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4th International Conference on Big Data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Jia, Jimao Software Engineering in College of Computer Science College of Software Inner Mongolia University Inner Mongolia Hohhot China

ISBN: (纸本)9798400709753

Chip analysis is an important means of chip research, and with the progress of the chip process, the traditional means are subject to great challenges. For this reason, the research and application of chip automation and intelligent technology is an important topic. In this paper, for the chip analysis in the layout presents special process holes, missing effective elements, poor image quality and other characteristics, the existing recognition algorithms have been applied to the difficulties of bad results, proposed deep learning-based improvement algorithms, basically solves the layout of the large holes, hollow holes, dark holes, and other special elements of the problem, the recognition effect reaches the expected. © 2024 Copyright held by the owner/author(s).

关键词： chip analysis deep learning special versioning traditional algorithms

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Graph-in-Graph Convolutional Network for Hyperspectral Image Classification

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IEEE Transactions on Neural Networks and Learning Systems 2024年第1期35卷 1157-1171页

作者： Jia, Sen Jiang, Shuguo Zhang, Shuyu Xu, Meng Jia, Xiuping College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Key Laboratory for Geo-Environmental Monitoring of Coastal Zone Shenzhen University Ministry of Natural Resources Shenzhen518060 China School of Engineering and Information Technology University of New South Wales CanberraACT 2612 Australia

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are increasing, and pixel-oriented classification has attracted much attention. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains and have been employed in HSI classification. But most methods based on GCN are hard to sufficiently exploit information of ground objects due to feature aggregation. To solve this issue, in this article, we proposed a graph-in-graph (GiG) model and a related GiG convolutional network (GiGCN) for HSI classification from a superpixel viewpoint. The GiG representation covers information inside and outside superpixels, respectively, corresponding to the local and global characteristics of ground objects. Concretely, after segmenting HSI into disjoint superpixels, each one is converted to an internal graph. Meanwhile, an external graph is constructed according to the spatial adjacent relationships among superpixels. Significantly, each node in the external graph embeds a corresponding internal graph, forming the so-called GiG structure. Then, GiGCN composed of internal and External graph convolution (EGC) is designed to extract hierarchical features and integrate them into multiple scales, improving the discriminability of GiGCN. Ensemble learning is incorporated to further boost the robustness of GiGCN. It is worth noting that we are the first to propose the GiG framework from the superpixel point and the GiGCN scheme for HSI classification. Experiment results on four benchmark datasets demonstrate that our proposed method is effective and feasible for HSI classification with limited labeled samples. For study replication, the code developed for this study is available at https://***/ShuGuoJ/***. © 2012 IEEE.

关键词： Convolution

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Spectral Reconstruction via Dual Cross-Scanning and Cross-Attention Mechanisms

Spectral Reconstruction via Dual Cross-Scanning and Cross-At...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Mingying Lin Ziqian Mo Hongtao Zhang Xiyou Fu Meng Xu College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

Hyperspectral images (HSIs) provide rich spectral information, but acquiring high-resolution data is costly and challenging, making spectral super-resolution essential. Inspired by the near-linear efficiency of state space models (SSM) in long-sequence tasks, we propose a spectral reconstruction method based on dual cross-scanning and cross-attention mechanisms (SR-CSCA). This novel network integrates Transformer and Mamba architectures, efficiently extracting multi-resolution contextual information and optimizing computational strategies to enhance spectral reconstruction performance. SR-CSCA method captures spectral and spatial details and dependencies by employing an optimized joint spatial-spectral dimensional scanning strategy. Furthermore, the cross-attention mechanism enhances feature fusion and information extraction. The efficacy of our approach is demonstrated through comparisons with multiple benchmark methods for spectral reconstruction. Experimental results indicate that SR-CSCA achieves state-of-the-art performance in spectral reconstruction while maintaining a low computational cost.

