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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

共 1869 条记录，以下是131-140 订阅

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BADA-LAT: Efficient Local Attention Transformer for Chinese Named Entity recognition with Boundary and LLM-Based Data Augmentation 27th

BADA-LAT: Efficient Local Attention Transformer for Chinese ...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Qiu, Xiaoping Yang, Ke Du, Shiling School of Computing and Artificial Intelligence Southwest Jiaotong University No. 999 Xi’an Road Sichuan Chengdu611756 China Tangshan Institute of Southwest Jiaotong University No. 6 Jingqu Road Hebei Tangshan063000 China National United Engineering Laboratory of Integrated and Intelligent Transportation No. 111 North Section 1 Second Ring Road Sichuan Chengdu610031 China Sichuan Key Laboratory of Manufacturing Industry Chain Collaboration and Information Support Technology No. 999 Xi’an Road Sichuan Chengdu610016 China Engineering Research Center of Sustainable Urban Intelligent Transportation No. 999 Xi’an Road Sichuan Chengdu611756 China Sichuan Institute of Industrial Software Technology No. 61 Sha’wan Road Sichuan Chengdu610031 China

Progress in Chinese Named Entity recognition (CNER)60;has highlighted lexicon-based methods that use word information to60;boost performance. However, these methods neglect two crucial aspects:60;the regulari... 详细信息

ISBN: (纸本)9783031784941

Progress in Chinese Named Entity recognition (CNER) has highlighted lexicon-based methods that use word information to boost performance. However, these methods neglect two crucial aspects: the regularity of word boundary and the characteristics of the NER task. To address these shortcomings, we introduce BADA-LAT, a Transformer architecture that incorporates word boundary information and introduce local attention computation. It can enhance entity boundary recognition and concentrate the attention weight of the target token on adjacent characters and matched words. Furthermore, to mitigate the issue of class imbalance, we augment the original training data using large language model (LLM). Our method outperforms other lexicon-based ones, as shown in experiments on four Chinese datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： boundary augmentation Chinese NER data augmentation local attention

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Intelligent Parking Big Data Cloud Platform Algorithm Research and Application 6

Intelligent Parking Big Data Cloud Platform Algorithm Resear...

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6th international conference on Computer Information Science and Application Technology, CISAT 2023

作者： Yan, Xiaobo Zou, Xue Cai, Shan Wang, Lin Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guizhou Minzu University Guiyang China College of Date Science and Information Engineering Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510668546

With the rapid development of the domestic economy and the increasing living standards of the people, the ownership of private cars has increased explosively. Currently, urban traffic congestion and parking difficulty have gradually become a hot topic of research from all walks of life. Parking difficulty is mainly reflected on the following aspects: first, the existing parking spaces in cities are not effectively utilized;the second reason is that drivers cannot grasp relevant information about parking spaces near the ground in real time. this article conducts in-depth research on the issue of parking difficulty. Based on the existing security monitoring systems in parking lots, real-time detection of parking spaces status is conducted based on computer vision, machine learning, and other methods. By building a comprehensive, synthesize, and multi-dimensional parking resources dynamic sensing system, a smart parking big data cloud platform is constructed to achieve the effect of intelligent indoor parking lots management. © 2023 SPIE.

关键词： Machine learning

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Leveraging Wavelets and Deep CNN for Sleep pattern recognition in Road Safety: An EEG Study 6th

Leveraging Wavelets and Deep CNN for Sleep Pattern Recogni...

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Arif, Saad Munawar, Saba Marie, Rashiq Rafiq Shah, Syed Aziz Department of Mechanical Engineering HITEC University Taxila47080 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Wah Campus Wah Cantt47040 Pakistan College of Computer Science and Engineering Taibah University Medina41477 Saudi Arabia Research Centre for Intelligent Healthcare Coventry University CoventryCV1 5FB United Kingdom

