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Towards trustworthy blockchain systems in the era of “Internet of value”: development, challenges, and future trends

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Science China(Information Sciences) 2022年第5期65卷 250-260页

作者： Hai JIN Jiang XIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Since the advent of cryptocurrencies such as Bitcoin, blockchain, as their underlying technologies,has drawn a massive amount of attention from both academia and the industry. This ever-evolving technology inherits the “genes” of distributed systems, offering significant advantages of immutability, transparency,auditability, and tamper-resistance. These benefits help blockchain re-establish public confidence, and hold the significant promise of reliable information sharing and value transfer. Therefore, blockchain has become the foundation of crucial strategic deployments in countries across the world, and the fundamental basis for building the next generation Web 3.0 — “Internet of value”. In this article, we will start with unraveling the essential ingredients of blockchain technology, and showing the characteristics of each of these ingredients in the context of distributed systems. We will then present the core technical challenges that need to be addressed prior to unleashing its full potential, including its performance, scalability, and cross-chain interoperability. Finally, we will introduce the recent developments of blockchain systems, and discuss the future trends of the blockchain ecosystem.

关键词： blockchain distributed systems internet of value challenges development

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A Novel Vectorization Tracking Algorithm for Maritime Emission Monitoring Assisted with E-Nose Enabled Unmanned Aerial Vehicle

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IEEE Sensors Journal 2024年 1-1页

作者： Yuan, Haiwen Xiao, Changshi Wang, Yanfeng Wen, Yuanqiao Li, Qiliang School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan 430205 China the Intelligent Transport Systems Center Wuhan University of Technology Wuhan 430063 China and National Engineering Research Center for Water Transport Safety Wuhan University of Technology Wuhan 430063 China School of Navigation Wuhan University of Technology Wuhan 430063 China and National Engineering Research Center for Water Transport Safety Wuhan University of Technology Wuhan 430063 China School of Navigation Wuhan University of Technology Wuhan 430063 China and Wuhan Technical College of Communications Wuhan 430063 China Intelligent Transport Systems Center Wuhan University of Technology Wuhan 430063 China and National Engineering Research Center for Water Transport Safety Wuhan University of Technology Wuhan 430063 China Department of Electrical & Computer Engineering George Mason University Fairfax VA 22030 USA

Monitoring the gas emission of maritime vessels is very challenging as the fields are almost inaccessible and the dispersion is very susceptible to the surrounding environment. In ultra-large-scale maritime scenes, general emission detection and tracking frameworks based on infotaxis will most likely fall into local turbulence traps. In this work, a novel vectorization tracking algorithm assisted with E-nose enabled unmanned aerial vehicle (UAV) is proposed for efficient monitoring the vessel emission. Firstly, a new prediction model using AIS data is developed to estimate globally vessel emission. Then, real-time measurements by the onboard E-nose are fused with the emission prediction in the vectorization framework. Based on the proposed tracking algorithm, the UAV’s flight velocity and heading can be adjusted towards the vessel emissions accurately and efficiently. Another contribution of the paper is that a series of real-world maritime experiments with a developed were carried out in order to evaluate the algorithm. The acquired results indicated that the proposed tracking algorithm and the developed E-nose enabled UAV are very efficient for such a maritime environment. IEEE

关键词： Forecasting

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Fast parallel Recovery for Transactional Stream Processing on Multicores

Fast Parallel Recovery for Transactional Stream Processing o...

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International Conference on Data engineering

作者： Jianjun Zhao Haikun Liu Shuhao Zhang Zhuohui Duan Xiaofei Liao Hai Jin Yu Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to system failures and power outages. Existing TSPEs mainly focus on performance improvement, but still face a significant challenge to guarantee fault tolerance while offering high-performance services. We revisit commonly-used fault tolerance approaches in stream processing and database systems, and find that these approaches do not work well on TSPEs due to complex data dependencies. In this paper, we propose a novel TSPE called MorphStreamR to achieve fast failure recovery while guaranteeing low performance overhead at runtime. The key idea of MorphStreamR is to record intermediate results of resolved dependencies at runtime, and thus eliminate data dependencies to improve task parallelism during failure recovery. MorphStreamR further mitigates the runtime overhead by selectively tracking data dependencies and incorporating workload-aware log commitment. Experimental results show that MorphStreamR can significantly reduce the recovery time by up to 3.1 x while experiencing much less performance slowdown at runtime, compared with other applicable fault tolerance approaches.