关键词： Technological innovation Computational modeling Superresolution Reconstruction algorithms Feature extraction Transformers Computational efficiency Time complexity Image reconstruction Hyperspectral imaging

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Adversarial Knowledge Transfer for Black-Box Model Inversion Attack

Adversarial Knowledge Transfer for Black-Box Model Inversion...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinhao Liu Zetao Lin Yingzhao Jiang Qiao Yan College of Computer Science & Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in model inversion attacks have raised privacy concerns, exploiting access to models to reconstruct private training data from inputs and outputs. These attacks are categorized as white-box, black-box, or label-only based on access level. We propose a new black-box model inversion attack, Label-Controlled Adversarial Knowledge Transfer (L-AdKT). L-AdKT leverages adversarial training with a Generative Adversarial Network (GAN) and a substitute model to extract knowledge from the target model. The substitute model minimizes discrepancies with the target model while guiding the generator to produce realistic samples. This approach enables white-box techniques to be applied in black-box settings. Experiments show that L-AdKT outperforms state-of-the-art black-box attacks by over 20% across benchmarks and remains robust against various defense mechanisms.

关键词： Training Closed box Training data Signal processing Generative adversarial networks Data models Speech processing Knowledge transfer Glass box Resilience

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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A Multiobjective Particle Swarm Optimizer based Localized Feature Selection for Imbalanced Fault Diagnosis 13

A Multiobjective Particle Swarm Optimizer based Localized Fe...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Gao, Lin Zhou, Yu Guo, Hainan Wu Kwong, Sam Tak College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China College of Management Shenzhen University Guangdong Shenzhen China Lingnan University Department of Computing and Decision Sciences Hong Kong

ISBN: (纸本)9798350308365

Modern fault diagnosis faces an imbalance issue at both the sample and feature levels. A common approach is to combine balancing strategies with feature selection algorithms to address this problem. However, this may result in feature subsets that cannot accurately represent the true data distribution. This paper proposes an imbalanced fault diagnosis method called RPLFS-MOBPSO, which combines region purity (RP) with local feature selection to achieve class balance implicitly by partitioning local regions. RP is used as a new objective in the optimization process to achieve accurate fault detection. The adaptive reference point method from Non-Dominated Sorting Genetic Algorithm III is employed to maintain a diverse and evenly distributed external archive. On both the simulated Tennessee Eastma (TE) benchmark datasets and the real bearing experimental bench datasets, the proposed method achieves the best results compared to two LFS methods and four imbalanced ensemble algorithms based on different balancing strategies. Our source codes are available at: https://***/EMRGSZU/papers-code/tree/main/RPLFS-MOBPSO. © 2024 IEEE.

关键词： Genetic algorithms

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UAV-aided 1D Wireless Power Transfer with Non-Linear Energy Harvesting

UAV-aided 1D Wireless Power Transfer with Non-Linear Energy ...

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IEEE International Conference on Communications in China (ICCC)

作者： Liang Ma Yueling Che Sheng Luo C. M. Victor Leung College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

This paper considers the unmanned aerial vehicle (UAV) aided wireless power transfer (WPT) to one-dimensionally (1D) deployed ground nodes (GNs) with practical non-linear energy harvesting (EH) modules. We jointly optimize the UAV’s tr’ajectory and its WPT decisions over time to minimize the UAV’s total energy consumption, subject to each GN’s EH constraint for meeting its energy requirement. However, this problem is a mixed-integer and non-convex optimization problem, and is generally difficult to solve optimally. First, we focus on the single-GN scenario. We reveal that the optimal successive hover-then-fly (SHF) UAV trajectory proposed in the literature for 1D deployed GNs with linear EH is no longer optimal for our considered problem. We propose a novel two-case based UAV trajectory design. For the case when the UAV is far from the GN, the UAV does not transmit energy and adopts the one-way flight trajectory with the optimally determined speed; and for the case when the UAV is close to the GN such that the GN can harvest non-zero energy, the UAV keeps transmitting energy, and adopts a newly proposed two-way round flight trajectory, where the flight rounds, UAV speed and the returning points are all optimally designed. Next, by extending the solutions from the single-GN scenario to the general multi-GN scenario, the original UAV energy minimization problem is solved efficiently. Finally, numerical results are provided to show the superior performance of our proposed scheme than the SHF-based benchmark.

