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3rd International Conference on information Science, Parallel and Distributed Systems, ISPDS 2022

作者： Zhang, Enjing Fang, Jiandong Zhao, Yudong Inner Mongolia University of Technology College of Information Engineering China Key Laboratory of Perception Technology and Intelligent Systems Inner Mongolia Autonomous Region Inner Mongolia Hohhot China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

This Compared with the single-dimensional ecologi-cal footprint evaluation, the three-dimensional ecological footprint evaluation of grass and livestock has the characteristics of categor-ical characterization of natural resource flow occupation and stock consumption status of grass and livestock, and at the same time can reflect the relationship between natural resources and sustain-able development more accurately. In this paper takes the grass-livestock balance relationship as the entry point, based on the eco-logical footprint and ecological carrying capacity, and uses the 3D ecological footprint improvement model to calculate the depth of grass-livestock footprint, the breadth of grass-livestock footprint and the 3D ecological footprint of grass-livestock in the agricul-tural and pastoral areas of each league and city in Inner Mongolia from 2018 to 2020, and then decodes the causes of formation. The results of the simulation experiment show that: at the social level, the more rural population, the larger the grass-livestock ecological footprint;at the economic level, industry accounts for a large pro-portion, there is industrial competition for food, and the grass-live-stock ecological footprint is small;at the natural level, the annual rainfall is more, and the corresponding grass-livestock ecological footprint is small. © 2022 IEEE.

关键词： Three dimensional computer graphics

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Tackling Sparse Facts for Temporal Knowledge Graph Completion 25

Tackling Sparse Facts for Temporal Knowledge Graph Completio...

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34th ACM Web Conference, WWW 2025

作者： Zhang, Yuchao Kong, Xiangjie Ye, Kailun Shen, Guojiang Zheng, Shangfei Zhejiang University of Technology College of Computer Science and Technology Hangzhou China Zhejiang Key Laboratory of Visual Information Intelligent Processing Hangzhou China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798400712746

Temporal knowledge graph completion (TKGC) seeks to develop more comprehensive knowledge representations by addressing missing relationships and entities within temporal knowledge graphs (TKGs), thereby enhancing reasoning and predictive capabilities in downstream tasks. Nonetheless, real-world knowledge—such as the progression of social network interactions and the unfolding of news events—is inherently dynamic, resulting in substantial sparsity issues in TKGs that profoundly impair the performance of TKGC models. To overcome this challenge, we introduce the Adaptive Neighborhood Enhancement Layer (ANEL), a novel module that can be effortlessly integrated into existing TKGC models to substantially elevate the representation quality of sparse entities. ANEL first derives initial entity embeddings through a base model and then uncovers concealed semantic relationships between entities via a latent relation module, enriching the explicit relationships within the knowledge graph. Furthermore, ANEL incorporates an adaptive latent information adjustment component, which dynamically calibrates the influence of latent information based on the entity’s relational structure: entities with fewer connections derive greater benefit from latent information, while entities with denser connections become less dependent on latent augmentation, ensuring precise and resilient representations. We conducted comprehensive experiments on four prominent benchmark datasets, and the results underscore the effectiveness and superiority of ANEL in TKGC tasks. © 2025 Copyright held by the owner/author(s).

关键词： Knowledge graph

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Few-Reference Image Quality Assessment with Multiple information Measurement Fusion 9th

Few-Reference Image Quality Assessment with Multiple Inform...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Shi, Shuang Wang, Simeng Liu, Yuchen Zhou, Chengxu Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

In the era of media information explosion, there is an urgent need for a fast and reliable image quality assessment (IQA) model to improve the actual application effect of images. To this end, we propose the multiple information measurement fusion metric (MMFM), which innovatively combines two types of information measures (IMs), i.e., local IM and global IM, using only a small number of references for IQA. First, inspired by the free energy theory, we combine 2-dimensional autoregressive model with sparse random sampling method as an inference engine on an input image to generate its associated predicted image. Second, by the inspiration of pixel-wise measurement, we obtain the local IM by calculating the information entropy of the residual error between the input image and its corresponding predicted one. Third, motivated by the histogram-based measurement, we acquire the global IM by computing the two kinds of divergences between the input image and its predicted one. Fourth, we systematically fuse three components, independently including one distance of local IM between the reference and corrupted images and two distances of global IMs between the reference and corrupted images, based on a linear function to derive the final IQA result. The results of experiment on the most popular LIVE database show that our designed algorithm with only one number used as few reference has achieved well performance as compared with several mainstream IQA models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Improving the Robustness of Low-Resource Neural Machine Translation with Adversarial Examples 1

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18th China Conference on Machine Translation, CCMT 2022

