检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Hao, Xiaohan Ren, Wei Xiong, Ruoting Zheng, Xianghan Zhu, Tianqing Xiong, Neal N. School of Computer Science China University of Geoscience Wuhan China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Mingbyte Technology QingDao China School of Software University of Technology Sydney UltimoNSW2007 Australia Dept. of Mathematics and Computer Science Northeastern State University United States

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Parameter estimation with the ordered 2 regularization via an alternating direction method of multipliers

arXiv

引用

arXiv 2019年

作者： Humayoo, Mahammad Cheng, Xueqi CAS Key Laboratory of Network Data Science & Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Regularization is a popular technique in machine learning for model estimation and for avoiding overfitting. Prior studies have found that modern ordered regularization can be more effective in handling highly correlated, high-dimensional data than traditional regularization. The reason stems from the fact that the ordered regularization can reject irrelevant variables and yield an accurate estimation of the parameters. How to scale up the ordered regularization problems when facing large-scale training data remains an unanswered question. This paper explores the problem of parameter estimation with the ordered 2-regularization via Alternating Direction Method of Multipliers (ADMM), called ADMM-O2. The advantages of ADMM-O2 include (i) scaling up the ordered 2 to a large-scale dataset, (ii) predicting parameters correctly by excluding irrelevant variables automatically, and (iii) having a fast convergence rate. Experimental results on both synthetic data and real data indicate that ADMM-O2 can perform better than or comparable to several state-of-the-art baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

Mixture-of-Experts for Distributed Edge computing with Channel-Aware Gating Function

arXiv

引用

arXiv 2025年

作者： Song, Qiuchen Jing, Shusen Zhang, Shuai Zhang, Songyang Huang, Chuan Shenzhen Future Network of Intelligence Institute School of Science and Engineering Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China Department of Radiation Oncology University of California San FranciscoCA94118 United States Department of Data Science New Jersey Institute of Technology NewarkNJ07102 United States Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States School of Science and Engineering Shenzhen Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China

In a distributed mixture-of-experts (MoE) system, a server collaborates with multiple specialized expert clients to perform inference. The server extracts features from input data and dynamically selects experts based on their areas of specialization to produce the final output. Although MoE models are widely valued for their flexibility and performance benefits, adapting distributed MoEs to operate effectively in wireless networks has remained unexplored. In this work, we introduce a novel channel-aware gating function for wireless distributed MoE, which incorporates channel conditions into the MoE gating mechanism. To train the channel-aware gating, we simulate various signal-to-noise ratios (SNRs) for each expert’s communication channel and add noise to the features distributed to the experts based on these SNRs. The gating function then utilizes both features and SNRs to optimize expert selection. Unlike conventional MoE models which solely consider the alignment of features with the specializations of experts, our approach additionally considers the impact of channel conditions on expert performance. Experimental results demonstrate that the proposed channel-aware gating scheme outperforms traditional MoE models. Copyright © 2025, The Authors. All rights reserved.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Faking photon number on a transition-edge sensor

arXiv

引用

arXiv 2021年

作者： Chaiwongkhot, Poompong Zhong, Jiaqiang Huang, Anqi Qin, Hao Shi, Sheng-Cai Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Bangkok10110 Thailand Purple Mountain Observatory and Key Laboratory of Radio Astronomy Chinese Academy of Sciences 10 Yuanhua road Nanjing210033 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China CAS Quantum Network Co. Ltd. 99 Xiupu road Shanghai201315 China Russian Quantum Center Skolkovo Moscow121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

We study potential security vulnerabilities of a single-photon detector based on superconducting transition-edge sensor. In a simple experiment, we show that an adversary could fake a photon number result at a certain wavelength by sending a larger number of photons at a longer wavelength. In another experiment, we show that the detector can be blinded by bright continuous-wave light and then, a controlled response simulating single-photon detection can be produced by applying a bright light pulse. We model an intercept- and-resend attack on a quantum key distribution system that exploits the latter vulnerability and, under certain assumptions, succeeds to steal the key. © 2021, CC BY.