关键词： Fault tolerance Runtime Fault tolerant systems Semantics parallel processing Real-time systems Power system reliability

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection

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IEEE Journal of Translational engineering in Health and Medicine 2024年 12卷 43-55页

作者： Chen, Li-Chin Hung, Kuo-Hsuan Tseng, Yi-Ju Wang, Hsin-Yao Lu, Tse-Min Huang, Wei-Chieh Tsao, Yu Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan Linkou Chang Gung Memorial Hospital Department of Laboratory Medicine Taoyuan City33342 Taiwan Taipei Veterans General Hospital Division of Cardiology Department of Internal Medicine Taipei112201 Taiwan Taipei Veterans General Hospital Department of Health Care Center Taipei112201 Taiwan National Yang Ming Chiao Tung University School of Medicine College of Medicine Department of Internal Medicine Taipei112304 Taiwan National Taiwan University Department of Biomedical Engineering Taipei10617 Taiwan

Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity;In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. Methods and procedures: GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for target vessel revascularization (TVR) detection. Results: The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority ( p ©;2023 IEEE.

关键词： Diseases

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Chlorine-substituted chiral 2D perovskite microwires with enhanced lattice rigidity for sensitive circularly polarized light detection

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Science China Chemistry 2023年第12期66卷 3602-3610页

作者： Meng Yuan Yuchen Qiu Yingjie Zhao Yuyan Zhao Hui Li Xiao Wei Gaosong Chen Jiangang Feng Hanfei Gao Jinjin Zhao Jiahui Zhao Lei Jiang Yuchen Wu Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and ChemistryChinese Academy of SciencesBeijing 100190China College of Chemistry Zhengzhou UniversityZhengzhou 450001China College of Chemistry Jilin UniversityChangchun 130012China Key Laboratory for Special Functional Materials of Ministry of Education National and Local Joint Engineering Research Center for High-Efficiency Display and Lighting TechnologyCollaborative Innovation Center of Nano Functional Materials and ApplicationsHenan UniversityKaifeng 475004China University of Chinese Academy of Sciences(UCAS) Beijing 100049China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117585Singapore Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China Ji Hua Laboratory Foshan 528000China Department of Physics Shanxi Datong UniversityDatong 037009China

Chiral two-dimensional(2D) perovskites offer numerous attractive features for optoelectronics owing to their soft, deformable lattices and a high degree of chemical tunability. While tremendous advances have been made in perovskite-based direct circularly polarized light(CPL) detection, the low circular polarization anisotropy factor and sensitivity of those photodetectors arising from large lattice distortion still limit practical applications. Herein, chiral 2D perovskite-based single-crystalline microwire arrays with enhanced circular dichroism(CD) absorption are fabricated with the synergy of the capillary-bridgeconfined assembly method and chlorine-substituted phenethylamine(Cl-MBA). Compared with phenethylamine(MBA)-inserted perovskites, the smaller lattice distortion and increased halogen–halogen interaction within Cl–MBA-inserted perovskites strengthen lattice rigidity and weaken electron–phonon coupling to improve carrier transport and thermal stability, resulting in high-performance CPL photodetectors with an anisotropy factor of 0.25, responsivity exceeding 95.7±9.3 A W^(-1)and detectivity exceeding(3.05±0.30)×10^(13)Jones. This work opens a new perspective to modulate circular polarization sensitivity and will be helpful to realize promising implementations in quantum computation and communication.

