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3rd International Conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Wang, Hao Si, Jinxin Zang, Xiuhuan Ding, Shiqi Liu, Chang Pan, Jianhong Shen, Junzhe Engineering Data Center State Grid Economic and Technological Research Institute Co. Ltd Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Digital Department State Grid Jilin Electric Power Co.Ltd Changchun China Planning and Operation Department of Economic and Technological Research Institute of State Grid Jilin Electric Power Co. LTD Changchun China

ISBN: (纸本)9798350340402

In the present digital era, data has become an increasingly valuable asset to both enterprises and society. Despite the vast amount of available data, the absence of a reliable means of transaction often hinders the direct benefits that data can bring. This results in a lack of willingness among data owners to conduct transactions, poor data mobility, and an inability to fully realize the value of data. In order to promote the transaction and circulation of data, this study Eposesa data auction model based on blockchain technology, which ensures transparency, security, and efficiency in data auctions. Furthermore, an optimal auction algorithm is devised, which demonstrates that in the process of data trading with multiple participants, the auction model can maximize the utility of data owners, improve their willingness to trade, enhance data liquidity, fully realize the value of data, and provide a platform for sustainable development of data trading. © 2023 IEEE.

关键词： Smart contract

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Multiplicative Fault Detection and Isolation in Dynamic Systems Using data-Driven K-Gap Metric based kNN Algorithm

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IFAC-PapersOnLine 2022年第6期55卷 169-174页

作者： Caroline Charlotte Zhu Linlin Li Steven X. Ding Institute of Automatic Control and Complex Systems (AKS) University of Duisburg-Essen Duisburg 47057 Germany Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing 100083 Beijing China

In this paper, a fault detection and isolation scheme for multiplicative faults in dynamic systems based on data-driven K-Gap metric and k-nearest neighbour (kNN) classification is proposed. To detect multiplicative faults, the standard classification task of kNN is studied from the viewpoint of system analysis. To this end, the data-driven stable kernel representation based on input/output data is presented for feature extraction capturing the dynamic of linear time-invariant (LTI) systems. data-driven K-Gap metric is used as an alternative tool for distance measure between two kernel subspaces in the kNN algorithm. A simulation example on the three-tank system (DTS200) demonstrates the successful detection and isolation of various multiplicative faults

关键词： Fault detection isolation multiplicative faults data-driven methods multiclass classification process monitoring K-Gap gap metrics

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Numerical analysis of added mass and damping of elastic hydrofoils

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Journal of Hydrodynamics 2020年第5期32卷 1009-1023页

作者： Jia-sheng Li Ye-gao Qu Hong-xing Hua School of Naval Architecture and Ocean Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Provincial Engineering Research Center of Data Techniques and Supporting Software for Ships(DTSSS) Wuhan 430074China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai 200240China

A numerical model is proposed for analyzing the effects of added mass and damping on the dynamic behaviors of *** coupled fluid-structure interactions(FSIs)of hydrofoils are analyzed by using the 3-D panel method for the fluid and the finite element method for the *** added mass and damping matrices due to the external fluid of the hydrofoil are asymmetric and computational *** computational inefficiencies associated with these asymmetric matrices are overcome by using a modal reduction technique,in which the first several wet mode vectors of the hydrofoil are employed in the analysis of the FSI *** discretized system of equations of motion for the hydrofoil are solved using the Wilson-6 *** present methods are validated by comparing the computed results with those obtained from the finite element *** is found that the stationary flow is sufficient for determining the wet modes of the hydrofoil under the condition of single-phase potential flow and without phase *** the case of relatively large inflow velocity,the added damping of the fluid can significantly affect the structural responses of the hydrofoil.

关键词： Fluid-structure interaction(FSI) hydroelastic response hydrofoil added mass added damping

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Once read is enough: domain-specific pretraining-free language models with cluster-guided sparse experts for long-tail domain knowledge 24

Once read is enough: domain-specific pretraining-free langua...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Fang Dong Mengyi Chen Jixian Zhou Yubin Shi Yixuan Chen Mingzhi Dong Yujiang Wang Dongsheng Li Xiaochen Yang Rui Zhu Robert Dick Qin Lv Fan Yang Tun Lu Ning Gu Li Shang China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Microsoft Research Asia Shanghai China School of Mathematics & Statistics University of Glasgow Bayes Business School City St George's University of London Department of Electrical Engineering and Computer Science University of Michigan Department of Computer Science University of Colorado Boulder School of Microelectronics Fudan University

ISBN: (纸本)9798331314385

Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, applying domain-specific pretraining to LMs is a common and indispensable practice. However, domain-specific pretraining can be costly and time-consuming, hindering LMs' deployment in real-world applications. In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and "long-tail" distributions as the leading cause for pretrained LMs' inferior downstream performance. Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model's gradient updates and, consequently, are not effectively memorized, leading to poor domain-specific downstream performance. Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs. During pretraining, a CSE layer efficiently clusters domain knowledge together and assigns long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers. With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, "outlier" clusters in an LM's representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of domain-specific-pretraining-free language models.

