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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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A Deep Learning Study On Understanding Banglish and Abbreviated Words Used in Social Media

A Deep Learning Study On Understanding Banglish and Abbrevia...

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international conference on ent computing and Control Systems (ICICCS)

作者： Suharto Das Md Sanzidul Islam Imran Mahmud Daffodil International University Dhaka Bangladesh

Day by Day, the trend of using social media(SM) has increased among the people. With the increasing rate, the use of Banglish(Merge of Bangla and English) and shortcut words has also being increased. In this research, Banglish and Shortcut words have been fully converted to English. For this type of conversation, we have used the CNNs method and the method consist of multiple layer and these layers are connected to each other. this method is considered to be the best method because it does not need any feature extraction. the convolutional neuron network is used in many areas such as image and pattern recognition, speech recognition, natural language processing and video analysis. In this research we have used CNN method because we have decided to use computer vision as some of the words are close to each other and at first we need to convert all the shortcut words into images for pre-processing the data. With the help of CNN method searched for the Banglish and shortcut words that people use for their daily conversation. At first we find the different representative of a single word and then we converted those shortcut and Banglish words to main word using CNNs method.

关键词： Deep learning Computer vision Social networking (online) Neurons Speech recognition Market research Feature extraction

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MFI: Multi-range Feature Interchange for Video Action recognition

MFI: Multi-range Feature Interchange for Video Action Recogn...

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international conference on pattern recognition

作者： Sikai Bai Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University Xi'an Shaanxi P. R. China

Short-range motion features and long-range dependencies are two complementary and vital cues for action recognition in videos, but it remains unclear how to efficiently and effectively extract these two features. In this paper, we propose a novel network to capture these two features in a unified 2D framework. Specifically, we first construct a Short-range Temporal Interchange (STI) block, which contains a Channels-wise Temporal Interchange (CTI) module for encoding short-range motion features. then a Graph-based Regional Interchange (GRI) module is built to present long-range dependencies using graph convolution. Finally, we replace original bottleneck blocks in the ResNet with STI blocks and insert several GRI modules between STI blocks, to form a Multi-range Feature Interchange (MFI) Network. Practically, extensive experiments are conducted on three action recognition datasets (i.e., Something-Something V1, HMDB51, and UCF101), which demonstrate that the proposed MFI network achieves impressive results with very limited computing cost.

关键词： three-dimensional displays Convolution Feature extraction Encoding pattern recognition Optical flow Videos

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Mitigation and models for determining premiums for natural disaster insurance due to excessive rainfall 10

Mitigation and models for determining premiums for natural d...

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10th international conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020

作者： Kalfin Sukono Supian, S. Mamat, M. Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Terengganu Malaysia

Natural disasters due to excessive rainfall often cause huge losses in the form of impacts including casualties and economic losses. To minimize casualties, disaster mitigation is needed, while financing for disasters (emergency response) and financing after disasters (rehabilitation and reconstruction) can be carried out with insurance. this paper aims to determine mitigation efforts and insurance premiums for natural disasters. the method as a mitigation effort is studied based on several methods that are suitable for the conditions of the community in areas that are often affected by disasters. Meanwhile, regarding loss insurance, the premium is calculated based on the number of cases, the rate set by the insurance company and the amount insured (loss). Based on the results of natural disaster mitigation studies, it is necessary to adjust it to the type of disaster. In the case of landslides, housing buildings must be standardized, while in cases of flooding, river normalization is required. Mitigation in general, namely the installation of disaster sirens and disaster response training. Regarding loss insurance, it is found that the optimal premium for the government and insurance companies is obtained. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Losses

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A Comparative Study on Autism Spectrum Disorder Detection via 3D Convolutional Neural Networks 25th

A Comparative Study on Autism Spectrum Disorder Detection vi...

