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MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

Computers, Materials & Continua 2019年第9期61卷 1105-1115页

作者： Dongjie Zhu Yuhua Wang Chuiju You Jinming Qiu Ning Cao Chenjing Gong Guohua Yang Helen Min Zhou School of Computer Science and Technology Harbin Institute of TechnologyWeihai264209China College of Information Engineering Sanming University365004SanmingChina Fujian Province University Key Lab for Industry Big Data Analysis and Application FujianChina College of Information Engineering Qingdao Binhai UniversityQingdaoChina Jiangsu Province Wireless Sensing System Application Engneering Technology Research and Development Centre China School of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

With the rapid development of the mobile Internet,users generate massive data in different forms in social network every day,and different characteristics of users are reflected by these social media *** to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services,marketing,and recommendation *** this paper,we propose Multi-source&Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a ***,we design their own feature extraction models for multiple heterogeneous data ***,we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task ***,we design each task’s own unique presentation layer for discriminant output of ***,we design a weighted loss function to improve the learning efficiency and prediction accuracy of each *** experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender,age and region of social media users.

关键词： User profiles multi-source multi-task learning social network

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A New Ontology Meta-Matching Technique with a Hybrid Semantic Similarity Measure 15th

A New Ontology Meta-Matching Technique with a Hybrid Semanti...

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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2019

作者： Lu, Jiawei Xue, Xingsi Lin, Guoxiang Huang, Yikun College of Information Science and Engineering Fujian University of Technology Fuzhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou China Concord University College Fujian Normal University Fuzhou China

ISBN: (纸本)9789811397097

Ontology is the kernel technique of semantic web, which can be used to describe the concepts and their relationships in a particular domain. However, different domain experts would construct the ontologies according to different requirements, and there exists a heterogeneity problem among the ontologies, which hinders the interaction between ontology-based intelligent systems. Ontology matching technique can determine the links between heterogeneous concepts, which is an effective method for solving this problem. Semantic similarity measure is a function to calculate to what extent two concepts are similar to each other, which is the key component of ontology matching technique. Generally, multiple semantic similarity measures are used together to improve the accuracy of the concept recognition. How to combine these semantic similarity measures, i.e., the ontology meta-matching problem, is a challenge in the ontology matching domain. To address this challenge, this paper proposes a new ontology meta-matching technique, which applies a novel combination framework to aggregate two broad categories of similarity measures. The experiment uses the famous benchmark provided by the Ontology Alignment Evaluation Initiative (OAEI). Comparing results with the participants of OAEI shows the effectiveness of the proposal. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Ontology

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Distributed State Estimation for Heterogeneous Mobile Sensor Networks with Varying Nodes

Distributed State Estimation for Heterogeneous Mobile Sensor...

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第三十九届中国控制会议

作者： Yingrong Yu Jie Ren Xiwang Dong Qingdong Li Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing Aerospace System Engineering Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

On background of cooperative air combat, state estimation of aerial maneuvering target with multi-sensor cooperative reconnaissance is discussed in this paper. Multiple moving platforms equipped with different kinds of detectors construct heterogeneous mobile sensor networks. Graph theory is adopted to describe the communication topology of the networks. In consideration of detection range limit, the number of sensors participating in the detection changes over time. An interactive multiple model algorithm（IMM） is utilized for target motion modeling. The local observation information is processed by Cubature Information Filter（CIF）. A weighted average consensus-based state estimation method for heterogeneous mobile sensor networks with varying nodes is put forward to fuse information from different sensors and obtain a consistent and precise tracking result. Moreover, the stability analysis of proposed method is provided detailed. Finally, numerical simulation results are given to illustrate the effectiveness of the theoretical results.

关键词： Cubature Information Filter consensus theory distributed state estimation mobile sensor networks stochastic boundedness

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Energy Saving Strategy of Power system Cluster Based on Container Virtualization

Energy Saving Strategy of Power System Cluster Based on Cont...

