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IET Blockchain 2024年第1期4卷 18-29页

作者： Ming, Li Wenpeng, Song Ankai, Li Ziming, Zhou Mianchen, Liu Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of Blockchain Shanghai China China Electronics Standardization Institute Beijing China School of Economics and Management Beihang University Beijing China Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations Beijing China

At present, cross-chain technology and cross-chain system pay more attention to cross-chain security and performance, and have shown great improvement in security, scalability and efficiency. However, the generality of cross-chain technology in different scenarios is poor, and it may not fit in different business fields. In order to solve the generality problem of cross-chain technology, this paper sorts out and analyzes the research of blockchain cross-chain protocol, and proposes a fusion protocol cross-chain method based on relay-chain technology. This paper will introduce the cross-chain framework and cross-chain transaction process of fusion protocol. Cross-chain framework includes blockchain cross-chain interaction architecture, cross-chain interaction governance mechanism and cross-chain privacy protection scheme. The transaction process is divided into three different stages. Finally, combined with cross-chain key technologies and landing application requirements, the future research direction of blockchain cross-chain technology is given. The fusion protocol cross-chain method based on relay-chain technology proposed here is a groundbreaking innovation in the field of blockchain cross-chain technology. By integrating the advantages of different blockchain systems and protocols, the fusion protocol is able to achieve a high degree of generality and compatibility, making it suitable for various business scenarios and applications. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Blockchain

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Method for identifying vital nodes in the scientific-citation double-layer hypernetwork based on the h-index

Method for identifying vital nodes in the scientific-citatio...

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2024 International Conference on computer Application and Information Security, ICCAIS 2024

作者： Guo, Lei Liu, Wei Zhang, Xin Hu, Feng School of Computer Qinghai Normal University Qinghai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9781510689312

Existing studies on scientific collaboration networks and citation networks are often limited to the analysis of a single relational dimension, making it challenging to comprehensively capture the interactions between collaboration and citation relationships within multilayered networks and their characteristics in knowledge dissemination. At the same time, traditional vital node identification methods are mostly based on evaluation metrics from single-layer networks, making it difficult to comprehensively and accurately identify vital nodes in multivariate, multilayer and multidimensional relationship hypernetworks. Therefore, based on the characteristics of scientific collaboration and citation networks, and in combination with hypernetwork theory based on hypergraphs, this paper proposes the construction of a scientific-citation double-layer hypernetwork model. Subsequently, based on the concept of aggregated networks, the double-layer hypernetwork is aggregated into a weighted hypernetwork. A method for identifying vital nodes in the hypernetwork based on the h-index is proposed, where the h-index is applied to the weighted aggregated hypernetwork and combined with the K-shell algorithm and hyperdegree metric to identify vital nodes. Finally, this method is applied to the empirical analysis of the scientific-citation double-layer hypernetwork constructed from the dataset of Acta Physica Sinica from 2014 to 2024. The results, based on the analysis of network propagation dynamics, network statistical characteristics, and AUC accuracy evaluation metrics, demonstrate that the proposed identification method is accurate and effective. This study provides valuable insights into how to construct multilayer hypernetwork models based on the characteristics of real-world networks, as well as the application of multilayer hypernetworks in real networks and the identification of vital nodes. © The Authors.

关键词： Multivariant analysis

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Broadband external rejection miniaturized SIW filter based on C-shaped symmetric structure

Broadband external rejection miniaturized SIW filter based o...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Xiao, Shuang Li, Minquan Qu, Xin Li, Chen Zhang, Zufeng Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Liaoyang Province Hefei230601 China

ISBN: (纸本)9781665452366

To effectively improve the frequency selectivity of the filter, a substrate integrated waveguide (SIW) filter with miniaturization and out-of-band rejection was designed. To achieve the desired filter response, symmetrical C-shaped resonators are etched on the top and bottom layers of the SIW filter. The C-shaped resonator etched on the top layer of SIW achieves miniaturization and filtering characteristics. In order to improve the frequency response, a two-stage SIW filter and a SIW filter with coupled gap-ring defected ground structure (CGRDGS) are proposed, and an equivalent circuit is given to verify the transmission response. The simulation results show that the filter achieves miniaturization, out-of-band rejection and low insertion loss, which can be used in 5G wireless communication The simulation results show that the filter achieves miniaturization, out-of-band rejection and low insertion loss, which can be used in 5G wireless communication. © 2022 IEEE.

关键词： Resonators

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CC-Net: Consistency Learning Combined with Contrastive Learning for Kidney Ultrasound Segmentation

CC-Net: Consistency Learning Combined with Contrastive Learn...

