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检索条件"任意字段=7th International Conference on Computer and Information Processing Technology, ISCIPT 2022"

共 4140 条记录，以下是171-180 订阅

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MicroConceptBERT: Concept-Relation Based Document information Extraction Framework 7

MicroConceptBERT: Concept-Relation Based Document Informatio...

引用

7th SLAAI - international conference on Artificial Intelligence, SLAAI-ICAI 2023

作者： Silva, Kanishka Silva, thushari Nanayakkara, Gayani School of Mathematics and Computer Science University of Wolverhampton United Kingdom University of Moratuwa Faculty of Information Technology Sri Lanka School of Computing Robert Gordon University United Kingdom

ISBN: (纸本)9798350319262

Extracting information from documents is a crucial task in natural language processing research. Existing information extraction methodologies often focus on specific domains, such as medicine, education or finance, and are limited by language constraints. However, more comprehensive approaches that transcend document types, languages, contexts, and structures would significantly advance the field proposed in recent research. this study addresses this challenge by introducing microConceptBERT: a concept-relations-based framework for document information extraction, which offers flexibility for various document processing tasks while accounting for hierarchical, semantic, and heuristic features. the proposed framework has been applied to a question-answering task on benchmark datasets: SQUAD 2.0 and DOCVQA. Notably, the F1 evaluation metric attains an outperforming 87.01 performance rate on the SQUAD 2.0 dataset compared to baseline models: BERT-base and BERT-large models. © 2023 IEEE.

关键词： Semantics

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Phrase Embedding for Matching Job Skills with Job Position with Low Resources 9

Phrase Embedding for Matching Job Skills with Job Position w...

引用

Joint 9th international conference on Digital Arts, Media and technology with 7th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Ronran, Chirawan Pairot, Pakamas Maejo University Information Technology Faculty of Science Chiang Mai Thailand Songkhla Rajabhat University Trading Innovation Management Faculty of Management Sciences Songkhla Thailand

ISBN: (纸本)9798350318241

Word embedding, a technique in natural language processing, has revolutionized various text analysis tasks. By representing words as dense vectors in a high-dimensional space, word embedding models capture semantic relationships and facilitate downstream applications. However, the inherent limitations of word embedding become evident when attempting to derive the meaning of phrases. this study explores the efficacy of phrase embedding techniques in matching job skills with job descriptions within low-resource environments. We compare the performance of two popular word embedding models: Continuous Bag-of-Words (CBOW) and Skip-gram. Using a dataset of ICT professionals, we achieved an average accuracy of 69.73% with CBOW and 77.62% with Skip-gram, demonstrating their ability to capture semantic relationships between job position and skill. We also explore the rationale behind pre-processing steps, such as replacing spaces with underscores to maintain the meaning of skill phrases. this research contributes valuable insights into applying NLP techniques for job and skill matching in human resource analysis. © 2024 IEEE.

关键词： Embeddings

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Interval prediction for wind speed based on box-plot data processing and optimized integrated modeling 7

Interval prediction for wind speed based on box-plot data pr...

引用

7th international conference on Electronic information technology and computer Engineering (EITCE)

作者： Liu, Yayu Li, Kun Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Liaoning Peoples R China

ISBN: (纸本)9798400708305

Accurate and reliable wind speed prediction is of great significance for efficient power generation by wind power equipment. In this paper, considering the uncertainty interference problem of noise, a hybrid prediction model integrating optimized variational modal decomposition (VMD) and stochastic configuration networks (SCNs) is proposed. the conventional point prediction model makes it difficult to describe the actual characteristics of wind speed, and this paper will seek its interval prediction range under the confidence interval of kernel density estimation. Firstly, the box-plot method is introduced to check the discrete points of the data;secondly, the singular spectrum analysis (SSA) is introduced to de-noise the wind speed data;and then the VMD is used to decompose the noise-reduced data in order to reduce the complexity of the series. In order to solve the problems of difficult-to-select parameters of SSA and lack of adaptive ability of VMD, the improved slime mould algorithm (SMA) is used for optimization. Finally, the SCNs are trained for each subsequence, and their prediction results are setsummed in its confidence interval, and its interval prediction range is calculated. the experimental results prove that the prediction method proposed in this paper can effectively reduce the noise interference in the wind speed.

关键词： Wind speed prediction singular spectrum analysis slime mould algorithm stochastic configuration networks variational modal decomposition

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A Novel Power Divider With Tunable Power Ratios and Reconfigurable Output Phase Differences 7

A Novel Power Divider With Tunable Power Ratios and Reconfig...

