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arXiv 2023年

作者： Wang, Zitai Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming SKLOIS Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University China BDKM University of Chinese Academy of Sciences China

Real-world datasets are typically imbalanced in the sense that only a few classes have numerous samples, while many classes are associated with only a few samples. As a result, a naïve ERM learning process will be biased towards the majority classes, making it difficult to generalize to the minority classes. To address this issue, one simple but effective approach is to modify the loss function to emphasize the learning on minority classes, such as re-weighting the losses or adjusting the logits via class-dependent terms. However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results. To bridge this gap, we propose a novel technique named data-dependent contraction to capture how these modified losses handle different classes. On top of this technique, a fine-grained generalization bound is established for imbalanced learning, which helps reveal the mystery of re-weighting and logit-adjustment in a unified manner. Furthermore, a principled learning algorithm is developed based on the theoretical insights. Finally, the empirical results on benchmark datasets not only validate the theoretical results but also demonstrate the effectiveness of the proposed method. © 2023, CC BY.

关键词： Learning algorithms

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Semi-supervised classification method of SAR images using spectral clustering in contourlet domain 4

Semi-supervised classification method of SAR images using sp...

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2019 4th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Jiang, Kaiwen Zhang, Degan Xu, Haixia Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education 300384 China Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Electronic and Information Engineering Tianjin Vocational Institute Tianjin300410 China

A new based on Semi-supervised classification theory for SAR images in contourlet domain is proposed, in this paper. Attempting to get better and faster performance, the PSO algorithm (Particle swarm optimization algorithm) and contourlet domain is proposed to instead of traditional k-means algorithm. PSO is used to find the global optimum by performing a global search in the whole solution space. And then, contourlet is applied in front of construct the similarity matrix to extract more effective eigenvalues. In section five, the proposed algorithm got better classification results than the traditional k-means algorithm which is proved by experimental results show that in terms of running time, classification accuracy and Kappa coefficient. © Published under licence by IOP Publishing Ltd.

关键词： Synthetic aperture radar

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Dual Interactive Graph Convolutional Networks for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2022年 60卷 1页

作者： Wan, Sheng Pan, Shirui Zhong, Ping Chang, Xiaojun Yang, Jian Gong, Chen Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia National Key Laboratory of Science and Technology on Atr National University of Defense Technology Changsha410073 China Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

Recently, graph convolutional network (GCN) has progressed significantly and gained increasing attention in hyperspectral image (HSI) classification due to its impressive representation power. However, existing GCN-based methods do not give full consideration to the multiscale spatial information, since the convolution operations are governed by fixed neighborhood. As a result, their performances can be limited, particularly in the regions with diverse land cover appearances. In this article, we develop a new dual interactive GCN (DIGCN) which introduces the dual GCN branches to capture spatial information at different scales. More significantly, the dual interactive module is embedded across the GCN branches, so that the correlation of multiscale spatial information can be leveraged to refine the graph information. To be concrete, the edge information contained in one GCN branch can be refined by incorporating the feature representations from the other branch. Analogously, improved feature representations can be generated in one GCN branch by fusing the edge information from the other branch. As such, the refined graph information can help enhance the representation power of the model. Furthermore, to avoid the negative effects of the manually constructed graph, our proposed model adaptively learns a discriminative region-induced graph, which also accelerates the convolution operation. We comprehensively evaluate the proposed method on four commonly used HSI benchmark data sets, and the state-of-the-art results can be achieved when compared with several typical HSI classification methods. © 1980-2012 IEEE.

关键词： Convolution

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

Epilepsy Classification for Mining Deeper Relationships betw...

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2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Xin Zheng, Yuanjie Niu, Yi Li, Chengiiang Shandong Normal University School of Information Science and Engineering Jinan250358 China Key Lab of Intelligent Computing and Information Security Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan250358 China Department of Electrical Engineering Information Technology Shandong University of Science and Technology Jinan250031 China

ISBN: (纸本)9781728194813

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy. © 2020 IEEE.

