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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Yu, Kai Xu, Yong Yang, Jian Shao, Ling Fu, Huazhu The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China The Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Terminus Group China

Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information, is a new and effective solution for fast MR imaging. However, current multi-contrast super-resolution (SR) methods tend to concatenate different contrasts directly, ignoring their relationships in different clues, e.g., in the high-intensity and low-intensity regions. In this study, we propose a separable attention network (comprising high-intensity priority attention and low-intensity separation attention), named SANet. Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image. SANet provides three appealing benefits: (1) It is the first model to explore a separable attention mechanism that uses the auxiliary contrast to predict the high-intensity and low-intensity regions regions, diverting more attention to refining any uncertain details between these regions and correcting the fine areas in the reconstructed results. (2) A multi-stage integration module is proposed to learn the response of multi-contrast fusion at multiple stages, get the dependency between the fused representations, and boost their representation ability. (3) Extensive experiments with various state-of-the-art multi-contrast SR methods on fastMRI and clinical in vivo datasets demonstrate the superiority of our model. © 2021, CC BY.

关键词： Magnetic resonance imaging

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RBDN: Residual Bottleneck Dense network for Image Super-Resolution

Research Square

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Research Square 2021年

作者： An, Zeyu Zhang, Junyuan Sheng, Ziyu Er, Xuanhe Lv, Junjie College of Electronics and Information Engineering Southwest University Chongqing400715 China College of Software Engineering Chengdu University of Information Technology Chengdu610200 China The Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing400715 China

Recent studies have shown that Super-Resolution Generative Adversarial network (SRGAN) can significantly improve the quality of single-image super-resolution. However, the existing SRGAN approaches also have drawbacks, such as inadequate of features utilization, huge number of parameters and poor scalability. To further enhance the visual quality, we thoroughly study three key components of SRGAN: network architecture, adversarial loss and perceptual loss, and propose a DenseNet with Residual-in-Residual Bottleneck Block (RRBB) named as Residual Bottleneck Dense network (RBDN) for single-image super-resolution. In particular, RBDN combines ResNet and DenseNet with different roles, in which ResNet refines feature values by addition and DenseNet memorizes feature values by concatenation. Specifically, the DenseNet adopts the Bottleneck structure to reduce the network parameters and improve the convergence rate. In addition, the proposed RRBB, as the basic network building unit, removes the batch normalization (BN) layer and employs the ELU function to reduce the opposite effects in the absence of BN. In this way, RBDN can enjoy the merits of the sufficient feature value refined by residual groups and the refined feature value memorized by dense connections, thus achieving better performance compared with most current residual blocks. © 2021, CC BY.

关键词： Generative adversarial networks

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Robust Artificial Noise-Aided Beamforming for A Secure MISO-NOMA Visible Light Communication System

引用

China Communications 2020年第11期17卷 42-53页

作者： Xiaodong Liu Zezong Chen Yuhao Wang Fuhui Zhou Shuai Ma School of Electronic Information Wuhan UniversityWuhan 430072China School of Information Engineering Nanchang UniversityNanchang 330031China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210000China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Shanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts&TelecommunicationsXi’an 710121China

Visible light communication(VLC)and non-orthogonal multiple access(NOMA)have been deemed two promising techniques in the next wireless communication *** this paper,secure communications in the presence of potential eavesdropper are investigated for a multiple-input single-output VLC system with *** artificial noise jamming and beamforming technologies are applied to improve secure performance.A robust resource allocation scheme is proposed to minimize the total transmit power taking into account the constraints on the quality of service requirement of the desired users and the maximum tolerable data rate of the eavesdropper,and the practical imperfect channel state information of both the desired users and the *** formulated non-convex optimization problem is tackled based onS-Procedure and semi-definite programming *** results illustrate that our proposed resource allocation scheme can effectively guarantee communication security and achieve transmit power ***,the height and number of LED can significantly affect system performance and the optimum LED height can be obtained for different LED numbers.

