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4th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2021

作者： Yang, Baifa Huang, Haixuan Feng, Botao Deng, Li College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781665432030

We propose a dual-polarized conformal Zhe-shaped patch antenna that is suitable for future millimeter wave (mmW) applications. The proposed antenna is composed of 3 different layers. A Zhe-shaped (喆) patch is printed on the upper surface of the top dielectric layer. In order to excite the Zhe-shaped radiating patch, two feeding networks are designed in the other two layers. The simulation result shows that a frequency bandwidth of 10.5% (24.75-27.5 GHz) and a good isolation performance ( © 2021 IEEE

关键词： Microstrip antennas

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An Offensive Language Identification Based on Deep Semantic Feature Fusion

An Offensive Language Identification Based on Deep Semantic ...

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International Conference on Computer and Communications (ICCC)

作者： Xiang Li Zhi Zeng Mingmin Wu Zhongqiang Huang Ying Sha Lei Shi 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

Various forms of social interactions are often char-acterized by toxic or offensive words that can be collectively referred to as offensive languages, which has become a unique linguistic phenomenon in social media platforms. How to detect and identify these offensive languages in social media platforms has become one of the important research in the field of natural language processing. Existing methods utilize machine learning algorithms or text representation models based on deep learning to learn the features of offensive languages and identify them, which have achieved good performances. However, traditional machine learning-based methods mainly rely on keyword identi-fication and blocking, deep learning-based methods do not ade-quately explore the fused deep semantic features of the content by combining word-level embeddings and sentence-level deep semantic feature representations of sentences, which cannot ef-fectively identify offensive languages that do not contain common offensive words but indicate offensive meanings. In this research, we propose a novel offensive language identification model based on deep semantic feature fusion, which uses the pre-trained model Bert to obtain word-level embedding representations of offensive languages, and then integrates the RCNN that combines with the attention mechanism to extract the fused deep semantic feature representations of offensive languages, and label encoder and offensive predictor to improve the identification accuracy and generalization ability of the model so that the performances of the model do not rely on the offensive language lexicon entirely and can identify offensive languages that do not contain common offensive words but indicate offensive meanings. Experimental results on Wikipedia and Twitter comment datasets show that our proposed model can better understand the context and discover potential offensive meanings, and outperforms existing methods.

关键词： Learning systems Social networking (online) Computational modeling Semantics Blogs Encyclopedias Predictive models

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PHY Secure Hybrid Precoding with Max-Min Fair Energy Harvesting for mm Wave MU-MIMO-SWIPT Communications

PHY Secure Hybrid Precoding with Max-Min Fair Energy Harvest...

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International Conference on Communication Technology (ICCT)

作者： Bowen Xue Chi Zhang Dandan Mao Zhengyu Zhu Wenxiang Shi Ning Wang School of Electrical and Information Engineering Zhengzhou University Zhengzhou China China Information Technology Designing and Consulting Institute Co. Ltd. Beijing China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou China State Key Laboratory of Millimeter Waves Southeast University Nanjing China

Physical Layer Secure (PLS) hybrid precoding for Simultaneous Wireless Information and Power Transfer (SWIPT) in a multi-user multiple-input-multiple-output (MU-MIMO) communication system over the millimeter wave (mmWave) band is investigated. Because a limited radio frequency (RF) chain architecture is adopted at the Base Station (BS) of the system, the precoding scheme is divided into two parts, i.e. the analog precoding part and the digital precoding part. Considering a typical use case where the users are Internet-of-Things (IoT)-type devices whose proper operations rely on the harvested energy from wireless power transfer, a fairness problem for harvested energy among users is considered, and a constrained max-min optimization problem is formulated accordingly. Given specific analog precoding based on quantized phase shifter array, the formulated problem aims to maximize the minimum received power of the users by jointly optimizing the digital precoding, the additive Artificial Noise (AN) for improved secrecy, and the power allocation, subject to the secrecy rate constraint and the total transmission power constraint. Due to the non-convexity property of the original optimization problem with the design variables coupled in both the objective function and the constraints, an iterative Successive Convex Approximation (SCA) method is employed to give sub-optimal solutions. Effectiveness and convergence of the proposed design are validated through extensive simulation with different system setting parameters, and tradeoffs between the secrecy rate, the wireless energy transfer, and the hardware complexity are discussed.

