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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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A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

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PhishGuard: Leveraging NLP and Machine Learning for Email Phishing Detection

PhishGuard: Leveraging NLP and Machine Learning for Email Ph...

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2024 International Conference on Big data Analytics in Bioinformatics, DABCon 2024

作者： Ahamed Fahim, Rifat Arman, Md.Shohel Sultana, Ishrat Tasnim, Nusrat Rifat Ahmed, Kazi Mahmud, Imran Daffodil International University Diu Data Science Lab Department of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350378047

Effective communication via email is indispensable in modern professional and per sonal contexts, facilitating efficient information exchange, collaboration, and connec tivity across global networks. Phishing emails pose a significant threat by potentially compromising personal and financial information, leading to identity theft, financial loss, and erosion of trust in online communication channels. Phishing emails pose a persistent threat to cybersecurity, necessitating robust detection, and classification mechanisms. This study evaluates the effectiveness of RandomForest and Naive Bayes classifiers in identifying phishing emails, employing the Natural Language Processing (NLP) techniques namely Term Frequency-Inverse Document Frequency (TF-IDF) and CountVectorizer methods for feature extraction, respectively. The RandomForest classifier demonstrates superior performance, achieving an impres sive accuracy rate of 98.91% with a precision score of 98.09%. In contrast, the Naive Bayes classifier yielded slightly lower accuracy at 98.44% and a precision score of 98.06%. These findings underscore the critical role of feature extraction techniques in enhancing the accuracy of phishing email detection. By leveraging machine learning algorithms and advanced feature extraction methods of NLP, organizations can bolster their defenses against phishing attacks, thereby safeguarding sensitive infor mation and mitigating potential risks. © 2024 IEEE.

关键词： Phishing

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Research on the Relationship Between New Energy Vehicles and Dual Carbon in Yangtze River Delta Based on GRA-LSTM Model 7

Research on the Relationship Between New Energy Vehicles and...

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7th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Zhou, Jinxuan Deng, Liang Tong, Yi Gao, Yan Wuhan Business University Department of Information Engineering Hubei China Data Science and Big Data Technology China Internet of Things Engineering China Software Engineering Big Data Technology

ISBN: (纸本)9798350339161

New energy automobile industry plays an important role in building a green, low-carbon and recycling industrial system. In this paper, the prediction simulation training and prediction accuracy comparison study are carried out with the help of the newly constructed GRA-LSTM model, Biological Neural Network model and the first-order one-variable gray GM (1, 1) model. The LSTM model is created and trained by Learning Rate Decay function. The Learning Rate Decay callback function is set, and the learning rate is gradually reduced during the training process to carry out simulation training, and finally the trained model is used to make predictions, and the time for the Yangtze River Delta region to reach carbon peak is 2028, and the time to reach carbon neutrality is 2060, and at the same time this paper analyzes and finds out that the higher the new energy automobile market ownership is, the shorter the time for carbon peak and carbon neutrality will be.2. © 2024 IEEE.

关键词： Forecasting

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A Tutorial on Federated Learning from Theory to Practice:Foundations,software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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Technological and Societal Shifts in Virtual Worlds: A Comparative Perspective 2

Technological and Societal Shifts in Virtual Worlds: A Compa...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Shubita, Ahmad F. Al-Banna, Abedal-Kareem Otoum, Nesreen University Of Petra Department Of Software Engineering Amman Jordan University Of Petra Department Of Ai And Data Science Amman Jordan

ISBN: (纸本)9798350394962

This paper aims at providing a comprehensive history and evolution of virtual worlds starting from their rudimentary beginnings in the form of text-based MUD games to their current day fully immersive 3D and augmented reality environments. This is done through a comparative study of the technology and user experiences throughout the different generations of virtual worlds with its impact evolving from being used purely for entertainment to having its hands in many other societies, such as the workforce. The methodology applied in this paper is based on an accurate study and review of the technology trends and user dynamics in the different generations of virtual worlds. This paper also addresses the perspective of these evolving environments, both positive and negative impacting on the users and the ethical consideration of the user to the environment, this paper emphasizes the need for further research in responsible innovation of virtual worlds for the betterment, showing the transformation journey and the foreseeable future. © 2024 IEEE.

关键词： Augmented reality

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Heart rate variability analysis in controls and epilepsy patients with or without receiving treatment: a clinical review and meta-analysis

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Neural Computing and Applications 2024年第29期36卷 18043-18063页

作者： Shahnawaz, Muhammad Bilal Dawooda, Hassan Iqbal, Uzair Department of Information Engineering Technology National Skills University Islamabad Islamabad4400 Pakistan Software Engineering Department University of Engineering and Technology Taxila Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The malfunctioning of cardiac autonomic control in epileptic patients develops ventricular tachyarrhythmia and causes sudden unexpected death in epilepsy patients (SUDEP). Various clinical studies investigated the effect of epilepsy on cardiac autonomic control by performing heart rate variability (HRV) analysis;however, results are unclear regarding whether sympathetic, parasympathetic, or both branches of the autonomic nervous system (ANS) are affected in epilepsy and also the impact of anticonvulsant treatment on the ANS. This study follows the systematic protocols to investigate epilepsy and its anticonvulsant treatment on cardiac autonomic control by using linear and nonlinear HRV analysis measures. The electronic databases of PubMed, Embase, and Cochrane Library were used for the collection of studies. Initially, 1475 articles were identified whereas after 2-staged exclusion criteria, 33 studies were selected for execution of the review process and meta-analysis. For meta-analysis, four comparisons were performed (epilepsy patients): (1) controls (healthy subject with no history of epilepsy) versus untreated patients;(2) treated (patients under treatment that have a seizure) versus untreated patients;(3) controls versus treated patients;and (4) refractory versus well-controlled (epilepsy patients that were seizure-free for last 1 year). For treated and untreated patients, there was no significant difference whereas well-controlled patients presented higher values as compared to refractory patients. Meta-analysis was performed for the time-domain, frequency-domain, and nonlinear parameters. Untreated patients in comparison with controls presented significantly lower HF (high-frequency) and LF (low-frequency) values. These LF (g = − 0.9;95% CI − 1.48 to − 0.37) and HF (g = − 0.69;95% confidence interval (CI) − 1.24 to − 0.16) values were affirming suppressed both, vagal and sympathetic activity, respectively. Additionally, LF and HF value was increased in most o

