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2019 4th International Conference on Intelligent Computing and Signal Processing, ICSP 2019

作者： Wang, Han Ye, Zhihui Chen, Dong Zhu, Dandan Li, Shouding College of Safety and Ocean Engineering China University of PetroleumBeijing Beijing102249 China State Key Laboratory of Petroleum Resources and Prospecting College of Petroleum Engineering China University of PetroleumBeijing Beijing102249 China Beijing Key Lab of Petroleum Data Mining Department of Computer Science and Technology Beijing102249 China Institute of Geology and Geophysics Chinese Academy of Sciences Key Laboratory of Shale Gas and Geoengineering Institute of Geology and Geophysics Chinese Academy of Sciences Beijing100029 China University of Chinese Academy of Sciences Beijing100083 China Institutions of Earth Science Chinese Academy of Sciences Beijing100029 China

Well logging data are important to evaluate rock and fluid properties underground. Many well logging data, as functions of well depth, are conserved as figures in the literature. Digitizing technics should be applied when utilizing the well logging data. In order to address this problem, a method was proposed to automatically identify and acquire the well logging data from well logging curves. Based on the color recognition, this algorithm is able to extract curve features based on pixels. Comparing to other data digitizing methods, the background grid lines could be easily filtered and removed. The algorithm is verified by digitizing some typical well logging curves. This main contribution of this work is to provide a new and efficient way for data digitizing from well logging curves. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： data acquisition

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The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

arXiv

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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A simple image encryption algorithm based on logistic map 5th

A simple image encryption algorithm based on logistic map

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5th Euro-China Conference on Intelligent data Analysis and Applications, ECC 2018

作者： Wu, Tsu-Yang Wang, King-Hang Chen, Chien-Ming Wu, Jimmy Ming-Tai Pan, Jeng-Shyang Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Shenzhen518055 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9783030037659

Based on the better properties of chaotic maps, the studies of chaotic-based image encryption have received much attentions by researchers in recent years. In this paper, an image encryption algorithm based on logistic map with substitution approach is proposed. Meanwhile, we make the line chart, histogram, and pixel loss analysis to show the performance of our algorithm. © Springer Nature Switzerland AG 2019.

关键词： Cryptography

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Mining Dual Emotion for Fake News Detection

arXiv

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arXiv 2019年

作者： Zhang, Xueyao Cao, Juan Li, Xirong Sheng, Qiang Zhong, Lei Shu, Kai Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing China Illinois Institute of Technology ChicagoIL United States

Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that conveyed by the publishers (i.e., publisher emotion). However, fake news often evokes high-arousal or activating emotions of people, so the emotions of news comments aroused in the crowd (i.e., social emotion) should not be ignored. Furthermore, it remains to be explored whether there exists a relationship between publisher emotion and social emotion (i.e., dual emotion), and how the dual emotion appears in fake news. In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. Further, we exhibit that our proposed features can be easily plugged into existing fake news detectors as an enhancement. Extensive experiments on three real-world datasets (one in English and the others in Chinese) show that our proposed feature set: 1) outperforms the state-of-the-art task-related emotional features;2) can be well compatible with existing fake news detectors and effectively improve the performance of detecting fake news.1 2 Copyright © 2019, The Authors. All rights reserved.

关键词： Fake detection

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Security analysis of a public key authenticated encryption with keyword search scheme 14th

Security analysis of a public key authenticated encryption w...

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14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018

作者： Wu, Tsu-Yang Chen, Chien-Ming Wang, King-Hang Wu, Jimmy Ming-Tai Pan, Jeng-Shyang Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

In order to solve the security problem that off-line keyword guessing attacks existed in PEKS or dPEKS scheme, Huang and Li introduced a new security model called PAEKS. In this paper, we show that their scheme didn’... 详细信息

ISBN: (纸本)9783030037444

关键词： Search engines

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Hierarchical semantic approximate multi-keyword ranked search over encrypted data 2nd

Hierarchical semantic approximate multi-keyword ranked searc...

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2nd International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2018

作者： Chen, Chien-Ming Zhang, Wenhao Wu, Tsu-Yang Wang, King-Hang Wu, Jimmy Ming-Tai Pan, Jeng-Shyang Shenzhen518055 China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9783030045845

With the rapid development of Cloud Computing, how to improve users search experience over encrypted data just like plaintext search has become a challenging task. In this paper, we propose a hierarchical semantic approximate multi-keyword ranked search (HSAMS) scheme over encrypted data. Performance analysis is demonstrated that our proposed scheme is efficient and effective while supporting semantic approximate match. © Springer Nature Switzerland AG 2019.

关键词： Semantics

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Multi-Agent Genetic Algorithm for Sparse Optical Phased Array Optimization

Multi-Agent Genetic Algorithm for Sparse Optical Phased Arra...

