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作者： Dou, Wanchun Liu, Bowen Duan, Jirun Dai, Fei Qi, Lianyong Xu, Xiaolong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Computer Science Qufu Normal University Qufu China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

In the past few years, latency-sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self-driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across-domain business task computing on distributed edge servers. With this observation, a trade-off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low-latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low-latency edge server. The experiments and comparison analysis verify the availability of our method. © 2022 John Wiley & Sons Ltd.

关键词： Economic and social effects

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An improved graphical convolutional network model based on interactive aspect-level sentiment analysis

An improved graphical convolutional network model based on i...

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2023 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Shi, Yuanhang Chen, Junjie College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Huhhot01 0018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Hohhot010018 China

ISBN: (纸本)9781510667662

Aspect-level text sentiment analysis is a more complete and specific task of text sentiment analysis, which determines the sentiment information expressed by a text with respect to each aspect. In recent years, the use of syntactic dependency trees as well as sentence structure information for aspect-level sentiment analysis has become more common, but they often ignore the role of sentence semantics involved in sentiment analysis and fail to combine them. In this paper, we improve and propose an interactive graph convolutional network (SInterGCN) model incorporating syntactic semantic structure for text-level sentiment analysis tasks. Specifically, a dependency graph is first constructed for each sentence in the syntactic relational dependency tree. This is followed by combining the graph of structural relations with the associated semantic graph, which is used to capture key aspect-oriented and contextual aspects. Furthermore, in order to interactively extract dependencies between aspect words and other aspects, an aspect-oriented GCN is used to model the representation learned by the aspect-oriented GCN based on the inter-aspect graph. Thus, the model can be aware of important contexts and aspect words when interactively learning aspect-specific sentiment features. Experimental results on four relevant benchmark datasets demonstrate that our proposed model outperforms the original interactive approach, achieving superior performance and validating the effectiveness of our model. © 2023 SPIE.

关键词： Sentiment analysis

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Improving multi-view document clustering: leveraging multi-structure processor and hybrid ensemble clustering module 30

Improving multi-view document clustering: leveraging multi-s...

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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： Bai, Ruina Bai, Qi Huang, Ruizhang Chen, Yanping Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center National Education Ministry College of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Mathematical Sciences Zhejiang University Hangzhou310058 China

ISBN: (纸本)9782493814104

We introduce a multi-view document clustering model called DMsECN (Deep Multi-structure Ensemble Clustering Network), comprising a multi-structure processor and a hybrid ensemble clustering module. Unlike existing models, DMsECN distinguishes itself by creating a consensus structure from multiple clustering structures. The multi-structure processor comprises two stages, each contributing to the extraction of clustering structures that preserve both consistency and complementarity across multiple views. Representation learning extracts both view and view-fused representations from multi-views through the use of contrastive learning. Subsequently, multi-structure learning employs distinct view clustering guidance to generate the corresponding clustering structures. The hybrid ensemble clustering module merges two ensemble methods to amalgamate multiple structures, producing a consensus structure that guarantees both the separability and compactness of clusters within the clustering results. The attention-based ensemble primarily concentrates on learning the contribution weights of diverse clustering structures, while the similarity-based ensemble employs cluster assignment similarity and cluster classification dissimilarity to guide the refinement of the consensus structure. Experimental results demonstrate that DMsECN outperforms other models, achieving new state-of-the-art results on four multi-view document clustering datasets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Structure (composition)

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Analysis of the Effectiveness of Carbon Emission Trading Market in China 10

Analysis of the Effectiveness of Carbon Emission Trading Mar...

