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FairCoRe: Fairness-Aware Recommendation Through Counterfactual Representation Learning

IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 4049-4062页

作者： Bin, Chenzhong Liu, Wenqiang Zhang, Feng Chang, Liang Gu, Tianlong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology School of Business Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Jinan University College of Cyber Security Engineering Research Center of Trustworthy AI Ministry of Education Guangzhou510632 China

Eliminating bias from data representations is crucial to ensure fairness in recommendation. Existing studies primarily focus on weakening the correlation between data representations and sensitive attributes, yet may inadvertently steer the user representations toward another potential bias direction of the target attribute. Furthermore, they often overlook the impact of user preferences on capturing sensitive information, incurring inadequate bias elimination. In this paper, we propose a Fair Counterfactual Representations (FairCoRe) learning framework, which aims to ensure the neutrality of representations among all bias directions. First, we intervene on sensitive attributes to construct a counterfactual scenario. Then, two opposing attribute prediction tasks are respectively performed in ground-truth and counterfactual scenarios to encode sensitive information along different bias directions. Second, we design a bias-aware enhancement learning method that quantifies the respective correlation of user preferences and sensitive attributes to enhance sensitive information encoding. Finally, we introduce two mutual information optimization methods that optimize the representations to capture users’ interests and disentangle sensitive factors. Moreover, we propose an attribute neutralization strategy that refines the learned representations, ensuring sensitive attribute neutrality. Extensive experiments demonstrate that our method achieves the optimal fairness and competitive accuracy compared to state-of-the-art methods. © 1989-2012 IEEE.

关键词： Accuracy Mutual information Correlation Data Visualization Medical Services Training Measurement Disentangled Representation Learning Semantics Recommender systems Recommender system Recommendation Fairness Representation Learning Counterfactual Representations Potential Bias Mutual information User Preferences Sensitive Attributes Counterfactual Scenario User Representation Time Complexity Generative Adversarial Networks Learning Objectives Demographic Groups Baseline Methods Recommender systems Minimum Method Prediction Loss Preference information Graph Convolution User Side Recommendation Model Counterfactual Thinking Maximum Mutual information Representation Learning Methods Maximization Method Recommendation Accuracy Recommendation Method Recommendation Task Notions Of Fairness Negative Samples Mutual information Estimation

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China 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 Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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GHTO: Improving Byzantine Robustness in Distributed Machine Learning Through Gradient Heterogeneity Twofold Optimization 6

GHTO: Improving Byzantine Robustness in Distributed Machine ...

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6th International Conference on Frontier Technologies of information and computer, ICFTIC 2024

作者： Li, Caiyun Yang, Ming Cao, Jian Wu, Xiaoming He, Yunpeng Wang, Xin Jinan250014 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China Shandong Evayinfo Technology Co. Ltd Jinan250101 China

ISBN: (纸本)9798331541750

Heterogeneous datasets induce local gradient variance and global gradient variance in distributed learning, collectively referred to as gradient heterogeneity. Gradient heterogeneity increases the system's vulnerability to Byzantine attacks, affecting performance. To address this issue, the paper proposes a Gradient Heterogeneity Twofold Optimization (GHTO) algorithm to enhance the Byzantine robustness of the system in non-independent and identically distributed environments. Specifically, a Local Gradient Correction (LGC) algorithm is proposed, which aims to reduce the local gradient variance. By further integrating LGC with momentum and bucketing techniques to achieve dual optimization of local and global gradient variance. Experimental results demonstrate the proposed LGC algorithm diminishes local gradient variance, and the GHTO method optimizes both local and global gradient variance, which further strengthens the robustness of the system against Byzantine attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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Fuzzing DBMS via NNLM 7

Fuzzing DBMS via NNLM

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7th IEEE International Conference on Data science in Cyberspace, DSC 2022

作者： Li, Yabin Nie, Yuanping Kuang, Xiaohui Beijing University of Posts and Telecommunications School of Computer Science Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Vulnerabilities in database management system (DBMS) can cause serious security problems affecting hundreds of millions of software systems. Fuzzing is an effective vulnerability mining technology, but few studies utilize neural network language model (NNLM) to fuzz DBMS. In this paper, we explore this technical roadmap and use the sequence model to automatically generate test cases to fuzz DBMS. One of the advantages of the method is that it can effectively test the DBMS in a black-box situation. We implemented our tool NNFuzz and evaluated it on SQLite. Experimental results show that NNFuzz can generate valid test cases and achieve higher code coverage than the initial training set. © 2022 IEEE.

