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Animal Sound Recognition Based on Double Feature of Spectrogram

Chinese Journal of Electronics 2019年第4期28卷 667-673页

作者： LI Ying HUANG Hongkeng WU Zhibin College of Mathematics and Computer Science Fuzhou University Key Laboratory of Information Security of Network Systems (Fuzhou University)

Due to existence of different environments and noises, the existing method is difficult to ensure the recognition accuracy of animal sound in low Signal-to-noise(SNR) conditions. To address these problems, we propose a double feature, which consists of projection feature and Local binary pattern variance(LBPV) feature, combined with Random forest(RF) for animal sound *** feature extraction, an operation of projecting is made on spectrogram to generate the projection ***, LBPV feature is generated by means of accumulating the corresponding variances of all pixels for every Uniform local binary pattern(ULBP) in the spectrogram. Short-time spectral estimation algorithm is used to enhance sound signals in severe mismatched noise conditions. In the experiments, we classify 40 kinds of common animal sounds under different SNRs with rain noise, traffic noise, and wind noise. As the experimental results show, the proposed framework consisting of shorttime spectrum estimation, double feature, and RF, can recognize a wide range of animal sounds and still remains a recognition rate over 80% even under 0dB SNR.

关键词： Animal sound recognition Local binary pattern variance(LBPV) Projection feature Random forest(RF) Sound enhancement

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Space-Air-Ground Integrated network with Native Intelligence(NI-SAGIN):Concept,Architecture,Access and networking

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China Communications 2022年第1期19卷 I0002-I0007页

作者： Zhenyu Xiao Qihui Wu Jiajia Liu Ning Zhang Tao Sun the School of Electronic and Information Engineering Beihang University IET China Institute of Communications and of Electronics the Nanjing University of Aeronautics and Astronautics(NUAA) key lab of Ministry of Industry and Information Technology of China the School of Cybersecurity Northwestern Polytechnical University Shaanxi Provincial Engineering Laboratory of Cyber Security Xi’an Unmanned System Security and Intelligent Communications ISTC Center IEEE IOT-AHSN TC IEEE Communications Society1 IEEE Vehicular Technology Society1 the Department of Electrical and Computer Engineering at University of Windsor1 China Mobile Research Institute1 GPP SA21 China Mobile’s 3GPP SA and CT groups

Ubiquitous coverage is one of the most important goals for mobile communication *** achieve this,integration of space,air,and ground networks is highly demanded,which expects to become the one of the enabling technolo... 详细信息

关键词： Access networks networking

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Discrete Curvature Graph information Bottleneck

arXiv

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arXiv 2024年

作者： Fu, Xingcheng Wang, Jian Gao, Yisen Sun, Qingyun Yuan, Haonan Li, Jianxin Li, Xianxian Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Institute of Artificial Intelligence Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

Graph neural networks(GNNs) have been demonstrated to depend on whether the node effective information is sufficiently passing. Discrete curvature (Ricci curvature) is used to study graph connectivity and information propagation efficiency with a geometric perspective, and has been raised in recent years to explore the efficient message-passing structure of GNNs. However, most empirical studies are based on directly observed graph structures or heuristic topological assumptions and lack in-depth exploration of underlying optimal information transport structures for downstream tasks. We suggest that graph curvature optimization is more in-depth and essential than directly rewiring or learning for graph structure with richer message-passing characterization and better information transport interpretability. From both graph geometry and information theory perspectives, we propose the novel Discrete Curvature Graph information Bottleneck (CurvGIB) framework to optimize the information transport structure and learn better node representations simultaneously. CurvGIB advances the Variational information Bottleneck (VIB) principle for Ricci curvature optimization to learn the optimal information transport pattern for specific downstream tasks. The learned Ricci curvature is used to refine the optimal transport structure of the graph, and the node representation is fully and efficiently learned. Moreover, for the computational complexity of Ricci curvature differentiation, we combine Ricci flow and VIB to deduce a curvature optimization approximation to form a tractable IB objective function. Extensive experiments on various datasets demonstrate the superior effectiveness and interpretability of CurvGIB. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Probability-based Global Cross-modal Upsampling for Pansharpening

arXiv

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arXiv 2023年

作者： Zhu, Zeyu Cao, Xiangyong Zhou, Man Huang, Junhao Meng, Deyu Xi'an Jiaotong University China School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China Nanyang Technological University Singapore

Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods used in these approaches only utilize the local information of each pixel in the low-resolution multispectral (LRMS) image while neglecting to exploit its global information as well as the cross-modal information of the guiding panchromatic (PAN) image, which limits their performance improvement. To address this issue, this paper develops a novel probability-based global cross-modal upsampling (PGCU) method for pan-sharpening. Precisely, we first formulate the PGCU method from a probabilistic perspective and then design an efficient network module to implement it by fully utilizing the information mentioned above while simultaneously considering the channel specificity. The PGCU module consists of three blocks, i.e., information extraction (IE), distribution and expectation estimation (DEE), and fine adjustment (FA). Extensive experiments verify the superiority of the PGCU method compared with other popular upsampling methods. Additionally, experiments also show that the PGCU module can help improve the performance of existing SOTA deep learning pansharpening methods. The codes are available at https://***/Zeyu-Zhu/PGCU. © 2023, CC BY-NC-ND.

关键词： Image enhancement

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LEGNN: A label Ensemble Perspective for Training a label-Noise-Resistant GNN with Reduced Complexity

arXiv

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arXiv 2024年

作者： Zhao, Rui Shi, Bin Liang, Zhiming Ruan, Jianfei Dong, Bo Lin, Lu School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China School of Distance Education Xi'an Jiaotong University China College of Information Sciences and Technology Penn State University United States

Graph Neural networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by label noise, that is, a small amount of incorrectly labeled nodes can substantially misguide model training. Mainstream solutions define node classification with label noise (NCLN) as a reliable labeling task, often introducing node similarity with quadratic computational complexity to more accurately assess label reliability. To this end, in this paper, we introduce the label Ensemble Graph Neural network (LEGNN), a lower complexity method for robust GNNs training against label noise. LEGNN reframes NCLN as a label ensemble task, gathering informative multiple labels instead of constructing a single reliable label, avoiding high-complexity computations for reliability assessment. Specifically, LEGNN conducts a two-step process: bootstrapping neighboring contexts and robust learning with gathered multiple labels. In the former step, we apply random neighbor masks for each node and gather the predicted labels as a high-probability label set. This mitigates the impact of inaccurately labeled neighbors and diversifies the label set. In the latter step, we utilize a partial label learning based strategy to aggregate the high-probability label information for model training. Additionally, we symmetrically gather a low-probability label set to counteract potential noise from the bootstrapped high-probability label set. Extensive experiments on six datasets demonstrate that LEGNN achieves outstanding performance while ensuring efficiency. Moreover, it exhibits good scalability on dataset with over one hundred thousand nodes and one million edges. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Adversarial Training for Image Captioning Incorporating Relation Attention 18th

Adversarial Training for Image Captioning Incorporating Rela...

引用

18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Chen, Tianyu Li, Zhixin Zhang, Canlong Ma, Huifang Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin541004 China College of Computer Science and Engineering Northwest Normal University Lanzhou730070 China

ISBN: (纸本)9783030891879

Image captioning methods with attention mechanism are leading this field, especially models with global and local attention. But there are few conventional models to integrate the relationship information between various regions of the image. In this paper, this kind of relationship features are embedded into the fused attention mechanism to explore the internal visual and semantic relations between different object regions. Besides, to alleviate the exposure bias problem and make the training process more efficient, we combine Generative Adversarial network with Reinforcement Learning and employ the greedy decoding method to generate a dynamic baseline reward for self-critical training. Finally, experiments on MSCOCO datasets show that the model can generate more accurate and vivid image captioning sentences and perform better in multiple prevailing metrics than the previous advanced models. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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StinAttack: A Lightweight and Effective Adversarial Attack Simulation to Ensemble IDSs for Satellite- Terrestrial Integrated network

StinAttack: A Lightweight and Effective Adversarial Attack S...

