检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Dong, Yanjie Giannakis, Georgios B. Chen, Tianyi Cheng, Julian Hossain, Md. Jahangir Leung, Victor C.M. Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electrical and Computer Engineering and Digital Technology Center University of Minnesota MinneapolisMN55455 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States School of Engineering University of British Columbia KelownaBCV1V 1V7 Canada College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

This work investigates fault-resilient federated learning when the data samples are non-uniformly distributed across workers, and the number of faulty workers is unknown to the central server. In the presence of adversarially faulty workers who may strategically corrupt datasets, the local messages exchanged (e.g., local gradients and/or local model parameters) can be unreliable, and thus the vanilla stochastic gradient descent (SGD) algorithm is not guaranteed to converge. Recently developed algorithms improve upon vanilla SGD by providing robustness to faulty workers at the price of slowing down convergence. To remedy this limitation, the present work introduces a fault-resilient proximal gradient (FRPG) algorithm that relies on Nesterov’s acceleration technique. To reduce the communication overhead of FRPG, a local (L) FRPG algorithm is also developed to allow for intermittent server-workers parameter exchanges. For strongly convex loss functions, FRPG and LFRPG have provably faster convergence rates than a benchmark robust stochastic aggregation algorithm. Moreover, LFRPG converges faster than FRPG while using the same communication rounds. Numerical tests performed on various real datasets confirm the accelerated convergence of FRPG and LFRPG over the robust stochastic aggregation benchmark and competing alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

来源：评论

学校读者我要写书评

暂无评论

Image Super-Resolution Based on Additional Self-Loop Supervision

引用

Journal of Physics: Conference Series 2021年第1期2025卷

作者： Hong Chen Li Xu Bing Luo Shuai Du Kunshu Xiao Siwei Li School of Computer and Software Engineering University of Xihua Chengdu China School of Science University of Xihua Chengdu China Xihua College of Xihua University University of Xihua Chengdu China

Relying on the prevalence of deep neural networks, only one image super-resolution (SR) research has taken a big step forward. Many network models use the difference between the real image and the generated super-resolution image as a loss function, ignoring the difference between the small-scale image and the super-resolution generated image. We adopt an end-to-end network model with two-way supervision, which can ensure that the image content is similar in both large and small scales. Only real natural images are used for supervision. As the scale of the image becomes larger, the difference becomes larger, and the effect of correcting image details becomes smaller. Adding the loss between the input image and the degraded image of the generated image can not only keep the content of the image similar on a small scale, but also ensure the image details. Compared with several state-of-the-arts, our method obtains the best results.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

arXiv

引用

arXiv 2020年

作者： Liu, Yi Garg, Sahil Nie, Jiangtian Zhang, Yang Xiong, Zehui Kang, Jiawen Shamim Hossain, M. School of Data Science and Technology Heilongjiang University Harbin China Electrical Engineering Department École de technologie supérieure Uni-versité du Québec Montréal Canada Energy Research Institute Interdisciplinary Graduate Programme School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Technology Wuhan University of Technology China Alibaba-NTU Joint Research Institute School of Computer Science and Engineering NTU Singapore Singapore Energy Research Institute Nanyang Technological University Singapore Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user’s private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-k selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that the proposed framework can accurately and timely detect anomalies and also reduce the communication overhead by 50% compared to the federated learning framework that does not use gradient compression scheme. Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Unsupervised feature selection via adaptive embedding and sparse learning for Parkinson’s disease diagnosis 1

引用

3rd International Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2019

作者： Huang, Zhongwei Lei, Haijun Chen, Guoliang Li, Shiqi Li, Hancong Elazab, Ahmed Lei, Baiying Key Laboratory of Service Computing and Applications Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China

ISBN: (数字)9783030323912

ISBN: (纸本)9783030323905

Parkinson’s disease (PD) is known as a progressive neurodegenerative disease in elderly people. Apart from decelerating the disease exacerbation, early and accurate diagnosis also alleviates mental and physical sufferings and provides timely and appropriate medication. In this paper, we propose an unsupervised feature selection method via adaptive manifold embedding and sparse learning exploiting longitudinal multimodal neuroimaging data for classification and regression prediction. Specifically, the proposed method simultaneously carries out feature selection and dynamic local structure learning to obtain the structural information inherent in the neuroimaging data. We conduct extensive experiments on the publicly available Parkinson’s progression markers initiative (PPMI) dataset to validate the proposed method. Our proposed method outperforms other state-of-the-art methods in terms of classification and regression prediction performance. © Springer Nature Switzerland AG 2019.

