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共 134 条记录，以下是71-80 订阅

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A Scene-Aware Prompt Template of Chinese Text Data Augmentation Method Based on Large Language Model

A Scene-Aware Prompt Template of Chinese Text Data Augmentat...

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pattern recognition and machine learning (prml), ieee international conference on

作者： Lingfei Kong Gang Wang Jingheng He Ying Wang Yunpeng Gao School of Data Science and Applications Inner Mongolia University of Technology Hohhot China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In Natural Language Processing (NLP) tasks, the target domain data is usually limited and the quality is poor. Text data augmentation is one of the effective methods to solve the problem of insufficient sample size in many NLP tasks. At present, on the one hand, the text data augmentation method is difficult to achieve the diversity and authenticity of the generated data text structure, on the other hand, it is also a challenge for the screening and elimination of low-quality samples in the generated data. therefore, this paper proposes a scene-aware template of chinese text data augmentation method (Scene-Aware Prompt Template, SAPT) based on large language model. the SAPT method first uses prompt learning to construct a Scene-Aware Prompt (SAP)template. Secondly, combined with the text understanding and generation ability of the large language model, the SAP is improved by analyzing the semantic and contextual features of the original data to generate scene prompt words. then, the original data is re-expressed into multiple data samples with different text structures but similar semantics by using the perfect SAP and large language model. Further, the quality of generated data samples is evaluated by fusing Rouge score and cosine similarity score, and low-quality data samples are filtered. the final generated data will be used for training the target domain task model. the experimental results on the Chinese news dataset thUCNews show that the overall performance of the classification model trained by the data generated by the SAPTDA method proposed in this paper reaches 89.89 %, which is 2.79 % higher than the baseline model, and has superior performance compared with the existing text data augmentation methods.

关键词： Training Large language models Semantics machine learning Data augmentation Natural language processing Data models pattern recognition Context modeling

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Modeling and Analyzing the Manifolds of Cortical-Muscle During Card-Grabbing by Preferential Subspace Identification

Modeling and Analyzing the Manifolds of Cortical-Muscle Duri...

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pattern recognition and machine learning (prml), ieee international conference on

作者： Jiyu Tan Yurong Li Jixiang Li Nan Zheng College of Electrical Engineering and Automation Fuzhou University Fuzhou China Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology Fuzhou University Fuzhou China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

the primary aim of this study is to construct a state-space model that reduces the complexity of the human sensorimotor control system and explore motion control theory based on manifolds. Low-dimensional neural and muscle manifolds, representing the population activity of the brain and muscles, quantify the sensorimotor function of the human cortex, revealing hidden information about how the brain controls movement. this study used EEG and EMG signals from 25 participants during a card grabbing task. Preferential subspace recognition (PSID) algorithm was used to construct a state space model of subjects to study the neural computational strategies for sensorimotor control in the preparation and execution phases of motion, as well as the differences between brain and muscle population activity. the results show that from the preparation stage to the execution stage, the population activity of cortical muscles follows the orthogonal neural computation strategy in the subspace. the degree of trajectory entanglement between neural manifolds and muscle manifolds is greater than that during exercise preparation. Moreover, neural manifolds are less entangled than muscle manifolds and show a more stable state in structure. these results show that the adopted approach effectively reveals the orthogonal neural mechanisms of brain control during movement, highlighting the differences in control output and feedback input in sensorimotor control. this offers a new perspective on how the brain controls complex movements.

关键词： Manifolds Computational modeling Muscles Aerospace electronics Control systems Brain modeling State-space methods Complexity theory Trajectory pattern recognition

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Supervised machine learning: Discriminative learning Methods for pattern Classification using HELM 5

Supervised Machine Learning: Discriminative Learning Methods...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Shirisha, N. Kiran, Ajmeera Prashanthi, A. Reddy, MrKamjula Lakshmi Kanth Mlr Institute of Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Computer Science and Engineering India Anurag University Department of Artificial Intelligence Hyderabad India

ISBN: (纸本)9798350321425

the ability to forecast the data is significantly helped by machine learning's involvement. At the present time, data analysis is playing a very significant part in both the Information Technology (IT) industry and academic institutions. Numerous real-time applications, such as translation in real time, facial recognition, musical compositions, and so on, have been developed through the various initiatives that have been carried out up to this point. According to the standards that must be met by every data analyst, data must first be preprocessed before any predictions can be made on that data. Predictions must also be based exclusively on the preprocessed data. this work demonstrates the data pre-processing along with the prediction of a churn model. the model contains a set of records of various customers, including information regarding whether or not the client possesses credit cards and whether or not the consumer is an active member. this research works on the construction of a hybrid ensemble learning model (abbreviated HELM) to collect heterogeneous weak learners in order to anticipate such analyses. In order to work on the Churn Model classification, a variety of machine learning algorithms are integrated with one another. the findings revealed the efficiency of each machine learning algorithm that was used, in addition to HELM's operational effectiveness. © 2023 ieee.

