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检索条件"机构=Key Laboratory of Signal Detection and Processing"

共 198 条记录，以下是121-130 订阅

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End-to-End Residual Stage Network for COVID-19 detection

End-to-End Residual Stage Network for COVID-19 Detection

引用

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Hongwei Chen Zhihua Huang Kexin Lu Hanzheng Liu Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang University Urumchi China

Currently, the COVID-19 virus is in a low-level epidemic state but still spreading in society. To timely cut off the transmission chain, early screening for COVID-19 is crucial. However, adding extra network modules during training may lead to module stacking errors and increased complexity. In this paper, we propose an optimization method based on residual stage networks (ResStage) and polarized self-attention mechanism to improve the detection ability of the model. This method provides better paths for information propagation across different layers of the network and extraction more refined and high-quality features. Results show that the fusion model based on ResStage and polarized self-attention can improve the detection accuracy of COVID-19 without increasing excessive parameters, achieving an AUC score of 86.53%. Moreover, this paper also subdivides the training tasks, including COVID-19 positive patients, COVID-19 positive patients with coughing, healthy individuals with coughing, asthma patients with coughing, and healthy asymptomatic individuals. These different tasks help to evaluate the performance of model and provide more references for practical applications.

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Scoreformer: Score Fusion-Based Transformers for Weakly-Supervised Violence detection

Scoreformer: Score Fusion-Based Transformers for Weakly-Supe...

引用

International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yang Xiao Liejun Wang Tongguan Wang Huicheng Lai Xinjiang Key Laboratory of Signal Detection and Processing College of Information Science and Engineering Xinjiang University Urumqi China

Violence detection is an application of anomaly detection, which is used to detect violence content in video clips. Using multimodal as input can improve the performance of violence detection. However, the existing MML Transformers-based fusion methods do not take into account the differences between non-homologous modals. The fusion of non-homologous modals makes features become noise between each other. This paper proposes a score fusion-based transformer framework, named Scoreformer. First of all, the optical flow, RGB and audio features pass through the independent self-attention transformer blocks. Second, the optical flow and RGB features pass through the cross-modal transformer blocks, after that they are fused with the audio features through the score fusion block. This method avoids the noise interference caused by the direct fusion of audio features and visual features. Experiments on the XD-Violence dataset show that the proposed method achieves 84.54% of the AP value, which exceeds at least 2.85% compared with the most advanced method (e. g. MSL, CRFD).

关键词： Visualization Interference signal processing Transformers Feature extraction Mobile handsets Explosions

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Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction

Pipeline Chain-of-Thought: A Prompt Method for Large Languag...

引用

International Conference on Asian Language processing (IALP)

作者： Hangtian Zhao Hakiz Yilahun Askar Hamdulla Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology XinJiang University Urumqi China

The development of language models has been influencing approaches to relation extraction (RE) problems. Although large language models (LLMs) have demonstrated breakthrough potential in certain aspects, they are still in the exploratory stage for RE tasks. Currently, the mainstream approach to improving the RE performance of LLMs is through prompt fine-tuning, but most methods require providing entity information in the prompt, which effectively only allows the LLMs to perform relationship classification tasks. We propose the Pipeline Chain-of-Thought (Pipeline-COT), which breaks down the RE task into steps and transforms it into reasoning tasks that have flat scaling curves, thereby enabling the use of Chain-of-Thought (COT) to enhance model inference. In addition, our method utilizes n-shot samples to provide signals for the Bayesian inference of the model by prompting the LLMs to focus on specific concepts to generate answers. We evaluated pipeline-COT on the Chinese dataset DuIE2.0, and compared with baseline methods that require including entity information in the prompt, our method still shows competitive performance.

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Log anomaly detection based on parallel fusion of CNN and GRU

Log anomaly detection based on parallel fusion of CNN and GR...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Cheng Li Jiaqing Mo Gang Zhou Guangming Li Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the development of deep learning and big data technology, system log anomaly detection technology has received widespread attention. Although many methods have achieved good results in anomaly detection in logs, it remains an extremely difficult task mining the rich dependencies hidden within log data from both global and local perspectives. In this paper, we propose a multi-scale parallel fusion network based on GRU and CNN, which more comprehensively captures the overall dependencies of log data. Specifically, we employ BERT to semantize the preprocessed log sequences, subsequently feeding the semantic vectors into the neural networks of a dual-branch CNN and a variant GRU. This approach enables the capture of both local and global features of the log sequences. Finally, we parallelly fuse the outputs of the dual-branch CNN and variant GRU. We conduct extensive experiments and evaluate three well-known benchmark datasets, BGL, Thunderbird, and Spirit. The results show that our method is better than other methods.

关键词： Deep learning Fuses Neural networks Semantics Network architecture Big Data Feature extraction

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An end-to-end algorithm for predicting missing load data based on waveGAIN

An end-to-end algorithm for predicting missing load data bas...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Jiang Zhang Gang Zhou Yuwei Feng Sen Yang Wanlin Wang Jiaqing Mo Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The accuracy of short-term load forecasting has improved with the development of deep learning on complete datasets. However, load data is often lost due to various complex factors in the power system, causing it is a challenge for incomplete load forecasting. To address this issue, this paper proposes an end-to-end load-completion and forecasting network. Specially, we designed the Wavelet-enhanced Generative Adversarial Imputation Net (waveGAIN) to improve the accuracy of data completion. Then, we propose an end-to-end training strategy that combines waveGAIN and Bidirectional Long Short-Term Memory (BiLSTM) networks to improve the accuracy of data filling and reduce the load forecasting error. The experimental results show that our proposed method improves the prediction accuracy in the case of different patterns and levels of missing data. And our method achieves good results even in case of 80% missing data.