关键词： Neuroimaging

来源：评论

学校读者我要写书评

暂无评论

SD-seq2seq : A Deep Learning Model for Bus Bunching Prediction Based on Smart Card Data

SD-seq2seq : A Deep Learning Model for Bus Bunching Predicti...

引用

International Conference on computer Communications and Networks (ICCCN)

作者： Zengyang Gong Bo Du Zhidan Liu Wei Zeng Pascal Perez Kaishun Wu College of Computer Science and Software Engineering Shenzhen University P. R. China SMART Infrastructure Facility University of Wollongong Australia School of Civil Mining and Environmental Engineering University of Wollongong Australia Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences P. R. China

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166087

Bus bunching, a phenomenon due to the failure of headway or timetable adherence, often causes low level of public transit service with poor bus on-time performance and excessive passenger waiting time. To mitigate bus bunching, an accurate and real-time prediction method plays an important role. In this paper, we propose a supply-demand seq2seq model called SD-seq2seq to predict bus bunching using smart card data. Features from both supply and demand sides of bus service are taken into account, like bus stop type, dwelling time, passenger demand and type, and so on. Extensive experiments on multiple bus routes in real world demonstrate that our method outperforms other baseline methods. The proposed method is expected to provide useful online information of bus operation to both bus operators and passengers.

关键词： Predictive models Trajectory Smart cards Data models Data mining Feature extraction Machine learning

来源：评论

学校读者我要写书评

暂无评论

Cross-Layer Scheduling and Beamforming in Smart-Grid Powered Cellular Networks With Heterogeneous Energy Coordination

arXiv

引用

arXiv 2020年

作者： Dong, Yanjie Hossain, Md Jahangir Cheng, Julian Leung, Victor C.M. Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada School of Engineering University of British Columbia KelownaBCV1V 1V7 Canada College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

—User scheduling, beamforming and energy coordination are investigated in smart-grid powered cellular networks (SGPCNs), where the base stations are powered by a smart grid and natural renewable energy sources. Heterogeneous energy coordination is considered in SGPCNs, namely energy merchandizing with the smart grid and energy exchanging among the base stations. A long-term grid-energy expenditure minimization problem with proportional-rate constraints is formulated for SGPCNs. Since user scheduling is coupled with the beamforming vectors, the formulated problem is challenging to handle via standard convex optimization methods. In practice, the beamforming vectors need to be updated over each slot according to the channel variations. User scheduling needs to be updated over several slots (frame) since the frequent scheduling of user equipment can cause reliability issues. Therefore, the Lyapunov optimization method is used to decouple the problem. A practical two-scale algorithm is proposed to schedule users at each frame, and obtain the beamforming vectors and amount of exchanged natural renewable energy at each slot. We prove that the proposed two-scale algorithm can asymptotically achieve the optimal solutions via tuning a control parameter. Numerical results verify the performance of the proposed two-scale algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Wireless networks

来源：评论

学校读者我要写书评

暂无评论

Dynamic Facial Models for Video-Based Dimensional Affect Estimation

Dynamic Facial Models for Video-Based Dimensional Affect Est...

