检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Wang, Song He, Yuting Kong, Youyong Zhu, Xiaomei Zhang, Shaobo Shao, Pengfei Dillenseger, Jean-Louis Coatrieux, Jean-Louis Li, Shuo Yang, Guanyu LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China Univ Rennes Inserm LTSI UMR1099 RennesF-35000 France Dept. of Radiology First Affliated Hospital Nanjing Medical University Dept. of Urology First Affliated Hospital of Nanjing Medical University Dept. of Medical Biophysics University of Western Ontario LondonON Canada

Renal compartment segmentation on CT images targets on extracting the 3D structure of renal compartments from abdominal CTA images and is of great significance to the diagnosis and treatment for kidney diseases. However, due to the unclear compartment boundary, thin compartment structure and large anatomy variation of 3D kidney CT images, deep-learning based renal compartment segmentation is a challenging task. We propose a novel weakly supervised learning frame- work, Cycle Prototype network, for 3D renal compartment segmenta- tion. It has three innovations: 1) A Cycle Prototype Learning (CPL) is proposed to learn consistency for generalization. It learns from pseudo labels through the forward process and learns consistency regularization through the reverse process. The two processes make the model robust to noise and label-efficient. 2) We propose a Bayes Weakly Supervised Module (BWSM) based on cross-period prior knowledge. It learns prior knowledge from cross-period unlabeled data and perform error correc- tion automatically, thus generates accurate pseudo labels. 3) We present a Fine Decoding Feature Extractor (FDFE) for fine-grained feature ex-traction. It combines global morphology information and local detail in- formation to obtain feature maps with sharp detail, so the model will achieve fine segmentation on thin structures. Our model achieves Dice of 79:1% and 78:7% with only four labeled images, achieving a significant improvement by about 20% than typical prototype model PANet[16]. © 2021, CC BY.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Generative networks as inverse problems with fractional wavelet scattering networks

arXiv

引用

arXiv 2020年

作者： Wu, Jiasong Zhang, Jing Wu, Fuzhi Kong, Youyong Yang, Guanyu Senhadji, Lotfi Shu, Huazhong Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Université de Rennes INSERM LTSI-UMR 1099 RennesF-35042 France Université de Rennes INSERM Southeast University Rennes France

Deep learning is a hot research topic in the field of machine learning methods and applications. Generative Adversarial networks (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but both of them are difficult to train since they need to train the generator (or encoder) and the discriminator (or decoder) simultaneously, which is easy to cause unstable training. In order to solve or alleviate the synchronous training difficult problems of GANs and VAEs, recently, researchers propose Generative Scattering networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain the features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate the image. The advantage of GSNs is the parameters of ScatNets are not needed to learn, and the disadvantage of GSNs is that the expression ability of ScatNets is slightly weaker than CNNs and the dimensional reduction method of Principal Component Analysis (PCA) is easy to lead overfitting in the training of GSNs, and therefore affect the generated quality in the testing process. In order to further improve the quality of generated images while keep the advantages of GSNs, this paper proposes Generative Fractional Scattering networks (GFRSNs), which use more expressive fractional wavelet scattering networks (FrScatNets) instead of ScatNets as the encoder to obtain the features (or FrScatNet embeddings) and use the similar CNNs of GSNs as the decoder to generate the image. Additionally, this paper develops a new dimensional reduction method named Feature-Map Fusion (FMF) instead of PCA for better keeping the information of FrScatNets and the effect of image fusion on the quality of image generation is also discussed. The experimental results on CIFAR-10 dataset and CelebA dataset show that the proposed GFRSNs can obtain better generated images than the original GSNs on the testing dataset. Copyright © 2020, The Author

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

A privacy-preserving logistics information system with traceability

arXiv

引用

arXiv 2021年

作者： Chen, Quanru Han, Jinguang Li, Jiguo Chen, Liquan Li, Song College of Information Engineering Nanjing University of Finance and Economics Nanjing China Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China College of Computer and Cyber Security Fujian Normal University Fuzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China School of Cyber Science and Engineering Southeast University Nanjing China

Logistics information System (LIS) is an interactive system that provides information for logistics managers to monitor and track logistics business. In recent years, with the rise of online shopping, LIS is becoming increasingly important. However, since the lack of effective protection of personal information, privacy protection issue has become the most problem concerned by users. Some data breach events in LIS released users' personal information, including address, phone number, transaction details, etc. In this paper, to protect users' privacy in LIS, a privacy-preserving LIS with traceability (PPLIST) is proposed by combining multi-signature with pseudonym. In our PPLIST scheme, to protect privacy, each user can generate and use different pseudonyms in different logistics services. The processing of one logistics is recorded and unforgeable. Additionally, if the logistics information is abnormal, a trace party can de-anonymize users, and find their real identities. Therefore, our PPLIST efficiently balances the relationship between privacy and traceability. © 2021, CC BY.

