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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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Efficient Sentiment Analysis on IMDb Movie Reviews with Synonym Augmentation and Global Average Pooling 3

Efficient Sentiment Analysis on IMDb Movie Reviews with Syno...

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3rd International Conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Krishna, Aaqil Raj Vardhini, Aluru S Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Universiti Tunku AbdulRahman Department of Internet Engineering & Computer Science Malaysia

ISBN: (纸本)9798350372120

With the advancement in the field of automated recommendations, efficient sentiment analysis of reviews has become immensely important. This study presents the application of the Global Average Pooling (GAP) model in efficient sentiment analysis that has traditionally been dominated by models such as Recurrent Neural Networks. Focusing on IMDb movie reviews this paper showcases the effectiveness of the GAP model in handling text data in extracting features and reducing dimensionality. Through this approach, we observed improvements in classification accuracy while using an efficient model. Our findings suggest that the GAP model could serve as an alternative to conventional sentiment analysis techniques. Furthermore, our research indicates benefits from exploring the integration of GAP, with deep learning methods to further enhance performance. © 2024 IEEE.

关键词： Sentiment analysis

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rafPS:A shapley-based visual analytics approach to interpret traffic

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Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

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HoLens:A visual analytics design for higher-order movement modeling and visualization

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Computational Visual Media 2024年第6期10卷 1079-1100页

作者： Zezheng Feng Fang Zhu Hongjun Wang Jianing Hao Shuang-Hua Yang Wei Zeng Huamin Qu Department of Computer Science and Engineering The Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Thrust of Computational Media and Arts(CMA) The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511458China Department of Computer Science University of ReadingBerkshire RG66AHUK Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement *** methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the ***,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban *** overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban *** mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral ***,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state *** real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using *** also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.

关键词： data visualization movement modeling state sequence visualization movement visualization urban visual analytics

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Stochastic Unit Commitment with High-Penetration Offshore Wind Power Generation in Typhoon Scenarios

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 535-546页

作者： Yanqi Liu Dundun Liu Hongcai Zhang State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao 999078China University of Macao Zhuhai UM Science and Technology Research Institute ZhuhaiChina

To tackle the energy crisis and climate change,wind farms are being heavily invested in across the *** China's coastal areas,there are abundant wind resources and numerous offshore wind farms are being *** secure operation of these wind farms may suffer from typhoons,and researchers have studied power system operation and resilience enhancement in typhoon ***,the intricate movement of a typhoon makes it challenging to evaluate its spatial-temporal *** published papers only consider predefined typhoon trajectories neglecting *** address this challenge,this study proposes a stochastic unit commitment model that incorporates high-penetration offshore wind power generation in typhoon *** adopts a data-driven method to describe the uncertainties of typhoon trajectories and considers the realistic anti-typhoon mode in offshore wind farms.A two-stage stochastic unit commitment model is designed to enhance power system resilience in typhoon *** formulate the model into a mixed-integer linear programming problem and then solve it based on the computationally-efficient progressive hedging algorithm(PHA).Finally,numerical experiments validate the effectiveness of the proposed method.

关键词： Unit commitment two-stage stochastic programming offshore wind farm typhoon

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Automation of Text Summarization using Hugging Face NLP 5

Automation of Text Summarization using Hugging Face NLP

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Asmitha, M. Danda, Aashritha Bysani, Hemanth Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer science Universiti Tunku Abdul Rahman Malaysia

ISBN: (纸本)9798350361155

Within the expansive domain of"Natural Language Processing" (NLP), the task of"text summarization" emerges as a foundational element, playing a pivotal role in distilling relevant information from extensive textual corpora. In the digital age, the importance of efficient summarization becomes increasingly critical, given the overwhelming volume of textual information. This comprehensive study delves into the intricacies of both extractive and abstractive summarization techniques, placing a specific focus on transformer-based models like BERT and GPT. These models, celebrated for their remarkable capabilities in context comprehension and coherent summarization, are rigorously evaluated alongside established methods like TF-IDF, TextRank, Sumy, Fine Tuning Transformers, Model-T5, LSTM, greedy, and beam search. The practical implications of text summarization extend across diverse fields, encompassing news stories, academic papers, and social media content, underscoring its broad utility in various domains. This study not only incorporates cutting-edge models but also explores a gamut of evaluation methods to discern the quality of summarization. By intertwining theory and application, this research positions itself at the forefront of evolving summarization approaches, shedding light on the transformative impact on information consumption patterns. The dynamic landscape of summarization methods underscores the need for continuous research and innovation, as technological advancements continue to reshape how individuals access and comprehend information. © 2024 IEEE.

