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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Zhu, Jizhao Duan, Wenyu Yu, Naitong Pan, Xinlong Fan, Chunlong Shenyang Aerospace University Shenyang China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China Naval Aviation University Yantai China

ISBN: (纸本)9783031466632

The extractive automatic summarization method is capable of quickly and efficiently generating summaries through the steps of scoring, extracting and eliminating redundant sentences. Currently, most extractive methods utilize deep learning technology to treat automatic summarization as a binary classification task. However, the effectiveness of automatic summarization for Chinese long text is limited by the maximum input length of the model, and it requires a large amount of training data. This paper proposes an unsupervised extractive automatic summarization method which solves the long text encoding problem by incorporating contextual semantics into sentence-level encoding. Firstly, we obtain the semantic representation of sentences by using the RoBERTa model. Secondly, we propose an improved k-Means algorithm to cluster sentence representations. By defining sparse and dense clusters, we improve the accuracy of summary sentence selection while preserving maximum semantic information from the original text. Experimental results on the CAIL2020 dataset show that our method outperforms baselines by 6.64/7.68/7.14% respectively on ROUGE-1/2/L. Moreover, we further enhance the automatic summarization results by 4.5/5.36/3.24% by adding domain rules tailored to the dataset’s characteristics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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An Alternative Algorithm for the Symmetry Classification of Ordinary Differential Equations

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Sound & Vibration 2022年第1期56卷 65-76页

作者： Yi Tian Jing Pang College of Data Science and Application Inner Mongolia University of TechnologyHohhot010080China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080China College of Science Inner Mongolia University of TechnologyHohhot010051China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010051China

This is the first paper on symmetry classification for ordinary differential equations(ODEs)based on Wu’s *** carry out symmetry classification of two ODEs,named the generalizations of the Kummer-Schwarz equations which involving arbitrary ***,Lie algorithm is used to give the determining equations of symmetry for the given equations,which involving arbitrary ***,differential form Wu’s method is used to decompose determining equations into a union of a series of zero sets of differential characteristic sets,which are easy to be solved *** branch of the decomposition yields a class of symmetries and associated *** algorithm makes the classification become direct and *** Dimitrov Bozhkov,and Pammela Ramos da Conceição have used the Lie algorithm to give the symmetry classifications of the equations talked in this paper in *** this paper,we can find that the differential form Wu’s method for symmetry classification of ODEs with arbitrary function(parameter)is effective,and is an alternative method.

关键词： Kummer-Schwarz equation ordinary differential equations(ODEs) differential form Wu’s method

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RRx-001 ameliorates inflammatory diseases by acting as a potent covalent NLRP3 inhibitor

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Cellular & Molecular Immunology 2021年第6期18卷 1425-1436页

作者： Yun Chen Hongbin He Bolong Lin Yun Chen Xianming Deng Wei Jiang Rongbin Zhou Hefei National Laboratory for Physical Sciences at Microscale the CAS Key Laboratory of Innate Immunity and Chronic DiseaseSchool of Basic Medical SciencesDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina Department of Geriatrics Gerontology Institute of Anhui Provincethe First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina CAS Centre for Excellence in Cell and Molecular Biology University of Science and Technology of ChinaHefeiChina State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkSchool of Life SciencesXiamen UniversityXiamenFujianChina

The NLRP3 inflammasome plays a crucial role in innate immune-mediated inflammation and contributes to the pathogenesis of multiple autoinflammatory,metabolic and neurodegenerative diseases,but medications targeting the NLRP3 inflammasome are not available for clinical ***-001 is a well-tolerated anticancer agent currently being investigated in phase III clinical trials,but its effects on inflammatory diseases are not ***,we show that RRx-001 is a highly selective and potent NLRP3 inhibitor that has strong beneficial effects on NLRP3-driven inflammatory ***-001 inhibits the activation of the canonical,noncanonical,and alternative NLRP3 inflammasomes but not the AIM2,NLRC4 or Pyrin ***,RRx-001 covalently binds to cysteine 409 of NLRP3 via its bromoacetyl group and therefore blocks the NLRP3-NEK7 interaction,which is critical for the assembly and activation of the NLRP3 *** importantly,RRx-001 treatment attenuates the symptoms of lipopolysaccharide(LPS)-induced systemic inflammation,dextran sulfate sodium(DSS)-induced colitis and experimental autoimmune encephalomyelitis(EAE)in ***,our study identifies RRx-001 as a new potential therapeutic agent for NLRP3-driven diseases.

关键词： RRx-001 NLRP3 inflammasome inflammatory diseases

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An Anti-Noise Hybrid Clustering Oversampling Technique for Imbalanced data Classification 5

An Anti-Noise Hybrid Clustering Oversampling Technique for I...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Pan, Xiaoying Feng, Congcong Liu, Chen Mu, Yuanzhen Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Oversampling techniques have always been favored in the field of imbalanced data classification. Noise processing plays an important role in the field of unbalanced data classification because the noise data directly affects the distribution of newly synthesized samples. We propose a new anti-noise hybrid clustering oversampling technique for imbalanced data classification to synthesize high-quality samples (ANCO). The algorithm is the first to propose a combination of noise filtering and optimization in an oversampling method. Firstly, based on the sample location information, the processing strategy of noise filtering and optimization is designed. Then, sample nearest neighbor interpolation is used to create a new sample. To compare the performance of our approach with representative oversampling, five datasets with variable imbalance rates in KEEL are chosen for testing. The results show that the ANCO algorithm improves the classifier's overall performance. © 2022 ACM.