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Asia Communications and Photonics Conference

作者： Chuxin Liu Weihan Xu Linjie Zhou Liangjun Lu Jianping Chen Shanghai Institute for Advanced Communication and Data Science Shanghai Key Lab of Navigation and Location Services State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering SJTU Shanghai China

We propose a multi-agent Genetic algorithm to suppress the peak sidelobe ratio (PSLR) in the OPA far-field diffraction pattern. The optimized 128-channel sparse array achieves beam-steering range of ±60° wit... 详细信息

ISBN: (纸本)9781943580705

关键词： Far field diffraction Free space optics Genetic algorithms Optical coherence tomography Phased arrays Ptychography

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A Public Auditing Scheme with data Recovery 12th

A Public Auditing Scheme with Data Recovery

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12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018

ISBN: (纸本)9789811358401

In order to solve the integrity verification of outsourced file in cloud, we based on the concept of regenerating code (RC) to propose a new public auditing scheme with data recovery in this paper. With the advantage of RC, our scheme provides a (k,n) recovery functionality, i.e., only encoded data blocks from any k cloud servers, user can recover the original data files, where n denotes the total number of cloud servers and k ≤ n. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Recovery

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Polyoxometalate-Cyclodextrin-Based Cluster-Organic Supramolecular Framework for Polysulfide Conversion and Guest–Host Recognition in Lithium-sulfur Batteries

引用

Angewandte Chemie 2023年第36期135卷

作者： Prof. Lubin Ni Jie Gu Xinyuan Jiang Hongjie Xu Zhen Wu Yuchao Wu Yi Liu Prof. Ju Xie Prof. Yongge Wei Prof. Guowang Diao School of Chemistry and Chemical Engineering Yangzhou University Yangzhou 225002 Jiangsu P. R. China Contribution: Conceptualization (lead) Data curation (lead) Funding acquisition (lead) Methodology (lead) Project administration (lead) Supervision (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Resources (equal) Software (equal) Contribution: Data curation (equal) Investigation (equal) Software (equal) Contribution: Data curation (equal) Investigation (equal) Software (equal) Validation (equal) Visualization (equal) Contribution: Validation (equal) Visualization (equal) Contribution: Methodology (equal) Visualization (equal) Key Lab of Organic Optoelectronics & Molecular Engineering of Ministry of Education Department of Chemistry Tsinghua University Beijing 100084 P. R. China Contribution: Conceptualization (equal) Project administration (equal) Resources (equal) Supervision (lead) Writing - original draft (equal) Writing - review & editing (lead) Contribution: Resources (lead) Supervision (equal) Writing - review & editing (lead)

Developing polyoxometalate-cyclodextrin cluster-organic supramolecular framework (POM-CD-COSF) still remains challenging due to an extremely difficult task in rationally interconnecting two dissimilar building blocks. Here we report an unprecedented POM-CD-COSF crystalline structure produced through the self-assembly process of a Krebs -type POM, [Zn 2 (WO 2 ) 2 (SbW 9 O 33 ) 2 ] 10− , and two β-CD units. The as-prepared POM-CD-COSF-based battery separator can be applied as a lightweight barrier (approximately 0.3 mg cm −2 ) to mitigate the polysulfide shuttle effect in lithium-sulfur batteries. The designed Li−S batteries equipped with the POM-CD-COSF modified separator exhibit remarkable electrochemical performance, attributed to fast Li + diffusion through the supramolecular channel of β-CD, efficient polysulfide-capture ability by the dynamic host–guest interaction of β-CD, and improved sulfur redox kinetics by the bidirectional catalysis of POM cluster. This research provides a broad perspective for the development of multifunctional supramolecular POM frameworks and their applications in Li−S batteries.

关键词： Cluster-Organic Supramolecular Framework Cyclodextrin Lithium–Sulfur Batteries Polyoxometalates Polysulfide

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A new algorithm for classification based on multi-classifiers learning 5th

A new algorithm for classification based on multi-classifier...

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5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Zheng, Yifeng Li, Guohe Zhang, Wenjie School of Computer Sciences Key Laboratory of Data Science and Intelligence Application Fujian Province University Minnan Normal University Zhangzhou China College of Geophysics and Information Engineering Beijing Key Lab of Data Mining for Petroleum Data University China University of Petroleum Beijing China

ISBN: (纸本)9789811308956

Quality and quantity are the two key factors to influence the accuracy of classification. In order to improve the classification accuracy, in this paper, we propose a new algorithm, called CMCM (Classification based on Multiple Classifier Models), which consists of two classification models. In Model1, we mainly focus on the improvement of quality, thus the best attribute value from both the items and their complements in the training set is selected as the first item of a classification rule. While in Model2, quantity is taken into consideration, so it constructs two candidate sets and uses the one-versus-many strategy to generate several rules at one time. The experiment results show that: (1) Model1 can extract sufficient high quality rules and achieve high classification accuracy. (2) Model2 can extract sufficient information and achieve high classification accuracy. (3) CMCM can achieve higher classification accuracy compare with traditional classification. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Classification (of information)

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