关键词： information use

来源：评论

学校读者我要写书评

暂无评论

FLSSM: A Federated Learning Storage Security Model with Homomorphic Encryption

arXiv

引用

arXiv 2025年

作者： Li, Yang Xia, Chunhe Li, Chang Li, Xiaojian Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing100191 China Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China Guangxi Collaborative Innovation Center Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China College of Computer Science and Information Technology Guangxi Normal University Guilin541004 China School of Cyber Science and Technology Beihang University Beijing100191 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

Federated learning based on homomorphic encryption has received widespread attention due to its high security and enhanced protection of user data privacy. However, the characteristics of encrypted computation lead to three challenging problems: "computation-efficiency", "attack-tracing" and "contribution-assessment". The first refers to the efficiency of encrypted computation during model aggregation, the second refers to tracing malicious attacks in an encrypted state, and the third refers to the fairness of contribution assessment for local models after encryption. This paper proposes a federated learning storage security model with homomorphic encryption (FLSSM) to protect federated learning model privacy and address the three issues mentioned above. First, we utilize different nodes to aggregate local models in parallel, thereby improving encrypted models’ aggregation efficiency. Second, we introduce trusted supervise nodes to examine local models when the global model is attacked, enabling the tracing of malicious attacks under homomorphic encryption. Finally, we fairly reward local training nodes with encrypted local models based on trusted training time. Experiments on multiple real-world datasets show that our model significantly outperforms baseline models in terms of both efficiency and security metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Memory-efficient Simulation Method of Grover’s Search Algorithm

引用

computers, Materials & Continua 2018年第11期57卷 307-319页

作者： Xuwei Tang Juan Xu Bojia Duan College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjingChina Institute for Quantum Computing University of WaterlooCanada Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationChina

Grover’s search algorithm is one of the most significant quantum algorithms,which can obtain quadratic speedup of the extensive search *** Grover's search algorithm cannot be implemented on a real quantum computer at present,its quantum simulation is regarded as an effective method to study the search *** simulating the Grover's algorithm,the storage space required is exponential,which makes it difficult to simulate the high-qubit Grover’s *** this end,we deeply study the storage problem of probability amplitude,which is the core of the Grover simulation *** propose a novel memory-efficient method via amplitudes compression,and validate the effectiveness of the method by theoretical analysis and simulation *** results demonstrate that our compressed simulation search algorithm can help to save nearly 87.5%of the storage space than the uncompressed *** under the same hardware conditions,our method can dramatically reduce the required computing nodes,and at the same time,it can simulate at least 3 qubits more than the uncompressed ***,our memory-efficient simulation method can also be used to simulate other quantum algorithms to effectively reduce the storage costs required in simulation.

关键词： Grover’s search algorithm probability amplitude quantum simulation memory compression

来源：评论

学校读者我要写书评

暂无评论

Homotopic gradients of generative density priors for MR image reconstruction

arXiv

引用

arXiv 2020年

作者： Quan, Cong Zhou, Jinjie Zhu, Yuanzheng Chen, Yang Wang, Shanshan Liang, Dong Liu, Qiegen The Department of Electronic Information Engineering Nanchang University Nanchang330031 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China Paul C. Lauterbur Research Center for Biomedical Imaging SIAT Chinese Academy of Sciences Shenzhen518055 China

Dee learnin articularl the enerative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently. Rather than the existing generative models that often optimize the density priors, in this work, by taking advantage of the denoising score matching, homotopic gradients of generative density priors (HGGDP) are exploited for magnetic resonance imaging (MRI) reconstruction. More precisely, to tackle the low-dimensional manifold and low data density region issues in generative density prior, we estimate the target gradients in higher-dimensional space. We train a more powerful noise conditional score network by forming high-dimensional tensor as the network input at the training phase. More artificial noise is also injected in the embedding space. At the reconstruction stage, a homotopy method is employed to pursue the density prior, such as to boost the reconstruction performance. Experiment results implied the remarkable performance of HGGDP in terms of high reconstruction accuracy;only 10% of the k-space data can still generate image of high quality as effectively as standard MRI reconstructions with the fully sampled data. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

4D-CBCT Reconstruction via Motion Compensataion Learning Induced Sparse Tensor Constraint

4D-CBCT Reconstruction via Motion Compensataion Learning Ind...

引用

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Jin Liu Yanqin Kang Dianlin Hu Yang Chen Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

The challenge in 4D-CBCT reconstruction is the lacked of projections at each phase, which result in a reconstruction image full of streak artifacts and motion blurring with the conventional analytical algorithms. Aiming at improving the overall quality of 4D-CBCT images, we propose a motion compensation learning induced sparse tensor constraint reconstruction (MCL-STCR) method which tries to explore the high correlation in 4D-CBCT images for different phases. In this method, we additionally conduct motion compensation on the 4D-CBCT volume by using trained image motion compensation convolutional neural network (CNN). Then the compensated 4D-CBCT volume is viewed as a pseudostatic sequence, of which the sparse tensor constraint was imposed on. The cost function of proposed MCL-STCR is optimized by a variable splitting algorithm. Its comparison to other methods through simulated dynamic phantom and lung data experiments demonstrated that the proposed method can lead to a promising improvement of 4D-CBCT reconstruction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mining hypernym-hyponym relations from social tags via tag embedding 5th

Mining hypernym-hyponym relations from social tags via tag e...

