检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Wang, Wen Jiang, Xuefeng Gao, Bo Lu, Jinda The Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Computer and Information Technology The Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China The School of Information Science and Technology University of Science and Technology of China Hefei China

Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end devices in EI, Federated Learning (FL) is proposed for collaborative training of shared AI models across multiple devices without compromising data privacy. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which suffers from unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) allows to update model parameters with logits transfer and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache. Empirical experiments on four datasets demonstrate that FedCache achiev

关键词： Distillation

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Age of Information Minimization in Intelligent Reflecting Surface-Aided Covert Communications

Age of Information Minimization in Intelligent Reflecting Su...

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IEEE Globecom Workshops

作者： Chao Wang Zan Li Yue Zhao Derrick Wing Kwan Ng Naofal Al-Dhahir State Key Laboratory of Integrated Service Network Xidian University Xi’an China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Department of Electrical and Computer Engineering the University of Texas at Dallas Richardson TX USA

ISBN: (纸本)9781665459761

In this work, we investigate the covert information freshness in intelligent reflecting surface (IRS)-aided multi antenna communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). Specifically, the covert transmission from Bob to Alice is shielded from Willie under the mask of Alice’s undisguised signal transmission. To minimize the covert information age, we exploit the channel statistics to jointly design the active beamforming at Alice and Bob, the passive beamforming at the IRS, and the blocklength of the confidential data packet under the constraints of the maximum tolerable age of information (AoI) at Bob and communication covertness. Although the considered joint optimization problem is nonconvex, we employ the successive convex approximation to design an iterative algorithm to obtain an efficient suboptimal solution. Numerical results demonstrate the superiority of our proposed joint optimization algorithm and further show that the enhanced covert channel quality can reduce the required blocklength to improve information freshness.

关键词： Array signal processing Error probability Simulation Full-duplex system Information age Approximation algorithms Reflector antennas

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Semantic Learning and Emulation Based Cross-platform Binary Vulnerability Seeker

arXiv

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arXiv 2022年

作者： Gao, Jian Jiang, Yu Liu, Zhe Yang, Xin Wang, Cong Jiao, Xun Yang, Zijiang Sun, Jiaguang The School of Software Tsinghua University Beijing National Research Center for Information Science and Technology Key Laboratory for Information System Security Ministry of Education Beijing100084 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Department of Electrical and Computer Engineering Villanova University VillanovaPA19085 United States The Department of Computer Science Western Michigan University KalamazooMI49008 United States

Clone detection is widely exploited for software vulnerability search. The approaches based on source code analysis cannot be applied to binary clone detection because the same source code can produce significantly different binaries due to different operating systems, microprocessor architectures and compilers. In this paper, we present BinSeeker, a cross-platform binary seeker that integrates semantic learning and emulation. With the help of the labeled semantic flow graph, BinSeeker can quickly identify M candidate functions that are most similar to the vulnerability from the target binary. The value of M is relatively large so this semantic learning procedure essentially eliminates those functions that are very unlikely to have the vulnerability. Then, semantic emulation is conducted on these M candidates to obtain their dynamic signature sequences. By comparing signature sequences, BinSeeker produces top-N functions that exhibit most similar behavior to that of the vulnerability. With fast filtering of semantic learning and accurate comparison of semantic emulation, BinSeeker seeks vulnerability precisely with little overhead. The experiments on six widely used programs with fifteen known CVE vulnerabilities demonstrate that BinSeeker outperforms three state-of-the-art tools Genius, Gemini and CACompare. Regarding search accuracy, BinSeeker achieves an MRR value of 0.65 in the target programs, whereas the MRR values by Genius, Gemini and CACompare are 0.17, 0.07 and 0.42, respectively. If we consider ranking a function with the targeted vulnerability in the top-5 as accurate, BinSeeker achieves the accuracy of 93.33 percent, while the accuracy of the other three tools is merely 33.33, 13.33 and 53.33 percent, respectively. Such accuracy is achieved with 0.27s on average to determine whether the target binary function contains a known vulnerability, and the time for the other three tools are 1.57s, 0.15s and 0.98s, respectively. Compared to the time used to manu

关键词： Semantics

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Refracting Reconfigurable Intelligent Surface Assisted URLLC for Millimeter Wave High-Speed Train Communication Coverage Enhancement

arXiv

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arXiv 2024年

作者： Liu, Changzhu He, Ruisi Niu, Yong Mao, Shiwen Ai, Bo Chen, Ruifeng The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China

