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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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Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Dopamine MoS_2 Biosensor Multiwalled carbon nanotubes Machine learning Complex mixture

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Systematic Selection of a Suitable Data Imputation Technique Based on Data Characteristics

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SN computer science 2025年第5期6卷 1-19页

作者： Sebastian, Anu Maria Peter, David Artificial Intelligence Lab Department of Computer Science Cochin University of Science and Technology Kerala Cochin India Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam India

Analyzing incomplete data is one of the prime concerns in data analysis. Discarding the missing records or values might result in inaccurate analysis outcomes or loss of helpful information, especially when the size of the data is small. A preferable alternative is to substitute the missing values using imputation such that the substituted values are very close to the actual missing values and this is a challenging task. In spite of the existence of many imputation algorithms, there is no universal imputation algorithm that can yield the best values for imputing all types of datasets. This is mainly because of the dependence of the imputation algorithm on the inherent properties of the data. These properties include type of data distribution, data size, dimensionality, presence of outliers, data dependency among the attributes, and so on. In the literature, there exists no straightforward method for determining a suitable imputation algorithm based on the data characteristics. The existing practice is to conduct exhaustive experimentation using the available imputation techniques with every dataset and this requires a lot of time and effort. Moreover, the current approaches for checking the suitability of imputations cannot be done when the ground truth data is not available. In this paper, we propose a new method for the systematic selection of a suitable imputation algorithm based on the inherent properties of the dataset which eliminates the need for exhaustive experimentation. Our method determines the imputation technique which consistently gives lower errors while imputing datasets with specific properties. Also, our method is particularly useful when the real-world data do not have the ground truth for missing data to check the imputation performance and suitability. Once the suitability of a DI technique is established based on the data properties, this selection will remain valid for another dataset with similar properties. Thus, our method can save time an

关键词： Data analysis Data distribution Data imputation Data preprocessing Imputation performance Machine learning Missing value handling

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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LWFDTL: lightweight fusion deep transfer learning for oral Squamous cell Carcinoma diagnosis using Histopathological oral Mucosa

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Multimedia Tools and Applications 2025年 1-25页

作者： Shabir, Aiza ahmed, Khawaja Tehseen Mujahid, Muhammad Kanwal, Khadija Department of Computer Science Bahauddin Zakariya University Multan Pakistan Institute of Computer Science and Information Technology The Women University Multan60000 Pakistan Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia

Oral squamous cell carcinoma (OSCC) is among the most lethal types of cancers, especially realizing the significance of timely diagnosis. Insufficient training and awareness among primary healthcare practitioners might result in more difficult treatments, longer hospital stays, and worse survival rates. This study aims to develop a lightweight model that diagnoses cancer in its early stages and incorporates deep transfer training methods with strategies based on convolution networks and domain-specific features of OSCC samples. This work proposes a new light sequential fusion model based on histopathological data for the diagnosis of OSCC with oral mucosa images. To boost detection performance, EfficientNet is incorporated with convolutional neural networks (CNNs) as well as with max-pooling layers. The application of max-pooling also brings down the spatial features and takes care of edges and texture which proves to be both curtailing the computational workload and enhancing the performance of the model. The legitimacy of the developed framework was assessed on 1224 histopathological images where images were categorized under normal and OSCC classes depending on malignancy grades. Moreover, to improve the model training, the framework applies fine preprocessing and augmentation of input parameters such as rotations, zooms, flips, contrasts, and crops. These augmentations help the model learn from different views and hence boost its detection capability across all the aspects of the data. Experimental findings shows that the proposed framework had an accuracy before enhancement of 91.0% and an accuracy after enhancement of 98.9%, which is even higher than such methods as the traditional single-model and the majority of modern methods. Additionally, our model is lightweight, requiring significantly less time than 0.05 segments on an average GPU. It is also conclude that the proposed model be run in clinical settings with modest computational capabilities. These resu

关键词： Diseases

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Cortex: Enhancing Resource Utilization in Edge Clusters Through Efficient Co-Location of LC and BE Workloads

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IEEE Internet of Things Journal 2025年第10期12卷 14733-14751页

作者： Zheng, Tianyang Yang, Pengfei Wang, Quan Zhang, Shuyu Li, Hui Lv, Wenkai Xidian University School of Computer Science and Technology Xi’an710071 China Tencent AI Lab Tencent AI Platform Department Shenzhen518000 China

In edge computing environments, the co-location of latency-critical (LC) services and best effort (BE) jobs is a key strategy for enhancing resource utilization. However, existing analysis-based co-location strategies incur high analytical costs and struggle to rapidly adapt to the evolving fields of edge computing and microservice architectures, often failing to effectively meet the demands of edge computing environments. Feedback-based co-location strategies, while reducing analytical overhead, lack sufficient research in multinode environments, resulting in overly coarse-grained deployment strategies. These strategies do not adequately consider the dynamic workloads and resource constraints inherent in edge computing, leading to improper resource allocation and degraded performance. This article introduces Cortex, a Kubernetes-based co-location framework for edge device clusters that addresses these challenges by innovatively transforming the co-location deployment problem into a minimum cost maximum flow (MCMF) problem and employing the network simplex algorithm (NSA) to optimize resource allocation and ensure Quality of Service (QoS) of LC services. Cortex also features a dynamic adjustment mechanism that adapts to changes in the request load of LC services, thereby minimizing the performance loss of BE jobs and reducing resource wastage. Our experiments in real edge device clusters demonstrate that Cortex significantly improves system resource utilization by 12.81%, increases the QoS satisfaction rate by 17.86%, and boosts the number of BE jobs by 51.96% compared to existing methods. © 2014 IEEE.

