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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Lu, Juhong Huang, Fangwan Yu, Zhiyong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

Sparse Representation-based Classification (SRC), which has achieved good performance in face recognition and other image classification, has been successfully extended to time series classification in recent years. As a generalization of the nearest subspace classifier, the performance of SRC depends on a rich set of training samples for each class, which can span as many variations of each class as possible under testing conditions. However, due to the difficulty of sample collection, many important applications can only provide a few or even a single sample for each class, which inevitably affects the performance of SRC. To address the problem of insufficient training samples, ESRC (Extended SRC) was proposed to put some samples that reflect the variations within the class into the dictionary, to obtain better generalization ability than SRC for under-sampled face recognition. In this paper, a new approach IESRC (Improved extend sparse representation based classification) is developed by splitting the dictionary into an original dictionary and auxiliary dictionary. The proposed model was first evaluated in 30 baseline data sets in the University of California, Riverside time series classification archive and then applied to classify the effects of aerobic exercise intervention in 24 young hypertensive patients based on the time series of the cardiopulmonary exercise test. Experimental results show that the proposed model can achieve better performance than SRC and ESRC in time series classification. © 2020, Springer Nature Switzerland AG.

关键词： Face recognition

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Using Deep Active Learning to Save Sensing Cost When Estimating Overall Air Quality 15th

Using Deep Active Learning to Save Sensing Cost When Estimat...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Lei, Dehao Yu, Zhiyong Li, Peiguan Han, Lei Huang, Fangwan College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

Air quality is widely concerned by the governments and people. To save cost, air quality monitoring stations are deployed at only a few locations, and the stations are actuated at partial time. Therefore, it is necessary to study how to actively collect a subset of air quality data to maximize the estimation accuracy of air quality at other locations and time. In order to solve this challenge, we propose the active variational adversarial model (AVAM) that selects the most valuable unlabeled samples through two iterative phases of active learning. In the first phase of our model, a candidate set with unlabeled samples is selected through traditional active learning. In the second phase, variational auto-encoder (VAE) is used to obtain the compressed representation of the candidate set and the training set with labeled samples, then a discriminator based on three-layer neural network is trained from the compressed representation. Finally the discriminator can output the most valuable unlabeled samples from the candidate set. The experimental results show that the AVAM proposed in this paper is superior to active learning models with the first or second phase only. © 2020, Springer Nature Switzerland AG.

关键词： Air quality

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Minimal Ternary Linear Codes from Vectorial Functions

arXiv

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arXiv 2024年

作者： Li, Yanjun Kan, Haibin Liu, Fangfang Peng, Jie Zheng, Lijing Zhuo, Zepeng Institute of Statistics and Applied Mathematics Anhui University of Finance and Economics Anhui Bengbu233030 China School of Computer Sciences Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai Engineering Research Center of Blockchain Shanghai Institute for Advanced Communication and Data Science Shanghai200433 China School of Mathematics and Statistics Huaibei Normal University Anhui Huaibei235000 China Mathematics and Science College Shanghai Normal University Guilin Road #100 Shanghai200234 China The School of Mathematics and Physics University of South China Hunan Hengyang421001 China

The study on minimal linear codes has received great attention due to their significant applications in secret sharing schemes and secure two-party computation. Until now, numerous minimal linear codes have been discovered. However, to the best of our knowledge, no infinite family of minimal ternary linear codes was found from vectorial functions. In this paper, we present a necessary and sufficient condition for a large class of ternary linear codes from vectorial functions such that those codes are minimal. Based on that, we construct several minimal ternary linear codes with three-weight from vectorial regular plateaued functions, and determine their weight distributions. Moreover, we also give a necessary and sufficient condition for a large family of ternary linear codes from vectorial functions such that the codes are minimal and violate the AB condition simultaneously. According to this characterization, we find several minimal ternary linear codes violating the AB condition. Notably, our results show that our method can be applied to solve a problem on minimal linear codes proposed by Li et al. Copyright © 2024, The Authors. All rights reserved.

