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Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212

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Journal of Systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

Hybrid optimization algorithm of particle swarm optimization with lagrangian relaxation for solving the multidimensional knapsack problem 2nd

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Hybrid optimization algorithm of particle swarm optimization...

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

作者： Luo, Jinyan Lin, Geng Zhang, Fuquan Xu, Lin Department of Mathematics Minjiang University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Digital Performance and Simulation Technology Laboratory School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783030045845

A hybrid algorithm that integrates PSO with Lagrangian relaxation is proposed for solving the multidimensional knapsack problem (MKP). An efficiency measure for MKP based on the LR dual information is defined to combine the object function and the constraints of the MKP together. The efficiency measure is used to determine the core problem for MKP with the aim of reducing the problem scale. Then a hybrid algorithm combines the Quantum Particle Swarm Optimization with a local search method is presented to solve the core problem. Numerical experiments are made on certain knapsack problems and computational results show that the proposed algorithm is very promising. © Springer Nature Switzerland AG 2019.

关键词： Efficiency

Interference-assisted kaleidoscopic meta-plexer for arbitrary spin-wavefront manipulation

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Light(Science & Applications) 2019年第1期8卷 1141-1150页

作者： He-Xiu Xu Guangwei Hu Ying Li Lei Han Jianlin Zhao Yunming Sun Fang Yuan Guang-Ming Wang Zhi Hao Jiang Xiaohui Ling Tie Jun Cui Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Air and Missile Defense College Air force Engineering University710051 Xi’anChina Hunan Provincial Key Laboratory of Intelligent Information Processing and Applications College of Physics and Electronics EngineeringHengyang Normal University421008 HengyangChina School of Natural and Applied Sciences Northwestern Polytechnical University710072 Xi’anChina Advanced Technique Department Key Lab of Aeronautics Computing Technique710075 Xi’anChina State Key Laboratory of Millimeter Waves Southeast University210096 NanjingChina

Achieving simultaneous polarization and wavefront control,especially circular polarization with the auxiliary degree of freedom of light and spin angular momentum,is of fundamental importance in many optical *** are typically undesirable in highly integrated photonic circuits and ***,we propose an interference-assisted metasurface-multiplexer(meta-plexer)that counterintuitively exploits constructive and destructive interferences between hybrid meta-atoms and realizes independent spin-selective wavefront *** kaleidoscopic meta-plexers are experimentally demonstrated via two types of single-layer spinwavefront multiplexers that are composed of spatially rotated anisotropic *** type generates a spinselective Bessel-beam wavefront for spin-down light and a low scattering cross-section for stealth for spin-up *** other type demonstrates versatile control of the vortex wavefront,which is also characterized by the orbital angular momentum of light,with frequency-switchable numbers of beams under linearly polarized wave *** findings offer a distinct interference-assisted concept for realizing advanced multifunctional photonics with arbitrary and independent spin-wavefront features.A variety of applications can be readily anticipated in optical diodes,isolators,and spin-Hall meta-devices without cascading bulky optical elements.

关键词： arbitrary momentum versatile

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

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arXiv 2024年

作者： Wang, Zi Xiao, Min Zhou, Yirong Wang, Chengyan Wu, Naiming Li, Yi Gong, Yiwen Chang, Shufu Chen, Yinyin Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Institute of Artificial Intelligence Xiamen University China Human Phenome Institute Fudan University China Department of Imaging Xiamen Cardiovascular Hospital of Xiamen University School of Medicine Xiamen University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University Department of Medical Imaging Shanghai Medical School Shanghai Institute of Medical Imaging China Fujian Province Key Clinical Specialty Construction Project Medical Imaging Department Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering and Imperial-X Imperial College London United Kingdom Department of Bioengineering Imperial College London United Kingdom

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

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arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

An Integrated and Intelligent Dental Healthcare System with Mobile Services 15th

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An Integrated and Intelligent Dental Healthcare System with ...

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Xu, Yueshen Lu, Yi Li, Rui Niu, Lin Du, Wenzhi An, Ni Liu, Yaning Liu, Xinyi Jiang, Yan Li, Zhenhua Guo, Jin Wang, Xiangdong School of Computer Science and Technology Xidian University Xi’an710071 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China Stomatological Hospital Xi’an Jiaotong University Xi’an710004 China Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Diseases College of Stomatology Xi’an Jiaotong University Xi’an China Shaanxi Lishengruihui Technology Co. Ltd. Xi’an710075 China

