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Study on Information Diffusion analysis in Social Networks and Its applications

International Journal of Automation and computing 2018年第4期15卷 377-401页

作者： Biao Chang Tong Xu Qi Liu En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer ScienceUniversity of Science and Technology of China

Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.

关键词： Information diffusion influence evaluation influence maximization information source detection social network.

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Designing preamplifier for sensing atmospheric electrostatic field strength via supercapacitive sensor

Designing preamplifier for sensing atmospheric electrostatic...

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2020 International Symposium on Electronic Information Technology and Communication Engineering, ISEITCE 2020

作者： Wu, Changhao Xiong, Yi Zeng, Wei National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Hefei230601 China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco-Dyeing and Finishing Wuhan Textile University Wuhan430073 China School of Physics and Technology Moe Key Laboratory of Artificial Micro- and Nano-Structures Center for Electron Microscopy Wuhan University Wuhan430072 China

In order to effectively obtain the signal from sensor, the analogy signal needs to be amplified and then converted into a digital signal for matching to the sensor characteristics. With a supercapacitive electric field sensor based on graphene aerogel, the response current signal from the electric field sensor is weak and unstable. Herein, a high gain and low noise preamplifier is developed, and an amplifier circuit with double T-type feedback network is proposed to reduce the Johnson noise for the amplifier. This design can reduce the thermal noise of resistance by using the smaller resistance under the same gain, and it can effectively reduce the interference of peak noise by adding the feedback capacitance, so as to improve the detection accuracy. The simulation results show that under the same gain condition, the Johnson noise can be reduced by 46% and the detection accuracy can be improved by 12% compared with the traditional T-type feedback network. © Published under licence by IOP Publishing Ltd.

关键词： Feedback amplifiers

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Remote sensing monitoring of areca yellow leaf disease based on UAV multi-spectral images

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 2020年第8期36卷 54-61页

作者： Zhao, Jinling Jin, Yu Ye, Huichun Huang, Wenjiang Dong, Yingying Fan, Lingling Ma, Huiqin Jiang, Jing National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei230601 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Hainan Key Laboratory of Earth Observation Sanya572029 China

Yellow leaf disease is a serious disease that endangers the growth of areca, it is urgent to monitor the infection severity and spatial distribution in time and accurately. However, the traditional monitoring methods are mainly depend on visual inspection and manual investigation, which affects the efficiency and spatial scope of monitoring. Low altitude UAV remote sensing technology can effectively solve the problems of insufficient optical satellite images acquisition caused by cloudy and rainy weather in areca planting area, and improve the real-time monitoring of areca yellow leaf disease. In this paper, in order to identify the severities and spatial distribution of areca yellow leaf disease, five band(including blue, green, red, near-infrared and red-edge wavebands) multispectral images were obtained by using the MicaSense RedEdge-M multispectral camera mounted on the DJI Phantom 4 Pro V2.0. Based on the Minimum Redundancy Maximum Relevance (mRMR), three sensitive features were selected from 15 potential vegetation indexes. Using Back Propagation Neural Network(BPNN), Random Forest(RF) and Support Vector Machine(SVM) classification algorithms respectively, a monitoring model of areca yellow leaf disease severity was constructed. A total sixty in-situ sampling points were selected and the disease index (DI) were obtained, according to the characteristics of the disease and the separability of remote sensing images, the severities of the disease were divided into three grades: health (DI © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.

关键词： Neural network models

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Spatial fuzzy C-means clustering and deep belief network for change detection in synthetic aperture radar images 5

Spatial fuzzy C-means clustering and deep belief network for...

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IET International Radar Conference 2020, IET IRC 2020

作者： Qi, Wenwen Wu, Lin Guo, Zhengwei Huang, Dan College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Department of Laboratory and Equipment Management Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

In this study, spatial fuzzy c-means (SFCM) clustering and deep belief network (DBN) method is presented for change detection in SAR images. There are three primary steps of this approach, they are given as follows: 1) Difference Image (DI) is firstly obtained by calculating the similarity of gray levels between multitemporal SAR images. 2) SFCM clustering is used to analyze DI, which combines the spatial information factor and the fuzzy cluster membership function, thus can effectively eliminate the interference of speckle noise. 3) DBN, a powerful data processing methodology that integrates deep learning techniques to extract underlying features and structures of complicated data, is trained to classify pixels to be changed or unchanged. The effectiveness and robustness of the proposed method are verified by experimental results with the Ottawa dataset. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Membership functions

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Simplifying graph contrastive learning with single feature fusion and column vector orthogonal noise

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Expert Systems with applications 2025年 288卷

作者： Lan You Zhen Zhang Qingxi Peng Christophe Claramunt Xing Zeng Hong Jin School of Computer Science and Information Engineering Hubei University Wuhan430062 Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan 430062 Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University) Wuhan 430062 School of Information Engineering Wuhan College Wuhan 430212 China Computer Science Naval Academy Research Institute Lanveoc-Poulmic BP 600 Brest 29240 France Okinawa Institute of Science and Technology Japan

Graph Contrastive Learning (GCL) has shown promise in recommendation systems by improving representation quality through data augmentation and consistency objectives. However, conventional GCL frameworks rely on multi-channel augmentation, which introduces significant computational overhead and limits scalability. To address this, we propose Single Feature Fusion Graph Contrastive Learning (S-GCL), a non-parametric and lightweight framework that simplifies contrastive learning by applying single feature fusion to the final prediction embeddings within each mini-batch. To further enhance S-GCL, we introduce Column Vector Orthogonal Noise (CVON), a feature-level augmentation strategy that injects orthogonal noise into each dimensional feature within the embedding space. CVON enriches representations while preserving semantic integrity, and incurs minimal computational overhead. Its integration within the S-GCL framework yields the proposed SCGCL method. Extensive experiments conducted on six publicly available benchmark datasets validate the effectiveness and generalizability of SCGCL. Notably, integrating SCGCL into LightGCN leads to substantial gains in both training efficiency and recommendation accuracy. For example, on the large-scale Alibaba-iFashion dataset, our method improves Recall@20 by 29 % and NDCG@20 by 33 %, while reducing training time by an impressive 84 %.