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10th International Conference on Information Technology and Quantitative Management, ITQM 2023

作者： Yu, Anqi Peng, Shuyang Li, Yizhi Guo, Kun School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Dornslife School of Arts and Science University of Southern California Los AngelesCA90007 United States Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

As global climate change intensifies, carbon emissions trading has become an important tool for countries to address climate change. China, as the world's largest carbon emitter, has been conducting carbon trading pilots since 2011, and officially launched a national carbon market in 2017. The construction of China's carbon market is crucial for China and global efforts to address climate change. Market efficiency is a key indicator of its success. This article aims to measure and analyze the effectiveness of China's carbon market, and to explore policy measures to enhance its effectiveness. It selected data from effective trading days and conducted an exploratory study on the effectiveness of China's pilot carbon markets. To analyze the effectiveness of the carbon market, we combined the efficient market hypothesis theory with the fractal market hypothesis theory, adopted the variance ratio test method, and the rescaled range analysis method to conduct a comprehensive analysis of the Chinese carbon market, while also accounting for the reasons behind their computational inconsistency. As our research shows that China's pilot carbon market has not reached the weak efficiency level, we have provided some suggestions based on the current operational status of China's carbon market. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative Management.

关键词： Carbon

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Transparent Four-Port MIMO DRAs Decoupled by Conformal Strips

Transparent Four-Port MIMO DRAs Decoupled by Conformal Strip...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： He, Chao Yang, Nan Leung, Kwok Wa Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Kowloon Hong Kong CityU Shenzhen Research Institute Information and Communication Technology Centre Shenzhen China

ISBN: (纸本)9798350355581

A four-port multi-input multi-output (MIMO) dielectric resonator (DR) antenna (DRA) is presented in this paper. Combing four rectangular DRs together can form a single DR element. With shape modification and conformal strips, the four ports can be decoupled. To verify the design idea, a transparent prototype can be designed and fabricated for 2.4-GHz band applications. The measured results show an impedance bandwidth of 5.8% and an isolation level of 24 dB. © 2024 IEEE.

关键词： Microwave antennas

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Enhancing Information Extraction from Low-sample Materials Science Literature by Transfer Learning

Enhancing Information Extraction from Low-sample Materials S...

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International Conference on big data and Information Analytics (bigDIA)

作者： Mengzhe Hei Qingbao Liu Xin Zhang National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Extracting information from materials science liter-ature is an important task that can provide data support for data-driven material design methods. This work is not straight-forward, especially in low-sample material domains where the data volume is insufficient to support the accurate training of extraction models. To tackle this problem, we proposes the use of a pre-trained model and a feature-based transfer learning approach to transfer the extraction capabilities learned from large-sample material domains to low-sample domains. To enhance the performance of information extraction, we propose a extraction method based on MatSciBERT and pointer networks. To evaluate the performance of the model, we have annotated datasets across three domains and conducted extensive experiments. The experiments demonstrate that our transfer method can improve extraction models in low-sample domains.

关键词： Training Materials science and technology Solid modeling Neuromorphics Transfer learning Metals Feature extraction Information retrieval data models data mining

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Mixup data Augmentation and Channel-Spatial Attention Mechanisms for Facial Emotion Recognition

Mixup Data Augmentation and Channel-Spatial Attention Mechan...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Dewei Yu Chuijian Kong Xianpiao Tang Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal Universitya Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the rapid advancement of computer vision, facial expression recognition has made significant progress in areas like human-computer interaction, mental health, driver assistance, and education. However, traditional facial expression recognition techniques exhibit poor robustness under complex backgrounds and varying lighting conditions, making them susceptible to environmental noise and challenging to accurately capture and classify complex emotional states. Additionally, these techniques often lack in data augmentation and model generalization capabilities, leading to overfitting on training samples and insufficient recognition of unseen samples. To address these limitations, this paper proposes a novel convolutional neural network model—HSCNet (A Hybrid data Augmentation and Channel-Spatial Attention Mechanisms Neural Network). The HSCNet model first employs the Mixup data augmentation technique to generate new training samples, then utilizes the Res2Net50 backbone network combined with the CBAM attention mechanism and average-max pooling techniques to extract significant features while suppressing redundant ones. This approach not only enhances feature extraction accuracy but also significantly improves the model’s generalization ability and robustness in facial expression recognition tasks. Finally, tested on the public dataset FERPlus, which includes eight types of emotions, the HSCNet model achieved an accuracy rate of 89.72%, demonstrating excellent performance and robustness.

