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4th International Conference on Artificial Intelligence, Automation and High Performance computing, AIAHPC 2024

作者： He, Jiangtao Wang, Licai Yu, Jintao You, Peihai Huang, Yixuan Big data research and development center North China Institute of Computing Technology China

ISBN: (纸本)9798400710049

The popularization and widespread application of high-resolution remote sensing imagery have made Instance Segmentation a hot research area. The difficulty of improving the instance segmentation accuracy of high-precision fine-grained remote sensing images has always been a research challenge. This paper proposes a new model, Oriented Bayes DCT, which effectively improves the accuracy of instance segmentation. The model utilizes a region proposal network to encode features, generate proposals, align the candidate boxes, and then uses Bayesian methods to predict class and box offsets. Compared to traditional methods of generating candidate boxes like RPN or Oriented RPN, the candidate boxes obtained using the Naive Bayes method are more accurate. Additionally, the model employs a mask prediction branch to predict segmentation. Through Discrete Cosine Transform (DCT), high-resolution binary grid masks are encoded into compact vectors, achieving precise instance segmentation of fine-grained remote sensing images. Compared to predicting the mask part using the Mask Head method, using the discrete cosine transform for encoding and decoding the ground truth masks results in lower error rates. Without tricks, Oriented Bayes DCT with ResNet50 achieves state-of-the-art segmentation accuracy on a commonly-used datasets iSAID for instance segmentation (37.7% mAP) and ResNet101 (38.5% mAP). © 2024 Copyright held by the owner/author(s).

关键词： Image segmentation

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Research on large model table question answering method based on thought of chain and vector matching filtering 3

Research on large model table question answering method base...

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3rd International Conference on Electronic Information Engineering and data Processing, EIEDP 2024

作者： Tang, Haichao Wang, Licai Hu, Xun Luo, Qibin Big data research and development center North China Institute of Computing Technology Beijing China

ISBN: (纸本)9781510680531

Implementing table-based question answering can fully leverage the rich information in tabular data, and the development of large language models has provided strong natural language processing and understanding capabilities. Current research on table-based question answering with large language models mainly focuses on how to improve text-to-SQL abilities and the development of table pre-training models, lacking in studies on feasible technical approaches for applying general large language models to engineering applications involving large-scale database table question answering. In response, combining techniques such as prompt learning and chain of thought,this paper constructs a database question answering chain of thought based on large language model, proposes a table filtering scheme based on vectorized matching of table structural information, specifically addressing poor table filtering effects, and constructs a database optimization chain of thought in the context of de-identification scenarios. Experimental verification shows that after adopting the chain of thought construction and vector matching filtering scheme, the database question answering capability of the large model has been effectively improved. © 2024 SPIE.

关键词： Information filtering

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Legal Event Detection Model Based on ERNIE 3.0-CRF and Ensemble Learning 5

Legal Event Detection Model Based on ERNIE 3.0-CRF and Ensem...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Wang, Siyu Wang, Licai Zhang, Ke North China Institute of Computing Technology Big Data Research and Development Center Beijing China

ISBN: (纸本)9798350385557

Recognizing events is a prerequisite for making judgments, so discovering events in legal documents is important for the analysis of legal case. Legal Event Detection aims to extract legal event trigger words from legal documents and categorize events and is the basis of many judicial AI applications. Oriented to the needs of understanding and analyzing legal events in the judicial trial process, and in response to the low accuracy of event detection caused by the many types of events and little sample data in the legal domain, this paper constructs a pre-training model oriented to event detection in the legal domain, which makes full use of ERNIE 3.0's powerful semantic representation learning capability and CRF's effective modeling capability for sequence annotation tasks. In addition, we further enhance the effectiveness of the model through data augmentation and ensemble learning. The experimental results show that the method works better compared to the baseline model and improves the prediction accuracy of legal event detection. © 2024 IEEE.

