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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Huang, Lei Cai, Hongguo Peng, Yuzhong Liao, Jianping Guangxi Key Laboratory of Human-Computer Interaction and Intelligent Decision-Making School of Computer and Information Engineering Nanning Normal University Guangxi Nanning530100 China Guangxi Academy of Sciences Guangxi Nanning530007 China

ISBN: (纸本)9789819619030

This paper addresses the challenges of mobile traffic sign detection in dynamic environments by proposing a lightweight model, RePCMA-YOLOv8n, to enhance feature extraction efficiency and detection performance. The RePCMA module integrates Partial Convolution (PConv), re-parameterization strategies, and multi-scale attention mechanisms (EMA), effectively reducing redundancy in feature extraction to lower the number of parameters and floating-point operations, thereby improving overall model performance. Specifically, to tackle the common issue of varying target sizes in traffic sign detection, this study introduces the Normalized Wasserstein Distance (NWD) as an optimization loss function, significantly enhancing the model’s detection accuracy for targets of different sizes. The model demonstrates exceptional adaptability across various scenarios. Experimental results show that on the LISA dataset, the algorithm achieved an mAP50 of 97.9%, and its outstanding performance was further validated on seven other public datasets using mAP50 and mAP50-90 metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

DRDM: A Disentangled Representations Diffusion Model for Syn...

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

作者： Huang, Enbo Zhang, Yuan Huang, Faliang Zhang, Guangyu Liu, Yang Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350368741

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM. © 2025 IEEE.

关键词： Diffusion Model Disentangled Representation human Parsing Person image synthesis

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Recognizing Adenoid Hypertrophy from Facial Images with Multi-scale Feature Fused State Space Model 18th

Recognizing Adenoid Hypertrophy from Facial Images with Mu...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Ma, Shuai He, Jinrong Wang, Yao Bi, Yingzhou Yang, Li College of Mathematics and Computer Science Yan’an University Yan’an716000 China Yan’an University Affiliated Hospital Yan’an716000 China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School Hospital Yan’an University Yan’an716000 China

ISBN: (纸本)9789819610709

Early diagnosis of pediatric adenoidal hypertrophy is crucial for timely identification of respiratory obstructions, thereby preventing declines in sleep quality that can lead to impaired growth, development, and cognitive function. Currently, the most common clinical diagnostic methods involve nasopharyngeal endoscopy and X-ray imaging assessments, which are time-consuming, labor-intensive, and costly. This paper presents an intelligent diagnostic model based on natural light facial images, employing a state space model (SSM). The model combines the local feature extraction capability of convolutional layers with the SSM’s ability to capture long-range dependencies for facial feature extraction. Furthermore, it incorporates a feature fusion strategy to enhance the features of facial images. Experimentation on a self-constructed dataset of adenoid facies images demonstrated that the model achieves an accuracy of 86.55% and an F1-score of 86.21%. The data and code are available at https://***/hejinrong/AF-SSM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation

arXiv

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arXiv 2025年

作者： Li, Xiangju Yang, Dong Zhu, Xiaogang Huang, Faliang Zhang, Peng Zhao, Zhongying College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266500 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China

Span-level emotion-cause-category triplet extraction represents a novel and complex challenge within emotion cause analysis. This task involves identifying emotion spans, cause spans, and their associated emotion categories within the text to form structured triplets. While prior research has predominantly concentrated on clause-level emotion-cause pair extraction and span-level emotion-cause detection, these methods often confront challenges originating from redundant information retrieval and difficulty in accurately determining emotion categories, particularly when emotions are expressed implicitly or ambiguously. To overcome these challenges, this study explores a fine-grained approach to span-level emotion-cause-category triplet extraction and introduces an innovative framework that leverages instruction tuning and data augmentation techniques based on large language models. The proposed method employs task-specific triplet extraction instructions and utilizes low-rank adaptation to fine-tune large language models, eliminating the necessity for intricate task-specific architectures. Furthermore, a prompt-based data augmentation strategy is developed to address data scarcity by guiding large language models in generating high-quality synthetic training data. Extensive experimental evaluations demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving at least a 12.8% improvement in span-level emotion-cause-category triplet extraction metrics. The results demonstrate the method’s effectiveness and robustness, offering a promising avenue for advancing research in emotion cause analysis. The source code is available at https://***/zxgnlp/InstruDa-LLM. Copyright © 2025, The Authors. All rights reserved.