关键词： Training Emotion recognition Attention mechanisms Accuracy Face recognition Lighting Feature extraction data augmentation data models Robustness

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Research on Multi-AGVs dynamic scheduling based on deep reinforcement learning 19

Research on Multi-AGVs dynamic scheduling based on deep rein...

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19th IEEE International Conference on e-Business engineering, ICEBE 2023

作者： Qin, Zhuorui Wu, Xiaoqian Zheng, Yimin Wu, Qingyao South China University of Technology School of Software Engineering Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Shenzhen Bangqi Technology Intelligent Development Co. Ltd Shenzhen China Pazhou Lab Guangzhou China

ISBN: (纸本)9798350325553

Driven by the latest advances in industrial artificial intelligence, automatic guided vehicles (AGVs) have been widely used for material handling in flexible workshops. In the pursuit of agile and flexible production transportation planning, the ability to handle unpredictable and dynamic events is becoming increasingly important. In order to achieve this real-time dynamic control, this study proposes a distributed multi-agent architecture to solve the dynamic Multi-AGVs scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agent, capture the relationship between production transportation operation information and scheduling goals, and make real-time scheduling decisions for flexible jobs with constant arrival intervals. Specialized state, action, and reward representations are proposed to handle variable specifications of dynamic scheduling problems. The performance of this method under different scene scales was verified through simulation studies. Numerical results show that compared with existing traditional heuristic scheduling strategies, this method not only has superior performance, but also its advantages persist even if the manufacturing system job scale changes. © 2023 IEEE.

关键词： Reinforcement learning

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Steel surface Defect Detection Algorithm Based on Improved YOLOv5s

Steel surface Defect Detection Algorithm Based on Improved Y...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Yingying Su Yuanyuan Deng Nengyang Zhou Hongyun Si Jie Peng College of Electrical Engineering Chongqing University of Science and Technology Chongqing China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

To address challenges in steel surface defect detection, such as low accuracy and slow processing speed, an enhanced algorithm is proposed. The C3 module is replaced with GSConv (multi-channel shuffle convolution) to improve parameter efficiency and detection accuracy. The nearest neighbor up-sampling is substituted with CARAFE (a lightweight operator) to enhance the receptive field and utilize semantic information. GhostNetV2 replaces the CBS convolutional module for efficient feature extraction through stacking operations. Additionally, the Hard Swish activation function is incorporated to boost accuracy. Experimental results on the NEU-DET dataset demonstrate that the average accuracy of the improved YOLOv5s algorithm is 81.2%, a 1.7% increase compared to the original algorithm, the amount of computation is 17.2GFLOPs and the number of model parameters is 8.65M. The model effectively enhances detection accuracy.

关键词： YOLO Accuracy Sensitivity Computational modeling Semantics Stacking Feature extraction

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LSBert: Lexical Simplification Based on BERT

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IEEE/ACM Transactions on Audio Speech and Language Processing 2021年 29卷 3064-3076页

作者： Qiang, Jipeng Li, Yun Zhu, Yi Yuan, Yunhao Shi, Yang Wu, Xindong Department of Computer Science Jiangsu Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100864 China

Lexical simplification (LS) aims at replacing complex words with simpler alternatives. LS commonly consists of three main steps: complex word identification, substitute generation, and substitute ranking. Existing LS methods focus on the contextual information of the complex word in the last step (substitute ranking). However, they miss out the following two facts: (1) The word complexity of a polysemous word is very closely related to its context;(2) The step of substitute generation regardless of the context will inevitably produce a large number of spurious candidates. Therefore, we propose a novel LS system LSBert based on pretrained language model BERT to address the aforementioned issues, which is capable of making use of the wider context when both identifying the words in need of simplification and generating substitute candidates for the complex words. Specifically, LSBert consists of a network for complex word identification by fine-tuning BERT and a network for substitute generation based on BERT. Experimental results show that LSBert performs well in both complex word identification and substitute generation, achieving state-of-the-art results in three benchmarks. © 2014 IEEE.

关键词： BERT Complex word identification Lexical simplification Sentence simplification

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