关键词： Learning systems

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RIF-Proto: A Few-Shot Relation Extraction for Prototype Networks Based on Relational Information Features 5

RIF-Proto: A Few-Shot Relation Extraction for Prototype Netw...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Zhang, Shuai Wang, Licai Huang, Yangchen North China Institute of Computing Technology Big Data Research and Development Center Beijing China

ISBN: (纸本)9798350385557

Relation extraction stands as a core task within the realm of information extraction, focusing on identifying the target relations between entities in text. While traditional relation extraction has been extensively researched, certain relations lack sufficient labeled data, rendering traditional models ineffective in practical research. Hence, the research focus in this field has shifted towards few-shot learning methods that leverage limited labeled data for training. Significant progress has been made in few-shot relation extraction, with prototype networks garnering attention from scholars for their outstanding performance in few-shot tasks. Prototype networks utilize the clustering idea to achieve relationship classification simply and efficiently, and their training process is mainly divided into three parts: generation of relationship prototypes, relationship classification, and loss calculation. The majority of current research is centered around leveraging external information to enhance the generation of prototypes and loss functions, but due to the difficulty of obtaining external data and the high complexity of data processing, it greatly increases the time cost and the difficulty of implementation. Given the aforementioned challenges, this paper introduces a method for prototype refinement leveraging relationship similarity among existing data. This approach aims to effectively address the issue of significant deviations in relationship prototypes. Initially, extract relationship vectors from the instances in both the support set and query set, followed by computing the relationship similarity between samples based on these vectors, and finally, this paper constructs a prototype correction module, which utilizes the relationship similarity to assign weights to the samples in the support set, to achieve the purpose of prototype correction. In the experiments against the standard dataset FewRel1.0., we observe that the method substantially improves the ex

关键词： Query processing

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A Novel Competition-Based Coordination Model With Dynamic Feedback for Multi-Robot Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2029-2031页

作者： Bo Peng Xuerui Zhang Mingsheng Shang Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 Chongqing School University of Chinese Academy of SciencesChongqing 400714China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China

Dear Editor, Task allocation strategies are important in multi-robot systems and have been intensely investigated by researchers because they are critical in determining the performance of the system. In this letter, a novel competition-based coordination model is proposed to solve the multi-robot task allocation problem and applied to a multi-robot object tracking scenario.

关键词： robot Robot Dynamic

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Large Language Model Based on Full-Text Retrieval for Temporal Knowledge Q&A Approach 5

Large Language Model Based on Full-Text Retrieval for Tempor...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Li, Zhidong Wang, Licai Luo, Qibin Qiao, Silong North China Institute of Computing Technology Big Data Research and Development Center Beijing China

ISBN: (纸本)9798350385557

Knowledge Q&A is one of the hot research topics in the field of natural language processing, and temporal knowledge Q&A is a difficult area of Q&A reasoning because it also needs to consider the temporal relationship of knowledge. Today's research usually focuses on the word vector similarity between knowledge and questions as an important basis for answering, while ignoring the sentence granularity semantic information embedded in the knowledge. In this paper, we propose a method of temporal knowledge Q&A for large language models based on full-text retrieval, firstly, the sentence granularity knowledge recall is performed by Elasticsearch so that large language models can learn the knowledge that is highly relevant to the problem, and then verify the temporal knowledge Q&A ability of large language models under Zero-shot, One-shot and Few-shot. The experiments were conducted on the ICEWS05-15 dataset, and the accuracy of answers was significantly improved, demonstrating the effectiveness of the temporal knowledge Q&A method for large language models based on Elasticsearch. © 2024 IEEE.