High-speed train (HST) has garnered significant attention from both academia and industry due to the rapid development of railways worldwide. Millimeter wave (mmWave) communication, known for its large bandwidth is an effective way to address performance bottlenecks in cellular network based HST wireless communication systems. However, mmWave signals suffer from significant path loss when traversing carriage, posing substantial challenges to cellular networks. To address this issue, reconfigurable intelligent surfaces (RIS) have gained considerable interest for its ability to enhance cell coverage by reflecting signals toward receiver. Ensuring communication reliability, a core performance indicators of ultra-reliable and low-latency communications (URLLC) in fifth-generation systems, is crucial for providing steady and reliable data transmissions along railways, particularly for delivering safety and control messages and monitoring HST signaling information. In this paper, we investigate a refracting RIS-assisted multi-user multiple-input single-output URLLC system in mmWave HST communications. We propose a sum rate maximization problem, subject to base station beamforming constraint, as well as refracting RIS discrete phase shifts and reliability constraints. To solve this optimization problem, we design a joint optimization algorithm based on alternating optimization method. This involves decoupling the original optimization problem into active beamforming design and packet error probability optimization subproblem, and discrete phase shift design subproblems. These subproblems are addressed exploiting Lagrangian dual method and the local search method, respectively. Simulation results demonstrate the fast convergence of the proposed algorithm and highlight the benefits of refracting RIS adoption for sum rate improvement in mmWave HST networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile telecommunication systems

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Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders 1

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2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020

作者： Chen, Chao Zhu, Xuequan Belkacem, Abdelkader Nasreddine Lu, Lin Hao, Long You, Jia Shin, Duk Tan, Wenjun Huang, Zhaoyang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Department of Computer and Network Engineering College of Information Technology UAE University Al Ain15551 United Arab Emirates Zhonghuan Information College Tianjin University of Technology Tianjin300380 China Department of Electronics and Mechatronics Tokyo Polytechnic University Atsugi243-0297 Japan School of Computer Science and Engineering Northeastern University Shenyang110189 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing100088 China

ISBN: (数字)9789811612886

ISBN: (纸本)9789811612879

Nowadays, Sleep disorder is a common disease, and spindle spindles are important features of the second stage non-rapid eye movement (NREM) sleep. In this paper, we propose an improved automatic detection method of spindles based on wavelet transform. The spindles automatic detector is mainly composed of wavelet transform and clustering. We collected the electroencephalography (EEG) signals of six patients with sleep disorders all night for ten hours, and then preprocessed the data and other operations, and then used our improved method to detect the sleep EEG signals by spindles. By comparing with the previous automatic detection method not improved and another automatic detection method, the results show that the accuracy of sleep spindles detection can be effectively improved. The accuracy of the improved detector is 5.19% higher than before, and 9.7% higher than that of another method based on amplitude threshold. Finally, we made a simple comparison between people with sleep disorders and normal people. We found that there were significant differences in spindle density between people with sleep disorders and people without sleep disorders. The average spindle density in the normal population averaged 2.59 spindles per minute. People with sleep disorders had an average spindle density of 1.32 spindles per minute. In future research, our research direction is to improve the accuracy of spindles automatic detection by improving the spindles detector and study the difference of spindles between patients with sleep disorders and normal people in a large number of samples, so that the difference of spindles can be used as the basis for the diagnosis of sleep disorders. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Wavelet transforms

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A sequence-to-sequence approach for document-level relation extraction

arXiv

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arXiv 2022年

作者： Giorgi, John Bader, Gary D. Wang, Bo Department of Computer Science University of Toronto Canada Department of Molecular Genetics University of Toronto Canada Department of Laboratory Medicine and Pathobiology University of Toronto Canada Terrence Donnelly Centre for Cellular & Biomolecular Research Canada Vector Institute for Artificial Intelligence The Lunenfeld-Tanenbaum Research Institute Sinai Health System Canada Princess Margaret Cancer Centre University Health Network Canada Peter Munk Cardiac Center University Health Network Canada

Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (DocRE). DocRE requires integrating information within and across sentences, capturing complex interactions between mentions of entities. Most existing methods are pipeline-based, requiring entities as input. However, jointly learning to extract entities and relations can improve performance and be more efficient due to shared parameters and training steps. In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components. Using a simple strategy we call entity hinting, we compare our approach to existing pipeline-based methods on several popular biomedical datasets, in some cases exceeding their performance. We also report the first end-to-end results on these datasets for future comparison. Finally, we demonstrate that, under our model, an end-to-end approach outperforms a pipeline-based approach. Our code, data and trained models are available at https://***/johngiorgi/seq2rel. An online demo is available at https://***/johngiorgi/seq2rel/main/***. © 2022, CC0.

关键词： Pipelines

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Dynamic nitrogen vacancy magnetometry by single-shot optical streaking microscopy

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Photonics research 2022年第9期10卷 2147-2156页

作者： MARK A.KEPPLER ZACHARY A.STEELMAN ZACHARY N.COKER MILOŠNESLÁDEK PHILIP R.HEMMER VLADISLAV V.YAKOVLEV JOEL N.BIXLER Department of Biomedical Engineering Texas A&M UniversityCollege StationTexas 77843USA SAIC JBSA Fort Sam HoustonTexas 78234USA National Research Council Research Associateship Program WashingtonDC 20001USA IMOMEC Division IMECB-3590 DiepenbeekBelgium Institute for Materials Research(IMO) Hasselt UniversityB-3590 DiepenbeekBelgium Czech Technical University in Prague 27201 KladnoCzech Republic Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTexas 77843USA Bioeffects Division Airman System DirectorateAir Force Research LaboratoryJBSA Fort Sam HoustonTexas 78234USA