引用

7th IEEE international conference on Electronic information and Communication technology, ICEICT 2024

作者： Huang, Jialin Guo, Xulong Xue, Shanshan Xu, Chang Huang, Yong Mao Wang, Xiaolong Jilin University College of Electronic Science and Engineering International Center of Future Science Changchun China Coordination Center of China Inner Mongolia Branch of National Computer Network Emergency Response Technical Team Hohhot China Xihua University School of Electrical and Electronic Information Chengdu China

ISBN: (纸本)9798350384437

In this letter, a novel power divider with a wide tunable range of power ratios and reconfigurable output phase differences is proposed. the tunable power ratio is implemented by replacing the branch transmission line of the conventional Gysel power divider. the branch transmission line equivalent circuit consists of three short-circuited varactors cascaded through two sections of transmission lines. Moreover, by selecting appropriate capacitances of the varactors, the proposed novel power divider can operate either in an in-phase mode or out-of-phase mode. Finally, the electromagnetic (EM) simulation of six examples was conducted and one experimental circuit of the proposed novel power divider was fabricated. the simulation and measurement results verified the feasibility of the proposed novel power divider. © 2024 IEEE.

关键词： Power transmission lines

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INTERPOLATION AND CONTEXT MAGNIFICATION FRAMEWORK FOR CLASSIFICATION OF SCENE IMAGES 16

INTERPOLATION AND CONTEXT MAGNIFICATION FRAMEWORK FOR CLASSI...

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16th international conference on computer Graphics, Visualization, computer Vision and Image processing, CGVCVIP 2022, 8th international conference on Connected Smart Cities, CSC 2022, 7th international conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th international conference on theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on computer Science and information Systems, MCCSIS 2022

作者： Balarabe, Anas Tukur Jordanov, Ivan School of Computing University of Portsmouth United Kingdom

ISBN: (纸本)9789898704429

Recently, there has been an upsurge in publicly available remote sensing image classification datasets. Standard CNNs and pre-trained architectures have been applied for scene classification tasks. However, transfer learning models accept specific image dimensions as the minimum required size for their respective image input layers. Depending on the size of the input image, the final feature map might not contain the discriminative information needed for accurately classifying the dataset categories. the proposed technique effectively enables and enhances a transfer learning model (Xception) to be applied to scene classification tasks. the model works on an adaptive framework that interpolates images and selects an appropriate dilation layer to enhance the quality of extracted features for improved classification. this approach is evaluated on the EuroSAT, a dataset with images of 64x64 pixels, UCM and AID datasets, respectively. We recorded 98.55%, 99.22%, and 96.15% accuracy for the EuroSAT, UCM, and AID datasets, respectively. Our model and the reported results have opened the potential of the Xception, which in our view, has not been given its fair share of attention, despite its efficient parameter utilisation. © MCCSIS *** rights reserved.

关键词： Classification (of information)

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PI-Net: Privileged information Assisted Referring Image Segmentation Network 7

PI-Net: Privileged Information Assisted Referring Image Segm...

引用

7th IEEE international conference on information Systems and computer Aided Education, ICISCAE 2024

作者： Liu, Baolong Liu, Yi Xin, Jianjia Li, Long Zhang, Yuanyu Feng, Taiyang School of Computer Science and Technology Xidian University Xi'an China INSPUR GROUP CO. LTD. China

ISBN: (纸本)9798350350760

—Referring Image Segmentation (RIS) aims to segment the corresponding region of a visual entity described by input referring expressions. Traditional approaches separately extract visual-linguistic knowledge from pre-trained models, neglecting the corresponding multi-modal information. In this paper, we propose a Privileged information assisted RIS network (PI-Net) aimed at enhancing the text-to-pixel multi-modal alignment capability. the incorporation of privileged information within visual-linguistic pre-trained models not only enriches the intrinsic feature extraction process but also enhances generalization capabilities. Furthermore, we propose a Global Grouped Spatial and Channel Attention (GGSCA), which serves to accentuate vital features while concurrently curtailing the salience of irrelevant features. To demonstrate the superior adaptability of our model on complex scenarios, we introduce a proprietary dataset called Ref-Hard. PI-Net shows best performance on Ref-Hard while also achieves 74.14%, 65.27%, and 63.56% respectively on RefCOCO, RefCOCO+ and RefCOCOg. ©2024 IEEE.

