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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Wanli Lyu Jiahao Deng Yu Zhang Dongshuo Yin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityKey Lab of Industrial Image Processing and Analysis of Anhui Province

ISBN: (纸本)9781510871076

In this paper,our proposed scheme using two steganography *** other data hiding schemes,this scheme studies the characteristics of the Tangram puzzle model and the secret message bytes that make up the eight binary bits that are hidden into the two *** matrix M *,horizontal and vertical coordinate range from 0 to 255,and the pixel pairs in the cover image are used to help hide the secret message bytes in our *** paired pixel points into single and double number hidden in two *** show that the program can hide relatively more pixels in a cover image with less losing image quality.

关键词： steganography images Tangram puzzle matrix M hide relatively scenario

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3D Clothed Human Reconstruction From One In-the-Wild RGB image

3D Clothed Human Reconstruction From One In-the-Wild RGB Ima...

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IEEE International Conference on image Processing

作者： Liangjing Shao Benshuang Chen Xinrong Chen Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In recent years, much achievement have been made in the field of 3D clothed human reconstruction. However, most of researches performed not well for reconstruction from in-the-wild images due to the domain gap between the synthetic images of training datasets and the in-the-wild images. In this study, a modular model, including clothes encoder, body encoder and cloth generator, is proposed to perform 3D clothed human reconstruction from one single-view in-the-wild RGB image. In particular, we introduce the adaptive aggregation of convolution and multi-head attention into the cloth encoder and apply the adjustment of the segmentation at the preprocessing stage. According to experiments on MSCOCO and 3DPW datasets, the proposed method achieves state-of-the-art performance on 3D clothed human reconstruction from in-the-wild images compared with previous works.

关键词： Training Solid modeling image segmentation Adaptation models Three-dimensional displays Convolution Data preprocessing

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Secure localization algorithms in wireless sensor networks: A review 2nd

Secure localization algorithms in wireless sensor networks: ...

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2nd International Conference on Computer, Communication and Computational Sciences, IC4S 2017

作者： Wang, Hua Wen, Yingyou Lu, Yao Zhao, Dazhe Ji, Chunlin School of Computer Science & Engineering Northeastern University Shenyang110819 China State Key Laboratory of Software Architecture Neusoft Corporation Shenyang110179 China Beijing Jiaotong University Beijing100044 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110819 China Kuang-Chi Institute of Advanced Technology Shenzhen518000 China

ISBN: (纸本)9789811303432

Localization plays a vital role in understanding the application context for wireless sensor networks (WSNs). However, it is vulnerable to various threats due to the open arrangement area, the nature of radio broadcasting, and resource constraint. Two kinds of attacks on localization process need to be investigated. On the one hand, adversaries may capture, impersonate, replicate, or fabricate nodes to mislead the target to an incorrect position. On the other hand, adversaries may tamper, interfere, replay, or modify jointly localization information to disturb the localization. This paper describes two kinds of localization attacks and gives a systematic survey of existing secure localization solutions, including the design ideas, application scopes, and limitations. Next, several novel secure localization schemes for special WSNs applications, including Underwater Wireless Sensor Networks (UWSNs), Wireless Body Area Network (WBAN) and three-dimensional (3D), are analyzed. Finally, many open problems for secure localization are presented. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Wireless sensor networks

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WaveFuse: A unified unsupervised framework for image fusion with discrete wavelet transform

arXiv

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arXiv 2020年

作者： Liu, Shaolei Wang, Manning Song, Zhijian Digital Medical Research Center School of Basic Medical Science Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assited Intervention

We propose an unsupervised image fusion architecture for multiple application scenarios based on the combination of multi-scale discrete wavelet transform through regional energy and deep learning. To our best knowledge, this is the first time that a conventional frequency method has been combined with deep learning for feature maps fusion. The useful information of feature maps can be utilized adequately through multi-scale discrete wavelet transform in our proposed method. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation. Moreover, it's worth mentioning that comparable fusion performance trained in COCO dataset can be obtained by training with a much smaller dataset with only hundreds of images chosen randomly from COCO. Hence, the training time is shortened substantially, leading to the improvement of the model's performance both in practicality and training efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： image fusion

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation

Multi-scale cyclical similarity prototype refinement for few...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Yingfeng Ou Xing Yang Jian Zhang Caiqing Jian Lihui Wang Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%.

关键词： image segmentation Ultrasonic imaging Breast tumors Semantics Prototypes Signal processing Data mining Few shot learning Residual neural networks

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MMBDE: A Two-stage Hybrid Feature Selection Method From Microarray Data

MMBDE: A Two-stage Hybrid Feature Selection Method From Micr...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Weidong Xie Yuhuan Chi Linjie Wang Kun Yu Wei Li Computer Science and Engineering Northeastern University Shenyang China College of Medicine and Bioinformation Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China

ISBN: (纸本)9781665429825

The discovery of diagnostically significant genes from microarray data is essential for disease diagnosis and drug research. However, the difficulty of analyzing microarray data comes from its high dimensionality and small sample size. Feature selection can effectively remove irrelevant and redundant features, reduce data dimensionality, and improve the accuracy of classifiers. This paper proposes a two-stage hybrid feature selection method MMBDE based on the improved min-Redundancy and Max-Relevance (mRMR) and the improved Binary Differential Evolution (BDE) algorithm. The improved mRMR is used to reduce the feature dimensionality at a coarse-scale significantly. In contrast, the improved BDE is used to refine the feature dimensionality at fine-scale further and select the best features. The experimental results show that MMBDE successfully reduces the dimensionality of microarray gene expression data, obtains high classification accuracy, and extracts effective features closely related to diseases from microarray gene expression data. The relevant datasets and codes can be obtained from https://***/xwdshiwo/MMBDE.

