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15th International Conference on Measurement Technology and Mechanical Automation, ICMTMA 2023

作者： Wang, Z.M. Li, M. Gao, C. Jin, X.M. School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

Complex lighting is one of the most challenging problems in automatic guided vehicle (AGV) vision recognition system. In order to overcome the influence of uneven illumination on the accuracy and robustness of path recognition, this paper proposes the LCS based visual recognition for AGV guide paths under complex illumination conditions method, which converts the collected image into an invariant image through logarithmic chromaticity space (LCS) to eliminate the influence of illumination, and the minimum average entropy angle is used as the projection angle to generate the invariant image to improve the speed of image conversion to the invariant image. Experimental results show that the proposed method can effectively improve the robustness of AGV vision recognition system under complex lighting conditions. © Published under licence by IOP Publishing Ltd.

关键词： Lighting

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Drug-Protein Interaction Prediction by Fusion of Attention and Graph Neural Network 42

Drug-Protein Interaction Prediction by Fusion of Attention a...

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42nd Chinese Control Conference, CCC 2023

作者： Chen, Tianrui Tuo, Shouheng Feng, Zengyu School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9789887581543

The identification of drug-protein interactions (DTIs) is a critical step in drug development and repositioning. However, detecting these interactions using scientific methods presents a formidable challenge. Existing models often struggle to extract relevant information from drug-protein sequences, and they fail to consider pre-existing interactions between pairs. Here, we introduce a deep learning model, DPAG, based on attention mechanisms and graph neural networks, which overcomes these limitations. DPAG leverages information from both the drug and protein sequences, as well as the association between drug-protein pairs, to predict DTIs. We conducted multiple experiments to evaluate the performance of the model in benchmark datasets, and the results showed that DPAG achieved excellent performance in terms of AUC, AUPR, precision, and recall. Our findings suggest that DPAG holds significant potential as a promising tool for predicting DTIs in drug development and repositioning. Code is available at https://***/veresse/DPAG. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Graph neural networks

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CQUPT-FL: Cross-Domain Sharing and Security Awareness and Early Warning Platform of Health Science big data 1

CQUPT-FL: Cross-Domain Sharing and Security Awareness and Ea...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： xiao, Yunpeng Zhang, Qunqing Zhao, Wanjing Li, Xufeng Peng, Jinhua Mo, Haonan Zhu, Haipeng Tang, Fei Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798350358780

Federated learning (FL) is a rapidly growing research area in machine learning, but it is problematic. It has been questioned whether or not existing FL libraries are practical in the area of medical privacy. To address these issues, we developed the CQUPT-FL system. The system focuses on resolving the conflict between data integrity and medical data privacy protection in cross-domain and cross-institution collaborative analysis. CQUPT-FL supports distributed computing and stand-alone simulation computing methods. To deal with the problems of heterogeneity, data domain diversity, and effective data scarcity, we adopted key technologies such as multi-party secure computing and holistic information representation and studied user identification, privacy protection, and heterogeneous user alignment to achieve sustainable Cross-domain and cross-platform data fusion of letters. The goal of introducing the CQUPT-FL system is to improve the level of data privacy protection and enhance the data privacy protection mechanism, solve the machine learning dilemma in the field of medical privacy, and provide a reliable solution for cross-domain collaborative analysis. © 2023 IEEE.

关键词： Machine learning

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Multi-Granular Differential Evolution Algorithm for Multi-Agent Task Allocation 9

Multi-Granular Differential Evolution Algorithm for Multi-Ag...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Gao, Peng Liu, Qun Jiang, Haihuan Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798350330076

The multi-agent task allocation presents a fundamental challenge in the field of multi-agent systems, especially in uncertain environments. Although extensive research has been conducted on the multi-agent task allocation in deterministic environments, discussions around the multi-agent task allocation in uncertain environments are relatively scarce. In reality, uncertain data is more common in practical decision-making processes. To address the multi-agent task allocation problem in uncertain environments, this study frames it as a noisy optimization problem and proposes a novel Multi-Granular Differential Evolution (MGDE) algorithm to solve it. MGDE combines the powerful differential evolution (DE) with the granular-ball computing which has high robustness in noise. The proposed MGDE is compared with other three state-of-the-art algorithms on 12 scenarios encompassing 6 agent and task quantity combinations and 2 uncertainty levels. Experimental results demonstrate the superior performance of MGDE. © 2023 IEEE.

