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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Fang, Yutong Huang, Qingqing Ye, Enjie Guo, Wenzhong Guo, Kun Chen, Xiaoqi Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Community detection is a trendy area in research on complex network analysis and has a wide range of real-world applications, like advertising. As people become increasingly concerned about privacy, protecting participants’ privacy in distributed community detection has become a new challenge. When applied to not identically and independently distributed (Non-IID) data, most federated graph algorithms suffer from the weight divergence problem caused by diversified training of local and global models, resulting in accuracy degradation. Furthermore, the privacy protection approaches based on anonymization, such as differential privacy (DP), and cryptography, such as homomorphic encryption (HE), incur accuracy loss and high time consumption, respectively. In this paper, we propose a globally consistent vertical federated graph autoencoder (GCVFGAE) algorithm, which builds a globally consistent model among the coordinator and all participants to solve the Non-IID graph data problem. As well, an attribute blinding strategy based on security aggregation is developed to protect the network privacy of each participant without losing accuracy. Both real-world and artificial networks’ experiments show that our algorithm reaches higher accuracy than the existing vertical federated graph neural networks (GNNs) and the simple distributed graph autoencoder without federated learning and detects communities identical to those found by the standard graph autoencoder (GAE). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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Image segmentation with texture-oriented superpixel 2

Image segmentation with texture-oriented superpixel

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2nd International Symposium on computer Applications and information Systems, ISCAIS 2023

作者： Song, Dongke Huang, Heming Zhang, Huiyun School of computer science and technology Qinghai Normal University Xining810008 China State Key Laboratory of Tibetan intelligent information processing and application Xining810008 China

ISBN: (纸本)9781510666665

Superpixel segmentation is an image preprocessing technique that uses pixel blocks instead of pixels to improve the efficiency of subsequent image tasks. Existing methods are not sensitive to image texture. To solve this problem, a texture-oriented superpixel (TOS) segmentation method is proposed. Firstly, an adaptive parameter function based on pixel boundary probability is used to calculate the distance. Secondly, a gradual merging strategy is used for merging. And finally, a loop iterative framework is used to optimize the seeds. The experimental results show that TOS can effectively preserve the smaller texture information, generate seeds that are more consistent with image texture, and better detect image texture. © 2023 SPIE.

关键词： Superpixels

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Horizontal Federated Traffic Speed Prediction Base on Secure Node Attribute Aggregation 17th

Horizontal Federated Traffic Speed Prediction Base on Secur...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Ye, Enjie Guo, Kun Guo, Wenzhong Chen, Dangrun Zhang, Zihan Li, Fuan Zheng, JiaChen Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923847

Federated graph learning has been widely used in distributed graph machine learning tasks. The data distribution of existing graph-based federated Spatio-temporal prediction methods is mainly segmented by graph topology. However, in the real-world Spatio-temporal traffic speed prediction task, a location will have data from different devices belonging to different companies. A node may have multi-party information in the real-world distributed traffic speed prediction scenario. The difference in multi-party information leads to the information not being fully utilized. Moreover, the direct transmission of node embedding in the federated learning process may also risk privacy leaks. Using homomorphic encryption and other encryption methods will bring a high computational overhead. Therefore we propose a new distributed privacy-preserving traffic speed prediction algorithm, which uses secure node attribute aggregation strategy(SNAAS) to apply to the multi-party collaborative traffic speed prediction scenario when the graph topology structure is public. At the same time, secret sharing technology is used in SNAAS to protect the attribute matrix and reduce the overhead of secret computing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A Robust Framework Based on Mutual information and Contrastive Learning for Multimodal Violence Detection in Videos 23

A Robust Framework Based on Mutual Information and Contrasti...

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15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Xiong, Guoding Zhou, Yongmei Zhou, Dong Lin, Nankai School of Information Science and Technology Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangdong University of Foreign Studies Guangzhou China School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781450398411

With the development of modern information, the security of online videos content is receiving more and more *** work often ignores the correlation between different modalities. In order to solve the above problem, the task of violence detection is proposed to identify and locate violent frames in videos. In this paper, we propose a robust framework based on mutual information and contrastive learning for multimodal violence detection in videos. We use mutual information maximization to improve the correlation between audio and visual modalities. Meanwhile, we leverage contrastive learning to control modality fusion to capture modality invariance. We propose a robust framework consisting of three modules: 1) utilize a graph convolutional neural network module to capture global relationships across multiple video frames;2) demonstrate a mutual information module to strengthen the correlation between audio and visual modalities;3) utilize the contrastive learning module to capture the modality invariance to facilitate modality fusion. For comparison with previous studies, we conduct extensive experiments on a public XD-Violence dataset. Experimental results show that our method achieves state-of-the-art performance. © 2023 ACM.

