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A survey on cross-user federated recommendation

science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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Hybrid mesh-neural representation for 3D transparent object reconstruction

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Computational Visual Media 2025年第1期11卷 123-140页

作者： Jiamin Xu Zihan Zhu Hujun Bao Weiwei Xu College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China College of Computer Science ETH ZurichZurich 8092Switzerland.

In this study,we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting *** combines the advantages of an explicit mesh and multi-layer perceptron(MLP)network as a hybrid representation to simplify the capture settings used in recent *** obtaining an initial shape through multi-view silhouettes,we introduced surface-based local MLPs to encode the vertex displacement field(VDF)for reconstructing surface *** design of local MLPs allowed representation of the VDF in a piecewise manner using two-layer MLP networks to support the optimization *** local MLPs on the surface instead of on the volume also reduced the search *** a hybrid representation enabled us to relax the ray–pixel correspondences that represent the light path constraint to our designed ray–cell correspondences,which significantly simplified the implementation of a single-image-based environment-matting *** evaluated our representation and reconstruction algorithm on several transparent objects based on ground truth *** experimental results show that our method produces high-quality reconstructions that are superior to those of state-of-the-art methods using a simplified data-acquisition setup.

关键词： transparent object 3D reconstruction environment matting neural rendering

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Efficient Distributed Sparse Relative Similarity Learning

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ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷 1-27页

作者： Yao, Dezhong Li, Sanmu Wang, Zhiwei Zhao, Peilin Wu, Gang Yu, Chen Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Tencent AI Lab Shenzhen China National Super Computing Center in Zhengzhou Zhengzhou University Zhengzhou China

Learning a good similarity measure for large-scale high-dimensional data is a crucial task in machine learning applications, yet it poses a significant challenge. Distributed minibatch Stochastic Gradient Descent (SGD) serves as an efficient optimization method in large-scale distributed training, allowing linear speedup in proportion to the number of workers. However, communication efficiency in distributed SGD requires a sufficiently large minibatch size, presenting two distinct challenges. Firstly, a large minibatch size leads to high memory usage and computational complexity during parallel training of high-dimensional models. Second, a larger batch size of data reduces the convergence rate. To overcome these challenges, we propose an Efficient Distributed Sparse Relative Similarity Learning (EDSRSL) framework. This framework integrates two strategies: local minibatch SGD and sparse relative similarity learning. By effectively reducing the number of updates through synchronous delay while maintaining a large batch size, we address the issue of high computational cost. Additionally, we incorporate sparse model learning into the training process, significantly reducing computational cost. This article also provides theoretical proof that the convergence rate does not decrease significantly with increasing batch size. Various experiments on six high-dimensional real-world datasets demonstrate the efficacy and efficiency of the proposed algorithms, with a communication cost reduction of up to 90.89% and a maximum wall time speedup of 5.66× compared to the baseline methods. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Mentor-Guided Learning in Immersive Virtual Environments: The Impact of Visual and Haptic Feedback on Skill Acquisition

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3547-3557页

作者： Lebrun, Flavien Simon, Cassandre Boukezzi, Assia Otmane, Samir Chellali, Amine Université Evry Paris-Saclay IBISC Lab France Algerian Higher National School of Computer Science IBISC France

In the early stages of learning a technical skill, trainees require guidance from a mentor through augmented feedback to develop higher expertise. However, the impact of such feedback and the different modalities used to communicate it remain underexplored in immersive virtual environments (IVE). This paper presents a study in which 27 participants were divided into three groups to learn a tool manipulation trajectory in an IVE. Two experimental groups received guidance from an expert using visual and/or haptic augmented feedback, while the control group received no feedback. The results indicate that both experimental groups showed significantly greater improvement in tool trajectory performance than the control group from pre- to post-test, with no significant differences between them. Analysis of their learning curves revealed similar performance improvements in tool trajectory across trials, outperforming the control group. Additionally, the visual-haptic feedback condition was linked to lower task load in three out of six dimensions of the NASA-TLX and a higher perceived interdependence with the expert's actions. These findings suggest that augmented feedback from an expert enhances the learning of tool manipulation skills. Although adding haptic feedback did not lead to better learning outcomes compared to visual feedback alone, it did enhance the overall user experience. These results offer valuable insights for designing IVEs that support mentor-trainee interactions through augmented feedback. © 1995-2012 IEEE.

