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A Novel Stacked Network Method for Enhancing the Performance of Side-Channel Attacks

Computers, Materials & Continua 2025年第4期83卷 1001-1022页

作者： Zhicheng Yin Lang Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangNormal UniversityHengyang421002China

The adoption of deep learning-based side-channel analysis(DL-SCA)is crucial for leak detection in secure *** previous studies have applied this method to break targets protected with *** the increasing number of studies,the problem of model *** research mainly focuses on exploring hyperparameters and network architectures,while offering limited insights into the effects of external factors on side-channel attacks,such as the number and type of *** paper proposes a Side-channel Analysis method based on a Stacking ensemble,called *** our method,multiple models are deeply *** the extended application of base models and the meta-model,Stacking-SCA effectively improves the output class probabilities of the model,leading to better ***,this method shows that the attack performance is sensitive to changes in the number of ***,five independent subsets are extracted from the original ASCAD database as multi-segment datasets,which are mutually *** method shows how these subsets are used as inputs for Stacking-SCA to enhance its attack *** experimental results show that Stacking-SCA outperforms the current state-of-the-art results on several considered datasets,significantly reducing the number of attack traces required to achieve a guessing entropy of ***,different hyperparameter sizes are adjusted to further validate the robustness of the method.

关键词： Side-channel analysis deep learning stacking ensemble learning model generalization

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Distributed Fixed-Time Dynamic Event-Triggered Algorithm for Economic Dispatch in Smart Grids Over Directed Networks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 173-180页

作者： Ji, Lianghao Liu, Qi Zhang, Cuijuan Yang, Shasha Li, Huaqing Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

A fresh algorithm is presented for tackling the economic dispatch problem (EDP) in smart grids with directed network topology. This algorithm is based on distributed consensus and aims to minimize the total cost of power generation while maintaining the balance between power supply and demand and complying with generation capacity restrictions. Even with unknown and bounded disturbances, this algorithm can achieve optimal dispatch within a fixed time. In addition, an expected settling time can be calculated, which is a more accurate upper bound estimation that relies solely on the generators' initial conditions. Meanwhile, the dynamic event-triggered strategy is employed for reducing sampling frequency and network resource consumption. Finally, numerical simulations validate the effectiveness of the designed algorithm. © 2024 IEEE.

关键词： Consensus algorithm

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Modeling, Estimation, and Applications of Phase Noise in Wireless Communications: A Survey

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 912-940页

作者： Chang, Zikai Xu, Yuntao Chen, Junjie Xie, Ning He, Yejun Li, Hongbin Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Phase noise is inevitably present in wireless communications, arising from both hardware impairments and imperfect channel estimation, and has substantial implications on the integrity and efficiency of communication systems. However, phase noise brings more to wireless systems than just negative effects. For instance, phase noise can be used as a physical layer feature to authenticate received signals and as a source for random number generation. There is a surprising lack of a comprehensive overview of the state-of-the-art estimation methods and applications of phase noise in wireless communications. In this paper, we conduct the first comprehensive survey on the modeling, estimation, and applications of phase noise in wireless communications. We review the state-of-the-art phase-noise models, phase-noise estimation, and mitigation techniques via joint transmit-receive design, as well as applications of phase noise in wireless communications, while highlighting contributions and critical issues of each reviewed work. Finally, we provide an expansive set of research directions, accompanying challenges, and potential solutions related to phase noise that could shape the future of phase noise research in wireless communications. © 1998-2012 IEEE.

关键词： Phase noise

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Cross-graph Knowledge Exchange for Personalized Response Generation in Dialogue Systems

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IEEE Internet of Things Journal 2025年第10期12卷 13769-13789页

作者： Dong, Yuezhou Qin, Ke Ke, Pei Liang, Shuang Luo, Guangchun National Key Laboratory of Intelligent Collaborative Computing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China National Key Laboratory of Intelligent Collaborative Computing Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

Recent advancements in language models have greatly improved dialogue systems, but they still face challenges in generating personalized responses that are consistent with the user's persona and dialogue context. Existing approaches typically model dialogue context and persona information together in a unified manner, but they lack fine-grained differentiation between the two, leading to inaccurate user modeling. This misalignment hinders the ability of dialogue systems to produce personalized responses. In this work, we propose CKE (Cross-Graph Knowledge Exchange), a novel algorithm designed to enhance personalized response generation in dialogue systems. CKE constructs separate dialogue user graphs for each party in the dialogue, representing both their dialogue context and persona information. These graphs are then utilized to perform cross-graph structured knowledge aggregation, where the aggregation is under supervised by both its own and the other party's persona and dialogue context, providing richer, more accurate representations. Furthermore, CKE introduces a hybrid prompt template that combines both discrete and continuous elements, improving the language model's ability to leverage graph-structured information. The experimental results demonstrate that CKE significantly outperforms existing baseline methods in generating more coherent, contextually appropriate, and personalized responses in dialogue systems. © 2014 IEEE.