关键词： Drugs Quantization (signal) Codes Conferences Feature extraction Gene expression medical diagnosis

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Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation

Space Engage: Collaborative Space Supervision for Contrastiv...

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International Conference on Computer Vision (ICCV)

作者： Changqi Wang Haoyu Xie Yuhui Yuan Chong Fu Xiangyu Yue School of Computer Science and Engineering Northeastern University Shenyang China Microsoft Research Asia Key Laboratory of Intelligent Computing in Medical Image Ministry of Education NEU China The Chinese University of Hong Kong

Semi-Supervised Semantic Segmentation (S4) aims to train a segmentation model with limited labeled images and a substantial volume of unlabeled images. To improve the robustness of representations, powerful methods introduce a pixel-wise contrastive learning approach in latent space (i.e., representation space) that aggregates the representations to their prototypes in a fully supervised manner. However, previous contrastive-based S4 methods merely rely on the supervision from the model’s output (logits) in logit space during unlabeled training. In contrast, we utilize the outputs in both logit space and representation space to obtain supervision in a collaborative way. The supervision from two spaces plays two roles: 1) reduces the risk of over-fitting to incorrect semantic information in logits with the help of representations; 2) enhances the knowledge exchange between the two spaces. Furthermore, unlike previous approaches, we use the similarity between representations and prototypes as a new indicator to tilt training those under-performing representations and achieve a more efficient contrastive learning process. Results on two public benchmarks demonstrate the competitive performance of our method compared with state-of-the-art methods.

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GALOPA: graph transport learning with optimal plan alignment 23

GALOPA: graph transport learning with optimal plan alignment

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yejiang Wang Yuhai Zhao Zhengkui Wang Ling Li School of Computer Science and Engineering Northeastern University China and Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China InfoComm Technology Cluster Singapore Institute of Technology Singapore

Self-supervised learning on graphs aims to learn graph representations in an unsupervised manner. While graph contrastive learning (GCL - relying on graph augmentation for creating perturbation views of anchor graphs and maximizing/minimizing similarity for positive/negative pairs) is a popular self-supervised method, it faces challenges in finding label-invariant augmented graphs and determining the exact extent of similarity between sample pairs to be achieved. In this work, we propose an alternative self-supervised solution that (i) goes beyond the label invariance assumption without distinguishing between positive/negative samples, (ii) can calibrate the encoder for preserving not only the structural information inside the graph, but the matching information between different graphs, (iii) learns isometric embeddings that preserve the distance between graphs, a by-product of our objective. Motivated by optimal transport theory, this scheme relies on an observation that the optimal transport plans between node representations at the output space, which measure the matching probability between two distributions, should be consistent with the plans between the corresponding graphs at the input space. The experimental findings include: (i) The plan alignment strategy significantly outperforms the counterpart using the transport distance; (ii) The proposed model shows superior performance using only node attributes as calibration signals, without relying on edge information; (iii) Our model maintains robust results even under high perturbation rates; (iv) Extensive experiments on various benchmarks validate the effectiveness of the proposed method.

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data *** data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data *** is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our *** the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied *** paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： Data silos Machine learning Federated learning Privacy protection Learning framework

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An Automatic Extraction Method of Coronary Arteries Centerline Based on Cellular Automata

An Automatic Extraction Method of Coronary Arteries Centerli...

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作者： Guangkun Ma Jinzhu Yang Hong Zhao School of Computer Science and Engineering Northeastern University School of Software Shenyang University of Technology Key Laboratory of Intelligent Computing in Medical Image Ministry of Education School of Computer Science and Engineering Northeastern University

Effectively obtaining the centerline of the coronary arteries is very important to the clinical medical diagnosis. Cellular automata are extensively used to find fine solutions in many complex hard. However, it has the problem of large amount of computation. In this paper, we propose an automatic extraction method of coronary centerline based on cellular automata. Different from the existing methods, the distance from boundary field is used to extract the centerline of the coronary arteries roughly, which can place the candidate voxel at the center of the coronary arteries and reduce the size of the data. Furthermore, the centerlines of the coronary arteries are extracted accurately by using cellular automata to find the shortest path. The proposed method can extract the centerlines of each branch of the coronary arteries simultaneously from a single source to multiple destinations. Furthermore, compared to the method in Ref [10], our proposed method works well, especially in the case of large vessel curvature, the voxels at the extracted centerline are relatively centered. In the last, quantitative evaluation is conducted on the coronary arteries and the results show that the proposed method achieves a smaller deviation distance.

关键词： Cellular automata Distance from Boundary Coronary arteries Computerized Tomography Angiography

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