关键词： Heart

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Sustainable Teaching and Learning E-learning Model towards Redesigning Transformative Learning Model for Knowledge Sharing & Infrastructure Enhancements Post COVID-19

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Recent Patents on engineering 2025年第2期19卷 1-12页

作者： Veeramanickam, M.R.M. Kaur, Gaganpreet Acakpovi, Amevi Vadrevu, Pavan Kumar Ciro, Rodriguez R. Kalpana, A.V. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Software Engineering Universidad Nacional Mayor de San Marcos Lima Peru Electrical and Electronic Engineering Department Accra Technical University Accra Ghana Andhra Pradesh India Department of Software Engineering Universidad Nacional Mayor de San Marcos UNMSM Lima Peru Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India

Background: Recently, e-learning has become a very basic, integral part of technology-based learning. Wide trends are increasing day by day because of the demands and its usage based on working remotely due to highly penetrated mobile handheld devices and digital media. The smart campus infrastructure has played a vital role to its full extent towards Z millennium students in the 20th century. The teaching and learning accessibility depends on terms of various cost-based afforda-ble platforms, either with synchronous learning or asynchronous mode of learning. Methods: The current patent research explores the changeling leading to infrastructural reforms as per the need for digital media for e-learning during and after COVID-19 spreads. The perspectives in 2 forms of research study are: 1st working on infrastructural needs and demands for the smart campuses and online learning challenges and 2nd is working on platforms technology utilization for better accessible resources for all learners. This work studied different aspects during and after COVID-19, leading to the importance of uninterrupted internet access, phone, hardware and relia-bility, etc. In this work, the importance of gamification study and flipped classrooms for enhancing learner performance to highly engage them in learning environments focused research model on learner engagement on Gamified perceiving study with Smart PLS-SEM was investigated. Promoting sustainability in its entirety through knowledge transfer and contributions to address various challenges in the redesign of learners' syllabi to meet educational needs, emphasizing online learning to integrate various modes of learner platforms, personalized teaching and learn-ing, peer-to-peer communication for learner enhancement, and student engagement through gami-fication are studied. Results: Learners who are enrolled at the school, college, and university levels of education increased exponentially post-COVID-19. More than 90% responded to sch

关键词： Curricula

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Towards Artist Recognition Based on Material Rendering. A Case Study for Recognition of Rembrandt and Van Dyck

Towards Artist Recognition Based on Material Rendering. A Ca...

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IS and T International Symposium on Electronic Imaging 2024: Computer Vision and Image Analysis of Art 2024

作者： Huang, Jing Elgammal, Ahmed Department of Big Data and Software Engineering Chongqing University Chongqing China Department of Computer Science Rutgers University NJ United States

Can artists be recognized from the way they render certain materials, such as fabric, skin, or hair? In this paper, we study this problem with a focus on recognizing works by Rembrandt, Van Dyck, and other Dutch and Flemish artists from the same era. This paper proposes a novel material-based approach based on Swin Transformer and Cascade Mask R-CNN to address artist recognition task. We report the performance on a dataset of 644 images. Additionally, the model’s robustness to image variations is studied. © 2024, Society for Imaging science and Technology.

关键词： Rendering (computer graphics)

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A Machine Learning-Based Approach for Sentiment Analysis of Movie Reviews on a Bangladeshi OTT Platform 6th

A Machine Learning-Based Approach for Sentiment Analysis of ...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Jahan, Hasnur Arman, Md. Shohel Hasan, Afia Bristy, Sabikun Nahar DIU Data Science Lab Department of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031501500

Sentiment analysis is the examination of feelings and viewpoints in any kind of literature. Opinion mining is another phrase for sentiment analysis. The data’s sentiment analysis is quite helpful, to convey the collective, group, or individual viewpoint. This method is employed to ascertain a person’s attitude about a specific source. Huge amounts of data are present on social media and other online platforms in the form of tweets, blogs, status, postings, etc. The movie reviews were examined in this research using a variety of methods. On demand of movies on OTT platform several Facebook reviewer pages has been created in Bangladesh. For this work, almost 1000 Bangla reviews were gathered, containing some English word from Facebook. Customer tones were assessed in movie reviews. We use Unigram, Bigram, and Trigram features with a variety of models, including Decision Tree, Random Forest, Multinomial Naive Bayes, K-Neighbors, and Linear Support Vector Machine in n-grams. Random Forest is the most accurate, with 92.35 percent and 90.03 percent accuracy in Unigram and Trigram, respectively. The most accurate model in Bigram is Decision Tree, which has an accuracy of 89.50 percent. This proposed system will help to analysis reviews and give feedback about a movie. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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