检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Gong, Maoguo Gao, Yuan Xie, Yu Qin, A.K. Pan, Ke Ong, Yew-Soon The School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'an Shaanxi Province710071 China The Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Taiyuan030006 China The Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed parties such as different institutions and may not be directly gathered and integrated due to various data policy constraints. As a result, some parties may suffer from insufficient data available for training machine learning models. In this paper, we propose a multi-party dual learning (MPDL) framework to alleviate the problem of limited data with poor quality in an isolated party. Since the knowledge sharing processes for multiple parties always emerge in dual forms, we show that dual learning is naturally suitable to handle the challenge of missing data, and explicitly exploits the probabilistic correlation and structural relationship between dual tasks to regularize the training process. We introduce a feature-oriented differential privacy with mathematical proof, in order to avoid possible privacy leakage of raw features in the dual inference process. The approach requires minimal modifications to the existing multi-party learning structure, and each party can build flexible and powerful models separately, whose accuracy is no less than non-distributed self-learning approaches. The MPDL framework achieves significant improvement compared with state-of-the-art multi-party learning methods, as we demonstrated through simulations on real-world datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

Epilepsy Classification for Mining Deeper Relationships betw...

引用

2020 International Conference on computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Xin Zheng, Yuanjie Niu, Yi Li, Chengiiang Shandong Normal University School of Information Science and Engineering Jinan250358 China Key Lab of Intelligent Computing and Information Security Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan250358 China Department of Electrical Engineering Information Technology Shandong University of Science and Technology Jinan250031 China

ISBN: (纸本)9781728194813

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy. © 2020 IEEE.

关键词： Electroencephalography

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Smart Insole: A Gait Analysis Monitoring Platform Targeting Parkinson Disease Patients Based on Insoles

arXiv

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arXiv 2022年

作者： Boucharas, Dimitrios G. Androutsos, Christos Gkois, George Tsakanikas, Vassilis D. Pezoulas, Vasileios C. Manousos, Dimitrios Skaramagkas, Vasileios Chatzaki, Chariklia Kontogiannis, Stathis Spandonidis, Christos Pantazis, Alexandros K. Tachos, Nikolaos S. Tsiknakis, Manolis Fotiadis, Dimitrios I. The Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina IoanninaGR-45110 Greece Crete HeraklionGR-70013 Greece Biomedical Informatics and eHealth Laboratory Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Crete HeraklionGR-71004 Greece The Department of Electric and Computer Engineering Hellenic Mediterranean University Crete HeraklionGR-71004 Greece PD Neurotechnology Ltd IoanninaGR-45110 Greece Prisma Electronics SA P.Faliro AthensGR-17564 Greece Crete HeraklionGR-70013 Greece Biomedical Research Institute FORTH IoanninaGR-45110 Greece The Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR-45110 Greece

During the preceding decades, human gait analysis has been the center of attention for the scientific community, while the association between gait analysis and overall health monitoring has been extensively reported. Technological advances further assisted in this alignment, resulting in access to inexpensive and remote healthcare services. Various assessment tools, such as software platforms and mobile applications, have been proposed by the scientific community and the market that employ sensors to monitor human gait for various purposes ranging from biomechanics to the progression of functional recovery. The framework presented herein offers a valuable digital biomarker for diagnosing and monitoring Parkinson’s disease that can help clinical experts in the decision-making process leading to corrective planning or patient-specific treatment. More accurate and reliable decisions can be provided through a wide variety of integrated Artificial Intelligence algorithms and straightforward visualization techniques, including, but not limited to, heatmaps and bar plots. The framework consists of three core components: the insole pair, the mobile application, and the cloud-based platform. The insole pair deploys 16 plantar pressure sensors, an accelerometer, and a gyroscope to acquire gait data. The mobile application formulates the data for the cloud platform, which orchestrates the component interaction through the web application. Utilizing open communication protocols enables the straightforward replacement of one of the core components with a relative one (e.g., a different model of insoles), transparently from the end user, without affecting the overall architecture, resulting in a framework with the flexibility to adjust its modularity. © 2022, CC BY.

关键词： computer architecture

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Anomaly detection on attributed networks via contrastive self-supervised learning

arXiv

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arXiv 2021年

作者： Liu, Yixin Li, Zhao Pan, Shirui Gong, Chen Zhou, Chuan Karypis, George The Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia The Alibaba Group Hangzhou310000 China The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100093 China The Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets. © 2021, CC BY.

关键词： Anomaly detection

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Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

arXiv

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arXiv 2021年

作者： Zhou, Guangyao Tian, Wenhong Buyya, Rajkumar Xue, Ruini Song, Liang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Laboratory Department of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning (DL) and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling. © 2021, CC BY.

