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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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Comprehensive recovery of W,V,and Ti from spent selective reduction catalysts

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Rare Metals 2023年第10期42卷 3518-3531页

作者： Li-Wen Ma Xiao-Li Xi Jia-Peng Chen Fan Guo Zi-Jie Yang Zuo-Ren Nie Key Laboratory of Advanced Functional Materials Ministry of EducationFaculty of Materials and ManufacturingBeijing University of TechnologyBeijing100124China Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of TechnologyBeijing100124China National Engineering Laboratory for Industrial Big-Data Application Technology Beijing University of TechnologyBeijing100124China

In this study,spent WO_(3)/V_(2)O_(5)-TiO_(2) catalysts used for selective catalytic reduction were treated by a hydrometallurgical process to comprehensively recover valuable metallic elements,such as W,V,and *** and Si impurities were preferentially removed by selective micro wave-assisted alkali leaching.W and V were leached by enhanced high-pressure leaching with efficiencies estimated at 95% and 81%.The leaching of W and V followed the nuclear shrinkage model controlled by the combination of product layer diffusion and interfacial chemical reaction.A synergistic extraction was applied to separate W and V using an extractant mixture of di-(2-ethylhexyl)phosphoric acid P204 and the primary amine *** extraction efficiencies of V and W reached 86.5% and 6.3%,respectively,with a separation coefficient(V/W) of *** product was precipitated after extraction to yield ammonium paratung state(APT) and NH_(4)VO_(3).The TiO_(2)catalyst carrier residue meets commercial specifications for *** comprehensive recovery process with the characteristics of high-pressure leaching and synergistic extraction realizes the resourceful utilization of the spent catalysts.

关键词： Spent selective catalytic reduction(SCR)catalysts Microwaveleaching Pressure leaching Synergistic extraction Comprehensive recovery

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Joint user profiling with hierarchical attention networks

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Frontiers of Computer Science 2023年第3期17卷 133-143页

作者： Xiaojian LIU Yi ZHU Xindong WU Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Information Engineering Yangzhou UniversityYangzhou 225009China Mininglamp Academy of Sciences MininglampBeijing 100193China

User profiling by inferring user personality traits,such as age and gender,plays an increasingly important role in many real-world *** existing methods for user profiling either use only one type of data or ignore handling the noisy information of ***,they usually consider this problem from only one *** this paper,we propose a joint user profiling model with hierarchical attention networks(JUHA)to learn informative user representations for user *** JUHA method does user profiling based on both inner-user and inter-user *** explore inner-user features from user behaviors(e.g.,purchased items and posted blogs),and inter-user features from a user-user graph(where similar users could be connected to each other).JUHA learns basic sentence and bag representations from multiple separate sources of data(user behaviors)as the first round of data *** this module,convolutional neural networks(CNNs)are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy *** this,we build another bag which contains a user-user ***-user features are learned from this bag using propagation information between linked users in the *** acquire more robust data,inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag ***,all of the bag representations are integrated to lean comprehensive user representation by the self-attention *** experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

关键词： user profiling hierarchical attention joint learning inner-user feature inter-user feature

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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Face micro-expression recognition algorithm based on ResNet depth model 8

Face micro-expression recognition algorithm based on ResNet ...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Wang, Liquan Zhan, Shu Hefei University of Technology Key Laboratory of Big Data Knowledge Engineering Ministry of Education School of Computer and Information Engineering Hefei China

ISBN: (纸本)9798350302455

Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small number of samples and uneven distribution of different categories, we propose a model framework to improve the accuracy of micro-expression recognition. The peak frames containing more key expression information in the micro-expression video sequences are extracted;SE-ResNeXt-50, an improved residual network with SE module, is used to extract features from the peak frames of micro-expressions, where the SE module can better learn the key information in the features, and ResNeXt simplifies the structure by replacing the dense structure with the sparse structure through group convolution, which improves the recognition efficiency. The recognition efficiency is improved by replacing the dense structure with the sparse structure by group convolution. At the same time, the Focal Loss loss function can better solve the model performance problem caused by the imbalance of micro-expression data. Simulation experiments are conducted on the micro-expression dataset CASME, and it is found that the improved residual network and peak frame improve the accuracy and F1 value of micro-expression recognition. The improved residual network and peak frame can reduce the effect of small data set, make the model have good fitting effect, and improve the performance of different categories, improve the recognition accuracy of micro-expressions, and have better recognition performance for micro-expression recognition. © 2023 IEEE.

关键词： Convolution

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Learning Self-Supervised Vision Transformers from Scratch for Aerial Person Re- Identification 13

Learning Self-Supervised Vision Transformers from Scratch fo...

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13th International Conference on Information technology in Medicine and education, ITME 2023

作者： Peng, Houfu Xu, Lili Lu, Xing Xia, Daoxun Guizhou Normal University Guizhou Key Laboratory of Information and Computing Science Guiyang China School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

In recent years, person re-identification (Re-ID) as a widely studied computer vision task, has reached a saturation state under closed-world setting, which encourages researchers to further explore more realistic scenarios. Among them, person Re- Idin aerial imagery is proposed and improved due to its unique practical importance in public security. However, since the aerial person images are taken by unmanned aerial vehicles (UAV s), influenced by camera height and angle of view, there are more serious problems such as weak appearance feature and occlusion than ground person images. Most of the current state-of-the-art person Re-ID methods on closed-world datasets are based on local convolution neural network, and hardly works well when applying them to aerial person Re- Idtasks directly. In this paper, we improve the emerging vision transformer (ViT) and apply it to the person Re- Idin aerial imagery. It should be noted that a large amount of data is required to be pretrained for ViTs to achieve competitive performance. Considering the limitations of data, computing power and flexibility in practical scenarios, we improve the pre-training process based on self-supervised learning, and achieve training ViTs from scratch with limited data. Specifically, in pre-training stage, the self-supervised paradigm based on parameter instance discrimination is applied to capture feature alignment and instance similarity, which alleviates the data-hungry of ViTs caused by the lack of inductive bias. Extensive comparative evaluation experiments are conducted on the aerial Re- Iddataset. Our method achieves a Rank-1 accuracy of 65.29% and a mean average precision (mAP) of 57.31%, which proves its effectiveness in aerial person Re-ID tasks. © 2023 IEEE.