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

作者： Luo, Yang Liu, Jun Ding, Mengqian School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665410205

Due to the scarcity of medical images with labels and the fact that medical image data is not easily accessible. Kidney ultrasound (KUS) images have speckle noise, low image quality, acoustic shadows, and other reasons, which pose significant challenges to the kidney ultrasound image segmentation task. To solve the above two problems, a novel consistency learning and contrastive learning-based kidney ultrasound image segmentation method (CC-Net) has been proposed. Specifically, the student-teacher model was adopted as the semi-supervised learning framework and uncertainty estimation was introduced. The Consensus Uncertainty Map (CUM) strategy was proposed to generate segmentation results for the teacher model when calculating uncertainty. To solve the problems of acoustic shadows and low image quality in kidney ultrasound images, a kidney ultrasound image data augmentation method (KUS-AUG) and a convolutional block (KUS-CONV) tailored to the characteristics of kidney ultrasound images were proposed. In addition, contrastive learning was implemented to enhance the encoder's ability to learn more semantic information, due to its inherent advantage in learning data distribution. Finally, a series of experiments were conducted on the ultrasound image dataset and the method achieved the best performance. © 2024 IEEE.

关键词： Contrastive Learning

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Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning 29

Improving Abstractive Dialogue Summarization with Speaker-Aw...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Geng, Zhichao Zhong, Ming Yin, Zhangyue Qiu, Xipeng Huang, Xuanjing School of Computer Science Fudan University Key Laboratory of Intelligent Information Processing Fudan University

Pre-trained models have brought remarkable success on the text summarization task. For dialogue summarization, the subdomain of text summarization, utterances are concatenated to flat text before being processed. As a result, existing summarization systems based on pretrained models are unable to recognize the unique format of the speaker-utterance pair well in the dialogue. To investigate this issue, we conduct probing tests and manual analysis, and find that the powerful pre-trained model can not identify different speakers well in the conversation, which leads to various factual errors. Moreover, we propose three speaker-aware supervised contrastive learning (SCL) tasks: Token-level SCL, Turn-level SCL, and Global-level SCL. Comprehensive experiments demonstrate that our methods achieve significant performance improvement on two mainstream dialogue summarization datasets. According to detailed human evaluations, pretrained models equipped with SCL tasks effectively generate summaries with better factual consistency. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Text processing

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DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Dual-Teacher Semi-Supervised Network for Kidney Ultrasound Image Segmentation

DHCT-DT: Dual-branch Hybrid CNN-Transformer Combined with Du...

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

ISBN: (纸本)9781665410205

Accurate segmentation of kidney ultrasound (KUS) images is of great significance for the diagnosis of kidney diseases. However, KUS images have challenges such as low contrast, large size variations, and acoustic shadow interference. In addition, obtaining labeled data is a major challenge in the field of medical image segmentation. This paper proposes a dual-branch hybrid CNN Transformer combined with a dual teacher semi-supervised segmentation network (DHCT-DT) based on the MT model. The reliability of guidance can be improved between two teachers by constructing consistency regularization constraints. The U-Net architecture is employed between the main teacher and student model. The auxiliary teacher model adopts a dual-branch hybrid CNN Transformer architecture. The multi-scale channel fusion attention mechanism (MSCF) is introduced to capture the feature correlation information of KUS images at different scales and channels. Two different training methods can combine their advantages to fully extract local and global information from images. To demonstrate the applicability of the proposed method, experiments were conducted on two datasets: a KUS dataset and a breast ultrasound (BUS) dataset. Both datasets demonstrated the most competitive segmentation performance. For example, in a semi-supervised experiment using only 10% labeled data in the BUS dataset, the Dice coefficient, Jaccard coefficient, HD, and ASSD reached 82.28%, 76,36%, 13.36mm, and 3.71mm. © 2024 IEEE.

关键词： Image segmentation

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Efficient DOA Estimation Based on Cauchy Loss Variable Step Size Algorithm 6

Efficient DOA Estimation Based on Cauchy Loss Variable Step ...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Xu, Xingchao Li, Yingsong College of Information and Communications Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9798350399059

The Cauchy loss function is robust to large outliers and has successful applications in signal processing. In this paper, a DOA estimation algorithm based on adaptive zero technology is proposed, which updates the weight of the filter by applying the Cauchy loss function, and further improves the algorithm performance by using the variable step size method. The algorithm step size is adjusted by accumulating errors and establishing a nonlinear function with instantaneous errors. The DOA estimation ability, estimation error and accuracy of the improved Cauchy algorithm in the impulse noise environment are analyzed. The algorithm is verified by simulation experiments. © 2023 IEEE.

