检索结果-内蒙古大学图书馆

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Wei, Hongxi Kang, Yanke Zhang, Hui School of Computer Science Inner Mongolia University Hohhot China Provincial Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9781728120584

To realize keyword spotting by means of query-by-example, learning efficient representation for word images is an essential issue. However, the amount of vocabulary at the training stage is often far less than the complete vocabulary of a certain language in various learning based representation approaches. Thus, unseen vocabularies might be taken as query keywords which may not exist in training set. Therefore, out-of-vocabulary (OOV) is frequently occurred in keyword spotting. In this paper, a sequence to sequence model with attention mechanism has been proposed to generate representation vectors of word images for solving the problem of OOV. After that, similarities can be calculated between each word image and a given query keyword image on their representation vectors. And then, a ranking list can be formed in descending order of the similarities for a collection of word images. Experimental results demonstrate that the proposed representation approach can be competent for the task of OOV keyword spotting and outperforms various baseline and state-of-the-art methods. © 2019 IEEE.

关键词： Image representation

来源：评论

学校读者我要写书评

暂无评论

Outer Synchronization Between Complex Delayed Networks with Both Uncertain Parameters and Unknown Topological Structure

Outer Synchronization Between Complex Delayed Networks with ...

引用

作者： Zhong Chen Xiaomei Tian Tianqi Lei Junyao Chen College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Department of Computer Engineering Ajou University

In this paper,synchronization of both non-identical unknown network and identical known network can be *** on Lyapunov stability theory,for the case of non-identical or identical network,

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Image Encryption Scheme Based on Poker Cross-Shuffling and Fractional Order Hyperchaotic System

A Novel Image Encryption Scheme Based on Poker Cross-Shuffli...

引用

作者： Zhong Chen Huihuang Zhao Junyao Chen College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Department of Computer Engineering Ajou University

A novel digital image encryption scheme based on Poker shuffling and fractional order hyperchaotic system is proposed. Poker intercrossing operation has nonlinearity and periodicity, and can form permutation group. Firstly, the plain image is transformed into block image, and each block is confused by Poker shuffling operation with key. Secondly, bit plane of every pixel in each block is confused. Lastly, the scrambled image is encrypted by fractional order hyperchaotic sequence. This encryption scheme provides a secure and efficient key stream, and guarantees a large key space. The experimental results and security analysis imply that the new encryption method has secure encryption effect and property of resisting common attacks.

关键词： Image encryption algorithm Poker shuffling operation Fractional order hyperchaotic system

来源：评论

学校读者我要写书评

暂无评论

End-to-End Model for Offline Handwritten Mongolian Word Recognition 8th

End-to-End Model for Offline Handwritten Mongolian Word Reco...

引用

8th CCF International Conference on Natural Language processing and Chinese Computing, NLPCC 2019

作者： Wei, Hongxi Liu, Cong Zhang, Hui Bao, Feilong Gao, Guanglai School of Computer Science Inner Mongolia University Hohhot010021 China Provincial Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783030322359

This paper proposed an end-to-end model for recognizing offline handwritten Mongolian words. To be specific, a sequence to sequence architecture with attention mechanism is used to perform the task of generating a target sequence from a source sequence. The proposed model consists of two LSTMs and one attention network. The first LSTM is an encoder which consumes a frame sequence of one word image. The second LSTM is a decoder which can generate a sequence of letters. The attention network is added between encoder and decoder, which allow the decoder to focus on different positions in a sequence of frames during the procedure of decoding. In this study, we have attempted two schemes for generating frames from word images. In the first scheme, frames are generated with overlapping. Each adjacent two frames overlap half a frame. In the second scheme, frames are generated without overlapping. In addition, the height of the frame is also taken into consideration in our study. By comparison, the better scheme for generating frames has been determined. Experimental results demonstrate that the proposed end-to-end model outperforms the state-of-the-art method. © 2019, Springer Nature Switzerland AG.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Skediff: Skeleton 3d Ct Diffusion Reconstruction Using 2d X-Ray

