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5th International Conference on Computer Science and Management technology, ICCSMT 2024

作者： Qiu, Han Sun, Chuanqiang Chen, Hongyun Zou, Baoyu Dong, Zizheng Huazhong University of Science and Technology Hubei Wuhan China Shandong Provincial E-Government Big Data Engineering Technology Research Center Shandong Jinan China Shandong Banner Information Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400709999

This study aims to address the issues of low efficiency and high manual involvement in existing government affairs processes by proposing an automated system based on large model *** system integrates modules such as data collection, natural language processing, business rule engine, process generation, and intelligent *** include using large models to transform unstructured government information into executable rules, automatically generating process models and documents, and implementing intelligent approval and automated verification at key *** results indicate that the system significantly enhances the automation level and execution efficiency of government affairs processes, providing a new solution for the intelligent transformation of government management. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： efficiency

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research on hydrological image restoration based on LGNet 24

Research on hydrological image restoration based on LGNet

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2024 7th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2024

作者： Li, Xiaojie Yu, Xiaomin Xia, Huan Xie, Rongdong School of Information Technology Guizhou University of Finance and Economics Guizhou Guiyang China E-commerce Big Data Marketing Engineering Research Center Guizhou University of Finance and Economics Guizhou Guiyang China School of Art Guizhou University of Finance and Economics Guizhou Guiyang China

ISBN: (纸本)9798400707254

There are a large number of literature materials about the Shui nationality, but these materials are badly damaged and difficult to identify, and the traditional restoration methods have limitations in time and space, which affects the inheritance of the Shui nationality culture. In this paper, a generative adversarial network of global Discriminator and local discriminator is designed. The local discriminator is used to reconstruct the restricted discriminator well, and the global discriminator ensures that the overall semantic information of the image remains unchanged. At the same time, residual links are built at each stage of the generated network, avoiding the problem of disappearing gradients and allowing deeper networks to learn effective features. This model provides an efficient, accurate and automatic method for the restoration of Shui script, and effectively speeds up the restoration process of Shui ancient characters. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Using machine learning and satellite data from multiple sources to analyze mining,water management,and preservation of cultural heritage

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地球空间信息科学学报（英文版） 2024年第3期27卷 552-571页

作者： Joaquim J.Sousa Jiahui Lin Qun Wang Guang Liu Jinghui Fan Shibiao Bai Hongli Zhao Hongyu Pan Wenjing Wei Vanessa Rittlinger Peter Mayrhofer Ruth Sonnenschein Stefan Steger Luís Paulo Reis School of Science and Technology University of Trás-Os-Montes E Alto DouroVila RealPortugal Centre for Robotics in Industry and Intelligent Systems(CRIIS) INESC Technology and SciencePortoPortugal Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Application system Department Ⅰ China Siwei Surveying and Mapping Technology Co.LtdBeijingChina China Aero Geophysical Survey and Remote Sensing Center for Natural Resources BeijingChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengduChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute for Earth Observation Eurac ResearchBolzanoItaly Faculty of Engineering University of PortoPortoPortugal

Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to *** high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential *** paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of earth observation data,with a particular emphasis on:(1)detecting mining sub-sidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)*** results showed that the efficient Channel Attention(eCA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(loU)indicators;(2)monitor-ing water conservancy and hydropower *** Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north *** dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural *** overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical *** new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time

关键词： Landslide detection InSAR time series analysis machine learning heritage preservation mining subsidence water conservancy infrastructures

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Quality assessment of segment based sensor data at motorways

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Transportation research Procedia 2023年 72卷 3316-3323页

作者： Michael Haberl Felix Hofinger Martin Fellendorf Paul Rosenkranz Jürgen Zajicek Siegfried Seebacher Mario Lovrić Han Tran Christian Partl Santokh Singh Robert Wenig Institute of Highway Engineering and Transport Planning Graz University of Technology Rechbauerstraße 12 Graz 8010 Austria AIT Austrian Institute of Technology GmbH Giefinggasse 4 1210 Vienna 1210 Austria ASFINAG Rotenturmstraße 5-9 Vienna 1010 Austria Know-Center GmbH - Research Center for Data-Driven Business & Big Data Analytics Inffeldgasse 13 Graz 8010 Austria

Road Operators like the ASFINAG in Austria use the advantages of various sensor technologies to record traffic on the motorway network. As the existing cross-sectional sensors do have some shortcomings, ten novel 24 GHz radar sensors with ultra-high resolution 4D/UHD+ technology and inbuilt video camera were tested at the motorway network to answer the question whether they have the capability to add valuable information to the current status of traffic situation detection for enhancing traffic management. This kind of segment based sensor systems can provide cross-sectional traffic count data as well as trajectory data. Therefore, the main objective of this publication was to show and evaluate the sensor quality during field tests. This quality assessment builds the knowledge base for further adoptions of new radar sensors within the ASFINAG road network in the future.

