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6th Conference on Signal and Information processing, Networking and Computers, ICSINC 2019

作者： Zhang, Miaohui Pang, Kangning Chen, Yunzhong Zhang, Bo Key Laboratory of Data Analysis and Processing of Henan Province Henan University Kaifeng China

ISBN: (纸本)9789811541629

Video surveillance has been using in daily life widely and has significant impact in security field. Reading time information in video has becoming an essential part. Because of the complex background of timestamp and the high similarity between targets, the features extracted by traditional machine learning algorithm cannot meet the requirements and cannot achieve the required detection speed and accuracy. In this paper, we improved the algorithm with multi-scale features. Features extracted from convolution layers of different scales are used to predict, which could improve the recognition accuracy of small targets. In addition, because the size of the collected dataset is 2860 and the amount of data is not large enough, we use data augmentation and transfer learning to train. Pre-training on the public dataset SVHN firstly, and then trains on the special dataset. Experiments show that the algorithm can recognize timestamps accurately. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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Optimal location query based on k nearest neighbours

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Frontiers of Computer Science 2021年第2期15卷 105-117页

作者： Yubao LIU Zitong CHEN Ada Wai-Chee FU Raymond Chi-Wing WONG Genan DAI School of Data and Computer Science Sun Yat-Sen UniversityGuangzhou510006China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006China Department of Computer Science and Engineering Chinese University of Hong KongHong Kong999077China Department of Computer Science and Engineering The Hong Kong University of Science and TechnologyHong Kong999077China

Optimal location query in road networks is a basic operation in the location intelligence *** a set of clients and servers on a road network,the purpose of optimal location query is to obtain a location for a new server,so that a certain objective function calculated based on the locations of clients and servers is *** works assume no labels for servers and that a client only visits the nearest *** assumptions are not realistic and it renders the existing work not useful in many *** this paper,we relax these assumptions and consider the k nearest neighbours(KNN)of *** introduce the problem of KNN-based optimal location query(KOLQ)which considers the k nearest servers of clients and labeled *** also introduce a variant problem called relocation KOLQ(RKOLQ)which aims at relocating an existing server to an optimal *** main analysis algorithms are proposed for these *** experiments on the real road networks illustrate the efficiency of our proposed solutions.

关键词： optimal location query k nearest neighbours road network

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PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs

arXiv

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arXiv 2023年

作者： Hu, Wentao Zheng, Jia Zhang, Zixin Yuan, Xiaojun Yin, Jian Zhou, Zihan Sun Yat-Sen University China Guangdong Key Laboratory of Big Data Analysis and Processing China Manycore Tech Inc. University of Electronic Science and Technology of China China

In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models. Existing methods for this problem reconstruct 3D models by back-projecting the 2D observations into 3D space while maintaining explicit correspondence between the input and output. Such methods are sensitive to errors and noises in the input, thus often fail in practice where the input drawings created by human designers are imperfect. To overcome this difficulty, we leverage the attention mechanism in a Transformer-based sequence generation model to learn flexible mappings between the input and output. Further, we design shape programs which are suitable for generating the objects of interest to boost the reconstruction accuracy and facilitate CAD modeling applications. Experiments on a new benchmark dataset show that our method significantly outperforms existing ones when the inputs are noisy or incomplete. © 2023, CC BY.

关键词： Computer aided design

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Inferring Prerequisite Relationships Among Learning Resources for HPC Education 11th

Inferring Prerequisite Relationships Among Learning Resource...

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Wu, Run Luo, Chenyu Hu, Miao Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9789811600098

For online HPC (High-Performance Computing) education, identifying the prerequisite relationships among different HPC learning resources is important for generating learning paths and personalized course recommendations for students. Previously, such prerequisite relationships were often created by domain experts manually. In this paper, we propose a novel framework to infer both concept and learning resource prerequisite relationships and integrate such a framework into an online HPC education platform. We first construct a bi-directional long short-term (BiLSTM) neural network with attention mechanism to automatically mine the latent semantic features from the formal description of concepts. Then, we input two kinds of features into a fully connected neural network for classification and obtain the relationships among concepts. Considering the asymmetry and directivity of the prerequisite relation, we adopt a network embedding model to learn the vector representations of each concept as the prerequisite and subsequent roles. The concept vectors are weighted and summed to generate the feature vectors of learning resources. For a given pair of learning resources, their feature vectors are input into a classifier that outputs the result of relation classification. Finally, we conduct a series of experiments on two large benchmark datasets. The experimental results show that our method outperforms existing methods with significant improvements. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Vectors

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Erase Then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning 39

Erase Then Rectify: A Training-Free Parameter Editing Approa...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Zhe-Rui Han, Jindong Wang, Chang-Dong Liu, Hao Sun Yat-sen University Guangzhou China The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)157735897X

Graph unlearning, which aims to eliminate the influence of specific nodes, edges, or attributes from a trained Graph Neural Network (GNN), is essential in applications where privacy, bias, or data obsolescence is a concern. However, existing graph unlearning techniques often necessitate additional training on the remaining data, leading to significant computational costs, particularly with large-scale graphs. To address these challenges, we propose a two-stage training-free approach, Erase then Rectify (ETR), designed for efficient and scalable graph unlearning while preserving the model utility. Specifically, we first build a theoretical foundation showing that masking parameters critical for unlearned samples enables effective unlearning. Building on this insight, the Erase stage strategically edits model parameters to eliminate the impact of unlearned samples and their propagated influence on intercorrelated nodes. To further ensure the GNN's utility, the Rectify stage devises a gradient approximation method to estimate the model's gradient on the remaining dataset, which is then used to enhance model performance. Overall, ETR achieves graph unlearning without additional training or full training data access, significantly reducing computational overhead and preserving data privacy. Extensive experiments on seven public datasets demonstrate the consistent superiority of ETR in model utility, unlearning efficiency, and unlearning effectiveness, establishing it as a promising solution for real-world graph unlearning challenges. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Weather classification method based on spiking neural network

