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Multi-Category Fusion Contrastive Learning with Core Data Selection for Robust RGB Image-based Dental Caries Classification

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Information Fusion 2025年 124卷

作者： Peiliang Zhang Yaru Chen Yunjiong Liu Chao Che Yongjun Zhu Key Laboratory of Advanced Design and Intelligent Computing (Dalian University) Ministry of Education Dalian 116622 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan 430070 China Department of Library and Information Science Yonsei University Seoul 03722 Republic of Korea Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey Surrey GU2 7NA United Kingdom School of Software Engineering Dalian University Dalian 116622 China

Dental caries represents one of the most prevalent diseases affecting humankind, particularly among adolescent populations. RGB images offer a convenient and cost-effective method for dental caries detection. However, the image data captured may suffer from blurriness, which, together with label errors introduced during manual annotations, can degrade the performance of the model learned for dental caries detection. To address this problem, we propose the Multi-Category Fusion Contrastive Learning with Core Data Selection (M3C) to improve the predictive performance of dental caries classification models. Instead of fine-tuning the backbone network structure, M3C focuses on improving the robustness of model to label errors from a novel perspective by identifying core data that is highly relevant to the dental caries category. We analyzed and validated that M3C has better robustness in dental caries detection from model architecture representation, theoretical analysis, and mutual information computation. Specifically, M3C quantifies the average mutual information between dental caries images and dental caries category centers based on Jensen-Shannon Divergence (JSD), which is then used for selecting the core data to mitigate the impact of label errors on model performance. Furthermore, we design inter-category contrastive learning to enhance the performance of the model in distinguishing the categories of dental caries by improving the feature representation for samples of different categories. With theoretical justification, we jointly optimized model training using prediction loss and confusion contrastive loss. Extensive experiments demonstrate that M3C significantly surpasses comparative data selection methods in dental caries detection on dental caries RGB image datasets. More excitingly, M3C achieves superior predictive performance using only 50% of the core data compared to state-of-the-art dental caries detection methods using the entire dataset. Our code is

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Divideup: A Generic Improvement Approach for Supervised Learning Using Dataset Partition with Finer Semantical Information

Divideup: A Generic Improvement Approach for Supervised Lear...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xinyue Zhang Qin Li Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Supervised learning technologies represented by neural networks have made great progress in many fields. In particular applications such as image recognition and natural language processing, the entire computing process is completely handed over to the machine learning algorithm to directly learn the mapping from the feature space to the expected output, without considering much of the semantical information and domain knowledge of the data. In this paper, we propose a generic data refinement approach called divideup, which incorporates finer semantical information into the dataset to obtain a prediction model capturing more detailed information in the training data. By providing the information theory, we have high confidence that the learned model trained with the refined dataset has better prediction accuracy than the original one. We conduct extensive experiments on different datasets with the state-of-the-art neural network architectures such as ResNet and DenseNet. The experimental results show that divideup improves the prediction accuracy of all these deep learning architectures on the original test set. The divideup approach is also applied to other machine learning models such as random forest, XGboost and SVM. The results supports the conclusion that the refined training data obtained by divideup produces better prediction accuracy of the learned model.

关键词： Training Supervised learning Neural networks Training data Predictive models Data models Random forests

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A unified cross-source context enhancement model for multi-source fake news detection

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Knowledge-Based Systems 2025年 324卷

作者： Ruiting Dai Haoran Meng Zhengdao Yuan Lisi Mo Wenwei Zhu Tao He School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054 Sichuan China The Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu 611731 Sichuan China College of Earth and Planetary Science Chengdu University of Technology Chengdu 610059 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 610054 Sichuan China University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Chengdu 611731 Sichuan China

The growing prevalence of misinformation across digital platforms poses a serious threat to public information integrity. While existing fake news detection methods have predominantly focused on single-source data, such approaches often fail to address the complexities introduced by multi-source information originating from diverse platforms. Detecting fake news in this multi-source context remains a relatively underexplored challenge. Traditional cross-domain methods typically transfer domain-invariant features using single-source modeling strategies, yet they neglect the rich contextual cues available across sources—an aspect critical for enhancing detection accuracy. To overcome these limitations, we propose a unified framework that effectively captures global contextual information by jointly leveraging intra-source and inter-source interactions. Our model integrates two principal components: (1) a cross-source global context learning module employing a context-augmented transformer to model long-range dependencies among multi-source instances, and (2) a dual-level contrastive learning mechanism that aligns representations at both local and global levels, reducing inconsistencies across feature spaces and source domains. Extensive experiments conducted on publicly available multi-source datasets demonstrate that our method achieves substantial improvements over existing state-of-the-art approaches. Specifically, it yields an approximate 5% gain in classification accuracy compared to leading models such as LIMFA, highlighting the robustness and effectiveness of our framework in advancing multi-source fake news detection.

关键词： Contrastive learning Cross-domain Domain-invariant features Fake news detection Single-source modeling

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SG-PBFT: A secure and highly efficient blockchain PBFT Consensus Algorithm for internet of vehicles

arXiv

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arXiv 2021年

作者： Xu, Guangquan Liu, Yihua Xing, Jun Luo, Tao Gu, Yonghao Liu, Shaoying Zheng, Xi Vasilakos, Athanasios V. The Big Data School Qingdao Huanghai University Qingdao266427 China College of Intelligence and Computing Tianjin University Tianjin300350 China The International Engineering Institute Tianjin University Tianjin300350 China The College of Intelligence and Computing Tianjin University Tianjin Tianjin300350 China The school of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China The Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China School of Informatics and Data Science Hiroshima University Japan The Department of Computing Macquarie University SydneyNSW2109 Australia The University of Technology Sydney Australia The Fuzhou University Fuzhou China Lulea University of Technology Lulea Sweden

The Internet of Vehicles (IoV) is an application of the Internet of things (IoT). It faces two main security problems: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To solve these problems, this paper proposes SG-PBFT: a secure and highly efficient PBFT consensus algorithm for Internet of Vehicles, which is based on a distributed blockchain structure. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional PBFT consensus algorithm by using a score grouping mechanism to achieve a higher consensus efficiency. The experimental result shows that our method can greatly improve the consensus efficiency and prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art consensus algorithm (PBFT). Our proposed SG-PBFT is versatile and can be used in other application scenarios which require high consensus efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Accelerated Latent Factor Analysis for Recommender Systems via PID Controller

Accelerated Latent Factor Analysis for Recommender Systems v...