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IEEE Symposium on computers and Communications (ISCC)

作者： Shangyuan Zhuang Jiyan Sun Hangsheng Zhang Xiaohui Kuang Ling Pang Haitao Liu Yinlong Liu School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Effective adversarial attacks simulation is essential for the deployment of ensemble Intrusion Detection Systems (en- semble IDSs) in Satellite-Terrestrial Integrated network (STIN). This is because it can automatically generate a large amount of adversarial samples to evaluate the robustness of different classifiers. Based on the result, it can further guide the STIN engineers to select proper classifiers in ensemble IDSs. Moreover, it can help the IDSs improve detect performance by their self- learning property in the adversarial attack process. However, the existing adversarial attack approaches suffer from the problems of low success rate and high overhead of communication and calculation due to the limited computing resources and long communication links of STIN. This results in their inefficiency in STIN. To address the above problems, we provide StinAttack as a robustness evaluation scheme for STIN. First, StinAttack provides a comprehensive and automatic robustness evaluation framework for IDSs in STIN with only few times interactions between terrestrial and satellite nodes. Second, StinAttack proposes an effective adversarial attack simulation based on lightweight gradient evaluation for ensemble IDSs. Third, we conduct experiments on 11 typical IDSs, 4 baseline popular adversarial attacks and our StinAttack. Experimental results show that our approach can effectively attack ensemble IDSs and the evaluation results based on real STIN dataset are instructive for designing secure networks.

关键词： computers 6G mobile communication Satellites Computational modeling Intrusion detection Robustness security

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Probability-based Global Cross-modal Upsampling for Pansharpening

Probability-based Global Cross-modal Upsampling for Pansharp...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zeyu Zhu Xiangyong Cao Man Zhou Junhao Huang Deyu Meng Xi'an Jiaotong University School of Computer Science and Technology Xi'an Jiaotong University Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University Nanyang Technological University Macau University of Science and Technology

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A FinBERT Framework for Sentiment Analysis of Chinese Financial News

A FinBERT Framework for Sentiment Analysis of Chinese Financ...

引用

computer Technology and information Science (ISCTIS), International Symposium on

作者： Yuesheng Huang Renyi Lin Jiawen Li Yiqi Li Jiajia Chen Jiaqi Liang Guanyuan Feng Jujian Lv School of Computer Science Guangdong Polytechnic Normal University Guangzhou China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China School of Finance and Economics Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In the era of big data, sentiment analysis of the Chinese stock market has become a research hotspot. As a vital reflection of market sentiment, financial news has a significant impact on stock market trends. In this paper, we propose a framework based on the FinBERT pre-trained model optimized for financial news sentiment analysis in China. In particular, the model utilizes the Bidirectional Encoder Representations from Transformers (BERT) to deal with semantics, syntactic features, and specialized vocabulary of financial texts through self-supervised learning. The fine-tuning stage further optimizes the model, so that it can accurately predict sentiment tendencies. Experimental results show that the proposed model outperforms others on the sentiment classification task, with an accuracy of up to 94.52%. Consequently, the model can provide participants in the financial industry with a valuable reference of market trends, which helps investors and analysts make decisions properly.

关键词： Analytical models Sentiment analysis Vocabulary Accuracy Bidirectional control Market research Transformers Data models Encoding Stock markets

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SPM-GCN: An adaptive reordering algorithm for sparse LU factorization via GCN

SPM-GCN: An adaptive reordering algorithm for sparse LU fact...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Huazeng Liu Junjie Wang Qi Liu Wei Du Zanjun Zhang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shaanxi Key Laboratory of Large Scale Electromagnetic Computing Xidian University Xi’an China

Sparse LU factorization is a critical kernel in scientific computing and engineering applications. A better nonzero pattern of sparse matrixes can accelerate LU factorization by reordering. Traditionally it’s difficult to predict which non-zero pattern is optimal for a sparse matrix. In this paper, we proposed a graph convolutional neural network (GCN) for adaptively selecting the optimal reordering algorithm from five candidate reordering algorithms in the Matrix preprocessing step, referred to as SPM-GCN. After using our SPM-GCN, the average numerical factorization time outperformed the other five algorithms, and the average numerical factorization time was reduced by 17.3% compared with the default method of swSuperLU.

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