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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Lin, Jiajun Jia, Zhenhong School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

In low-illumination environments, the contrast between targets and the background sharply decreases, and imaging sensors introduce complex random noise while capturing more light, leading to severe distortion in the target features of visible light images. Under these conditions, target feature-based detection algorithms face significant challenges. In this paper, we propose a coarse-to-fine change detection algorithm that detects targets in low-illumination environments by focusing on the change features generated by the targets. First, a binary classifier based on ResNet18 is trained with a specially curated dataset of targets and noise. Second, this classifier is applied to identify potential change objects in test data, preserve targets, and extract corresponding bi-temporal local regions of interest. Third, novel difference feature extraction operators are employed to generate local difference images. Next, a Laplacian-of-Gaussian-based graph cut algorithm is used to perform binary segmentation, distinguishing foreground from background in the images. We validated the feasibility of this algorithm in three challenging night scenes. Compared with the current state-of-the-art unsupervised change detection algorithms, the proposed algorithm shows overall better detection performance. © 2024 SPIE.

关键词： Change detection

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Tor encrypted traffic recognition and classification technology based on flow information graph and deep learning

Tor encrypted traffic recognition and classification technol...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Zhang, Yu Jia, Zhenhong Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei Lu, Changwu School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

ISBN: (纸本)9781510681514

Internet traffic analysis is the core approach to network management and security. In the rapidly changing environment of encrypted traffic, traditional plaintext-based analysis methods have become obsolete. Although there are currently some methods for analysing encrypted traffic, they overlook the inherent logic and hierarchy of different encrypted traffic analysis requirements and lack research into the essential characteristics of encrypted *** article proposes a framework FM-ENet based on Graph Neural Network and Deep learning for Tor encrypted traffic classification to meet the practical needs of network management and security monitoring.A Flow Information Graph has been designed on the basis of Graph Neural Network, which can effectively alleviate the problem of concept *** the area of deep neural networks, we have developed an end-to-end multi-level spatio-temporal feature fusion enhanced network module ST-FENet, which has the advantage of automatically learning the non-linear relationship between input and output data without the need for manual feature extraction,this structure can complement each other in classification performance and solve the problem of low classifier efficiency. We compared FM-ENet to current popular methods using the public ISCX-Tor dataset. FM-ENet can achieve higher performance while saving costs, with a 9% improvement in accuracy compared to FlowPrint and a 4.4%, 4.5% and 4.4% improvement in PR, RC and F1 indicators compared to TSCRNN. © 2024 SPIE.

关键词： Network security

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Video image change detection under a wide field of view in foggy weather

Video image change detection under a wide field of view in f...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Li, Wen Jia, Zhenhong Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei School of Computer Science and Technology Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

Addressing the problem of poor visual effect and noise interference in foggy video images collected by hawk-eye image sensors, we suggest an approach which utilizes difference images (DIs) fusion and two-stage clustering for detecting changes in monitoring video pictures. First, we utilize a dehazing algorithm based on the color attenuation prior theory to effectively eliminate the haze interference. Then, two DIs are generated using the compressed logarithmic ratio and maximum mean pixel ratio operators. Fusion of two DIs using multi-scale Laplace pyramid and regional energy algorithms, and filtered using the adaptive median filtering algorithm to obtain the final DIs. Finally, the soft threshold function and the FCM clustering algorithm are used to perform two-stage clustering on the DI to obtain the change detection result. Experimental results demonstrate that this approach can effectively and accurately detect subtle changes in hawk-eye surveillance video images under foggy conditions. © 2024 SPIE.

关键词： Image fusion

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Multi-speaker Emotional Speech Synthesis Based on Self-Supervised Speech Representations 5

Multi-speaker Emotional Speech Synthesis Based on Self-Super...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Yin, Mingming Huang, Zhihua Lu, Kexin Zhou, Haiyang School of Computer Science and Technology Xinjiang University Urumqi China School of Computer Science and Technology Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (纸本)9798350355925

In recent years, emotional speech synthesis has shown considerable progress. However, some existing emotional speech synthesis methods only model emotion from a single scale, resulting in only global or average emotional expression, unable to synthesize significant emotional speech. In this paper, we propose a multi-scale multi-speaker emotional speech synthesis method based on self-supervised speech model HuBERT. Firstly, the emotion information is extracted from the feature extractor HuBERT. Secondly, the extracted emotion information is modeled by coarse-grained emotion modeling and fine-grained emotion modeling respectively to obtain utterance-level emotion embedding and phoneme-level emotion embedding. Through subjective and objective evaluation, we verify that the method is significantly better than the comparison model in terms of speech quality and emotional expression. © 2024 IEEE.

