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2022 International Conference on Informatics, Networking and Computing, ICINC 2022

作者： Zhou, Nannan Long, Shigong Guizhou University Guizhou Provincial Key Laboratory of Public Big Data Guiyang550025 China Guizhou University College of Computer Science and Technology Guiyang550025 China

ISBN: (纸本)9798350309690

Social networks contain sensitive information, and direct press conferences disclose people's privacy. The published social network data needs privacy protection. The currently released social network data has seriously damaged the network structure due to privacy protection. This paper proposes a social network graph publishing model that preserves the social network structure. This model builds an uncertainty graph based on privacy protection probability to generate social network data for publication. It is proved theoretically that the social network graph publishing model satisfies the definition of differential privacy. This paper uses three real social network datasets to evaluate the effectiveness of the proposed method, showing that the method effectively retains the network structure. © 2022 IEEE.

关键词： Graphic methods

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Vision BEV-MAE: Masked Bird’s Eye View Autoencoders for Camera-based 3D Object Detection

Vision BEV-MAE: Masked Bird’s Eye View Autoencoders for Cam...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Zhenguo Cao, Yuanzhouhan Zhang, Hui Chen, Naiyue Ren, Chao Li, Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education China School of Computer Science and Technology Beijing Jiaotong University China College of Electronics and Information Engineering Sichuan University China

ISBN: (纸本)9798350368604

3D object detection is one of the fundamental perception tasks in autonomous driving. In recent years, perception in the Bird’s Eye View (BEV) has attracted tremendous attention due to its outstanding ability in preserving geometric cues. However, like other supervised learning tasks, BEV-based perception suffers from inadequate labeled training data. In this work, we propose the Vision BEV-MAE, a self-supervised learning approach for BEV perception from camera images. Specifically, we design a feature-level masking strategy to mask the mid-level representations, i.e., the encoded BEV features instead of directly masking input multi-view images, and reconstruct BEV features with a light-weight decoder. Our Vision BEV-MAE is independent of any geometric data such as point clouds, or any additional supervision, and can be easily implemented within any state-of-the-art BEV perception frameworks to improve the performance. We conduct experiments on benchmark dataset to validate our approach. © 2024 IEEE.

关键词： Self-supervised learning

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Towards sustainable adversarial training with successive perturbation generation

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Frontiers of Information technology & Electronic Engineering 2024年第4期25卷 527-539页

作者： Wei LIN Lichuan LIAO College of Computer Science and Mathematics Fujian University of TechnologyFuzhou 350118China College of Economics and Management Xi’an University of TechnologyXi’an 710048China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou 350118China

Adversarial training with online-generated adversarial examples has achieved promising performance in defending adversarial attacks and improving robustness of convolutional neural network ***,most existing adversarial training methods are dedicated to finding strong adversarial examples for forcing the model to learn the adversarial data distribution,which inevitably imposes a large computational overhead and results in a decrease in the generalization performance on clean *** this paper,we show that progressively enhancing the adversarial strength of adversarial examples across training epochs can effectively improve the model robustness,and appropriate model shifting can preserve the generalization performance of models in conjunction with negligible computational *** this end,we propose a successive perturbation generation scheme for adversarial training(SPGAT),which progressively strengthens the adversarial examples by adding the perturbations on adversarial examples transferred from the previous epoch and shifts models across the epochs to improve the efficiency of adversarial *** proposed SPGAT is both efficient and effective;e.g.,the computation time of our method is 900 min as against the 4100 min duration observed in the case of standard adversarial training,and the performance boost is more than 7%and 3%in terms of adversarial accuracy and clean accuracy,*** extensively evaluate the SPGAT on various datasets,including small-scale MNIST,middle-scale CIFAR-10,and large-scale *** experimental results show that our method is more efficient while performing favorably against state-of-the-art methods.

