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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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Characterizing Interaction Uncertainty in Human-Machine Teams 4

Characterizing Interaction Uncertainty in Human-Machine Team...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Wenskovitch, John Fallon, Corey Miller, Kate Dasgupta, Aritra Artificial Intelligence & Data Analytics Pacific Northwest National Laboratory RichlandWA United States New Jersey Institute Of Technology Department Of Data Science NewarkNJ United States

ISBN: (纸本)9798350315790

With the increasing use and adoption of artificial intelligence (AI), the reliability of modern data systems will be driven by a tighter teaming between human experts and intelligent machine teammates. As in the case of human-human teams, the success of human-machine teams will also rely on clear communication about mutual goals and actions. In this paper, we combine related literature from cognitive psychology, human-machine teaming, uncertainty in data analysis, and multi-agent systems to propose a new form of uncertainty: interaction uncertainty for characterizing bidirectional communication in human-machine teams. We map the causes and effects of interaction uncertainty and outline potential ways to mitigate uncertainty for mutual trust in a high-consequence real-world scenario. © 2024 IEEE.

关键词： Multi agent systems

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Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python 1

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16th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2023 and 14th International Conference on EUropean Transnational Education, ICEUTE 2023

作者： Melgar-García, Laura Troncoso-García, Ángela Gutiérrez-Avilés, David Torres, José Francisco Troncoso, Alicia Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Computer Science University of Seville Seville Spain

ISBN: (数字)9783031425196

ISBN: (纸本)9783031425189

Explainable artificial intelligence aims to describe an artificial intelligence model and its predictions. In this research work, this technique is applied to a subject of a Computer science degree where the programming language changed from Octave to Python. Experiments are performed to analyze the explainability using the SHapley Additive exPlanations algorithm for XGBoost regressor model (for numerical grade prediction) and XGBoost classifier model (for class grade prediction). After the validation and training process, several conclusions are drawn that validate the idea of changing the programming language to a more popular one such as Python. For example, regarding classification problems, the most important feature for the insufficient class in the Octave courses is the practical exam. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Python

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Quality of life issues in rural settlements:assessment by social media users

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data science and Management 2024年第4期7卷 283-292页

作者： Evgeniy V.Shchekotin Viacheslav L.Goiko Darya O.Dunaeva Polina A.Basina Julia K.Aleksandrova Department of Philosophy and History Siberian State University of Telecommunications and Information ScienceNovosibirsk630102Russia Laboratory of Applied Big Data Analysis Tomsk State UniversityTomsk634050Russia

Disparity in the quality of life of people living in rural and urban areas is among the major problems,often leading to greater depression among the rural population compared to those in urban areas who have access to a high standard of *** goal of this study was to identify the issues of the quality of life that are fundamental from the point of view of rural settlements and typical for the rural population in various regions of the Russian Federation with diverse geographical,socioeconomic,and demographic *** practical relevance of our study is based on the identification of the scope of typical problems related to the quality of life in rural *** fundamental value of this study is the significance of digital tracks,which can serve as a source of *** data sources included messages and posts discussing various quality of life aspects of the rural population published by *** this study,we used messages and posts with negative implications from communities in rural settlements in ten Russian *** issues include housing infrastructure and utilities,transportation,the environment,telecommunications,banking,healthcare,and education.

关键词： Rural settlements Quality of life Social media Russia Digital footprint

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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Clustering and Analysis of Maternal Mortality Causes in Rwanda: Implications for Targeted Health Interventions

Clustering and Analysis of Maternal Mortality Causes in Rwan...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Ngizimana, Boaz Nizeyimana, Pacifique School of Sciences and technologies Adventist University Of Central Africa Department of Big Data Analytics Kigali City Rwanda African Institute of Mathematical Sciences Kigali City Rwanda