关键词： Forecasting

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Chinese Nested Named Entity recognition in the Medical Field Based on BiLSTM and Multi-Head Global Pointer

Chinese Nested Named Entity Recognition in the Medical Field...

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pattern recognition and machine learning (prml), ieee international conference on

作者： Xinke Zhao Hankiz Yilahun Askar Hamdulla School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

the technology of named entity recognition has been gradually refined. However, current research is primarily focused on non-nested named entity recognition, with less attention devoted to nested named entity recognition (NNER). the challenge of accurately extracting nested entities from complex medical texts remains. To address the issue of low accuracy in identifying nested entities in medical named entity recognition, we propose a Chinese nested named entity recognition model (Multi-ABGP) based on adversarial training, bidirectional long short-term memory (BiLSTM) and multi-headed global pointer network. this model employs a BiLSTM network to extract bidirectional and long-distance text features, enhancing the ability of the global pointer network to extract features from longer text. Furthermore, adversarial training is employed to reduce the impact of noise on the model's performance and enhance its robustness. this enables the model to ignore irrelevant features introduced by noise and instead focus on entity-related features. Finally, a multi-head global pointer network is utilized to fuse the features of multiple global pointer networks. this reduces the erroneous prediction of a single pointer network and improves the overall performance of the model. the experimental results demonstrate that the proposed model achieves Micro-F1, Micro-P, and Micro-R values of 73.71%, 74.96%, and 72.50%, respectively, on the CMeEE-V2 dataset of Chinese nested named entity recognition in the medical specialty. this significantly improves the effectiveness of Chinese nested entity recognition in the medical field. And we also compare it with the large language model (LLM), the experimental results show that the model outperforms the ChatGLM in the nested entity recognition task. Additionally, it exhibits favorable results on the non-nested entity recognition dataset People's Daily.

关键词： Training Accuracy Large language models Noise Semantics Named entity recognition Predictive models Feature extraction Robustness pattern recognition

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Identification of Atrial Septal Defects in Congenital Heart Disease Patients Using Deep learning-based Chest Radiographs

Identification of Atrial Septal Defects in Congenital Heart ...

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pattern recognition and machine learning (prml), ieee international conference on

作者： Hongmin Shao Yunfei Ling Jiyun Pang Qijun Zhao Yongjun Qian College of Computer Science Sichuan University Chengdu China Department of Cardiovascular Surgery West China Hospital Sichuan University Chengdu China West China School of Medicine Sichuan University Chengdu China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Objective: Chest X-rays are a non-invasive and cost-effective diagnostic method; however, they are not commonly used for diagnosing atrial septal defects (ASD). Typically, the diagnosis of ASD requires more expensive tests. We have discovered that an enlarged heart shadow can be an initial indicator of ASD. We have developed and validated a model using deep learning techniques to determine whether a patient has an atrial septal defect based on their chest X-ray. Method: this study involved chest X-ray examinations of 1459 patients diagnosed with atrial septal defects at West China Hospital of Sichuan University from January 8, 2009, to June 25, 2022, as well as 1580 individuals who underwent general health check-ups at the West China Hospital's Examination Center from July 21, 2022, to August 13, 2022. Results: this study included chest X-ray examinations of 3,039 patients (44.3% male; median age 33 years with an interquartile range of 28–42 years). For the classification task, the deep learning model achieved an accuracy of 0.96, sensitivity of 0.97, specificity of 0.96, and an AUC (Area Under the Curve) of 0.9922. Conclusion: Current research indicates that chest X-ray analysis based on deep learning can determine the presence of atrial septal defects, a congenital heart condition. these findings suggest that deep learning methods can offer an objective diagnosis for atrial septal defects in routine health screenings, offering a low-cost and non-invasive approach.

关键词： Heart Deep learning Analytical models Sensitivity Hospitals pattern recognition Diagnostic radiography X-ray imaging Biomedical imaging Diseases

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Intracardiac Electrogram Signals Key Feature Points recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

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pattern recognition and machine learning (prml), ieee international conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. the feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. the dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. the final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. the final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. the research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data Medical services Myocardium Feature extraction pattern recognition Testing

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Text-to-CQL Based on Large Language Model and Graph pattern Enhancement

Text-to-CQL Based on Large Language Model and Graph Pattern ...

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pattern recognition and machine learning (prml), ieee international conference on

作者： Xinke Zhao Hankiz Yilahun Askar Hamdulla School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

