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Privacy-preserving explainable AI: a survey

science China(information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Enhanced Acceleration for Generalized Nonconvex Low-Rank Matrix Learning

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Chinese Journal of Electronics 2025年第1期34卷 98-113页

作者： Hengmin Zhang Jian Yang Wenli Du Bob Zhang Zhiyuan Zha Bihan Wen School of Electrical and Electronic Engineering Nanyang Technological University School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Science and Engineering East China University of Science and Technology Department of Electrical and Computer Engineering University of Macau

Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.

关键词： Learning systems Image recognition Minimization Computational efficiency Complexity theory Matrix decomposition Optimization Image reconstruction Singular value decomposition Convergence

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One model, two skills: active vision and action learning model for robotic manipulation

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science China(information sciences) 2025年第6期68卷 331-349页

作者： Guokang WANG Yanhong LIU Huaping LIU School of Electrical and Information Engineering Zhengzhou University Department of Computer Science and Technology Tsinghua University

The perception in most existing vision-based reinforcement learning(RL) models for robotic manipulation relies heavily on static third-person or hand-mounted first-person cameras. In scenarios with occlusions and limited maneuvering space, these carefully positioned cameras often struggle to provide effective visual observations during manipulation. Taking inspiration from human capabilities, we introduce a novel RL-based dual-arm active visual-guided manipulation model(DAVMM), which simultaneously infers “eye” actions and “hand” actions for two separate robotic arms(referred to as the vision-arm and the worker-arm) based on current observations, empowering the robot with the ability to actively perceive and interact with its environment. To handle the extensive redundant observation-action space, we propose a decouplable target-centric reward paradigm to offer stable guidance for the training process. For making fine-grained manipulation action decisions, alongside a global scene image encoder, we utilize an independent encoder to extract local target texture features,enabling the simultaneous acquisition of both global and detailed local information. Additionally, we employ residual-RL and curriculum learning techniques to further enhance our model's sample efficiency and training stability. We conducted comparative experiments and analyses of DAVMM against a set of strong baselines on three occluded and narrow-space manipulation tasks. DAVMM notably improves the success rates across all manipulation tasks and showcases rapid learning capabilities.

关键词： robotic manipulation visual learning reinforcement learning active sensing machine vision

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Single-And Dual-Beam Pattern Reconfigurable Patch Antenna With Dual Switchable CP for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 21904-21914页

作者： Wang, Shi-Tong Zhu, Lei University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

In this article, a novel method is proposed to facilitate the design of compact, low-profile, pattern reconfigurable antennas with fixed or switchable circular polarization (CP) for Internet of Vehicles (IoV) applications. The proposed method eliminates the need for a complex beamforming or phase-tuning network by innovatively exploiting four resonant modes of a square patch antenna. Specifically, the TM01 and TM10 modes are utilized to accomplish the single-beam CP pattern, while, the dualbeam CP pattern is achieved by combining the TM11 and TM02 modes. An electric current model is developed to investigate the far-field radiation, revealing that the radiation pattern of TM11 mode can be effectively reshaped from quad-beam to dual-beam by manipulating the surface current distribution. The complete design method is verified by an antenna prototype working at 3.5 GHz, which consists of a radiative patch, a pair of open-ended stubs, a pair of slots, and four equivalent switchable pins. Different from the conventional pin-loading method, the proposed equivalent switchable pins attain excellent CP performance during switching by compensating for the detrimental electric coupling. The prototype is fabricated and measured. Its radiation can be switched between single-beam (7.8 dBic) and dual-beam CP pattern (3.5 dBic), exhibiting an overlapped CP coverage from -123° to 73°. Furthermore, another prototype with the extended switchable CP capability is realized. Without increasing the number of p-i-n diodes, this prototype achieves four operating states with independent pattern (single-and dual-beam) and polarization (right-and left-handed CP) reconfiguration. The merits of compact size, low profile, and flexible reconfigurability make the proposed method and design cases excellent candidates for IoV applications and intelligent transportation systems (ITSs). © 2014 IEEE.

