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Predictability of real temporal networks

national Science Review 2020年第5期7卷 929-937页

作者： Disheng Tang Wenbo Du Louis Shekhtman Yijie Wang Shlomo Havlin Xianbin Cao Gang Yan School of Electronic and Information Engineering Beihang University Shanghai Institute of Intelligence Science and Technology Tongji University Network Science Institute Northeastern University Department of Physics Bar Ilan University CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation School of Physics Science and Engineering Tongji University

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and *** evidence has shown that the temporal nature of links in many real-world networks is not ***, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly,we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes.

关键词： temporal network predictability network entropy predictive algorithm

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Combining spectral and textures of digital imagery for wheat aboveground biomass estimation

Combining spectral and textures of digital imagery for wheat...

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2022 International Conference on Electronic Information technology, EIT 2022

作者： Zheng, Ling Tao, Jianpeng Bao, Qian Weng, Shizhuang Zhang, Yakun Zhao, Jinling Huang, Linsheng National Engineering Research Venter for Agro-Ecological Big Data Analysis and Application Anhui University Hefei230601 China College of Agricultural Equipment Engineering Henan University of Science and Technology Luoyang471003 China

ISBN: (纸本)9781510655096

The aboveground biomass (AGB) is a key index for predicting wheat yield. In the case of high biomass, the AGB estimation of single spectral feature or image texture is poor. Therefore, this study evaluated the ability of fusion of spectral reflectance and texture to predict wheat AGB. Among them the reflectance spectrum of the wheat canopy was collected by near-earth spectrometer, and the texture features of three bands of RGB were extracted by gray co-occurrence matrix. Partial least squares regression (PLS) model was used to evaluate the relationship between fusion features and AGB. The experimental results based on the validated data set show that the AGB estimation effect of feature fusion is better than that of single feature (R2 = 0.70;RMSE = 0.06). This shows that the combination of spectral reflectance and texture can improve the accuracy of AGB estimation in the later stage. © 2022 SPIE. All rights reserved.

关键词： Textures

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MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

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Computer Modeling in engineering & Sciences 2023年第7期136卷 381-401页

作者： Yi Chen Jin Zhou Qianting Gao Jing Gao Wei Zhang School of Computer Science Central China Normal UniversityWuhan430079China Computer Science School of ScienceRensselaer Polytechnic InstituteTroy12180USA National Engineering Laboratory of Educational Big Data Application Technology Central China Normal UniversityWuhan430079China

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of *** learning is a strategy of learning through groups or *** cooperative learning behavior occurs,each student in the group should participate in teaching *** showed that students who are actively involved in a class gain *** behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning *** studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching *** this paper,student engagement is automatically analyzed based on computer *** tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning *** multi-modal solution was evaluated in a real collaborative *** results show that the model can accurately predict students’performance in the collaborative learning environment.

关键词： Engagement facial expression deep network gaze

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Local global information aggregation graph convolution for skeleton-based action recognition

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Neurocomputing 2025年 640卷

作者： Shichong Xie Shengze Li Peng Chen Bing Wang Jun Zhang School of Artificial Intelligence Anhui University Hefei 230601 China School of Electrical Engineering and Automation Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Institute of Health Sciences and Technology & School of Internet Anhui University Hefei 230601 China Anhui Rocvision Intelligent Technology Co. Ltd Hefei 230001 China School of Management Science and Engineering Anhui University of Finance & Economics Bengbu 233030 China

In the field of human action recognition based on skeletal data, how to capture the complex features of skeletal sequences in both spatial and temporal dimensions is a significant challenge. Existing Graph Convolutional Network (GCN) methods typically focus on extracting spatial features using various approaches, while neglecting the temporal characteristics inherent in human actions. In this paper, we propose the L(ocal) G(lobal)-GCN, a novel approach that emphasizes the under-explored temporal dimension by segmenting time to capture both local and global information. We also introduce a relative position embedding method tailored for skeletal data to enhance the representation capability of graph convolution. Additionally, we propose a partitioning method that reduces computational complexity while retaining all information, leveraging the sequential nature of skeletal data. Our LG-GCN achieves and surpasses the state-of-the-art accuracy across three widely employed datasets: NTU RGB+D with 97.3% (X-View), NTU RGB+D 120 with 91.1% (X-Set), and NW-UCLA with 96.6%.

关键词： Action recognition Global and local processing Graph convolutional network Sequence reordering

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Class-Dynamic and Hierarchy-Constrained Network for Entity Linking 28th

Class-Dynamic and Hierarchy-Constrained Network for Entity...

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28th International Conference on database Systems for Advanced applications, DASFAA 2023

作者： Wang, Kehang Liu, Qi Zhang, Kai Liu, Ye Tao, Hanqing Huang, Zhenya Chen, Enhong Auhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9783031306716

Entity Linking (EL) aims to map mentions in a text to corresponding entities in a knowledge base. Existing EL methods usually rely on sufficient labeled data to achieve the best performance. However, the massive investment in data makes EL systems viable only to a limited audience. There is ample evidence that introducing entity types can provide the model prior knowledge to maintain the model performance in low-data regimes. Unfortunately, current low-data EL methods usually employ entity types by rule constraints, which are in a shallow manner. Furthermore, they usually ignore fine-grained interaction between mention and its context, resulting in insufficient semantic information of mention representation in low-data regimes. To this end, we propose a Class-Dynamic and Hierarchy-Constrained Network (CDHCN) for entity linking. Specifically, we propose a dynamic class scheme to learn a more effective representation for each entity type. Besides, we formulate a hierarchical constraint scheme to reduce the matching difficulty of the given mention and corresponding candidate entities by utilizing entity types. In addition, we propose an auxiliary task called mention position prediction (MPP) to obtain an informative mention representation in low-data regimes. Finally, extensive in-domain and out-of-domain experiments demonstrate the effectiveness of our method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge based systems