关键词： Convolutional neural networks

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React Agent-Based Question Answering Method for Biology in National College Entrance Examination 24

React Agent-Based Question Answering Method for Biology in N...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Chen, Yulin Luo, Jing Tu, Xinhui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China College of Computer Science Central China Normal University Hubei Wuhan China

ISBN: (纸本)9798400717826

Recent advancements in large language models have led to significant improvements in various natural language processing tasks, including automated question answering. However, these models still struggle with providing accurate responses to complex biology questions, such as those found in the National College Entrance Examination. To address this issue, researchers have explored retrieval-augmented generation techniques, which incorporate external knowledge sources to enhance answer accuracy. While these methods have shown promise in improving efficiency, they often lack the ability to perform in-depth analysis of complex questions. In response to these limitations, the paper proposes a novel ReAct agent-based question answering method. This method combines biology textbook document libraries with Google search engine capabilities, leveraging the strengths of large language models. By integrating these components, this method can engage in autonomous decision-making and multi-step reasoning, allowing for a more thorough analysis of complex biology questions. Experimental results demonstrate the effectiveness of this new approach. When compared to responses generated directly by ChatGPT and GPT-4o models, the method showed significant improvements in accuracy for biology questions from the National College Entrance Examination. Specifically, the new method achieved accuracy increases of 11.1% and 4.64% over ChatGPT and GPT-4o, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Question answering

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Reducing Equalization-enhanced Phase Noise via Carrier Phase Recovery in the Presence of Variable Matched-filter Bandwidth

Reducing Equalization-enhanced Phase Noise via Carrier Phase...

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Yi, Xingwen Wang, Huaiyin Zhang, Jing Sun Yat-Sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510006 China University of Electronic Science and Technology of China Key Laboratory of Optical Fiber Sensing and Communications Sichuan Chengdu610000 China

ISBN: (纸本)9798350312843

Equalization-enhanced phase noise (EEPN) can be a major limitation in coherent receivers, when the large accumulated dispersion is compensated for high baud rate signals. One unique feature of EEPN is that its phase component can be larger than the amplitude one. Then the carrier phase recovery algorithm can squeeze the phase component, and therefore, the overall EEPN is also reduced. In this paper, we show that the EEPN and its ovality are both affected by the matched filter bandwidth. The reduction of EEPN by BPS can be more pronouncing when the bandwidth is larger. © 2023 IEEE.

关键词： Phase noise

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Robust Video Object Segmentation with Restricted Attention 48

Robust Video Object Segmentation with Restricted Attention

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhang, Huaizheng Guo, Pinxue Le, Zhongwen Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing China Fudan University School of Computer Science Shanghai China Fudan University Academy for Engineering and Technology Shanghai China

ISBN: (纸本)9781728163277

This paper focuses on the two problems of the similar objects distraction and the lack of robustness for unseen object categories in semi-supervised video object segmentation task. Existing methods have achieved great results on the benchmark dataset, but these two problems still have not been completely solved. We propose the Robust Video Object Segmentation With Restricted Attention (RVOSR), which can suppress the effects caused by similar objects and filter out noise confusion from other irrelevant regions. Meanwhile augmenting the semantic information of features, which makes the features more suitable for video object segmentation task. Extensive experiments demonstrate the effectiveness of our approach and achieve the state-of-the-art performance on the widely-used VOS benchmarks including DAVIS-2016 (92.1% J& F), DAVIS-2017 (86.8% J& F) and YouTubeVOS-2019 (84.8%). © 2023 IEEE.

关键词： Semantics

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Energy management of solid oxide fuel cell/lithium battery hybrid system

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CSEE Journal of Power and Energy Systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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Polyp Segmentation Neural Network with Simple Encoding Structure and Reverse Attention Module 13

Polyp Segmentation Neural Network with Simple Encoding Struc...

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13th International Conference on information technology in Medicine and Education, ITME 2023

作者： Wang, Jie Li, Zuoyong Xu, Haiping Cheng, Xuesong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou350001 China College of Mathematics and Data Science Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350319156

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation. © 2023 IEEE.

关键词： Diseases

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Optimizing label correlation in deep learning-based side-channel analysis

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Microelectronics Journal 2025年 162卷

作者： Xia, Shengcheng Li, Lang Ou, Yu Xiang, Jiahao College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang421002 China

Label distribution learning techniques can significantly enhance the effectiveness of side-channel analysis. However, this method relies on using probability density functions to estimate the relationships between labels. The settings of parameters play a crucial role in the impact of the attacks. This study introduces a non-parametric statistical method to calculate the distribution between labels, specifically employing smoothing with the Gaussian kernel function and adjusting bandwidth. Then, the aggregation of the results from each label processed by the Gaussian kernel facilitates a hypothesis-free estimation of the label distribution. This method accurately represents the actual leakage distribution, speeding up guess entropy convergence. Secondly, we exploit similarities between profiling traces, proposing an analysis scheme for sample correlation locally of label distribution learning. Furthermore, Signal to-Noise Ratio (SNR) is employed to re-extract and reduce dataset dimensions to 500 power consumption points, resulting in noise reduction. Our results showcase the successful training of 800 profiling traces using our method for sample correlation locally of label distribution learning, with the findings indicating its exceptional performance. © 2025

关键词： Gaussian distribution

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