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10th IEEE Smart World Congress, SWC 2024

作者： Hao, Huijuan Zhao, Hongge Zhu, Sijian Feng, Yue Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China JinanChina

ISBN: (纸本)9798331520861

The rise of the Industrial Internet of Things (IIOT) has brought new opportunities for the mechanical equipment condition monitoring. The trend towards intelligent and automated production of mechanical equipment has become mainstream. However, in the actual operational environment, mechanical equipment is often mixed with a large amount of dirty data unrelated to equipment failures, which seriously affects equipment fault diagnosis and wastes maintenance resources. To improve the reliability of mechanical equipment health status analysis, this paper proposes a new method for dirty data identification suitable for mechanical equipment condition monitoring. First, utilizing the time-space position encoding (TSPE) to map positional relationships between sequences. Next, designing a time-dimension attention (TDA) mechanism to capture the correlation between multiple temporal attribute features and the temporal dependencies between preceding and subsequent data. Then, building a dirty data identification model using an improved Transformer network and Generative Adversarial network (GAN). Finally, employing an adaptive thresholding method to dynamically set thresholds, determining the degree of data anomalies by comparing anomaly scores with the threshold. The effectiveness of the proposed method are validated through one simulation experiment and one real-world example. The F1 Score of this method on the simulated missing data and drifting data is 0.9874 and 0.9838. On the drive-end and fan-end is 0. 9 7 0 0 and 0.9882 at Case Western Reserve University (CWRU). Compared to other dirty data identification methods in the field, this method has better dirty data identification capability. © 2024 IEEE.

关键词： Reliability analysis

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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

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Frontiers of computer Science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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A Concatenated Mapping ZC Sequences Based Large-Capacity Grant-Free Random Access Preamble Design Method for Satellite Internet of Things 7

A Concatenated Mapping ZC Sequences Based Large-Capacity Gra...

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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Ma, Mingke Zhen, Li Li, Shuchang Pei, Chenchen Qin, Hao Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Xidian University School of Telecommunication Engineering Xi'an China

ISBN: (纸本)9798350384437

Satellite communication has garnered widespread attention due to its wide coverage and flexible deployment, and is expected to be a promising solution for achieving reliable access of ubiquitous Internet of Things (IoT) devices. Given the efficiency of grant-free random access (GF-RA) in massive machine-type communications, this paper investigates the preamble sequence design and detection issue for GF-RA in satellite-based IoT. Specifically, a concatenated mapping preamble is designed by concatenating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences processed through Kronecker product operations, which can effectively reduce the collision probability of preambles and further improve the detection performance of active users. In particular, the proposed method remarkably expands the capacity of the preamble set, thereby the users colliding in orthogonal sequences can be identified by detecting non-orthogonal sequences. Furthermore, we propose a two-stage multi-user detection method and theoretically derive the success detection probability of active users. Simulation results show that our proposed method can have a notable improved detection performance as compared to the existing ones. © 2024 IEEE.

关键词： active user detection concatenated mapping preambles grant-free random access Internet of Things (IoT) Satellite communication

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DTGCN: a method combining dependency tree and graph convolutional networks for Chinese long-interval named entity relationship extraction

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13361-13373页

作者： Li, Taoyuan Ma, Liangli Qin, Jiwei Ren, Wei College of Electronic Engineering Naval University of Engineering Wuhan China School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

The extraction of relationships between military entities and descriptive entities is a key task for automated construction of military knowledge graphs. This category of text unstructured imposed problems such as unfixed entity length, possibly overlong intervals between entities, and excessive pronouns. Many studies rely on traditional pattern matching and statistical learning methods. Thanks to the expansion of data volume, deep learning methods are generally used to address similar problems. However, the traditional single neural network model lacks the ability to extract the relationship information of long-interval entities and cannot tackle the above problems well. To solve these problems, a new named entity relationship extraction model called DTGCN is proposed combining dependency tree and GCN (Graph Convolutional networks). By comparing with traditional models, the experimental results demonstrate that the method proposed in this paper can enhance F1-value by 2.28–11.11% and can provide support for the extraction of Chinese long-interval relationships. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Knowledge graph

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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DBKEM-AACS: a distributed key escrow model in blockchain with anonymous authentication and committee selection