关键词： Adversarial training Adversarial attack Stochastic weight average Machine learning Model generalization

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GACP:graph neural networks with ARMA flters and a parallel CNN for hyperspectral image classification

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International Journal of Digital Earth 2023年第1期16卷 1770-1800页

作者： Jing Yang Jie Sun Yaping Ren Shaobo Li Shujie Ding Jianjun Hu School of Mechanical Engineering Guizhou UniversityGuiyangPeople’s Republic of China State Key Laboratory of Public Big Data Guizhou UniversityGuiyangPeople’s Republic of China School of Intelligent Systems Science and Engineering Jinan UniversityZhuhaiPeople’s Republic of China Department of Computer Science and Engineering University of South CarolinaColumbiaSCUSA

In recent years,the use of convolutional neural networks(CNNs)and graph neural networks(GNNs)to identify hyperspectral images(HSIs)has achieved excellent results,and such methods are widely used in agricultural remote sensing,geological exploration,and marine remote *** many generalization classification algorithms are designed for the purpose of learning a small number of samples,there is often a problem of a low utilization rate of position information in the empty spectral *** on this,a GNN with an autoregressive moving average(ARMA)-based smoothingfilter samples the node information in the null spectral domain and then captures the spatial information at the pixel level via spatial feature convolution;then,the null spectral domain position information lost by the CNN is located by a coordinate attention(CA)***,autoregressive,spatial convolution,and CA mechanisms are combined into multiscale features to enhance the learning capacity of the network for tiny *** conducted on the widely used Indian Pines(IP)dataset,the Botswana(BS)dataset,Houton 2013(H2013),and the WHU-Hi-HongHu(WHU)benchmark HSI dataset demonstrate that the proposed GACP technique can perform classification work with good accuracy even with a small number of training examples.

关键词： Graph neural networks word convolutional neural networks hyperspectral image classification attention mechanisms

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation 17

Penta-Encoder with Medical Transformer for Incomplete Multim...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Yu, Guohui Wang, Lihui Chen, Qijian Wang, Li Tang, Kun Cheng, Xinyu Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France

ISBN: (纸本)9798350387384

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets. © 2024 IEEE.

关键词： Semantic Segmentation

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A Solution-Based Tabu Search for Quadratic Knapsack Problem with Conflict Graphs

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SSRN 2024年

作者： Xu, Zhenghao Dong, Xueshi College of Computer Science and Technology Qingdao University Qingdao266071 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

The quadratic knapsack problem with conflict graph (QKPCG) is a variant of the classical knapsack problem (KP). This problem incorporates additional constraints represented by quadratic profits and conflict graphs, which is widely applied in areas such as telecommunications and logistics. However, the inclusion of pairwise interaction items and conflict graph constraints significantly increases the computational complexity of the problem, making it challenging to solve large-scale instances within reasonable time frames. This work proposes a novel hybrid solution-based tabu search (HSBTS) method for the QKPCG. The method integrates a greedy construction heuristic that produces a guaranteed initial solution, a solution-based tabu search (SBTS) strategy that executes local optimization, and a perturbation procedure to explore potential solutions. The proposed method is evaluated on the benchmark instances from the literature and shows competitive performance compared to existing state-of-the-art algorithms. It matches the best-known solutions for all 45 benchmark instances and improves the average solution quality. Meanwhile, computational efficiency has been greatly enhanced. © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Diffusion-Based Multi-Agent Reinforcement Learning with Communication

Diffusion-Based Multi-Agent Reinforcement Learning with Comm...

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IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

作者： Xinyue Qi Jianhang Tang Jiangming Jin Yang Zhang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Laboratory of Public Big Data Guizhou University China Qingcheng AI

ISBN: (数字)9798350361704

ISBN: (纸本)9798350361711

Multi-agent systems (MAS) have been widely used as a modeling tool to analyze the behaviors of members in groups, such as swarms, autonomous vehicle fleets, and smart Internet of Things devices. With their distributed architecture and model-free nature, reinforcement learning (RL) is often deployed to address issues of decision-making and action-taking in multi-agent systems to optimize overall system performance. However, as multi-agent systems involve agent coordination and interactions, this leads to extensive communication and computation overhead, resulting in decreased performance and slower convergence of multi-agent reinforcement learning. This work integrates a diffusion model into RL training to accelerate decision-making, taking into account communication among system agents. The effectiveness of this method is validated in a grid-based multi-agent predator-prey system scenario, where agents interact and confront each other via mutual communication networks. Experimental results show significant improvements in terms of learning efficiency and task performance.

