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检索条件"任意字段=Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition"

共 711 条记录，以下是121-130 订阅

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Causal Inference and Counterfactual Reasoning 2020

Causal Inference and Counterfactual Reasoning

引用

Joint international conference on 7th ACM IKDD international conference on Data Science (CoDS) and 25th international conference on Management of Data (COMAD)

作者： Sharma, Amit Kiciman, Emre Microsoft Res New York NY 10011 USA

As computing systems are more frequently and more actively intervening to improve people9;s work and daily lives, it is critical to correctly predict and understand the causal effects of these interventions. Conven... 详细信息

ISBN: (纸本)9781450377386

As computing systems are more frequently and more actively intervening to improve people's work and daily lives, it is critical to correctly predict and understand the causal effects of these interventions. Conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal analysis. this tutorial will introduce participants to concepts in causal inference and counterfactual reasoning, drawing from a broad literature on the topic from statistics, social sciences and machine learning. We will motivate the use of causal inference through examples in domains such as recommender systems, social media datasets, health, education and governance. To tackle such questions, we will introduce the key ingredient that causal analysis depends on-counterfactual reasoning-and describe the two most popular frameworks based on Bayesian graphical models and potential outcomes. Based on this, we will cover methods suitable for doing causal inference with large-scale online data, including randomized experiments, observational methods like matching and stratification, and natural experiment-based methods such as instrumental variables and regression discontinuity. We will also focus on best practices for evaluation and validation of causal inference techniques, drawing from our own experiences. We will show application of these techniques using DoWhy, a Python library for causal inference. throughout, the emphasis will be on considerations of working with large-scale data, such as logs of user interactions or social data.

关键词： pattern recognition

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the AffectMove 2021 Challenge - Affect recognition from Naturalistic Movement Data 9

The AffectMove 2021 Challenge - Affect Recognition from Natu...

引用

9th international conference on Affective computing and Intelligent Interaction (ACII)

作者： Olugbade, Temitayo Sagoleo, Roberto Ghisio, Simone Gold, Nicolas Williams, Amanda C. de C. de Gelder, Beatrice Camurri, Antonio Volpe, Gualtiero Bianchi-Berthouze, Nadia UCL London England Univ Genoa Genoa Italy Maastricht Univ Maastricht Netherlands

ISBN: (纸本)9781665400213

We ran the first Affective Movement recognition (AffectMove) challenge that brings together datasets of affective bodily behaviour across different real-life applications to foster work in this area. Research on automatic detection of naturalistic affective body expressions is still lagging behind detection based on other modalities whereas movement behaviour modelling is a very interesting and very relevant research problem for the affective computing community. the AffectMove challenge aimed to take advantage of existing body movement datasets to address key research problems of automatic recognition of naturalistic and complex affective behaviour from this type of data. Participating teams competed to solve at least one of three tasks based on datasets of different sensors types and real-life problems: multimodal EmoPain dataset for chronic pain physical rehabilitation context, weDraw-1 Movement dataset for maths problem solving settings, and multimodal Unige-Maastricht Dance dataset. To foster work across datasets, we also challenged participants to take advantage of the data across datasets to improve performances and also test the generalization of their approach across different applications.

关键词： affective computing bodily challenge datasets movement

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Advances in Soft computing - 9th Mexican international conference on Artificial Intelligence, MICAI 2010, proceedings

Advances in Soft Computing - 9th Mexican International Confe...

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9th Mexican international conference on Artificial Intelligence, MICAI 2010

ISBN: (纸本)3642167721

the proceedings contain 84 papers. the topics discussed include: music composition based on linguistic approach;a practical robot coverage algorithm for unknown environments;an algorithm for the automatic generation of human-like motions based on examples;line maps in cluttered environments;fuzzy cognitive maps for modeling complex systems;semantic representation and management of student models: an approach to adapt lecture sequencing to enhance learning;towards one-class pattern recognition in brain activity via neural networks;real time tracking of musical performances;recognizing dactylogical symbols with image segmentation and a new differentiated weighting scheme;selecting candidate labels for hierarchical document clusters using association rules;recognizing textual entailment using a machine learning approach;detection of different authorship of text sequences through self-organizing maps and mutual information function;and supervised machine learning for predicting the meaning of verb-noun combinations in Spanish.

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Study on Discharge Mode and Characteristics of Insulation Defects in GIS Equipment 9

Study on Discharge Mode and Characteristics of Insulation De...

