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A Graph with Adaptive AdjacencyMatrix for Relation Extraction

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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Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

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Science China(Technological Sciences) 2024年第8期67卷 2579-2593页

作者： LIU JiaJun ZHANG Jie LENG JieWu Shanghai Engineering Research Center of Industrial Big Data and Intelligent System Institute of Artificial IntelligenceDonghua UniversityShanghai 201620China State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of TechnologyGuangzhou 510006China

Supplier selection is an important business activity in order to realize the purchasing function in supply chain *** supplier selection process includes four stages,i.e.,bidding inviting,bidding,group decision-making,and results disclosure,involving the participation of manufacturing service demanders(MSDs),manufacturing service suppliers(MSSs),and ***,all the participants have raised concerns about the increased transparency in supplier ***,this study proposes a transparent supplier selection method by considering the engagement of *** this method,the Bayesian best-worst method(Bayesian BWM)is used to aggregate decision-makers'preferences into the overall optimal weights of the alternative MSSs,and the MSS with the largest weight is considered the suitable MSS for ***,blockchain is introduced to record the decision-making process information about supplier selection through a customized smart contract,where MSSs act as supervisors to supervise the decision-making process through the distributed consensus mechanism rather than directly participate in the decision-making ***,a case study of supplier selection in purchasing vibration acceleration sensors is *** result shows that the proposed method can support MSDs in selecting suitable MSS from alternative MSSs by aggregating decision-makers'preferences,and blockchain can provide credible information about the supplier selection process for MSSs,MSDs,and *** this way,the transparency of supplier selection is enhanced.

关键词： blockchain supplier selection supply chain transparency Bayesian BWM group decision-making

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Web-Based Optimization and Expansion of Multimodal High School Entrance Examination data Visualization 12

Web-Based Optimization and Expansion of Multimodal High Scho...

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12th International Conference of Information and Communication technology, ICTech 2023

作者： Zhang, Wei Jian, Duan Wang, HongLiang Zhang, YongHong Shengyang Institute of Computing Technology University of Chinese Academy of Sciences Beijing China Shandong University Big Data Technology and Cognitive Intelligence Laboratory Beijing China

ISBN: (纸本)9798350332919

The reform of the new college entrance examination system makes students and their parents pay more attention to the feasibility and accuracy of the college entrance examination. The college entrance examination informatization can help the local admissions departments to mine and analyze the data rules from the massive college entrance examination data, and provide help and guidance for each link of volunteer application. Therefore, this paper, in order to comply with the development trend of the new college entrance examination information construction, proposes the optimization method of data visualization chart rendering and innovative combination of ideas to improve the Web rendering performance in order to enhance the interactive experience of users and the platform. © 2023 IEEE.

关键词： data visualization

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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

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Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis Sun: fundamental parameters techniques: image processing

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A Probabilistic Framework for Temporal cognitive Diagnosis in Online Learning Systems

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Journal of Computer Science & technology 2023年第6期38卷 1203-1222页

作者：刘嘉聿汪飞马海平黄振亚刘淇陈恩红苏喻 School of Data Science University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Cognitive Intelligence Hefei 230088China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230026China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Computer Science and Artificial Intelligence Hefei Normal UniversityHefei 230061China

cognitive diagnosis is an important issue of intelligent education systems,which aims to estimate students'proficiency on specific knowledge *** existing studies rely on the assumption of static student states and ig-nore the dynamics of proficiency in the learning process,which makes them unsuitable for online learning *** this paper,we propose a unified temporal item response theory(UTIRT)framework,incorporating temporality and random-ness of proficiency evolving to get both accurate and interpretable diagnosis ***,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness ***,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects the temporality aspect and enables us to get analytical maximization(M-step)in the expectation maximization(EM)algorithm when estimating model *** UTIRT is a framework containing unified training and inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.

关键词： cognitive diagnosis probabilistic graphical model item response theory(IRT) stochastic process expectation maximization(EM)algorithm

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Abnormal Clustering and Cross Slicing Transformer for Insect Fine-Grained Image Classification 2

Abnormal Clustering and Cross Slicing Transformer for Insect...

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2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Mei, Aokun Huo, Hua Big Data and Computing Intelligence Engineering Technology Research Center School of Information Engineering Henan University of Science and Technology Big Data Analysis Laboratory of Henan Medical Luoyang471023 China

ISBN: (纸本)9798350390254

Insect fine-grained image classification is an application scenario in fine-grained image classification. It not only has the characteristics of small inter-class differences and large intra-class differences, but also has the difficulty that some categories have multiple life-stage forms, which makes the general fine-grained image classification model difficult to play a role in insect scenes. To this end, based on the Vision Transformer, we design a fine-grained classification network for insect images based on abnormal clustering and cross slicing, called ACCS-Trans. In the first stage of the model, we use segmentation and clustering operations to distinguish the special morphology of insects in those few-shot life stages. The model can avoid the interference of few-sample abnormal morphology on the class feature extraction of the current class during training. The second stage is the cross slicing module, which uses the anchor box of the image sample segmentation region in the first stage to cut the original sample image to form the main target image, which is used as the information supplement area of the original image. Finally, the image is divided into doubling patch groups by two vertical patch operations. In the third stage, we concatenate the multiplication patch group and input it into the Vision Transformer network for class feature extraction. We fully experiment with ACCS-Trans on two insect image datasets. Compared with the current mainstream fine-grained image classification models, ACCS- Trans achieves state-of-the-art effects on both datasets, and we do ablation experiments for each module. The effect of each module on our ACCS- Trans is analyzed. The excellent performance of our ACCS- Trans in insect scenes is verified in these experiments, these provide new ideas for the task of fine-grained classification of insect images. © 2024 IEEE.

