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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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Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem:Review and Analysis

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Tsinghua science and technology 2024年第5期29卷 1368-1389页

作者： Lin Gui Xinyu Li Qingfu Zhang Liang Gao State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Computer Science City University of Hong KongHong KongChina

Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable *** combining domain knowledge of the specific optimization problem,the search efficiency and quality of meta-heuristic algorithms can be significantly improved,making it crucial to identify and summarize domain knowledge within the *** this paper,we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem(JSP).Firstly,this paper delves into the importance of domain knowledge in optimization algorithm *** that,the development of different methods for the JSP are reviewed,and the domain knowledge in it for meta-heuristic algorithms is summarized and *** of this domain knowledge are analyzed,showing it is indispensable in ensuring the optimization performance of meta-heuristic ***,this paper analyzes the relationship among domain knowledge,optimization problems,and optimization algorithms,and points out the shortcomings of the existing research and puts forward research *** paper comprehensively summarizes the domain knowledge in the JSP,and discusses the relationship between the optimization problems,optimization algorithms and domain knowledge,which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.

关键词： domain knowledge job-shop scheduling problem meta-heuristic algorithm

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Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing

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Discover Artificial Intelligence 2025年第1期5卷 1-29页

作者： Islam, Md. Rezaul Chowdhury, Aniruddha Islam Shama, Sharmin Lamyea, Md. Masudul Hasan Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet3114 Bangladesh Department of Educational Technology and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur1750 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1212 Bangladesh

Thyroid disorders are increasingly prevalent, making early detection crucial for reducing mortality and complications. Accurate prediction of disease progression and understanding the interplay of clinical features are essential for effective diagnosis and treatment. Our study addresses these challenges by employing a standard machine learning model, enhanced with comprehensive clinical feature analysis and an ensemble learning technique. By leveraging machine learning, we can identify key risk factors and improve diagnostic accuracy. To achieve optimal prediction outcomes, we evaluated seventeen machine learning models and implemented an Ensemble ML classifier using a hard voting strategy. Class balancing techniques, particularly random oversampling, significantly improved classification performance. Our experimental results demonstrate that the proposed model outperforms existing methods, achieving 100% sensitivity and 99.72% accuracy using the XGBoost algorithm and SelectKBest feature selection. By addressing feature reduction and high class-imbalance, the ensemble ML classifier with hard voting proves more effective in handling classification challenges. © The Author(s) 2025.

关键词： Adversarial machine learning

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3GPP Edge–Fog federation: Transparent 3rd-party authentication and application mobility

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computer Communications 2024年 222卷 220-229页

作者： Ali, Asad Islam, Minhajul Mallick, Tushin Islam, Mohammad Sakibul Sakib, Sadman Shohrab Hossain, Md. Lin, Ying-Dar National Institute of Cyber Security Ministry of Digital Affairs Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh

3GPP edge and fog computing paradigms provide computational services to users at low latency. These paradigms alone are not enough to fulfill the users’ requirements completely. Therefore, a federation among these computing paradigms is necessary. To realize such federation, there is a need of an authentication mechanism where subscribers of a fog, can access 3GPP edge's services, or vice versa, without buying new subscription, and an application mobility mechanism for continuous service during handover from 3GPP edge to fog, or vice versa, without re-authentication. In this work, we propose: (1) a proxy-based state transfer and third-party authentication (PS3A), that uses a transparent proxy to transfer the authentication and application state information between 3GPP edge and fog, and (2) a token-based state transfer and proxy-based third-party authentication (TSP3A), that uses the proxy to transfer the authentication information and tokens to transfer application state information between 3GPP edge and fog. The proxy plays different roles, via virtual counterparts of entities involved in these protocols, to provide transparency. When the 3GPP edge, using EPS-AKA, receives an authentication request, the proxy relays and behaves as a virtual Home Subscriber Server (vHSS) for the 3GPP edge, and behaves as a virtual user for the fog, which is using OIDC. We applied PS3A and TSP3A to three federation scenarios among 3GPP edge and fog. Experimental results show that PS3A and TSP3A provide authentication within 0.345–2.858 s for a 0–100 Mbps proxy load. The results further show that TSP3A provides application mobility, while taking 40%–52% less time than PS3A, using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloud-based service, via tokens and prefetching. © 2024 Elsevier B.V.