关键词： emotional speech synthesis self-supervised speech representation speech synthesis

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End-to-end Speech Enhancement Using Self-Attention Generative Adversarial Networks 6

End-to-end Speech Enhancement Using Self-Attention Generativ...

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6th International Symposium on Computer and Information processing Technology, ISCIPT 2021

作者： Cao, Zhonghui Huang, Zhihua Xinjaing University College of Information Science and Engineering Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumqi830001 China

ISBN: (纸本)9781665441377

Generative adversarial networks (GAN) achieve a good level of success in the speech enhancement (SE) task in recent years. However, traditional GAN for SE generates enhanced speech only as a function of local points, such that the model has difficulity in capturing long-range dependency and non-local information. In this paper, a model named self-attention GAN (ASEGAN) is proposed to estabilish connections between every sample and the rest samples in the time domain. Attention mechanism is used to model long-range dependency modeling for SE tasks. Armed with self-attention, the generator can generate the enhanced speech in which fine details at every sample are carefully coordinated with fine details in distant portions of the speech. Moreover, the discriminator can also more accurately enforce constraints on the global speech structure, thus the quality of enhanced speech by generator is better. Experimental results demonstrate that the proposed approach outperforms the competing methods measured by objective quality and speech intelligibility. © 2021 IEEE.

关键词： Generative adversarial networks

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RoPE-BAM: Nested Entity Recognition Based on Rotary Position Embedding and Biaffine Attention Mechanism 3

RoPE-BAM: Nested Entity Recognition Based on Rotary Position...

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3rd International Conference on Advanced Algorithms and Neural Networks, AANN 2023

作者： Deng, Jinxin Qin, Xizhong Yang, Rong Lv, Xiaoyi School of Information Science and Engineering Xinjiang University of China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China The Fifth Affiliated Hospital of Xinjiang Medical Urumqi830017 China

ISBN: (纸本)9781510668355

Named entity recognition (NER) involves two main types: nested NER and flat NER. The span-based approach classifies entity types by head-tail pair span representations and can handle nested and flat entities uniformly. However, the span-based approach uses a single feature and ignores the relative position information between head-tail pairs, which affects the precision of entity recognition. Therefore, we propose a nested entity recognition model that combines rotary position embedding and biaffine attention mechanism (RoPE-BAM) to improve the model performance by adding relative position features to the span representations. Concretely, we first obtain the head sequence and tail sequence representations through two feedforward networks. Then, to incorporate the relative position features, rotary position embedding is applied to both head and tail sequences. Finally, we use a biaffine attention mechanism to capture the span representations while generating the relative position information in the span. Extensive experiments were conducted on five widely-used benchmark datasets to demonstrate the effectiveness of our proposed RoPE-BAM model. © 2023 SPIE.

关键词： Embeddings

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Improving ECAPA-TDNN Performance with Coordinate Attention

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Journal of Shanghai Jiaotong University (Science) 2024年 1-7页

作者： Liu, Shuanghong Song, Zhida He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The current mainstream networks, such as squeeze and excitation residual neural network (SE-ResNet) and emphasized channel attention, propagation and aggregation based time delay neural network (ECAPA-TDNN), enhance the capability of speaker embedding extractors to extract more discriminative speaker embeddings by incorporating squeeze and excitation (SE) attention within the convolutional blocks. However, the SE attention focuses solely on encoding inter-channel information, overlooking the importance of spatial positional information and time-frequency information, which are crucial for the model’s performance. In this paper, we first experimentally compare the effectiveness of several mainstream attention mechanisms in the computer vision domain for the ECAPA-TDNN model. Next, we focus on the substantial improvements that coordinate attention (CA) brings to the ECAPA-TDNN model. The introduction of CA can help the model embed time-frequency information into the channel representation. Even without using AS-Norm, our proposed model achieves relative reductions of about 5.3% equal error rate (EER) and 5.5% minimum detection cost function (minDCF) on both the Voxceleb-O and Voxceleb-H test sets compared to the ECAPA-TDNN baseline model. In addition, the EER is relatively reduced by 9.46% on the CN-Celeb1 test set. This result strongly demonstrates that the CA module can effectively improve the generalization ability of the ECAPA-TDNN model. © Shanghai Jiao Tong University 2024.

