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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 333-353页

作者： Rao, Bosen Zhang, Jiale Wu, Di Zhu, Chengcheng Sun, Xiaobing Chen, Bing Yangzhou University School of Information Engineering Yangzhou225127 China University of Southern Queensland School of Mathematics Physics and Computing ToowoombaQLD4350 Australia Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

The emergence of new machine learning methods has led to their widespread application across various domains, significantly advancing the field of artificial intelligence. However, the process of training and inferring machine learning models relies on vast amounts of data, which often include sensitive private information. Consequently, the privacy and security of machine learning have encountered significant challenges. Several studies have demonstrated the vulnerability of machine learning to privacy inference attacks, but they often focus on specific scenarios, leaving a gap in understanding the broader picture. We provide a comprehensive review of privacy attacks in machine learning, focusing on two scenarios: centralized learning and federated learning. This article begins by presenting the architectures of both centralized learning and federated learning, along with their respective application scenarios. It then conducts a comprehensive review and categorization of related inference attacks, providing a detailed analysis of the different stages involved in these attacks. Moreover, the article thoroughly describes and compares the existing defense methods. Finally, the article concludes by highlighting open questions and potential future research directions, aiming to contribute to the ongoing competition between privacy attackers and defenders. © 2020 IEEE.

关键词： Data privacy

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Dynamic Pricing Models in E-Commerce: Exploring Machine Learning Techniques to Balance Profitability and Customer Satisfaction

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IEEE Access 2025年 13卷 72994-73002页

作者： Guo, Xiaochen Zhang, Lei Management School of Anhui Business and Technology College Hefei230041 China Anhui Business and Technology College School of Information Engineering Hefei230041 China University of Technology MARA College of Computing Informatics and Mathematics Selangor Shah Alam40450 Malaysia

This research investigates the application of Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, paired with gradient-based optimization techniques for dynamic pricing in e-commerce. The primary objective is to develop a pricing model that effectively balances profitability with customer satisfaction by leveraging sequential data, such as time-series and customer behavior patterns. The approach utilizes LSTM’s ability to capture long-term dependencies in sequential data, while optimization methods like Stochastic Gradient Descent (SGD) enhance model convergence and performance. Key findings include the superior predictive accuracy of LSTM-based models over traditional approaches like Linear Regression and Decision Trees, particularly in real-time data updates and price elasticity scenarios. Additionally, the analysis revealed that LSTM models could efficiently adapt pricing strategies in response to market dynamics, significantly improving profitability while maintaining customer satisfaction. This study provides valuable insights into the application of advanced machine learning techniques in e-commerce pricing. The results suggest that LSTM-based dynamic pricing models could optimize revenue generation, offering substantial implications for pricing strategy development in modern retail environments. Future work may explore hybrid models and multi-objective optimization techniques to further refine these models. © 2013 IEEE.

关键词： User experience

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Hamiltonian boundary value methods applied to KdV-KdV systems

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Applied Numerical mathematics 2025年 214卷 1-27页

作者： Luo, Qian Xiao, Aiguo Luo, Xuqiong Yan, Xiaoqiang School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Hunan 411105 China School of Mathematics and Statistics Changsha University of Science and Technology Changsha410114 China National Center for Applied Mathematics in Hunan Xiangtan University Hunan 411105 China

In this paper, we propose a highly accurate scheme for two KdV systems of the Boussinesq type under periodic boundary conditions. The proposed scheme combines the Fourier-Galerkin method for spatial discretization with Hamiltonian boundary value methods for time integration, ensuring the conservation of discrete mass and energy. By expanding the system in Fourier series, the equations are firstly transformed into Hamiltonian form, preserving the original Hamiltonian structure. Applying the Fourier-Galerkin method for semi-discretization in space, we obtain a large-scale system of Hamiltonian ordinary differential equations, which is then solved using a class of energy-conserving Runge-Kutta methods, known as Hamiltonian boundary value methods. The efficiency of this approach is assessed, and several numerical examples are provided to demonstrate the effectiveness of the method. © 2025 IMACS

关键词： Ordinary differential equations

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Generative Adversarial Network for Image Reconstruction from Human Brain Activity

Generative Adversarial Network for Image Reconstruction from...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Tanner, Tim Cutsuridis, Vassilis School of Computer Science University of Lincoln Lincoln United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom

Decoding of brain activity with machine learning has enabled the reconstruction of thoughts, memories and dreams. In this study, we designed a methodology for reconstructing visual stimuli (digits) from human brain activity recorded during passive visual viewing. Using the MindBigData EEG dataset, we preprocessed the signals and cleaned them from noise, muscular artifacts and eye blinks. Using the Common Average Reference (CAR) method and past studies’ results we reduced the available electrodes from 14 to 4 keeping only those containing discriminative features associated with the visual stimulus. A convolutional neural network (CNN) was then trained to encode the signals and classify the images. A 92% classification performance was achieved post-CAR. Three variations of an auxiliary conditional generative adversarial network (AC-GAN) were evaluated for decoding the latent feature vector with its class embedding and generating black-and-white images of digits. Our objective was to create an image similar to the presented stimulus through the previously trained GANs. An average 65% reconstruction score was achieved by the AC-GAN without a modulation layer, a 60% by the AC-GAN with modulation layer and multiplication, and a 63% by the AC-GAN with modulation and concatenation. Rapid advances in generative modeling promise further improvements in reconstruction performance. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Generative adversarial networks

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STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario

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IEEE Transactions on Multimedia 2025年 27卷 2666-2681页

作者： Liu, Renyang Lam, Kwok-Yan Zhou, Wei Wu, Sixing Zhao, Jun Hu, Dongting Gong, Mingming Yunnan University School of Information Science and Engineering Kunming650500 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Yunnan University School of Software Engineering Research Center of Cyberspace Kunming650500 China University of Melbourne Melbourne Centre for Data Science School of Mathematics and Statistics ParkvilleVIC3010 Australia

Extensive studies have revealed that deep neural networks (DNNs) are vulnerable to adversarial attacks, especially black-box ones, which can heavily threaten the DNNs deployed in the real world. Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness. Despite the significant progress made recently, existing black-box attack methods still suffer from unsatisfactory performance due to the vast number of queries needed to optimize desired perturbations. Besides, the other critical challenge is that adversarial examples built in a noise-adding manner are abnormal and struggle to successfully attack robust models, whose robustness is enhanced by adversarial training against small perturbations. There is no doubt that these two issues mentioned above will significantly increase the risk of exposure and result in a failure to dig deeply into the vulnerability of DNNs. Hence, it is necessary to evaluate DNNs' fragility sufficiently under query-limited settings in a non-additional way. In this paper, we propose the Spatial Transform Black-box Attack (STBA), a novel framework to craft formidable adversarial examples in the query-limited scenario. Specifically, STBA introduces a flow field to the high-frequency part of clean images to generate adversarial examples and adopts the following two processes to enhance their naturalness and significantly improve the query efficiency: a) we apply an estimated flow field to the high-frequency part of clean images to generate adversarial examples instead of introducing external noise to the benign image, and b) we leverage an efficient gradient estimation method based on a batch of samples to optimize such an ideal flow field under query-limited settings. Compared to existing score-based black-box baselines, extensive experiments indicated that STBA could effectively improve the imperceptibility of the adversarial examples and remarkably boost the attack success rate u

关键词： Structured Query Language

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Enhance Statistical Features with Changepoint Detection for Driver Behaviour Analysis 21st

Enhance Statistical Features with Changepoint Detection for ...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Maktoubian, Jamal Tran, Son N. Shillabeer, Anna Amin, Muhammad Bilal Sambrooks, Lawrence School of Information and Communication Technology University of Tasmania Hobart Australia School of Information Technology Deakin University VIC Australia School of Computing Mathematics and Engineering Charles Sturt University NSW Australia

ISBN: (纸本)9789819601240

Driver behaviour modelling is a critical field that addresses complex and dynamic driving behaviours on roads with the goal of enhancing road safety, reducing air pollution, and improving vehicle performance. Recent advancements in sensor technology and machine learning (ML) techniques have facilitated the capture and analysis of driver behaviour patterns. Nonetheless, the efficacy of ML models heavily relies on the quality of the data used. Therefore, developing feature extraction techniques that provide high-quality inputs is crucial. In this paper, we conceptualised, implemented, and evaluated a novel feature model called Changepoint-based Statistical Feature (C-bSF). Initially, we extracted various statistical functions from raw sensor data, which were then aggregated using lagging windows. Following this, a changepoint detection method was used to derive the C-bSF feature. We compared the performance metrics of this new approach with other feature extraction methods, demonstrating the superiority of C-bSF in driver behaviour classification tasks across three datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Grading Model for Jasmine Flowers Using Deep Learning and Quantum Machine Learning 4

A Grading Model for Jasmine Flowers Using Deep Learning and ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pratiksha Cauvery, K.P. Thrisha, R. Harshitha, T. Panigrahi, Suman Thangavel, Senthil Kumar Somasundaram, K. Boopathy, C.P. Amrita School of Physical Sciences Department of Mathematics Tamil Nadu Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Engineering Department of Elelctical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331523923

The flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. The already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. This paper handles these limitations by introducing automation in the grading process using Deep Learning and Quantum Machine Learning. The conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. The deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. This DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. The findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture. © 2025 IEEE.