关键词： Natural language processing systems

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Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

arXiv

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arXiv 2025年

作者： Li, Qiang Lin, Xinze Lv, Shenghao Huang, Faliang Li, Xiangju Shandong Women’s University School of Data and Computer Science Jinan250002 China Shandong University of Science and Technology College of Computer Science and Engineering Qiangdao266500 China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning530100 China

Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user’s reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models∗ Copyright © 2025, The Authors. All rights reserved.

关键词： Recommender systems

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

DRDM: A Disentangled Representations Diffusion Model for Syn...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Enbo Huang Yuan Zhang Faliang Huang Guangyu Zhang Yang Liu Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photorealistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM

关键词： Limbs Image synthesis Shape Clothing Semantics Production Feature extraction Diffusion models Sampling methods Speech processing

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Corrigendum to "DSTF: A diversified spatio-temporal feature extraction model for traffic flow prediction" [Neurocomputing 621 (2025) 129280] (Neurocomputing (2025) 621, (S0925231224020514), (10.1016/***.2024.129280))

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Neurocomputing 2025年 638卷

作者： Wang, Xing Wang, Xiaojun Huang, Faliang Zou, Fumin Liao, Lyuchao Zeng, Ruihao Fujian Normal University College of Computer and Cyber Security China Fujian Normal University Digital Fujian Institute of Big Data Security Technology China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision China Fujian University of Technology Fujian Key Laboratory of Automotive Electronic and Electrical Drive Technology China The University of Sydney School of Civil Engineering Sydney Australia

The authors regret to inform that: (1) During the resubmission stage of the revised manuscript, in compliance with reviewers' recommendations to show standard deviations in the experimental results (In Section 5. Experiment implementation), a batch replacement operation (intended to replace "+" with "±" for data formatting) inadvertently replaced the "+" symbols in the mathematical formulas and the explanatory text with "±" symbols (In Section 3. Problem statement and Section 4. Methodology). (2) In proof stage, we sincerely apologize for failing to identify this issue during the final proofreading stage. Correction statement and details: As this error may affect readers' understanding, we request a corrigendum to rectify this error. In Section 3. Problem statement and Section 4. Methodology, all instances of "±" symbols within mathematical formulas and their explanatory text should be reverted to the original "+" symbols. The authors would like to apologize for any inconvenience caused. © 2025 The Authors

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BGNN-TC: BERT GNN Based Text Classification Algorithm 1

BGNN-TC: BERT GNN Based Text Classification Algorithm

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Zhao, Jianbo Qin, Xiao Peng, Lei Zhang, Jinyong Wang, Wenji Zheng, Hongchun Nanning Normal University Guangxi Key Laboratory of Human-Computer Interaction and Intelligent Decision Making Nanning China

ISBN: (纸本)9798331504434

The traditional Text-Level-GNN effectively captures the structural information of texts but fails to fully extract the semantic information of words during text-level composition. To address this limitation, we employ BERT to initialize the word nodes within text-level graphs, constructing a graph network that integrates both spatial structure and semantic information. Based on BERT optimization, we proposes a BGNN-TC based text classification algorithm to enhance the performance of existing methods, particularly on long-text classification tasks. This approach leverages the strengths of large-scale pre-trained models and text-level graph neural networks. We perform text category prediction using both BERT+GNN and BERT models based on word features from text-level compositions and train the model with a mixed loss of the two prediction results. Experiments on the R8, R52, and Ohsumed datasets demonstrate that the proposed method achieves outstanding performance in handling long texts, effectively extracting both semantic and structural information. The results highlight the robustness and generalization capabilities of the model. © 2024 IEEE.

