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Information Systems and Computing technology (ISCTech), International Conference on

作者： Yingli Liu Guangtao Zhang Tao Shen Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China

Table, as an information carrier with high information density and direct display form, are widely used in scientific literature and social production documents. Accurately identifying tables from various documents will significantly promote the intelligent application of documents. The table’s structural information is crucial for comprehending how the text content in the table relates to one another, but teaching a computer to fully understand a table’s structure is still a very difficult undertaking. In this research work, we provided a unique edge-based method to recognize table structures. Specifically, we define the inter-cell relationships as an edge prediction problem and leverage graph convolutional networks to learn representations of the cells. These learned representations are then utilized to directly predict the connections between the cells. The experimental results conducted on three publicly available datasets for table structure recognition provide compelling evidence supporting the feasibility and usefulness of our presented methodology.

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: heterogeneous hypergraph attention network with counterfactual learning for multimodal sentiment analysis

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Complex & Intelligent Systems 2025年第4期11卷 1-16页

作者： Huang, Changqin Lin, Zhenheng Huang, Qionghao Huang, Xiaodi Jiang, Fan Chen, Jili Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China School of Education Zhejiang Normal University Jinhua China School of Computer Science and Technology Zhejiang Normal University Jinhua China School of Computing Mathematics and Engineering Charles Sturt University Albury Australia

Multimodal sentiment analysis (MSA) has garnered significant attention for its immense potential in human-computer interaction. While cross-modality attention mechanisms are widely used in MSA to capture inter-modality interactions, existing methods are limited to pairwise interactions between two modalities. Additionally, these methods can not utilize the causal relationship to guide attention learning, making them susceptible to bias information. To address these limitations, we introduce a novel method called Heterogeneous Hypergraph Attention Network with Counterfactual Learning $$(\text {H}^2\text {CAN}).$$ The method constructs a heterogeneous hypergraph based on sentiment expression characteristics and employs Heterogeneous Hypergraph Attention Networks (HHGAT) to capture interactions beyond pairwise constraints. Furthermore, it mitigates the effects of bias through a Counterfactual Intervention Task (CIT). Our model comprises two main branches: hypergraph fusion and counterfactual fusion. The former uses HHGAT to capture inter-modality interactions, while the latter constructs a counterfactual world using Gaussian distribution and additional weighting for the biased modality. The CIT leverages causal inference to maximize the prediction discrepancy between the two branches, guiding attention learning in the hypergraph fusion branch. We utilize unimodal labels to help the model adaptively identify the biased modality, thereby enhancing the handling of bias information. Experiments on three mainstream datasets demonstrate that $$\text {H}^2\text {CAN}$$ sets a new benchmark.

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Long-range Brain Graph Transformer

arXiv

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arXiv 2025年

作者： Yu, Shuo Jin, Shan Li, Ming Sarwar, Tabinda Xia, Feng School of Computer Science and Technology Dalian University of Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China School of Software Dalian University of Technology China Zhejiang Institute of Optoelectronics China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Computing Technologies RMIT University Australia

Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integration across the entire brain. However, previous studies generally focused on the short-range dependencies within brain networks while neglecting the long-range dependencies, limiting an integrated understanding of brain-wide communication. To address this limitation, we propose Adaptive Long-range aware TransformER (ALTER), a brain graph transformer to capture long-range dependencies between brain ROIs utilizing biased random walk. Specifically, we present a novel long-range aware strategy to explicitly capture long-range dependencies between brain ROIs. By guiding the walker towards the next hop with higher correlation value, our strategy simulates the real-world brain-wide communication. Furthermore, by employing the transformer framework, ALERT adaptively integrates both short- and long-range dependencies between brain ROIs, enabling an integrated understanding of multilevel communication across the entire brain. Extensive experiments on ABIDE and ADNI datasets demonstrate that ALTER consistently outperforms generalized state-of-the-art graph learning methods (including SAN, Graphormer, GraphTrans, and LRGNN) and other graph learning based brain network analysis methods (including FBNETGEN, BrainNetGNN, BrainGNN, and BrainNETTF) in neurological disease diagnosis. Cases of long-range dependencies are also presented to further illustrate the effectiveness of ALTER. The implementation is available at https://***/yushuowiki/ALTER. Copyright © 2025, The Authors. All rights reserved.

关键词： Neurons

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Independent Pitch Controller based on Fuzzy Adaptive Tuning PI for Load Mitigation of Large Land Wind Turbine 2

Independent Pitch Controller based on Fuzzy Adaptive Tuning ...