关键词： Knowledge graph

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Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Unsupervised Domain Transfer for Object Classification in 3D Point Clouds via Hierarchical Prompt Learning

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IEEE Signal processing Letters 2025年 32卷 1750-1754页

作者： Luo, Huan Fu, Kaiwei Fang, Lina Fuzhou University College of Computer and Data Science China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing Fuzhou350003 China Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Haixi Institute Fujian Quanzhou362216 China

Traditional object classification in 3D point cloud scenes relies heavily on large-scale labeled training data, which is both time-consuming and labor-intensive to obtain. Unsupervised Domain Transfer (UDT) mitigates this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. However, existing UDT-based methods often require complex neural architectures and substantial computational resources. This letter proposes a novel UDT framework that integrates hierarchical prompt learning with a 3D foundational model. The proposed method consists of a modality alignment stage and an unsupervised transfer stage. In the modality alignment stage, cross-modal hierarchical prompts are employed to align the Visual-Language (V-L) modality in the 3D foundational model through a V-L coupling module. In the unsupervised transfer stage, cross-domain hierarchical prompts and a Target-Source (T-S) coupling module facilitate the alignment of multi-scale contextual information across domains, ensuring efficient and accurate knowledge transfer. Extensive experiments conducted on multiple datasets collected from various laser scanners demonstrate the effectiveness of our proposed approach. © 1994-2012 IEEE.

关键词： Visual languages

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RIS Enabled SCLAM: An Approach for Simultaneous Radio Communication, Localization, and Mapping

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IEEE Internet of Things Journal 2025年第10期12卷 14663-14676页

作者： Zhao, Jinqiu Bai, Zhiquan Guo, Shuaishuai Ma, Dejie Li, Na Kwak, Kyung Sup Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Information Science and Engineering Qingdao266237 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration School of Control Science and Engineering Jinan250061 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan250200 China INHA University Graduate School of Information Technology and Telecommunications Incheon22212 Korea Republic of

Radio-based simultaneous localization and mapping (SLAM) facilitates unmanned systems to fulfill self-localization and navigation in complex environments. However, the existing studies overlooked communication between devices, which may lead to low efficiency and high costs when performing SLAM tasks. Reconfigurable intelligent surface (RIS) can satisfy the growing demands of users and improve SLAM accuracy in dynamic and complex environments. This paper proposes a RIS enabled simultaneous radio communication, localization, and mapping (SCLAM) system, where an unmanned aerial vehicle (UAV) can perform concurrent communication and SLAM with the help of RIS by utilizing communication signals from base station (BS). The proposed method enables the UAV to simultaneously accomplish SLAM and ensure communication with the BS, while also enhancing the spectrum utilization efficiency. We derive the Bayesian Fisher information matrix (BFIM) for joint SLAM and symbol detection of the proposed system, in which the trade-off between the BFIM of SLAM and the upper bound of the ergodic mutual information is illustrated. Then, a weighted factor based BFIM is presented to further achieve a performance trade-off between SLAM and communication. We formulate an optimization problem of joint BS active beamforming and RIS passive beamforming to maximize the log determinant of weighted BFIM. Numerical results verify the superiority of the proposed SCLAM system on position error bound (PEB), mapping error bound (MEB), and spectral efficiency (SE). The performance trade-off between communication and SLAM is also discussed and explored. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Relaxing Distillation Constraints for Improved New Class Learning in Continual Semantic Segmentation

Relaxing Distillation Constraints for Improved New Class Lea...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Ti, Zheng Hao, Xuze Chen, Renhai College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9798350368741

Continual Semantic Segmentation (CSS) aims to continuously learn new classes while mitigating catastrophic forgetting. Existing CSS methods primarily address this challenge through knowledge distillation. While they focus on maintaining stability for old classes, this emphasis often restricts plasticity in learning new ones. In this work, we propose two plug-and-play methods to better balance stability and plasticity. First, for feature map distillation, we replace pixel-level constraints with statistical information extraction within a window, improving the learning of new classes while preserving structural integrity to overcome forgetting. Second, we use relative ranks of predictions to replace exact probabilistic values to loosen the constraints in the logit distribution of old classes. Extensive experiments demonstrate that our methods outperform state-of-the-art methods when integrating into several baselines across various CSS scenarios. © 2025 IEEE.

关键词： Continual Learning Knowledge Distillation Semantic Segmentation

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Trajectory optimization for UAV-enabled relaying with reinforcement learning

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Digital Communications and Networks 2025年第1期11卷 200-209页

作者： Chiya Zhang Xinjie Li Chunlong He Xingquan Li Dongping Lin School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhenChina Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhenChina Peng Cheng Laboratory(PCL) ShenzhenChina National Mobile Communications Research Laboratory Southeast UniversityNanjingChina Shenzhen Institute of Information Technology ShenzhenChina Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen UniversityShenzhenChina

In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the *** solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based ***,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL *** simulation results show that both approaches can solve the proposed optimization problem and obtain good ***,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.

关键词： Unmanned aerial vehicle Emergency communications Trajectory optimization Convex optimization Reinforcement learning

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