关键词： chiral perovskites microwire arrays lattice rigidity circular polarization photodetectors

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Stable Extended U-Net for Noise-Robust Speaker Verification

Stable Extended U-Net for Noise-Robust Speaker Verification

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Zonghui Fang, Zhihua He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798350368741

With advancements in deep learning, speaker verification systems have significantly improved their performance in noisy environments. researchers typically demonstrate the effectiveness of their improved models by comparing performance on specific datasets, such as the VoxCeleb benchmark. However, in diverse real-world noise conditions, the out-of-domain generalization ability is also a crucial factor in evaluating a model's performance improvement. research on stable learning indicates that eliminating the spurious correlation between training and testing data can enhance the generalization of the model. Building on this idea, we propose an improved speaker verification system with high generalization based on the extended U-Net (ExU-Net). It uses the sample reweighting method from stable learning to eliminate sample correlations and retains more effective speaker information through sub-pixel convolutions and coordinate attention mechanisms. We validate the effectiveness of this approach through extensive evaluations on VoxCeleb1, VOiCES, and other out-of-domain noise test sets, highlighting its generalization capability and model robustness. © 2025 IEEE.

关键词： noisy environments out-of-domain generalization Speaker verification stable learning

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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A Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs 12th

A Transformer-Based Spatio-Temporal Graph Neural Network for...

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12th CCF Conference on BigData, BigData 2024

作者： Gao, Yuanjun Zhu, Quntao Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819610235

Dynamic graphs have emerged as a pivotal data structure underpinning real-world network applications. Against this backdrop, detecting anomalies in dynamic graphs has become particularly important, serving as a foundation for multiple downstream tasks. However, existing research has been limited by only considering the simplistic spatial features of the target edge and being confined to local spatial structures. This confinement imposes significant limitations on model performance. Furthermore, current models have not adequately addressed the issue of missing node attributes, relying solely on one-hot encoding to represent nodes, which restricts the expressive capacity of spatial features. In response to these challenges, we propose an end-to-end transformer-based spatio-temporal graph neural network model called TransSTGNN for detecting anomalous edges on dynamic graphs. This model innovatively integrates the graph convolution network with the transformer model to extract multi-dimensional spatial features of the target edge. Simultaneously, TransSTGNN incorporates a novel node enhancement module that reinforces the representation capability of spatial features by encoding the spatial attributes of nodes, effectively tackling the problem of missing node attributes. Extensive experiments confirm that TransSTGNN surpasses the state-of-the-art methods in anomaly detection on six benchmark datasets and effectively resolves the challenges mentioned above. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

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WSpeller: Robust Word Segmentation for Enhancing Chinese Spelling Check

WSpeller: Robust Word Segmentation for Enhancing Chinese Spe...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Li, Fangfang Shan, Youran Duan, Junwen Mao, Xingliang Huang, MinLie School of Computer Science and Engineering Central South University China Institute of Big Data and Internet Innovation Hunan University of Technology and Business China Beijing National Research Center for Information Science and Technology Tsinghua University China

Chinese spelling check (CSC) detects and corrects spelling errors in Chinese texts. Previous approaches have combined character-level phonetic and graphic information, ignoring the importance of segment-level information. According to our pilot study, spelling errors are always associated with incorrect word segmentation. When appropriate word boundaries are provided, CSC performance is greatly enhanced. Based on these findings, we present WSpeller, a CSC model that takes into account word segmentation. A fundamental component of WSpeller is a W-MLM, which is trained by predicting visually and phonetically similar words. Through modification of the embedding layer's input, word segmentation information can be incorporated. Additionally, a robust module is trained to assist the W-MLM-based correction module by predicting the correct word segmentations from sentences containing spelling errors. We evaluate WSpeller on the widely used benchmark datasets SIGHAN13, SIGHAN14, and SIGHAN15. Our model is superior to state-of-the-art baselines on SIGHAN13 and SIGHAN15 and maintains equal performance on SIGHAN14. © 2022 Association for Computational Linguistics.

关键词： Errors

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