关键词： Costs

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Real-Time On-Site Ground-Motion Prediction Using ResNet

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Seismological Research Letters 2025年第3期96卷 1834-1845页

作者： Yang, Tzu-Yi Chen, Da-Yi Chin, Tai-Lin Mobile Computing and Networking Lab Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center of Central Weather Administration Taipei Taiwan

On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023. © Seismological Society of America.

关键词： Convolutional neural networks

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Hybrid Attendance Management System: Utilizing computer Vision and Deep Learning 18th

Hybrid Attendance Management System: Utilizing Computer Visi...

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18th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2024

作者： Noekhah, Shirin Mahmood, Mohammed Hamid Mohammed, Bawan Hiwa Bahrami, Mohammad Reza Department of Computer Science Komar University of Science and Technology Sulaimani Iraq Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Lab of Cyber-Physical Systems Innopolis University Innopolis Russia

ISBN: (纸本)9789819764686

A hybrid attendance management system has gained widespread popularity since its inception. Traditional methods such as the QR (Quick Response)-code-based approach and the Haar-cascade technique have been used widely for their cost-effectiveness. However, modern techniques such as RFID with IoT (Internet of Things) and Convolutional Neural Networks (CNN) are powerful tools that offer location-based tracking capabilities and improve accuracy. This study introduces a novel Hybrid Attendance Management System to overcome traditional attendance systems’ challenges, including pose invariance, illumination, and partial occlusion. It combines traditional and modern techniques to automate attendance tracking to improve accuracy and efficiency. This hybrid system captured attendance data by employing computer vision, a deep learning-based facial biometric recognition approach, and its integration with RFID technology. The results showed the accuracy of the attendance management system under different circumstances such as lightning, external objects, and inactivity of students. It also provided valuable insights into students’ attendance patterns, including the number of exits and entries, the percentage of inactivity during class, and time spent outside the classroom. These data facilitated categorizing students as present, late, or absent. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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HiLM-D: Enhancing MLLMs with Multi-scale High-Resolution Details for Autonomous Driving

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International Journal of computer Vision 2025年 1-17页

作者： Ding, Xinpeng Han, Jianhua Xu, Hang Zhang, Wei Li, Xiaomeng Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Kowloon Hong Kong Noah’s Ark Lab Huawei Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Kowloon Hong Kong

Recent efforts to use natural language for interpretable driving focus mainly on planning, neglecting perception tasks. In this paper, we address this gap by introducing ROLISP (Risk Object Localization and Intention and Suggestion Prediction), which towards interpretable risk object detection and suggestion for ego car motions. Accurate ROLISP implementation requires extensive reasoning to identify critical traffic objects and infer their intentions, prompting us to explore the capabilities of multimodal large language models (MLLMs). However, the limited perception performance of CLIP-ViT vision encoders in existing MLLMs struggles with capturing essential visual perception information, e.g., high-resolution, multi-scale and visual-related inductive biases, which are important for autonomous driving. Addressing these challenges, we introduce HiLM-D, a resource-efficient framework that enhances visual information processing in MLLMs for ROLISP. Our method is motivated by the fact that the primary variations in autonomous driving scenarios are the motion trajectories rather than the semantic or appearance information (e.g., the shapes and colors) of objects. Hence, the visual process of HiLM-D is a two-stream framework: (i) a temporal reasoning stream, receiving low-resolution dynamic video content, to capture temporal semantics, and (ii) a spatial perception stream, receiving a single high-resolution frame, to capture holistic visual perception-related information. The spatial perception stream can be made very lightweight by a well-designed P-Adapter, which is lightweight, training-efficient, and easily integrated into existing MLLMs. Experiments on the DRAMA-ROLISP dataset show HiLM-D’s significant improvements over current MLLMs, with a 3.7% in BLEU-4 for captioning and 8.7% in mIoU for detection. Further tests on the Shikra-RD dataset confirm our method’s generalization capabilities. The DRAMA-ROLISP is available at https://***/xmed-lab/HiLM-D. © The Aut

关键词： Depth perception

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Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

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computers, Materials & Continua 2025年第4期83卷 1023-1039页

作者： Faten S.Alamri Tariq Sadad Ahmed S.Almasoud Raja Atif Aurangzeb Amjad Khan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan AIDA Lab Department of Information SystemsCollege of Computer&Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science International Islamic UniversityIslamabad44000Pakistan

Mango farming significantly contributes to the economy,particularly in developing ***,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial *** development of deep learning models for detecting crop diseases is an active area of research in smart *** study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)*** datasets were *** first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery *** second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and *** datasets were obtained from publicly available *** proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS *** results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more *** system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual ***,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.

关键词： ConvNeXt model fusion mango disease smart agriculture vision transformer

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