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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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Design and Simulation of Intelligent Matching Algorithm for Online Primary School Mathematics Curriculum Based on Deep Learning 3rd

Design and Simulation of Intelligent Matching Algorithm for ...

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3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Mo, Yi Tang, Jishen Chang, Xunyun Wu, Dong Hechi University Guangxi Yizhou546300 China Foreign Languages Department Sichuan Vocational and Technical College Sichuan Suining629000 China

ISBN: (纸本)9783031631382

With the continuous development of new information technologies such as big data, cloud computing, and Internet of Things, people's access to knowledge and information has undergone profound changes. New concepts such as machine learning, wisdom education, and educational big data are changing the traditional educational ecology, teaching form, and learning methods, and at the same time, promoting the deep development of educational modernization to informationization. People's needs in the fields of artificial intelligence such as information retrieval, automatic question and answer, dialogue system, etc. begin to appear, and intelligent matching algorithms are needed to meet the high demand of users. Text matching algorithm is the core problem in natural language processing technology. In the traditional text matching field, the dimension disaster of text representation and data sparseness have affected the development of natural language processing field. It is necessary for the times and students’ development to carry out subject teaching under the guidance of deep learning, which is of great significance. Unit review class is an important class type of primary school mathematics, which is of great value to students’ development, but there are many problems in actual teaching. This paper presents a multi-view collaborative teaching network based on sparse interactive data. The matching network is mainly composed of two parts, a text-based matching model and a relationship-based matching model. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Big data

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Multi-source data distribution network patrol system based on wireless network 3

Multi-source data distribution network patrol system based o...

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3rd international conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Huang, Lei Fang, Xi Wang, Shi Deng, Fengyi Luo, Bo Tongren Power Supply Bureau of Guizhou Power Grid Co. LTD 54300 China Guizhou Power Grid Co. LTD Tongren Wanshan Power Supply Bureau 554200 China Guizhou Power Grid Co. LTD Tongren Jiangkou Power Supply Bureau 554400 China

ISBN: (纸本)9781510671423

Patrol inspection is an important part of the daily work of the power plant. The quality of patrol inspection is often related to the safe operation of the power plant. In the modern power system, there are many and complex parameters of the equipment. It is a time-consuming and laborious matter to judge whether the parameters exceed the standard only by manual work. Therefore, how to use the powerful computing and storage capabilities of the ironing network to reduce the analysis of manual operation while ensuring information security is a problem worth studying. The beneficial effects of the power plant patrol management system studied in this paper are: the use of data source signal acquisition, including temperature, humidity, vibration, etc., reflects the operation status of the equipment more comprehensively, and the GPS positioning and identity verification are set up at the same time. The use of cloud computing greatly reduces the calculation and storage of the local server. © 2023 SPIE.

关键词： Inspection

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Overview of Second Shared Task on Homophobia and Transphobia Detection in Social Media Comments 3

Overview of Second Shared Task on Homophobia and Transphobia...

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3rd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Chakravarthi, Bharathi Raja Ponnusamy, Rahul Subramanian, Malliga Buitelaar, Paul García-Cumbreras, Miguel Ángel Jiménez-Zafra, Salud María García-Díaz, José Antonio Valencia-García, Rafael Jindal, Nitesh School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Kongu Engineering College Tamil Nadu India Universidad de Jaén Spain Universidad de Murcia Spain University of Galway Ireland

ISBN: (纸本)9789544520847

We present an overview of the second shared task on homophobia/transphobia Detection in social media comments. Given a comment, a system must predict whether or not it contains any form of homophobia/transphobia. The shared task included five languages: English, Spanish, Tamil, Hindi, and Malayalam. The data was given for two tasks. Task A was given three labels, and Task B fine-grained seven labels. In total, 75 teams enrolled for the shared task in Codalab. For Task A, 10 teams submitted systems for English, 7 for Tamil, 4 for Spanish, 7 for Malayalam, and seven teams for Hindi. For Task B, 8 teams were submitted for English, 7 for Tamil, and 6 for Malayalam. We present and analyze all submissions in this paper. © 2023 LTEDI 2023 - 3rd Workshop on Language Technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent Advances in Natural Language processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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Research on Digital Twin Testing Theory and Technology for Intelligent Connected Vehicle 3

Research on Digital Twin Testing Theory and Technology for I...

