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Probabilistic fault diagnosis of clustered faults for multiprocessor systems

Journal of computer Science & technology 2023年第4期38卷 821-833页

作者：孙雪丽樊建席程宝雷王岩张力 School of Computer Science and Technology Soochow UniversitySuzhou 215006China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China

With the development of high-performance computing and the expansion of large-scale multiprocessor sys-tems,it is significant to study the reliability of *** fault diagnosis is of practical value to the reliabilityanalysis of multiprocessor *** this paper,we design a linear time diagnosis algorithm with the multiprocessor sys-tem whose threshold is set to 3,where the probability that any node is correctly diagnosed in the discrete state can be ***,we give the probabilities that all nodes of a d-regular and d-connected graph can be correctly diag-nosed in the continuous state under the Weibull fault distribution and the Chi-square fault *** prove thatthey approach to 1,which implies that our diagnosis algorithm can correctly diagnose almost all nodes of the graph.

关键词： regular graph clustered fault probabilistic diagnosis algorithm Preparata Metze Chien model(PMCmodel)

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DECTNet: A detail enhanced CNN-Transformer network for single-image deraining

Cognitive Robotics

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Cognitive Robotics 2025年 5卷 48-60页

作者： Wang, Liping Gao, Guangwei Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

Recently, Convolutional Neural Networks (CNN) and Transformers have been widely adopted in image restoration tasks. While CNNs are highly effective at extracting local information, they struggle to capture global context. Conversely, Transformers excel at capturing global information but often face challenges in preserving spatial and structural details. To address these limitations and harness both global and local features for single-image deraining, we propose a novel approach called the Detail Enhanced CNN-Transformer Network (DECTNet). DECTNet integrates two key components: the Enhanced Residual Feature Distillation Block (ERFDB) and the Dual Attention Spatial Transformer Block (DASTB). In the ERFDB, we introduce a mixed attention mechanism, incorporating channel information-enhanced layers within the residual feature distillation structure. This design facilitates a more effective step-by-step extraction of detailed information, enabling the network to restore fine-grained image details progressively. Additionally, in the DASTB, we utilize spatial attention to refine features obtained from multi-head self-attention, while the feed-forward network leverages channel information to enhance detail preservation further. This complementary use of CNNs and Transformers allows DECTNet to balance global context understanding with detailed spatial restoration. Extensive experiments have demonstrated that DECTNet outperforms some state-of-the-art methods on single-image deraining tasks. Furthermore, our model achieves competitive results on three low-light datasets and a single-image desnowing dataset, highlighting its versatility and effectiveness across different image restoration challenges. © 2025

关键词： Restoration

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Attention-assisted dual-branch interactive face super-resolution network

Cognitive Robotics

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Cognitive Robotics 2025年 5卷 77-85页

作者： Wan, Xujie Xu, Siyu Gao, Guangwei Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

We propose a deep learning-based Attention-Assisted Dual-Branch Interactive Network (ADBINet) to improve facial super-resolution by addressing key challenges like inadequate feature extraction and poor multi-scale information handling. ADBINet features a multi-scale encoder-decoder architecture that captures and integrates features across scales, enhancing detail and reconstruction quality. The key to our approach is the Transformer and CNN Interaction Module (TCIM), which includes a Dual Attention Collaboration Module (DACM) for improved local and spatial feature extraction. The Channel Attention Guidance Module (CAGM) refines CNN and Transformer fusion, ensuring precise facial detail restoration. Additionally, the Attention Feature Fusion Unit (AFFM) optimizes multi-scale feature integration. Experimental results demonstrate that ADBINet outperforms existing methods in both quantitative and qualitative facial super-resolution metrics. © 2025

关键词： Feature extraction

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge Computing Environments

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Tsinghua Science and technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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HRAM-VITON: High-Resolution Virtual Try-On with Attention Mechanism

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computers, Materials & Continua 2025年第2期82卷 2753-2768页

作者： Yue Chen Xiaoman Liang Mugang Lin Fachao Zhang Huihuang Zhao College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.

关键词： Virtual try-on attention mechanism high-resolution image generation

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

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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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Fusion of data dimensionality reduction algorithms baced on category representation theory 4

Fusion of data dimensionality reduction algorithms baced on ...