关键词： artificial noise non-orthogonal multiple access physical-layer secrecy robust resource allocation visible light communication

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New (k, l, m)-verifiable multi-secret sharing schemes based on XTR public key system

arXiv

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arXiv 2020年

作者： Yang, Jing Fu, Fang-Wei Chern Institute of Mathematics and LPMC Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China

Secret sharing was proposed primarily in 1979 to solve the problem of key distribution. In recent decades, researchers have proposed many improvement schemes. Among all these schemes, the verifiable multi-secret sharing (VMSS) schemes are studied sufficiently, which share multiple secrets simultaneously and perceive malicious dealer as well as participants. By pointing out that the schemes presented by Dehkordi and Mashhadi in 2008 cannot detect some vicious behaviors of the dealer, we propose two new VMSS schemes by adding validity check in the verification phase to overcome this drawback. Our new schemes are based on XTR public key system, and can realize GF (p6) security by computations in GF (p2) without explicit constructions of GF (p6), where p is a prime. Compared with the VMSS schemes using RSA and linear feedback shift register (LFSR) public key cryptosystems, our schemes can achieve the same security level with shorter parameters by using trace function. What’s more, our schemes are much simpler to operate than those schemes based on Elliptic Curve Cryptography (ECC). In addition, our schemes are dynamic and threshold changeable, which means that it is efficient to implement our schemes according to the actual situation when participants, secrets or the threshold needs to be *** Codes 94A62, 94A60, 11T71, 68R01 © 2020, CC-BY-NC-ND.

关键词： Shift registers

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Embedding 3D models in offline physical environments

Embedding 3D models in offline physical environments

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作者： Ertugrul, Egemen Zhang, Han Zhu, Fang Lu, Ping Li, Ping Sheng, Bin Wu, Enhua Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space-efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user-friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low-cost autonomous robots, and 3D printing. © 2020 John Wiley & Sons, Ltd.

关键词： Digital storage

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Learning continuous face representation with explicit functions

arXiv

引用

arXiv 2021年

作者： Zhang, Liping Li, Weijun Sun, Linjun Yu, Lina Ning, Xin Dong, Xiaoli Xu, Jian Qin, Hong Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering School of Microelectronics University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Institute of Semiconductors Beijing100083 China Cognitive Computing Technology Joint Laboratory Wave Group Beijing100083 China

How to represent a face pattern? While it is presented in a continuous way in our visual system, computers often store and process the face image in a discrete manner with 2D arrays of pixels. In this study, we attempt to learn a continuous representation for face images with explicit functions. First, we propose an explicit model (EmFace) for human face representation in the form of a finite sum of mathematical terms, where each term is an analytic function element. Further, to estimate the unknown parameters of EmFace, a novel neural network, EmNet, is designed with an encoder-decoder structure and trained using the backpropagation algorithm, where the encoder is defined by a deep convolutional neural network and the decoder is an explicit mathematical expression of EmFace. Experimental results show that EmFace has a higher representation performance on faces with various expressions, postures, and other factors, compared to that of other methods. Furthermore, EmFace achieves reasonable performance on several face image processing tasks, including face image restoration, denoising, and transformation. Copyright © 2021, The Authors. All rights reserved.

关键词： Functions

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Fuzzified Federated Multi-task Unlearning for Efficient and Privacy-Preserving Swarm Consumer Electronics Systems

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IEEE Transactions on Consumer Electronics 2025年

作者： Qu, Y. Neil Kong, Xiangjie Shen, Shigen Zhang, Aiqing Gao, Huynh Thi Thanh Binh Longxiang Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Huzhou University School of Information Engineering Huzhou313000 China Anhui Normal University School of Physics and Electronic Information Wuhu China

The proliferation of consumer electronics systems in swarm environments has heightened the need for robust privacy-preserving techniques in federated learning (FL) frameworks. Traditional FL methods, while effective in distributing model training, often fall short in efficiently handling data removal requests and safeguarding against inference attacks. Existing solutions, such as FL with Differential Privacy (FL+DP), offer some improvements but still face challenges in balancing performance, privacy, and communication efficiency. In this paper, we introduce FL+Fuzzy Unlearning, a novel FL framework that integrates Fuzzy Logic to address these challenges in federated multi-task learning for swarm consumer electronics systems. Our method aims to maintain high model performance while ensuring robust privacy and efficient communication. We evaluated FL+Fuzzy Unlearning against Traditional FL and FL+DP using two benchmark datasets: MNIST and MovieLens. Our results demonstrate that the proposed approach attains comparable or superior accuracy (0.86 on MNIST and 0.88 on MovieLens) while achieving robust unlearning performance. Moreover, FL+Fuzzy Unlearning substantially enhances privacy protection, evidenced by elevated inference attack failure rates (0.75 for MNIST and 0.78 for MovieLens), and effectively reduces communication overhead, underscoring its efficiency in data exchange. Overall, FL+Fuzzy Unlearning presents a promising, balanced solution for privacy, efficiency, and adaptability, making it highly suitable for real-world applications in swarm consumer electronics systems. © 1975-2011 IEEE.