引用

5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhang, Mengyi Wu, Tianxing Ji, Qiu Qi, Guilin Sun, Zhixin School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Modern Posts and Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China YuanTong Express Co. LTD. Shanghai China

ISBN: (纸本)9783030242701

With the rapid development of Internet of Thing, mobile Internet, cloud computing and other technologies, network data increases dramatically and Folksonomy plays an important role in web systems. How to obtain valuable knowledge, especially hypernym-hyponym relations, becomes a popular research topic in the field of artificial intelligence. For Folksonomy, hypernym-hyponym relation identification aims to recognize the"is-a" relation between two social tags. Most existing works about identifying hypernym-hyponym relations are based on statistical and heuristic approaches, but their performance still needs to be improved. In this paper, we propose a novel supervised learning approach to identify hypernym-hyponym relations from social tags using tag embeddings. First, we use a neural network model to learn tag embeddings. This model relies on not only the hypernym and hyponym tags, but also the contextual information between them. We then apply such embeddings as features to identify hypernym-hyponym relations using a supervised learning method. Our experimental results demonstrate that the proposed approach significantly outperforms other state-of-the-art approaches over a labeled dataset. The accuracy and F1-score of our approach achieve 0.91 and 0.86 respectively. © Springer Nature Switzerland AG 2019.

关键词： Heuristic methods

来源：评论

学校读者我要写书评

暂无评论

Brain Extraction from Magnetic Resonance Imaging Using Deep Neural network with Edge Prior

Brain Extraction from Magnetic Resonance Imaging Using Deep ...

引用

2019 IEEE International Conference on Signal, information and Data Processing, ICSIDP 2019

作者： Bin, Zhou Wang, Linsong Jian, Zhu Wu, Jiasong Youyong, Kong Huazhong, Shu School of Computer Science and Engineering Southeast University Sipailou 2 Nanjing210096 China College of Chien-Shiung Wu Honor Southeast University Nanjing China Shandong Cancer Hospital Shandong Academy of Medical Sciences Jinan China International Joint Laboratory of Infomration Integration Southeast University Nanj ing China Key Laboratory of Computer Network and Infomration Display and Visualization Southeast University Nanjing China

ISBN: (纸本)9781728123455

Brain extraction from Magnetic resonance imaging (MRI) is the separation of brain tissue from non-brain tissue in MRI brain images and removal of extra brain tissue. Automatic extraction of brain tissue is an important step in neuroimage processing for clinical applications and scientific research. Accurate delineation of the region can be challenged due to the weak contrast between the brain and non-brain tissue. To overcome this issue, this paper proposed a novel brain extraction approach integrating the boundary information under the deep neural network framework. The boundary of the brain tissues was first detected by an edge detection neural network. The inception module was then utilized in the convolutional stage to capture multi-scale features. A dense decoder shortcut connection structure was finally introduced to leverage the multi-level features in the upsampling stage. The proposed method can achieve Dice overlap coefficients of 98.15% and 98.09%, for the OASIS and LPBA40 datasets respectively. The proposed method outperforms the commonly used brain extraction tools by qualitative and quantitative assessments on two publicly available benchmark datasets. © 2019 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Sensing-Communication-Computing-Control Closed-Loop Optimization for 6G Digital Twin-Empowered Unmanned Robotic Systems

引用

IEEE Journal on Selected Areas in Communications 2025年

作者： Fang, Xinran Lei, Chengleyang Feng, Wei Chen, Yunfei Xiao, Ming Ge, Ning Wang, Cheng-Xiang Tsinghua University State Key Laboratory of Space Network and Communications Department of Electronic Engineering Beijing100084 China University of Durham Department of Engineering DurhamDH1 3LE United Kingdom Royal Institute of Technology School of Electrical Engineering and Computer Science Department of Information Science and Engineering Sweden Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

In recent decades, cyber-physical systems (CPSs) have received great attention due to their broad applications. This paper investigates CPS deployment in remote areas, specifically focusing on a digital twin-empowered unmanned robotic system. The system consists of a multifunctional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that connects sensors and actuators - forming reflex-arc-like sensing-communication-computing-control (SC3) loops. A digital twin is integrated into the EIH to emulate the system's behavior and assist in the decision-making. To alleviate resource limitations in remote areas, we propose a goal-oriented closed-loop optimization scheme. The proposed scheme takes the SC3 loop as an integrated structure and jointly optimizes uplink and downlink (UL&DL) communication and computing resources to minimize the total linear quadratic regulator (LQR) cost. To address the non-convex optimization problem, we derive the closed-form solution for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate the superiority of the proposed scheme, which achieves a two-tier task-level balance within and across the SC3 loops. © 1983-2012 IEEE.

关键词： Drones

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：