关键词： Quality control

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Covid-19 Classification using Fine-tuned EfficientNet Architecture 9

Covid-19 Classification using Fine-tuned EfficientNet Archit...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sruthi, Srigiri Emadaboina, Siddharth MacHavarapu, Pradyumna Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering And Computer Science Malaysia

ISBN: (纸本)9798350394474

This work utilizes an Efficient Net-based deep learning architecture, leveraging its efficiency and scalability for image classification tasks. We employ a Dataset of chest X-ray images, including COVID-19 cases as well as other relevant Pathology, to train and evaluate the model. The training process involves data augmentation, model optimization, and early stopping to ensure robust and generalizable performance. It specifies the image size, channels, and class count, and then proceeds to create a pre-trained model using the EfficientNetB0 architecture. The model is built upon the pre-trained EfficientNetB0 model, with additional layers including Layer Normalization, Dense, and Dropout layers for fine-tuning on the Covid-19 data. Subsequently, the model is rigorously evaluated using standard performance metrics, including precision, recall, F1-score, and accuracy, and has proven to outperform other state-of-art models by a 3% increase in F1-score value. © 2024 IEEE.

关键词： Deep learning

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A Machine Learning Approach for Credit Card Fraud Detection in Massive Datasets Using SMOTE and Random Sampling 6

A Machine Learning Approach for Credit Card Fraud Detection ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sreenivas Prasad, B. Akash Babu, N. Reddy, Harthikeswar Singh, Rimjhim Padam Kanchan, Sneha Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering & Computer science Malaysia

ISBN: (纸本)9798350381689

The surge in digital transactions has paved the way for an alarming rise in credit card fraud, compelling the need for robust detection systems. The swift progress of technology has transformed customer payment habits, driving them toward a cashless society, with the rise of digital payments making it easier for fraudsters to commit fraud. The impact of credit card fraud on revenue can be significant. No matter the scale of the businesses, credit card fraud will have a significant effect on the businesses. This paper addresses the escalating threat by proposing a fraud detection model utilizing the 'IEEE CIS Credit Fraud Detection' dataset from Kaggle, constructing a machine learning model aimed at enhancing the accuracy of fraudulent transaction identification, presenting an integrated approach to credit card fraud detection that combines user separation and innovative techniques for improved accuracy and real-world impact, ultimately strengthening the global financial ecosystem. © 2024 IEEE.

关键词： Electronic money

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Predicting the Coronary Heart Disease Risk Over the Next Decade through Ensemble Methodologies 3

Predicting the Coronary Heart Disease Risk Over the Next Dec...

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3rd International Conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Arun, Aashna Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer Science Malaysia

ISBN: (纸本)9798350372120

Ascertaining diagnosis for Coronary Heart Disease (CHD) is a difficult procedure & must be completed precisely within a stipulated period. Numerous researchers have taken a gander at coronary illness risk factors & recommended specific Machine Learning (ML) & Artificial Intelligence (AI) models. This paper attempts to demonstrate the development of various predictive models for the risk of CHD over the next decade using the Framingham dataset with relevant features. The data undergoes thorough preprocessing, including handling missing values, outliers, & duplicates based on which a predictive model was built & evaluated. The work encapsulates essential steps, emphasizing the approach to predicting CHD & providing insights for potential healthcare applications. Attempting to improvise the predictive accuracy the work proposes implementing several ensemble approaches, combining the strengths of multiple models which showcased exceptional performance. Through the ensemble techniques, the individual models were integrated to mitigate weaknesses & enhance overall predictive performance that contributes to a more advanced and accurate prediction of CHD. The various strategic ensemble approaches enhance the reliability of the predictive model, offering a more effective tool for healthcare practitioners in assessing the likelihood of CHD in subjects. © 2024 IEEE.

关键词： Diagnosis

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Time Series Forecasting for COVID-19 Confirmed Cases Using Transformer Based Stacked LSTM Model 8th

Time Series Forecasting for COVID-19 Confirmed Cases Using T...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Penumadi, Sri Naga Jathin Nambiar, Vaishakh Vignesh, G.S. Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Perak Malaysia

ISBN: (纸本)9783031736162

The worldwide impact of COVID-19 necessitates accurate forecasting for informed decision-making by governments and health entities. This study proposes neural network architectures for time series forecasting of confirmed COVID-19 cases based on historical data. The models, including variants of LSTM networks, CNN-GRU hybrids, RBMs, DBNs, and self-attention mechanisms, are trained on comprehensive datasets of confirmed cases, deaths, recoveries, and other variables across various countries. Comparative analysis indicates that CNN-GRU and LSTM models exhibit superior performance in forecasting accuracy, surpassing RBM, DBN, and self-attention models across metrics like Mean Absolute Error and Root Mean Squared Error. This research emphasizes the viability of employing advanced neural networks for precise COVID-19 spread predictions, pivotal in shaping public health policies during global crises. © IFIP International Federation for Information Processing 2025.

关键词： Time series

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