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4th International Conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Xu, Xiaoxiang Li, Fanzhang Zhang, Li School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9781510687615

For a long time, people have believed that representation problems are one of the bottlenecks in the field of machine learning. Therefore, it is a long-term and exploratory work to study machine learning representation methods. Due to this, we use category theory to study the fusion representation of data dimensionality reduction. We propose the basic concept of category representation for data dimensionality reduction and provide a data dimensionality reduction fusion representation framework. We have conducted research and analysis on algorithms such as PCA, KPCA, and LDA, identified the essential connection between PCA, KPCA, and LDA and proposed a data dimensionality reduction fusion representation algorithm based on a data dimensionality reduction fusion representation framework. Finally, we demonstrated the feasibility of the proposed method through experiments. © 2025 SPIE.

关键词： Dimensionality reduction

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Learning Unbiased Rewards with Mutual information in Adversarial Imitation Learning 48

Learning Unbiased Rewards with Mutual Information in Adversa...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhang, Lihua Liu, Quan Huang, Zhigang Wu, Lan Soochow University School of Computer Science and Technology Suzhou China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou China

ISBN: (纸本)9781728163277

A powerful method for automated decision systems is Adversarial Imitation Learning (AIL). It is based on a generative adversarial framework that alternately optimizes a generator (learner) and a discriminator (reward function). In the popular mind, a high-accuracy discriminator results in informative rewards, thus AIL must balance the performance between a generator and a discriminator. However, we find that the AIL reward function is biased and also results in informative rewards. Thus, we theoretically analyze the bias in the AIL reward function and find that balancing the performance of a generator and a discriminator is not necessary when we recover an unbiased reward function. Further, we propose a mutual information based auxiliary reward function. Experiments on continuous control tasks indicate that MI-GAIL is able to address the bias problem of the AIL reward function and further improve sample efficiency and training stability compared with up-to-date algorithms. © 2023 IEEE.

关键词： Adversarial imitation learning Continuous control Mutual information Potential-based reward function Reward bias

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A Perturbation-Based Policy Distillation Framework with Generative Adversarial Nets 48

A Perturbation-Based Policy Distillation Framework with Gene...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Zhang, Lihua Liu, Quan Zhang, Xiongzhen Xu, Yapeng Soochow University School of Computer Science and Technology Suzhou China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou China

ISBN: (纸本)9781728163277

We study the problem of imitation learning in automated decision systems, in which a learner is trained to imitate an expert demonstrator. A widely used method is adversarial imitation learning that alternately optimizes a generator (learner) and a discriminator (reward function). However, the discriminator is biased during the initial and intermediate training stages. Consequently, the gradient descent direction of the learner is misguided, which leads to unstable training and sample complexity. In this paper, we propose deep imitation learning through a guidance-based policy distillation (GIL) algorithm. First, GIL proposes a teacher model, the guidance-based variational autoencoder, which is pre-trained with expert demonstrations. Then, GIL proposes a perturbation-based policy distillation method that uses the teacher model to guide the learner in the correct optimization direction, enabling the learner to imitate the expert policy with fewer detours. The experimental results show that our approach achieve higher sample efficiency compared with multiple baselines. © 2023 IEEE.

关键词： Distillation

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Side-Channel Leakage Analysis of Inner Product Masking

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computers, Materials & Continua 2024年第4期79卷 1245-1262页

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

The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of *** previous work unfolds when certain(adversarial and implementation)conditions are met,and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected *** this paper,we investigate the security characteristics of IPM under different *** adversarial condition,the security properties of first-order IPMs obtained through parametric characterization are preserved in the face of univariate and bivariate *** implementation condition,we construct two new polynomial leakage functions to observe the nonlinear leakage of the IPM and connect the security order amplification to the nonlinear *** observe that the security of IPMis affected by the degree and the linear component in the leakage *** addition,the comparison experiments from the coefficients,signal-to-noise ratio(SNR)and the public parameter show that the security properties of the IPM are highly implementation-dependent.

关键词： Side-channel analysis inner product masking mutual information nonlinear leakage

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