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RGCNU: Recurrent Graph Convolutional Network With Uncertainty Estimation for Remaining Useful Life Prediction

IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1640-1642页

作者： Qiwu Zhu Qingyu Xiong Zhengyi Yang Yang Yu School of Big Data&Software Engineering Chongqing UniversityChongqing 400044 Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of EducationChongqing 400044China School of Biology and Engineering Guizhou Medical UniversityGuiyang 550025China

Dear Editor,This letter focuses on the problem of remaining useful life(RUL)prediction of equipment. Existing graph neural network(GCN)-based approaches merely provide the point estimation of RUL. However,the estimated RUL often varies widely due to the model parameters and the noise in data. It is important to know the uncertainty in predictions for reliable risk analysis and maintenance decision *** map the relationship between noisy condition monitoring data and RUL with uncertainty.

关键词： letter estimation prediction

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Exploring implicit influence for social recommendation based on GNN

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Soft computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation Based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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EEiF: Efficient Isolated Forest with e Branches for Anomaly Detection 24

EEiF: Efficient Isolated Forest with e Branches for Anomaly ...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhang, Yifan Xiang, Haolong Zhang, Xuyun Xu, Xiaolong Fan, Wei Zhang, Qin Qi, Lianyong School of Software Nanjing University of Information Science and Technology China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China School of Computing Macquarie University Australia University of Oxford Uffield Department of Women's & Reproductive Health United Kingdom College of Computer Science and Software Engineering Shenzhen University China Faculty of Science and Engineering China

ISBN: (纸本)9798331506681

Anomaly detection is a popular research topic in Artificial Intelligence and has been widely applied in network security, financial fraud detection, and industrial equipment failure detection. Isolation forest based methods are the base algorithms to detect anomalies in these scenarios for their simplicity and efficiency, which has been further exploited with multi-folk trees and learning mechanisms to realize the optimal isolation forest for high detection accuracy. However, the optimal isolation forest is time-consuming with the learning mechanisms, resulting in the task failing of time-constrained applications. Moreover, the original optimal isolation forest fails to construct the optimal tree structure restricted by the time complexity. To address the above challenges, we propose an efficient anomaly detection method called EEIF, which realizes the real e-folk structure of the optimal isolation forest in our practical algorithm design. Specifically, we design a distribution that perfectly matches the e-branch theory to construct the optimal isolation forest. Then, we design an FR clustering scheme to achieve fast training of the isolation forest with learning to hash and provide related proofs of accuracy and efficiency. Besides, a parallel algorithm is integrated into our method to reduce prediction time. Finally, extensive experiments are conducted on a large amount of real-world datasets and the results demonstrate that our method significantly improves efficiency while ensuring effectiveness, compared with the state-of-the-art methods. © 2024 IEEE.

关键词： Anomaly Detection Efficient FR clustering Parallel Algorithm

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research Center of Financial Computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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Enhancing Topic Interpretability for Neural Topic Modeling Through Topic-Wise Contrastive Learning 40

Enhancing Topic Interpretability for Neural Topic Modeling T...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Gao, Xin Lin, Yang Li, Ruiqing Wang, Yasha Chu, Xu Ma, Xinyu Yu, Hailong Software Engineering Institution School of Computer Science Peking University Beijing China Key Lab of High Confidence Software Technologies Ministry of Education Beijing China Peking University National Engineering Research Center of Software Engineering Beijing China School of Computer Science Center on Frontiers of Computing Studies Peking University China

ISBN: (纸本)9798350317152

Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment with the central goals of data mining and knowledge discovery which is to reveal interpretable insights from large data repositories. Overemphasizing likelihood maximization without incorporating topic regularization can lead to an overly expansive latent space for topic modeling. In this paper, we present an innovative approach to NTMs that addresses this misalignment by introducing contrastive learning measures to assess topic interpretability. We propose a novel NTM framework, named ContraTopic, that integrates a differentiable regularizer capable of evaluating multiple facets of topic interpretability throughout the training process. Our regularizer adopts a unique topic-wise contrastive methodology, fostering both internal coherence within topics and clear external distinctions among them. Comprehensive experiments conducted on three diverse datasets demonstrate that our approach consistently produces topics with superior interpretability compared to state-of-the-art NTMs. © 2024 IEEE.

关键词： Data mining

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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Computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Advanced Mechanisms of Perception in the Digital Hide and Seek Game Based on Deep Learning 5

Advanced Mechanisms of Perception in the Digital Hide and Se...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Livada, Časlav Hodak, David Department of Software Engineering Faculty of Electrical Engineering Computing and Information Osijek Croatia Faculty of Electrical Engineering Computing and Information Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

The goal of this paper is to demonstrate the power of machine learning and deep learning combined with powerful tools used in creating virtual simulations. This is made possible by the Unity Game Engine, a software used for the development of 2D and 3D video games and simulations, and the ML-agents plugin, which contains algorithms for training intelligent agents. The basic principles and algorithms of Deep Learning are described. Agent uses these methods to perceive the environment and solve certain tasks. In this paper, a simulation is created in which the agent tries to find hidden objects using machine learning. When the agent finds an item, a new location for the item is randomly generated. It must also complete the task in a specified amount of time. With the right implementation and parameters, the agent could successfully complete the task. © 2022 IEEE.