关键词： Training Deep learning Accuracy Load forecasting Predictive models Data models Power systems

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LinWA:Linear Weights Attention for Time Series Forecasting

LinWA:Linear Weights Attention for Time Series Forecasting

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Qiang Li Jiwei Qin Dezhi Sun Fei Shi Daishun Cui Jiachen Xie School of Computer Science and Technology Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Transformer has achieved excellent results in the field of time series forecasting. However, some recent studies have pointed out that the existing Transformer models are ineffective in preserving the temporal order information. To solve this problem, we propose a Linear Weighted Attention mechanism (LinWA), a new, simple, and effective attention weights calculation method. Linwa calculates the linear weights and the attention weights, respectively, and combines them to reduce the proportion of the original attention weights to use the order information effectively and improve the forecasting performance. Additionally, we introduce an adaptive parameter adjustment mechanism to adjust the proportion between linear weights and attention weights dynamically. Experimental results on six publicly available datasets show that LinWA preserves temporal order well and improves performance on many Transformer models.

关键词： Adaptation models Attention mechanisms Time series analysis Neural networks Benchmark testing Transformers Forecasting

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Two-Branch Network with Selective Kernel Convolution for Time-Domain Speech Enhancement

Two-Branch Network with Selective Kernel Convolution for Tim...

引用

International Symposium on Chinese Spoken Language processing

作者： Hui Li Zhihua Huang Chuangjian Guo Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang University Urumqi China

ISBN: (纸本)9798350397970

Speech enhancement methods based on deep neural networks usually take noisy speech as input and clean speech as output. However, there is still a correlation between predicted speech and removed noise, which is not fully utilized. And the common convolutional codec has mostly the same receptive field at each layer, which may be difficult to represent the multi-scale features of speech. In this paper, we propose a two-branch interactive time-domain speech enhancement model combining selective kernel units (TBSK-Net). We introduce Selective Kernel Convolution into each layer of the encoder to adaptively encode the information of noisy speech at multiple scales. The stacked selective kernel units are designed to construct two branches to learn speech and noise features where insert exchange information modules. The results show that the proposed model has obvious advantages in dealing with unknown noise than the baseline and other references. Introducing the Selective Kernel mechanism can effectively improve the enhancement performance. Ablation experiments are also performed to demonstrate the effectiveness of different components in the structure.

关键词： Adaptation models Convolution Speech coding Neural networks Speech enhancement Feature extraction Noise measurement

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Fake News detection Based on Cross-modal Co-attention

Fake News Detection Based on Cross-modal Co-attention

引用

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Yadong Gu Mijit Ablimit Askar Hamdulla School of Information Science and Engineering Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

The spread of rumors has brought many negative impacts to society. Nowadays, rumors on social media platforms often exist in the form of both images and text. In response to this, many methods for multimodal rumor detection have been proposed, but most of these methods only utilize extracted text features and image features for simple fusion, without considering the connections between modalities. In this study, we employ ConvNet to extract image features, BERT to extract text features, and take into account the complementarity between modalities. We use a co-attention module to enhance the extracted features of individual modalities, aiming to achieve the interaction of modality features and the complementary nature between modality information. The obtained features are then concatenated to achieve the fusion of image and text features. The multimodal fusion model used in this study was experimented on Twitter and Weibo, achieving accuracy rates of 81.20% and 88.15% respectively.

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Efficient Channel Attention for Speech Emotion Recognition With Asymmetric Convolution Module

Efficient Channel Attention for Speech Emotion Recognition W...

引用

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： TianQi Wu Liejun Wang Shuli Cheng Jiang Zhang School of Information Science and Engineering Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

Speech emotion recognition (SER) is a challenging task due to the diversity and complexity of emotions. There are some unresolved issues, such as too much redundant information in the extracted features, the problems of hard-to-classify samples and category imbalance. To address these issues, we propose asymmetric convolution module (ACM) capture the spatial information of feature maps by improving CNNs structure and efficient channel attention (ECA) to establish the dependency relationship between channels. The combination of both extract more useful emotion information in the feature map and improve classification accuracy. In this paper, our proposed model achieves state-of-the-art results on IEMOCAP improvised data, with 70.99% and 89.78% unweighted accuracy (UA) on the full IEMOCAP and EMO-DB corpora.

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Visible Light Wireless Data Center Links with Distinct Beam Configurations 11

Visible Light Wireless Data Center Links with Distinct Beam ...

引用

11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Ding, Jupeng Chih-Lin, I. Wang, Lili Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region School of Information Science and Engineering Xinjiang University Urumqi China China Mobile Research Institute Beijing China School of Information and Electrical Engineering Ludong University Yantai China

ISBN: (纸本)9781643682402

Visible light communication (VLC) is being explored as one promising approach to enable wireless data centers (WDC). Up to now, the visible light wireless data center links are still limited to the conventional Lambertian beam paradigm. The potential coverage gain relevant to the optical beam space is waiting for sufficient investigation. For addressing this issue, in this paper, the dynamic optical beam based WDC coverage enhancement scheme is introduced, and for each transmitter, the best candidate asymmetrical optical beam is selected to load the data signal. Numerical evaluation shows that, compared with the conventional static beam configuration, up to 6.76 dB peak signal to noise ratio (SNR) gain and 4.46 dB average SNR gain could be provided by the proposed dynamic beam scheme. Moreover, this SNR dynamic range is reduced to 36.65 dB while the counterpart of the static non-Lambertian beam configuration is up to 44.78 dB. © 2022 The authors and IOS Press.

关键词： Visible light communication

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