关键词： Image segmentation

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Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model

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Plant Phenomics 2023年第3期5卷 501-519页

作者： Qianding Huang Xingcai Wu Qi Wang Xinyu Dong Yongbin Qin Xue Wu Yangyang Gao Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang 550025China National Key Laboratory of Green Pesticide Guizhou UniversityGuiyang 550025China

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food *** detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating ***,existing methods are still limited to single crop disease *** importantly,the existing model has a large number of parameters,which is not conducive to deploying it to agricultural mobile ***,reducing the number of model parameters tends to cause a decrease in model *** solve these problems,we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple *** detail,we design 2 strategies to build 4 different lightweight models as student models:the YOLOR-Light-v1,YOLOR-Light-v2,Mobile-YOLOR-v1,and Mobile-YOLOR-v2 models,and adopt the YOLOR model as the teacher *** develop a multistage knowledge distillation method to improve lightweight model performance,achieving 60.4%mAP@.5 in the PlantDoc dataset with small model parameters,outperforming existing ***,the multistage knowledge distillation technique can make the model lighter while maintaining high *** only that,the technique can be extended to other tasks,such as image classification and image segmentation,to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart *** code is available at https://***/QDH/MSKD.

关键词： Plant diagnosis image

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Cooperative Classification and Rationalization for Graph Generalization 24

Cooperative Classification and Rationalization for Graph Gen...

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33rd ACM Web Conference, WWW 2024

作者： Yue, Linan Liu, Qi Liu, Ye Gao, Weibo Yao, Fangzhou Li, Wenfeng Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei China Douyin Vision Co. Ltd Beijing China

ISBN: (纸本)9798400701719

Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data environment, yet accessing the environment information is complex. Besides, another promising approach involves rationalization, extracting invariant rationales for predictions. However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of theclassification and therationalization module. Specifically, we first assume that multiple environments are available in theclassification module. Then, we introduce diverse training distributions using an environment-conditional generative network, enabling robust graph representations. Meanwhile, therationalization module employs a separator to identify relevant rationale subgraphs while the remaining non-rationale subgraphs are de-correlated with labels. Next, we align graph representations from theclassification module with rationale subgraph representations using the knowledge distillation methods, enhancing the learning signal for rationales. Finally, we infer multiple environments by gathering non-rationale representations and incorporate them into theclassification module for cooperative learning. Extensive experimental results on both benchmarks and synthetic datasets demonstrate the effectiveness of C2R. Code is available at https://***/yuelinan/Codes-of-C2R. © 2024 ACM.

关键词： Classification (of information)

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A Knowledge-Injected Curriculum Pretraining Framework for Question Answering 24

A Knowledge-Injected Curriculum Pretraining Framework for Qu...

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33rd ACM Web Conference, WWW 2024

作者： Lin, Xin Su, Tianhuang Huang, Zhenya Xue, Shangzi Liu, Haifeng Chen, Enhong School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen China University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Knowledge-based question answering (KBQA) is a key task in natural language processing research, and also an approach to access the web data and knowledge, which requires exploiting knowledge graphs (KGs) for reasoning. In the literature, one promising solution for KBQA is to incorporate the pretrained language model (LM) with KGs by generating KG-centered pretraining corpus, which has shown its superiority. However, these methods often depend on specific techniques and resources to work, which may not always be available and restrict its application. Moreover, existing methods focus more on improving language understanding with KGs, while neglect the more important human-like complex reasoning. To this end, in this paper, we propose a general K nowledge-I njected C urriculum P retraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). Specifically, the KI module first injects knowledge into the LM by generating KG-centered pretraining corpus, and generalizes the process into three key steps that could work with different implementations for flexible application. Next, the KA module learns knowledge from the generated corpus with LM equipped with an adapter as well as keeps its original natural language understanding ability to reduce the negative impacts of the difference between the generated and natural corpus. Last, to enable the LM with complex reasoning, the CR module follows human reasoning patterns to construct three corpora with increasing difficulties of reasoning, and further trains the LM from easy to hard in a curriculum manner to promote model learning. We provide an implementation of the general framework, and evaluate the proposed KICP on four real-word datasets. The results demonstrate that our framework can achieve higher performances, and have good generalization ability to other QA tasks. © 2024 ACM.

关键词： Curricula

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A Keypoint-Based Object Detection Algorithm 7

A Keypoint-Based Object Detection Algorithm

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7th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Hou, Xiangdan Chen, Quansong Liu, Hongpu School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Hebei Province Key Laboratory of Big Data Computing Tianjin China

ISBN: (纸本)9798350370782

An improved algorithm is proposed for the omission and re-detection problems in the point cloud object detection method CenterPoint. The algorithm firstly adds Focal sparse convolution module to the feature extraction network to increase the proportion of important information in the features;then redefines the generation function of the detection head heatmap, and the supervisory signal generated by the elliptical Gaussian kernel is more closely matched to the real shape of the object to inhibit the generation of negative samples;finally, the overall regression of the bounding box information is applied to the training, so that the back propagation loss gain of the single dimensional information is regulated by the associated information. The improved algorithm is trained and evaluated on the KITTI 3D object detection dataset, and the overall mean Average Precision (mAP) is improved by 3.29% compared with the base model CenterPoint. The experimental results show that the improved algorithm effectively suppresses the redetection and omission problems of CenterPoint. © 2024 IEEE.

关键词： CenterPoint Focal sparse convolution Gaussian kernel object detection overall regression

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