关键词： Virtual environments

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User-trust centric lightweight access control for smart IoT crowd sensing applications in healthcare systems

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Personal and Ubiquitous Computing 2025年第1期29卷 31-44页

作者： Mahmood, Zahid Ashraf, Zeeshan Iqbal, Muddesar Farooq, Beenish Department of Computer Science & IT University of Kotli Azad Jammu & Kashmir Kotli Pakistan Department of Computer Science The University of Chenab Gujrat Pakistan Renewable Energy Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia

The Internet of Things (IoT) enables healthcare systems to handle emergencies, where multiple authorities interact to perform tasks. Prevention of unauthorized access and defining access domains for legitimate users are crucial. Attribute-Based Access Control System (ABACS) techniques play a vital role in defining boundaries in a multi-agent environment. However, adopting traditional ABAC in IoT-based resource-constrained networks is not feasible. This research analyzes the effects of attributes as key performance metrics, including execution time, memory overhead, and computational complexities. To address these challenges, this research proposes a Physical-Social Attributes Access Control Policy (PS-ABACS) framework that secures Multiparty Computation (SMC), symmetric encryption, and randomization-based access control methods. PS-ABASC introduces a lightweight two-party set intersection technique to generate an access policy. The analysis shows that the proposed technique is efficient in computing access policy and session key generation, and less number of attributes based on randomness characteristics is appropriate for resource-constrained networks. Moreover, it demonstrates advancements by reducing memory usage up to 0.048 KB for 60 attributes. The framework generates session keys proficiently, encrypts data, and minimizes computational expenses through a randomized attribute vector. In terms of communication overhead, the framework surpasses expectations by supporting up to 100 attributes, resulting in a reduction of transmission costs to 1120 bits. Overall, this framework improves security, reduces resource consumption, and enhances data exchange efficiency in IoT ecosystems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Access control

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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science China(Information sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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Perturb, Attend, Detect and Localize (PADL): Robust Proactive Image Defense

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IEEE Access 2025年 13卷 81755-81768页

作者： Bartolucci, Filippo Masi, Iacopo Lisanti, Giuseppe University of Bologna Computer Science and Engineering Dept. CVLab Bologna Italy University of Rome Computer Science Dept. OmnAI Lab Sapienza Rome Italy

Image manipulation detection has gained significant attention due to the rise of Generative Models (GMs). Passive detection methods often overfit to specific GMs, limiting their effectiveness. Recently, proactive approaches have been introduced to overcome this limitation. However, these methods suffer from two vulnerabilities: i) the manipulation detector is not robust to noise and hence can be easily fooled;ii) they rely on fixed perturbations for image protection, which offers an exploit for malicious attackers, enabling them to evade detection. To overcome these issues, we propose PADL, a novel solution that is able to create image-specific perturbations for protecting images. PADL's key objective is to provide a secure and adaptive protection mechanism that ensures the authenticity of images by detecting and localizing manipulations, drastically reducing the possibility of reverse engineering. The method consists of two key components: an encoder, which conditions a learnable perturbation on the input image to ensure uniqueness and robustness against attacks, and a decoder, which extracts the perturbation and leverages it for manipulation detection and localization. PADL can detect manipulation of a protected image and pinpoint regions that have undergone alterations. Unlike previous proactive defenses that rely on a finite set of perturbations, PADL's tailored protection significantly reduces the risk of reverse engineering. Although being trained only on images of faces manipulated with STGAN, PADL generalizes to a range of unseen models with diverse architectural designs, such as StarGANv2, CycleGAN, BlendGAN, DiffAE, StableDiffusion, and StableDiffusionXL and also to unseen data domains. Finally, we propose a novel evaluation protocol that fairly assesses localization performance in relation to detection accuracy, providing a better reflection of real-world scenarios. Future research will aim to extend PADL to work on more challenging scenarios, including v

关键词： Reverse engineering

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Hypergraph Regularized Semi-supervised Least Squares Twin Support Vector Machine for Multilabel Classification 27th

Hypergraph Regularized Semi-supervised Least Squares Twin Su...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Rastogi, Reshma Nirwal, Dev Lab Department of Computer Science South Asian University Delhi India

ISBN: (纸本)9783031783821

In a multi-label learning problem, each instance is associated with multiple labels simultaneously. However, problem becomes more complicated when labels are missing. Many multi-label based real-life applications, such as medical diagnosis, protein function prediction, image annotations are framed as networks which are used to model interactions between complex entities. Although, in many scenarios these interactions may not be pairwise, rather should described as higher-order interactions. For such a scenario Hypergraphs are preferred rather than simple Laplacian. Multilabel twin support vector machines (MLTSVM) has become popular due its performance for multilabel classification. In this paper, to deal with missing label scenario, we propose a semi-supervised framework termed as Hypergraph Least Squares Twin Support Vector Machine for Multi-label Learning (HMLLSTSVM) wherein we have used Hypergraph Laplacian to train our classifier utilizing both labeled and unlabeled samples. We incorporate the idea of Hypergraph along with least squares loss function into MLTSVM, which improves the efficacy in terms of classification accuracy and speed of our proposed model. Taking motivation from KNN-based Least Squares Twin Support Vector Machine (KNNLSTSVM), we have incorporated the intrinsic similarity information among the samples in our proposed model’s objective function, which makes our classifier HMLLSTSVM less sensitive to outliers. Experimental results on benchmark multilabel datasets proves the efficacy of the classifier. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image annotation

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