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Demonstration and Analysis of a 100-Mbps Real-Time Daytime OWC System

Demonstration and Analysis of a 100-Mbps Real-Time Daytime O...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shixuan Wang Weibo Zheng Shulei Wang Yuanzhe Qu Shidong Yuan Yongchun Yuan Wentao Li Junjie Zhang Qianwu Zhang Key laboratory of Optics Fiber and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Engineering No. 1 Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Shanghai China Teralink Optical Corporation (Shanghai) Shanghai China

We propose a daytime OWC system based on VCSEL and APD with misalignment tolerance of 18 degrees. FPGA-Based real-time experimental results show that 140m @100Mbps transmission distance can be achieved with 47300 lux ... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Ultraviolet sources Asia Avalanche photodiodes Real-time systems Optical fiber communication Vertical cavity surface emitting lasers Photonics

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Cross-Domain Animal Pose Estimation with Skeleton Anomaly-Aware Learning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Han, Le Chen, Kaixuan Zhao, Lei Jiang, Yangbo Wang, Pengfei Zheng, Nenggan Zhejiang Hangzhou310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310007 China Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou310007 China Institute of Blockchain and Data Security. China Zhejiang University School of Software Technology Ningbo China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou310007 China Zhejiang Provincial Government ZJU Collaborative Innovation Center for Artificial Intelligence by MOE Hangzhou310007 China Bengbu University School of Computer and Information Engineering Bengbu233030 China

Animal pose estimation is often constrained by the scarcity of annotations and the diversity of scenarios and species. The pseudo-label generation based unsupervised domain adaptation paradigm, which discriminates the predicted keypoints of unlabeled data based on the skeleton position consistency, has demonstrated effectiveness for such problems. However, existing methods generate pseudo-labels with massive false positives, because they cannot effectively distinguish sample pairs with the same errors. In this study, we propose a cross-domain animal pose estimation model from a novel perspective of skeleton anomaly learning. We construct a graph contrastive learning mechanism to acquire the skeleton anomaly-aware knowledge, which enables the generation of accurate pseudo-labels for target domain and imposes graph constraint on unlabeled data. And a skeleton anomaly-feedback based domain adaptation framework is designed to facilitate implicit alignment of object-specific features and joint training of cross-domain. Besides, we propose a novel rat pose dataset named UDARP-9.4K to address the gap of small-sized animal pose datasets encompassing diverse experimental scenarios. The related datasets are reviewed and evaluated in detail. Extensive experiments are conducted on UDARP-9.4K and two public datasets to demonstrate the superiority of the proposed model in cross-scenarios and cross-species animal pose estimation tasks. Further analysis reveals the effectiveness of the proposed model for skeleton structure feature learning. The UDARP-9.4K dataset is available here. © 1991-2012 IEEE.

关键词： Invertebrates

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Surface crack width measurement based on digital image processing

Surface crack width measurement based on digital image proce...

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Chinese Control and Decision Conference, CCDC

作者： Zongyuan Xie Hongyan Ma Yongxue Ye Jiaming Dou Shengyan Li Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China National Virtual Simulation Experimental Center for Smart City Education Beijing China

ISBN: (纸本)9781665478977

Cracks are common and dangerous hazards in buildings. Aiming at the defects of low accuracy and poor feasibility in traditional crack width detection, proposing a crack width measurement method based on an orthogonal skeleton line. Firstly, the crack image is processed by grayscale. Secondly, the processed image is median filtering and threshold segmentation. Finally, the skeleton and outline of the crack image were extracted by this method, and the measurement and comparison of simulated cracks and actual cracks are completed. The simulation results show that this method has high accuracy and feasibility and can meet the accuracy of engineering practice.

关键词： Image segmentation Simulation Buildings Process control Position measurement Gray-scale Skeleton

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EAPS: Edge-Assisted Privacy-Preserving Federated Prediction Systems

EAPS: Edge-Assisted Privacy-Preserving Federated Prediction ...

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IEEE Conference on Wireless Communications and Networking

作者： Daquan Feng Guanxin Huang Chenyuan Feng Bin Cao Zhenzhong Wang Xiang-Gen Xia Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen University Shenzhen China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Technical Management Center China Media Group Beijing China Department of Electrical and Computer Engineering University of Delaware Newark USA

To reduce the delay and network congestion for content delivery in wireless networks, proactive caching scheme has attracted lots of attentions from both academia and industry. However, traditional caching prediction methods require to collect user data in a centralized server, which is becoming unreliable and impractical due to regulatory restrictions. To circumvent this issue, deploying caching prediction system in a federated learning (FL) fashion becomes a promising solution. However, there still exist privacy risks, and even worse, the FL is vulnerable to low-cost attacks. To solve this problem, a novel federated prediction system (FPS) is studied to provide high robustness and privacy. Firstly, to keep a balance between further enhancing privacy protection and alleviating the performance degradation caused by additional protection schemes, we propose an edge-assisted, robust and privacy-preserving FPS framework based on the local differential privacy (LDP) scheme. Secondly, to mitigate the impact of heterogeneous data, we add a regularization term to the local loss function. Furthermore, an attention-based aggregation scheme is proposed to defend against Byzantine attacks during the training process. Finally, the experiment results are provided to show the superiority of our proposed algorithm in terms of prediction accuracy and robustness.