关键词： Multi agent systems

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Few-Shot Object Detection Based On Label Constrained data Augmentation 9

Few-Shot Object Detection Based On Label Constrained Data Au...

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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Shi, Yan Wang, Guoyin Liu, Qun Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798350330076

The majority of object detection methods typically depend on a significant quantity of annotated data, while few-shot object detection (FSOD) endeavors to identify novel classes of objects using a limited number of training instances. However, the limited number of samples leads to the problem of disparate data distributions between the source and target domains, which makes the generalization ability of the detector usually weak. In this paper, we combine data augmentation with fine-tuning to design a pseudo-labeled constrained model called LCDA, aiming to obtain high-quality pseudo-labels to effectively enrich the training examples. Furthermore, we leverage the pre-trained CLIP model to enhance the quality of pseudo-labels by restricting the category information as well as the designed bounding box consistency criterion. The experimental outcomes demonstrate that our model outperforms the existing models on two public datasets across various shot scenarios. The average enhancement of our method on different shots is 1.2AP, 1.6AP, 2.3AP, 2.7AP, and 4.2AP. We also validate the performance of the model in real applications of the USV dataset which shows an improvement of 1.9AP over the baseline methods. All demonstrate the effectiveness of our model. © 2023 IEEE.

关键词： Object detection

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A New Model Based on Differential Evolutionary Algorithm and Markov Clustering for Identifying Protein Complexes 42

A New Model Based on Differential Evolutionary Algorithm and...

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42nd Chinese Control Conference, CCC 2023

作者： Feng, Zengyu Tuo, Shouheng Chen, Tianrui School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9789887581543

As the material basis for all life activities, proteins play a crucial role in performing life activities. Most real-life proteins perform various functions in the form of protein complexes, so it is essential for understanding of life activities to accurately identify protein complexes. Most of the existing approaches learn protein interactions directly from protein topology, ignoring the biological characteristics of the actual proteins. Nevertheless, actual protein complexes are usually composed of protein cores and protein attachments. In this study, a new MP-DE algorithm is designed from the core-attachment structure to generate protein complex cores using Markov clustering, while searching for attached proteins in the second-order neighborhood of core protein complexes based on differential evolution(DE) algorithm. The experimental results show that the proposed method has high detection accuracy and efficiency on the current mainstream protein-protein interaction(PPI) database. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Proteins

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SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1717-1719页

作者： Jiufang Chen Ye Yuan xin Luo the College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China IEEE

Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.

关键词： representation preserving undirected

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A Low-Rank Matching Attention Based Cross-Modal Feature Fusion Method for Conversational Emotion Recognition

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IEEE Transactions on Affective computing 2024年第2期16卷 1177-1189页

作者： Shou, Yuntao Liu, Huan Cao, xiangyong Meng, Deyu Dong, Bo Xi'An Jiaotong School of Computer Science and Technology Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi'an710049 China Xi'An Jiaotong School of Computer Science and Technology Shaanxi Xi'an710049 China BigKEJoint Innovation Center SHMEEA Shanghai China Xi'An Jiaotong University School of Mathematics and Statistics Xi'an China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. © 2024 IEEE.

关键词： Semantics

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LeapGNN: Accelerating Distributed GNN Training Leveraging Feature-Centric Model Migration 23

LeapGNN: Accelerating Distributed GNN Training Leveraging Fe...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Chen, Weijian He, Shuibing Qu, Haoyang Zhang, Xuechen The State Key Laboratory of Blockchain and Data Security Zhejiang University China Zhejiang Lab China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Washington State University Vancouver United States

ISBN: (纸本)9781939133458

Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the transfer of massive graph vertex features to GNN models, which leads to a significant communication bottleneck. Recognizing that the model size is often significantly smaller than the feature size, we propose LeapGNN, a feature-centric framework that reverses this paradigm by bringing GNN models to vertex features. To make it truly effective, we first propose a micrograph-based training strategy that leverages a refined structure to enhance locality, combined with the model migration technique, to minimize remote feature retrieval. Then, we devise a feature pre-gathering approach that merges multiple fetch operations into a single one to eliminate redundant feature transmissions. Finally, we employ a micrograph-based merging method that adjusts the number of micrographs for each worker to minimize kernel switches and synchronization overhead. Our experimental results demonstrate that LeapGNN achieves a performance speedup of up to 4.2× compared to the state-of-the-art method, namely P3 © 2025 FAST. All Rights Reserved.

关键词： Graph neural networks

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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