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3D Hand Pose Estimation Based on External Attention

3D Hand Pose Estimation Based on External Attention

引用

Natural language processing (ICNLP), international conference on

作者： Shoukun Li Xiaoying Pan Beibei Wang Jialong Gao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi’an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

3D hand pose for a single depth image is an important topic in computer vision and human-computer interaction, and although significant progress has been made in this field in recent years, there is still room for improvement in accuracy for some specific application scenarios. To address this problem, a 3D hand pose estimation algorithm based on external attention is proposed. First, the target features are extracted by an hourglass network; then, a HEM (Hard Example Mining) loss based on a mean-variance loss function is proposed, which firstly calculates the L2 loss values of all N keypoints, and then sorts these loss values, and back-propagates the gradient only to the first m loss values. Meanwhile, external attention is introduced to enhance the ability to perceive the global information of the target, and the recognition ability of the features is improved by giving the features different influences through the attention weights. Experimental results show that the algorithm achieves an average distance error of 5.42 mm on the ICVL dataset and 7.11 mm on the MSRA dataset, which further improves the detection performance of the 3D hand pose estimation algorithm.

关键词： Human computer interaction Computer vision three-dimensional displays Accuracy Target recognition Pose estimation Feature extraction Prediction algorithms Natural language processing

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Enhanced Document Segmentation at Paragraph Level

Enhanced Document Segmentation at Paragraph Level

引用

Electronics and Communication, Network and Computer technology (ECNCT), international conference on

作者： Jiafu Ma Sai Han Shiwei Ye Guangquan Wang University of Chinese Academy of Sciences Beijing China China Unicom Research Institute Beijing China

ISBN: (数字)9798350366143

ISBN: (纸本)9798350366150

the rapid growth of digital communications has led to an exponential increase in textual data, necessitating efficient methods for document segmentation. the readability can be improved by segmenting long documents into semantically coherent sections. Downstream Natural language processing (NLP) tasks, such as information retrieval and document summarization, can also be greatly assisted. However, previous studies have predominantly focused on sentence-level segmentation, which often failed to utilize the inherent paragraph structure of documents effectively. In this paper, a novel approach to document segmentation that leverages the characteristics of paragraphs is proposed. Pretrained language models (PLMs) are employed to identify and segment distinct topics within documents by analyzing the structure and semantic coherence between paragraphs. To improve the model performance, two new auxiliary tasks are integrated into the training process, providing additional signals that helps in better understanding context coherence and document structure. these auxiliary tasks are designed to align with the primary objective of segmentation while enhancing the model's capability to generalize across various types of documents. A novel dataset with approximately 10,000 documents focused on communications domain, characterized by detailed paragraph and topic structure, is crawled for training and evaluating. A diverse set of other domain specific documents were also evaluated with our model. It is demonstrated that the results of integrating auxiliary tasks and leveraging paragraph-level features significantly outperform baseline models.

关键词： Training Analytical models Computational modeling Semantics Coherence information retrieval Digital communication Natural language processing Communication networks Context modeling

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Community Asset Ontology for Modeling Community Data using information Extraction 22

Community Asset Ontology for Modeling Community Data using I...

引用

Proceedings of the 2022 6th international conference on Natural language processing and information Retrieval

作者： Md Towhidul Absar Chowdhury Naveen Sharma Golisano College of Computing and Information Science Rochester Institute of Technology USA

ISBN: (纸本)9781450397629

In this paper, we analyze some data-related challenges to building resilient and sustainable communities, particularly how to computationally model the social and economical dynamic that exists within a community. To that end, we propose the Community Asset Ontology (CAO) for a knowledge graph that can encapsulate community data as modeled in existing social science literature. We utilize existing information extraction paradigms to map natural language community data to CAO and evaluate the usefulness of such an ontology-based approach compared to a baseline open information extraction approach.

关键词： information extraction knowledge graph ontologies

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Improvement of Timing Synchronization Algorithm for FBMC-OQAM System Based on Training Sequence

Improvement of Timing Synchronization Algorithm for FBMC-OQA...

引用

Natural language processing (ICNLP), international conference on

作者： Xihai Xie Ruichao Ning School of Communications and Information Engineering Xi’an University of Posts & Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

In the context of 5G with high rate transmission, the filter bank multicarrier-offset orthogonal amplitude modulation (FBMC-OQAM) technique has attracted much attention as an alternative to OFDM technology because the FBMC system reduces the wastage of spectral resources and has better time-frequency focusing characteristics, as well as better robustness to channel frequency selective fading. However, the inter-symbol overlap and interference caused by this modulation can make timing synchronization difficult. To address this problem, this paper designs a special training sequence based on existing synchronization algorithms, which exploits the conjugate symmetry property of the FBMC system for timing synchronization, and consists of four symbols, the first two of which are computed to have conjugate symmetry in the time domain; and the last two of which are zero-valued. the length of the correlation sequence is enlarged compared to the traditional algorithm, and the number of symbols used for training is increased. theoretical analysis and simulation results show that the proposed algorithm has higher timing accuracy and better synchronization performance.