关键词： Decision making

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Efficiency of a cylindrical wave energy converter with different bottom shapes in a chambered breakwater

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Marine Systems and Ocean Technology 2025年第2期20卷 1-15页

作者： Aiswaria, K. Balaji, Ramakrishnan Krishnendu, P. Jodha, Shweta Department of Civil Engineering Indian Institute of Technology Bombay Mumbai400076 India School of Engineering Computing and Mathematics University of Plymouth Drake Circus PlymouthPL48AA United Kingdom Naval Architecture and Ocean Engineering Indian Maritime University Visakhapatnam Andhra Pradesh Visakhapatnam531035 India

Ocean waves are one of the high-density renewable energy resources and can work as a generous, sustainable, and clean energy source. The energy capturing of a floating point absorber system is designed to be maximum when it is close to resonance. Integrating coastal protection structures with a wave energy converter can be a promising solution to extract wave energy economically. This paper investigates the influence of bottom shapes on the hydrodynamic performance of a cylindrical heaving floating point absorber wave energy converter stand-alone and integrated with breakwater conditions. The study is carried out for constant mass and constant draft model cases, providing identical conditions for comparison. It is seen that a cylinder with a 90° conical bottom has better performance for the same waterline diameter than all the other models. After the integration with a chambered breakwater, the power performance of the wave energy device was found to increase by a maximum of 3.5 times more than that of the stand-alone case for all the models at quasi-resonance conditions, especially when the WEC is positioned at locations of amplified wave elevations inside the chamber. © The Author(s), under exclusive licence to Sociedade Brasileira de Engenharia Naval 2025.

关键词： Wave power

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Modification of Maxwell model for conductivity prediction of carbon nanotubes-filled polymer composites with tunneling effect

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Applied mathematics and Mechanics(English Edition) 2025年第1期46卷 25-36页

作者： Jue ZHU Longyuan LI Ningtao ZHU Zhejiang Provincial Engineering Research Center for the Safety of Pressure Vessel and Pipeline Ningbo 315211Zhejiang ProvinceChina Key Laboratory of Impact and Safety Engineering(Ningbo University) Ministry of EducationNingbo 315211Zhejiang ProvinceChina School of Engineering Computing and MathematicsUniversity of PlymouthPlymouth PL48AAU.K. Xi'an Zhanshi Testing&Engineering Co.Ltd. Xi'an 710018China

Carbon nanotubes(CNTs)have garnered great attention in recent years due to their outstanding electrical,thermal,and mechanical *** incorporation of small amounts of CNTs in polymers can substantially improve the sensitivity of the polymer's electrical *** paper presents a modified Maxwell model to evaluate the electrical conductivity of CNTs-filled polymer composites by introducing a transition zone to account for the tunneling *** this modified Maxwell model,the CNTs-filled polymer composite is modeled as a three-phase composite,consisting of a matrix(polymer),inclusions(CNTs),and a transition zone(tunneling zone).The effective electrical conductivity(EEC)of the composite is calculated based on the volume fractions and electrical conductivities of the matrix,inclusions,and transition *** model's validity is confirmed through the use of available test data,which demonstrates its capability to accurately capture the nonlinear conductivity behavior observed in CNTs-polymer *** study offers valuable insights into the design of high-performance conductive polymer nanocomposites,and enhances the understanding of electrical conduction mechanisms in CNT-dispersed polymer composites.

关键词： carbon nanotube(CNT) polymer composite electrical conductivity tunneling Maxwell model

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A DECOUPLED,LINEARLY IMPLICIT AND UNCONDITIONALLY ENERGY STABLE SCHEME FOR THE COUPLED CAHN-HILLIARD SYSTEMS

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Journal of Computational mathematics 2025年第3期43卷 708-730页

作者： Dan Zhao Dongfang Li Yanbin Tang Jinming Wen School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China College of Information Science and Technology Jinan UniversityGuangzhou 510632China

We present a decoupled,linearly implicit numerical scheme with energy stability and mass conservation for solving the coupled Cahn-Hilliard *** time-discretization is done by leap-frog method with the scalar auxiliary variable(SAV)*** only needs to solve three linear equations at each time step,where each unknown variable can be solved *** is shown that the semi-discrete scheme has second-order accuracy in the temporal *** convergence results are proved by a rigorous analysis of the boundedness of the numerical solution and the error estimates at different *** examples are presented to further confirm the validity of the methods.

关键词： Coupled Cahn-Hilliard system Leap-frog method Scalar auxiliary variable Error estimate

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