检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Deep Plug-and-Play Image Restoration with Total Variation Regularization for Low-Dose Ct

SSRN

引用

SSRN 2024年

作者： Ma, Yinjin Zhang, Yajuan Chen, Lin Jiang, Qiang Wei, Biao School of Data Science Tongren University Tongren554300 China Department of Electrical Automation Hebei University of Water Resources and Electric Engineering Cangzhou061001 China Tongren City People’s Hospital Tongren554300 China Key Laboratory of Optoelectronic Technology and Systems Ministry of Education Chongqing University Chongqing400044 China

X-ray computed tomography (CT) is extensively exploited for screening and diagnosing for clinic which allows acquiring high-resolution images of internal tissue and organ structures of the patients in a non-invasive way. This had attracted more and more public attention on the cumulative radiation risk of X-ray to the patients. Low-dose CT reduces the radiation dose while leads to increased noise and artifacts which seriously affects the diagnostic results of the radiologists. Low-dose CT restoration is a challenging task in medical image processing. To improve the quality of low-dose CT image and take into consideration the flexibility and effectiveness of plug-and-play based methods in image restoration, in this paper, we propose a novel deep plug-and-play denoising method for low-dose CT image restoration. Specifically, we first set up a deep residual block convolutional neural network (DRBNet) with residual noise learning. Next we train the DRBNet on using a hybrid loss function which combines L1 and multi-scale structural similarity (M-SSIM) losses. Meanwhile, the training procedure is regularized by total variation (TV). We then plug the trained DRBNet as a deep denoiser prior into a half quadratic splitting based method to solve low-dose CT image restoration. The experiments show that the proposed plug-and-play method with DRBNet prior outperforms other state-of-the-art methods on suppressing noise and artifacts, preserving the textual and edge information in comparison with standard full-dose CT scans. A blind reader study with two experienced radiologists further demonstrates performance of the proposed method surpassing other compared methods. © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous Information Networks

引用

IEEE Transactions on Multimedia 2021年 23卷 2019-2032页

作者： Lei Sang Min Xu Shengsheng Qian Matt Martin Peter Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW Australia Institute of Automation Chinese Academy of Sciences Beijing China INTERACT Technology Sydney NSW Australia Mininglamp Academy of Sciences Mininglamp Technology Beijing China

With the emergence of online social networks (OSNs), video recommendation has come to play a crucial role in mitigating the semantic gap between users and videos. Conventional approaches to video recommendation primarily focus on exploiting content features or simple user-video interactions to model the users’ preferences. Although these methods have achieved promising results, they fail to model the complex video context interdependency, which is obscure/hidden in heterogeneous auxiliary data from OSNs. In this paper, we study the problem of video recommendation in Heterogeneous Information Networks (HINs) due to its excellence in characterizing heterogeneous and complex context information. We propose a Context-Dependent Propagating Recommendation network (CDPRec) to obtain accurate video embedding and capture global context cues among videos in HINs. The CDPRec can iteratively propagate the contexts of a video along links in a graph-structured HIN and explore multiple types of dependencies among the surrounding video nodes. Then, each video is represented as the composition of the multimodal content feature and global dependency structure information using an attention network. The learned video embedding with sequential based recommendation are jointly optimized for the final rating prediction. Experimental results on real-world YouTube video recommendation scenarios demonstrate the effectiveness of the proposed methods compared with strong baselines.

关键词： Semantics Collaboration YouTube Australia Visualization Context modeling

来源：评论

学校读者我要写书评

暂无评论

EAPS: Edge-Assisted Privacy-Preserving Federated Prediction Systems

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels

arXiv

引用

arXiv 2022年

作者： Ma, Zhongchen Li, Lisha Mao, Qirong Chen, Songcan The School of Computer Science and Communications Engineering Jiangsu University Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China Nanjing211106 China

Contrastive learning (CL) has shown impressive advances in image representation learning in whichever supervised multi-class classification or unsupervised learning. However, these CL methods fail to be directly adapted to multi-label image classification due to the difficulty in defining the positive and negative instances to contrast a given anchor image in multi-label scenario, let the label missing one alone, implying that borrowing a commonly-used way from contrastive multi-class learning to define them will incur a lot of false negative instances unfavorable for learning. In this paper, with the introduction of a label correction mechanism to identify missing labels, we first elegantly generate positives and negatives for individual semantic labels of an anchor image, then define a unique contrastive loss for multilabel image classification with missing labels (CLML), the loss is able to accurately bring images close to their true positive images and false negative images, far away from their true negative images. Different from existing multi-label CL losses, CLML also preserves low-rank global and local label dependencies in the latent representation space where such dependencies have been shown to be helpful in dealing with missing labels. To the best of our knowledge, this is the first general multi-label CL loss in the missing-label scenario and thus can seamlessly be paired with those losses of any existing multi-label learning methods just via a single hyperparameter. The proposed strategy has been shown to improve the classification performance of the Resnet101 model by margins of 1.2%, 1.6%, and 1.3% respectively on three standard datasets, MSCOCO, VOC, and NUS-WIDE. Code is available at https://***/chuangua/ContrastiveLossMLML. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

CUE: Compound Uniform Encoding for Writer Retrieval

CUE: Compound Uniform Encoding for Writer Retrieval

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jiakai Luo Hongwei Lu Xin Nie Shenghao Liu Xianjun Deng Chenlu Zhu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Chutian Expressway Digital Technology Co. Ltd. China

Writer retrieval is crucial in document forensics and historical document analysis. However, due to the difference in syntactic structure between Chinese and other languages, the existing methods may not be directly applied to Chinese writer retrieval. Previous work on Chinese writer retrieval does not overcome the performance degradation problem when the number of samples grows. In this paper, we propose a novel compound uniform encoding algorithm (CUE) for Chinese writer retrieval, which mainly consists of a combined feature extraction module (CFE) and a prototype substitution module (PS). The CFE module combines two complementary features from image filter response and character contour. It counts local symmetries and edge co-occurrence pairs. PS module substitutes the outliers with the class prototypes to alleviate the influence of the outliers. Finally, the weighted Chi-square distance is applied to measure the similarity between writer and text. To verify the superiority of our proposed method, experiments are conducted on four public datasets and our built dataset. The results validate that CUE outperforms the state-of-the-art algorithms on mAP metric.

关键词： Weight measurement Measurement Text analysis Image edge detection Prototypes Syntactics Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Single-cell technologies:From research to application

引用

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

来源：评论

学校读者我要写书评

暂无评论

Secrecy Energy Efficiency Maximization for UAV-Enabled FDMA systems

Secrecy Energy Efficiency Maximization for UAV-Enabled FDMA ...

引用

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

作者： Jie Sun Zhichao Sheng Ali A. Nasir Yanzan Sun Yong Fang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Department of Electrical Engineering and Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

In this paper, we consider an unmanned aerial vehicle (UAV), which serves multiple legitimate ground users with frequency division multiple access (FDMA) approach in the presence of a potential eavesdropper (EV) with uncertain location information. We also assume that UAV must also avoid flying over the no-fly-zone (NFZ). We aim to maximize the secrecy energy efficiency (SEE) by the robust joint design of the rotary-wing UAV three dimensional (3D) trajectory, bandwidth allocation, and transmit power. However, the optimization problem becomes challenging to solve optimally due to the non-convexity of the problem and the imperfect location information of the EV. We develop an efficient iterative algorithm to obtain its high-quality suboptimal solution. The numerical results indicate that our proposed joint optimization algorithm outperforms the benchmark schemes, resulting in significant enhancement of the energy efficiency of the system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：