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20th International Conference on Natural Computation, Fuzzy Systems and knowledge Discovery, ICNC-FSKD 2024

作者： Dong, Jie Fang, Wenyu Zheng, Wanling Liu, Jinkun Liu, Yanhua College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China

ISBN: (纸本)9798350356328

In order to achieve more efficient and accurate DDoS detection while ensuring data privacy, this paper proposes a DDoS detection method based on FLAD. Firstly, this paper uses the FLAD algorithm to train a global DDoS detection model without leaving local traffic data, protecting the privacy and security of traffic data between different hosts, and improving aggregation efficiency by dynamically adjusting the aggregation weights to adapt to different sub-dataset increments. Secondly, a DDoS traffic detection method based on the integration of LSTM and CNN is proposed, which extracts and analyzes the temporal correlation of traffic data by calculating the statistical characteristics of traffic data within a time period, to achieve real-time detection of traffic feature data. Again, combined with the concept of SDN, real-time defense against DDoS based on ODL-API is implemented, and precise matching of DDoS detection results with network entity information is achieved, realizing the technology of real-time and precise issuance of multiple flow rules, effectively blocking DDoS malicious attack traffic, protecting important entities in the topology, and maintaining stable traffic in the topology. This paper focuses on solving the detection problem of DDoS traffic data increments and uneven data distribution through the FLAD algorithm. Experimental results show that the proposed method improves the accuracy of DDoS attack detection by more than 4% and the F1 Score by more than 7% compared to the FedAvg aggregation algorithm. © 2024 IEEE.

关键词： Differential privacy

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Research on Weld Detection Based on Weighted Feature Fusion Network 23

Research on Weld Detection Based on Weighted Feature Fusion ...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Wang, Mengxue Ma, Xing Mu, Chunyang Hou, Bing College of Electronic and Information Engineering North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing North Minzu University Yinchuan China

ISBN: (纸本)9798400708237

Weld seam grinding is a crucial process for welding cast products. Not only are the polished welds more attractive and durable, but they also have superior stress effects. However, the workplace is harsh and can significantly impact health. The first task for automated weld grinding is weld detection. This paper, a pyramid feature fusion network (YOLOv5-BiCA) learning model with embedded coordinate attention as a weighting condition that fully utilizes the shallow and deep network information. An online data augmentation strategy employing Mosaic+Mixup is proposed to address the problem of insufficient sample size. We use Focal Loss to enhance the classification function and increase the positive influence of positive samples on the loss function during the training process to address the problem of an imbalance between positive and negative sample. Our proposed YOLOv5-BiCA performs better in weld detection, mAP improves by 3.5%, and recall improves by 7.97%, according to experimental results based on homegrown datasets. © 2023 ACM.

关键词： Welds

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Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention 30

Refining Idioms Semantics Comprehension via Contrastive Lear...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wu, Mingmin Su, Guixin Zhang, Yongcheng Huang, Zhongqiang Sha, Ying College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China

ISBN: (纸本)9782493814104

Chinese idioms on social media demand a nuanced understanding for correct usage. The Chinese idiom cloze test poses a unique challenge for machine reading comprehension due to the figurative meanings of idioms deviating from their literal interpretations, resulting in a semantic bias in models' comprehension of idioms. Furthermore, given that the figurative meanings of many idioms are similar, their use as suboptimal options can interfere with optimal selection. Despite achieving some success in the Chinese idiom cloze test, existing methods based on deep learning still struggle to comprehensively grasp idiom semantics due to the aforementioned issues. To tackle these challenges, we introduce a Refining Idioms Semantics Comprehension Framework (RISCF) to capture the comprehensive idioms semantics. Specifically, we propose a semantic sense contrastive learning module to enhance the representation of idiom semantics, diminishing the semantic bias between figurative and literal meanings of idioms. Meanwhile, we propose an interference-resistant cross-attention module to attenuate the interference of suboptimal options, which considers the interaction between the candidate idioms and the blank space in the context. Experimental results on the benchmark datasets demonstrate the effectiveness of our RISCF model, significantly outperforming state-of-the-art methods. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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A two-lane lattice hydrodynamic model with mixed traffic of connected and non-connected vehicles 6

A two-lane lattice hydrodynamic model with mixed traffic of ...

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6th International Conference on Traffic engineering and Transportation System, ICTETS 2022

作者： Bai, Yingqiang Zhang, Ying School of Information and Engineering Zhejiang Ocean University Zhoushan316000 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9781510663053

The development of intelligent transportation is of great significance to improving the effectiveness and safety of traffic. A new two-lane lattice hydrodynamics model (LHM) is proposed by simultaneously considering the effects of lane-changing and mixed traffic of connected and non-connected vehicles (CNVs). Using linear analysis, the stability conditions are derived, which indicates that lane-changing has a positive impact on traffic stability. In addition, the numerical simulation results provide support for theoretical analysis and confirm the correctness. © 2023 SPIE.