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MRI-derived radiomics models for diagnosis,aggressiveness,and prognosis evaluation in prostate cancer

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2023年第8期24卷 663-681页

作者： Xuehua ZHU Lizhi SHAO Zhenyu LIU Zenan LIU Jide HE Jiangang LIU Hao PING Jian LU Department of Urology Peking University Third HospitalBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100080China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information Technology of the People’s Republic of ChinaBeijing 100191China Department of Urology Beijing Tongren HospitalCapital Medical UniversityBeijing 100730China

Prostate cancer(PCa)is a pernicious tumor with high heterogeneity,which creates a conundrum for making a precise diagnosis and choosing an optimal treatment *** magnetic resonance imaging(mp-MRI)with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of ***,using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence(AI)and image data *** acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise *** models provide a reliable and noninvasive alternative to aid in precision medicine,demonstrating advantages over traditional models based on clinicopathological *** purpose of this review is to provide an overview of related studies of radiomics in PCa,specifically around the development and validation of radiomics models using MRI-derived image *** current landscape of the literature,focusing mainly on PCa detection,aggressiveness,and prognosis evaluation,is reviewed and *** than studies that exclusively focus on image biomarker identification and method optimization,models with high potential for universal clinical implementation are ***,we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical *** review will encourage researchers to design models based on actual clinical needs,as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.

关键词： Magnetic resonance imaging(MRI) Radiomics Prostate cancer Predictive model

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Network Situation Awareness Model Based on Incomplete Information Game 5th

Network Situation Awareness Model Based on Incomplete Inform...

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5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Zhen School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science and Technology Shijiazhuang050000 China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

Game theory has been widely used in network security situational awareness. However, most of the currently proposed game-based offensive and defensive situational awareness methods are for traffic data, and there are fewer models or methods for analysis using vulnerability data. To overcome these issues, this paper proposes collecting periodic security vulnerability information in the network and utilizing the change in vulnerability status to achieve network security situational awareness. At this time, a network attack and defense game model based on incomplete information is proposed, which uses the state changes of the vulnerability life cycle to model the attack and defense behavior, calculates the benefits of both attack and defense through the evaluation of the exploitability of the vulnerability, and then quantifies the security situation value. We carried out the experiments using the vulnerability dataset, which was obtained by scanning the IP addresses of several enterprises in Hebei Province, China. The experimental results show that the approach of using network security vulnerabilities to assess network security status is feasible. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

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Temporal Hierarchical Graph Attention Network for Traffic Prediction with Prompt Learning

Temporal Hierarchical Graph Attention Network for Traffic Pr...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Cheng Li Pei-Yuan Lai Yu-Xuan Zhou Chang-Dong Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China South China Technology Commercialization Center Guangzhou China School of Information Engineering Guangdong University of Technology Guangzhou Guangdong China Beijing Normal University - Hong Kong Baptist University United International College Zhuhai China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Traffic prediction is a complex task. In the traditional forecasting methodologies, there are limitations regarding generalization, as specifically observed in the pretraining approaches. This is particularly evident in models involving temporal and spatial attributes, where finetuning often encounters numerous challenges. With the emergence of graph prompt learning, a new finetuning paradigm has gained increasing application. In the context of the traffic prediction methods involving spatio-temporal attributes, we endeavor to enhance its performance in the downstream tasks through graph pretraining and prompt-based finetuning. In particular, we propose a traffic prediction model based on graph prompt learning. This model deeply mines the hierarchical information of traffic data and its temporal attributes, by employing the method of graph pretraining and prompt learning to enhance the model’s generalizability and accuracy. Extensive experiments conducted on two real-world traffic prediction datasets confirm the effectiveness of the proposed method.