关键词： Database systems

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Spiderweb Protector: Biomimetic Defense Against Targeted Attacks on Graph Neural Networks 21

Spiderweb Protector: Biomimetic Defense Against Targeted Att...

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21st International computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2024

作者： Zhou, Zeyu Huang, Yuanyuan Wang, Yongli Li, Siyu Lu, Jiazhong He, Peilin Chengdu University of Information Technology Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Dazhou Key Laboratory Government Data Security Dazhou635000 China University of Pittsburgh Department of Informatics and Networked Systems Pittsburgh15260 United States

ISBN: (纸本)9798331519254

Graph Neural Networks (GNNs) have emerged as powerful tools across diverse applications, including social networks, bioinformatics, and recommendation systems. However, their susceptibility to adversarial attacks, particularly targeted attacks, poses a significant challenge. Targeted attacks exploit the message-passing mechanism of GNNs to induce severe performance degradation, even with minimal perturbations. Inspired by the self-repair and adaptive behaviors of spider webs, this paper introduces SpiderWeb Protector, a biomimetic defense strategy designed to enhance GNN robustness. The proposed method integrates three core components: Community-Centric Graph Update, which selectively reinforces key graph structures;Topology Feature Merging, which improves feature representation by incorporating structural insights;and Edge Pruning, which dynamically removes adversarial connections. Comprehensive experiments on three datasets (Cora, Citeseer and Cora_ml) and two GNN models (GCN and GIN) demonstrate the superior performance of SpiderWeb Protector against both direct and influence-targeted attacks, achieving state-of-the-art results. © 2024 IEEE.

关键词： Graph neural networks

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Adaptive Granulation for Efficient Classification on Noise Labeled Images 9

Adaptive Granulation for Efficient Classification on Noise L...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Cai, Kecan Zhang, Hongyun Miao, Duoqian Tongji University Department of Computer Science and Technology Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (纸本)9798350352214

Current semi-supervised learning-based sample selection methods for noisy label image classification typically utilize all clean and noisy samples for model training. However, not all noisy samples contribute positively to model training. This paper introduces a novel semi-supervised image data granulation method that employs adaptively generated granular noisy sample subsets in place of the original noisy samples to enhance classification efficiency. The granular data generated retain the essential features of the original data, thereby improving efficiency without compromising classification accuracy. The quality of the granular data is assessed using coverage and specificity criteria, standard metrics for evaluating information granules. The proposed method consists of three main components: (i)selecting clean and noisy samples through network co-training, (ii)calculating granular noisy sample subsets by adaptive granulation, and (iii)optimizing the network model using a semi-supervised strategy. Experimental results on benchmark datasets with varying noise rates demonstrate that our method significantly improves the efficiency of noisy label image classification while maintaining accuracy. © 2024 IEEE.

关键词： Self-supervised learning

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DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices 38

DapperFL: Domain Adaptive Federated Learning with Model Fusi...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Jia, Yongzhe Zhang, Xuyun Hu, Hongsheng Choo, Kim-Kwang Raymond Qi, Lianyong Xu, Xiaolong Beheshti, Amin Dou, Wanchun State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas San Antonio United States College of Computer Science and Technology China University of Petroleum [East China China School of Computer and Software Nanjing University of Information Science and Technology China

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL. © 2024 Neural information processing systems foundation. All rights reserved.

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Sharing of Encrypted Lock keys in the Blockchain-Based Renting House system from Time- and Identity-Based Proxy Reencryption

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China Communications 2022年第5期19卷 164-177页

作者： Zhiwei Wang Liping Qian Danwei Chen Guozi sun School of Computer Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Guangxi Key Laboratory of Cryptography and Information Security Department of Computer SciencesGuilin University of Electronic TechnologyGuilin 541004China

To design an efficient protocol for sharing the encrypted lock keys in the renting house system,we introduce a new notion called time-and identitybased proxy reencryption(TIPRE)and the blockchain *** CPA secure TIPRE scheme is constructed from Green et al.’s identity-based proxy reencryption scheme by adding the time *** every time period,a time stamp authority generates a public key embedded with the current time stamp for each *** our protocol for the renting house system,the TIPRE scheme is the primary building block,and the blockchain platform serves instead of a trusted third party,such as a real estate agency between landlords and *** TIPRE scheme allows the landlord to change the lock key at each time period for *** blockchain platform allows the landlords and tenants to directly interact,and all of the interactions are recorded in the blockchain database to provide the desired security requirements,such as nonrepudiation and ***,we provide the secure analysis of our protocol and test its performance by implementing it in the MacBook Pro and the Intel Edison development platforms.

关键词： identity-based proxy reencryption time stamp renting houses system blockchain

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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