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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ren, Haopeng Zeng, Yushi Cai, Yi Ye, Zhenqi Yuan, Li Zhu, Pinli School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

ISBN: (纸本)9782493814104

Much of commonsense knowledge in real world is in the form of procudures or sequences of steps to achieve particular goals. In recent years, knowledge extraction on procedural documents has attracted considerable attention. However, they often focus on procedural text but ignore a common multimodal scenario in the real world. Images and text can complement each other semantically, alleviating the semantic ambiguity suffered in text-only modality. Motivated by these, in this paper, we explore a problem of grounded multimodal procedural entity recognition (GMPER), aiming to detect the procedural entity and the corresponding bounding box groundings in images (i.e., visual entities). A new dataset (Wiki-GMPER) is built and extensive experiments are conducted to evaluate the effectiveness of our proposed model. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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A novel hybrid butterfly optimization algorithm for feature selection with sine cosine velocity in the high-dimensional classification data

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 369-391页

作者： Zhang, Li Chen, Xiaobo Key Laboratory of Data Science and Intelligence Education Hainan Normal University Ministry of Education Haikou Hainan China School of Computer Engineering Jiangsu University of Technology Changzhou Jiangsu China Changzhou City Center Branch People's Bank of China Changzhou Jiangsu China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

Aiming at the shortcomings of the traditional butterfly optimization algorithm in solving the high-dimensional classification feature selection problem, which has low convergence and is prone to fall into local optimal solutions, a new hybrid butterfly optimization algorithm is proposed, i.e., HBOA-SCV (A novel hybrid butterfly optimization algorithm with sine cosine velocity). The algorithm is applied to solve a high-dimensional classification feature selection problem. Firstly, the algorithm's global exploration and local exploitation ability can be dynamically balanced by introducing inertia weight coefficients w based on multiple learning strategies. Secondly, using the updated speed position formula of the sine-cosine acceleration strategy, individual butterflies' autonomous search ability and convergence speed can be further improved. Finally, according to the fitness value of each butterfly individual, the moving step length and direction of the butterfly individual are automatically adjusted better to fit the actual search process of the butterfly individual, increase the search ability in the global range, and avoid the algorithm from falling into the local optimum. To verify the algorithm's effectiveness, 18 high-dimensional classification numbers are selected to carry out simulation and comparison experiments between HBOA-SCV and traditional BOA algorithm, five improved BOA algorithms and other comparative algorithms for high-dimensional classification data successively. The experimental results show that the average fitness value and classification accuracy of the HBOA-SCV algorithm are better than the comparison algorithm, thus verifying the superiority of the HBOA-SCV algorithm. © 2024 - IOS Press. All rights reserved.

关键词： Optimization algorithms

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A multi-level FW-UAV fault diagnosis solution based on multi-task learning under small samples 14

A multi-level FW-UAV fault diagnosis solution based on multi...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Zhang, Yizong Yang, Lei An, Xue Li, Shaobo Zhang, Ansi Liao, Zihao School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9798350301359

Deep learning provides more advanced solutions for Unmanned Aerial Vehicle (UAV) fault diagnosis, but most existing deep learning methods are designed for a single fault diagnosis task. A UAV is a complex system with multiple coupled components, requiring the simultaneous execution of several different Faulty Component Diagnosis (FCD) level 1 tasks and Fault Level Detection (FLD) level 2 tasks. In addition, scarcity of fault samples is a recognized problem. Therefore, we propose a Multi-Level Fault Diagnosis (MLFD) method for Fixed-Wing UAV (FW-UAV) based on multi-task learning. MLFD jointly trains the FCD and FLD tasks by Cross-stitch units under small samples, adaptively sharing the public information of the two tasks, enriching the fault diagnosis knowledge, and at the same time improving the model's ability to handle the two tasks. Finally, we validate the effectiveness of the proposed method in real FW-UAV failure data. © 2023 IEEE.

关键词： deep learning fault diagnosis multi-task learning shared information UAV

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Generative Replay Method Based on Integrating Random Gating and Attention Mechanism 9

Generative Replay Method Based on Integrating Random Gating ...