关键词： Differential privacy

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IoT Device Recognition Framework Based on network Protocol keyword Query 6th

IoT Device Recognition Framework Based on Network Protocol K...

引用

6th International Conference on Artificial Intelligence and security, ICAIS 2020

作者： Xu, Zi-Xiao Dai, Qin-yun Xu, Gang Huang, He Chen, Xiu-Bo Yang, Yi-Xian Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China Shanghai Branch Coordination Center of China National Computer Network Emergency Response Technical Team Shanghai201315 China State Key Laboratory of Public Big Data Guizhou University GuiyangGuizhou550025 China

ISBN: (纸本)9783030578831

The boost of 5G technology has brought IoT with enhanced feasibility, it is predictable to have a dramatically increased number of the IoT devices with Internet access. However, there will be certain numbers of devices that are extremely vulnerable, particularly with low level of security coding built in. Hackers might have a greater chance to launch DDoS attack. Therefore, identifiable IoT devices linked to the network security vulnerability database might mitigate the risks contributing to a safer cyberspace. This is of great significance to the security of the entire cyberspace. This paper proposes an IoT device identification technology based on network protocol keyword query. Firstly, the traffic packets sent by the IoT device are collected in real time. Then the traffic data carried by different network protocols is extracted by parsing traffic packets. Secondly, the traffic data is filtered and converted into identifiable information related to the identification attribute of the IoT device. Finally, with the search engine of the major Internet of Things e-commerce website, the information about the IoT device is obtained by inputting the protocol keyword. © 2020, Springer Nature Switzerland AG.

关键词： Internet of things

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Dynamics and Resonance Fluorescence from a Superconducting Artificial Atom Doubly Driven by Quantized and Classical Fields

arXiv

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arXiv 2024年

作者： Ruan, Xinhui Wang, Jia-Heng He, Dong Song, Pengtao Li, Shengyong Zhao, Qianchuan Kuang, L.M. Tsai, Jaw-Shen Zou, Chang-Ling Zhang, Jing Zheng, Dongning Astafiev, O.V. Liu, Yu-Xi Peng, Zhihui Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Integrated Circuits Tsinghua University Beijing100084 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Center for Quantum Computing RIKEN Saitama351-0198 Japan Graduate School of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku Tokyo162-0825 Japan CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Skolkovo Institute of Science and Technology Nobel str. 3 Moscow143026 Russia Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny141701 Russia Royal Holloway University of London Surrey EghamTW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical one by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anti-crossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches to the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric lines

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DBDNet: Learning bi-directional dynamics for early action prediction 28

DBDNet: Learning bi-directional dynamics for early action pr...

引用

28th International Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Pang, Guoliang Wang, Xionghui Hu, Jian-Fang Zhang, Qing Zheng, Wei-Shi Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9780999241141

Predicting future actions from observed partial videos is very challenging as the missing future is uncertain and sometimes has multiple possibilities. To obtain a reliable future estimation, a novel encoder-decoder architecture is proposed for integrating the tasks of synthesizing future motions from observed videos and reconstructing observed motions from synthesized future motions in an unified framework, which can capture the bi-directional dynamics depicted in partial videos along the temporal (past-to-future) direction and reverse chronological (future-back-to-past) direction. We then employ a bi-directional long short-term memory (Bi-LSTM) architecture to exploit the learned bi-directional dynamics for predicting early actions. Our experiments on two benchmark action datasets show that learning bi-directional dynamics benefits the early action prediction and our system clearly outperforms the state-of-the-art methods. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

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