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Telecommunications, Optics and computer science (TOCS), IEEE Conference on

作者： Changchang Zeng Shaobo Li Bin Chen Chengdu Institute of Computer Application in Chinese Academy of Sciences University of Chinese Academy of Sciences Chengdu China State Key Laboratory of Public Big Data Guizhou University Guiyang China Chongqing Research Institute of HIT Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665424998

Machine reading comprehension (MRC) is a challenging research hotspot in the field of Artificial Intelligence (AI). It can be applied to many scenarios, such as intelligent question answering, intelligent document retrieval, and so on. In this article, we focus on the machine reading comprehension of high-tech industry policy texts in China. First, we create a cloze style machine reading comprehension dataset of Chinese high-tech industrial policies. Next, we propose a new pre-training objective named multi-segment ordering discriminator, and we also use domain-specific dictionary to improve the MLM pre-training process. Finally, on our dataset, we trained a new pre-trained language model for machine reading comprehension of Chinese industrial policies. Experiment results show that our pre-trained language model surpasses existing models such as BERT and RoBERTa in the new dataset.

关键词： Industries Vocabulary Computational modeling Bit error rate Food industry Petrochemicals Optics

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Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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Engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

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RadioGAT: A Joint Model-based and data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks

arXiv

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arXiv 2024年

作者： Li, Xiaojie Zhang, Songyang Li, Hang Li, Xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan Xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Two-Stage Attention-Based Model for Code Search with Textual and Structural Features

Two-Stage Attention-Based Model for Code Search with Textual...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Ling Xu Huanhuan Yang Chao Liu Jianhang Shuai Meng Yan Yan Lei Zhou Xu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

Searching and reusing existing code from a large scale codebase can largely improve developers' programming efficiency. To support code reuse, early code search models leverage information retrieval (IR) techniques to index a large-scale code corpus and return relevant code according to developers' search query. However, IR-based models fail to capture the semantics in code and query. To tackle this issue, developers applied deep learning (DL) techniques to code search models. However, these models either are too complex to determine an effective method efficiently or learning for semantic correlation between code and query *** bridge the semantic gap between code and query effectively and efficiently, we propose a code search model TabCS (Two-stage Attention-Based model for Code Search) in this study. TabCS extracts code and query information from the code textual features (i.e., method name, API sequence, and tokens), the code structural feature (i.e., abstract syntax tree), and the query feature (i.e., tokens). TabCS performs a two-stage attention net-work structure. The first stage leverages attention mechanisms to extract semantics from code and query considering their semantic gap. The second stage leverages a co-attention mechanism to capture their semantic correlation and learn better code/query representation. We evaluate the performance of TabCS on two existing large-scale datasets with 485k and 542k code snippets, respectively. Experimental results show that TabCS achieves an MRR of 0.57 on Hu et al.'s dataset, outperforming three state-of-the-art models CARLCS-CNN, DeepCS, and UNIF by 18%, 70%, 12%, respectively. Meanwhile, TabCS gains an MRR of 0.54 on Husain et al.'s, outperforming CARLCS-CNN, DeepCS, and UNIF by 32%, 76%, 29%, respectively.

关键词： Deep learning Correlation Semantics Syntactics Programming Feature extraction Information retrieval

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Design and analysis of a 2D discrete memristive map

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Physica Scripta 2025年第7期100卷 075219-075219页

作者： Haiwei Sang Qiao Wang Yuling Chen Xiong Yu Feifei Wu Guizhou Key Laboratory of Artificial Intelligence and Brain-inspired Computing College of Mathematics and Big Data Guizhou Education University Guiyang 550018 People’s Republic of China School of artificial intelligence Guangzhou University 510006 People’s Republic of China State Key Laboratory of Public Big Data Guizhou University Guiyang 550025 People’s Republic of China Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor 43600 Malaysia

This study proposes a novel 2D memristive hyperchaotic map (2DMHM) with hyperbolic tangent and absolute value functions. The 2DMHM exhibits an infinite of fixed points in a set of lines on the y-axis, with stability characteristics partitioned across memristor parameters and initial condition planes. System dynamics are systematically investigated through bifurcation analysis, Lyapunov exponent spectra, and basin of attraction, trajectory plots, revealing remarkable multistability and initial-sensitive chaotic behavior. The map demonstrates superior spectral entropy (SE) complexity across critical parameter ranges, significantly outperforming conventional chaotic systems. A microcontroller-based digital implementation validates the physical realizability of 2DMHM, while the NIST test success rate demonstrates its exceptional performance in pseudorandom number generation. These results establish 2DMHM as a promising candidate for secure communication systems and cryptographic applications.