关键词： Semantics

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Collaborative Framework for circRNA-Disease Associations Prediction using Dual Variational Graph

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IEEE Transactions on Big Data 2025年

作者： Liu, Changchun Wang, Lei Zhao, Bowei Wei, Mengmeng Li, Yang Lu, Mianshuo Wei, Yu Liang, Sizhe The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Key Lab of Human-machine Interaction and Intelligent Decision Guangxi Acad emy of Science Nanning530007 China The School of Computer Science and Information Engineer ing Hefei University of Technology Hefei230601 China The College of Computer and Information Science School of Software University of Chinese Academy of Sciences Urumqi830011 China

Many experiments have shown that circular RNA (circRNA) can act as biomarkers for complex diseases and play significant regulatory roles in multiple pathological processes. However, most circRNA-disease associations remain unknown, and discovering these associations through biological experimental approach is expensive and time-consuming. Taking into account the shortcomings of current methods, we introduce a new collaborative framework that utilizes multi-heterogeneous graphs, along with variational graph auto-encoders (VGAE) to predict associations between circRNA and diseases. First, we build multi-similarity networks using various biological attributes of circRNA and diseases, and integrate these similarity networks. Two subnetworks are constructed from association matrix and combined similarity network, which included a circRNA-based network and a disease-based network. We then employ random walk with restart and Singular Value Decomposition, for feature extraction from the similarity matrix. Finally, we use collaborative framework to predict the circRNA-disease association scores based on the two subnetworks. We integrate the two score matrices to obtain a final prediction scoring matrix. Using 5-fold cross-validation on the CircR2Disease dataset, our model achieved an AUC score of 0.9828 and an AUPR score of 0.9820. Additionally, among the top 30 highest-scoring circRNA-disease association pairs, 26 associations have already been validated. Our model shows strong performance and can accurately predict associations between circRNA and diseases, according to experimental results. © 2015 IEEE. All rights reserved.

关键词： Variational techniques

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Implementation of Person Re-Identification Algorithm Based on Huawei Mindspore and Pytorch Hybrid Architecture 1

Implementation of Person Re-Identification Algorithm Based o...

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Deng, Chao Yang, Xiaochen Qin, Xiao Peng, Lei Zhang, Jinyong Wang, Wenji Qian, Quanmei Zhao, Jianbo Nanning Normal University Guangxi Key Laboratory of Human-Computer Interaction and Intelligent Decision Nanning China

ISBN: (纸本)9798331504434

Based on Huawei's Mindspore and Pytorch hybrid architecture, this paper carries out localization operation of the current mainstream pedestrian re-recognition algorithm - AGW model, and makes it successfully run correctly on the domestic hardware platform. In this paper, the construction mode of Mindspore map and the technical characteristics of Mindspore branch structure in the process of localization of pedestrian rerecognition model are deeply analyzed, and the difficulties of localization of AGW model are explored. The effects of the implementation of data processing logic in different frameworks on the running performance of AGW localization model are studied. Develop a cross-framework parameter mapping module to map the parameters in Pytorch to the Mindspore framework in order to maintain the performance of the localized AGW model. Finally, the visualization module proposed in this paper can track the feature map changes in the domestic AGW model. In this paper, the domestic AGW model was tested on SYSU-MM01 and RegDB data sets, and its performance in RANK1 index and mAP index reached more than 80% of the original model. The experimental results show that the localized AGW model based on Huawei's Mindspore and Pytorch hybrid architecture runs successfully and correctly on the localized platform. © 2024 IEEE.

关键词： hybrid architecture localization Mindspore pedestrian re-recognition Pytorch

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