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9th International Conference on Computer and Communications, ICCC 2023

作者： He, Yao Yang, Jing Li, Qinglang An, Hongchao Zhang, Fengyuan Guizhou University School of Mechanical Engineering Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9798350317251

Artificial neural networks aspire to mimic human intelligence by constantly learning from a series of tasks without forgetting past knowledge. The most typical way to achieve this kind of learning is to store previously learned task data and replay previously stored data when training a new task. However, the performance of these methods will be affected with the reduction of memory during training. To solve this problem, a new generative replay algorithm is proposed in this paper - - A generative replay method based on the fusion of random gating and attention mechanism. This method is used to generate visual significance features for the model to remember past sample information. First, we introduce an attention mechanism into the generated model to address the issue of the quality of the generated replay data. This mechanism enhances the data salience of feature information during large-scale model training and allows the generated replay network to produce visual salience features, which essentially mimic the way a human observes. Secondly, because of the overfitting phenomenon in the training process of the model, a random gating mechanism is introduced to improve the generalization of the model and improve the accuracy of the model. Finally, we use the CIFAR100 and Permuted MNIST datasets to validate the efficacy of our method. © 2023 IEEE.

关键词： Neural networks

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SLMP: A Scientific Literature Management Platform Based on Large Language Models 15

SLMP: A Scientific Literature Management Platform Based on L...

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15th IEEE International Conference on knowledge Graph, ICKG 2024

作者： Guo, Menghao Jiang, Jinling Wu, Fan Sun, Shanxin Zhang, Chen Li, Wenhui Sun, Zeyi Chen, Guangyong Wu, Xindong Research Center for Life Sciences Computing Zhejiang Lab Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Hefei China

ISBN: (纸本)9798331508821

This paper presents a Scientific Literature Management Platform (SLMP, demo link1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge. © 2024 IEEE.

关键词： Question answering

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Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

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Computer Modeling in engineering & Sciences 2024年第2期138卷 1695-1712页

作者： Xiaodong Yang Runze Diao Tao Liu Haoqi Wen Caifen Wang College of Computer Science and Engineering Northwest Normal UniversityLanzhou730070China Institute of China TelecomWanwei China TelecomWanwei Information Technology Co.Ltd.Lanzhou730030China College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Key Laboratory of Cryptography and Data Analytics Northwest Normal UniversityLanzhou730070China

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading *** signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity ***,most certificateless signatures still suffer fromvarious security *** present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature *** ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and *** scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota *** addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing *** results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

关键词： Electricity carbon trading certificateless signature blockchain forgery attack carbon quota

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A data-driven CEEMDAN-DRSCN-based RUL prediction method for UAV batteries 14

A data-driven CEEMDAN-DRSCN-based RUL prediction method for ...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Liao, Zihao Zhang, Ansi Li, Shaobo Leiyang Zhang, Yizong Zhou, Peng State Key Laboratory of Public Big Data Guizhou University Guiyang China School of Mechanical Engineering Guizhou University Guiyang China

ISBN: (纸本)9798350301359

Electric UAV propulsion systems have an advantage over fuel-powered UAVs. As the "heart" of an electric UAV, the remaining service life prediction of its electrical system is an important estimation parameter in battery health management. In this paper, we propose a data-driven incremental prediction method that integrates empirical modal decomposition and incremental learning models. First, a stochastic configuration network (SCN)-based incremental prediction model is designed to update the model input parameters by adding new data to overcome the in-flight data variation and employing Decay Regularization (DR) to avoid the output weight overfitting problem. Then, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is introduced to perform nonlinear mode decomposition on the UAV battery service data. Finally, experiments are conducted on the NASA UAV battery dataset. The experimental results show that the proposed CEEMDAN-DRSCN has good prediction performance and stability, prediction error is as low as 0.0004. © 2023 IEEE.

