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14th International Conference on computer Engineering and Networks, CENet 2024

作者： Zhang, Qifan Ma, Yimeng Shen, Jie Yan, Qinghua Yang, Houqun School of Computer Science and Technology Hainan University Hainan Haikou 570228 China Haikou Key Laboratory of Deep Learning and Big Data Application Technology Hainan University Hainan Haikou 570228 China

ISBN: (纸本)9789819640157

Time series classification is a pivotal task in machine learning and data mining and has garnered widespread attention in recent years. With the advent of the significant data era, the application of time series data in finance, healthcare, transportation, energy, and other fields has been increasing, making the research and application of time series classification increasingly significant. This paper summarizes and organizes the relevant research methods from the perspective of traditional time series classification methods and time series classification methods based on deep learning. Traditional methods encompass distance-based, feature-based, interval-based, and dictionary-based approaches. Deep learning-based methods, on the other hand, include convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), gated recurrent units (GRU), and Transformer models. Furthermore, this paper also introduces the characteristics of time series data and common indicators for evaluating time series classification performance and classifies and organizes public time series datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep Learning-based Time Series Classification Methods Time Series Classification Traditional Time Series Classification Methods

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A real-time framework for dangerous behavior detection based on deep learning 22

A real-time framework for dangerous behavior detection based...

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Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Heng Yang Pengjie Liu Shiyuan Li Hongyu Liu Haofeng Wang Ark key laboratory Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9781450398343

Identifying dangerous behaviors of workers in complex production operation scenarios is an important research field of intelligent monitoring technology. When implementing practical applications, it often has complex and diverse function requirements and business logic. To this end, this paper proposes a unified and simple framework for monitoring unsafe behavior based on deep learning technology. We first conduct data analysis and demand logic disassembly based on six actual production scenarios and decouple the complex task requirement as a coupled problem of object detection function, adaptive and variable scene recognition function, behavior analysis function, and safety logic reasoning function. Then we build a unified detection framework and use four sub-modules for real-time and high-efficiency detection, which are a perceptron-based efficient scene recognition module, a Yolov5s-based real-time object detection module, an area-based behavior judgment module, and a configurable safety rule inference module. While maintaining the characteristics of splitting and combining these modules for different application requirements, their effects on the test set have also reached the best. Among them, the accuracy rate of the scene recognition module has reached 100.00%, and the mAP of the object detection module has reached 99.06%, average FPS of the overall framework reached 72.05.

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Unsharp Mask Sharpening Detection via Global Analysis 35

Unsharp Mask Sharpening Detection via Global Analysis

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35th Irish Systems and Signals Conference, ISSC 2024

作者： Lu, Rong-Hui Chen, Tzong-Jer Pan, Junhong Wuyi University Information Technology and Laboratory Management Center Fujian Wuyishan354300 China School of Mathematics and Computer Science Wuyi University Fujian Wuyishan354300 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry China

ISBN: (纸本)9798350352986

Image sharpening prediction plays a significant role in image restoration, forensics, and computer vision. This work proposes a new method for detecting sharpening and indexing in digital images. Images initially undergo manipulation using Gaussian filters and Unsharp Masking (UM) with various degrees of intensity. This is followed by transformation into the frequency domain using Fourier transform. The resulting amplitude values are calculated, and their absolute values are summed along the frequency axis to produce a profile curve. These data curves are subsequently smoothed. The UM curves exhibit concavity and symmetry relative to the curve of the original image. The values of these curves at the concave positions, corresponding to various UM intensities, can be utilized as a sharpness index by comparing them to the curve values of the original image at the same positions. These sharpness indices are dependent on the UM amplification factors. This methodology finds utility in medical image processing and computer vision systems for sharpness detection and indexing. The features identified in this study, based on Fourier power spectrum analysis, can be input into a machine learning classifier for classification. © 2024 IEEE.

