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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Muhammad Aamir Sibghatullah Bazai Uzair Aslam Bhatti Zaheer Ahmed Dayo Jing Liu Kun Zhang College of Computer Science Huanggang Normal Univeristy Huanggang China Department of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences Quetta Pakistan School of Information and Communication Engineering Hainan University Haikou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China

With the continuous development of information technology and medical data information, more and more clinicians recognize artificial intelligence or will completely change medical practice by using advanced machine learning methods. The potential of using machine learning and predictive analysis to customize specific treatments for individuals is currently under research. Machine learning can learn a large number of medical data and explore the dependencies in data concentration, forming a corresponding medical model that can quickly and accurately predict new data, which is conducive to the early diagnosis of diseases and assisted clinical decisions. Clinical medicine faces the status quo of the relative shortage of medical resources and the identification and rapid diagnosis and treatment of critically ill emergency patients. In the era of big data, clinical demand is driven by clinical needs, and intelligent medical care provided by machines is the key to tackling the aforementioned issues.

关键词： Industries Wireless communication Sociology Machine learning Medical services Big data Predictive models

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A Novel Machine Learning Based Framework for Bridge Condition Analysis

A Novel Machine Learning Based Framework for Bridge Conditio...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Masum, Mohammad Anjum, Nafisa Hossain Faruk, Md Jobair Shahriar, Hossain Sakib, Nazmus Valero, Maria Karim, M.A. Rahman, Akond Wu, Fan Cuzzocrea, Alfredo San Jose State University Department of Applied Data Science San Jose United States Kennesaw State University Department of Information Technology United States Kennesaw State University Department of Software Engineering and Game Development United States Kennesaw State University Department of Civil and Environmental Engineering United States Auburn University Department of Computer Science and Software Engineering United States Tuskegee University Department of Computer Science United States University of Calabria IDEA Lab Rende Italy

ISBN: (纸本)9781665480451

Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country's social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country's 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score. © 2022 IEEE.

关键词： Forecasting

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G-GCSN: Global Graph Convolution Shrinkage Network for Emotion Perception from Gait 15th

G-GCSN: Global Graph Convolution Shrinkage Network for Emoti...

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Zhuang, Yuan Lin, Lanfen Tong, Ruofeng Liu, Jiaqing Iwamoto, Yutaro Chen, Yen-Wei College of Computer Science and Technology Zhejiang University Hangzhou310000 China College of Information Science and Engineering Ritsumeikan University Kyoto525-8577 Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou310000 China

ISBN: (纸本)9783030697556

Recently, emotion recognition through gait, which is more difficult to imitate than other biological characteristics, has aroused extensive attention. Although some deep-learning studies have been conducted in this field, there are still two challenges. First, it is hard to extract the representational features of the gait from video effectively. Second, the input of body joints sequences has noise introduced during dataset collection and feature production. In this work, we propose a global link, which extends the existing skeleton graph (the natural link) to capture the overall state of gait based on spatial-temporal convolution. In addition, we use soft thresholding to reduce noise. The thresholds are learned automatically by a block called shrinkage block. Combined with the global link and shrinkage block, we further propose the global graph convolution shrinkage network (G-GCSN) to capture the emotion-related features. We validate the effectiveness of the proposed method on a public dataset (i.e., Emotion-Gait dataset). The proposed G-GCSN achieves improvements compared with state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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Efficient Serverless Function Scheduling at the Network Edge

arXiv

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arXiv 2023年

作者： Lou, Jiong Tang, Zhiqing Yuan, Shijing Li, Jie Wu, Chengtao Department of Computer Science and Engineering Shanghai Jiao Tong University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China

Serverless computing is a promising approach for edge computing since its inherent features, e.g., lightweight virtualization, rapid scalability, and economic efficiency. However, previous studies have not studied well the issues of significant cold start latency and highly dynamic workloads in serverless function scheduling, which are exacerbated at the resource-limited network edge. In this paper, we formulate the Serverless Function Scheduling (SFS) problem for resource-limited edge computing, aiming to minimize the average response time. To efficiently solve this intractable scheduling problem, we first consider a simplified offline form of the problem and design a polynomial-time optimal scheduling algorithm based on each function’s weight. Furthermore, we propose an Enhanced Shortest Function First (ESFF) algorithm, in which the function weight represents the scheduling urgency. To avoid frequent cold starts, ESFF selectively decides the initialization of new function instances when receiving requests. To deal with dynamic workloads, ESFF judiciously replaces serverless functions based on the function weight at the completion time of requests. Extensive simulations based on real-world serverless request traces are conducted, and the results show that ESFF consistently and substantially outperforms existing baselines under different settings. © 2023, CC BY.

关键词： Polynomial approximation

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QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows

QoS-Aware and Cost-Efficient Dynamic Resource Allocation for...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Hao Wu Junxiao Deng Hao Fan Shadi Ibrahim Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Inria Univ. Rennes CNRS IRISA France

Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely on offline prediction, which limit their efficiency. In this paper, we introduce CE-scaling – a Cost-Efficient autoscaling framework for serverless ML work-flows. During the hyperparameter tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during model training. We implement and evaluate CE-scaling on AWS Lambda using various ML models. Evaluation results show that compared to state-of-the-art static resource allocation methods, CE-scaling can reduce the job completion time and the monetary cost by up to 63% and 41% for hyperparameter tuning, respectively; and by up to 58% and 38% for model training.

