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Journal of Network Intelligence 2023年第1期8卷 76-88页

作者： Wang, Jinshui Chen, Junyan Ou, Xuewen Han, Qingfeng Tang, Zhengyi School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China China National Petroleum Corporation China

Automatic Essay Scoring (AES) is one of the significant and challenging research topics in the Natural Language Processing(NLP) area. However, existing AES models majorly consider features derived from vocabulary while failing to integrate sentence and chapter information from multi-level perspectives. In this study, we proposed a multi-level feature fusion model, which was used to capture the multi-level features from different perspectives to improve the accuracy of scoring. Our model consisted of three components to respectively capture the vocabulary-level, sentence-level, and chapter-level features, which were then fed into a CNN and BiLSTM network for the final essay scor-ing. The results show that the proposed model outperforms a set of state-of-the-art AES models on the dataset of the Kaggle Automated Student Assessment Prize (ASAP) com-petition. The average Quadratic Weighted Kappa (QWK) value reaches 0.816, which verifies the efficacy of the model in the task of automated essay scoring. © 2023, Taiwan Ubiquitous Information CO LTD.

关键词： Automation

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First-principles study on the interfacial bonding strength and segregation at Mg/MgZn_(2)matrix interface

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Journal of Magnesium and Alloys 2024年第10期12卷 4053-4062页

作者： Longke Bao Peng Du Shengkun Xi Cuiping Wang Kaihong Zheng Rongpei Shi Guoqiang Xie Xingjun Liu State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhen 518055PR China College of Materials Science and Engineering and Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055PR China College of Materials Science and Engineering Xiamen UniversityXiamen 361005PR China Institute of New Materials Guangdong Academy of SciencesGuangzhou 510650PR China

To understand the interface characteristics between the precipitateβ2'and the Mg matrix,and thus guide the development of new Mg-Zn alloys,we investigated the atomic interface structure,work of adhesion(Wad),and interfacial energy(γ)of Mg(0001)/β2’(MgZn_(2))(0001)interface,as well as the effect of segregation behavior of the introduced transition metal atoms(3d,4d and 5d)on interfacial bonding *** calculated works of adhesion and interfacial energies dementated that the Zn2-terminated MT+HCP configuration is the most stable structure for all considered *** the Zn2-MT+HCP interface as the research object,estimated segregated energies(Eseg)reveal that added transition metal atoms prefer to segregate at Mg-I and Mg-II *** predicted Wad and charge density difference results reveal that the segregation of alloying additives employed may all strengthen Mg(0001)/MgZn_(2)(0001)interface,with the enhancement effect of Os,Re,Tc,W,and Ru at the Mg-II site being the most pronounced.

关键词： Mg/MgZn_(2)interface Work of adhesion Interfacial energy Interface segregation

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suitable for Natural

EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suit...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Yuzhe Chen, Jiahao Duan, Xiaodong Li, Zhuohui College of Mechanical and Electronic Engineering Dalian China College of Computer Science and Engineering Dalian China Seac Key Laboratory of Big Data Applied Technology Dalian China

ISBN: (纸本)9781665410205

Weed detection is crucial for the healthy growth of crops, yet existing detection models struggle to perform high-accuracy real-time detection of weeds in natural environments on edge computing devices. This paper introduces EMPA-YOLO, a model designed for rapid, accurate, real-time weed detection on low-performance edge computing devices. It incorporates an efficient multi-scale convolutional structure, C3EMSC, and a lightweight, adaptive weight subsampling layer, LAWDS, into YOLOv5s. Additionally, a logical distillation algorithm, AlignSoftTarget, is proposed for knowledge distillation. Validation on a mixed dataset of crops and weeds showed that EMPA-YOLO improved mAP50 by 11.2%, reduced parameter count by 4.8M, decreased computational load by 8.7GFLOPs, and increased inference frame rate by 58% compared to the original YOLOv5s algorithm. When compared to YOLOv3, YOLOv5s, YOLOv6, YOLOv8s, and RT-DETR, inference speed improved by 89.2%, 60.8%, 77.5%, 76.5%, and 94.7%, respectively, with mean accuracy enhancements of 7.3%, 11.2%, 16.7%, 0.9%, and 1.2%. Real-world testing on edge computing devices met real-time detection requirements, proving its efficacy and practicality in weed detection. © 2024 IEEE.

关键词： edge computing knowledge distillation lightweight model model compress ion YOLO

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iCREPCP:A deep learning-based web server for identifying base-resolution cis-regulatory elements within plant core promoters

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Plant Communications 2023年第1期4卷 213-216页

作者： Kaixuan Deng Qizhe Zhang Yuxin Hong Jianbing Yan Xuehai Hu College of Informatics Hubei Engineering Technology Research Center of Agricultural Big DataHuazhong Agricultural UniversityWuhanHubeiP.R.China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070P.R.China

Dear Editor,A central question of plant biology is how to specify the temporal and spatial patterns as well as the quantitative levels of gene expression,which are significantly associated with important agronomic *** has been a growing consensus in the past decade that the two key factors determining gene expression level are cis-regulatory modules(CRMs)and trans-acting factors(TAFs)(Schmitz et al.,2022).Common CRMs include gene-proximal promoters and distal enhancers,which are both considered to be complex assemblies of cis-regulatory elements(CREs).It is the binding or interaction between CREs and TAFs(often transcription factors[TFs])in a ubiquitous or cell-specific manner that determines in which cell,at what time,and at what level a gene is ***,the identification of plant CRMs or critical CREs will not only help us understand transcriptional regulatory mechanisms in plants but also serve as an essential prerequisite for plant breeding 4.0—breeding by genome editing(Gao,2021).

关键词： server web breeding

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Efficient Deep learning-based Real-time Migration Strategies for Containers in SDN 6

Efficient Deep learning-based Real-time Migration Strategies...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Huang, Shan Liu, Yanan Li, Yanuo He, Feili Duan, Xiaodong School of Computer Science and Engineering Dalian Minzu University Dalian China Key Laboratory of Big Data Application Technology of the State Ethnic Affairs Commission Dalian China Dalian Ethnic Culture Digital Technology Key Laboratory Dalian China

ISBN: (纸本)9798350388374

Container technology is widely used and improve the efficiency of container real-time migration has become an important research topic. Existing studies mainly focus on optimizing iterative dumping of containers without fully considering the impact of migration node selection on migration efficiency. Software-defined networking (SDN), achieves centralized control of the network by separating the control plane and the forwarding plane. Applying deep learning to SDN makes tasks such as traffic prediction and network optimization more intelligent. This paper proposes Prediction Model based on CLCB (CNN-LSTM-CBAM) uses real-time collected load data as input, extracts feature components of load data using one-dimensional convolutional neural networks, and then predicts using LSTM. To improve prediction accuracy, a lightweight CBAM attention module is introduced, and the prediction results are output through fully connected layers. The Network Traffic Prediction Model based on GCGRU-Conv (GCN-GRU-CNN) considers both spatial and temporal features of network traffic. GCN and GRU are used to extract spatial and temporal features of network traffic, respectively, and then these features are fused together using convolutional neural networks. To address the gradient problem caused by increasing network depth, a residual network structure is introduced. Experimental results show that the method proposed can effectively reduce the container migration time and has better prediction results. © 2024 IEEE.

关键词： Convolutional neural networks

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Rare earth element Ce enables high thermoelectric performance in n-type SnSe polycrystals

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Journal of Materials science & technology 2023年第12期143卷 234-241页

作者： Shan Li Li Yin Yijie Liu Xiaodong Wang Chen Chen Qian Zhang School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

SnSe exhibits excellent thermoelectric performance in both n-and p-type single crystals,but its n-type polycrystals are restricted because of the lower electrical ***,we dually introduced rare earth element Ce and PbTe to optimize the thermoelectric properties of n-type SnSe *** is demonstrated that Ce is an effective cationic dopant to convert SnSe from p-to n-type conductor,and an enhanced peak zT value of∼0.9 at 823 K was obtained in Sn 0.97 Ce 0.03 Se due to the improved power ***,PbTe alloying not only reduced the band gap to increase the carrier concentration,but also enhanced the density-of-states effective mass,and hence further increased the power factor in the whole measured temperature ***,the lattice thermal conductivity was significantly reduced owing to the enhanced phonon scattering by the mass and strain *** a result,the peak zT value was increased to∼1.3 for Sn 0.9 Pb 0.07 Ce 0.03 Se 0.93 Te 0.07 together with a high average zT value of∼0.52 in the temperature range of 300 to 823 K.

