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Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction 27

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

引用

27th International Conference on Asian Language processing, IALP 2023

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

ISBN: (纸本)9798350330786

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. © 2023 IEEE.

关键词： Pipelines

来源：评论

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Fake News detection Based on Cross-modal Co-attention 4

Fake News Detection Based on Cross-modal Co-attention

引用

4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

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

ISBN: (纸本)9798350324303

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. © 2023 IEEE.

关键词： Feature extraction

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Efficient Channel Attention for Speech Emotion Recognition with Asymmetric Convolution Module 4

Efficient Channel Attention for Speech Emotion Recognition w...

引用

4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

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

ISBN: (纸本)9798350324303

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. © 2023 IEEE.

关键词： Convolution

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Heavy snow removal in video based on tensor dimensionality reduction and simultaneous sparse coding

Heavy snow removal in video based on tensor dimensionality r...

引用

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

作者： Wei, Guofeng Jia, Zhenhong Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

We propose a novel snow removal algorithm. First, we make full use of the high-dimensional character- istics of video data and propose a tensor dimensionality reduction-based robust principal component analysis (TDR-RPCA) method to reconstruct clean background, which improves the traditional RPCA’s ability to handle four-dimensional data and solves the problems with artifacts residue. Then a penalty regularization term-based outlier detection method is used to detect moving objects and glob- al large snowflakes. A binary mask containing moving object and snowflakes is obtained. According to the mask, we design an adaptive thresholding strategy to remove snowflakes. Our scheme can effectively remove global snowflakes. Considering the sparse characteristic of snowflakes, tinny snowflakes in front of moving objects can be regarded as noise;hence, we deal with these tiny snowflakes using Gaussian-scale mixture-based simultaneous sparse coding. Experiments show that our method can remove snowflakes in videos captured during heavy snow conditions and eliminate artifact residue, which outperforms other state-of-the-art methods. © 2024 SPIE.

关键词： Tensors

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Design and Application of Pocket Experiment System Based on STM32F4 8

Design and Application of Pocket Experiment System Based on ...

引用

8th International Conference on Information Technology: IoT and Smart City, ICIT 2020

作者： Zhou, Yanyun Shi, Fei Chen, Juan School of Information Science and Engineering Xinjiang University and Key Laboratory of Signal Detection and Processing China

ISBN: (纸本)9781450388559

Aiming at the disadvantages of embedded system in practical teaching, a pocket experiment system based on STM32F4 is developed. The system includes input unit, output unit, logic control unit, power supply unit, peripheral unit and data transmission unit. It has the characteristics of rich resources, low cost, and easy to carry, which meets the needs of students' mobility experiments and extra-curricular innovation practice training. Practical application shows that the system not onlystimulates students' enthusiasm for learning, but also trains their creative ability and practical application ability. © 2020 ACM.

关键词： Computer programming

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Classification of X-ray images of COVID-19 based on CNN and improved swin transformer model 3

Classification of X-ray images of COVID-19 based on CNN and ...

引用

3rd International Conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Song, Fubao Mo, Jiaqing Zhang, Jiangwei Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510668522

Faced with the rapid spread of COVID-19, nucleic acid testing methods can detect positive cases relatively quickly. Still, the time-consuming detection and frequent false-negative issues have led to a sharp increase in the demand for alternative diagnostic tools for COVID-19. In this paper, using medical imaging technology and deep learning technology, a model combining a convolutional neural network and an improved Swin Transformer network is designed to detect chest X-ray images of COVID-19. The image is input into the convolutional layer to extract the local details of the image. Then, to solve the problem that some heads do not play a role in calculating multi-head self-Attention due to too small a weight, a learnable bias parameter is added to each individual computing head to enhance the specific weight of each head. Experiments show that this method has a recognition rate of 98.25% for chest X-ray images of COVID-19. Indicators such as recall rate and F1 score have been improved compared with some current methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： COVID-19

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Medical image segmentation based on adaptive threshold prediction and feature fusion

Medical image segmentation based on adaptive threshold predi...

