 2025年第4期34卷

作者： Chen, Chong Su, Jianzhong Chen, Jiachi Wang, Yanlin Bi, Tingting Yu, Jianxing Wang, Yanli Lin, Xingwei Chen, Ting Zheng, Zibin Sun Yat-sen University Guangzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China University of Western Australia Perth Australia Zhejiang University Hangzhou China University of Electronic Science and Technology of China Chengdu China

With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attention, showcasing great capabilities in code analysis tasks. In this article, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT's effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT's performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT's F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions;and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Empirical Study Smart Contracts Large Language Models ChatGPT Vulnerabilities

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