关键词： Hydrodynamics

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MDIot: IoT device identification method based on traffic characteristics 1

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19th Chinese Intelligent Systems Conference, CISC 2023

作者： Zheng, Hanxi Liu, Ruijun Yin, Huanpu Li, Haisheng School of Computer and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China

ISBN: (数字)9789819968824

ISBN: (纸本)9789819968817

In recent years, the rapid growth of the Internet of Things (IoT) devices has brought with it security risks, highlighting the need for management for IoT devices. Identification and classification are key components of devices management. However, existing work on IoT device identification is based on prior knowledge to manually extract features, resulting in too many redundant features and reducing devices identification accuracy. In this paper, we propose amodel called MDIoT. Our model use the multi-votingmethod for feature selection, and is combined with Multiobjective Genetic Algorithm to reduce redundancy features and noise. Experimental results show that our feature selection method is more efficient in feature selection and the classification accuracy is over 95%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Internet of things

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A Survey of COVID-19 Diagnosis Based on Deep Learning from X-ray Images and CT-Scan Images 29th

A Survey of COVID-19 Diagnosis Based on Deep Learning from X...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Wang, Jiaji Chen, Shuwen Zhu, Huisheng School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (纸本)9783031449468

The Coronavirus pandemic has been going on for three years. Undoubtedly, it caused huge damage to people, physically and mentally. The earlier the doctor diagnoses, the sooner the patient will recover In this paper, we review the more advanced applications of deep learning in COVID-19 diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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3D Target Detection of Tractor in Agricultural Scene Based on Lidar 8

3D Target Detection of Tractor in Agricultural Scene Based o...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Zhibo, Chen Caicong, Wu Banghao, Lin College of Information and Electrical Engineering China Agricultural University Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing China

ISBN: (纸本)9798350305838

Real time and accurate detection of agricultural machinery in the working scene is an important basis to improve the safety of agricultural machinery driverless and autonomous operation. This paper presents a 3D target detection method of tractor based on GNSS/INS/Lidar/camera data. Firstly, an on-board data acquisition platform based on multi-source sensors is constructed to obtain the tractor image, point cloud and position and attitude data of the acquisition vehicle in the common working scenes of agricultural machinery (machinery depot, tractor road and farmland), the data are time synchronized and space calibrated. Then, distortion correction of point cloud, the point cloud and image are jointly labelled, and the tractor test data set of images, point cloud and GNSS/IMU information is constructed. 643 frames of machinery depot scene data, 321 frames of tractor track scene data, and 320 frames of farmland scene data are obtained. The number of tractors selected is 6250. Finally, according to the characteristics of tractor driving and operation, the region of interest of tractor target detection is defined, the point cloud within its range is extracted, and the ground point cloud is filtered by RANSAC method. Then the data set is input into PVRCNN++ network for training according to the proportion of 5:1 between training set and test set, and finally the tractor 3D target detection model is obtained. The test results show that the detection accuracy of the model for tractors in machinery depot, tractor road and farmland are 90.21%, 87.59%, and 99.85% respectively, the recall rates are 86.72%, 95.13%, and 98.89% respectively, and the F1 scores are 88.43%, 91.20%, and 99.37% respectively. The model can better adapt to the agricultural scene. © 2023 IEEE.

关键词： Tractors (agricultural)

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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Multi-modal Denoising Diffusion Pre-training for Whole-Slide Image Classification 24

Multi-modal Denoising Diffusion Pre-training for Whole-Slide...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Lou, Wei Li, Guanbin Wan, Xiang Li, Haofeng The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen China

ISBN: (纸本)9798400706868

Whole-slide image (WSI) classification methods play a crucial role in tumor diagnosis. Most of them use hematoxylin and eosin (H&E) stained images, while Immunohistochemistry (IHC) staining provides molecular markers and protein expression information that highlights cancer regions. However, obtaining IHC-stained images requires higher costs in practice. In this work, we propose a multi-modal denoising diffusion pre-training framework that harnesses the advantages of IHC staining to learn visual representations. The framework is trained with the H&E-to-IHC re-staining task and IHC-stained image reconstruction task, which helps capture the structural similarity and staining difference between two image modalities. The trained model can then provide IHC-guided features, by taking only H&E-stained images as inputs. Besides, we build a new class-constraint constrastive loss to achieve the semantic consistency between dual-modal features from our pre-training framework. To integrate with WSI classifiers based on multi-instance learning, we further propose a bag feature augmentation strategy to extend bags with the features extracted by our pre-trained model. Experimental results on three datasets show that our pre-training framework effectively improves WSI classification and surpasses the state-of-the-art pre-training approaches. Code and model are released via https://***/lhaof/MDDP. © 2024 ACM.

关键词： Image reconstruction

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