ISBN: (纸本)9783031530814

A major cause of fatal traffic accidents and injuries worldwide is drowsy driving. the fatal aftereffects can be considerably reduced, and road safety can rise, with earlier and more efficient diagnosis. In this study, electroencephalography (EEG) biosignals-based brain-computer interface (BCI) is established for early identification of human inattentiveness during driving activities. EEG signals are recorded from six EEG electrode positions viz Fp1, Fp2, F7, F8, O1, and O2. Spectral information of the alpha and beta frequency bands is extracted in terms of continuous wavelet transform (CWT) with a temporal correlation for complete experiments. CWT components are processed as scalograms which are color images used for visual inspection of brain states. the scalogram images of 10 s epochs are stored as the image dataset. the SqueezeNet deep convolutional neural network (CNN) is fine-tuned with transfer learning for effective drowsiness detection from CWT scalograms. Transfer-learned CNN classification model achieved the best results with 89.8% accuracy, 88.0% precision, 96.1% recall, 89.9% F1 -score, 87.7% specificity, and 79.7% Matthew’s correlation coefficient. the statistical significance of the achieved results is validated with a p-value of less than 0.05. Using wavelet information of EEG biosignals, the suggested BCI methodology offers a viable method for quicker and more accurate driving drowsiness detection. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electroencephalography

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Wafer Map Defect pattern recognition using Imbalanced Datasets 13

Wafer Map Defect Pattern Recognition using Imbalanced Datase...

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13th international conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Tziolas, theodoros theodosiou, theodosis Papageorgiou, Konstantinos Rapti, Aikaterini Dimitriou, Nikolaos Tzovaras, Dimitrios Papageorgiou, Elpiniki University of Thessaly Gaiopolis Campus Dept. of Energy Systems Larisa41500 Greece Information Technologies Institute Centre for Research and Technology Hellas 6th Km Charilaou-Thermi Road Thermi-Thessaloniki57001 Greece

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

the accurate and automatic inspection of wafer maps is vital for semiconductor engineers to identify defect causes and to optimize the wafer fabrication process. this research work seeks to address the pattern recognition task for the identification of defects in wafer maps, by developing a deep Convolutional Neural Network (CNN) classifier. the proposed CNN-based model utilizes various pre- and post-processing tools and is applied on the public but highly imbalanced industrial dataset WM-811K. To handle imbalance, a methodology of treating each class individually is proposed by applying different processing techniques for down-sampling, splitting and data augmentation based on the number of samples. the proposed model achieves 95.3% accuracy and 93.78% macro F1-score and outperformes other models in the related literature concerning the identification of the majority of classes. © 2022 IEEE.

关键词： Convolutional neural networks

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6th international Joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

6th International Joint Conference on Asia-Pacific Web (APWe...

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6th international Joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

ISBN: (纸本)9783031251573

the proceedings contain 125 papers. the special focus in this conference is on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM). the topics include: Neighborhood Constraints Based Bayesian Personalized Ranking for Explainable Recommendation;hierarchical Aggregation Based Knowledge Graph Embedding for Multi-task Recommendation;mixed-Order Heterogeneous Graph Pre-training for Cold-Start Recommendation;MISRec: Multi-Intention Sequential Recommendation;MARS: A Multi-task Ranking Model for Recommending Micro-videos;how to Share Medical Data Belonging to Multiple Owners in a Secure Manner;LAP-BFT: Lightweight Asynchronous Provable Byzantine Fault-Tolerant Consensus Mechanism for UAV Network Trusted Systems;a Secure Order-Preserving Encryption Scheme Based on Encrypted Index;computing Online Average Happiness Maximization Sets over Data Streams;A Deep Reinforcement Learning-Based Approach for Android GUI Testing;RoFL: A Robust Federated Learning Scheme Against Malicious Attacks;FDP-LDA: Inherent Privacy Amplification of Collapsed Gibbs Sampling via Group Subsampling;multi-modal Fake News Detection Use Event-Categorizing Neural Networks;unified Proof of Work: Delegating and Solving Customized Computationally Bounded Problems in a Privacy-Preserving Way;TSD3: A Novel Time-Series-Based Solution for DDoS Attack Detection;executing Efficient Retrieval Over Blockchain Medical Data Based on Exponential Skip Bloom Filter;IMPGA: An Effective and Imperceptible Black-Box Attack Against Automatic Speech recognition Systems;FD-Leaks: Membership Inference Attacks Against Federated Distillation Learning;when Self-attention and Topological Structure Make a Difference: Trajectory Modeling in Road Networks;A Penetration Path Planning Algorithm for UAV Based on Laguerre Diagram;block-Join: A Partition-Based Method for Processing Spatio-Temporal Joins;RN-Cluster: A Novel Density-Based Clustering Approach for Road Network Partition;fine-Grained Urban Flow Inferring via Condit

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Deep Learning in Automated Worm Identification and Tracking for C. Elegan Mating Behaviour Analysis 27th

Deep Learning in Automated Worm Identification and Trackin...