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3rd international conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Jiang, Huajun Dong, Hongliang Zhang, Yuan Fang, Zhiyi Taizhou Vocational and Technical College 788 Xueyuan Rd Jiaojiang District Zhejiang Taizhou China Jilin University No.2699 Qianjin Street Changchun City China

ISBN: (纸本)9781510671485

Due to the difficulty and high cost of conducting sufficient real-world road tests, it is widely accepted in the industry to use a digital twin testing system that combines virtual simulation testing with real-world road testing to test and evaluate autonomous driving systems. Digital twin testing can simulate a road test environment using technologies such as sensor simulation and vehicle dynamics simulation, advanced graphics processing, traffic flow simulation, digital simulation, and road modelling. We integrated simulation testing tools, V2X communication devices, and real testing vehicles to achieve digital twin autonomous driving testing based on virtual simulation and real environments. It generates scene data based on virtual scene simulation software, constructs simulated scenarios mapping to reality, and uses V2X communication devices to transmit simulated scene environment data in real time to the real testing vehicles. It also receives real-time status data from the vehicles and feeds them back to the scene controllers, enabling autonomous vehicles to test different scenarios on real roads. In active safety-related testing, virtual scenes can avoid accidents and save unnecessary equipment losses. The system has high repeatability, and after a large number of repetitive in-vehicle tests, it can significantly reduce functional defects in the tested system, reducing the workload of actual vehicle field testing and road testing and improving testing efficiency and cost savings. © 2023 SPIE.

关键词： Autonomous vehicles

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Optimized Detection of Continuous Gravitational-Wave signals using Convolutional Neural Network

Optimized Detection of Continuous Gravitational-Wave Signals...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Premkumar Duraisamy Yuvaraj Natarajan V. Niranjani K Parvathy Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore India

Identifying gravitational waves produced by binary black hole mergers has sparked an unheard-of revolution in physics and astronomy. However, due to the low magnitudes of gravitational wave signals and the inevitability of detector noise, proper processing of gravitational wave data is in high demand. In this research, we propose a deep learning technique termed convolution neural network based on a set of time-series data from “***/datasets/andradaolteanu/g2net-gravitational-wave-dataset” that contains simulated gravitational waves with noise. This work uses a convolution neural network and Fourier transformation-based gravitational wave signal recognition model (CNN). According to experimental findings, the frequency-domain signal graph describes the gravitational wave signal’s features more accurately than the time-domain signal. The signal recognition accuracy has been improved using CNN’s convolutional processing.

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Automated Number Plate Detection and Recognition Using Machine Learning Techniques 3rd

Automated Number Plate Detection and Recognition Using Machi...

引用

3rd international conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Shuva, Taslima Ferdaus Hasan, Nahid Rahman, Md. Tanvir Khan, Risala Tasin Daffodil International University Dhaka Bangladesh Mawlana Bhashani Science and Technology University Tangail Bangladesh Jahangirnagar University Savar Bangladesh

ISBN: (纸本)9789819739363

The official collection of numbers and letters shown on the front and rear of a road vehicle is called a license plate. License plate recognition is crucial for many applications, including traffic monitoring, vehicle tracking, and law enforcement. Automatic License Plate Recognition (ALPR) is one of the applications that benefited tremendously from convolutional neural network (CNN) processing, which is now the de facto processing method for complex data. This paper offers a comprehensive analysis and comparison of deep learning-based approaches for identifying license plates. The study also looks a dataset from Kaggle, pre-process the dataset for removing noise and artifact, utilized to train these models into test, train, and validation dataset. Additionally, the study examines the difficulties and prospective possibilities in deep learning-based license plate recognition, highlighting the potential uses of cutting-edge innovations such object detection using attention mechanisms and graph convolutional networks. In this study we utilized six transfer learning model which is VGG16, VGG19, DenseNet121, AlexNet, EfficientNet, and YOLOv4 where DenseNet121 provide the best or highest accuracy 98.66%. The goal of the study is to guide researchers and industry professionals toward powerful license plate detecting systems by employing deep learning approaches by way of this evaluation and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An ultra wideband (UWB) localization system for automatic guided vehicles (agvs) in smart warehouses 3

An ultra wideband (UWB) localization system for automatic gu...