关键词： Electroencephalography

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The research of the expansion property and chain mixing property of topological group 12

The research of the expansion property and chain mixing prop...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

作者： Zhan-Jiang, Ji Wei, Shi School of Information and Electronic Engineering Wuzhou University China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou University China Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University China Party Committee Office Wuzhou Vocational College China

ISBN: (纸本)9781728170817

The expansion property and chain mixing property have an important significance in terms of theory and application. The concept of the expansion property and chain mixing property are introduced in this paper. We study their dynamical properties. The following result are obtained: (1)Letbe the hyperspace of. Ifis expansive, thenis expansive;(2)Letbe the hyperspace of. Ifis chain mixing, thenis chain mixing;(3)Letbe the hyperspace of. Ifis chain transitive, thenis chain transitive;These results enriched the theory of the expansion property and chain mixing property of topological group. © 2020 IEEE.

关键词： Topology

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The research of G-chain transitive and G-chain mixing on the hyperspace of topological group action 12

The research of G-chain transitive and G-chain mixing on the...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

ISBN: (纸本)9781728170817

G-chain transitive and G-chain mixing have an important significance in terms of theory and application. According to the definition of chain transitive and chain mixing, we give the concept of G-chain transitiveand G-chain mixing in this paper. By inference, some conclusions of the hyperspace were extended to the hyperspace of topological group action. We have the following result: (1)Letbe the hyperspace of. Ifis a G-chain transitive map, thenis a G-chain transitive map;(2)Letbe the hyperspace of. Ifis a G-chain mixing map, thenis a G-chain mixing map;(3)Letbe the hyperspace ofandbe a equivariant map. All non-empty open set are invariable for. Thenis a G-exact map if and only ifis a G-exact map. These results enriched the theory of the G-chain transitive and G-chain mixing on the hyperspace of topological group action. © 2020 IEEE.

关键词： Group theory

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The research of g-mixing and g-equicontinuity on the hyperspace of topological group action 12

The research of g-mixing and g-equicontinuity on the hypersp...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

ISBN: (纸本)9781728170817

G-mixing and G-equicontinuity have an important significance in terms of theory and application. According to the definition of mixing and equicontinuity, we give the concept of G-mixing and G-equicontinuity in this paper. By inference, some conclusions of the hyperspace were extended to the hyperspace of topological group action. We have the following result: (1)Letbe the hyperspace ofandbe a pseudo equivariant map. All non-empty open set are invariable for. Ifis a G-mixing map, thenis a G-mixing map;(2)Letbe the hyperspace of andbe a equivariant map. Thenis a G-mild mixing map if and only ifis a G-mild mixing map;(3)Letbe the hyperspace ofandbe a equivariant map. Thenis a G-equicontinuity map if and only ifis a G-equicontinuity map. These results enriched the theory of the G-mixing and G-equicontinuity on the hyperspace of topological group action. © 2020 IEEE.

关键词： Mixing

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Accurate Latent Factor Analysis via Particle Swarm Optimizers

Accurate Latent Factor Analysis via Particle Swarm Optimizer...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jia Chen Xin Luo MengChu Zhou School of Cyber Science and Technology Beihang University Beijing China Chongqing Key Lab. of Big Data and Intelligent Computing and the Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chongqing School University of Chinese Academy of Sciences Chongqing China New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781665442084

A stochastic-gradient-descent-based Latent Factor Analysis (LFA) model is highly efficient in representative learning of a High-Dimensional and Sparse (HiDS) matrix. Its learning rate adaptation is vital in ensuring its efficiency. Such adaptation can be realized with an evolutionary computing algorithm. However, a resultant model tends to suffer from two issues: a) the pre-mature convergence of the swarm of learning rates as caused by an adopted evolution algorithm, and b) the pre-mature convergence of the LFA model as caused jointly by evolution-based learning rate adaptation and an optimization algorithm. This paper focuses on the methods to address such issues. A Hierarchical Particle-swarm-optimization-incorporated Latent factor analysis (HPL) model with a two-layered structure is proposed, where the first layer pre-trains desired latent factors with a position-transitional particle-swarm-optimization-based LFA model, and the second layer performs latent factor refining with a newly-proposed mini-batch particle swarm optimizer. With such design, an HPL model can well handle the pre-mature convergence, which is supported by the positive experimental results achieved on HiDS matrices from industrial applications.