关键词： Energy consumption Wireless power transfer Benchmark testing Autonomous aerial vehicles Minimization Trajectory Topology

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Human Action Recognition in Multi-Level Convolutional Temporal Attention Network

Human Action Recognition in Multi-Level Convolutional Tempor...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qian Huang ZhongQi Chen Chang Li WeiWen Qian College of Computer Science and Software Engineering Hohai University NanJing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recognition performance and difficulty in distinguishing similar actions. To solve this problem, this paper proposes a model based on Multilevel Convolutional Time Series Attention Network (MCTSANet). By Multi-ResCNN to pay attention to different levels of features and using Time Series Attention (TSA) to pay attention to the more important data in the channel, so as to improve the ability of confusable action recognition. Experiments on three public datasets show that the proposed method outperforms state-of-the-art sensor-based HAR approaches.

关键词： Human computer interaction Convolution Time series analysis Noise Speech recognition Medical services Human activity recognition Speech processing Older adults Faces

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Spatially Compact Dense Block Mining in Spatial Tensors 25

Spatially Compact Dense Block Mining in Spatial Tensors

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Weike Tang Dingming Wu Tsz Nam Chan Kezhong Lu College of Computer Science & Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798400712456

Spatial tensors have been extensively used in a wide range of applications, including remote sensing, geospatial information systems, conservation planning, and urban planning. We study the problem of Spatially Compact Dense (SCD) block mining in a spatial tensor, which targets for discovering dense blocks that cover small spatial regions. However, most of existing dense block mining (DBM) algorithms cannot solve the SCD-block mining problem since they only focus on maximizing the density of candidate blocks, so that the discovered blocks are spatially loose, i.e., covering large spatial regions. Therefore, we first formulate the problem of mining top-k Spatially Compact Dense blocks (SCD-blocks) in spatial tensors, which ranks SCD-blocks based on a new scoring function that takes both the density value and the spatial coverage into account. Then, we adopt a filter-refinement framework that first generates candidate SCD-blocks with good scores in the filtering phase and then uses the traditional DBM algorithm to further maximize the density values of the candidates in the refinement phase. Due to the NP-hardness of the problem, we develop two types of solutions in the filtering phase, namely the top-down solution and the bottom-up solution, which can find good candidate SCD-blocks by approximately solving the new scoring function. The evaluations on four real datasets verify that compared with the dense blocks returned by existing DBM algorithms, the proposed solutions are able to find SCD-blocks with comparable density values and significantly smaller spatial coverage.

关键词： dense block

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Poster: Towards Accurate and Fast Federated Learning in End-Edge-Cloud Orchestrated Networks 43

Poster: Towards Accurate and Fast Federated Learning in End-...

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43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023

作者： Li, Mingze Sun, Peng Zhou, Huan Zhao, Liang Liu, Xuxun Leung, Victor C. M. College of Computer and Information Technology China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electronic and Information Engineering South China University of Technology Guangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350339864

This work proposes a novel three-layer federated learning (FL) framework with parameter selection and pre-synchronization (PSPFL) to achieve fast and accurate model training. The basic idea of PSPFL is that clients select partial model parameters for transmission and then base stations aggregate them cooperatively (i.e., pre-synchronization) and send the aggregated results to the server for global model update periodically. However, there is an intrinsic trade-off between parameter transmission overhead and model training loss. To strike a desirable balance between them, we investigate the optimal parameter pre-synchronization round and local training round under PSPFL. Specifically, we propose a Deep Q-Network (DQN)-based method to obtain the local training round and parameter pre-synchronization round. Finally, extensive experiments are conducted to evaluate the performance of the proposed method on commonly used datasets. The results show that the proposed method can reduce the sum of FL completion time and training loss by an average of 8.17%-18.82% compared to benchmarks. © 2023 IEEE.

关键词： Synchronization

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