关键词： Fourier transforms

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Avoid attacks: A Federated Data Sanitization Defense in IoMT Systems

Avoid attacks: A Federated Data Sanitization Defense in IoMT...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Chen, Chong Gao, Ying Huang, Siquan Yan, Xingfu School of Computer Science and Technology South China University of Technology Guangzhou China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application China School of Computer Science South China Normal University Guangzhou China

ISBN: (纸本)9781665494274

Malicious falsification of medical data destroys the training process of the medical-aided diagnosis models and causes serious damage to Healthcare IoMT Systems. To solve this unsupervised problem, this paper finds a robust data filtering method for various data poisoning attacks. First, we adapt the federated learning framework to project all of the clients' data features into the public subspace domain, allowing unified feature mapping to be established while their data remains stored locally. Then we adopt the federated clustering to re-group their features to clarify the poisoned data. The federated clustering is based on the consistent association of data and its semantics. Finally, we do the data sanitization with a simple yet efficient strategy. Extensive experiments are conducted to evaluate the accuracy and efficacy of the proposed defense method against data poisoning attacks. © 2023 IEEE.

关键词： Semantics

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Long-Term and Short-Term Perception in Knowledge Tracing 1

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11th CCF Big Data Conference, BigData 2023

作者： Chen, Zihang Zhu, Mengxiao Wang, Fei Shen, Shuanghong Huang, Zhenya Liu, Qi Anhui Province Key Laboratory of Science Education and Communication Institute of Advanced Technology and School of Humanities and Social Sciences University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science and School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9789819989799

ISBN: (纸本)9789819989782

Knowledge Tracing (KT) is a fundamental task in contemporary intelligent educational systems, which tracks the knowledge states of the learners based on their response sequences. KT is crucial for the effectiveness of computer-assisted intelligent educational systems, such as intelligent tutoring systems and educational resource recommendation systems. In recent years, KT models benefited from the deep learning approaches and improved dramatically compared with the traditional probabilistic approaches. However, deep learning based KT models also have significant limitations. For example, Recurrent Neural Network (RNN)-based KT models can not capture dependencies in long-term sequences effectively, and attention mechanism-based KT models rely on the positional encoding to perceive sequential information, which may disrupt the semantics of the original embeddings. Moreover, the data sparsity question remains a big challenge in existing KT models. This study is based on the observation that KT shows a stronger sequential dependence in the long term than in the short term. In this paper, we propose a novel KT model called "Long-term and Short-term perception in knowledge tracing (LSKT)", which uses multilayer perceptrons and attention mechanism to capture the long-term and short-term dependencies, and employs 2PL-IRT based embedding to alleviate the data sparsity question. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed LSKT model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Deep Learning Educational Data Mining Intelligent Education Knowledge Tracing

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Deep semantic network for hyperspectral image classification using enhanced bifurcation and light self-Gaussian attention mechanism

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Wan, Xiaoqing Chen, Feng He, Yupeng Hengyang Normal University College of Computer Science and Technology Hengyang China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang China

Convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI) classification due to their strong feature extraction capabilities. Nevertheless, CNN-based classification methods face challenges in capturing deep semantic features effectively, and as the number of layers increases, computational expenses escalate significantly. In addition, spatial locations and spectral bands in HSI data have varying discriminative power. To address these challenges, we propose a novel approach, DSNet, which integrates a multiway attention mechanism with a CNN-based deep semantic network to improve HSI classification. First, we extract shallow abstract features using a module that combines 3D and 2D convolution layers, followed by transformation through a Gaussian-weighted feature tokenizer. Second, we introduce an enhanced bifurcation attention mechanism with dynamically adjustable perceptual ranges to capture spatial semantic information from both horizontal and vertical perspectives. Third, a convolutional multilayer perceptron module is employed to extract high-level feature representations in both spatial and spectral dimensions, which are then refined by a light self-Gaussian attention module. Finally, a linear layer is used to determine the sample labels. Experiments conducted on three publicly available hyperspectral datasets illustrate that the proposed DSNet achieves superior performance compared with several contemporary state-of-the-art methods, even when trained with limited samples. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Convolutional neural networks

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Solar Observation with the Fourier Transform *** Results of Solar Spectrum near the CO 4.66μm and Mg I 12.32μm

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Research in Astronomy and Astrophysics 2023年第12期23卷 128-137页