关键词： Authentication

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Learning Discriminatory Information for Object Detection on Urine Sediment Image

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computer Modeling in Engineering & sciences 2024年第1期138卷 411-428页

作者： Sixian Chan Binghui Wu Guodao Zhang Yuan Yao Hongqiang Wang College of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China The Hefei Institutes of Physical Science Chinese Academy of SciencesHefei230071China Department of Digital Media Technology Hangzhou Dianzi UniversityHangzhou310018China School of Computer Science University of Nottingham Ningbo ChinaNingbo315100China

In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad *** the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and ***,manual urine sediment examination is labor-intensive,time-consuming,and *** machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine *** learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image ***,only a limited number of publicly available urine sediment datasets are currently *** alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert *** main challenges are associated with our dataset:a large number of small objects and the occlusion between these small *** manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this ***,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and *** propose an improved lightweight one-stage object detection algorithm called *** proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the *** local context attention module captures context information beyond the object region,

关键词： Object detection attention mechanism medical image urine sediment

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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READS: Resource efficient attack detection system for drones

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Multimedia Tools and Applications 2024年 1-20页

作者： Vishnu, Aditya Arora, Sumedha Kerala University of Digital Sciences Innovation and Technology Kerala Thiruvananthapuram India Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahemdabad India

Unmanned Aerial Vehicles (UAVs), commonly known as aerial drones, have gained popularity due to their versatility and applications in various industries. However, their increasing use has raised concerns about cybersecurity and the vulnerability of networked UAVs to malicious attacks. A deep learning-based Attack Detection System (ADS) is essential for mitigating cyber threats to UAVs. It can provide proactive protection even against unknown attacks. Also, for resource constraints of UAVs, ADS must be resource-efficient to optimize the deep learning model for embedded hardware, making a low code footprint. Therefore, this work focuses on developing a Resource Efficient Attack Detection System (READS) for drones that uses a lite deep learning-based network activity classification algorithm for detecting cyber threats on UAVs. The entire system has been implemented in three phases, where the first phase includes dataset training. The second phase involves running multiple conventional models to choose the best conventional model for intrusion detection using accuracy, precision, and recall. This is followed by phase three, which converts the selected conventional Dense Neural Network (DNN) model into a lite weight (Tiny) model that works on UAVs. The selected Lite DNN model is evaluated on a real-time testbed. The optimized model could lower the storage space and generates 97.69% accuracy. The proposed system consumes 30% of the original power. Hence, it allows efficient threat detection while minimizing the impact on UAV resources. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Drones

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Topic Modelling and Sentimental Analysis of Students’Reviews

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computers, Materials & Continua 2023年第3期74卷 6835-6848页

作者： Omer S.Alkhnbashi Rasheed Mohammad Nassr Information and Computer Science Department King Fahd University of Petroleum and Minerals31261Kingdom Saudi Arabia Computer Science Department School of Computing and Digital TechnologyBirmingham City UniversityBirminghamB55JUUnited Kingdom

Globally,educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 *** fundamental concern has been the continuance of *** a result,several novel solutions have been developed to address technical and pedagogical ***,these were not the only difficulties that students *** implemented solutions involved the operation of the educational process with less regard for students’changing circumstances,which obliged them to study from *** should be asked to provide a full list of their *** a result,student reflections,including those from Saudi Arabia,have been analysed to identify obstacles encountered during the COVID-19 ***,most of the analyses relied on closed-ended questions,which limited student *** delve into students’responses,this study used open-ended questions,a qualitative method(content analysis),a quantitative method(topic modelling),and a sentimental *** study also looked at students’emotional states during and after the COVID-19 *** terms of determining trends in students’input,the results showed that quantitative and qualitative methods produced similar *** had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face ***,topic modelling has revealed that the majority of difficulties are more related to the environment(home)and social *** were less accepting of online *** a result,it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot,such as social interaction and effective eye-to-eye communication.

关键词： Topic modelling sentimental analysis COVID-19 students’input

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

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Tsinghua science and technology 2024年第6期29卷 1776-1784页

作者： Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering&TechnologyHyderabad 500090India Institute of Computer Science and Digital Innovation(ICSDI) UCSI UniversityKuala Lumpur 56000Malaysia also with Department of ECE KoneruLakshmaiah Education FoundationHyderabad 500090India ICSDI UCSI UniversityKuala Lumpur 56000Malaysia Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah 21448Kingdom of Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk 47512Kingdom of Saudi Arabia

In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.

关键词： wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al)

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