关键词： Semantics

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Enhancing Recommendation with Denoising Auxiliary Task

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Journal of Computer Science & Technology 2024年第5期39卷 1123-1137页

作者： Peng-Sheng Liu Li-Nan Zheng Jia-Le Chen Guang-Fa Zhang Yang Xu Jin-Yun Fang College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

The historical interaction sequences of users play a crucial role in training recommender systems that can accurately predict user ***,due to the arbitrariness of user behaviors,the presence of noise in these sequences poses a challenge to predicting their next actions in recommender *** address this issue,our motivation is based on the observation that training noisy sequences and clean sequences(sequences without noise)with equal weights can impact the performance of the *** propose the novel self-supervised Auxiliary Task Joint Training(ATJT)method aimed at more accurately reweighting noisy sequences in recommender ***,we strategically select subsets from users’original sequences and perform random replacements to generate artificially replaced noisy ***,we perform joint training on these artificially replaced noisy sequences and the original *** effective reweighting,we incorporate the training results of the noise recognition model into the recommender *** evaluate our method on three datasets using a consistent base *** results demonstrate the effectiveness of introducing the self-supervised auxiliary task to enhance the base model’s performance.

关键词： auxiliary task learning recommender system sequence denoising

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A Progressive Skip Reasoning Fusion Method for Multi-Modal Classification 24

A Progressive Skip Reasoning Fusion Method for Multi-Modal C...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Qian Liang, Xinyan Qian, Yuhua Cui, Zhihua Wen, Jie Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798400706868

In multi-modal classification tasks, a good fusion algorithm can effectively integrate and process multi-modal data, thereby significantly improving its performance. Researchers often focus on the design of complex fusion operators and have proposed numerous fusion operators, while paying less attention to the design of feature fusion usage, specifically how features should be fused to better facilitate multi-modal classification tasks. In this article, we propose a progressive skip reasoning fusion network (PSRFN) to make some attempts to address this issue. Firstly, unlike most existing multi-modal fusion methods that only use one fusion operator in a single stage to fuse all view features, PSRFN utilizes the progressive skip reasoning (PSR) block to fuse all views with a fusion operator at each layer. Specifically, each PSR block utilizes all view features and the fused features from the previous layer to jointly obtain the fused features for the current layer. Secondly, each PSR block utilizes a dual-weighted fusion strategy with learnable parameters to adaptively allocate weights during the fusion process. The first level of weighting assigns weights to each view feature, while the second level assigns weights to the fused features from the previous layer and the fused features obtained from the first level of weighting in the current layer. This strategy ensures that the PSR block can dynamically adjust the weights based on the actual contribution of features. Finally, to enable the model to fully utilize feature information from different levels for feature fusion, the skip connections are adopted between PSR blocks. Extensive experiment results on six real multi-modal datasets show that a better usage for fusion operator is indeed able to improve performance. © 2024 ACM.

关键词： Classification (of information)

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A data representation method using distance correlation

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Frontiers of Computer Science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Few-shot relation extraction based on multi-level feature metric learning 3

Few-shot relation extraction based on multi-level feature me...

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3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Fang, YaXin Liu, ZhongLin Pan, Shuang Li, Qian North China Institute of Computing Technology Big data analysis department Beijing China

ISBN: (纸本)9781510663473

Relationship extraction is an important task in natural language processing and knowledge mapping. Traditional entity relationship extraction methods have achieved high accuracy in practical ***,when faced with the task of entity relationship extraction that is not easy to obtain large-scale supervision training data sets, traditional methods can not get good results. In this paper, a few-shot relation extraction method based on multi-level feature metric learning is proposed. This method takes the prototype network as the baseline network to generate a class prototype. Firstly, a multilevel feature extraction module is proposed. This module combines the multi-level features of the text with the multi-level attention mechanism, which can fully extract the features of the text. Secondly, a loss function based on label value and negative sample distance is proposed. This algorithm introduces an evaluation mechanism of negative sample distance on the basis of the prototype network, so that the model can adaptively allocate parameters and improve the clustering ability of small *** are conducted on FewRel1.0 which is a small sample relational data set. Experiment results show:compared with other models, our model can improve classification accuracy. © 2023 SPIE.

关键词： Extraction

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