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9th international conference on Condition Monitoring and Diagnosis (CMD)

作者： Shi, Haonan Tian, Yi Wang, Yanru Zhu, Guixin Wang, Shuai Li, Zhiwei Huang, Xinbo Xian Polytech Univ Elect Informat Coll Xian 710048 Peoples R China

ISBN: (纸本)9784886864314

Partial discharge caused by different insulation defects has different degrees of damage to the insulation performance of gas insulating switchgear (GIS). To timely and effectively find the early insulation defects in the equipment, the insulation status is evaluated and diagnosed. therefore, it is necessary to study the discharge mode and characteristics of insulation defects in GIS equipment. Firstly, this paper builds an equal scale GIS internal defect simulation platform in the laboratory. A variety of different insulation defects were simulated and the relevant partial discharge signals were collected. Data augmentation of the discharge pattern training set using conditional generative adversarial networks. Finally, the improved depth residual network is used to extract the discharge spectrum features of each type of defects to classify them, and the recognition accuracy of different types of defects is obtained. the experimental results show that the proposed method can effectively distinguish different typical partial discharge defect types in GIS, and has a good application prospect in engineering practice.

关键词： GIS Insulation defects Partial discharge pattern recognition

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Research on Reinforcement Learning-Based Dynamic Power Management for Edge Data Center 9

Research on Reinforcement Learning-Based Dynamic Power Manag...

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9th IEEE international conference on Software Engineering and Service Science (ICSESS)

作者： Guo, Qianying Huo, Ru Meng, Hao Xinhua, E. Liu, Jiang Huang, Tao Beijing Univ Technol Beijing Adv Innovat Ctr Future Internet Technol Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781538665657

Mobile Edge computing (MEC) is a supplement to traditional cloud computing. Its characteristics are low latency and high reliability, and it will be widely used in the future. However, their dense deployment pattern raises a big concern on the system-wide energy consumption, Dynamic power management (DPM) method is an important method to solve energy consumption problems, it saves energy by shutting down servers in the EDC that are idle or have low utilization. In this paper, a DPM method based on reinforcement learning was proposed, it achieves the trade-off between EDC service performance and energy consumption by learning the global optimal dynamic timeout threshold power management strategy by trial and error. Experiments have shown that the proposed method saves no less than 6.35% energy consumption compared to the expert-based method.

关键词： dynamic power management timeout threshold reinforcement learning energy-efficiency mobile edge computing

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An Efficient Convolutional Neural Network Approach for Facial recognition 10

An Efficient Convolutional Neural Network Approach for Facia...

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10th international conference on Cloud computing, Data Science and Engineering (Confluence)

作者： Mangal, Aayushi Malik, Himanshu Aggarwal, Garima Amity Univ ASET Dept Comp Sci Noida UP India

ISBN: (纸本)9781728127910

Data security being the main concern now a days, has faced a lot of threat in terms of breaching of information which requires immediate attention. Biometrics have served a long-run for this purpose which is a part of Deep Learning. In the recent past, face recognition has become a very important tool for safety and security purposes. this paper presents the application of face recognition technique, making use of Convolutional Neural Network (CNN) with Python and a comparison is drawn between the other techniques such as Principal Component Analysis (PCA), Local Binary pattern (LBP) and K Nearest Neighbour (KNN). Unlike conventional methods, the proposed scheme uses four Convolutional layers with ReLu layers, four pooling layers, a fully connected layer and a Softmax Loss Layer to normalize the probability distribution. the dataset consists of 1500 images with different facial expressions and the model is trained and tested in order to acquire an accuracy using CNN method. Experimental results show that the proposed Neural Network scored an accuracy of 96.96%.

关键词： Facial recognition Deep Learning Face Database Convolutional Neural Networks K Nearest Neighbour Principal Component Analysis Local Binary pattern Histogram

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Intelligent Decision Technologies 2016 1st ed. 2016

引用

丛书名： Smart Innovation, Systems and Technologies

2016年

作者： Ireneusz Czarnowski Alfonso Mateos Caballero Robert J. Howlett Lakhmi C. Jain