关键词： Wireless communication Accuracy Computational modeling Predictive models data models Task analysis Forecasting

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Feature Compression for Cloud-Edge Multimodal 3D Object Detection

arXiv

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arXiv 2024年

作者： Tian, Chongzhen Li, Zhengxin Yuan, Hui Hamzaoui, Raouf Shen, Liquan Kwong, Sam The School of Control Science and Engineering Shandong University Jinan250061 China The School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China The School of Data Science Lingnan University Hong Kong

Machine vision systems, which can efficiently manage extensive visual perception tasks, are becoming increasingly popular in industrial production and daily life. Due to the challenge of simultaneously obtaining accurate depth and texture information with a single sensor, multimodal data captured by cameras and LiDAR is commonly used to enhance performance. Additionally, cloud-edge cooperation has emerged as a novel computing approach to improve user experience and ensure data security in machine vision systems. This paper proposes a pioneering solution to address the feature compression problem in multimodal 3D object detection. Given a sparse tensor-based object detection network at the edge device, we introduce two modes to accommodate different application requirements: Transmission-Friendly Feature Compression (T-FFC) and Accuracy-Friendly Feature Compression (A-FFC). In T-FFC mode, only the output of the last layer of the network’s backbone is transmitted from the edge device. The received feature is processed at the cloud device through a channel expansion module and two spatial upsampling modules to generate multi-scale features. In A-FFC mode, we expand upon the T-FFC mode by transmitting two additional types of features. These added features enable the cloud device to generate more accurate multi-scale features. Experimental results on the KITTI dataset using the VirConv-L detection network showed that T-FFC was able to compress the features by a factor of 6061 with less than a 3% reduction in detection performance. On the other hand, A-FFC compressed the features by a factor of about 901 with almost no degradation in detection performance. We also designed optional residual extraction and 3D object reconstruction modules to facilitate the reconstruction of detected objects. The reconstructed objects effectively reflected the shape, occlusion, and details of the original objects. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

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Towards Accurate Search for E-Commerce in Steel Industry: A knowledge-Graph-Based Approach 16th

Towards Accurate Search for E-Commerce in Steel Industry: A ...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Chen, Maojian Shen, Hailun Huang, Ziyang Luo, Xiong Yin, Junluo School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong528399 China Ouyeel Co. Ltd. Shanghai201999 China

ISBN: (纸本)9783030675363

Mature artificial intelligence (AI) makes human life more and more convenient. However, in some application fields, it is impossible to achieve the satisfactory results only depending on the traditional AI algorithm. Specifically, in order to avoid the limitations of traditional searching strategies in e-commerce field related to steel, such as the inability to analyzing long technical sentences, we propose a collaborative decision making method in this field, through the combination of deep learning algorithms and expert systems. Firstly, we construct a knowledge graph (KG) on the basis of steel commodity data and expert database, and then train a model to accurately extract steel entities from long technical sentences, while using an advanced bidirectional encoder representation from transformers (BERT), a bidirectional long short-term memory (Bi-LSTM), and a conditional random field (CRF) approach. Finally, we develop an intelligent searching system for e-commence in steel industry, with the help of the designed KG and entity extraction model, while improving the searching performance and user experience in such system. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Decision making

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Remaining Useful Life Prediction with Spatio-Temporal Graph Transform and Weakly Supervised Adversarial Network: An Application in Power Components

SSRN

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SSRN 2024年

作者： Deng, Shuhan Lan, Hao Yue, Ke Chen, Zhuyun Zhang, Xuning Huang, Ruyi Huang, Zhicong Li, Weihua Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China School of Mechanical & Automotive Engineering South China University of Technology Guangzhou China State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology Guangzhou China Department of Industrial Engineering and Decision Analytics The Hong Kong University of Science and Technology Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Singapore

Predictive maintenance is crucial for improving the reliability of power components and having attracted widespread attention, for which remaining useful life (RUL) prediction is a precondition. Owing to the heterogeneity of the power components, accuracy RUL prediction through different components remains challenging. To make things worse, the structural property of the data itself is also neglected. Herein, take the insulated gate bipolar transistors (IGBTs) as the sample, a novel RUL prediction framework is proposed in this paper. In brief, a time window and Radius distance metric function is adopted to generate graph information matrix. Specifically, spatio-temporal fusion graph network (STFGN) is proposed to obtain unique IGBTs data structure information and incorporate past and future moments aging features to construct health index (HI). Then, weakly supervised adversarial learning strategy is considered to realize the cross-domain RUL prediction, which conditional domain adaptation (CDA)-based adversarial method is adopted to reduce distribution discrepancies between distinct domains and weakly supervised training strategy with some target domain labels is also used to enhance positive transfer. Moreover, the proposed method is proved by four transfer tasks and the results show that overcomes the challenge of unavailability of labeled failure samples and outperforms state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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