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2023 2nd International Conference on Energy Internet and Power Systems, ICEIPS 2023

作者： Li, Shengshan Du, Ming Li, Yanggui He, Jun Ma, Fuliang Chen, Qingyun Department of Computer Science Qinghai University Qinghai Xining China Geospatial and Natural Resources Big Data Center Qinghai Xining China Key Laboratory of Geospatial Information Technology and Application Qinghai Xining China College of Chemical Engineering Qinghai University Qinghai Xining China Qinghai Telecom Qinghai Xining China

With the development of wind power generation units towards the mega-scale, the blade root and tower have large load and vibration problems during the operation of wind turbines. In order to extend the operational life of wind turbines, a PI independent pitch control (IPC) strategy with fuzzy adaptive tuning (FAT) is introduced based on the existing wind turbine model and control strategy to achieve optimal control of wind turbine loads. Simulations are carried out by the FAST software of the US Renewable Energy laboratory to compare the independent pitch and the cooperative pitch. The results show that IPC reduces the load on the blade roots and improves the quality of the electrical energy. © 2023 Published under licence by IOP Publishing Ltd.

关键词： Wind turbines

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Deep Learning Assisted Enhance Vertical Field Confinement of Spot Size Converter

Deep Learning Assisted Enhance Vertical Field Confinement of...

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Cross Strait Quad-Regional Radio Science and Wireless technology Conference (CSRWTC)

作者： Qinghao Tan Jiandao Huang Zan Hui Chen School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China

We propose a spot size converter using pixelated nano-dots waveguide, to enhance the vertical field confinement. Theoretical analysis shows a converter efficiency of -2.1 dB for the TE mode at $\lambda_{0}=1.55$ ...

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A Multi-Signal Perception Network for Textile Composition Identification 48

A Multi-Signal Perception Network for Textile Composition Id...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Peng, Bo He, Liren Wu, Dong Chi, Mingmin Chen, Jintao Shanghai Key Laboratory of Data Science Fudan University School of Computer Science Shanghai China Zhongshan PoolNet Technology Co. Ltd Zhongshan Fudan Joint Innovation Center Zhongshan China Shanghai Fabric Eyes Artificial Intelligence Technology Co. Ltd Shanghai China Zhengzhou Zhongke Institute of Integrated Circuit and System Application China

ISBN: (纸本)9781728163277

Textile composition identification (TCI) is an essential basic link in the textile industry. Methods based on computer vision or near-infrared (NIR) signal processing have shown potential for the nondestructive TCI task. However, these methods ignore that the integration of NIR signals and visual information may help the model learn a better representation through information complementarity. This paper propose a Multi-Signal Perception Network (MSPNet) for nondestructive textile composition identification, allowing the model to benefit from the advantages of multimodal data. Firstly, a two-way feature extraction network is used to obtain multi-modal features. After that, we propose a multimodal signal fusion module to control the aggregation granularity among multimodal data. Specifically, the target areas of the image are perceived by a target area perception module (TAP). Then a bi-gated aggregation (Bi-GFA) is designed to capture consistent semantic information from signal to image and image to signal. The quantitative and qualitative results of the proposed MSPNet are significantly improved compared to both single and multimodal approaches. © 2023 IEEE.

关键词： computer vision

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections 8

Semi-supervised Segmentation and Quantization Algorithm for ...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Cai, Yulong Chen, Xiaopan Hu, Qi Dong, Lingyun Yuan, Caihong School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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Long-range brain graph transformer 24

Long-range brain graph transformer

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Shuo Yu Shan Jin Ming Li Tabinda Sarwar Feng Xia School of Computer Science and Technology Dalian University of Technology China and Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology) Ministry of Education China School of Software Dalian University of Technology China Zhejiang Institute of Optoelectronics China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Computing Technologies RMIT University Australia

ISBN: (纸本)9798331314385

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification 8

Lightweight Transformer Network and Self-supervised Task for...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Li, Yunwei Li, Danyang Dong, Lingyun Chen, Xiaopan School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results. © 2022 IEEE.

关键词： Image recognition

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Simulation Study on Broadband Hive Receiving Coil for Small-Loop Transient Electromagnetic Detection 11

Simulation Study on Broadband Hive Receiving Coil for Small-...

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11th International Conference on Environmental and Engineering Geophysics, ICEEG 2024

作者： Wang, Shengjie Su, Guoying Sun, Yishu Jia, Wenfeng Wang, Weicheng Wang, Qingshan The First Monitoring and Application Center China Earthquake Administration Tianjin300180 China College of Computer and Electrical Engineering Hunan University of Arts and Science Changde415000 China Qingdao Zhongdian Zhongyi Intelligent Technology Development Co. Ltd Qingdao266108 China Key Laboratory of Geophysics Exploration Equipment Ministry of Education of PRC Jilin University Changchun130061 China

The multi-turn small-loop transient electromagnetic (TEM) receiving coils can be used for urban underground space survey and mine detection because of their versatility and adaptability to complex environments. However, reducing the size of the receiving coil increases the parasitic parameters while narrowing the frequency band, resulting in the loss of high-frequency signals that contains shallow geological information. We propose a hive-receiving coil, and use finite element simulation to compare multi-turn hive coils with circular receiving coils in the same size. It is proved that the hive coil has lower parasitic parameters and wider passband, which can meet the detection requirements of small-loop TEM. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Electromagnetic transients

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