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An Improved Honey Badger Algorithm for Electric Vehicle Charge Orderly Planning

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 332-346页

作者： Chen, Ran-Fan Luo, Hao Huang, Kuan-Chun Nguyen, Trong-The Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China School of Aeronautics and Astronautics Zhejiang University HangZhou310000 China Department of Industrial and Systems Engineering Chung Yuan University Taoyuan320-33 Taiwan Multimedia Communications Lab VNUHCM-University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Multiple electric vehicles simultaneously connected to a distribution load network will significantly impact the stability of the power grid. This paper proposes an improved Honey-badger algorithm (IHBA) through elite reverse learning, spiral update, and wild dog survival strategies to optimize the orderly charging of electric vehicles (EV). Objective functions of EV charge orderly planning are taken by using load fluctuation satisfaction, user cost satisfaction, and user convenience satisfaction to achieve the high efficiency of orderly charging. The benchmark test function and the electric vehicle se-quential charging problem were employed to evaluate the proposed IHBA approach. The obtained results are compared with the other algorithms in the literature, which indicate that the IHBA algorithm does not only provides more accurate outcome but better con-vergence speed than the other competitors. © 2022;Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Food products

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A Method of Realizing External Network Access to Intranet for Embedded Web Server 2

A Method of Realizing External Network Access to Intranet fo...

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2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Zhang, Xian Qu, Taiguo Tang, Pengju Liu, Yiwen Key Lab. of Intelligent Contr. Technol. for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China School of Computer Science and Engineering Huaihua University Huaihua China

ISBN: (纸本)9781665464017

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects. © 2022 IEEE.

关键词： Intranets

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A Routing Optimization in Wireless Sensor Networks Based on Reverse Elite Sparrow Search Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第4期6卷 763-775页

作者： Ning, Chun-Yuan Liao, Lyu-Chao Nguyen, Trong-The Huang, Kuan-Chun Pan, Jeng-Shyang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Multimedia Communications Lab University of Information Technology VNU-HCM Viet Nam Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

This study suggests a solution to the routing optimization insufficiency based on the reverse elite sparrow search algorithm (RESSA) for energy balancing and network lifetime prolonging in wireless sensor networks (WSN). The node’s consumption and the remaining energy priority to balance consumption in the data forwarding to the cluster head are considered the optimal path evaluation function. The obtained results of the experimental the suggested scheme are compared with the other methods show that the proposed scheme increases data forwarding round numbers and consumes less energy, the remaining energy more, and higher efficiency of node energy consumption. © The Authors.

关键词： Energy utilization

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LEARNING SYSTEM DYNAMICS WITHOUT FORGETTING

arXiv

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arXiv 2024年

作者： Zhang, Xikun Song, Dongjin Jiang, Yushan Chen, Yixin Tao, Dacheng Generative AI Lab College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Computer Science and Engineering University of Connecticut StorrsCT United States Washington University in St. Louis St. LouisMO United States

Observation-based trajectory prediction for systems with unknown dynamics is essential in fields such as physics and biology. Most existing approaches are limited to learning within a single system with fixed dynamics patterns. However, many real-world applications require learning across systems with evolving dynamics patterns, a challenge that has been largely overlooked. To address this, we systematically investigate the problem of Continual Dynamics Learning (CDL), examining task configurations and evaluating the applicability of existing techniques, while identifying key challenges. In response, we propose the Mode-switching Graph ODE (MS-GODE) model, which integrates the strengths LG-ODE and sub-network learning with a mode-switching module, enabling efficient learning over varying dynamics. Moreover, we construct a novel benchmark of biological dynamic systems for CDL, Bio-CDL, featuring diverse systems with disparate dynamics and significantly enriching the research field of machine learning for dynamic systems. Our code available at https://***/QueuQ/MS-GODE. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning systems

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Time Series-based Active labeling Framework for Curating a Multispectral Sentinel 2 Imagery dataset for Crop Type Mapping

Time Series-based Active Labeling Framework for Curating a M...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： V. Mehmood R. Murtaza Z. Zafar M. Shahzad K. Berns M. M. Fraz School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Data Science in Earth Observations Technical University of Munich Germany Robotics Research Lab Technical University of Kaiserslautern Germany

Acquiring ground-truth data for crop mapping is a challenging task in developing countries. Limited resources, inconsistent agricultural practices across the country, and inadequate infrastructure at the administrative level pose a significant challenge in collecting dataset information. This study proposes an active labelling framework for automated ground truth data generation in areas with limited or no ground truth information available. Sentinel 2 images and vegetative indices are visually interpreted for the identification of Wheat and Rice fields. This hand-labelled data is incorporated into the framework as training data to generate crop maps at 10m resolution using light weight, ConvLSTM model. Testing is performed for two spatially distinct districts with different field sizes and crop distribution in Pakistan. The results are consistent with an accuracy of 77.8%, F1 score of 87.3% and IOU of 70.7% for Gujranwala and 76%, 82% and 70% for Sargodha, promising the applicability of the proposed approach to generate large-scale crop labels, thereby enhancing the efficiency of crop mapping efforts in developing countries

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