关键词： Adaptation models Analytical models Computational modeling Refining Predictive models Sparse matrices Particle swarm optimization

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Exploiting Probabilistic Siamese Visual Tracking with a Conditional Variational Autoencoder

Exploiting Probabilistic Siamese Visual Tracking with a Cond...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wenhui Huang Jason Gu Peiyong Duan Sujuan Hou Yuanjie Zheng School of Information Science and Engineering Shandong Normal University China Dalhousie University Halifax Canada Key Lab of Intelligent Computing & Information Security in Universities of Shandong Shandong Normal University China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Shandong Normal University China

Visual tracking is a fundamental capability for robots tasked with humans and environment interaction. However, state-of-the-art visual tracking methods are still prone to failures and are imprecise when applied to challenging stereos, and their results are generally confidence agonistic. These methods depend on an embedded deep learning model to provide deterministic features or regression maps. A deterministic output with low confidence can result in disastrous consequences and lacks evidence needed for subsequent operations. Moreover, training data ambiguities or noise in the observations (so-called data uncertainty) can also lead to inherent uncertainty. In this paper, we focus on exploiting probabilistic Siamese visual tracking with a conditional variational autoencoder (CVAE). First, we build a bridge between the Siamese architecture and the CVAE and propose a novel Bayesian visual tracking method. Second, the proposed method generates a complete probability distribution that enables the production of multiple plausible tracking outputs. Third, CVAE conditioned by ground truth data encodes a low-dimensional latent space and conducts noise-injection training to prevent overfitting. Our proposed tracking method outperformed the state-of-the-art trackers on the VOT2016, VOT2018 and TColor-128 datasets.

关键词： Bridges Training Visualization Uncertainty Training data Probabilistic logic Probability distribution

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Cooperative Task Offloading Strategy for Vehicular Edge computing Based on Multi-Agent Deep Reinforcement Learning

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Future Generation Computer Systems 2026年 174卷

作者： Yuya Cui Degan Zhang Honghu Li Hao Qiang Haitao Zhao School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing 210003 China School of Internet of Things Engineering Jiangsu Vocational College of Information Technology Wuxi 214153 China Tianjin Key Lab of Intelligent Computing and Novel Software Key Lab of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300384 China

Vehicular Edge computing (VEC) and Vehicle- to-Vehicle (V2V) offloading can significantly reduce in-vehicle task latency. This paper investigates a cooperative task offloading strategy in VEC, where latency-sensitive and computation -intensive tasks can be offloaded to Road Side Units (RSUs) using 5 G connectivity. Additionally, these tasks can be shared among nearby vehicles through V2V links. Joint VEC and V2V cooperative offloading can not only minimizes task execution delays but also prevents network congestion. When vehicles are in motion, dynamic migration of computation tasks is necessary to maintain service continuity. We propose a two-phase distributed task offloading and migration strategy for multiple vehicles. In the first phase, vehicles select the optimal service vehicle based on inter-vehicle link quality and offloading willingness. In the second phase, to minimize system cost, we introduce a multi-agent reinforcement learning (MARL) based distributed task offloading and migration strategy. This strategy allows vehicles to choose the optimal edge node in a dynamic environment without fully offloading information. Moreover, we implement a counterfactual multi- agent (COMA) reinforcement learning approach to address the inefficiency caused by the credit allocation problem in multi-agent systems. Extensive evaluations demonstrate that the algorithm proposed in this paper perform better in terms of average system latency and overall task completion rate. Compared with related scheme, the proposed method can reduce latency by up to 54 % and improve task completion rate by up to 15 % in different scenarios.

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