关键词： Training Industries Privacy Federated learning Wireless networks Predictive models Prediction algorithms

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Deconfound Semantic Shift and Incompleteness in Incremental Few-shot Semantic Segmentation 39

Deconfound Semantic Shift and Incompleteness in Incremental ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wu, Yirui Xia, Yuhang Li, Hao Yuan, Lixin Chen, Junyang Liu, Jun Lu, Tong Wan, Shaohua Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing and Communication Lancaster University Lancaster United Kingdom National Key Lab for Novel Software Technology Nanjing University Nanjing China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)157735897X

Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the trained model to segment new-class images with few samples. However, semantic meanings may shift from background to object class or vice versa during incremental learning. Moreover, new-class samples often lack representative attribute features when the new class greatly differs from the pre-learned old class. In this paper, we propose a causal framework to discuss the cause of semantic shift and incompleteness in IFSS, and we deconfound the revealed causal effects from two aspects. First, we propose a Causal Intervention Module (CIM) to resist semantic shift. CIM progressively and adaptively updates prototypes of old class, and removes the confounder in an intervention manner. Second, a Prototype Refinement Module (PRM) is proposed to complete the missing semantics. In PRM, knowledge gained from the episode learning scheme assists in fusing features of new-class and old-class prototypes. Experiments on both PASCAL-VOC 2012 and ADE20k benchmarks demonstrate the outstanding performance of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Single-cell technologies:From research to application

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The Innovation 2022年第6期3卷 92-108页

作者： Lu Wen Guoqiang Li Tao Huang Wei Geng Hao Pei Jialiang Yang Miao Zhu Pengfei Zhang Rui Hou Geng Tian Wentao Su Jian Chen Dake Zhang Pingan Zhu Wei Zhang Xiuxin Zhang Ning Zhang Yunlong Zhao Xin Cao Guangdun Peng Xianwen Ren Nan Jiang Caihuan Tian Zi-Jiang Chen Biomedical Pioneering Innovation Centre(BIOPIC) Peking UniversityBeijing 100871China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China School of Chemical Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Mozhuo Biotech(Zhejiang)Co. Ltd.TongxiangJiaxing 314500China Geneis(Beijing)Co. Ltd.Beijing 100102China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhou 510530China Department of Medical Oncology Zhongshan HospitalFudan UniversityShanghai 200032China School of Food Science and Technology Dalian Polytechnic UniversityDalian 116034China State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationBeijing Advanced Innovation Center for Biomedical EngineeringSchool of Engineering MedicineBeihang UniversityBeijing 100083China Department of Mechanical Engineering City University of Hong KongHong Kong 999077China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Center of Peony Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Flower Crops(North China)Key Laboratory of Biology and Genetic Improvement of Horticultural CropsMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Hepatic Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Advanced Technology Institute University of SurreyGuildfordSurreyGU27XHUK National Physical Laboratory TeddingtonMiddlesex TW110LWUK Institute of Clinical Science Zhongshan HospitalFudan UniversityShanghai 200032China West China School of Basic Medical Sciences&Forensic Medicine Sichuan UniversityChengdu 610041China Jinfeng Laboratory Chongqing 401329China Center for Reproduc

In recent years,more and more single-cell technologies have been developed.A vast amount of single-cell omics data has been generated by large projects,such as the Human Cell Atlas,the Mouse Cell Atlas,the Mouse RNA Atlas,the Mouse ATAC Atlas,and the Plant Cell *** on these single-cell big data,thousands of bioinformatics algorithms for quality control,clustering,cell-type annotation,developmental inference,cell-cell transition,cell-cell interaction,and spatial analysis are *** powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence,the molecular landscape at the single-cell level can be revealed.

关键词： Atlas landscape application

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Dual Progressive Transformations for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2022年

作者： Huo, Dongjian Su, Yukun Wu, Qingyao School of Software and Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Nanyang Technological University Singapore Pazhou Lab Guangzhou China

Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision. The current state-of-the-art CNN-based methods usually adopt Class-Activation-Maps (CAMs) to highlight the potential areas of the object, however, they may suffer from the part-activated issues. To this end, we try an early attempt to explore the global feature attention mechanism of vision transformer in WSSS task. However, since the transformer lacks the inductive bias as in CNN models, it can not boost the performance directly and may yield the over-activated problems. To tackle these drawbacks, we propose a Convolutional Neural Networks Refined Transformer (CRT) to mine a globally complete and locally accurate class activation maps in this paper. To validate the effectiveness of our proposed method, extensive experiments are conducted on PASCAL VOC 2012 and CUB-200-2011 datasets. Experimental evaluations show that our proposed CRT achieves the new state-of-the-art performance on both the weakly supervised semantic segmentation task the weakly supervised object localization task, which outperform others by a large margin. Code is available at https://***/huodongjian0603/crt Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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