Weather classification method based on spiking neural networ...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Meng Tian Xuefei Chen Hongkuo Zhang Peng Zhang Kejing Cao Ruiyi Wang School of Computer and Information Engineering Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9781665406314

People's life and production activities are directly or indirectly affected by the weather. It is very necessary to accurately and quickly predict weather conditions. At present, the weather prediction system needs a series of sensors and manual assistance, but it cannot be arranged in high density due to high cost, which leads to inaccurate weather prediction. Computer vision technology can classify weather conditions through images, which reduces the cost and can be arranged in high density to ensure the accuracy of weather prediction. Because the training and reasoning of traditional p Convolutional Neural Network has very large energy consumption, while Spiking Neural Network has the characteristics of ultra-low energy consumption, which can further reduce the energy cost. In this paper, a shallow Spiking Neural Network for weather classification is constructed, which is trained and tested on a dataset containing four categories (cloudy, rainy, sunny and sunrise). Experiments show that the classification accuracy of the model is 93.45%, which is higher than that of the Convolutional Neural Network based on vgg19. In addition, the computational complexity of Spiking Neural Network and Convolutional Neural Network are analyzed to show the advantages of Spiking Neural Network in energy consumption.

关键词： Training Energy consumption Costs Neural networks Weather forecasting Cognition Sensor systems

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Vision Transformer Based Hash Coding for Efficient Image and Audio Retrieval with Global and Local Equilibrium Distance Constraints

Vision Transformer Based Hash Coding for Efficient Image and...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ye Liu Yan Pan Jian Yin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Artificial Intelligence and Big Data Department Lizhi Inc. Beijing & Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the application of efficient retrieval in information systems and retrieval augmented generation with vector database for large language models, hash coding algorithms have made progress in recent years. The rise of transformer technology in the field of deep learning has brought the possibility to further improve the effect of hash coding algorithms. We introduce the vision transformer framework to both images and audios, and propose a novel approach for the tasks of multi-label image retrieval and audio event retrieval. In the proposed hash coding model, global and local equilibrium distance constraints are integrated, so that the hash codes for images can be better obtained through the global hash centers and local similar samples. In order to realize end-to-end training and hash code generation for audios, we adopt the adapter of mel spectrogram, thus the proposed approach can be simply converted and applied to audio hash coding. Comparative experiments verify that better results can be achieved on multiple image and audio datasets.

关键词： Training Computer vision Image coding Codes Large language models Image retrieval Transformers Encoding Vectors Spectrogram

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AMHF-TP: Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期 44-58页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing School of Electronic Engineering Xi'an University of Posts and Telecommunications

Multifunctional therapeutic peptides(MFTP) hold immense potential in diverse therapeutic contexts, yet their prediction and identification remain challenging due to the limitations of traditional methodologies, such as extensive training durations, limited sample sizes, and inadequate generalization capabilities. To address these issues, we present AMHF-TP, an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance performance. The AMHF-TP is composed of four key components: a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences; a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures; a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences; and a hierarchical feature extraction module that integrates multimodal peptide sequence features. Compared with leading methods,the proposed AMHF-TP demonstrates superior precision, accuracy, and coverage, underscoring its effectiveness and robustness in MFTP recognition. The comparative analysis of separate hierarchical models and the combined model, as well as with five contemporary models, reveals AMHF-TP's exceptional performance and stability in recognition tasks.

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Dynamic Residual Classifier for Class Incremental Learning

Dynamic Residual Classifier for Class Incremental Learning

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International Conference on Computer Vision (ICCV)

作者： Xiuwei Chen Xiaobin Chang School of Artificial Intelligence Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R.China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The rehearsal strategy is widely used to alleviate the catastrophic forgetting problem in class incremental learning (CIL) by preserving limited exemplars from previous tasks. With imbalanced sample numbers between old and new classes, the classifier learning can be biased. Existing CIL methods exploit the long-tailed (LT) recognition techniques, e.g., the adjusted losses and the data re-sampling methods, to handle the data imbalance issue within each increment task. In this work, the dynamic nature of data imbalance in CIL is shown and a novel Dynamic Residual Classifier (DRC) is proposed to handle this challenging scenario. Specifically, DRC is built upon a recent advance residual classifier with the branch layer merging to handle the model-growing problem. Moreover, DRC is compatible with different CIL pipelines and substantially improves them. Combining DRC with the model adaptation and fusion (MAF) pipeline, this method achieves state-of-the-art results on both the conventional CIL and the LT-CIL benchmarks. Extensive experiments are also conducted for a detailed analysis. The code is publicly available 1 .

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Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

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International Journal of Agricultural and Biological Engineering 2022年第1期15卷 241-250页

作者： Dongyan Zhang Mengru Zhang Fenfang Lin Zhenggao Pan Fei Jiang Liang He Hang Yang Ning Jin National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475004HenanChina School of Informatics and Engineering Suzhou UniversitySuzhou 234000AnhuiChina National Meteorological Center Beijing 100081China Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100101China

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing *** Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing *** this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in *** random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the *** spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by *** the top nine features were selected to form an optimal feature *** the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature *** overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.

关键词： Google Earth Engine regional scale winter wheat rapid mapping

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