关键词： Training Wireless communication Performance evaluation System performance Decision making Reinforcement learning Diffusion models

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Hybrid Dynamic Diversified Solution-Based Tabu Search for Generalized Quadratic Multiple Knapsack Problem

SSRN

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SSRN 2024年

作者： Xu, Zhenghao Dong, Xueshi Shan, Yongchang College of Computer Science and Technology Qingdao University Qingdao266071 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

The computationally complex generalized quadratic multiple knapsack problem (GQMKP) extends the classical quadratic multiple knapsack problem (QMKP) by introducing the classification and setup restrictions of items. There are many real-world applications of such problems, such as portfolio management and the location of railway stations. In this study, a hybrid algorithm (HDSB-TS) applying the main search procedure of the solution-based tabu search (SBTS) strategy and the dynamic diversification mechanism to discover other local optimum of higher quality is proposed for solving the GQMKP. Meanwhile, HDSB-TS employs the streamlined gain update strategy to quickly evaluate candidate solutions. The computational results of the proposed algorithm on two sets of 96 benchmark instances show its significant competitiveness. Extensive computational experiments perfomed on two sets of 96 benchmark instances show that the algorithm proposed in this paper is competitive compared to the reference algorithms. Specifically, 15 large-scale instances with improved computational results are reported, with 24 instances showing results consistent with the best-known results. Computational efficiency is also improved in most instances. Additional experiments are performed on key parts of the algorithm to explore their impact on the performance of the algorithm. © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Self-supervised fast reconstruction method for diffusion MRI based on multi-directional assistance 17

Self-supervised fast reconstruction method for diffusion MRI...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Wu, Yuxin Zhang, Jian Wang, Lihui Cao, Ying Wu, Linhai Wu, Yunjia Ye, Chen Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 France

ISBN: (纸本)9798350387384

Diffusion MRI (dMRI) is widely used for the detection and diagnosis of various pathologies due to its non-invasive nature. However, its signal acquisition process is relatively slow and susceptible to motions. In order to accelerate reconstruction process, many deep learning-based methods have been developed in recent years to achieve promising results, while most of them are supervised based methods and require fully sampled data which are difficult to obtain in practical applications to train the network. Self-supervised based methods were proposed to deal with this problem to a certain extent, however, they are devoted only to conventional MRI data, ignoring the correlation between the directions of dMRI data. To address this issue, we propose a self-supervised fast reconstruction method for dMRI based on multi-directional assistance. The method better utilizes the information between dMRI data directions by Partition-Replace operation, cleverly constructing data pairs and designing effective self-supervised strategy for bi-directional prediction, which can overcome the problem of data incompleteness and guide the model to reconstruct unsampled signals in spite of the difficulty of acquiring fully sampled data. Experimental results show that our method achieves better reconstruction in the absence of fully sampled data, even with an 8-fold acceleration factor. © 2024 IEEE.

关键词： Self-supervised learning

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Event-driven Robotic Tactile data Learning Using Temporal Spike Sequence Backpropagation Method

Event-driven Robotic Tactile Data Learning Using Temporal Sp...

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2022 Chinese Automation Congress, CAC 2022

作者： Hou, Qing Liu, Tingqing Yang, Jing Ji, Xiaoyang Li, Qinglang Li, Jian Yin, Baofan Guizhou University School of Computer Science and Technology Guiyang China Guizhou University School of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University State Key Laboratory of Public Big Data Guizhou University School of Mechanical Engineering Guiyang China Guizhou University School of Mechanical Engineering Guiyang China

ISBN: (纸本)9781665465335

Tactile perception is indispensable for intelligent robots to interact intelligently like humans. Therefore, the effective use of deep learning methods to acquire tactile features has become an important focus of tactile perception research. Satisfactory time-driven characteristics and the ability to process spatiotemporal information efficiently of spiking neural networks are advantageous for event-based data. We apply a temporal spike sequence learning backpropagation method that can handle continuous spikes to improve the spike neural network for tactile object recognition based on event-driven data. We prove the effectiveness of the temporal spike sequence error backpropagation method in practical applications to address the problem of losing temporal information of data using approximate derivatives. In practical application, we have proved the validity of the back propagation method of temporal spike sequence error in solving the problem of losing temporal information of data using approximate derivatives © 2022 IEEE.

关键词： Backpropagation

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