关键词： Antennas

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Real-time Student Classroom Feedback with the RFMNet Framework in a Smart Classroom 13

Real-time Student Classroom Feedback with the RFMNet Framewo...

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13th International Conference on Information technology in Medicine and education, ITME 2023

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

ISBN: (纸本)9798350319156

Facial expression recognition is the ability to interpret human feelings from facial features. Teachers can use it to visualize the state of emotion and estimate students' learning effects to take corresponding intervention strategies to improve teaching. The current technology has shortcomings such as network performance degradation and feature information loss, which cannot meet the needs of sentiment analysis of student feedback. Therefore, RFMNet, a novel network model based on deep learning theory, is proposed in this paper. Featuring higher extraction ability, it is conducive to accurately analysing students' expressions. First of all, this model introduces a relation-aware global attention (RGA) module to better integrate the more discriminative expression features in the image and achieve more refined sentiment analysis. Second, the Mish activation function and the focal loss function are employed. The Mish activation function is introduced to avoid disappearing feature information due to neuron deactivation when the ReLU gradient is 0. The model achieves an average recognition accuracy of 89.62%, as confirmed through a sequence of trials conducted on the public dataset FERPlus. The model helps achieve intelligent processing of teaching information and obtaining real-time feedback by detecting, extracting, and analysing the students' facial expressions. © 2023 IEEE.

关键词： Students

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PDDD-PreTrain:A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

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Plant Phenomics 2023年第2期5卷 313-332页

作者： Xinyu Dong Qi Wang Qianding Huang Qinglong Ge Kejun Zhao Xingcai Wu Xue Wu Liang Lei Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China The School of Physics and Optoelectronic Engineering Guangdong University of TechnologyGuangzhou 510006China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationGuizhou UniversityGuiyang 550025China

Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural *** intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming,costly,inefficient,and subjective *** a mainstream AI method,deep learning has substantially improved plant disease detection and diagnosis for precision *** the meantime,most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased ***,the commonly used pre-trained models are from the computer vision dataset,not the botany dataset,which barely provides the pre-trained models sufficient domain knowledge about plant ***,this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic *** address this issue,we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease *** addition,we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification,plant disease detection,plant disease segmentation,and other *** extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time,thereby supporting the better diagnosis of plant *** addition,our pre-trained models will be open-sourced at https://***/and Zenodo platform https://***/10.5281/zenodo.7856293.

关键词： diagnosis Plant thereby

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NAGNet: A Convolutional Neural Network for Real-Time Sentiment Analysis of Students 12

NAGNet: A Convolutional Neural Network for Real-Time Sentime...

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12th International Conference on Information technology in Medicine and education, ITME 2022

作者： Zhu, Huihui Hu, Pengyun Tang, Xianpiao Xia, Daoxun Guizhou Normal University School of Big Data and Computer Science Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350310153

Students' emotional state during class can reflect their understanding of classroom knowledge. Analysing the emotional data of students is helpful for ensuring that teachers understand students' learning effect and improve their teaching quality. With the rapid development of information education, student emotion analysis has been widely studied in the field of educational technology. However, due to the complexity and inconsistency of educational scenes, traditional facial expression recognition algorithms have low accuracy, losing information while extracting facial expression features, and student emotion analysis faces several technical issues. Therefore, based on the theory of deep learning, this paper proposes a novel network model NAGNet for recognizing and analysing students' expressions in classroom environments, to assist teachers in carrying out reasonable teaching effect evaluations. In this model, Res2Net50 is used as the backbone network to enhance the feature extraction ability of the model. Moreover, non-local attention module is added to integrate the global facial expression feature information to achieve fine-grained sentiment analysis. In addition, we replace the original pooling algorithm with generalized mean (GeM) pooling to pool the features of the last layer. The experimental results on the public affection dataset FERPlus, which includes eight emotions, show that the NAGNet model achieves an accuracy of 89.3%. Thus, the proposed model has higher accuracy than existing methods. In a real-world application scenario, the NAGNet model can analyse and detect students' emotions in real time. What's more, it has good robustness. © 2022 IEEE.

关键词： Students

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A Graph with Adaptive AdjacencyMatrix for Relation Extraction

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Computers, Materials & Continua 2024年第9期80卷 4129-4147页

作者： Run Yang YanpingChen Jiaxin Yan Yongbin Qin Engineering Research Center of Text Computing&Cognitive Intelligence Ministry of EducationGuizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The relation is a semantic expression relevant to two named entities in a *** a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named *** related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency ***,this approach has two main ***,parsing a sentence heavily relies on external toolkits,which can be ***,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic *** this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural *** of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between *** elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a *** model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,*** experimental results show that our model excels in the relation extraction task,outperforming prior models.

关键词： Relation extraction graph convolutional neural network adaptive adjacency matrix

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