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International Conference on Computer and Communications (ICCC)

作者： Ying Bai Dongsheng Yang Hongliang Wang Changyi Deng Wei Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In recent years, the use of skeleton data for human action recognition has received increasing attention in fields such as intelligent monitoring, human-object interaction, and video retrieval. Among them, Modeling Human Skeleton as Spatial-Temporal Graph Convolutional Network (ST-GCN) has made significant progress. However, methods based on Graph Convolution (GCN) generally face two issues: on one hand, the receptive fields of the spatial and temporal graphs are not flexible enough, limiting their feature representation capabilities. On the other hand, the model is overly parameterized, leading to high computational costs. In this paper, we first propose a spatial-temporal transpose convolution module to overcome the inflexibility of the spatial-temporal graph receptive field. Secondly, we introduce 1 *1 convolutions and depth-wise separable convolution layers into the network model, effectively reducing the computational complexity of the model. On the two subsets of the large-scale skeleton action recognition dataset NTU RGB-D, experimental results show that the proposed method achieved Top1 accuracy metrics of 84.6% and 92.5% respectively, while the model size was reduced by 13.48 times compared to the classic ST-GCN method, and the computational complexity was reduced by approximately 17.18 times.

关键词： Hands Accuracy Convolution Computational modeling Benchmark testing Approximation algorithms Skeleton Partitioning algorithms Computational complexity Monitoring

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Keyword-Enhanced Semantic Retrieval and Multi-Dimensional Relevance Ranking in RAG 10

Keyword-Enhanced Semantic Retrieval and Multi-Dimensional Re...

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10th International Conference on Computer and Communications, ICCC 2024

作者： Zhao, Kui Wang, Guoyu Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

In the task of generating medical hot topics, efficiently and accurately retrieving relevant information based on hot keywords and generating credible topics is a challenging problem. Addressing the limitations of existing retrieval and ranking methods in terms of accuracy and relevance, this paper proposes a RAG-based keyword-enhanced semantic retrieval and multi-dimensional relevance ranking approach. First, we construct keyword vectors using the BGE model and perform fast similarity searches to achieve preliminary retrieval. Then, we optimize the relevance of the retrieval results by setting a similarity threshold and applying a cluster and de-duplication strategy. Building on this, multi-dimensional similarity measurement is introduced, combining a cross-encoder model and feature extraction to implement reordering, further improving the accuracy of retrieval results. Experimental results show that this method significantly enhances RAG's performance in generating hot topics from medical news and information, providing an effective solution for complex information retrieval tasks. © 2024 IEEE.

关键词： Online searching

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An Automatic Tooth Position and Dental Disease Detection Algorithm based on YOLOv8

An Automatic Tooth Position and Dental Disease Detection Alg...

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International Conference on Computer and Communications (ICCC)

作者： Hongliang Wang Ke Diao Lin Wu Xuerao Li Xin Zhou Xiaozhou Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the development of digital dental imaging technology, panoramic tomographic images have become an effective tool for diagnosing dental diseases. However, due to the diversity and complexity of dental lesions, accurately identifying tooth positions and pathological conditions from these images remains challenging. To address the issue of low target detection accuracy caused by the high similarity and complex textures of teeth, this paper introduces YOLOv8-Teeth, an enhanced teeth detection algorithm designed for accurate identification of teeth positions and dental diseases from panoramic radiographs. Key innovations include integrating the C2f_ConvNeXtv2 module for improved feature extraction, employing the ASPP module for optimized multi-scale feature capture, and utilizing Focal-EloU Loss for refined bounding box regression. Experimental results on a custom dataset of 476 panoramic radiographs demonstrate significant performance gains, achieving a mean average precision (mAP) of 91.3%, precision of 93.6%, and recall of 88.7%. These improvements offer promising potential for early diagnosis and treatment planning in dental care.

关键词： Technological innovation Accuracy Computational modeling Teeth Tomography Feature extraction Real-time systems Dentistry Detection algorithms Diseases

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A Novel Grades Prediction Method for Undergraduate Students by Learning Explicit Conditional Distribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4837-4848页

作者： Zhang, Na Liu, Ming Wang, Lin Liu, Shuangrong Sun, Runyuan Yang, Bo Zhu, Shenghui Li, Chengdong Yang, Cheng Cheng, Yuhu China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Quan Cheng Laboratory Jinan250100 China Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China Shandong Jianzhu University School of Information and Electrical Engineering Jinan250101 China Ministry of Education Engineering Research Center of Intelligent Control for Underground Space Xuzhou221116 China