ISBN: (纸本)9783030301453

Medical informatization takes a key role in medical and healthcare industries, which is a necessary way of improving service quality and treatment experience in a hospital. In this paper, we design and implement an integrated and intelligent healthcare system with mobile services, specific to dental healthcare. The developed system contains four components, including WeChat official account, intelligent question and answering (Q&A) system, mobile follow-up care mini program and AI speech assistant. The developed WeChat official account and intelligent Q&A system provide a large amount of professional knowledge on dental health, which can help narrow the knowledge gap between doctors and patients. The two components facilitate patients to follow up our applications without downloading any extra softwares, as the developed functions (e.g., voice service) are provided through mobile services. These two components also facilitate follow-up management, decreasing manpower and resource usage in hospital. Our system is developed to serve patients as well as dentists, and provides a group of interactive healthcare services in a low cost. In this paper, we elaborate the whole system architecture and implementation detail of each component. We also report the performance test result and training process of the classifier. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Hospitals

Privacy-preserving medical treatment system through nondeterministic finite automata

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arXiv 2020年

作者： Yang, Yang Deng, Robert H. Liu, Ximeng Wu, Yongdong Weng, Jian Zheng, Xianghan Rong, Chunming School of Information Systems Singapore Management University Singapore College of Mathematics and Computer Science Fuzhou University Fujian China State Key Laboratory of Integrated Services Networks Xidian University Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Systems Singapore Management University Singapore Singapore Department of Computer Science Jinan University Guangdong China College of Mathematics and Computer Science Fuzhou University Fujian China Department of Electronic Engineering and Computer Science University of Stavanger Norway MingByte Technology Qingdao China

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

Positive-Unlabeled Learning for Sentiment Analysis with Adversarial Training 15th

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Positive-Unlabeled Learning for Sentiment Analysis with Adve...

15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Xu, Yueshen Li, Lei Huang, Jianbin Yin, Yuyu Shao, Wei Mai, Zhida Hei, Lei School of Computer Science and Technology Xidian University Xi’an710071 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China School of Computer Hangzhou Dianzi University Hangzhou310018 China School of Science RMIT University MelbourneVIC3001 Australia Xanten Guangdong Development Co. Ltd. Foshan528200 China Center of Journal Publication Xidian University Xi’anShaanxi710071 China

ISBN: (纸本)9783030301453

Sentiment classification is a critical task in sentiment analysis and other text mining applications. As a sub-problem of sentiment classification, positive and unlabeled learning or positive-unlabeled learning (PU learning) problem widely exists in real-world cases, but it has not been given enough attention. In this paper, we aim to solve PU learning problem under the framework of adversarial training and neural network. We propose a novel model for PU learning problem, which is based on adversarial training and attention-based long short-term memory (LSTM) network. In our model, we design a new adversarial training technique. We conducted extensive experiments on two real-world datasets. The experimental results demonstrate that our proposed model outperforms the compared methods, including the well-known traditional methods and state-of-the-art methods. We also report the training time, and discuss the sensitivity of our model to parameters. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Long short-term memory

Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network

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arXiv 2020年

作者： Jiang, Yenan Li, Ying Zou, Shanrong Zhang, Haokui Bai, Yunpeng School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710129 China School of Computer Science University of Adelaide AdelaideSA5005 Australia National Key Laboratory of Science and Technology on Space Microwave Xi’an710000 China School of Computing and Information Systems University of Melbourne MelbourneVIC3010 Australia

In recent years, deep convolutional neural networks (CNNs) have demonstrated impressive ability to represent hyperspectral images (HSIs) and achieved encouraging results in HSI classification. However, the existing CNN-based models operate at the patch-level, in which pixel is separately classified into classes using a patch of images around it. This patch-level classification will lead to a large number of repeated calculations, and it is hard to identify the appropriate patch size that is beneficial to classification accuracy. In addition, the conventional CNN models operate convolutions with local receptive fields, which cause the failure of contextual spatial information modeling. To overcome these aforementioned limitations, we propose a novel end-to-end, pixel-to-pixel fully convolutional spatial propagation network (FCSPN) for HSI classification. Our FCSPN consists of a 3D fully convolution network (3D-FCN) and a convolutional spatial propagation network (CSPN). Specifically, the 3D-FCN is firstly introduced for reliable preliminary classification, in which a novel dual separable residual (DSR) unit is proposed to effectively capture spectral and spatial information simultaneously with fewer parameters. Moreover, the channel-wise attention mechanism is adapted in the 3D-FCN to grasp the most informative channels from redundant channel information. Finally, the CSPN is introduced to capture the spatial correlations of HSI via learning a local linear spatial propagation, which allows maintaining the HSI spatial consistency and further refining the classification results. Experimental results on three HSI benchmark datasets demonstrate that the proposed FCSPN achieves state-of-the-art performance on HSI classification. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution