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2022 Conference on Empirical Methods in Natural Language processing, EMNLP 2022

作者： Sun, Tianxiang He, Junliang Qiu, Xipeng Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

Automatic evaluation metrics are crucial to the development of generative systems. In recent years, pre-trained language model (PLM) based metrics, such as BERTScore (Zhang et al., 2020), have been commonly adopted in various generation tasks. However, it has been demonstrated that PLMs encode a range of stereotypical societal biases, leading to a concern on the fairness of PLMs as metrics. To that end, this work presents the first systematic study on the social bias in PLM-based metrics. We demonstrate that popular PLM-based metrics exhibit significantly higher social bias than traditional metrics on 6 sensitive attributes, namely race, gender, religion, physical appearance, age, and socioeconomic status. In-depth analysis suggests that choosing paradigms (matching, regression, or generation) of the metric has a greater impact on fairness than choosing PLMs. In addition, we develop debiasing adapters that are injected into PLM layers, mitigating bias in PLM-based metrics while retaining high performance for evaluating text generation. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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PCRTAM-Net:A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation

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Journal of Computer Science & Technology 2023年第3期38卷 567-581页

作者：汪华登李紫正保罗黎兵兵潘细朋刘振丙蓝如师罗笑南 Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin 541004China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin 541004China Department of Pathology Ganzhou Municipal HospitalGanzhou 341000China

Retinal images play an essential role in the early diagnosis of ophthalmic *** segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast *** the same time,automated models struggle to capture representative and discriminative retinal vascular *** fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the *** addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature *** evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and *** model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.

关键词： retinal image segmentation triple attention mechanism atrous convolution residual network

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Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts

Late Prompt Tuning: A Late Prompt Could Be Better Than Many ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Liu, Xiangyang Sun, Tianxiang Huang, Xuanjing Qiu, Xipeng School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

Prompt tuning is a parameter-efficient tuning (PETuning) method for utilizing pre-trained models (PTMs) that simply prepends a soft prompt to the input and only optimizes the prompt to adapt PTMs to downstream tasks. Although it is parameter- and deployment-efficient, its performance still lags behind other state-of-the-art PETuning methods. Besides, the training cost of prompt tuning is not significantly reduced due to the back-propagation through the entire model. Through empirical analyses, we shed some light on the lagging performance of prompt tuning and recognize a trade-off between the propagation distance from label signals to the inserted prompt and the influence of the prompt on model outputs. Further, we present Late Prompt Tuning (LPT) that inserts a late prompt into an intermediate layer of the PTM instead of the input layer or all layers. The late prompt is obtained by a neural prompt generator conditioned on the hidden states before the prompt insertion layer and therefore is instance-dependent. Through extensive experimental results across various tasks and PTMs, we show that LPT can achieve competitive performance to full model tuning and other PETuning methods under both full-data and few-shot scenarios while possessing faster training speed and lower memory cost. © 2022 Association for Computational Linguistics.

关键词： Backpropagation

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Linear Extended State Observer-based Sliding Mode control of Active Magnetic Bearing Systems 41

Linear Extended State Observer-based Sliding Mode Control of...

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第41届中国控制会议

作者： Ziheng Yu Yifeng Tang Bowen Xu Xiang Huang Hai-Tao Zhang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Controland the State Key Lab of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In the last decades,as a typical nonlinear system,active magnetic bearings(AMB) system has been widely applied in manufacturing systems.A sliding mode control(SMC) scheme for the AMB system is proposed with the disturbance observation of the linear extended state observer(LESO) in this *** chattering of the AMB system has been reduced by LESO-SMC by at least 60%.Sufficient BIBO(bounded input-bounded output) stability conditions of the closed-loop AMB system governed by the proposed LESO-SMC are derived by Lyapunov ***,experiments are conducted to verify the effectiveness and superiority of the proposed LESO-SMC than conventional SMC.

关键词： Active magnetic bearing systems state observer sliding mode control chattering

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Minimum Strong Radius of the Strong Product of Paths 2

Minimum Strong Radius of the Strong Product of Paths

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2nd International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2022

作者： Liu, Shuyang Li, Feng College of Computer Science Qinghai Normal University Xi'ning810003 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xi'ning810003 China

ISBN: (纸本)9781510663183

Let D is a strong oriented graph, the strong distance sd(u, v) is the number of arcs of D containing u and v. For a vertex v of D, the strong eccentricity se(v) is the strong distance between v and a vertex farthest from v. The strong radius srad(D) is the minimum strong eccentricity among the vertices of D. The minimum strong radius srad(G) of a graph D is the minimum strong radius over all strong orientations of G. And G1 ⊗ G2 denote the strong product of graphs G1 and G2. In this paper, according to the order of paths we give the result of the minimum strong radius of the strong product of paths, and propose a new directed network design method. © 2023 SPIE.

关键词： Graph theory

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High precision localisation of the sources of noise of a bent-axis motor

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Noise and Vibration Worldwide 2023年第7-8期54卷 350-359页

作者： Xu, Chen Xiangming, Meng Qinghong, Gong Hui, Huang Shumei, Chen Heng, Du Yuzheng, Li Xiufang, Lin School of Mechanical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of Fluid Power and Intelligent Electro-Hydraulic Control Fuzhou University Fuzhou China College of Physics and Electronic Information Engineering Minjiang University Fuzhou China

The hydraulic motor will generate noise in operation and it has fluid-structure coupling and compact internal structure. The sound intensity measurement is difficult to distinguish the noise sources of motor. In this paper, a high-precision localization method for noise sources of bent-axis motor is studied to improve the resolution of the sound intensity images to locate noise sources accurately. Firstly, the structural characteristics of the motor are analyzed, and the sound intensity images are obtained through the experiment. Then the high-resolution optimization method based on compressed sensing is designed according to the characteristics of the sound intensity field. Finally, the sound intensity images with high resolution are constructed. The high-resolution images can distinguish the position of the prominent points, and the error is less than 0.4 dB. The identification distance of noise sources can reach 30 mm, and it breaks through the highest resolution of the traditional sound intensity test. © The Author(s) 2023.

关键词： Compressed sensing

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Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid 4

Event-Triggered Online Learning Distributionally Robust Ener...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study. © 2022 IEEE.

关键词： Ammonia

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Identification of Rice Species and Adulteration Using Gas Chromatography-ion Mobility Spectrometry and Multi-classification Maximum-interval Twin Support Vector Machine 3

Identification of Rice Species and Adulteration Using Gas Ch...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Lian, Feiyu Fu, Maixia College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Key Laboratory of Grain Information Processing and Control Henan University of Technology Ministry of Education Zhengzhou450001 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Gas chromatography-ion mobility spectrometry (GC-IMS) was applied to identify rice varieties and adulteration in order to address the inefficiencies caused by the dependence on intricate biochemical procedures for the identification of rice species and *** original migration profile data for the identification of rice varieties and adulteration were provided by testing the volatile flavor substances of 5 kinds of rice. The original data was compressed through principal component analysis (PCA), which was then used as the classifier's input. An improved multi-classification maximum-interval twin support vector machine (MMTSVM) was proposed to construct a multi-classifier with increased classification efficiency and accuracy compared to the conventional support vector machine. The experimental results on the testing set demonstrated that the accuracy of rice variety identification was close to 94.00%, which was obviously better than that of conventional support vector machines, decision trees, and other models. At the same time, the experimental results also showed that the accuracy of the GC-IMS method inidentifying adulterated rice reached 93.3%, outperforming the traditional chromatographic or spectral analysis methods. © 2023 SPIE.

关键词： Principal component analysis

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Path Planning Method of Unmanned Surface Vessel Based on Strategy Integration 23rd

Path Planning Method of Unmanned Surface Vessel Based on Str...

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23rd IFToMM China International Conference on Mechanism and Machine Science and Engineering, IFToMM CCMMS 2022

作者： Yang, Caipei Zhao, Yingqi Liu, Jie Zhou, Kaibo MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811993978

In the task of path planning, the unmanned surface vessels (USV) are required to reach the destination while avoiding obstacle. However, it is difficult for USV to prioritize the two sub-target tasks of destination arrival and obstacle avoidance in a complex dynamic environment using traditional path planning methods. Based on the above considerations, a path planning method for USVs based on strategy integration is proposed. Firstly, the path planning scenario containing dynamic obstacles and two sub-target scenarios are set up. In order to strengthen its ability to reach the destination and avoid dynamic obstacles, the dynamic obstacles and the destination are removed separately in the two sub-target scenarios. Furthermore, the model for strategy selection is trained on action decisions in sub-target scenarios through double deep Q-Network with prioritized experience replay. Finally, a framework for strategy integration is designed to optimize strategy selection for USV. The simulation results show that the proposed method achieves a good overall performance and a higher path planning success rate than the traditional path planning method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Ensemble prediction modeling of flotation recovery based on machine learning

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International Journal of Mining Science and Technology 2024年第12期34卷 1727-1740页

作者： Guichun He Mengfei Liu Hongyu Zhao Kaiqi Huang Jiangxi Provincial Key Laboratory of Low-Carbon Processing and Utilization of Strategic Metal Mineral Resources Ganzhou 341000China School of Resources and Environmental Engineering Jiangxi University of Science and TechnologyGanzhou 341000China Hefei GoldStart Intelligent Control Technical Co. Ltd.Hefei 230088China School of Mechanical and Electrical Engineering Jiangxi University of Science and TechnologyGanzhou 341000China

With the rise of artificial intelligence(AI)in mineral processing,predicting the flotation indexes has attracted significant research ***,current prediction models suffer from low accuracy and high prediction ***,this paper utilizes a two-step ***,the outliers are pro-cessed using the box chart method and filtering ***,the decision tree(DT),support vector regression(SVR),random forest(RF),and the bagging,boosting,and stacking integration algorithms are employed to construct a flotation recovery prediction *** experiments compared the prediction accuracy of six modeling methods on flotation recovery and delved into the impact of diverse base model combinations on the stacking model’s prediction *** addition,field data have veri-fied the model’s *** study demonstrates that the stacking ensemble approaches,which uses ten variables to predict flotation recovery,yields a more favorable prediction effect than the bagging ensemble approach and single models,achieving MAE,RMSE,R2,and MRE scores of 0.929,1.370,0.843,and 1.229%,*** hit rates,within an error range of±2%and±4%,are 82.4%and 94.6%.Consequently,the prediction effect is relatively precise and offers significant value in the context of actual production.

关键词： Machine learning Stacking Bagging Flotation recovery rate Filtering algorithm

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