作者： Xianyong Bai Zhiwei Feng Zhiyong Zhang Yuanyong Deng Xiaoming Zhu Kaifan Ji Song Feng Xiao Yang Yongliang Song National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Yunnan Observatories Chinese Academy of SciencesKunming 650011China Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and TechnologyKunming 650500China

An infrared solar spectrum observed by ground-based telescopes is seriously affected by the background radiation both from the telescope and sky,relative to the visible *** accuracy is also influenced by the spectral resolution of the Fourier transform *** the paper,we developed a CO_(2)gas cell and installed it in the sample compartment to calibrate the spectral resolution of the Bruker IFS-125HR at infrared *** measured spectral resolution is 0.00342±0.00086 cm^(-1)and 0.0059±0.00024 cm^(-1)at the wavenumbers of798 cm^(-1)and 2136 cm^(-1),*** also updated a fully reflective sunlight feeding system to observe the solar spectrum near CO 4.66μm and Mg I 12.32μ*** quickly pointing the sunlight feeding system about1 degree away from the solar disk center,we are able to measure the background radiation from the telescope and the sky at Huairou Solar Observing *** removing the background radiation,our observed solar spectrum at CO 4.66μm is consistent with that from the National Solar *** Mg I 12.32μm working line selected by the Accurate Infrared Magnetic Field Measurements of the Sun(AIMS)project is also *** method is helpful not only for the spectral resolution calibration and background radiation correction of AIMS but also for other infrared astronomical telescopes.

关键词： Sun infrared-methods observational-instrumentation interferometers

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Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

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Journal of Earth Science 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

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computer Modeling in Engineering & Sciences 2024年第11期141卷 1305-1325页

作者： Yongsheng Zhu Chong Liu Chunlei Chen Xiaoting Lyu Zheng Chen Bin Wang Fuqiang Hu Hanxi Li Jiao Dai Baigen Cai Wei Wang School of Automation and Intelligence Beijing Jiaotong UniversityBeijing100044China Institute of Computing Technologies China Academy of Railway Sciences Corporation LimitedBeijing100081China School of Computer Science and Technology Beijing Jiaotong UniversityBeijing100044China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong UniversityBeijing100044China Institute of Infrastructure Inspection China Academy of Railway Sciences Corporation LimitedBeijing100081China Zhejiang Key Laboratory of Multi-Dimensional Perception Technology Application and CybersecurityHangzhou310053China

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system *** learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private ***,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s *** this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated *** experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s ***,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

关键词： Privacy-preserving intelligent railway transportation system federated learning poisoning attacks defenses

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Research on Metaverse Multi-person Linkage Using Mobile Edge Computing Based on Extended Reality Under the Immersive Experience of Zhijiang Peace Culture Memorial Hall 16th

Research on Metaverse Multi-person Linkage Using Mobile Edge...

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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Liu, Yiwen Fu, Jinrong Yan, Haobo Gao, Yanxia Peng, Ling Qu, Taiguo School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (纸本)9783031580529

This study aims to deeply explore the impact of mobile edge computing based on extended reality technology on the multi-person linkage of the Zhijiang Peace Culture Memorial Hall under the immersive experience. In the part of research background and significance, it discusses the rise of the application of XR technology in the field of culture and education, emphasizing the importance of education on the history of revolution and its value in the social sense. Then, it expounds the core topic of this research aimed at integrating XR technology with education on the history of revolution, and presenting visitors with more realistic and immersive historical scenes by creating an immersive experience of multi-person linkage. The use of these technologies enables visitors to transcend time and space barriers, directly participate in historical events, and deeply appreciate the intrinsic value of red culture. In order to achieve multi-person linkage, this study adopts mobile edge computing technology to ensure that multiple visitors can realize real-time interaction in the virtual scene and jointly build a collective experience of the Metaverse. This research has achieved positive results in the fields of XR technology application, education on the history of revolution, and mobile edge computing. It provides a useful reference for the modernization and upgrading of the Zhijiang Peace Culture Memorial Hall, and also provides a new paradigm for the cultural promotion of multi-person linkage experience in Metaverse. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Mobile edge computing

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