关键词： Image segmentation

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Feature Enhancement Method for Multimodal Nighttime Pedestrian Detection 7

Feature Enhancement Method for Multimodal Nighttime Pedestri...

引用

7th international conference on Electrical, Mechanical and computer Engineering, ICEMCE 2023

作者： Wang, Yonggui Zhu, Zhongpan Li, Jian Wang, Zhipeng Chang, Qi He, Bin School of Electronic Information and Artificial Intelligence Shanxi University of Science & Technology Xi'an China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai China

ISBN: (纸本)9798350382877

Nighttime Pedestrian Detection in Low-Light Conditions poses a challenging task, as previous processing methods often introduced noise and limited model generalization due to the low image quality in low-light conditions. this study presents a feature enhancement algorithm tailored for multimodal nighttime pedestrian detection, harnessing the advantages of infrared and visible light data. Our approach utilizes an adaptive weight fusion algorithm and a spatial detail module for multimodal fusion and spatial detail feature extraction, respectively, to accomplish the nighttime pedestrian detection task. Experimental results demonstrate that our method, compared to the baseline algorithm, achieves performance improvements of 3.3%, 11.9%, and 4.5% on the LLVIP dataset. © 2023 IEEE.

关键词： Feature extraction

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Latency-aware Load Balancing Virtual Network Embedding Algorithm 7

Latency-aware Load Balancing Virtual Network Embedding Algor...

引用

7th international conference on computer and information processing technology, iscipt 2022

作者： Zong, Yue Li, Dedi Lai, Xinjie Luo, Yuanlin Xu, Han Power China Huadong Engineering Corporation Limited Hangzhou311122 China Country College of Information Science & Electronic Engineering Zhejiang University Hangzhou310027 China Country School of Mechanical Engineering Tianjing University Tianjing300072 China College of Economics and Management North China Electric Power University Beijing102206 China

To overcome network ossification, network slicing has been proposed as a promising method in 5G network. Software defined networking (SDN) and network function virtualization (NFV) as emerging enabled technology support multiple logical networks sharing physical infrastructure. Virtual network embedding (VNE) is one of the main challenges for network slicing. To avoid link congestion and satisfy the latency requirement of virtual network requests, latency model of network and load balancing link weight formulation are proposed. then we design a load balancing link reconfiguration embedding algorithm and latency-aware load balancing VNE algorithm. Compared with the baseline algorithms, simulation results show that our proposed algorithm has better performance. © 2022 Copyright for this paper by its authors.

关键词： Network function virtualization

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Overview of Underwater Object Detection Based on Image processing Techniques 7

Overview of Underwater Object Detection Based on Image Proce...

引用

7th international conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Aamir, Muhammad Gao, Jinxiong Zhang, Yonghui Tang, Hao Bhatti, Uzair Aslam College of Computer Science Huanggang Normal University Huanggang438000 China School of Information and Communication Engineering Hainan University Haikou570100 China

ISBN: (纸本)9798350350890

underwater object detection and recognition are critical technologies in marine science and engineering, enabling advancements in environmental monitoring, resource exploration, and autonomous underwater vehicles. the rapid progression of computing power, underwater robotics, and artificial intelligence has driven substantial academic and industrial interest in this domain. this paper presents a comprehensive overview of the current methodologies in image-based underwater object recognition, where optical images or videos captured under various lighting conditions are processed to detect and identify underwater objects. the discussion includes an analysis of the prevailing algorithms, a review of the contemporary research landscape, and an exploration of emerging trends. Additionally, this paper offers insights into the anticipated technological advancements that will shape the future of underwater object recognition. © 2024 IEEE.

关键词： Underwater imaging

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SOCIAL MEDIA DATA VISUALIZATION: A REVIEW 16

SOCIAL MEDIA DATA VISUALIZATION: A REVIEW

引用

作者： Asiri, Hanan Aksoy, Mehmet Sabih Department of Information Systems King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9789898704429

With the huge amount of data available on social media, it will be challenging to analyze, summarize, and present useful information extracted from social media in an understood, effective, and communicable form, which facilitates the decision-making process. In this paper, the visual analytics of social media is discussed. Started with a brief description of the social media concept. then, a review of a piece of works in social media data analysis is shown. Finally, we summarize state-of-the-art visual analytics of social media data since 2015 categorized based on their applications into four categories: geographical information visualization, text visualization, disasters management, general-purpose visualization tools of social media data. © MCCSIS *** rights reserved.

关键词： Data visualization

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