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IEEE Asia Power and Energy engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan Zhang 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 Wuhan China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

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Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration

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national Science Review 2021年第2期8卷 60-73页

作者： Philippe Ciais Yitong Yao Thomas Gasser Alessandro Baccini Yilong Wang Ronny Lauerwald Shushi Peng Ana Bastos Wei Li Peter A.Raymond Josep G.Canadell Glen P.Peters Rob J.Andres Jinfeng Chang Chao Yue A.Johannes Dolman Vanessa Haverd Jens Hartmann Goulven Laruelle Alexandra G.Konings Anthony W.King Yi Liu Sebastiaan Luyssaert Fabienne Maignan Prabir K.Patra Anna Peregon Pierre Regnier Julia Pongratz Benjamin Poulter Anatoly Shvidenko Riccardo Valentini Rong Wang Grégoire Broquet Yi Yin Jakob Zscheischler Bertrand Guenet Daniel S.Goll Ashley-P.Ballantyne Hui Yang Chunjing Qiu Dan Zhu State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Northwest A&F University CSIRO Oceans and Atmosphere 15. Institute for Geology CEN—Center for Earth System Research and SustainabilityUniversity of Hamburg 16. Department of Earth System ScienceStanford University 17. Department of Earth System ScienceStanford UniversityStanford 18. School of Geographical SciencesNanjing University of Information Science and Technology 19. Department of Ecological SciencesVrije Universiteit Amsterdam 20. Research Institute for Global ChangeJAMSTEC Laboratoire des Sciences du Climat et de l'Environnement CEA-CNRS-UVSQ-UPSACLAY Center for Environmental Remote Sensing Chiba University Institute of Soil Science and Agrochemistry Siberian Branch of the Russian Academy of Sciences (SB RAS) Tuva State University Republic of Tuva Max Planck Institute for Meteorology NASA Goddard Space Flight Center Biospheric Sciences Lab. 26. Department for Innovation in BiologicalAgro-food and Forest systems (DIBAF)University of Tuscia RUDN University Department of Environmental Science and Engineering Shanghai Key Laboratory of Atmospheric Particle Pollution and PreventionInstitute of Atmospheric SciencesFudan University 29. Division of Geological and Planetary SciencesCalifornia Institute of Technology 30. Climate and Environmental Physics and Oeschger Centre for Climate Change ResearchUniversity of Bern 31. Department of Ecosystem and Conservation Sci

Resolving regional carbon budgets is critical for informing land-based mitigation policy. For nine regions covering nearly the whole globe, we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing. The net land–atmospheric carbon exchange(NEE) was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade, and riverine-carbon export to the ocean. Summing up NEE from all regions, we obtained a global ‘bottom-up’ NEE for net land anthropogenic CO2uptake of –2.2 ± 0.6 Pg C yr-1consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000–2009. This estimate is so far the most comprehensive global bottom-up carbon budget accounting, which set up an important milestone for global carbon-cycle studies. By decomposing NEE into component fluxes, we found that global soil heterotrophic respiration amounts to a source of CO2of 39 Pg C yr-1with an interquartile of 33–46 Pg C yr-1—a much smaller portion of net primary productivity than previously reported.

关键词： carbon budget human appropriation of ecosystems soil carbon

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research on Pre-view Method of Safety Level of Cascading Trip for Power Grid 6th

Research on Pre-view Method of Safety Level of Cascading Tri...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Deng, Huiqiong Li, Qinbin Zheng, Rongjin Li, Peiqiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Fujian Provincial University Engineering Research Center of Smart Grid Simulation Analysis and Integrated Control Fuzhou Fujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China College of Mechanical & Electrical Engineering National Taipei University of Technology No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Department of Business Administration North Borneo University College Lot 47 Block-F Alamesra Permai Plaza 2 Jln Sulaman Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9783030697167

This article introduces a method by which the power grid's security level can be observed in advance based on the expected initial failure. Firstly, based on the general form of cascading trip, this paper gives a mathematical expression of grid tripping based on cascading trip relay protection action behavior. According to the mathematical expression, the relationship between grid tripping and grid node injection power is discussed. According to this relationship, the distance between the node injection power under the current operation state and the nearest critical state node injection power is taken as the index to measure the power grid's security for cascading trip. According to this index, combined with various power grid constraints before and after the initial fault, a mathematical model for obtaining this safety index is given. This model is an optimized mathematical model. For the model, this paper gives a solution idea based on particle swarm. Finally, a few cases are performed in the IEEE-14 node system, and the calculations of the cases proved the accuracy of the proposed model and method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electric load flow

