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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Luyi Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Zheng, Haibin Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Hangzhou Innovation Institute of Beihang University Hangzhou310052 China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031714665

With the rapid proliferation of integration of blockchain and IoT, traditional centralized architectures and single-node processing have been effectively solved, but scalability of blockchain have become inadequate to meet the demands of high-load environments. Sharded blockchain brings an innovative approach to tackle the scalability of blockchain and has the potential to greatly enhance the performance of IoT based on blockchain. This paper introduces CTS towards IoT – complete tree sharding that overlaps shards to enable a little shards to retain records of other shards, facilitating the validation of cross-shard transactions as intra-shard transactions. A tree sharding consensus mechanism that relies on the complete tree architecture is proposed to ensure consistency among interconnected shards during the execution of cross-shard transactions. A prototype for CTS towards IoT is simulated and the findings demonstrate that CTS can enhance transaction throughput by a factor of 1.3 when compared to state of the art layered sharding systems observed in a system consisting of 15 shards and 1500 nodes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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Graph external attention enhanced transformer 24

Graph external attention enhanced transformer

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Proceedings of the 41st International Conference on Machine Learning

作者： Jianqing Liang Min Chen Jiye Liang Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan Shanxi China

The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings. Despite making some progress, existing works tend to overlook external information of graphs, specifically the correlation between graphs. Intuitively, graphs with similar structures should have similar representations. Therefore, we propose Graph External Attention (GEA)--a novel attention mechanism that leverages multiple external node/edge key-value units to capture inter-graph correlations implicitly. On this basis, we design an effective architecture called Graph External Attention Enhanced Transformer (GEAET), which integrates local structure and global interaction information for more comprehensive graph representations. Extensive experiments on benchmark datasets demonstrate that GEAET achieves state-of-the-art empirical performance. The source code is available for reproducibility at: https://***/icm1018/GEAET.

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Wearable and interactive multicolored photochromic fiber display

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Light(Science & Applications) 2024年第3期13卷 454-465页

作者： Pan Li Yuwei Wang Xiaoxian He Yuyang Cui Jingyu Ouyang Ju Ouyang Zicheng He Jiayu Hu Xiaojuan Liu Hang Wei Yu Wang Xiaoling Lu Qian Ji Xinyuan Cai Li Liu Chong Hou Ning Zhou Shaowu Pan Xiangru Wang Huamin Zhou Cheng-Wei Qiu Yan-Qing Lu Guangming Tao State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and Engineering and Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Vascular Aging(HUST) Ministry of EducationWuhan 430030China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollege of Engineering and Applied Sciencesand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China School of Performing Arts Wuhan Conservatory of MusicWuhan 430060China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhan 430074China School of Fashion Beijing Institute of Fashion TechnologyBeijing 100029China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Tongji Medical College Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringDonghua UniversityShanghai 201620China

Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent,wearable interactive ***,significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight ***,we introduce a mass-produced,wearable,and interactive photochromic fiber that provides uniform multicolored light *** designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the *** impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission *** addition,we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single *** prepared fibers on mass-produced kilometer-long using the thermal drawing *** fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as *** work provides a new perspective and inspiration to the existing field of fiber display interaction,paving the way for future human–machine integration.

关键词： fiber interactive colored

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Cascaded mixed pyramid pooling for surface defect segmentation in solid oxide fuel cell 3

Cascaded mixed pyramid pooling for surface defect segmentati...

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3rd International Conference on Electronic information Engineering, Big Data, and computer technology, EIBDCT 2024

作者： Wang, Yaokun Fu, Xiaowei Li, Xi Xu, Wei College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9781510680449

To address the issues of defect leakage and mis-segmentation in the surface defect segmentation of solid oxide fuel cells, we propose a cascaded mixed pyramid pooling segmentation method. In the encoding part, attention-based depth-wise separable convolution is implemented to replace the traditional convolution. This reduces the computational complexity and achieves hierarchical feature extraction. Secondly, cascaded sampling is applied to the last three layers of the encoder to promote multi-level feature fusion, and mixed pyramid pooling consisting of atrous convolution and strip pooling is adopted to achieve feature extraction for long-range band defects. Lastly, an adaptive loss function is constructed to supervise the training process, balancing the relative importance among the losses and improving the learning of defect features by the network. The experiments demonstrate that the proposed method improves segmentation accuracy, as evidenced by better results in mean absolute error, F1 score, intersection over union, and Pratt's figure of merit. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Convolution

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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Research and Application of Smart Water Meter Monitoring System Based on NB-IoT 7

Research and Application of Smart Water Meter Monitoring Sys...