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Resilience-oriented Valuation for Energy Storage Amidst Extreme Events

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Chinese Journal of Electrical engineering 2023年第3期9卷 15-25页

作者： Youzhen Wu Jianxiao Wang Yiyang Song Yunyun Xie College of Engineering Peking UniversityBeijing 100871China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China School of Automation Nanjing University of Science and TechnologyNanjing 210094China

In power grids,the frequency is increasing of extreme accidents which have a low probability but high risk such as natural disasters and deliberate *** has sparked discussions on the resilience of power ***-storage systems(ESSs)are critical for enhancing the resilience of power ***,with their mechanism of flexible charging and discharging,adjust energy usage as needed during disasters,thereby mitigating the impact on the grid and enhancing security and ***,in turn,ensures the power system’s stable ***,there is limited systematic research quantifying the economic value of energy storage in resilience ***,a model and methodology were proposed to quantify the value of energy storage systems for enhancing grid resilience during extreme events.A two-stage stochastic optimization mathematical model was *** first stage involves pre-deployment based on day-ahead expectations,and the second stage involves simulating potential failure scenarios through real-time *** the temporal dimension,the energy storage systems with flexible regulation capabilities was used as emergency power sources to reduce occurrences of ***,a novel index was proposed that quantifies the resilience value of energy storage as the economic value of energy storage per unit of capacity,as reflected in the emergency dispatch *** index helps determine the balance between the energy storage investment cost and resilience ***,an IEEE-30 node transmission system was used to verify the feasibility and effectiveness of the proposed *** findings revealed a significant improvement in the resilience value,with a 23.49%increase observed when energy storage systems were implemented compared to the scenario without energy storage *** optimal capacity configurations for the flywheel,lithium-ion batteries,and pumped hydro storage were 10 MW,11 MW,and 12 MW,respectively,highligh

关键词： Energy storage dispatch model power system resilience resilience-oriented valuation two-stage optimization model

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Global Exponential Rendezvous Control of Nonholonomic Unicycle Vehicles with Directed Communication Topology

Global Exponential Rendezvous Control of Nonholonomic Unicyc...

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Chinese Intelligent Systems Conference, CISC 2020

作者： Zhao, Yixin Xing, Bin Huang, Wei Luo, Shiyu Li, Peng Xie, Wenjing The School of Computer and Information Science Southwest University Chongqing400715 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd. National Engineering Laboratory for Industrial Big-Data Application Technology Chongqing400065 China

ISBN: (纸本)9789811584572

This paper studies the full-state rendezvous control problem of multiple nonholonomic unicycle systems. A distributed state feedback time-varying control law is derived by exploiting model transformation, input-output feedback linearization technology, linear cooperation control theory and graph theory. Based on the carefully constructed output-like variable, the input-output feedback linearization approach overcomes the underactuated control challenge, makes the choice of controller parameters independent of initial states of vehicles, and allows the linear control theory applicable, rather than using the Lyapunov method and hence requiring the information flow undirected. Stability analysis proves that all the unicycle vehicles globally exponentially converge to a common fixed location and orientation, provided that the communication topology is directed and having a spanning tree. A numerical simulation is implemented for five nonholonomic unicycle vehicles, demonstrating the effectiveness of the proposed control scheme. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicles

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An Alternative Algorithm for the Symmetry Classification of Ordinary Differential Equations

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Sound & Vibration 2022年第1期56卷 65-76页

作者： Yi Tian Jing Pang College of Data Science and Application Inner Mongolia University of TechnologyHohhot010080China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080China College of Science Inner Mongolia University of TechnologyHohhot010051China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010051China

This is the first paper on symmetry classification for ordinary differential equations(ODEs)based on Wu’s *** carry out symmetry classification of two ODEs,named the generalizations of the Kummer-Schwarz equations which involving arbitrary ***,Lie algorithm is used to give the determining equations of symmetry for the given equations,which involving arbitrary ***,differential form Wu’s method is used to decompose determining equations into a union of a series of zero sets of differential characteristic sets,which are easy to be solved *** branch of the decomposition yields a class of symmetries and associated *** algorithm makes the classification become direct and *** Dimitrov Bozhkov,and Pammela Ramos da Conceição have used the Lie algorithm to give the symmetry classifications of the equations talked in this paper in *** this paper,we can find that the differential form Wu’s method for symmetry classification of ODEs with arbitrary function(parameter)is effective,and is an alternative method.

关键词： Kummer-Schwarz equation ordinary differential equations(ODEs) differential form Wu’s method

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Mobilecount: An efficient encoder-decoder framework for real-time crowd counting 1

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2nd Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019

作者： Gao, Chenyu Wang, Peng Gao, Ye School of Computer Science Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Beijing China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

In this work, we propose a computation-efficient encoder-decoder architecture, named MobileCount, which is specifically designed for high-accuracy real-time crowd counting on mobile or embedded devices with limited computation resources. For the encoder part, MobileNetV2 is tailored in order to significantly reduce FLOPs at a little cost of performance drop, which has 4 bottleneck blocks preceded by a max pooling layer of stride 2. The design of decoder is motivated by Light-weight RefineNet, which further boosts counting performance with only a 10% increase of FLOPs. In comparison with state-of-the-arts, our proposed network is able to achieve comparable counting performance with 1/10 FLOPs on a number of benchmarks. © Springer Nature Switzerland AG 2019.

关键词： Network coding

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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Computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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