Educational data mining (EDM) offers an effective solution to predict students' course grades in the next term. Conventional grade prediction methods can be viewed as regressing an expectation of the probability distribution of the student's grade, typically called single-value grade prediction. The reliable prediction outcomes of these methods depend on the complete input information related to students. However, next-term grade prediction often encounters the challenge of incomplete input information due to the inaccessibility of future data and the privacy of data. In this scenario, single-value grade prediction struggles to assess students' academic status, as it may not be represented and assessed by relying on a singular expectation value. This limitation increases the risk of misjudgment, and may lead to errors in educational decision-making. Considering the challenge of collecting complete input information, we shift from traditional single-value predictions to forecasting the explicit probability distribution of the course grade. The probability distribution of the grade can assess the students' academic status by providing probabilities corresponding to all possible grade values rather than relying solely on an expectation value, which offers the foundation to support the educators' decision-making. In this article, the course grade distribution prediction (CGDP) model is proposed, aiming to estimate an explicit conditional probability distribution of course grades in the next term. This model can identify at-risk students, offering comprehensive decision-making information for educators and students. To ensure precise distribution predictions, a calibration method is also employed to improve the alignment between predicted and actual probabilities. Experimental results verify the effectiveness of the proposed model in early grade warning for undergraduates, based on real university data. © 2020 IEEE.

关键词： Probability distributions

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Hybrid Variational Autoencoder for Collaborative Filtering 25

Hybrid Variational Autoencoder for Collaborative Filtering

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Jinxin Xiao, Yingyuan Zhu, Ke Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance. © 2022 IEEE.

关键词： Collaborative filtering

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Bilinearization-reduction approach to the classical and nonlocal semi-discrete modified Korteweg-de Vries equations with nonzero backgrounds

arXiv

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arXiv 2024年

作者： Deng, Xiao Chen, Hongyang Zhao, Song-Lin Ren, Guanlong Research Center for Data Hub and Security Zhejiang Lab Zhejiang Province Hangzhou311100 China School of Mathematical Sciences Zhejiang University of Technology Zhejiang Province Hangzhou310023 China Research Center for Space Computing System Zhejiang Lab Zhejiang Province Hangzhou311100 China

Quasi double Casoratian solutions are derived for a bilinear system reformulated from the coupled semi-discrete modified Korteweg-de Vries equations with nonzero backgrounds. These solutions, when applied with the classical and nonlocal reduction techniques, also satisfy the corresponding classical and nonlocal semi-discrete modified Korteweg-de Vries equations with nonzero backgrounds. They can be expressed explicitly, allowing for an easy investigation of the dynamics of systems. As illustrative examples, the dynamics of solitonic, periodic and rational solutions with a plane wave background are examined for the focusing semi-discrete Korteweg-de Vries equation and the defocusing reverse-space-time complex semi-discrete Korteweg-de Vries equation. © 2024, CC BY-NC-SA.

关键词： Korteweg-de Vries equation

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Voxelization Algorithm based on Octree Spatial Averaging

Voxelization Algorithm Based on Octree Spatial Averaging

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Information and Communication Technology (ICTech), International Conference of

作者： HongChao Lv Jun Wang HongLiang Wang XingYu Zhou Shenyang University of Chemical Technology Shandong China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

This paper proposes a high-precision and efficient voxelization algorithm for the development of voxelization in the preprocessing stage of industrial simulation. The main idea is to establish an octree data structure to evenly divide the three-dimensional mesh model space into voxel sets. By using the separation axis method and the improved scan line method, the position relationship between voxels and triangles can be directly determined by skipping the calculation of intersection between voxels and triangles. The innovation of this article lies in the use of the separation axis algorithm and the improved scanning line method for voxelization, and the simplification of the algorithm's operations through the octree data structure, thereby reducing the time and spatial complexity of the algorithm.

关键词： Technological innovation Solid modeling Octrees Data models Information and communication technology Complexity theory

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Keyword-Enhanced Semantic Retrieval and Multi-Dimensional Relevance Ranking in RAG

Keyword-Enhanced Semantic Retrieval and Multi-Dimensional Re...

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International Conference on Computer and Communications (ICCC)

作者： Kui Zhao Guoyu Wang Chinese Academy of Sciences Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Chinese Academy of Sciences Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

关键词： Measurement Accuracy Databases Computational modeling Semantics Buildings Information retrieval Feature extraction Vectors Reliability

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Algorithm for Gastrointestinal Tumor Detection based on Multimodal Large-scale Models in the Medical Domain

Algorithm for Gastrointestinal Tumor Detection based on Mult...

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Information and Communication Technology (ICTech), International Conference of

作者： Hu Li Hongliang Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Beijing China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

A new Chinese gastrointestinal tumor visual language medical model is proposed. This model integrates text and image analysis capabilities, allowing it to simultaneously explain gastrointestinal tumor images and text. The model adopts a framework design that combines a visual transformer and a foundation language model in two parts. It enhances the abilities of feature matching and instruction learning in two stages through deep training. This method improves the model's ability to perform medical summarization and answer complex medical questions. In addition, a gastrointestinal tumor medical image dataset has been organized. It contains more than 20,000 medical images paired with corresponding text. These medical resources have been organized to facilitate comprehensive interpretation of gastrointestinal tumors, which is conducive to assisting gastrointestinal tumor detection and identification. In summary, the introduction of this model and dataset can greatly promote Chinese gastrointestinal tumor detection and recognition, and provide help for many medical workers and patients.

关键词： Training Analytical models Visualization Accuracy Feature extraction Transformers Gastrointestinal tract Data mining Biomedical imaging Tumors

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