ISBN: (数字)9783319396309

ISBN: (纸本)9783319396293

the KES-IDT-2016 proceedings give an excellent insight into recent research, both theoretical and applied, in the field of intelligent decision making. the range of topics explored is wide, and covers methods of grouping, classification, prediction, decision support, modelling and many more in such areas as finance, linguistics, medicine, management and transportation. this proceedings contain several sections devoted to specific topics, such as: Specialized Decision Techniques for Data Mining, Transportation and Project Management pattern recognition for Decision Making Systems New Advances of Soft computing in Industrial and Management Engineering Recent Advances in Fuzzy Systems Intelligent Data Analysis and Applications Reasoning-based Intelligent Systems Intelligent Methods for Eye Movement Data Processing and Analysis Intelligent Decision Technologies for Water Resources Management Intelligent Decision Making for Uncertain Unstructured Big Data Decision Making theory for Economics Interdisciplinary Approaches in Business Intelligence Research and Practice pattern recognition in Audio and Speech Processing the KES-IDT conference is a well-established international annual conference, interdisciplinary in nature. these two volumes of proceedings form an excellent account of the latest results and outcomes of recent research in this leading-edge area.

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9th international conference on Big Data, BigData 2020, held as part of the Services conference Federation, SCF 2020

9th International Conference on Big Data, BigData 2020, held...

引用

9th international conference on Big Data, BigData 2020, held as part of the Services conference Federation, SCF 2020

ISBN: (纸本)9783030596118

the proceedings contain 18 papers. the special focus in this conference is on Big Data. the topics include: Validating Goal-Oriented Hypotheses of Business Problems Using Machine Learning: An Exploratory Study of Customer Churn;the Collaborative Influence of Multiple Interactions on Successive POI Recommendation;Chemical XAI to Discover Probable Compounds Spaces Based on Mixture of Multiple Mutated Exemplars and Bioassay Existence Ratio;clinical Trials Data Management in the Big Data Era;cross-Cancer Genome Analysis onCancer Classification Using Both Unsupervised and Supervised Approaches;heavy Vehicle Classification through Deep Learning;spatial Association pattern Mining Using In-Memory Computational Framework;Dissecting Biological Functions for BRCA Genes and their Targeting MicroRNAs Within Eight Clusters;a Performance Prediction Model for Spark Applications;Predicting the DJIA with News Headlines and Historic Data Using Hybrid Genetic Algorithm/Support Vector Regression and BERT;processing Big Data Across Infrastructures;fake News Classification of Social Media through Sentiment Analysis;scalable Reference Genome Assembly from Compressed Pan-Genome Index with Spark;a Web Application for Feral Cat recognition through Deep Learning;MCF: Towards Window-Based Multiple Cuckoo Filter in Stream computing;A Data-Driven Method for Dynamic OD Passenger Flow Matrix Estimation in Urban Metro Systems.

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A Complete Piping Identification Solution for Piping and Instrumentation Diagrams 25

A Complete Piping Identification Solution for Piping and Ins...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Shengyong Li, Zhongtao Zhao, Shuai Yang, Lei Zhao, Fu Ge, Chuan University of Jinan School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Jinan Housing and Urban-Rural Development Bureau Jinan China The Shandong Cyberspace Affairs Commission Jinan China Shandong Xiaoya Holding Group Co. Ltd Jinan China Shandong University Jinan China

ISBN: (纸本)9798350330014

this study proposes a pipeline identification method for identifying pipelines in Piping and Instrumentation Diagrams (P&IDs) in image format. Automating this process is an important issue for the process plant industry, as image P &IDs are currently identified manually by humans. As part of research on technology for automatic conversion of image-format piping and instrumentation diagram (P&ID) into digital P&ID, this study proposes a method combining deep learning and traditional image detection to identify various types of pipelines in image format P&ID. the proposed method includes image pipeline extraction, pipeline simulation data augmentation, pipeline identification and fusion. For multiple test P &IDs, using yolov8 based on split coordinate spatial attention and augmented with simulated data, the average precision and recall are 90.5 % and 88.5 %, respectively. Combined with LSD line detection, it takes an average of 100 seconds to recognize a complete image, and the overall pipeline recognition rate can reach 95.5 %. © 2023 IEEE.

关键词： Object recognition

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Intelligent Decision Technologies 2016 1st ed. 2016

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丛书名： Smart Innovation, Systems and Technologies

2016年

作者： Ireneusz Czarnowski Alfonso Mateos Caballero Robert J. Howlett Lakhmi C. Jain

ISBN: (数字)9783319396279

ISBN: (纸本)9783319396262

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