SSRN

引用

SSRN 2024年

作者： Gao, Yuan Ge, Rongjun Wu, Zhan Li, Yuanhang Gu, Jiabing Chen, Yang Zhou, Mingle Li, Min Li, Gang Coatrieux, Jean-Louis Qilu University of Technology Shandong Academy of Sciences No. 19 Keyuan Road Lixia District Shandong Jinan250013 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science No. 19 Keyuan Road Lixia District Shandong Jinan250013 China Shandong SCICOM Information and Economy Research Institute Co. Ltd. No. 2000 Shunhua Road High-tech Zone Shandong Jinan250101 China Southeast University No. 2 Sipailou Xuanwu District Jiangsu Nanjing210096 China The Affiliated Hospital of Qingdao University No. 16 Jiangsu Road South District Shandong Qingdao266001 China University of Rennes 1 Breizh Rennes35042 France

In orthopedic disease examination, 2D X-ray imaging is the major source used in emergency situations, due to its wide accessibility and clinical effectiveness as well as its easy operation and low radiation effect. However, the 2D nature of these images do not provide a full access to the skeletal structures available through 3D CT. To address this issue, we propose an algorithm, named SkeDiff, to achieve a skeleton 3D CT reconstruction from two standard 2D X-ray images. SkeDiff establishes a mapping between the 2D space and the 3D space by means of a skeleton feature latent spatial diffusion process called $DM_{3DL}$. To fully utilize the information of a given X-ray image to guide the reconstruction direction of the diffusion process, we design a cross-dimensional conditional encoder $E_{Cond}$ in order to get 2D image priors for the 3D diffusion process $DM_{3DL}$. Furthermore, we propose a diffusion-based scoliosis classifier $D_{SC}$ for orthopedic to achieve scoliosis disease classification during the reconstruction of 3D CT images. The SkeDiff solution is evaluated on spine, hip and knee datasets by using standard metrics. These experiments show a better performance of SkeDiff when quantitatively and visually compared to recent algorithms. Our code is available on GitHub: https://***/YuanGao1201/SkeDiff. © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

引用

Signal Transduction and Targeted Therapy 2021年第5期6卷 1585-1600页

作者： Yiyue Ge Tingzhong Tian Suling Huang Fangping Wan Jingxin Li Shuya Li Xiaoting Wang Hui Yang Lixiang Hong Nian Wu Enming Yuan Yunan Luo Lili Cheng Chengliang Hu Yipin Lei Hantao Shu Xiaolong Feng Ziyuan Jiang Yunfu Wu Ying Chi Xiling Guo Lunbiao Cui Liang Xiao Zeng Li Chunhao Yang Zehong Miao Ligong Chen Haitao Li Hainian Zeng Dan Zhao Fengcai Zhu Xiaokun Shen Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijingChina NHC Key laboratory of Emeric Pathogenic Microbiology Jiangsu Provincial Center for Diseases Control and PreventionNanjingJiangsu ProvinceChina Shanghai Institute of Materia Medica Chinese Academy of SciencesShanghaiChina Silexon Al Technology Co. Ltd.NanjingJiangsu ProvinceChina Department of Computer Science University of Illinois at Urbana-ChampaignIllinoisILUSA School of Pharmaceutical Sciences Tsinghua UniversityBeijingChina School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanHubei ProvinceChina Institute of Pathology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanHubei ProvinceChina Department of Automation Tsinghua UniversityBeijingChina Inner Mongolia Alashan League Organization Establishment Committee Office Electronic Support Center AlashanInner MongoliaChina Convalife(Shanghai)Co. Ltd.ShanghaiChina Advanced Innovation Center for Human Brain Protection Beijing Tiantan HospitalCapital Medical UniversityBeijingChina Beijing Advanced Innovation Center for Structural Biology Tsinghua-Peking Joint Center for Life SciencesDepartment of Basic Medical SciencesSchool of MedicineTsinghua UniversityBeijingChina Center for Global Health Nanjing Medical UniversityNanjingJiangsu ProvinceChina