关键词： Impulse noise

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Improved YOLOv6s Algorithm for Automatic Detection of Elements of Qinghai Farmer Painting and Thangka 5

Improved YOLOv6s Algorithm for Automatic Detection of Elemen...

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5th International Conference on Electronic Engineering and Informatics, ICEEI 2023

作者： Guo, Shaodong Peng, Chunyan Zhang, Xiaojuan School of Computer Science Qinghai Normal University Xining810016 China State Key Laboratory of Intelligent Processing and Application of Tibetan Language Qinghai Normal University Xining810016 China

ISBN: (纸本)9781643685021

Qinghai Farmer Painting and Thangka are important intangible cultural heritages in Qinghai province, and the elements in the picture have the characteristics of abstract, dense and clutter. In order to quickly and accurately understand the meanings of the elements in the images, the Qinghai Farmer Painting dataset and the Thangka dataset are constructed, their element characteristics are analyzed. An improved algorithm based on YOLOv6s is proposed in this study to achieve accurate and fast detection of traditional painting elements of Qinghai Farmer Painting and Thangka. The algorithm incorporates a coordinate attention module into the YOLOv6s to enhance the emphasis on the location information of the object. Experimental results demonstrate that the improved algorithm yields an increase in mAP of 2.1% and 1.1% in the Qinghai Farmer Painting dataset and the Thangka dataset, respectively., compared with YOLOv6s. To verify the generality of the improved algorithm, the training was validated in the PASCAL VOC2012 dataset, and the calculated mAP was improved by 0.3% compared to YOLOv6s. © 2024 The Authors.

关键词： Object detection

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The Dual Path Multi-Feature Fusion Model combining CNN and Transformer for Age Estimation of Caenorhabditis elegans

The Dual Path Multi-Feature Fusion Model combining CNN and T...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ding, Mengqian Liu, Jun Luo, Yang Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350386226

The relatively simple genome, short life cycle, and easy observation of Caenorhabditis elegans (C. elegans) make it an excellent model organism for aging research. Age-stage prediction of C. elegans can help to assess the effectiveness of anti-aging interventions, better explore the role of individual differences in the aging process, and thus promote the development of medicine. However, there are still some problems in accurately predicting the age stages of C. elegans. On the one hand, there may be individual differences among C. elegans due to natural variation and environmental factors. On the other hand, nematode data sets are scarce and image information is complex. In this paper, we perform the age prediction task by studying the body contour morphology, biomarkers, and internal organ changes of C. elegans. Specifically, to address the effects of individual differences and datasets, this paper combines the body contour features, protein aggregation features, and pharyngeal terminal bulb features of C. elegans. In addition, considering the complex information of C. elegans images, this paper proposes a hybrid CNN and Transformer feature extraction method (DFCT), which can fully extract the global and local features of the image. A Dual Path Adaptive Feature Fusion Module (DAFM) is used to fuse the features. A series of experiments are conducted to verify the effectiveness of the proposed method. The experimental results show that the method proposed in this paper outperforms other methods in terms of accuracy and precision, especially in dividing 25 age stages into 3 categories, with an accuracy of 85.93%. © 2024 IEEE.

关键词： age estimation biological image analysis Caenorhabditis elegans CNN and Transformer image classification

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Design and Implementation of Warning Signs for Vehicles Based on Automatic Navigation Algorithms 1

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13th International Conference on computer Engineering and Networks, CENet 2023

作者： Wang, Fangyan Jiao, Ge Lu, Jinjia Yuan, Yiping College of Computer Science and Technology Hengyang Normal University Hunan Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hunan Hengyang421002 China

ISBN: (数字)9789819992393

ISBN: (纸本)9789819992386

Traditional warning signs use human retrograde placement;there are problems such as short warning distances, imprecise placement positions, and difficult positioning of accident points. According to statistics, the user's reaction time after facing a car accident is generally 1.05–1.28 s, and the reaction time to light is 0.225 s. The reaction time for the user to deal with a car accident independently is completely insufficient, and these reasons are the main reasons for secondary traffic accidents. In order to solve this social pain point, we designed a new intelligent triangle warning sign. The intelligent warning sign is installed with an intelligent base at the bottom, and it gets the traffic accident point precisely located when the intelligent warning sign is working, and the system gets the vehicle accident point and reminds the vehicles behind to pay attention to avoid, realizing the three-way interconnection of drivers and passengers, traffic control departments, and the personnel of the vehicles behind, and using the automatic navigation algorithm to realize the automatic navigation of the warning sign to protect the safety of drivers and passengers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Vehicles

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