关键词： Traffic Radar Sensor Quality Assessment Traffic Sensor Analysis Real-time traffic information (RTTI) services Trajectory data Analysis Intelligent Transport Systems Segment Based Sensor data

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Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with epidemic Intervention—London,UK,2020-2022

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China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COVID-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COVID-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:Analysis of London’s daily COVID-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This robust tool has proven invaluable for analyzing COVID-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

arXiv

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arXiv 2022年

作者： Xu, Fei Xu, Jianian Chen, Jiabin Chen, Li Shang, Ruitao Zhou, Zhi Liu, Fangming The Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University 3663 N. Zhongshan Road Shanghai200062 China The School of Computing and Informatics University of Louisiana at Lafayette 301 East Lewis Street LafayetteLA70504 United States The Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Sun Yat-sen University 132 E. Waihuan Road Guangzhou510006 China The National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan430074 China

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings severe performance interference among co-located inference workloads, as motivated by an empirical measurement study of DNN inference on eC2 GPU instances. While existing works on guaranteeing inference performance service level objectives (SLOs) focus on either temporal sharing of GPUs or reactive GPU resource scaling and inference migration techniques, how to proactively mitigate such severe performance interference has received comparatively little attention. In this paper, we propose iGniter, an interference-aware GPU resource provisioning framework for cost-efficiently achieving predictable DNN inference in the cloud. iGniter is comprised of two key components: (1) a lightweight DNN inference performance model, which leverages the system and workload metrics that are practically accessible to capture the performance interference;(2) A cost-efficient GPU resource provisioning strategy that jointly optimizes the GPU resource allocation and adaptive batching based on our inference performance model, with the aim of achieving predictable performance of DNN inference workloads. We implement a prototype of iGniter based on the NVIDIA Triton inference server hosted on eC2 GPU instances. extensive prototype experiments on four representative DNN models and datasets demonstrate that iGniter can guarantee the performance SLOs of DNN inference workloads with practically acceptable runtime overhead, while saving the monetary cost by up to 25% in comparison to the state-of-the-art GPU resource provisioning strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Gsgm: A Group-Sequence-Aware Graph Model for Personalized News Recommender System

SSRN

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SSRN 2024年

作者： Lv, Pengtao Zhang, Qinghui Guan, Zhenhan Deveci, Muhammet Shi, Lei Zhong, Kaiyang Wang, Jingping Xu, Heliang Zhang, Yana Ministry of Education Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China Henan Grain Big Data Analysis and Application Engineering Research Center Henan University of Technology Henan Zhengzhou450001 China The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul4940 Turkey State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages Minzu University of China Beijing100081 China Southwestern University of Finance and Economics China

News recommender system is an important method of helping readers identify interesting stories. Although human behaviors exhibit sequential patterns, most current studies on news recommendations ignore the influence of reading sequences. Leveraging sequential patterns in news recommendation is, however, very challenging, as the capture of reading behavior sequence at the individual level may be impossible due to severity of sparsity in the field of news. To address this challenge, we proposed a group-sequence-aware graph model (GSGM) to learn representative reading sequences of groups instead of individuals, where groups are viewed as latent variables and automatically learnt from users' reading behaviours. Besides, when readers select different groups, they are likely to have distinct reading preferences. This finding is used to learn group-dependent personal reading preferences in GSGM. A wide range of experiments are conducted to verify the efficiency and effectiveness of the GSGM on real datasets. experimental results demonstrate that GSGM outperforms the state-of-the-art competing methods. © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Deep Learning to Improve Heart Disease Risk Prediction 1st