关键词：

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Advances in artificial intelligence techniques drive the application of radiomics in the clinical research of hepatocellular carcinoma

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iLIVER 2022年第1期1卷 49-54页

作者： Jingwei Wei Meng Niu Ouyang Yabo Yu Zhou Xiaoke Ma Xue Yang Hanyu Jiang Hui Hui Hongyi Cao Binwei Duan Hongjun Li Dawei Ding Jie Tian Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China Department of Interventional Radiology The First Affiliated Hospital of China Medical UniversityShenyangLiaoning110000China Beijing YouAn Hospital Capital Medical UniversityBeijing Institute of HepatologyBeijing100069China School of Life Science and Technology Xidian UniversityXi'anChina School of Computer Science and Technology Xidian UniversityXi'anShaanxiChina Department of Radiology Beijing Youan HospitalCapital Medical UniverstiyBeijing100069China Department of Radiology West China HospitalSichuan UniversityChengduSichuan 610041China Department of Pathology College of Basic Medical ScienceChina Medical UniversityShenyangLiaoning110000China The Department of General Surgery Center Beijing YouAn HospitalCapital Medical UniversityChina School of Bioengineering Beihang UniversityBeijing100191China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine Beihang UniversityBeijing100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'anShaanxi710126China

Hepatocellular carcinoma(HCC)remains the most common malignancy to threaten public health *** advances in artificial intelligence techniques,radiomics for HCC management provides a novel perspective to solve unmet needs in clinical settings,and reveals pixel-level radiological information for medical imaging big data,correlating the radiological phenotype with targeted clinical *** radiomics pipelines depend on handcrafted engineering features,and further deep learning-based radiomics pipelines are supplemented with deep features calculated via self-learning *** the past decade,radiomics has been widely applied in accurate diagnoses and pathological or biological behavior evaluation,as well as in prognosis *** this review,we systematically introduce the main pipelines of artificial intelligence-based radiomics and their efficacy in the clinical studies of HCC.

关键词： Artificial intelligence Diagnosis Hepatocellular carcinoma Prognosis Radiomics

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric sciences 2025年第6期42卷 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of St.Thomas School of EngineeringMinnesota 55105USA NSF National Center for Atmospheric Research BoulderColorado 80307USA University of Auckland Auckland 0630New Zealand National Oceanic and Atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA National Oceanic and Atmospheric Administration National Centers for Environmental InformationAshevilleNC 28801USA National Research Council(CNR)Institute of Marine Sciences(ISMAR) Rome 00133Italy Mercator Ocean International Toulouse 31400France Department of Earth and Environmental Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA Institute of Oceanology Chinese Academy of SciencesQingdao 266071China National Marine Environmental Forecasting Center Ministry of Natural Resources of ChinaBeijing 100081China North Carolina Institute for Climate Studies(NCICS) North Carolina State UniversityAshevilleNC 28804USA Oceanographic Data Center Chinese Academy of SciencesQingdao 266071China College of Oceanography Hohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South China Sea Institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMD 20740USA Computer Network Information Center Chinese Academy of SciencesBeijing 100083China Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment AdministrationMinistry of Ecology and EnvironmentGuangzhou 510611China National Meteorological Information Center China Meteorological AdministrationBeijing 100081China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Xiamen Universit

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

关键词： ocean heat content sea surface temperature ocean temperature global warming climate

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Face Mask Integrated with Flexible and Wearable Manganite Oxide Respiration Sensor

SSRN

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SSRN 2023年

作者： Ye, Lianxu Wu, Fan Xu, Ruixing Zhang, Di Lu, Juanjuan Wang, Chuanlong Dong, Anjiang Xu, Sichen Xue, Lejun Fan, Zixin Xu, Longjie Li, Kaifeng Li, Dong Kursumovic, Ahmed Zhao, Run Tang, Rujun Qiu, Lei Wang, Haiyan MacManus-Driscoll, Judith L. Jing, Qingshen Li, Weiwei Yang, Hao College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Microelectronics & Data Science Anhui University of Technology Maanshan243002 China School of Materials Engineering School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Jiangsu Key Laboratory of Thin Films School of Physical Science and Technology Soochow University Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom James Watt School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing211106 China

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices. © 2023, The Authors. All rights reserved.

关键词： Mica

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Learning from pseudo lesion: A self-supervised framework for covid-19 diagnosis

arXiv

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arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

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Remote Online Firmware Upgrade System for Embedded Devices based on ZigBee

Remote Online Firmware Upgrade System for Embedded Devices b...

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Electronic Information technology and Smart Agriculture (ICEITSA), International Conference on

作者： Xian Zhang Pengju Tang Taiguo Qu Yiwen Liu School of Computer Science and Engineering Huaihua University Huaihua China College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua China

ISBN: (纸本)9781665413015

To reduce the upgrade and maintenance cost of embedded devices effectively, according to the requirement of a remote firmware update of embedded devices, this paper proposed a remote online firmware upgrade technology based on ZigBee. Combined with the requirement of remote online firmware upgrade application of the tower top amplifier, this paper analyzes and designs the system requirements, ZigBee module selection, and design the software, fault analysis, and other aspects. Designed Experiments to test the ZigBee connection success rate under the device startup state and normal operation state, the relationship between transmission firmware size and transmission success rate, system CPU resource consumption, transmission distance, transmission rate, and other performance indicators. The experimental results show that the remote online firmware upgrade technology of embedded devices based on ZigBee can better meet the performance requirements of all aspects of the system. The results show that this scheme has the advantages of simplicity, low cost, and high reliability and has a broad application prospect.

关键词： Performance evaluation Costs Codes Poles and towers Zigbee Maintenance engineering Software

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