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AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition

arXiv

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arXiv 2022年

作者： Xu, Yongling Du, Yang Zou, Jing Zhou, Tianying Xiao, Lushan Liu, Li Ma, Pengcheng The Big Data Center Nanfang Hospital Southern Medical University Guangzhou510515 China The Brainup Research Lab Naolu Technology Co. Ltd. Beijing100124 China The Department of Psychology and Economics University of California Los Angeles Los AngelesCA90024 United States The Department of Electrical Engineering and Computer Sciences UC Berkeley BerkeleyCA94720 United States The Department of Infectious Diseases Nanfang Hospital Southern Medical University Guangzhou510515 China

Affective computing is an important subfield of artificial intelligence, and with the rapid development of brain-computer interface technology, emotion recognition based on EEG signals has received broad attention. It is still a great challenge to effectively explore the multi-dimensional information in the EEG data in spite of a large number of deep learning methods . In this paper, we propose a deep model called Attention-based Multiple Dimensions EEG Transformer (AMDET), which can leverage the complementarity among the spectral-spatial-temporal features of EEG data by employing the multi-dimensional global attention mechanism. We transformed the original EEG data into 3D temporal-spectral-spatial representations and then the AMDET would use spectral-spatial transformer encoder layer to extract effective features in the EEG signal and concentrate on the critical time frame with a temporal attention layer. We conduct extensive experiments on the DEAP, SEED, and SEED-IV datasets to evaluate the performance of AMDET and the results outperform the state-of-the-art baseline on three datasets. Accuracy rates of 97.48%, 96.85%, 97.17%, 87.32% were achieved in the DEAP-Arousal, DEAP-Valence, SEED, and SEED-IV datasets, respectively. We also conduct extensive experiments to explore the possible brain regions that influence emotions and the coupling of EEG signals. Remarkably, AMDET can perform as well even with few channels which are identified by visualizing what the trained model learned. The accuracy could achieve over 90% even with only eight channels and it is of great use and benefit for practical applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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Identifying stepping-stones within small wetlands in highly urbanized areas and under network construction: A case study of the Guangdong–Hong Kong–Macao greater Bay Area, China

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Ecological Indicators 2025年 176卷

作者： Ye, Ying Chen, Caixia Yu, Chunxue Sun, Caige Geng, Shoubao Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangdong Province Engineering Laboratory for Geographic Spatio-Temporal Big Data Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China School of Geography South China Normal University Guangzhou510631 China Research Center for Eco-environmental Engineering Dongguan University of Technology Dongguan523808 China

In highly urbanized areas, small wetlands can provide multiple ecosystem services while benefiting the well-being of citizens. Although existing studies on wetland ecological networks have prioritized large wetland conservation measures, insufficient attention has been paid to the distribution patterns of small wetlands and their role in ecological networks. Utilizing the Guangdong–Hong Kong–Macao Greater Bay Area as a case study, we constructed a wetland ecological network to identify ecological pinch points while identifying small wetlands suitable to function as stepping-stones. This approach aims to establish potential corridors within the network and to assess the connectivity and robustness of the network under various scenarios. Our findings identified 297 ecological pinch points within the wetland ecological network alongside 53 small wetlands designated as stepping-stones, predominantly located in Foshan and Zhongshan cities in the central and western regions of the study area. The stepping-stone network comprised of 241 ecological corridors, with enhancements observed in the α, β, and γ indices, thereby significantly expanding the network's connectivity. Robustness index alterations in the stepping-stone network were moderated in relation to the ecological network, demonstrating that small wetlands can effectively bolster ecological network stability. It is recommended that, considering the distribution of ecological stepping stones of small wetlands, priority be given to the conservation and restoration of small wetlands in Foshan and Zhongshan within the central and western regions of the Guangdong–Hong Kong–Macao Greater Bay Area. This includes delineating conservation zones for small wetlands or establishing clusters of wetland parks centered on small wetlands, thereby maximizing their ecosystem value in supporting the urban agglomeration of the Guangdong–Hong Kong–Macao Greater Bay Area. © 2025 The Author(s)