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Learning the Compositional Visual Coherence for Complementary Recommendations

arXiv

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arXiv 2020年

作者： Li, Zhi Wu, Bo Liu, Qi Wu, Likang Zhao, Hongke Mei, Tao Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science University of Science and Technology of China Columbia University School of Computer Science and Technology University of Science and Technology of China College of Management and Economics Tianjin University JD AI Research

Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a Global Coherence Learning (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a Focal Coherence Learning (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Textures

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Long-term joint scheduling for urban traffic

arXiv

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arXiv 2019年

作者： Liang, Xianfeng Wu, Likang Chen, Joya Liu, Yang Yu, Runlong Hou, Min Wu, Han Ye, Yuyang Liu, Qi Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China

Recently, the traffic congestion in modern cities has become a growing worry for the residents. As presented in Baidu traffic report [1], the commuting stress index 1, has reached surprising 1.973 in Beijing during rush hours, which results in longer trip time and increased vehicular queueing. Previous works have demonstrated that by reasonable scheduling, e.g, rebalancing bike-sharing systems [2] and optimized bus transportation [3], the traffic efficiency could be significantly improved with little resource consumption. However, there are still two disadvantages that restrict their performance: (1) they only consider single scheduling in a short time, but ignoring the layout after first reposition, and (2) they only focus on the single transport. However, the multi-modal characteristics of urban public transportation are largely under-exploited. In this paper, we propose an efficient and economical multi-modal traffic scheduling scheme named JLRLS based on spatio-temporal prediction, which adopts reinforcement learning to obtain optimal long-term and joint schedule. In JLRLS, we combines multiple transportation to conduct scheduling by their own characteristics, which potentially helps the system to reach the optimal performance. Our implementation of an example by PaddlePaddle is available at https://***/bigdata-ustc/Long-term-Joint-Scheduling, with an explaining video at https://***/t5M2wVPhTyk. Copyright © 2019, The Authors. All rights reserved.

关键词： Traffic congestion

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data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

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SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Dekker, Andre Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Background and Objective: Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Conducting centralized training to enhance model performance is impractical in real-world scenarios. Although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. Methods: In this work, we propose a novel federated semi-supervised learning framework (FSSL) for complex and realistic scenario, which can efficiently leverage the limited labeled data and abundant unlabeled data from multiple clinical sites. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining all local models during communication round. Results: We conducted extensive comparative and ablation experiments to validate the effectiveness of our proposed method. When only a limited amount of labeled data is available in the partially labeled sites, even without loading the pre-trained model, our approach consistently achieves the highest average Dice scores of 0.7383, 0.4768, and 0.6629 on the internal test sets, external unseen test sets, and pre-training test sets, respectively. Upon loading the pre-trained model, the scores on the three test sets further improve to 0.8205, 0.6185, and 0.7334, respectively. Conclusion: Our proposed method is capable of collaboratively training a global model with robust segmentation performance and generalizability by efficiently utilizing unlabeled data from different sites, even when only a limited amount of labeled da

关键词： Federated learning

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data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

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SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Andre, Dekker Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Centralized model training faces privacy constraints, although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. In this work, we propose a novel federated semi-supervised learning framework for complex and realistic scenario, which can efficiently leverage the limited labeled data and more homogeneous unlabeled data from multiple clinical sites for collaborative model training. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining local models during each communication round to enhance the robustness and generalizability of the global model. Extensive comparative and ablation experiments confirm the efficacy of our proposed methodology, demonstrating improvement over recent state-of-the-art federated semi-supervised segmentation methods in scenarios with only a small amount of labeled data. This approach highlights the significant potential of multi-source and heterogeneous unlabeled data in facilitating the training of federated deep learning models and promotes clinical applications, assists radiologists in diagnosis and improves patient outcomes. The source code will be released on https://***/zhenweishi/FedPSR © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Constructing Educational Concept Maps with Multiple Relationships from Multi-Source data

Constructing Educational Concept Maps with Multiple Relation...

引用

IEEE International Conference on data Mining (ICDM)

作者： Xiaoqing Huang Qi Liu Chao Wang Haoyu Han Jianhui Ma Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China iFLYTEK Research

Concept map is an useful tool to help people organize and improve knowledge. Particularly in educational domain, it is beneficial for students and teachers to improve the learning and teaching quality. Traditionally, manual educational concept maps, provided by teachers, are quite time-consuming and limited to teachers' experience. Thus, it is meaningful to automatically construct high-quality concept maps. However, existing data-driven solutions only focus on either separate data source or single pedagogic relationship, which are not sufficient to satisfy actual demands. To this end, we propose a novel framework, named Extracting Multiple Relationships Concept Map (EMRCM), to construct multiple relations concept maps from Multi-source data. Specifically, we design various targeted evidences to explore diverse information of multi-source data from different perspectives. Then, we employ three classic classifiers to bulid the predictive model for extracting key concepts and multiple concept relationships using the proposed evidences. We create a real dataset for empirically studying this problem. Extensive experiments on a real-world dataset show the effectiveness of our method.

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