With the wide application of knowledge graphs in various scenarios and Neo4j graph databases becoming excellent knowledge graph carriers, Cypher (CQL for short) has become the most popular graph database query language. However, when performing graph database retrieval, the complex pattern and syntax make constructing CQL statements a complicated and time-consuming task. therefore, similar to Text-to-SQL, it is necessary and urgent to study an effective method for end-to-end transformation of natural languages into CQL. there have been many advances in Text-to-SQL for traditional relational databases, but these methods cannot be well applied to Text-to-CQL, so there is still a lack of effective work on Text-to-CQL for graph databases. In recent years, as large language models have been widely applied to different tasks with good results, and considering that the fundamental difference between CQL and SQL is the representation of graph patterns. We propose in this paper PA-LLM, a Text-to-CQL method that utilizes large language models combined with graph patterns enhancement, which combines large language models and subdivides the graph patterns into three categories according to their respective characteristics. they are simple query patterns, multi-hop query patterns, and function query patterns. For different graph patterns, the method optimizes the process of generating CQL for the model, which can be subdivided into four sub-methods, simple pattern enhancement method, multi-hop pattern enhancement method, function pattern enhancement method, and entity-relationship enhancement method. the experimental results show that the method improves the quality of the CQL statements generated by the model, including the logical accuracy ACC LX and the execution result accuracy ACC EX , and achieves better results on the SpCQL dataset proposed by the National Defense University of Science and Technology (NDUST). It provides an effective solution for the task of converti

关键词： Structured Query Language Accuracy Quantization (signal) Large language models Natural languages Knowledge graphs Relational databases Syntactics Real-time systems pattern recognition

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An Improved Causal Disentanglement Single-Source Domain Generalization Method for Bearing Fault Diagnosis 5

An Improved Causal Disentanglement Single-Source Domain Gene...

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5th international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Wang, Hongqi Wang, Yujing Kang, Shouqiang Wang, Qingyan Harbin University of Science and Technology Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception Harbin China

ISBN: (纸本)9798331529192

In the field of domain generalization for fault diagnosis, the majority of approaches concentrate on extracting domain-invariant features from multi-source domains. However, collecting samples from multi-source domains is extremely difficult, and the data typically originate from a single-source domain. To tackle the issue of inadequate generalization capability in unknown target domains when trained on only a single source domain, an improved prototypeguided causal disentanglement domain generalization network (ICDDG) is proposed for mechanical fault diagnosis. this network combines feature mean, similarity, and triplet loss to construct an improved prototype-based triplet loss function, which reduces the influence of outlier samples and achieves more effective prototype learning. the improved triplet loss function effectively guides the causal disentanglement network to separate causal features from non-causal features better, enhancing the model's adaptability and robustness when encountering unseen domains. Diagnostic experiments performed using two bearing datasets substantiate the efficacy of the ICDDG method. © 2024 ieee.

关键词： bearing causal disentanglement fault diagnosis single-source domain generalization triplet loss

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Alignment Offset Based Adaptive Training for Simultaneous machine Translation 5

Alignment Offset Based Adaptive Training for Simultaneous Ma...

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5th international conference on Natural Language Processing, ICNLP 2023

作者： Liang, Qiqi Liu, Yijin Meng, Fandong Xu, Jinan Chen, Yufeng Zhou, Jie Beijing Jiaotong University Beijing China Pattern Recognition Center WeChat Ai Tencent Inc Beijing China

ISBN: (纸本)9798350302219

Given incomplete source sentences as inputs, it is generally difficult for Simultaneous machine Translation (SiMT) models to generate a target token once its aligned source tokens are absent. How to measure such difficulty and further conduct adaptive training for SiMT models are not sufficiently studied. In this paper, we propose a new metric named alignment offset (AO) to quantify the learning difficulty of target tokens for SiMT models. Given a target token, its AO is calculated by the offset between its aligned source tokens and the already received source tokens. Furthermore, we design two AO-based adaptive training methods to improve the training of SiMT models. Firstly, we introduce token-level curriculum learning based on AO, which progressively switches the training process from easy target tokens to difficult ones. Secondly, we assign an appropriate weight to the training loss of each target token according to its AO. Experimental results on four datasets demonstrate that our methods significantly and consistently outperform all the strong baselines. © 2023 ieee.

关键词： Curricula

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machine learning Approach for Mixed type Wafer Defect pattern recognition by ResNet Architecture 5

Machine Learning Approach for Mixed type Wafer Defect Patter...

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Remya, K.P. Sajith, V. ISRO Liquid Propulsion Systems Centre Trivandrum India College of Engineering Trivandrum Department of Electronics and Communication Trivandrum India

ISBN: (纸本)9798350334128

Semiconductor manufacturing process involves various steps: fabrication of Wafers, testing of wafers, assembly of each single die and package level final test. Out of which, testing of wafer for defect detection and classification is the most important step where defective dies are eliminated. this improves the efficiency of the overall manufacturing process. Semiconductor wafer defects can be basic defects or mixed defects containing two or more basic defects. Since these defects varied from wafer to wafer and are complex, the accuracy obtained from traditional deep learning model is poor. To address this problem, a semiconductor mixed defect pattern recognition based on Residual Network (ResNet) Architecture is attempted. Among the ResNet architectures, ResNet50 is selected for wafer defect pattern recognition which has a 50 layer structure. the model is evaluated based on data set with 15 mixed defect patterns which were generated from WM-811K wafer map data set. Training, testing and validation of the data set is done. the performance parameters are generated and compared the performance with four different architectures including Convolutional Neural Network(CNN). the training results indicate that the ResNet50 shows a better performance in terms of accuracy, precision, recall and F1 score. © 2023 ieee.

关键词： Scanning electron microscopy

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