关键词： Photons

来源：评论

学校读者我要写书评

暂无评论

Dynamic MCMC sampling

arXiv

引用

arXiv 2019年

作者： Feng, Weiming He, Kun Sun, Xiaoming Yin, Yitong State Key Laboratory for Novel Software Technology Nanjing University CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

The Markov chain Monte Carlo (MCMC) methods are the primary tools for sampling from Gibbs distributions arising by various graphical models, e.g. Markov random fields (MRF). Traditional MCMC sampling algorithms are focused on a classic static setting, where the input is fixed. In this paper we study the problem of sampling from a MRF when the graphical model itself is changing dynamically with time. The problem is well motivated by the growing volume and velocity of data in today's applications of the MCMC methods. For the two major MCMC approaches, respectively for the approximate and perfect sampling, namely, the Gibbs sampling and the coupling from the past (CFTP), we give dynamic versions for the respective MCMC sampling algorithms. On MRF with n variables and bounded maximum degrees, these dynamic sampling algorithms can maintain approximate samples within 1/Poly(n) total variation errors, or perfect samples, while the MRF is dynamically changing. Furthermore, the dynamic sampling algorithms are efficient with O(n) space cost, and O(log2 n) incremental time cost upon each local update to the input MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains. These decay conditions were well known in the literature of couplings for rapid mixing of Markov chains, and now for the first time, are used to imply efficient dynamic sampling algorithms. Consequently, we have efficient dynamic (approximate or perfect) sampling algorithms with O(n) space cost and O (log2 n) incremental time cost, for the following models when the maximum degree is bounded: generalMRF satisfying the Dobrushin-Shlosman condition (for approximate sampling);Ising model with temperature β where e-2|β| > 1 - 2 β+1 (for both approximate and perfect samplings);hardcore model with fugacity λ 2λ (for approximate sampling);or q > 2Δ2 + 3Δ (for perfect sampling). These results show that the coupling of single-site Markov chains that

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Facial Expression Recognition for In-the-wild Videos

Facial Expression Recognition for In-the-wild Videos

引用

International Conference on Automatic Face and Gesture Recognition

作者： Hanyu Liu Jiabei Zeng Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

In this paper, we propose a method for facial expression recognition for in-the-wild videos. Our method combines Deep Residual network (ResNet) and Bidirectional Recurrent Neutral network with Long-Short-Term Memory Unit (BLSTM). This method won the 2 nd place in the seven basic expression classification track of Affective Behavior Analysis in-the-wild Competition held in conjunction with the IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2020, achieving 66.9% accuracy and 40.8% final metric on the test set. We also visualize the learned attention maps and analyze the importance of different regions in facial expression recognition.

关键词： Face recognition Videos Feature extraction Training Conferences Computer vision Mouth

来源：评论

学校读者我要写书评

暂无评论

Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

引用

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

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

来源：评论

学校读者我要写书评

暂无评论

Design and Application of a Portable Sleep Inertia Detection System Based on EEG Signals

Design and Application of a Portable Sleep Inertia Detection...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yunzhi Cui Fuze Tian Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Ministry of Education Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Lanzhou China

ISBN: (纸本)9781665429825

Sleep inertia is a transitional state from sleep to wakefulness, accompanied by groggy feelings and cognitive impairment. Previous research on sleep inertia mainly used expensive and cumbersome equipment, and the analysis of physiological signals relied on computers. This work introduces a sleep inertia detection system that consists of a wearable low-power electroencephalogram (EEG) acquisition module based on STM32WB55 and ADS1299, and a data processing module based on the Xilinx ® Zynq ® -7000 XC7Z020. This work recorded the EEG signals of ten subjects in the alert and sleep inertia states to extract the delta power, alpha power, beta power, EEG vigilance, and sample entropy. A linear support vector machine (SVM) was then used to classify the two states based on all subjects’ EEG signals, with an accuracy of 72.5%, and the average accuracy based on a single participant was 8S.9%. Finally, the feature extraction algorithm and SVM parameters were entered into the Zynq ® system-on-chip (SoC) development board to realize onboard processing of the algorithm. The system is capable of evaluating the severity of human sleep inertia, which has reference significance for the practical application of sleep inertia detection.