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IEEE International Conference on Networking, Sensing and Control

作者： Jinli Li Xuke Wu Ye Yuan Yajuan Wu Kangkang Ma Yue Zhou Computer School China West Normal University Nanchong Sichuan China Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of information and software engineering University of Electronic Science and Technology of China Chengdu Sichuan China University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

High-dimensional and sparse (HiDS) matrices generated by recommender systems (RSs) contain rich knowledge. A latent factor (LF) model can address such data effectively. Stochastic gradient descent (SGD) is an efficient algorithm for building a LF model on an HiDS matrix. However, it suffers slow convergence. To address this issue, this study proposes to implement a LF model with a proportional integral derivative (PID) controller. The main idea is to continuously apply a correction for SGD to accelerate the training process. Based on such design, a PID-based LF (PLF) model is proposed. Empirical studies on two HiDS matrices from RSs indicate that a PLF model outperforms an LF model in terms of both convergence rate and prediction accuracy for missing data.

关键词： Training Predictive models Data models Sparse matrices Acceleration Recommender systems Convergence

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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A novel method of android malware detection based on ensemble learning algorithm 8

A novel method of android malware detection based on ensembl...

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2018 8th International Workshop on Computer Science and engineering, WCSE 2018

作者： Zhao, Jingyi Mo, Xiuliang Zheng, Qiao Tianjin Intelligent Computing and Software New Technology Key Laboratory School of computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)9789811178610

Android is popular for mobile devices in recent years. As the risk of malware is sharply increasing in Android platform, Android malware detection has become an important research topic. However, the current work shows that the detection of malware still needs to be reformed. Many current results suggest a modification of permissions or a combination of permissions and intents, but Android's fragmentation issues and requiring rooting, hindering the widespread adoption of those methods. Many approaches have been proposed to detect this attack by modifying the Android OS. Existing anti-viruses depend on signature databases that need to be updated from time to time and are unable to detect zero-day malware. The Android Operating system allows inter-application communication through the use of component reuse by using intents. A malware detection model is developed based on ensemble learning algorithms and on the basis of the random forest in WEKA. The method we proposed can take a better efficiency and precision. © Proceedings of 2018 the 8th International Workshop on Computer Science and engineering, WCSE 2018. All rights reserved.

关键词： Android (operating system)

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A reinforcement-learning-based energy-efficient framework for multi-task video analytics pipeline

arXiv

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arXiv 2021年

作者： Zhao, Yingying Dong, Mingzhi Wang, Yujiang Feng, Da Lv, Qin Dick, Robert P. Li, Dongsheng Lu, Tun Gu, Ning Shang, Li School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Department of Computing Imperial College London London United Kingdom Software Service Company Limited Beijing China University of Colorado Boulder BoulderCO United States Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann ArborMI United States Microsoft Research Asia Shanghai China School of Computer Science Fudan University Shanghai China

Deep-learning-based video processing has yielded transformative results in recent years. However, the video analytics pipeline is energy-intensive due to high data rates and reliance on complex inference algorithms, which limits its adoption in energy-constrained applications. Motivated by the observation of high and variable spatial redundancy and temporal dynamics in video data streams, we design and evaluate an adaptive-resolution optimization framework to minimize the energy use of multitask video analytics pipelines. Instead of heuristically tuning the input data resolution of individual tasks, our framework utilizes deep reinforcement learning to dynamically govern the input resolution and computation of the entire video analytics pipeline. By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency. Governed by reinforcement learning, optical flow is incorporated into the framework to minimize unnecessary spatio-temporal redundancy that leads to re-computation, while preserving accuracy. The proposed framework is applied to video instance segmentation which is one of the most challenging computer vision tasks, and achieves better energy efficiency than all baseline methods of similar accuracy on the YouTube-VIS dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

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Bilingual Word Embedding with Sentence Combination CNN for 1-to-N Sentence Alignment 20

Bilingual Word Embedding with Sentence Combination CNN for 1...

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Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval

作者： Xinyuan Ren Xiangling Fu Xuesi Zhou Chunsheng Liu Songfeng Gao Lei Peng School of Software Engineering Beijing University of Posts and Telecommunications Beijing China and Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Hai Dian Beijing China and Multimedia Signal and Intelligent Information Processing Lab Tsinghua University Hai Dian Beijing China BUPT and Huarong Joint lab of Smart Finance Beijing P. R. China and HuaRong RongTong (Beijing) Technology Co. Ltd Beijing P. R. China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Convolutional Neural Network

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

Epilepsy Classification for Mining Deeper Relationships betw...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xin Chen Yuanjie Zheng Yi Niu Chengiiang Li School of Information Science and Engineering Shandong Normal University Jinan China Key Lab of Intelligent Computing and Information Security Universities of Shandong Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan China Shandong University of Science and Technology Jinan China

ISBN: (数字)9781728194813

ISBN: (纸本)9781728194820

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy.

关键词： Epilepsy Electroencephalography Convolution Brain modeling Feature extraction Correlation Task analysis

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