关键词： Classification (of information)

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ICTNET at Temporal Summarization Track TREC 2014 23

ICTNET at Temporal Summarization Track TREC 2014

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23rd Text REtrieval Conference, TREC 2014

作者： Chen, Lei Zhang, Hainan Li, Siying Ji, Zhiyuan Liu, Qian Liu, Yue Wu, Dayong Cheng, Xueqi Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Web Data Science and Technology CAS 100190 China University of Chinese Academy of Science Beijing100190 China

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Image recognition and detection based on fast area convolutional neural network

Image recognition and detection based on fast area convoluti...

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2021 International Conference on Computer, Communication, Control, Automation and Robotics, CCCAR 2021

作者： Zhang, Hanwen Qin, Zhen Xie, Hua School of Automation Engineering UESTC Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China China

Nowadays, image recognition and detection technologies based on traditional artificial neural networks and convolutional neural networks are slightly inadequate in terms of training and recognition time and accuracy, and are difficult to deploy on devices with limited hardware resources. Therefore, this article proposes a recognition and detection technology based on fast regional convolutional neural networks. We use RPN (Region Proposal network) instead of Selective Search method to rebuild the network, and add a new ROI pooling layer before the fully connected layer of CNN. Determine the category. The average detection accuracy on our data set can reach 83.8%, and the training time is only 0.34 hours. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

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Temporal Knowledge Graph Question Answering: A Survey

arXiv

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arXiv 2024年

作者： Su, Miao Li, Zixuan Chen, Zhuo Bai, Long Jin, Xiaolong Guo, Jiafeng CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a growing interest in Temporal Knowledge Graph Question Answering (TKGQA), an emerging task to answer temporal questions. However, this field grapples with ambiguities in defining temporal questions and lacks a systematic categorization of existing methods for TKGQA. In response, this paper provides a thorough survey from two perspectives: the taxonomy of temporal questions and the methodological categorization for TKGQA. Specifically, we first establish a detailed taxonomy of temporal questions engaged in prior studies. Subsequently, we provide a comprehensive review of TKGQA techniques of two categories: semantic parsing-based and TKG embedding-based. Building on this review, the paper outlines potential research directions aimed at advancing the field of TKGQA. This work aims to serve as a comprehensive reference for TKGQA and to stimulate further research. © 2024, CC BY.

关键词： Question answering

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KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction

arXiv

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arXiv 2024年

作者： Li, Zixuan Zeng, Yutao Zuo, Yuxin Ren, Weicheng Liu, Wenxuan Su, Miao Guo, Yucan Liu, Yantao Li, Xiang Hu, Zhilei Bai, Long Li, Wei Liu, Yidan Yang, Pan Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

In this paper, we propose KnowCoder, a Large Language Model (LLM) to conduct Universal Information Extraction (UIE) via code generation. KnowCoder aims to develop a kind of unified schema representation that LLMs can easily understand and an effective learning framework that encourages LLMs to follow schemas and extract structured knowledge accurately. To achieve these, KnowCoder introduces a code-style schema representation method to uniformly transform different schemas into Python classes, with which complex schema information, such as constraints among tasks in UIE, can be captured in an LLM-friendly manner. We further construct a code-style schema library covering over 30,000 types of knowledge, which is the largest one for UIE, to the best of our knowledge. To ease the learning process of LLMs, KnowCoder contains a two-phase learning framework that enhances its schema understanding ability via code pretraining and its schema following ability via instruction tuning. After code pretraining on around 1.5B automatically constructed data, KnowCoder already attains remarkable generalization ability and achieves relative improvements by 49.8% F1, compared to LLaMA2, under the few-shot setting. After instruction tuning, KnowCoder further exhibits strong generalization ability on unseen schemas and achieves up to 12.5% and 21.9%, compared to sota baselines, under the zero-shot setting and the low resource setting, respectively. Additionally, based on our unified schema representations, various human-annotated datasets can simultaneously be utilized to refine KnowCoder, which achieves significant improvements up to 7.5% under the supervised setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Research on Image Description Generation Method Based on G-AoANet 5

Research on Image Description Generation Method Based on G-A...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Qiao, Pingan Shen, Ruixue Li, Yuan Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Most of the image description generation methods in the attention-based encoder-decoder framework extract local features from images. Despite the relatively high semantic level of local features, it still has two problems to be solved, one is object loss, where some important objects may be lost when generating image descriptions, and the other is prediction error, as an object may be identified in the wrong class. In this paper, a G-AoANet model is proposed to solve the above problems. The model uses an attention mechanism to combine global features with local features. In this way, our model can selectively focus on both object and contextual information, improving the quality of the generated descriptions. Experimental results show that the model improves the initially reported best CIDEr-D and SPICE scores on the MS COCO dataset by 9.3% and 5.1% respectively. © 2022 ACM.

关键词： Semantics

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