作者： Sun, Shuo Hou, Hongxu Wu, Nier Yang, Zongheng Wang, Yisong Wang, Pengcong Jian, Weichen Inner Mongolia Key Laboratory of Mongolian Information Processing Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9789811979606

ISBN: (纸本)9789811979590

Weak robustness and noise adaptability are major issues for Low-Resource Neural Machine Translation (NMT) models. That is, once some tiny perturbs are added to the input sentence, the model will produce completely different translation with high confidence. Adversarial example is currently a major tool to improve model robustness and how to generate an adversarial examples that can degrade the performance of the model and ensure semantic consistency is a challenging task. In this paper, we adopt reinforcement learning to generate adversarial example for low-resource NMT. Specifically, utilizing the actor-critic algorithm to modify the source sentence, the discriminator and translation model in the environment are used to determine whether the generated adversarial examples maintain semantic consistency and the overall deterioration of the model. Furthermore, we also install a language model reward to measure the fluency of adversarial examples. Experimental results on low-resource translation tasks show that our method highly aggressive to the model while maintaining semantic constraints greatly. Moreover, the model performance is significantly improved after fine-tuning with adversarial examples. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural machine translation

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Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning

arXiv

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arXiv 2024年

作者： Yang, Shuo Shang, Zirui Wang, Yongqi Deng, Derong Chen, Hongwei Cheng, Qiyuan Wu, Xinxiao Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China

This paper proposes a novel framework for multi-label image recognition without any training data, called data-free framework, which uses knowledge of pre-trained Large Language Model (LLM) to learn prompts to adapt pre-trained Vision-Language Model (VLM) like CLIP to multi-label classification. Through asking LLM by well-designed questions, we acquire comprehensive knowledge about characteristics and contexts of objects, which provides valuable text descriptions for learning prompts. Then we propose a hierarchical prompt learning method by taking the multi-label dependency into consideration, wherein a subset of category-specific prompt tokens are shared when the corresponding objects exhibit similar attributes or are more likely to co-occur. Benefiting from the remarkable alignment between visual and linguistic semantics of CLIP, the hierarchical prompts learned from text descriptions are applied to perform classification of images during inference. Our framework presents a new way to explore the synergies between multiple pre-trained models for novel category recognition. Extensive experiments on three public datasets (MS-COCO, VOC2007, and NUS-WIDE) demonstrate that our method achieves better results than the state-of-the-art methods, especially outperforming the zero-shot multi-label recognition methods by 4.7% in mAP on MS-COCO. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Dimension Reduction Based on Grouped Feature Selection Strategy for Fluorescence Molecular Tomography 46

Dimension Reduction Based on Grouped Feature Selection Strat...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Li, Linxin Zhang, Lizhi Zhao, Yizhe Xie, Qiong He, Xiaowei He, Xuelei Yi, Huangjian Northwest University School of Information Sciences and Technology Shaanxi Xi'an710069 China The Xi'an Key Laboratory of Radiomics and Intelligent Perception No. 1 Xuefu Avenue Shaanxi Xi'an710127 China The First Hospital of Hunan University of Chinese Medicine Hunan Changsha410007 China

ISBN: (纸本)9798350371499

Fluorescence Molecular Tomography (FMT) is an optical molecular imaging technique with high sensitivity. Multi-point excitation and multi-view measurement have improved the recovered results while also resulting in high computation and memory requirements. In this work, we proposed a grouped feature selection strategy (GFS) based on correlation and information entropy to reduce the size of the system matrix. It further reduced the computational complexity. Firstly, a similarity is constructed based on the correlation between the matrix rows. Then a group is created by the surface measurements. Secondly, information entropy is defined to measure the information contained in the surface measurement. Finally, these two measurement methods are combined with minimum redundancy and maximum relevance (mRMR) to construct the optimal subset of the original data, which completes the dimensionality reduction of the matrix. Numerical simulation experiments show that GFS outperforms the traditional principal component analysis (PCA) method in terms of reconstruction accuracy and reconstruction *** Relevance - The primary objective of this work is to enhance the reconstruction speed of FMT. Current experiments are focused on simulation experiments, while the proposed downscaling strategy will facilitate rapid clinical diagnosis and treatment decisions in the future © 2024 IEEE.