引用

International Conference on computer Vision Workshops (ICCV Workshops)

作者： Siyang Song Enrique Sánchez-Lozano Mani Kumar Tellamekala Linlin Shen Alan Johnston Michel Valstar University of Nottingham UK Samsung AI Centre Cambridge UK College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Dimensional affect estimation from a face video is a challenging task, mainly due to the large number of possible facial displays made up of a set of behaviour primitives including facial muscle actions. The displays vary not only in composition but also in temporal evolution, with each display composed of behaviour primitives with varying in their short and long-term characteristics. Most existing work models affect relies on complex hierarchical recurrent models unable to capture short-term dynamics well. In this paper, we propose to encode these short-term facial shape and appearance dynamics in an image, where only the semantic meaningful information is encoded into the dynamic face images. We also propose binary dynamic facial masks to remove 'stable pixels' from the dynamic images. This process allows filtering of non-dynamic information, i.e. only pixels that have changed in the sequence are retained. Then, the final proposed Dynamic Facial Model (DFM) encodes both filtered facial appearance and shape dynamics of a image sequence preceding to the given frame into a three-channel raster image. A CNN-RNN architecture is tasked with modelling primarily the long-term changes. Experiments show that our dynamic face images achieved superior performance over the standard RGB face images on dimensional affect prediction task.

关键词： Face Heuristic algorithms Shape Task analysis Image sequences Estimation Standards

来源：评论

学校读者我要写书评

暂无评论

Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery

arXiv

引用

arXiv 2022年

作者： Ventura, Jonathan Pawlak, Camille Honsberger, Milo Gonsalves, Cameron Rice, Julian Love, Natalie L.R. Han, Skyler Nguyen, Viet Sugano, Keilana Doremus, Jacqueline Andrew Fricker, G. Yost, Jenn Ritter, Matt Department of Computer Science & Software Engineering California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107-0354 United States Department of Biological Sciences California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107-0401 United States Department of Social Sciences California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107-0329 United States Department of Political Science California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107-0329 United States Orfalea College of Business California Polytechnic State University 1 Grand Ave San Luis ObispoCA93107 United States

Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically don’t provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future. © 2022, CC BY.

关键词： Aerial photography

来源：评论

学校读者我要写书评

暂无评论

Text Cover Steganography Using Font Color of the Invisible Characters and Optimized Primary Color-intensities

Text Cover Steganography Using Font Color of the Invisible C...

引用

International Conference on Communication Technology (ICCT)

作者： Feno Heriniaina Rabevohitra Yantao Li School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China

Text cover steganography is concerned with using text support for hiding information. Despite its long and old history, the art of cover writing is still attracting the attention of researchers in the domain of information hiding. Unfortunately, only a few related works exist in terms of text-covered writing. We present a visual attack on the previous related work. Then, we use invisible characters to hide secret data within the text support. We further use a combination and variation of the three primary colors intensities to vehiculate the message when painting non-printing characters. The proposed algorithm makes use of the low-value color spaces to decrease the color variations of the embedded secret message. It also further strengthens the hiddenness of the non-printing characters even when replaced by visible characters.

关键词： Color Image color analysis Painting software Text processing Visualization Writing

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient resource allocation for NOMA enabled MEC networks with imperfect CSI

arXiv

引用

arXiv 2020年

作者： Fang, Fang Wang, Kaidi Ding, Zhiguo Leung, Victor C.M. Department of Engineering Durham University DurhamDH1 3LE United Kingdom Department of Electrical and Electronic Engineering University of Manchester M13 9PL United Kingdom College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) can significantly improve the spectrum efficiency beyond the fifth-generation network. In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions. To minimize the energy consumption, we consider jointly optimizing the task assignment, power allocation and user association. As the main contribution, with imperfect CSI, the optimal closed-form expressions of task assignment and power allocation are analytically derived for the two-BS case. Specifically, the original formulated problem is nonconvex. We first transform the probabilistic problem into a non-probabilistic one. Subsequently, a bilevel programming method is proposed to derive the optimal solution. In addition, by incorporating the matching algorithm with the optimal task and power allocation, we propose a low complexity algorithm to efficiently optimize user association for the multi-user and multi-BS case. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme but also the identical results with lower complexity from the exhaustive search with the small number of BSs. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

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：