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7th IEEE information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Zhao, Yuanyuan Zhang, Tixiang Hao, Huijuan Zhang, Xiaojie Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science No. 19 Keyuan Road Shandong Jinan250014 China Shandong Yonate Environmental Technology Co. Ltd. Shandong Jinan250014 China

ISBN: (纸本)9798350334197

With the increasing shortage of water resources and the improvement of environmental awareness, intelligent water meter monitoring systems have become a hot research topic in the field of water conservation. This paper studies and designs a smart water meter system based on NB-IoT, and discusses its effect in practical applications. This smart water meter monitoring system adopts STM8 microcontroller as the main control chip of the water meter, and adopts the low-power NB-IoT module BC28 module to upload the monitored voltage data, network signal quantity, current water consumption and collection time to the TLink cloud platform in real time, which solves the problems of unstable communication in the working environment, high power consumption of long-term sensor collection and transmission data. At the same time, the water meter can achieve the function of remote control of the water valve switch by driving the stepper motor, which perfectly solves the problems and shortcomings of the current traditional water meter and smart water meter, and greatly meets the development needs of the water industry. © 2023 IEEE.

关键词： Electric power utilization

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Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification

arXiv

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arXiv 2024年

作者： Ji, Zhangjian Cheng, Donglin Feng, Kai the Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan030006 China

Due to some complex factors (e.g., occlusion, pose variation and diverse camera perspectives), extracting stronger feature representation in person re-identification remains a challenging task. In this paper, we proposed a novel self-supervision and supervision combining transformer-based person re-identification framework, namely SSSC-TransReID. Different from the general transformer-based person re-identification models, we designed a self-supervised contrastive learning branch, which can enhance the feature representation for person re-identification without negative samples or additional pre-training. In order to train the contrastive learning branch, we also proposed a novel random rectangle mask strategy to simulate the occlusion in real scenes, so as to enhance the feature representation for occlusion. Finally, we utilized the joint-training loss function to integrate the advantages of supervised learning with ID tags and self-supervised contrastive learning without negative samples, which can reinforce the ability of our model to excavate stronger discriminative features, especially for occlusion. Extensive experimental results on several benchmark datasets show our proposed model obtains superior Re-ID performance consistently and outperforms the state-of-the-art ReID methods by large margins on the mean average accuracy (mAP) and Rank-1 accuracy. © 2024, CC BY-NC-SA.

关键词： Self-supervised learning

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Text information Mining in Cyberspace: An information Extraction Method Based on T5 and keyBERT 9

Text Information Mining in Cyberspace: An Information Extrac...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Liu, Kuan Li, Yumei Qi, Yuanhua Qi, Ning Zhai, Mengran Qilu University of Technology Shandong Academy of Sciences Information Research Institute Shandong Academy of Sciences Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Institute of Economy and Informatization Development Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Shandong Institute of Economy and Informatization Development Jinan China

ISBN: (纸本)9798350391367

With the development of the digital economy and the advent of the big data era, the rapid growth of textual information in cyberspace has placed higher demands on information processing technology. This paper proposes an information extraction technique based on the Text-to-Text Transfer Transformer, known as T5, and keyBERT models, aiming to efficiently distill the key content from textual information in cyberspace. This method combines automatic summarization and keyword extraction to form information briefs. The experiments selected representative policy documents related to data elements as input data and employed information entropy and ROUGE scores as metrics to evaluate the automatic summarization model. The results were compared with the outputs of similar models. Experimental results indicate that the T5 model outperforms other models in terms of summarization effectiveness, and the proposed information extraction method shows significant advantages in readability and processing efficiency, demonstrating practical application value. © 2024 IEEE.

关键词： Spatio-temporal data

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VIPNet: Combining Viewpoint information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions 9

MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions

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9th International Conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Chen, Zili Peng, Yilong Yang, Yaokai Jiao, Jiadong Wang, Laigang Wang, Aiguo Liu, Jianjun Lin, Wei Guo, Yan School of Tourism Henan Normal University Xinxiang China Institute of Agricultural Information Technology Henan Academy of Agricultural Sciences Zhengzhou China Zhengzhou Tobacco Research Institute of Cntc Zhengzhou China Henan Provincial Tobacco Company of Cntc Zhengzhou China College of Computer and Information Engineering Henan Normal University Xinxiang China Henan Provincial Key Laboratory of Educational Artificial Intelligence and Personalized Learning Xinxiang China Ministry of Agriculture and Rural Affairs Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology Zhengzhou China

ISBN: (纸本)9798350376548

For tobacco leaf disease spots with varying scales and shapes causing difficulties in segmentation and low accuracy, a novel MD-Unet algorithm is proposed for precise segmentation of tobacco leaf disease spots, aiding in scientific prevention and control of tobacco diseases. Innovative design includes Multi-scale Convolution Module (MC) and Dense Residual Dilated Convolution Module (MVCR) to enhance feature extraction of multiscale disease spots and perception of global contextual information. Utilizing Residual Unit Fusion Framework (RUFF) in the upsampling section effectively compensates for spatial details loss and strengthens network representation capability. Additionally, integration of Channel Spatial Attention Mechanism (CBAM) and Attention Gate (AG) further enhances the network's focusing ability on key information and fine-grained feature extraction capability. The MD-Unet algorithm achieves significant improvements in spot CPA, Recall, IoU, F1 metrics with 92.75%, 90.94%,84.93%,91.81% respectively, and an overall Dice score of 94.67%. Compared to mainstream methods like Unet, PSP, DeepLab v3+, FCN, SegNet, UNET++, DoubleU-Net, it shows remarkable enhancements, providing theoretical basis and technical support for precise segmentation of tobacco leaf disease spots and other plant diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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