Deep Learning to Improve Heart Disease Risk Prediction

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1st International Workshop on Machine Learning and Medical engineering for Cardiovasvular Healthcare, MLMeCH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STeNT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Sajeev, Shelda Maeder, Anthony Champion, Stephanie Beleigoli, Alline Ton, Cheng Kong, Xianglong Shu, Minglei Flinders Digital Health Research Centre School of Nursing and Health Sciences Flinders University Adelaide Australia Department of Big Data Engineering Technology Research Center of E-Government JinanShandong China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology Jinan China

ISBN: (纸本)9783030333263

Disease prediction based on modeling the correlations between compounded indicator factors is a widely used technique in high incidence chronic disease prevention diagnosis. Predictive models based on personal health information have been developed historically by using simple regression fitting over relatively few factors. Regression approaches have been favored in previous prediction modeling approaches because they are simplest and do not assume any non-linearity in the model for contributions of the chosen factors. In practice, many factors are correlated and have underlying non-linear relationships to the predicted outcome. Deep learning offers a means to construct a more complex modeling approach, along with automation and adaptation. The aim of this paper is to assess the ability of a deep learning model to predict the heart disease incidence using a common benchmark dataset (University of California, Irvine (UCI) dataset). The performance of deep learning model has been compared with four popular machine learning models (two linear and two nonlinear) in predicting the incidence of heart disease using data from 567 participants from two cohorts taken from UCI database. The deep learning model was able to achieve the best accuracy of 94% and an AUC score of 0.964 when compared to other models. The performance of deep learning and nonlinear machine learning models was significantly better compared to the linear machine learning models with increase in the dataset size. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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An eeG study on college students’ attention levels in a blended computer science class

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PLeT: Programmed Learning & educational technology 2024年第4期61卷

作者： Hengtao Tang Miao Dai Xu Du Jui-Long Hung Hao Li a Department of Educational Studies University of South Carolina Columbia SC USA b National Engineering Research Center for E-Learning Central China Normal University Wuhan Hubei China c National Engineering Laboratory for Educational Big Data Central China Normal University Wuhan Hubei Chinad Department of Educational Technology Boise State University Boise Idaho USA

ABSTRACTBlended learning has been widely integrated in college-level computer science education. Despite evidence about benefits of blended learning, students’ in-class activities remain underexplored. To afford effective blended learning experience, supporting students in both modalities is essential. This study thus took an initial step to fill the gap by investigating college students’ in-class activities in a blended course from the perspective of attention. Using non-intrusive electroencephalography (eeG) instruments to collect attentional data, this study found students’ attention in in-class activities positively correlated with their learning gains. Students’ attention also varied across in-class activities, reaching a higher level in group discussions than in pre-tests and lectures. Linear regression analysis indicated students’ length of time spent viewing online resources and their pre-test scores significantly predicted their in-class attention. The findings of the study provide insight into course design and facilitation for effective blended computer science courses.

关键词： electroencephalography (eeG) attention blended learning computer science higher education

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Gas sensing with mode-phase-difference photothermal spectroscopy assisted by a long period grating in a dual-mode negative-curvature hollow-core optical fiber

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Optics letters 2020年第20期45卷 5660-5663页

作者： Pengcheng Zhao Hoi Lut Ho Wei Jin Shangchun Fan Shoufei Gao Yingying Wang Pu Wang School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China Department of Electrical Engineering and Photonics Research Center The Hong Kong Polytechnic University Hong Kong China Photonics Research Center The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen China e-mail: eewjin@polyu.edu.hk (eewjin@polyu.edu.hk) Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China e-mail: shangcfan@*** Institue of Photonics Technology Jinan University Guangzhou China Beijing Engineering Research Centre of Laser Technology Beijing University of Technology Beijing China

We demonstrate sensitive gas detection with mode-phase-difference photothermal spectroscopy assisted by a long period grating (LPG) inscribed on a dual-mode negative-curvature hollow-core fiber (NC-HCF). The LPG is inscribed using a pulsed laser, which enables pump propagation in the fundamental mode to achieve maximum photothermal phase modulation while exciting both the and modes at the probe wavelength to form a dual-mode interferometer for detection of the phase difference. With a 1533 nm pump and a 1620 nm probe, a noise equivalent concentration of ∼2.2 ppb acetylene is achieved with an 85-cm-long NC-HCF gas cell and 1 s lock-in time constant.

关键词： Amplified spontaneous emission Hollow core fibers Phase modulation Photonic crystal fibers Photothermal spectroscopy Tunable diode lasers

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