关键词： Circular polarization

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Metarelation2vec:A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

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Tsinghua science and technology 2024年第2期29卷 553-575页

作者： Lei Chen Yuan Li Yong Lei Xingye Deng School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consisting of multiple,simple metarelations must be driven by domain *** sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this ***,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given ***,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node ***,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link ***,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the *** experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.

关键词： metarelation random walk heterogeneous network metapath representation learning

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REDUCTION OF CO2 EMISSION AS A RESULT OF THE USE OF 48-VOLT electrical INSTALLATIONS IN PASSENGER CARS

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Archives of Transport 2023年第2期66卷 7-20页

作者： Kozyra, Jacek Lukasik, Zbigniew Kuśmińska-Fijalkowska, Aldona Janota, Aleš Faculty of Transport Electrical Engineering and Computer Science University of Technology and Humanities Radom Poland Faculty of Electrical Engineering and Information Technology University of Žilina Žilina Slovakia

The introduction of a new standard of 48-volt electrical systems in cars comes at an additional cost to the vehicle. Acceptance of these costs is justified because it becomes a way to achieve lower CO2 emissions and lower fuel consumption. An important factor in favor of adopting 48-volt systems is the reduction in CO2 due to the use of a highly efficient 48-volt motor-generator unit coupled to a DC/DC converter. A methodology for testing new solutions to quantify CO2 savings and reductions therefore becomes crucial. This methodology must be capable of demonstrating the CO2 benefits primarily of the innovative technology proven in real-world driving conditions and with a large amount of realistic statistical data. The introduction of new eco-innovations must take into account the linkage and impact on other environmentally oriented ecoinnovative solutions. When implementing new technical solutions, a necessary aspect is the interaction with other innovations installed in vehicles with new electrical installation standards. Therefore, for the expected synergy of solutions to occur, two or more innovative technologies must be installed. Then the combined savings from one of them will affect the performance of the other technologies, and vice versa. The new technology of a high-efficiency 48-volt motor-generator unit cooperating with a 48V/12V DC/DC converter fits very well in creating interactions with other implemented solutions aimed at reducing CO2 emissions. The article discusses the problems of the introduced new technology of a high-efficiency 48-volt motor-generator unit cooperating with a 48V/12V DC/DC converter. The publication analyzes the impact of increasing the voltage rating of current passenger car installations to 48V. Based on the methodology for determining the reduction of CO2 emissions of a vehicle with a 48V/12V DC/DC voltage converter installed, the mass of fuel per unit of engine operation time was determined. The amount of fuel saved was determ

关键词： Carbon dioxide

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A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266000China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the College of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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Cache-aided MISO-NOMA concept

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International Journal of Wireless and Mobile Computing 2024年第3期26卷 270-281页

作者： Paunkoska-Dimoska, Natasa Kafedziski, Venceslav University of Information Science and Technology Ohrid Macedonia Faculty of Electrical Engineering and Information Technologies Skopje Macedonia

Coded caching, non-orthogonal multiple access (NOMA), and massive multiple-input multiple-output (MIMO) are challenging and attractive technologies for rapidly emerging mobile networks, offering numerous significant advantages. The literature shows that a match between two diverse techniques outperforms the current existing benefits. This paper proposes a new joint scheme assuming cooperation between all three methods. Namely, a multiple-input single-output (MISO) base station equipped with multiple antennas serves a group of cached-aided end-users using the NOMA pairing approach to deliver requested files. Using MISO allows simultaneous transmissions;NOMA uses SIC (successive interference cancellation) to decode the signal consisting of more messages;coded caching speeds up the interference elimination and file decoding by applying CIC (cache-enabled interference cancellation). The zero-forcing method deals with inter-pair interference cancellation. This newly proposed concept improves the achievable sum rate for file delivery compared with other joint conventional constructions. Hence, simulation results show that the schemes using join CIC and SIC decoding processes exceed the sum-rate performance. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Decoding

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： Sun: activity Sun: flares (Sun:) sunspots Sun: magnetic fields magnetohydrodynamics (MHD)

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