关键词： Neural network models

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Network Intrusion detection Based on Balanced Data 24

Network Intrusion Detection Based on Balanced Data

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4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Yu, Xuecheng Jia, Zhenhong Huang, Xiaohui Song, Sensen Wang, Jiajia Zhou, Gang Shi, Fei Xinjiang University Urumqi Xinjiang China The Autonomous University Key Laboratory of signal and Information Processing Laboratory Xinjiang University Urumqi Xinjiang China

ISBN: (纸本)9798400710247

Network intrusion detection is a kind of network security measures. The current Internet presents a lot of information characteristics, continuous growth of traffic, uneven distribution of features, network intrusion detection puts forward higher requirements. In view of the current existing detection methods in there is still a high leakage rate and low detection accuracy problems, this paper carries out the network intrusion detection in the study of balanced data. For network intrusion detection, the difference between malicious traffic and normal traffic is too large, making some malicious traffic detection accuracy is low. A generative adversarial network model is proposed based on the improvement of bidirectional long and short-term memory network, the model has a very strong ability to restore the real data distribution, the use of generators and discriminators can be generated to simulate real-world data, the use of the model balanced dataset, which can significantly improve the accuracy of the detection of abnormal traffic as well as the accuracy rate. In the classification phase of network intrusion detection using a bidirectional long and short-term memory network model that incorporates multiple convolutional layers for detection, as well as the dataset used is the CIC-IDS 2017 dataset, the final results show that the model trained using the balanced dataset and then performing network intrusion detection has better detection results compared to other models, i.e., 99.3% accuracy, 99.35% precision, 99.34% recall, and 99.29% F1-score. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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AMIMC: Based on Improved Meta-Learning Contrast Network with Attention Mechanism for Few Shot Text Classification

AMIMC: Based on Improved Meta-Learning Contrast Network with...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Senyan Li Ye Dong Xizhong Qin Xinjiang Signal Detection and Processing Key Laboratory Xinjiang University Urumqi China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Currently, meta-learning is the mainstream approach to solving the problem of scarce data in few-shot text classification. Still, challenges remain, such as embedding vectors not being compact enough, suboptimal meta-task sampling strategies, and the rigidity of convolution operations in traditional Text CNNs. To address this, we propose the Attention Mechanism-based Improved Meta-learning Contrast Network (AMIMC), which enhances intra-class aggregation, increases inter-class separation and improves embedding quality. Additionally, Double Dynamic Similar Sampling (DDSS) generates more challenging meta-tasks, and the attention mechanism enhances the flexibility of Text CNNs, significantly boosting accuracy on five few-shot text classification datasets.

关键词： Metalearning Attention mechanisms Reviews Text categorization Semantics Vectors Robustness Rigidity Noise measurement Software engineering

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Mixformer: Feature Mixed Transformer for Rainfall Forecasting

Mixformer: Feature Mixed Transformer for Rainfall Forecastin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liao, Yuanyuan Li, Boyuan Li, Xiuhong School of Computer Science and Technology Xinjiang University Urumgi830046 China Xinjiang University Key Labaratory of Signal Detection and Processing. Xinjiang Uygur Auonomous Region Urumqi830046 China

ISBN: (纸本)9781665410205

In the xinjiang region of China, water is scarce and unevenly distributed, and due to factors such as global warming, extreme rainfall events occur frequently, posing serious threats to people's lives and property safety. To accurately predict short-term precipitation, we propose a Mixformer model. Specifically, we first use a combination of min-max normalization and reversible instance normalization to reduce the impact of feature numerical ranges on modeling while preserving the distribution of the original data. Next, to enhance the representation of multivariate time series data, we propose a feature mixing module. This module enhances the representation of inter-feature correlation information by calculating the correlation between different sequences, thus improving prediction effectiveness. Finally, to enhance its non-linear characteristics, we introduce a residual prediction method. This method models seasonal and trend components separately and also pays special attention to the residual component. We have validated the proposed method extensively, proving that it outperforms existing state-of-the-art (SOTA) methods in this field. © 2024 IEEE.

关键词： Rain

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