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Science China(information Sciences) 2023年第3期66卷 289-290页

作者： Axin XIANG Hongfeng GAO Youliang TIAN Liang WAN College of Computer Science and Technology Guizhou University Institute of Cryptography and Data Security Guizhou University Network and Information Management Center Guizhou University State Key Laboratory of Public Big Data Guizhou University Institute of Computer Software and Theory Guizhou University

Dear editor, key security is of great practical significance and demand to guarantee the security of digital assets in the blockchain system. At present, users prefer to escrow their assets on centralized institutions, but this phenomenon has gone against the unique characteristics of decentralization and anonymity in the blockchain. Among them, the suspense incidents of assets lock or lost are enough to prove that the security of escrowed keys on the exchanges is questionable.

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Angle-resolved photoemission study of NbGeSb with non-symmorphic symmetry

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Chinese Physics B 2024年第2期33卷 464-470页

作者：马欢谭宁吴徐传李满王义炎路洪艳夏天龙王善才 Department of Physics Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing 100872China Beijing Key Laboratory of Opto-Electronic Functional Materials&Micro-Nano Devices Renmin University of ChinaBeijing 100872China School of Information Network Security People’s Public Security University of ChinaBeijing 100038China Institute of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Physics and Physical Engineering Qufu Normal UniversityQufu 273165China

We investigate the electronic structure of NbGeSb with non-symmorphic *** employ angle-resolved photoemission spectroscopy(ARPES)to observe and identify the bulk and surface states over the Brillouin *** utilizing high-energy photons,we identify the bulk Fermi surface and bulk nodal line along the direction X–R,while the Fermi surface of the surface state is observed by using low-energy *** observe the splitting of surface bands away from the high-symmetry point *** density functional theory calculations on bulk and 1 to 5-layer slab models,as well as spin textures of NbGeSb,verify that the band splitting could be attributed to the Rashba-like spin–orbit coupling caused by space-inversion-symmetry breaking at the *** splitted surface bands cross with each other,forming two-dimensional Weyl-like crossings that are protected by mirror *** findings provide insights into the two-dimensional topological and symmetry-protected band inversion of surface states.

关键词： non-symmorphic symmetry nodal line splitting of surface bands angle-resolved photoemission spectroscopy

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Joint User Activity Detection and Channel Estimation for Grant-Free Temporal-Correlated Random Access in LEO Satellite Based IoT 7

Joint User Activity Detection and Channel Estimation for Gra...

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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Zhong, Sheng Zhen, Li Li, Shuchang Dong, Ping Qin, Hao Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an China Xidian University School of Telecommunication Engineering Xi'an China

ISBN: (纸本)9798350384437

Low-earth-orbit (LEO) satellite communication has been regarded as a promising solution for providing ubiquitous Internet of Things (IoT) services, due to its capability of global seamless coverage. In this paper, we focus on resolving the joint activity detection and channel estimation (JADCE) problem for realizing the efficient grant-free random access in LEO satellite based IoT. According to the sporadic user traffic and the temporal correlations over consecutive frames, we formulate the JADCE problem as a dynamic compressive sensing problem. Especially, a probabilistic model is built to represent both the time variation of user activities and the evolution of channel coefficients by steady Markov processes. Furthermore, we develop a correlated information aided hybrid generalized approximate message passing (GAMP) algorithm to solve this problem, which utilizes the GAMP algorithm for detecting active users and estimating channels, and further employs the standard message passing algorithm to update user activity likelihood and channel coefficients. Simulation results show that the proposed algorithm can effectively reduce user activity detection errors and improve channel estimation performance as compared to the existing benchmark algorithms. © 2024 IEEE.

关键词： Markov processes

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Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年 PP卷 1-11页

作者： Lan, Wei Chen, Jianwei Liu, Mingyang Chen, Qingfeng Liu, Jin Wang, Jianxin Chen, Yi-Ping Phoebe Wei Lan is School of Computer Electronic and Information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning Guangxi China School of Computer Electronic and Information Guangxi University Nanning Guangxi China School of Computer Electronic and Information and State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi University Nanning Guangxi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha Hunan China Department of Computer Science and Information Technology La Trobe University Melbourne Victoria Australia

By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE

关键词： RNA

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