关键词： Support vector machines Sleep Conferences Feature extraction data processing Electroencephalography Physiology

来源：评论

学校读者我要写书评

暂无评论

Sea-land Segmentation of Synthetic Aperture Radar Imagery Using Deep Neural network Models

Sea-land Segmentation of Synthetic Aperture Radar Imagery Us...

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Yinfei Zhou Gang Zheng Jingsong Yang Xiaofeng Li Bin Liu Jingliang Shao Han Jiang Ren Ren Peng Chen Lin Ren Bin Zhang Ocean College Zhejiang University Zhoushan China Ministry of Natural Resources State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography Hangzhou China Chinese Academy of Sciences CAS Key Laboratory of Ocean Circulation and Waves Big Data Center Institute of Oceanology Qingdao China Chinese Academy of Sciences Center for Ocean Mega-Science Institute of Oceanology Qingdao China College of Marine Sciences Shanghai Ocean University Shanghai China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665409889

The waterline is the transient boundary between land and sea, and the waterline extraction is of great significance for coastal research and monitoring. Spaceborne synthetic aperture radar (SAR) has the ability to penetrate clouds and thus can image large coastal areas with high-resolution under all-weather and all-day conditions. These advantages make SAR well complement the waterline observations by optical satellites. However, SAR images of coastal areas usually have complex patterns because of the diverse topography and geomorphology, and the SAR-imaging mechanism also results in large coherent speckle noise in the images. The above poses challenges to the waterline extraction from SAR imagery. Traditional manual annotation is highly accurate, but is laborious and time-consuming. Therefore, a more efficient method is urgent. The procedure of waterline extraction is sea-land segmentation followed by boundary-extraction where accurate segmentation is critical. From the perspective of the computer vision, sea-land segmentation can be viewed as the image semantic segmentation task. In recent years, deep neural network (DNN) models have achieved great success in the semantic segmentation task. The U-Net, named for its U-shape architecture, is one of the most popular DNN models for the task. Compared with others, the U-Net has a lighter structure that efficiently extract and fuse the multi-scale image features. In this study, we applied the U-Net and its variants to sea-land segmentation and systematically investigated the models’ performances. All models can segment the sea and land efficiently, and the results are overall consistent with the manual annotation. Our study demonstrates the application potential of DNN technology in waterline monitoring from SAR imagery.

关键词： Deep learning Image segmentation Neural networks Semantics Sea measurements Manuals Radar polarimetry

来源：评论

学校读者我要写书评

暂无评论

Extending Registration and Authentication Processes of FIDO2 External Authenticator with QR Codes

Extending Registration and Authentication Processes of FIDO2...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Chengqian Guo Quanwei Cai Qiongxiao Wang Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering CAS Data Assurance and Communication Security Research Center CAS School of Cyber Security University of Chinese Academy of Sciences School of Cyber Security University of Science and Technology of China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

FIDO2, the newest set of FIDO specifications, enables the user to leverage an external authenticator for the authentication in both mobile and desktop environments (referred to as user agent). For the secure registration and authentication, FIDO2 requires the external authenticator and user agent to establish a confidential and mutually authenticated data transport channel through either USB interfaces, Near Field Communication (NFC) or Bluetooth. However, the external authenticator and host may not be equipped with one of the above physical media simultaneously, for example, a desktop may only have USB interfaces while an external authenticator (e.g., a smartphone) may have no USB inferfaces. This affects the wide adoption of FIDO2. In this paper, we extend the registration and authentication processes of FIDO2 external authenticator with QR code, which enables the external authenticator being equipped with a camera to be used for the authentication at any user agent. During the registration process, our scheme requires the user to provide the original credential and a one-time password displayed on the authenticator, and therefore ensures the correct user will only be bound with the expected authenticator. The security of our scheme has been formally analyzed based on the Dolev-Yao style model, a widely adopted model for the analysis of web systems. We have implemented the prototype, and the performance evaluation demonstrated the efficiency of our scheme, which needs 373 ms for registration and 141 ms for authentication in our environment.

关键词： Analytical models Privacy Authentication Prototypes Near field communication Universal Serial Bus Security

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：