关键词： Biotic

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CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

arXiv

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arXiv 2022年

作者： Gu, Ran Wang, Guotai Lu, Jiangshan Zhang, Jingyang Lei, Wenhui Chen, Yinan Liao, Wenjun Zhang, Shichuan Li, Kang Metaxas, Dimitris N. Zhang, Shaoting School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering ShanghaiTech University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Sensetime Research Shanghai China Department of Radiation Oncology Sichuan Cancer Hospital and Institute University of Electronic Science and Technology of China Chengdu China West China Hospital-SenseTime Joint Lab West China Biomedical Big Data Center Sichuan University Chengdu China Department of Computer Science Rutgers University PiscatawayNJ08854 United States Shanghai AI Lab Shanghai China

Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to disentangle domain-specific and domain-invariant features is key for achieving Domain Generalization (DG). However, existing DG methods can hardly achieve effective disentanglement to get high generalizability. To deal with this problem, we propose an efficient Contrastive Domain Disentanglement and Style Augmentation (CDDSA) framework for generalizable medical image segmentation. First, a disentangle network is proposed to decompose an image into a domain-invariant anatomical representation and a domain-specific style code, where the former is sent to a segmentation model that is not affected by the domain shift, and the disentangle network is regularized by a decoder that combines the anatomical and style codes to reconstruct the input image. Second, to achieve better disentanglement, a contrastive loss is proposed to encourage the style codes from the same domain and different domains to be compact and divergent, respectively. Thirdly, to further improve generalizability, we propose a style augmentation method based on the disentanglement representation to synthesize images in various unseen styles with shared anatomical structures. Our method was validated on a public multi-site fundus image dataset for optic cup and disc segmentation and an in-house multi-site Nasopharyngeal Carcinoma Magnetic Resonance Image (NPC-MRI) dataset for nasopharynx Gross Tumor Volume (GTVnx) segmentation. Experimental results showed that the proposed CDDSA achieved remarkable generalizability across different domains, and it outperformed several state-of-the-art methods in domain-generalizable segmentation. Code is available at https://***/Hilab-git/DAG4MIA. © 2022, CC BY.

关键词： Magnetic resonance imaging

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Novel Hemorrhagic Risk Score in Elderly Patients with Coronary Artery Disease and Gastrointestinal Malignant Tumor Comorbidity:A 10-year Clinical Inpatient data Analysis from 2 Medical centers

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Cardiology Discovery 2021年第3期1卷 163-172页

作者： Nandi Bao Wanling Wang Huitao Wu Yabin Wang Hebin Che Wenwen Meng Jiaxin Miao Dong Han Fan Yin National Clinical Research Center for Geriatric Diseases The Second Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China Medical School of Chinese People's Liberation Army General Hospital Beijing 100853China National Engineering Laboratory for Medical Big Data Application Technology The First Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China Department of Oncology The Second Medical Center of Chinese People's Liberation Army General HospitalBeijing 100853China

Objective:Older patients with comorbidity,such as coronary heart disease(CHD)and malignant gastrointestinal tumors,are at a high risk of bleeding ***,risk prediction models based on risk factor assessment remain *** study aimed to establish an individualized bleeding risk assessment system based on the analysis of 10-year inpatient clinical big ***:Total clinical data of 56,819 patients with CHD and 25,988 patients with malignant digestive tract tumors(admitted from January 2008 to December 2017)were retrospectively collected at the First and Second Medical centers of Chinese People's Liberation Army General *** them,1307 patients with CHD and malignant digestive tract tumors were screened as the derivation *** dependent variable was the occurrence of major clinical bleeding *** statistics and hypothesis tests of differences were performed for independent variables according to the occurrence of *** Tree,eXtreme Gradient Boosting(XGBoost),logistic regression,and random forest models were used for *** accuracy,sensitivity,specificity,and area under the receiver operating characteristic curve(AUC-ROC)were applied as the criteria for evaluating and verifying model *** evaluate this developed model,another cohort comprising 454 patients(admitted from January 2018 to December 2019)was prospectively enrolled as the validation cohort based on the same inclusion and exclusion ***:Among the 64 variables with<50%missing values,the recursive feature elimination method with a random forest model was used to screen the selected *** highest accuracy was obtained following the selection of 10 scalars,and the final model was constructed *** demonstrated the best performance *** AUC-ROC of this model was 0.981,with an accuracy,sensitivity,and specificity of 0.939,0.950,and 0.927,*** the validation cohort,the AUC-ROC,accurac

关键词： Coronary disease Gastrointestinal cancers Bleeding Multivariate analysis Risk factors Machine learning

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