关键词： Deep reinforcement learning

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A survey of static formal methods for building dependable industrial automation systems

arXiv

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arXiv 2021年

作者： Sinha, Roopak Patil, Sandeep Gomes, Luis Vyatkin, Valeriy Department of IT and Software Engineering Auckland University of Technology Auckland New Zealand Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå97187 Sweden Electrical and Computer Engineering Department NOVA School of Science and Technology NOVA University of Lisbon Portugal Centre of Technology and Systems UNINOVA Caparica2829-517 Portugal Computer Technologies International Laboratory ITMO University Saint Petersburg197101 Russia Department of Electrical Engineering in Automation Aalto University Aalto00076 Finland

Industrial automation systems (IAS) need to be highly dependable;they should not merely function as expected but also do so in a reliable, safe, and secure manner. Formal methods are mathematical techniques that can greatly aid in developing dependable systems and can be used across all phases of the system development life cycle (SDLC), including requirements engineering, system design and implementation, verification and validation (testing), maintenance, and even documentation. This state-of-the-art survey reports existing formal approaches for creating more dependable IAS, focusing on static formal methods that are used before a system is completely implemented. We categorize surveyed works based on the phases of the SDLC, allowing us to identify research gaps and promising future directions for each phase. Copyright © 2021, The Authors. All rights reserved.

关键词： Formal verification

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A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

arXiv

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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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Mapping global lake dynamics reveals the emerging roles of small lakes (October, 10.1038/s41467-022-33239-3, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-12页

作者： Pi, Xuehui Luo, Qiuqi Feng, Lian Xu, Yang Tang, Jing Liang, Xiuyu Ma, Enze Cheng, Ran Fensholt, Rasmus Brandt, Martin Cai, Xiaobin Gibson, Luke Liu, Junguo Zheng, Chunmiao Li, Weifeng Bryan, Brett A. Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) The University of Hong Kong Department of Urban Planning and Design Hong Kong SAR China (GRID:grid.194645.b) (ISNI:***) The University of Hong Kong Urban Systems Institute Hong Kong SAR China (GRID:grid.194645.b) (ISNI:***) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) University of Copenhagen Department of Geosciences and Natural Resource Management Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Biology Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Lund University Department of Physical Geography and Ecosystem Science Lund Sweden (GRID:grid.4514.4) (ISNI:0000 0001 0930 2361) Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) University of Copenhagen Department of Geosciences and Natural Resource Management Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Chinese Academy of Sciences Institute of Geodesy and Geophysics Wuhan China (GRID:grid.9227.e) (ISNI:***) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) North China University of Water Resources and Electric Power School of Water Conservancy Zhengzhou China (GRID:grid.412224.3) (ISNI:0000 0004 1759 6955) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263

Lakes are important natural resources and carbon gas emitters and are undergoing rapid changes worldwide in response to climate change and human activities. A detailed global characterization of lakes and their long-term dynamics does not exist, which is however crucial for evaluating the associated impacts on water availability and carbon emissions. Here, we map 3.4 million lakes on a global scale, including their explicit maximum extents and probability-weighted area changes over the past four decades. From the beginning period (1984–1999) to the end (2010–2019), the lake area increased across all six continents analyzed, with a net change of +46,278 km2, and 56% of the expansion was attributed to reservoirs. Interestingly, although small lakes (<1 km2) accounted for just 15% of the global lake area, they dominated the variability in total lake size in half of the global inland lake regions. The identified lake area increase over time led to higher lacustrine carbon emissions, mostly attributed to small lakes. Our findings illustrate the emerging roles of small lakes in regulating not only local inland water variability, but also the global trends of surface water extent and carbon *** are essential components of the hydrological and biogeochemical cycles. Here, Pi et al develop a global lake dataset called GLAKES via high-resolution satellite images and deep learning to examine global lake changes over four decades.

关键词： Hydrology Limnology

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Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing

arXiv

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arXiv 2022年

作者： Faroughi, Salah A. Pawar, Nikhil M. Fernandes, Célio Raissi, Maziar Das, Subasish Kalantari, Nima K. Mahjour, Seyed Kourosh Geo-Intelligence Laboratory Ingram School of Engineering Texas State University San MarcosTX78666 United States Associate Laboratory of Intelligent Systems Institute for Polymers and Composites Polymer Engineering Department School of Engineering University of Minho Campus of Azurém Guimarães4800-058 Portugal Department of Applied Mathematics University of Colorado Boulder BoulderCO610101 United States Artificial Intelligence in Transportation Lab Ingram School of Engineering Texas State University San MarcosTX78666 United States Computer Science and Engineering Department Texas A&M University College StationTX77843 United States

Recent breakthroughs in computing power have made it feasible to use machine learning and deep learning to advance scientific computing in many fields, including fluid mechanics, solid mechanics, materials science, etc. Neural networks, in particular, play a central role in this hybridization. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data is sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multi-physics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers are presented. This critical review provides researchers and engineers with a solid starting point to comprehend how to integrate different layers of physics into neural networks. © 2022, CC BY.

关键词： Fluid mechanics

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反向连边在大型分层网络中的影响（英文）

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engineering 2024年第5期 240-249页

作者：曹浩森胡斌斌莫小雨陈都鑫高建喜袁烨陈关荣 Tamás Vicsek 管晓宏张海涛 MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Mechanical and Aerospace Engineering Nanyang Technological University Jiangsu Key Laboratory of Networked Collective Intelligence School of Mathematics Southeast University Department of Computer Science Rensselaer Polytechnic Institute Department of Electronic Engineering City University of Hong Kong Department of Biological Physics E?tv?s University MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi'an Jiaotong University

Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks,and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes, such as lagging birds' howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of ‘‘bunchingo effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

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