关键词： Training Time-frequency analysis Analytical models Accuracy OFDM Simulation Symbols Interference Synchronization Time-domain analysis

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Recognition of Epidemic Cases in Social web texts 17th

Recognition of Epidemic Cases in Social Web texts

引用

17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Alexiou, Eleftherios Antonakakis, Apostolos Jevtic, Nemanja Sideras, Georgios Farmaki, Eftichia Foutsitzi, Sofronia Kermanidis, Katia-Lida Ionian Univ Dept Informat 7 Tsirigoti Sq Corfu Greece

ISBN: (纸本)9783030791599;9783030791575;9783030791568

Since December 2019, Covid-19 has been spreading rapidly across the world. Unsurprisingly, conversation in social networks about Covid-19 is increasing as well. the aim of this study is to identify tentative Covid-19 infection cases through social networks and, specifically, on Twitter, using machine learning techniques. Tweets were collected using the data set "Covid-19 Twitter", between November 1, 2020 and December 30, 2020, and manually marked by the authors of this study as positive (describing a tentative Covid-19 infection case) or negative (pertaining to any other Covid-19 related issue) cases of Covid-19, creating a smaller but more focused dataset. this study was conducted in three phases: a. data collection and data cleaning, b. processing and analysis of tweets by machine learning techniques, and c. evaluation and qualitative/quantitative analysis of the achieved results. the implementation was based on Gradient Boosting Decision Trees, Support Vector Machines (SVM) and Deep Learning algorithms.

关键词： Covid Machine learning Twitter Natural language processing

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Enhancing Code Completion with Position Embedding of Abstract Syntax Tree

Enhancing Code Completion with Position Embedding of Abstrac...

引用

Frontiers technology of information and Computer (ICFTIC), IEEE international conference on

作者： Jiahao Li Junyu Lu Linbo Zhu AHU-IAI AI Joint Laboratory Hefei Comprehensive National Science Center Anhui University Institute of Artificial Intelligence Hefei China School of Data Science University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Code completion is a crucial feature in integrated development environments (IDEs) that enhances both the efficiency and experience of developers. By analyzing the context of preceding code, real-time suggestions can be provided, significantly aiding developers in their work. Recent research has highlighted that the abstract syntax tree (AST) contains rich syntactic and structural information, which can assist deep learning models in better understanding code. As a result, AST-based code completion methods have garnered substantial attention. However, existing approaches often overlook the positional information of nodes within the AST, limiting the model's ability to capture structural relationships in the code. this paper explores the positional relationships between AST nodes and proposes a tree structure position embedding method tailored to the AST. Experimental results validate that this position embedding significantly improves the performance of code completion.

关键词： Deep learning Codes Limiting Computational modeling Syntactics Real-time systems Natural language processing

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A residual gated convolution summary generation model based on contrast learning 22

A residual gated convolution summary generation model based ...

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Proceedings of the 2022 6th international conference on Electronic information technology and Computer Engineering

作者： Xiaohan Guan Shuo Liang School of Information Science and Technology North China University of Technology China

ISBN: (纸本)9781450397148

Contrastive learning achieves better results in several tasks in natural language processing. However, there are fewer studies on Chinese text summary generation tasks. In this paper, a new summary generation model is proposed. this model uses the reference summary of random word order as a negative sample. Let the model learn to predict the distribution of representations more reasonably. Using RoBERTa pre-trained language model to encode the text, the summary text is generated using the gated convolutional unit with the residual mechanism. the experimental results of the LCSTS dataset show that the model performs in this paper better than the baseline model.

关键词： Summary generation pre-trained language model contrast learning RoBERTa gated convolution

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Preface: the 6th international Workshop on Knowledge Discovery in Healthcare Data (KDH)

引用

CEUR Workshop Proceedings 2023年 3479卷

作者： Ibrahim, Zina Wu, Honghan Wiratunga, Nirmalie China

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A Novel Hindi Voice-Based Assistant System Architecture For E-Commerce 6th

A Novel Hindi Voice-Based Assistant System Architecture For ...

引用

6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Kujur, Avinash Paul Fahad, Md Shah Birla Institute of Technology Mesra Jharkhand Ranchi835215 India

ISBN: (纸本)9783031813412

this paper presents a novel architecture for a Hindi voice-based assistant system tailored for e-commerce applications. the architecture integrates key components including speech recognition, machine translation, keyword extraction, intent classification, a dictionary-based question-answering model, and speech synthesis. the system efficiently converts spoken Hindi queries into text, translates across languages, identifies intent, extracts keywords, generates relevant database queries, and provides accurate responses through synthesized speech. this holistic approach enhances user interaction, accessibility, and convenience within the e-commerce domain. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Improving the Transferring the Files in Cloud by Using Vogel's Approximation Method

Improving the Transferring the Files in Cloud by Using Vogel...

引用

international conference on Engineering technology and their Applications (IICETA)

作者： Hawraa Mahdi Salih Mahdi S. Almhanna College of Information Technology University of Babylon Babylon Iraq

Cloud computing is a technology that allows for the virtual integration and deployment of computing resources at remote locations. various users on the World Wide web request various files spread on many servers across the world, which consumes time and increases the cost of sending the information under cloud computing. Reducing the time it takes to download a file is the same as lowering the cost of product delivery. the shortest amount of time necessary to provide a product to a client at the lowest attainable cost. the system's basic idea selects which server assists in delivering the file promptly to the client seeking this file. the suggested system reduces the time necessary to download the material by picking the best server that delivers the file in the lowest amount of time and is an efficient approach. VAM is used to determine the lowest cost for each file to be provided to the user inside the middle server processing client requests, as well as to specify the server from which each client would download the content. In this study, we use a variety of optimization approaches to increase query performance by reducing the time transferring the file using Vogel's Approximation Method (VAM) and the proposed system consists of three parts which are the cloud computing part, server part, and client part and used three files which is (File1, File2, File3) with (6.5, 2, 1.3) GB respectively. the results showed that the VAM contributed to reducing the cost of file transferring

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