The global spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019(COVID-19).In this study,we developed an integrative drug repositioning framework,which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph,literature and transcriptome data to discover the potential drug candidates against *** in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1(PARP1)inhibitor,CVL218,currently in Phase I clinical trial,may be repurposed to treat *** in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic *** addition,we showed that CVL218 can interact with the nucleocapsid(N)protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

关键词： prevention respiratory acute

来源：评论

学校读者我要写书评

暂无评论

Vertex-disjoint paths in the generalized hypercube under 1-restricted connectivity 21

Vertex-disjoint paths in the generalized hypercube under 1-r...

引用

作者： Wang, Guijuan Lin, Cheng-Kuan Fan, Jianxi Cheng, Baolei Liu, Zhao School of Computer Science and Technology Soochow University Suzhou215006 China College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing210003 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

ISBN: (纸本)9781728120584

The generalized hypercube is an excellent interconnection network since it includes many interconnection topologies and it can be used to construct many data center networks. Considering the probability that, in general, all neighbors of one vertex becoming faulty at the same time is extremely low, we assume that each vertex has at least one fault-free neighbor. An r-dimensional generalized hypercube is denoted by G(m-r, m-r-1,⋅, m-1). In this paper, we proposed an efficient algorithm which can construct at least κ^1(G) disjoint paths based on any two adjacent vertices in G(m-r, m-r-1, m-1) in O(rm) time where κ^1(G) is the 1-restricted connectivity of G(m-r, m-r-1, m-1). The maximum length of these disjoint paths is bounded by 7. © 2019 IEEE.

关键词： Fault tolerance

来源：评论

学校读者我要写书评

暂无评论

JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution

arXiv

引用

arXiv 2021年

作者： Gao, Guangwei Tang, Lei Wu, Fei Lu, Huimin Yang, Jian The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The College of Automation Nanjing University of Posts and Telecommunications Nanjing China The Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu804-8550 Japan The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

With the growing importance of preventing the COVID-19 virus in cyber-manufacturing security, face images obtained in most video surveillance scenarios are usually low resolution together with mask occlusion. However, most of the previous face super-resolution solutions can not efficiently handle both tasks in one model. In this work, we consider both tasks simultaneously and construct an efficient joint learning network, called JDSR-GAN, for masked face super-resolution tasks. Given a low-quality face image with mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some competitive methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A novel TSK fuzzy system incorporating multiview collaborative transfer learning for personalized epileptic EEG detection

arXiv

引用

arXiv 2021年

作者： Li, Andong Deng, Zhaohong Lou, Qiongdan Choi, Kup-Sze Shen, Hongbin Wang, Shitong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Centre for Smart Health Hong Kong Polytechnic University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset. © 2021, CC BY-NC-ND.

关键词： Electrophysiology

来源：评论

学校读者我要写书评

暂无评论

Anomalydae: Dual autoencoder for anomaly detection onattributed networks

arXiv

引用

arXiv 2020年

作者： Fan, Haoyi Zhang, Fengbin Li, Zuoyong School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121

Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactions between network structure and node attribute. In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings. Specifically, Anoma-lyDAE consists of a structure autoencoder and an attribute autoencoder to learn both node embedding and attribute embedding jointly in latent space. Moreover, attention mechanism is employed in structure encoder to learn the importance between a node and its neighbors for an effective capturing of structure pattern, which is important to anomaly detection. Besides, by taking both the node embedding and attribute embedding as inputs of attribute decoder, the cross-modality interactions between network structure and node attribute are learned during the reconstruction of node attribute. Finally, anomalies can be detected by measuring the reconstruction errors of nodes from both the structure and attribute perspectives. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed *** Codes 68T30 Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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：