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High-Altitude Pedestrian Detection Based on Improved YOLOv3 1

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6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Tian, Qing Cao, Pengfei Fan, Haoyi Hu, Rong Li, Zuoyong School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China University of Sevilla Sevilla Spain School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

As one of the main tasks in the field of computer vision, pedestrian detection aims to find out all pedestrians in the image or video. The existing YOLOv3 is a relatively mature object detection method. However, for the long-distance pedestrian detection task in high-altitude scenes, YOLOv3 has the limitations of low detection speed and low detection accuracy. This paper proposes an improved YOLOv3 method briefly called YOLOv3-M for the high-altitude pedestrian detection, which replaces the feature extraction module called darknet53 in YOLOv3 with MobileNetv1. Specifically, YOLOv3-M first constructs the dataset with the small objects of high-altitude pedestrians as the detection object. Then, it uses the K-means + + algorithm to re-cluster the high-altitude pedestrian dataset. Next, it uses the Distance Intersection over Union (DIoU) loss function to alleviate the problem of high-altitude pedestrian overlapping. Experimental results show that the proposed YOLOv3-M improves the detection precision and the detection speed compared to YOLOv3. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Constructions of Lightweight MDS Diffusion Layers from Hankel Matrices

Constructions of Lightweight MDS Diffusion Layers from Hanke...

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作者： Qiuping Li Lang Li Jian Zhang Junxia Zhao Kangman Li Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

Maximal Distance Separable（MDS） matrices are used as optimal diffusion layers in many block ciphers and hash ***,the designers paid more attentions to the lightweight MDS matrices because it can reduce the hardware *** this paper,we give a new method to construct the lightweight MDS *** provide some theoretical results and two kinds of 4 x4 lightweight Hankel MDS *** also prove that the 2～8 x 2～8 involution Hankel MDS matrix is not exist in finite ***,we searched the 4 x4 Hankel MDS matrices over GL（4,F） and GL（8,F） that have the better s-XOR counts until now.

关键词： Diffusion Layer Lightweight MDS Matrix Hankel Matrix

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Node transfer with graph contrastive learning for class-imbalanced node classification

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Neural Networks 2025年 190卷

作者： Yangding Li Xiangchao Zhao Yangyang Zeng Hao Feng Jiawei Chai Hao Xie Shaobin Fu Shichao Zhang College of Information Science and Engineering Hunan Normal University Changsha China Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China

In graph representation learning, the class imbalance problem is a significant challenge that has received much attention from academics. Although current approaches have shown promising results, they have not adequately addressed the problems of node quantity imbalance and feature space imbalance in datasets. This research presents a node transfer with graph contrastive learning framework (NT-GCL) that aims to improve the representation capabilities of graph neural networks for minority classes nodes by balancing node quantity and feature space distributions. First, the proposed node transfer algorithm redistributes misclassified nodes from majority classes to achieve a balanced distribution of node quantity and feature space. This approach effectively prevents the feature space of minority classes from being compressed by majority classes during information propagation, further mitigating potential imbalance issues. Subsequently, the self-supervised contrastive learning strategy is employed to train the model without relying on labels, reducing the bias introduced by labeled data. Experiments conducted with various encoders on six public datasets demonstrate that NT-GCL exhibits strong competitiveness in class-imbalanced node classification.

关键词： Class imbalance Graph contrastive learning Graph neural network Node classification Node transfer

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Transfer learning-based few-shot sample deep learning-assisted template attacks

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Physical Communication 2025年 72卷

作者： Zhiying Feng Lang Li Yu Ou Lianrui Deng College of Computer Science and Technology Hengyang Normal University Hengyang 421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang 421002 China

