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IEEE International Conference on Data Science in Cyberspace (DSC)

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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

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.

关键词： Measurement Biological system modeling Cyberspace Text summarization information processing information retrieval Transformers Data models Data mining information entropy

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Incremental algorithms for the maximum internal spanning tree problem

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Science China(information Sciences) 2021年第5期64卷 77-84页

作者： Xianbin ZHU Wenjun LI Yongjie YANG Jianxin WANG School of Computer Science and Engineering Central South University Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Chair of Economic Theory Saarland University

The maximum internal spanning tree（MIST） problem is utilized to determine a spanning tree in a graph G,with the maximum number of possible internal *** incremental maximum internal spanning tree（IMIST） problem is the incremental version of MIST whose feasible solutions are edge-sequences e1,e2,…,en-1such that the first k edges form trees for all k ∈ [n-1].A solution's quality is measured using maxk∈[n-1]opt（G,k）/|In（Tk）| with lower being ***,opt（G,k） denotes the number of internal vertices in a tree with k edges in G,which has the largest possible number of internal vertices,and |In（Tk）| is the number of internal vertices in the tree comprising the solution's first k *** first obtained an IMIST algorithm with a competitive ratio of 2,followed by a 12/7-competitive algorithm based on an approximation algorithm for MIST.

关键词： maximum internal spanning tree incremental problem approximation algorithm competitive ratio

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

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

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6th International Conference on Intelligent Computing and Signal processing (ICSP)

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

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

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.

关键词： Location awareness Accuracy Shape Convolution Signal processing algorithms Logic gates Feature extraction Robustness Lesions Diseases

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Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning

arXiv

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arXiv 2025年

作者： He, Yuting Wang, Boyu Ge, Rongjun Chen, Yang Yang, Guanyu Li, Shuo Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing China School of Instrument Science and Engineering Southeast University Nanjing China Department of Computer Science Western University LondonONN6A 3K7 Canada Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH44106 United States

Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We proposes a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels’ correspondence under the condition of topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via gradient. We also proposes a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code at a companion website. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Student Behavior Recognition in Remote Video Classrooms 16th

Student Behavior Recognition in Remote Video Classrooms

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16th International Conference on Intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of information technology, Applications and Tools, FITAT 2020

作者： Wang, Rongrong Zhang, Guanglong Zhang, Fuquan Dong, Zhiwei Qi, Mingzhu Computer School Beijing Institute of Technology Beijing100081 China Computer School Jilin University Changchun130012 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Botou Vocational College Botou062150 China Shandong Dezhou Experimental Middle School Dezhou253000 China

ISBN: (纸本)9789813367562

With the development of high-definition camera equipment and Internet communication technology, remote video teaching methods have emerged. This new type of teaching method has led to changes in traditional classrooms. On the one hand, remote video teaching has broken the constraints of time and space and effectively improved the inequality of educational resources. On the other hand, in actual video courses, teachers cannot effectively monitor students’ learning actions and states, so they cannot communicate well. In order to solve this problem, this paper proposes an algorithm based on deep learning to monitor the behaviors of students in real time during the learning process, understand the learning status of students, and strengthen the real-time communication between teachers and students. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Students

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An End-to-End Network for Single Image Dedusting 7

An End-to-End Network for Single Image Dedusting

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7th International Conference on information Science and Control Engineering, ICISCE 2020

作者： Huang, Jiayan Yu, Zhaochai Li, Zuoyong Zheng, Xiangpan Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9781728164069

Dust storm not only reduces visibility of natural scene, but also affects capture capability of outdoor monitoring equipment. Image dedusting aims to remove dust storm in images and make image scenes clearer. However, most of existing image enhancement-based methods have focused on defogging and deraining, and have paid little attentions to image dedusting. Therefore, research of image dedusting is necessary. In this paper, we presented an end-to-end network for single image dedusting. First, the proposed method uses an encoder module to encode a dusty image into image feature maps. Second, it uses the latest smoothed dilated residual convolution module to alleviate gridding artifacts caused by a widely-used dilated convolution. Third, it adopts an attention-based module to fuse image features at different levels and extract more contextual image information. Finally, it utilizes a decoder module, which decodes the feature maps, to obtain a residue, and adds it to the input dusty image to obtain the final image dedusting result. Experiments demonstrate the effectiveness of the proposed method in image dedusting. © 2020 IEEE.