关键词： CEEMDAN Incremental learning Remaining useful life the Decay Regularization

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UAV Flight data Anomaly Detection based on Parameter Selection and Multiple Regression 14

UAV Flight Data Anomaly Detection based on Parameter Selecti...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Yang, Lei Zhang, Yizong Li, Shaobo Zhang, Ansi Liao, Zihao Li, Chuanjiang School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (纸本)9798350301359

Unmanned aerial vehicles (UAVs) are important in military and civilian applications. Flight data anomaly detection is an essential part of ensuring the safety and reliability of UAVs and has received extensive attention and research. The UAV system is complex and has many parameters. However, many unrelated parameters do not positively impact the model performance. In addition, since there are many anomaly types of UAV flight data, the adaptability of different anomaly types for anomaly detection methods remains a significant challenge. This paper proposes a data-driven method based on parameter selection and multiple regression for UAV flight data anomaly detection, which integrates correlation analysis and long short-term memory (CA-LSTM). First, a correlation-based analysis is performed to select parameters with correlation to reduce the model complexity. Second, an LSTM-based multiple regression model is designed to map flight parameters. Finally, the effectiveness of the proposed method is verified on real UAV flight data injected with two common anomaly types. © 2023 IEEE.

关键词： anomaly detection correlation analysis long short-term memory (CA-LSTM) Unmanned aerial vehicle (UAV)

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A Hybrid Approach for data-Literature Linking 23

A Hybrid Approach for Data-Literature Linking

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7th International Conference on Computer Science and Application engineering, CSAE 2023

作者： Li, Jiao Huang, Yongwen Sun, Tan Xian, Guojian Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing National Press and Publication Administration Agricultural Information Institute of Caas Beijing100081 China Key Laboratory of Agricultural Big Data Ministry of Agriculture and Rural Affairs Chinese Academy of Agricultural Sciences Beijing100081 China

ISBN: (纸本)9798400700590

Current solutions that link research data with scientific literature are primarily constrained in the dimension and depth of relations, which is a necessity for the sharing and reuse of scholarly resources and accelerating scientific progress. This paper presents a data-literature linking framework that combines data enrichment techniques of entity extraction, discipline classification & subject indexing based on semantic knowledge bases, and further implement multi-scenario data-literature link discovery with data from persistent identifier provisions - dataCite and CrossRef, including research data retrieval and faceted browsing, data-literature and data-data bidirectional discovery, entity-based link browsing and navigation. © 2023 ACM.

关键词： knowledge based systems

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Predictive control for greenhouse temperature and humidity and energy optimization by improved NMPC objective function algorithm

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International Journal of Agricultural and Biological engineering 2024年第5期17卷 128-136页

作者： Lina Wang Ying Zhang Mengjie Xu Qiuhui Liu Binrui Wang Institute of Mechanical and Electrical Engineering China Jiliang UniversityHangzhou 310018China Zhejiang Province Key Laboratory of On-line Testing Equipment Calibration Technology Research China Jiliang UniversityHangzhou 310018China Zhejiang Provincial Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability and Application China Jiliang UniversityHangzhou 310018China

Persistent low temperatures in autumn and winter have a huge impact on crops,and greenhouses rely on solar radiation and heating equipment to meet the required indoor *** the energy cost of frequent operation of the actuators is exceptionally *** relationship between greenhouse environmental control accuracy and energy consumption is one of the key issues faced in greenhouse *** this study,a non-linear model predictive control method with an improved objective function was *** improved objective function used tolerance intervals and boundary constraints to optimize the objective *** nonlinear model predictive control(NMPC)controller design was based on the wavelet neural network(WNN)data-driven model and applied the interior point method to solve the optimal solution of the objective function control,thus balancing the contradiction between energy consumption and control *** simulation results showed that the improved NMPC method reduced energy consumption by 21.02%and 9.54%compared with the model predictive control and regular NMPC,which proved the method achieved good results in a low-temperature *** research can provide an important reference for the field as it offers a more efficient approach to managing greenhouse climates,potentially leading to substantial energy savings and enhanced sustainability in agricultural practices.

关键词： greenhouse environmental control greenhouse energy optimization nonlinear model predictive control objective function improvement

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