引用

2023 International Conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Liu, Wei Mo, Jiaqing Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510677685

In the realm of computer-aided diagnosis, image segmentation techniques have garnered significant attention over the years. However, medical image data present distinctive characteristics, such as confidentiality, class imbalance, and challenges in acquiring accurate labels. Of certain rare medical conditions, it is arduous to obtain an adequate number of data samples within a limited scope for training models. Moreover, complex medical conditions may lead to diagnostic omissions concerning the locations of minor lesions. Consequently, our primary objective in this article is to address the challenge of enhancing the segmentation accuracy of lesion locations in complex medical conditions when working with limited data. Additionally, we believe that threshold selection and feature choice are particularly crucial for medical image segmentation tasks. However, existing medical image segmentation methods often prioritizes model complexity while overlooking the impact of threshold variations on segmentation performance. In this paper, we propose a multi-feature fusion network with adaptive threshold prediction and introduce a novel paradigm of semi-supervised pseudo-label generation to facilitate supervised learning. The multi-feature fusion module ensures the comprehensive utilization of dimension-sensitive features, while the adaptive threshold predictor enables the model to generate distinct segmentation thresholds based on varying inputs. We employed publicly available medical image datasets, namely Kvasir-SEG, Rim-one r2, and ISIC 2018, to train and validate our model and approach. In comparison to other supervised and semi-supervised methods, the model and the approach we proposed demonstrate superior segmentation performance. © 2024 SPIE.

关键词： Computer aided diagnosis

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Semi-supervised Medical Image Classification Based on DenseNet and Capsule Network Combination Model

Semi-supervised Medical Image Classification Based on DenseN...

引用

2024 International Conference on Remote Sensing, Mapping, and Image processing, RSMIP 2024

作者： Shen, Hao Mo, Jiaqing Ren, Jinxun Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510680012

Medical image classification plays a crucial role in clinical treatment and medical education. However, traditional classification methods have reached performance limits and require substantial time and effort for feature extraction and selection. In this study, we introduce a novel deep learning model that combines the feature extraction part of DenseNet with the dynamic routing mechanism of capsule networks. DenseNet, known for its dense connections, aids in gradient propagation and feature reuse. Capsule networks introduce capsule layers with sensitivity to spatial relationships. By merging these two structures, our aim is to enhance the model's ability to model object hierarchy and pose variations. Our approach initially incorporates the dynamic routing mechanism of capsule networks on top of DenseNet, achieving an effective fusion of the two structures. Experimental results on the Chest X-Ray14 dataset demonstrate a significant improvement in accuracy and robustness compared to traditional DenseNet and capsule networks. To address the issue of limited annotation in medical image data, we introduce a semi-supervised learning approach and improve training balance through anti-curriculum pseudo-labeling. Furthermore, to obtain more accurate pseudo-labels, we adopt a mixed model for pseudo-label generation. This method not only effectively balances the pseudo-label predictions generated by two models but also mitigates the challenges associated with threshold-based confidence intervals in the context of multi-class, multi-label scenarios. In summary, the combination of deep convolutional neural networks and capsule networks, along with semi-supervised learning and anti-curriculum pseudo-labeling, holds great promise for medical image classification. It has significant application value in clinical practice and presents a wide-ranging prospect for the future. © 2024 SPIE.

关键词： Feature extraction

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Scoreformer: Score Fusion-Based Transformers for Weakly-Supervised Violence detection 48

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

引用

48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Xiao, Yang Wang, Liejun Wang, Tongguan Lai, Huicheng Xinjiang University College of Information Science and Engineering Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830046 China

ISBN: (纸本)9781728163277

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). © 2023 IEEE.

关键词： Optical flows

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Deep neural network for MIMO-SCMA detection 5

Deep neural network for MIMO-SCMA detection

引用

2023 5th International Conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023

作者： Zhang, Shiwei Ge, Wenping Key Laboratory of Signal Detection and Processing College of Information Science and Engineering Xinjiang University Xinjiang Urumqi830017 China

ISBN: (纸本)9781510667440

This article introduces deep learning into the multiple-input multiple-output (MIMO) sparse code multiple access (SCMA) system and proposes a MIMO-SCMA detection scheme based on deep neural networks (DNN) to improve bit error rate (BER) performance. The DNN learns the codebook of each user through channel feature learning on different transmission antennas. The fully connected DNN is designed as the decoder at the receiving end, which does not require traditional multi-antenna detection and multi-user detection, and can obtain user data with one decoding operation. The encoder and decoder are trained using an end-to-end training method. All learning models of the DNN are generated offline and the learned models are used for online testing. In this model, the received signal and channel coefficients are set as input data, and the label corresponding to the transmitted symbol is set as output data for offline learning. After offline learning is completed, the model can be deployed online with fixed weights and biases. Through simulation experiments, the proposed DNN encoder-decoder method can reduce the BER and computational complexity of the receiver in the MIMO-SCMA system. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep neural networks

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