Deep learning-assisted template attack (DLATA) is a novel side-channel attack (SCA) method proposed by Lichao Wu at CHES2022. It utilizes a triplet network to assist template attacks (TA), avoiding the redundant training and hyperparameter tuning required in traditional DL-based SCA methods. However, the training of the triplet network requires a large number of power samples due to its unique structure. We propose a new optimization scheme, in which the transfer learning (TL) technology is used to train multiple models on several similar datasets with fewer power traces, to mitigation the problem. The approach allows us to leverage pre-trained models to product a new mode on the another target dataset by fine-tuning weights so that significantly reduce the training cost for the triplet network while maintaining attack effectiveness. We remould the structure and dimensionality of similar datasets so that the models trained on them can perform effective transfer learning for training on the target dataset. Concretely, some of parameters and features obtained from pretraining can be used directly for the target task, while the rest only require fine-tuning. Evaluation and experimental validation on the public ASCAD dataset demonstrate that our method achieves or even surpasses the performance of the original method with a 90% reduction in the training set. These findings highlight the effectiveness of the proposed TL strategy in achieving robust attack performance in low-sample training environments.

关键词： Deep learning

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A Jacobi Collocation Method for the Fractional Ginzburg-Landau Differential Equation

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Advances in applied mathematics and Mechanics 2020年第1期12卷 57-86页

作者： Yin Yang Jianyong Tao Shangyou Zhang Petr V.Sivtsev Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtanHunan 411105China Department of Mathematical Sciences University of DelawareNewarkDE 19716USA International Scientific and Research Laboratory of Multiscale Model Reduction and High Performance Computing Ammosov North Eastern UniversityKulakovskogo677013YakutskRussia

In this paper,we design a collocation method to solve the fractional Ginzburg-Landau equation.A Jacobi collocation method is developed and implemented in two ***,we space-discretize the equation by the Jacobi-Gauss-Lobatto collocation(JGLC)method in one-and two-dimensional *** equation is then converted to a system of ordinary differential equations(ODEs)with the time variable based on *** second step applies the Jacobi-Gauss-Radau collocation(JGRC)method for the time ***,we give a theoretical proof of convergence of this Jacobi collocation method and some numerical results showing the proposed scheme is an effective and high-precision algorithm.

关键词： The fractional Ginzburg-Landau equation Jacobi collocation method convergence

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Relations of the class u(λ) to other families of functions

arXiv

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arXiv 2021年

作者： Li, Liulan Ponnusamy, Saminathan Wirths, Karl-Joachim College of Mathematics and Statistics Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang Hunan421002 China Department of Mathematics Indian Institute of Technology Madras Chennai600 036 India Institute of Algebra and Analysis Technical University Braunschweig Braunschweig38106 Germany

In this article, we consider the family of functions f analytic in the unit disk |z| ′(0) − 1 and satisfying the condition |(z/f(z))2f′(z) − 1| MSC Codes Primary: 30C45, 30C62, 30C80, 30J10, 31C05, Secondary: 30C20,... 详细信息

关键词： Convolution

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The Graph Structure of Chebyshev Permutation Polynomials over Ring pk

arXiv

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arXiv 2022年

作者： Li, Chengqing Lu, Xiaoxiong Tan, Kai Chen, Guanrong The School of Computer Science Xiangtan University Hunan Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing & Information Processing Ministry of Education Xiangtan University Hunan Xiangtan411105 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Understanding the underlying graph structure of a nonlinear map over a particular domain is essential in evaluating its potential for real applications. In this paper, we investigate the structure of the associated functional graph of Chebyshev permutation polynomials over a ring pk, with p being a prime number greater than three, where every number in the ring is considered as a vertex and the existing mapping relation between two vertices is regarded as a directed edge. Based on some new properties of Chebyshev polynomials and their derivatives, we disclose how the basic structure of the functional graph evolves with respect to parameter k. First, we present a complete and explicit form of the length of a path starting from any given vertex. Then, we show that the strong patterns of the functional graph that the number of cycles of any given length always remains constant as k increases. Moreover, we rigorously prove the rules on the elegant structure of the functional graph and verify them experimentally. Our results could be useful for studying the emergence of the complexity of a nonlinear map in digital computers and security analysis of its cryptographic *** Codes 94A55 Copyright © 2022, The Authors. All rights reserved.

关键词： Chebyshev polynomials

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