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27th international conference on pattern recognition, ICPR 2024

作者： Akpu, Chukwuma Hilary Wei, Hong Hong, Xia University of Reading ReadingRG6 6AY United Kingdom

ISBN: (纸本)9783031781216

this study is concerned with computer vision technologies applied in C. elegans (diminutive nematodes) mating behaviour analysis, more specifically object detection and tracking to find contacts of male and female worms in worm mating videos. Advanced deep learning algorithms, such as YOLOv8 and DeepSORT, are adapted in the automated worm identification and tracking system. A modified DeepSORT algorithm is developed to cope with appearance similarity of C. elegans for improving the tracking accuracy. In addition, a male worm detection and tracking algorithm, utilising the male worm’s mobility characteristic, assists the modified DeepSORT in accurate male worm tracking. Finally, worm contact detection is implemented by calculating the Euclidean distance between the male and female worms. the developed system, named as M1 and M2, is trained and evaluated under two sets of data, bounding boxes and segmented worms, respectively. Furthermore, we compared the effectiveness of including SAM segmentation optional module in experiments. the evaluation results have shown that YOLOv8 has excellent performance in worm detection to cope with deformable worm shape, and the modified DeepSORT significantly outperforms the default DeepSORT in worm tracking. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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2by2: Weakly-Supervised Learning for Global Action Segmentation 27th

2by2: Weakly-Supervised Learning for Global Action Segmenta...

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27th international conference on pattern recognition, ICPR 2024

作者： Bueno-Benito, Elena Dimiccoli, Mariella Institut de Robòtica i Informàtica Industrial CSIC-UPC Llorens i Artigas 4-6 Barcelona08028 Spain

ISBN: (纸本)9783031781247

this paper presents a simple yet effective approach for the poorly investigated task of global action segmentation, aiming at grouping frames capturing the same action across videos of different activities. Unlike the case of videos depicting all the same activity, the temporal order of actions is not roughly shared among all videos, making the task even more challenging. We propose to use activity labels to learn, in a weakly-supervised fashion, action representations suitable for global action segmentation. For this purpose, we introduce a triadic learning approach for video pairs, to ensure intra-video action discrimination, as well as inter-video and inter-activity action association. For the backbone architecture, we use a Siamese network based on sparse transformers that takes as input video pairs and determine whether they belong to the same activity. the proposed approach is validated on two challenging benchmark datasets: Breakfast and YouTube Instructions, outperforming state-of-the-art methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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6th international Joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

6th International Joint Conference on Asia-Pacific Web (APWe...

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6th international Joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

ISBN: (纸本)9783031252006

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Incremental Object 6D Pose Estimation 27th

Incremental Object 6D Pose Estimation

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27th international conference on pattern recognition, ICPR 2024

作者： Tian, Long Sorrenti, Amelia Pang, Yik Lung Bellitto, Giovanni Palazzo, Simone Spampinato, Concetto Oh, Changjae Queen Mary University of London London United Kingdom Southwest Jiaotong University Chengdu China University of Catania Catania Italy

ISBN: (纸本)9783031783944

We present a novel setting for 6D object pose estimation, where a model progressively adapts its parameters to estimate the pose of new objects without forgetting. this capability is crucial for real-world applications, particularly in scenarios where a deployed model must accommodate new objects while mitigating the risk of forgetting previously seen objects. To tackle this challenge, we propose a replay-based incremental learning technique designed to retain key information about previously seen objects when the model is exposed to a new one. Our approach relies on a memory buffer comprising keyframes of previously encountered objects, serving to regularize the model parameters based on past experiences while allowing for the update of model features to perform pose estimation on new objects. We validate the effectiveness of our method on the standard Linemod and YCB-Video datasets, demonstrating how our method surpasses baseline approaches in incremental learning at the task at hand. the project website is available at: https://***/ILPose. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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6th international Joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

6th International Joint Conference on Asia-Pacific Web (APWe...

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6th international Joint conference on Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM), APWeb-WAIM 2022

ISBN: (纸本)9783031251979

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