Nitrogen vacancy diamonds have emerged as sensitive solid-state magnetic field sensors capable of producing diffraction limited and sub-diffraction field ***,for the first time,to our knowledge,we extend those measurements to high-speed imaging,which can be readily applied to analyze currents and magnetic field dynamics in circuits on a microscopic *** overcome detector acquisition rate limitations,we designed an optical streaking nitrogen vacancy microscope to acquire two-dimensional spatiotemporal *** demonstrate magnetic field wave imaging with micro-scale spatial extent and~400μs temporal *** validating this system,we detected magnetic fields down to 10μT for 40 Hz magnetic fields using single-shot imaging and captured the spatial transit of an electromagnetic needle at streak rates as high as 110μm/*** design has the capability to be readily extended to full 3D video acquisition by utilizing compressed sensing techniques and a potential for further improvement of spatial resolution,acquisition speed,and *** device opens opportunities to many potential applications where transient magnetic events can be isolated to a single spatial axis,such as acquiring spatially propagating action potentials for brain imaging and remotely interrogating integrated circuits.

关键词： vacancy utilizing overcome

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SPP-CNN: An Efficient Framework for network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

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Remote Sensing Plateau Forest Segmentation with Boundary Preserving Double Loss Function Collaborative Learning

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Journal of New Media 2022年第4期4卷 165-177页

作者： Ying Ma Jiaqi Zhang Pengyu Liu Zhihao Wei Lingfei Zhang Xiaowei Jia School of Physics and Electronic Information Engineering Qinghai Minzu UniversityXining810007China The Information Department Beijing University of TechnologyBeijing100124China Advanced Information Network Beijing Laboratory Beijing100124China Computational Intelligence and Intelligent Systems Beijing key Laboratory Beijing100124China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing100871China Department of Computer Science University of Pittsburgh15260USA

Plateau forest plays an important role in the high-altitude ecosystem,and contributes to the global carbon *** forest monitoring request in-suit data from field *** recent development of the remote sensing technic,large-scale satellite data become available for surface *** to the various information contained in the remote sensing data,obtain accurate plateau forest segmentation from the remote sensing imagery still remain *** developed deep learning(DL)models such as deep convolutional neural network(CNN)has been widely used in image processing tasks,and shows possibility for remote sensing ***,due to the unique characteristics and growing environment of the plateau forest,generate feature with high robustness needs to design structures with high *** at the problem that the existing deep learning segmentation methods are difficult to generate the accurate boundary of the plateau forest within the satellite imagery,we propose a method of using boundary feature maps for collaborative *** are three improvements in this ***,design a multi input model for plateau forest segmentation,including the boundary feature map as an additional input label to increase the amount of information at the ***,we apply a strong boundary search algorithm to obtain boundary value,and propose a boundary value loss ***,improve the Unet segmentation network and combine dense block to improve the feature reuse ability and reduces the image information loss of the model during *** then demonstrate the utility of our method by detecting plateau forest regions from ZY-3 satellite regarding to Sanjiangyuan nature *** experimental results show that the proposed method can utilize multiple feature information comprehensively which is beneficial to extracting information from boundary,and the detection accuracy is generally higher than several state-of-art algorit

关键词： Remote sensing forest segmentation boundary preserving double loss function

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K-Space-Aware Cross-Modality Score for Quality Assessment of Synthesized Neuroimages

arXiv

引用

arXiv 2023年

作者： Xie, Guoyang Wang, Jinbao Huang, Yawen Lyu, Jiayi Zheng, Feng Zheng, Yefeng Jin, Yaochu Southern University of Science and Technology Department of Computer Science Shenzhen518055 China CATL Department of Intelligent Manufacturing Ningde352000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Tencent Youtu Lab Jarvis Research Center Shenzhen510310 China University of Chinese Academy of Sciences the School of Engineering Science Beijing100049 China Westlake University the School of Engineering Hangzhou310030 China

The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural features but neglect the crucial lesion location and fundamental k-space speciality of medical images. To overcome this problem, we propose a new metric K-CROSS to spur progress on this challenging problem. Specifically, K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location, together with a tumor encoder for representing features, such as texture details and brightness intensities. To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty. The structure-shared encoders are designed and constrained with a similarity loss to capture the intrinsic common structural information for both modalities. As a consequence, the features learned from lesion regions, k-space, and anatomical structures are all captured, which serve as our quality evaluators. We evaluate the performance by constructing a large-scale cross-modality neuroimaging perceptual similarity (NIRPS) dataset with 6,000 radiologist judgments. Extensive experiments demonstrate that the proposed method outperforms other metrics, especially in comparison with the radiologists on NIRPS. To forster reproducibility and accessibility, the source codeis uploaded to the website: https://***/M-3LAB/K-CROSS © 2023, CC BY.

关键词： Magnetic resonance imaging

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