关键词： Reinforcement learning

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Facial Expression Recognition Based on TripletLoss and Attention Mechanism 28

Facial Expression Recognition Based on TripletLoss and Atten...

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28th International Conference on Automation and computing, ICAC 2023

作者： Gou, Rongqiang Gai, Qiuyan Xu, Zhijie Xu, Yuanping Zhang, Chaolong Jin, Jin He, Jia Shi, Yajing School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (纸本)9798350335859

The paper proposes a facial expression recognition method called Triplet-Loss Attention Network (TAN), which aims to address the problem of large intra-class and inter-class distances in facial expression recognition. The method uses a self-attention mechanism to calculate the weight of triplet expression samples, which helps in determining the influence of key regions on expression recognition. The method uses images with attention weights above a certain threshold as training samples to form new hard triplets. The distance between high-weight samples in the three sets of samples is calculated using the Mahalanobis distance formula, and the difference in distance between high-weight groups of triplets is calculated using the Maharaja Loss function. The Triplet Loss is mainly used as the loss function in TAN, and the model is jointly optimized using Triplet Loss, Mahalanobis Loss, and Cross Entropy Loss functions to improve the performance of the model in facial expression recognition. Experimental results show that TAN performs well in alleviating the intra-class and inter-class distance problem and has good robustness and generalization performance. On the RAF-DB and FERPlus datasets, TAN achieves recognition accuracies of 88.40% and 88.73%, respectively, which is 1.37% and 0.18% higher than the previous state-of-the-art methods. © 2023 IEEE.

关键词： Face recognition

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RAHN: A Reputation Based Hourglass Network for Web Service QoS Prediction 36

RAHN: A Reputation Based Hourglass Network for Web Service Q...

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36th International Conference on software engineering and Knowledge engineering, SEKE 2024

作者： Chen, Xia Du, Yugen Tang, Guoxing Luo, Yingwei Ma, Benchi Software Engineering Institute East China Normal University Shanghai200062 China Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China

ISBN: (纸本)1891706594

As the homogenization of Web services becomes more and more common, the difficulty of service recommendation is gradually increasing. How to predict Quality of Service (QoS) more efficiently and accurately becomes an important challenge for service recommendation. Considering the excellent role of reputation and deep learning (DL) techniques in the field of QoS prediction, we propose a reputation and DL based QoS prediction network, RAHN, which contains the Reputation Calculation Module (RCM), the Latent Feature Extraction Module (LFEM), and the QoS Prediction Hourglass Network (QPHN). RCM obtains the user reputation and the service reputation by using a clustering algorithm and a Logit model. LFEM extracts latent features from known information to form an initial latent feature vector. QPHN aggregates latent feature vectors with different scales by using Attention Mechanism, and can be stacked multiple times to obtain the final latent feature vector for prediction. We evaluate RAHN on a real QoS dataset. The experimental results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of RAHN are smaller than the six baseline methods. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Web services

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Buffer with Feature Tokens: Combating Confusion in Medical Image Continual Learning

Buffer with Feature Tokens: Combating Confusion in Medical I...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fan, Zheming Chen, Weiting Fan, Jiahao Yu, Ruizhi East China Normal University Shanghai Key Laboratory of Trustworthy Computing Moe Research Center of Software/Hardware Co-Design Engineering China

ISBN: (纸本)9798350386226

Due to its complexity and privacy concerns, medical data is often difficult to collect fully at once. New data emerges with the discovery of new diseases and advances in medical technology, but privacy concerns limit its storage. When deep learning models require training on datasets of new diseases, they often suffer from catastrophic forgetting, where the models' performance on previously learned data degrades significantly. Recently, pre-trained continual learning methods have been proposed. Unlike earlier continual learning methods, these new approaches do not require learning excessive amounts of new knowledge, furthermore, they can still achieve good performance even when the pre-trained feature extractor is completely frozen. For these approaches, the performance degradation is not primarily due to catastrophic forgetting, but rather confusion. To combat confusion, we propose a method using a buffer with feature tokens (BWFT) which is cheaper and safer than directly storing the original data. Additionally, we propose a feature merge method to further reduce storage and computation costs. In the class-incremental scenario, our method outperforms state-of-the-art methods on multiple medical datasets as well as natural image datasets. Code is available at https://***/1345475677/BWFT. © 2024 IEEE.

关键词： Contrastive Learning

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