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arXiv 2022年

作者： Liu, Minmin Li, Xuechen Gao, Xiangbo Chen, Junliang Shen, Linlin Wu, Huisi Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China University of California Irvine United States

Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution. When training a detector to detect the cancer cells in a WSI (Whole Slice Image) image captured from the TCT (Thinprep Cytology Test) specimen, head categories (e.g. normal cells and inflammatory cells) typically have a much larger number of samples than tail categories (e.g. cancer cells). Most existing state-of-the-art long-tailed learning methods in object detection focus on category distribution statistics to solve the problem in the long-tailed scenario, without considering the "hardness" of each sample. To address this problem, in this work we propose a Grad-Libra Loss that leverages the gradients to dynamically calibrate the degree of hardness of each sample for different categories, and re-balance the gradients of positive and negative samples. Our loss can thus help the detector to put more emphasis on those hard samples in both head and tail categories. Extensive experiments on a long-tailed TCT WSI image dataset show that the mainstream detectors, e.g. RepPoints, FCOS, ATSS, YOLOF, etc. trained using our proposed Gradient-Libra Loss, achieved much higher (7.8%) mAP than that trained using cross-entropy classification loss. Copyright © 2022, The Authors. All rights reserved.

关键词： Cytology

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Transformers in Medical Image Analysis: A Review

arXiv

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arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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Multi-Level Chaotic Maps for 3D Textured Model Encryption 2nd

Multi-Level Chaotic Maps for 3D Textured Model Encryption

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Jin, Xin Zhu, Shuyun Wu, Le Zhao, Geng Li, Xiaodong Zhou, Quan Lu, Huimin Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan

ISBN: (纸本)9783030177621

With the rapid progress of virtual reality and augmented reality technologies, 3D contents are the next widespread media in many applications. Thus, the protection of 3D models is primarily important. Encryption of 3D models is essential to maintain confidentiality. Previous work on encryption of 3D surface model often considers the point clouds, the meshes, and the textures individually. In this work, a multi-level chaotic maps model for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons, and textures. For vertices which make main contribution for recognizing, we use high-level 3D Lu chaotic map to encrypt them. For polygons and textures which make relatively smaller contributions for recognizing, we use 2D Arnold’s cat map and 1D logistic map to encrypt them, respectively. The experimental results show that our method can get similar performance with the other method and use the same high-level chaotic map for point cloud, polygons, and textures, while we use less time. Besides, our method can resist more method of attacks such as statistic attack, brute-force attack, and correlation attack. © 2020, Springer science and Business Media Deutschland GmbH. All rights reserved.

关键词： Textures

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Verifiable Privacy Preservation Scheme for Outsourcing Medi-cal Image to Cloud Through ROI Based Crypto-Watermarking

Verifiable Privacy Preservation Scheme for Outsourcing Medi-...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Chuan Zhou Yi Zhou XingHan An Yan Liu Min Wang XiangZhi Liu Tianjin University Tianjin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China College of Environmental Science and Engineering Beijing Forestry University Beijing China Inspur Software Co. Ltd. Jinan China The Party School of cpc Jinan Municipal Committee Jinan China

A novel verifiable privacy preservation scheme for outsourcing medical image to cloud through ROI based crypto-watermarking is proposed in the paper. In the proposed scheme, data owner firstly carries out substitution of S-box for the region of interest (ROI) of medical image, and then separates the image into 4 most significant bits (MSBs) plane and 4 least significant bits (LSBs) plane images. Secondly, the hash value of ROI is embedded into the two separated bit plane images using reversible watermarking algorithm. Lastly, some selected hash values are transformed into the initial parameters of chaotic maps, and the two sharing secrets, which are produced through chaos based encryption algorithm, are finally outsourced to two different cloud servers. For authorized users, they can get shares from different cloud servers, and then can losslessly recover the original medical image through a series of decryption operations and extraction of watermarking. Furthermore, the users can verify whether the original image is completely reconstructed or not, they even can locate the tampered parts inside ROI if anyone of the sharing secrets is damaged. Some experiments analyses and comparisons are given to show the security and effectiveness of proposed scheme.

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A Distributed Approximation Algorithm for the Total Dominating Set Problem

SSRN

引用

SSRN 2022年

作者： Wang, Limin Zhang, Zhao Du, Donglei Mao, Yaping Zhang, Xiaoyan State Key Laboratory for Novel Software Technology Nanjing University Jiangsu210023 China College of Mathematics and Computer Sciences Zhejiang Normal University Zhejiang321004 China Faculty of Management University of New Brunswick FrederictonNBE3B 5A3 Canada School of Mathematics and Statistics Qinghai Normal University Qinghai810008 China Academy of Plateau Science and Sustainability Qinghai Xining810008 China School of Mathematical Science Institute of Mathematics Nanjing Normal University Jiangsu210023 China

Given a network graph $G=(V,E)$, a subset $T\subseteq V$ is said to be a total dominating set (TDS) if every $v\in V$ is adjacent to at least one node in $T$. In this paper, we present a distributed algorithm for the TDS problem via the LP relaxation techniques. For a positive integer $k$ and maximum degree $\Delta$, the proposed algorithm outputs a fractional total dominating set of expected size $O(k\Delta^\frac{2}{k})$. It is the first distributed algorithm for TDS which obtains an approximation ratio in $O(k^2)$ communication rounds where the algorithm use messages of size $O(\log\Delta)$;then the fractional solution is rounded to obtain an integer total dominating set for the original problem. © 2022, The Authors. All rights reserved.