关键词： Surface measurement

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Semantic Data Augmentation for Few-Shot Biomedical Named Entity Recognition

Semantic Data Augmentation for Few-Shot Biomedical Named Ent...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Ying Wang, Weihua College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798350368741

Biomedical Named Entity Recognition (BioNER) aims to identify and classify entities in biomedical text. This task struggles with data scarcity due to limited annotated data. Although data augmentation is effective, existing methods fail to handle the complex semantic mappings on biomedical terminology, which results in the generated samples lacking semantic diversity. In this paper, we propose a data augmentation method to create semantically diverse and coherent training samples for few-shot BioNER. The method utilizes the Unified Medical Language System (UMLS) for entity replacement and combines with a Masked Language Model (MLM) to generate contextually relevant words. Experimental results show that our method improves BioNER performance in few-shot scenarios. Compared to all baseline models, our method achieves an average F1 score improvement of 7.6% and 11.6% on the NCBI and JNLPBA datasets, respectively. Our source code and data are available at https://***/ABC8184/SDA. © 2025 IEEE.

关键词： BioNER data augmentation few-shot learning masked language model UMLS

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Optimization Deployment of Temperature Measurement Points for CZ Silicon Single Crystal Growth Equipment Based on Improved Fuzzy C-Means Clustering

Optimization Deployment of Temperature Measurement Points fo...

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第43届中国控制会议

作者： Weichao Huang Siyuan Cao Ding Lui School of Automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the problems of low accuracy and difficult selection of temperature measurement point locations. To solve this problem, this paper proposes an optimization algorithm for measurement point locations based on improved fuzzy c-means clustering(IFCM). The method first generates the initial clustering center through a specific density function and introduces the Mahalanobis distance to solve the problem that the traditional fuzzy c-means clustering results depend on the initial clustering center and the results are unstable. Then, an optimized deployment algorithm based on IFCM temperature measurement points is proposed and compared with other mean value clustering algorithms. Finally, the optimal temperature measurement position on the crystal surface is selected by the IFCM temperature measurement point optimization deployment algorithm on the CZ(Czochraski)silicon single crystal growth equipment, and the inversion results of the randomly selected temperature measurement point scheme and the optimized temperature measurement point scheme are compared by the real-time inversion algorithm based on the dynamic matrix control multi-boundary heat flux, and the results of the optimized temperature measurement point scheme are obtained to be better than that of the randomly selected temperature measurement point scheme.

关键词： Fuzzy c-means clustering IFCM Temperature measurement point optimization Inverse heat transfer

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Acceleration method for target HRRP generation based on DDPM 2

Acceleration method for target HRRP generation based on DDPM

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Shen, Xitai Zhou, Qiang Yu, Bingqian Zhang, Xin Wang, Yanhua Zhang, Liang Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

The target high resolution range profile (HRRP) is the sum of the vector projected on the radar line of sight of the target scatters, which can reflect the radial structure characteristics of the target and is one of the important means of radar target recognition. Existing HRRP-based target recognition methods adopt the idea of machine learning classification, which usually require sufficient training samples to obtain satisfactory recognition performance. To solve the dilemma of insufficient radar HRRP data caused by factors such as non-cooperative target and complexity of data acquisition process, this paper uses denoising diffusion probabilistic models (DDPM) to generate HRRP data. Furthermore, to solve the problem that the DDPM generation speed is too slow to meet the actual needs, this paper designs a fast DDPM HRRP (FDH) data generation method based on progressive knowledge distillation. Finally, the experimental results show that the generation speed is increased by about 25 times compared with the initial model under the premise of losing 7.6% similarity. © 2024 IEEE.

关键词： Radar target recognition

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Residual Hyperbolic Graph Convolution Networks

arXiv

引用

arXiv 2024年

作者： Xue, Yangkai Dai, Jindou Lu, Zhipeng Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Hyperbolic graph convolutional networks (HGCNs) have demonstrated representational capabilities of modeling hierarchical-structured graphs. However, as in general GCNs, over-smoothing may occur as the number of model layers increases, limiting the representation capabilities of most current HGCN models. In this paper, we propose residual hyperbolic graph convolutional networks (R-HGCNs) to address the over-smoothing problem. We introduce a hyperbolic residual connection function to overcome the over-smoothing problem, and also theoretically prove the effectiveness of the hyperbolic residual function. Moreover, we use product manifolds and HyperDrop to facilitate the R-HGCNs. The distinctive features of the R-HGCNs are as follows: (1) The hyperbolic residual connection preserves the initial node information in each layer and adds a hyperbolic identity mapping to prevent node features from being indistinguishable. (2) Product manifolds in R-HGCNs have been set up with different origin points in different components to facilitate the extraction of feature information from a wider range of perspectives, which enhances the representing capability of R-HGCNs. (3) HyperDrop adds multiplicative Gaussian noise into hyperbolic representations, such that perturbations can be added to alleviate the over-fitting problem without deconstructing the hyperbolic geometry. Experiment results demonstrate the effectiveness of R-HGCNs under various graph convolution layers and different structures of product manifolds. © 2024, CC BY.

关键词： Convolution

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