ISBN: (纸本)9798350376838

Maternal mortality remains a critical public health challenge in Rwanda, with various factors contributing to adverse outcomes across different geographic regions. This study aimed to identify patterns and cluster mortality causes using Principal Component Analysis (PCA) and K-means clustering. K-means was chosen for its effectiveness in partitioning data into distinct, non-overlapping groups based on similarity of features, as well as its simplicity and scalability in handling multidimensional data. The analysis utilized a comprehensive dataset from the Civil Registration and Vital Statistics (CRVS) system, encompassing maternal death records in Rwanda. K-means clustering on the data revealed four distinct clusters characterized by unique patterns of causes of death, geographic distribution, and underlying conditions. The results highlighted the prevalence of post-partum hemorrhage in Cluster 1, the largest with 49 cases, showing high frequencies in Southern Province (14 cases), Western Province (12), and Kigali City (9). Cluster 2 (18 cases) focused on atonic postpartum hemorrhage, with a more even distribution across provinces. Pre-eclampsia superimposed on hypertension was prominent in Cluster 3, exclusively located in Kigali City (Nyarugenge district) with 11 cases. Geographical disparities were evident, with clusters showing varying concentrations in specific provinces and districts. Cluster 4 uniquely featured renal failure following unspecified abortion, with 15 cases spread across provinces but highest in Western Province (6 cases). The findings underscore the importance of tar-geted interventions, including enhanced medical training, focused healthcare programs, infrastructure improvements, community engagement, and data-driven strategic planning. By addressing the identified challenges through evidence-based approaches tailored to each cluster's specific characteristics, decision-makers can prioritize resources and develop targeted policies to improve mat

关键词： clustering analysis maternal mortality Principle component Analysis public health

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Explaining the Effects of Preprocessing on Constraint Satisfaction Search 30th

Explaining the Effects of Preprocessing on Constraint Sat...

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30th Irish Conference on Artificial Intelligence and Cognitive science, AICS 2022

作者： Wallace, Richard J. Insight Centre for Data Analytics and Department of Computer Science University College Cork Cork Ireland

ISBN: (纸本)9783031264375

Preprocessing constraint satisfaction problems is a much studied method for improving the performance of subsequent solution search. The traditional explanation for its beneficial effects is "problem reduction", where possible values that cannot take part in a solution are discarded, leaving fewer possibilities to explore during search. Here, we show that this is not the only or even the main factor when dynamic variable ordering heuristics are used. Multiple lines of evidence indicate that under these conditions domain reductions effected by preprocessing serve to inform the heuristic as to which variables should be chosen for instantiation before others. It is suggested that an information transmission model is needed to account for such effects, and it is argued that an extension of this approach can incorporate simple domain reduction effects as well. © 2023, The Author(s).

关键词： Constraint satisfaction problems

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big OLAP data Cube Compression Algorithms in Column-Oriented Cloud/Edge data Infrastructures 9

Big OLAP Data Cube Compression Algorithms in Column-Oriented...

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9th IEEE International Conference on Multimedia big data, bigMM 2023

作者： Cuzzocrea, Alfredo University of Calabria The Big Data Engineering and Analytics Laboratory Rende Italy University of Paris City Dept. of Computer Science Paris France

This paper provides a brief overview on the innovative problem of devising and implementing big OLAP data cube compression algorithms in column-oriented Cloud/Edge data infrastructures, an emerging need for next-gener... 详细信息

ISBN: (纸本)9798350360004

关键词： data analytics

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Detectability of hierarchical communities in networks

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Physical Review E 2024年第3期110卷 034306页

作者： Leto Peel Michael T. Schaub Department of Data Analytics and Digitalisation School of Business and Economics Department of Computer Science

We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analyzed in detail and a phase transition has been identified below which the partition cannot be detected. Here we show that, in the hierarchical setting, there exist additional phases in which the presence of multiple consistent partitions can either help or hinder detection. Accordingly, the detectability limit for nonhierarchical partitions typically provides insufficient information about the detectability of the complete hierarchical structure, as we highlight with several constructive examples.

关键词： Community structure Complex systems Network structure Patterns in complex systems Block models data analysis

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Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

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Computers, Materials & Continua 2024年第3期78卷 3021-3039页

作者： Shoukun Xu Lujun Zhang Guangqi Jiang Yining Hua Yi Liu School of Computer Science and Artificial Intelligence Aliyun School of Big Data and School of SoftwareChangzhou UniversityChangzhou213164China Department of Computer Science University of AberdeenKing’s CollegeAberdeenAB243FXUK

This paper focuses on the task of few-shot 3D point cloud semantic *** some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic *** tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding ***,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their ***,we designed a multi-prototype enhancement module to enhance the prototype ***,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich ***,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category *** studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic ***,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

关键词： Few-shot point cloud semantic segmentation CapsNets

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