关键词： Approximation algorithms

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Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization

arXiv

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arXiv 2023年

作者： Wu, Yanan Chi, Zhixiang Wang, Yang Plataniotis, Konstantinos N. Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Edward S Rogers Sr. ECE Department University of Toronto TorontoM5S3G8 Canada Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and domain information is separately embedded in the weight matrix and batch normalization (BN) layer. Previous works normally update the whole network naively without explicitly decoupling the knowledge between label and domain. As a result, it leads to knowledge interference and defective distribution adaptation. In this work, we propose to reduce such learning interference and elevate the domain knowledge learning by only manipulating the BN layer. However, the normalization step in BN is intrinsically unstable when the statistics are re-estimated from a few samples. We find that ambiguities can be greatly reduced when only updating the two affine parameters in BN while keeping the source domain statistics. To further enhance the domain knowledge extraction from unlabeled data, we construct an auxiliary branch with label-independent self-supervised learning (SSL) to provide supervision. Moreover, we propose a bi-level optimization based on meta-learning to enforce the alignment of two learning objectives of auxiliary and main branches. The goal is to use the auxiliary branch to adapt the domain and benefit main task for subsequent inference. Our method keeps the same computational cost at inference as the auxiliary branch can be thoroughly discarded after adaptation. Extensive experiments show that our method outperforms the prior works on five WILDS real-world domain shift datasets. Our method can also be integrated with methods with label-dependent optimization to further push the performance boundary. Our code is available at https://***/ynanwu/MABN. © 2023, CC BY.

关键词： Time domain analysis

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Intermediate Value Size Aware Coded MapReduce

Intermediate Value Size Aware Coded MapReduce

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yamei Dong Bin Tang Baoliu Ye Zhihao Qu Sanglu Lu National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer and Information Hohai University Nanjing China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

MapReduce is a commonly used framework for parallel processing of data-intensive tasks, but its performance usually suffers from heavy communication load incurred by the shuffling of intermediate values (IVs) among computing servers. Recently, the Coded MapReduce framework is proposed which uses a coding scheme named coded distributed computing (CDC) to trade the communication load with extra computation in MapReduce. CDC can achieve the optimal computation-communication tradeoff when all the IVs have the same size. However, in many practical applications, the sizes of IVs can vary over a large range, leading to inferior performance. In this paper, we introduce a generalized CDC scheme which takes the sizes of IVs into account and then propose a combinatorial optimization problem aiming to minimize the communication load when the computation load is fixed. We show that the problem is NP-hard, and further propose a very efficient algorithm which achieves an approximation ratio of 2. Experiments conducted on Alibaba Cloud show that, compared to the original CDC scheme, our proposed IV size aware approach can significantly reduce the communication load and achieve a lower total execution time.

关键词： Parallel processing Approximation algorithms Heterogeneous networks Encoding Servers Task analysis Optimization

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CSPs: Catchy short passwords making offline and online attacks impossible

引用

International Journal of Information and computer Security 2019年第3期11卷 255-274页

作者： Shen, Jaryn Zeng, Qingkai State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

This paper proposes to address online and offline attacks to passwords without increasing users' efforts in choosing and memorising their passwords. In CSPS, a password consists of two parts, a user-chosen short password and a server-generated long password. The short password should be memorised and secured by its user while the long password be encrypted and stored on the server side. To keep the secret key for protecting the long password secure, an additional sever is introduced to store the secret key and provide encryption/decryption services. On top of balloon, CSPS integrates expensive hash with secure encryption. It is mathematically proved that computationally unbounded attackers cannot succeed in offline dictionary or brute-force attacks or a combination of offline and online attacks. The criteria of security are established, which quantifies the security. To our best knowledge, CSPS is the first technique to make security quantifiable in password authentication mechanisms. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Web services

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TrustCross: Enabling confidential interoperability across blockchains using trusted hardware

arXiv

引用

arXiv 2021年

作者： Lan, Ying Gao, Jianbo Wang, Ke Zhang, Jiashuo Wu, Zhenhao Zhu, Yuesheng Chen, Zhong School of Electrical and Computer Engineering Peking University Shenzhen China Department of Computer Science and Technology EECS Peking University Beijing China Key Laboratory of High Confidence Software Technologies Peking University MoE Beijing China

With the rapid development of blockchain technology, different types of blockchains are adopted and interoperability across blockchains has received widespread attention. There have been many cross-chain solutions proposed in recent years, including notary scheme, sidechain, and relay chain. However, most of the existing platforms do not take confidentiality into account, although privacy has become an important concern for blockchain. In this paper, we present TrustCross, a privacy-preserving cross-chain platform to enable confidential interoperability across blockchains. The key insight behind TrustCross is to encrypt cross-chain communication data on the relay chain with the assistance of trusted execution environment and employ fine-grained access control to protect user privacy. Our experimental results show that TrustCross achieves reasonable latency and high scalability on the contract calls across heterogeneous blockchains. © 2021, CC BY.

关键词： Interoperability

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