关键词： Convolution

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FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment

arXiv

引用

arXiv 2023年

作者： Lin, Qinghua Li, Zuoyong Zeng, Kun Fan, Haoyi Li, Wei Zhou, Xiaoguang School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Automation Beijing University of Post and Telecommunications Beijing100876 China School of Economics and Management Minjiang University Fuzhou350121 China

Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process of data labeling is particularly costly and time-consuming, especially when dealing with videos. Considering the limited quantity of labeled video data, we propose a semi-supervised fire detection model called FireMatch, which is based on consistency regularization and adversarial distribution alignment. Specifically, we first combine consistency regularization with pseudo-label. For unlabeled data, we design video data augmentation to obtain corresponding weakly augmented and strongly augmented samples. The proposed model predicts weakly augmented samples and retains pseudo-label above a threshold, while training on strongly augmented samples to predict these pseudo-labels for learning more robust feature representations. Secondly, we generate video cross-set augmented samples by adversarial distribution alignment to expand the training data and alleviate the decline in classification performance caused by insufficient labeled data. Finally, we introduce a fairness loss to help the model produce diverse predictions for input samples, thereby addressing the issue of high confidence with the non-fire class in fire classification scenarios. The FireMatch achieved an accuracy of 76.92% and 91.81% on two real-world fire datasets, respectively. The experimental results demonstrate that the proposed method outperforms the current state-of-the-art semi-supervised classification methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Fires

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Joint Sparsification and Quantization for Wireless Federated Learning under Communication Constraints

Joint Sparsification and Quantization for Wireless Federated...

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IEEE Workshop on Signal processing Advances in Wireless Communications (SPAWC)

作者： Junshen Su Xijun Wang Xiang Chen Ray-Guang Cheng School of Electronics and Information Technology Sun Yat-Sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Communication efficiency is one of the key bottlenecks in Federated Learning (FL). Compression techniques, such as sparsification and quantization, are used to reduce communication overhead. However, joint designs of these techniques under communication constraints are not well-explored. This paper investigates the joint uplink compression problem in communication-constrained FL systems. We propose a Block-TopK sparsification scheme to reduce the proportion of bits used for locating entries of a sparsified vector. Considering the communication constraints, an optimization formulation is proposed to minimize the compression error. By solving the optimization problem, our joint compression method provides a better trade-off between sparsity budget and bit width. Numerical results demonstrate that our approach achieves 99.96% of baseline accuracy with only 1.56% of the baseline communication overhead when training ResNet-18 on CIFAR-10.

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Lightweight Vision Model-based Multi-user Semantic Communication Systems

arXiv

引用

arXiv 2025年

作者： Jiang, Feibo Tu, Siwei Dong, Li Wang, Kezhi Yang, Kun Liu, Ruiqi Pan, Cunhua Wang, Jiangzhou Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Semantic Communication (SemCom) is a promising new paradigm for next-generation communication systems, emphasizing the transmission of core information, particularly in environments characterized by uncertainty, noise, and bandwidth constraints. However, existing image SemCom systems face several challenges, such as inefficient knowledge base construction, insufficient semantic encoding, and lack of multi-user semantic sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-user Semantic Communication System (LVM-MSC). First, we construct a Lightweight Knowledge Base (LKB) based on the fast Segment Anything Model (SAM). LKB incorporates the extensive image knowledge of the SAM model while significantly reducing the number of parameters through its convolutional architecture. Next, we design an Efficient Semantic Codec (ESC) based on the Masked AutoEncoder (MAE) architecture. ESC enhances semantic compression at both the pixel and semantic levels and implements lightweight semantic decoding tailored for user devices. Furthermore, we propose a Multi-user Semantic Sharing (MSS) transmission for the multi-user SemCom. By calculating the similarity of semantic information among different users in the sharing semantic space, we unify the transmissions of similar semantic information through broadcasting, further improving the transmission efficiency. Finally, simulation results demonstrate the feasibility and effectiveness of the proposed LVM-MSC system. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

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A Photonics-aided MMW OFDM Joint Radar and Communication System with Velocity Accuracy Improvement

A Photonics-aided MMW OFDM Joint Radar and Communication Sys...

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Applied Sensing Conference (APSCON), IEEE

作者： Lanfeng Peng Dongxiang Luo Yaoqiang Xiao Fan Li School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Sun Yat-Sen University Guangzhou China Institute of Semiconductors South China Normal University Guangzhou China School of Chemistry and Chemical Engineering/Huangpu Hydrogen Innovation Center Guangzhou University Guangzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China

Photonics-aided millimeter wave (MMW) Orthogonal frequency division multiplexing (OFDM) system is a novel approach to realize the joint radar and communication system. The phase noise introduced by the laser will degrade the system performance. In this paper, we analyze the mechanism of phase noise acting on OFDM radar system. A method that combines chirp-Z transform (CZT) and compensation enabled by a reference signal is adopted to improve the velocity accuracy. RF pilot (RFP) and blind phase search (BPS) algorithm are adopted to improve communication performance. A simulation is demonstrated at 30 GHz with 2 GHz communication bandwidth. A twodimensional radar imaging with a range resolution of 0.075 m and velocity resolution of 0.980 m/s and a communication capacity of 6.4 Gbps are obtained in the proposed joint radar and communication system.

关键词： Phase noise Laser radar Image resolution Communication systems OFDM Bandwidth Radar imaging

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