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3rd international conference on signal processing and Machine Learning, CONF-SPML 2023

作者： Huang, Hao Huang, Ziyu Wang, Dexin Wang, Jiarui Xu, Xiaotian Yao, Peixin Shenzhen College of International Education Shenzhen518043 China The University of Edinburgh EdinburghEH9 3JU United Kingdom New York University Abu Dhabi P.O. Box 129188 Abu Dhabi United Arab Emirates Pacific Academy Irvine Irvine92064 United States Nanjing University of Information Science and Technology Nanjing210044 China

As the main component of indoor positioning technology, UWB technology plays an important role in promoting the development of indoor and outdoor seamless positioning technology. This paper presents a proposed localization system of a propagation channel measurement campaign performed in a smart warehouse environment whose dimension is 100 meters ∗ 100 meters and provides an extensive model for this environment. Three different arrangements of basestations are simulated to analyze the effects of basestation constellations and virtual racks are set up to determine the approximate line-of-sight (LOS) probability. At the same time, by considering the influences of varying transmit power, using weighting and using two-way ranging, the distribution of errors and the reliability threshold are drawn and the required bandwidths are determined. Finally, according to the simulation results, the optimal layout and modulation parameters are recommended, and improvements in channel models and algorithms for future work are concluded. © Published under licence by IOP Publishing Ltd.

关键词： Automatic guided vehicles

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Deep Wyner-Ziv Decoding Using Auxiliary Hierarchical Features 3

Deep Wyner-Ziv Decoding Using Auxiliary Hierarchical Feature...

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2023 3rd international conference on Electrical, Electronics, and Computing Technology, EECT 2023

作者： Zhou, Junwei Yi, Xiangbo School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

Wyner-Ziv (WZ) video coding shifts the burden of complex calculations from the encoder to the decoder, making it suitable for video coding scenarios with limited resources. Motivated by deep learning has shown superior performance over traditional methods, this paper proposes deep WZ video coding with the help of auxiliary hierarchical features in the decoder. It uses an autoencoder to encode WZ frames. In the decoder side, an inter-frame correlation model called SI-Net is employed to enhance WZ frame quality with Key frames. The auxiliary hierarchical features are extracted from Key frames through multi-level downsampling and employed in autoencoder to optimize feature extraction, avoiding gradient disappearance caused by deep networks. Since the auxiliary hierarchical features of Key frames describe spatial information and expand the network's perception of video frame features, a high-quality WZ frame reconstructed can be obtained. Compared with previous work, our method shows obvious superiority on four video datasets with different degrees of motion. © Published under licence by IOP Publishing Ltd.

关键词： Video signal processing

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Research on Information System Cloud Computing Scheduling Method Based on Swarm Intelligence Algorithm

Research on Information System Cloud Computing Scheduling Me...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Wang Xiang Wang Yancheng School of Computing and Data Science Xiamen University Malaysia Jalan Sunsuria Bandar Sunria Sepang Selangor Malaysia

With the advancement of cloud computing technology, research on cloud computing task scheduling has become a hot topic. This paper mainly studies the cloud computing scheduling method of information systems based on swarm intelligence algorithm, including cloud computing scheduling model based on particle swarm optimization(PSO), improved Invasive Tumor Growth Optimization(ITGO) model structure, and multi-objective cloud computing task scheduling based on improved artificial bee colony algorithm. This paper concludes that by introducing multi-objective optimization terms, replacing multi-objective optimization terms with fitness functions, initializing tumor cell set functions, and improving search strategies and selection strategies, the efficiency and quality of cloud computing task scheduling can be improved. In order to realize the optimal utilization of cloud computing resources and meet the needs of users.

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