关键词： Thermoelectric N-type SnSe Ce doping PbTe alloying

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Ensemble Learning Based Employment Recommendation Under Interaction Sparsity for College Students 9th

Ensemble Learning Based Employment Recommendation Under Inte...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Zhu, Haiping Zhao, Yifei Wu, Yuchen Chen, Yan Li, Wenhao Zheng, Qinghua Tian, Feng Department of Computer Science and Technology Xi’an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9783031466632

Recommendation systems play a crucial role in helping college students find job opportunities. However, the sparsity of interactions in employment recommendation for college students poses a challenge for models based on historical user preferences. To address this issue, we propose a novel model called Ensemble Learning based Employment Recommendation under Interaction Sparsity for College Students (EERIS). The model comprises two components: a similarity information component that uses pooled users to determine the nearest neighbor in user similarity measurement, and a global interaction component that uses interaction vectors of user groups to enhance interactions. To evaluate the missing interactions, we propose a loss function called CellLoss. These components are combined based on ensemble learning to improve the model’s generalization and scalability. Our experiments on two real-world datasets demonstrate the superior performance of the EERIS model. Ablation experiments further confirm that each component positively contributes to the model’s performance. Additionally, we design a revised metric for better model testing. Overall, the proposed EERIS model effectively addresses the interaction sparsity in employment recommendation for college students and provides satisfactory recommendations to students. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Upconversion luminescence and optical thermometry behaviors of Yb^(3+)and Ho^(3+)co‑doped GYTO crystal

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Frontiers of Optoelectronics 2023年第3期16卷 179-189页

作者： Chuancheng Zhang Shoujun Ding Miaomiao Wang Hao Ren Xubing Tang Yong Zou Renqin Dou Wenpeng Liu School of Microelectronics and Data Science Anhui University of TechnologyMaanshan 243002China Anhui Institute of Optics and Fine Mechanics Chinese Academy of SciencesHefei 230031China Advanced Laser Technology Laboratory of Anhui Province Hefei 230037China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan 243002China

Optical thermometry based on the upconversion(UC)luminescence intensity ratio(LIR)has attracted considerable attention because of its feasibility for achievement of accurate non-contact temperature *** with traditional UC phosphors,optical thermometry based on UC single crystals can achieve faster response and higher sensitivity due to the stability and high thermal conductivity of the single *** this study,a high-quality 5 at%Yb^(3+)and 1 at%Ho^(3+)co-doped Gd_(0.74)Y_(0.2)TaO_(4)single crystal was grown by the Czochralski(Cz)method,and the structure of the as-grown crystal was ***,the UC luminescent properties and optical thermometry behaviors of this crystal were *** 980 nm wavelength excitation,green and red UC luminescence lines at 550 and 650 nm and corresponding to the^(5)F_(4)/^(5)S_(2)→^(5)I_(8)and^(5)F_(5)→^(5)I_(8)transitions of Ho^(3+),respectively,were *** green and red UC emissions involved a two-photon mechanism,as evidenced by the analysis of power-dependent UC emission *** temperature-dependent UC emission spectra were measured in the temperature range of 330–660 K to assess the optical temperature sensing *** 660 K,the maximum relative sensing sensitivity(S_(r))was determined to be 0.0037 K^(−1).These results highlight the signifcant potential of Yb,Ho:GYTO single crystal for optical temperature sensors.

关键词： Yb,Ho:GYTO Optical temperature sensor Luminescence intensity ratio Upconversion luminescence

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation 42

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Haozhao Xu, Wenchao Fan, Yunfeng Li, Ruixuan Zhou, Pan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350334142

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods. © 2023 IEEE.

关键词： Learning systems

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