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Zhang, Kaijie Wang, Wei Guo, Yijun Shan, Caifeng Wang, Liang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Beijing China Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Artificial Intelligence Research Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030687625

the prevalence of Autism Spectrum Disorder (ASD) in the United States has increased by 178% from 2000 to 2016. However, due to the lack of well-trained specialists and the time-consuming diagnostic process, many children are not able to be promptly diagnosed. Recently, several research have taken steps to explore automatic video-based ASD detection systems with the help of machine learning and deep learning models, such as support vector machine (SVM) and long short-term memory (LSTM) model. However, the models mentioned above could not extract effective features directly from raw videos. In this study, we aim to take advantages of 3D convolution-based deep learning models to aid video-based ASD detection. We explore three representative 3D convolutional neural networks (CNNs), including C3D, I3D and 3D ResNet. In addition, a new 3D convolutional model, called 3D ResNeSt, is also proposed based on ResNeSt. We evaluate these models on an ASD detection dataset. the experimental results show that, on average, all of the four 3D convolutional models can obtain competitive results when compared to the baseline using LSTM model. Our proposed 3D ResNeSt model achieves the best performance, which improves the average detection accuracy from 0.72 to 0.85. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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Factor Screening using Bayesian Active Learning and Gaussian Process Meta-Modelling

Factor Screening using Bayesian Active Learning and Gaussian...

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international conference on pattern recognition

作者： Cheng Li Santu Rana Andrew Gill Dang Nguyen Sunil Gupta Svetha Venkatesh School of Computing National University of Singapore Singapore Applied Artificial Intelligence Institute Deakin University Australia Joint and Operations Analysis Division Defence Science Technology Group Australia

In this paper we propose a data-efficient Bayesian active learning framework for factor screening, which is important when dealing with systems which are expensive to evaluate, such as combat simulations. We use Gaussian Process meta-modelling with the Automatic Relevance Determination covariance kernel, which measures the importance of each factor by the inverse of their associated length-scales in the kernel. this importance measures the degree of non-linearity in the simulation response with respect to the corresponding factor. We initially place a prior over the length-scale values, then use the estimated posterior to select the next datum to simulate which maximises the mutual entropy between the length-scales and the unknown simulation response. Our goal-driven Bayesian active learning strategy ensures that we are data-efficient in discovering the correct values of the length-scales compared to either a random-sampling or uncertainty-sampling based approach. We apply our method to an expensive combat simulation and demonstrate the superiority of our approach.

关键词： Gaussian processes Length measurement Entropy Bayes methods pattern recognition Kernel

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CKG: Dynamic Representation Based on Context and Knowledge Graph

CKG: Dynamic Representation Based on Context and Knowledge G...

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international conference on pattern recognition

作者： Xunzhu Tang Tiezhu Sun Rujie Zhu Shi Wang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Momenta Suzhou China University of Central Florida Orlando FL USA Institute of Computing Technology Chinese Academy beijing China

Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance. As a result, they have achieved state-of-the-art results in a large range of language tasks. However, there exists other valuable semantic information such as similar, opposite, or other possible meanings in external knowledge graphs (KGs). We argue that entities in KGs could be used to enhance the correct semantic meaning of language sentences. In this paper, we propose a new method CKG: Dynamic Representation Based on Context and Knowledge Graph. On the one side, CKG can extract rich semantic information of large corpus. On the other side, it can make full use of inside information such as co-occurrence in large corpus and outside information such as similar entities in KGs. We conduct extensive experiments on a wide range of tasks, including QQP, MRPC, SST-5, SQuAD, CoNLL 2003, and SNLI. the experiment results show that CKG achieves SOTA 89.2 on SQuAD compared with SAN (84.4), ELMo (85.8), and BERTBase (88.5).

关键词： Semantics Data mining Task analysis Context modeling

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Improving Batch Normalization with Skewness Reduction for Deep Neural Networks

Improving Batch Normalization with Skewness Reduction for De...

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international conference on pattern recognition

作者： Pak Lun Kevin Ding Sarah Martin Baoxin Li School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ

Batch Normalization (BN) is a well-known technique used in training deep neural networks. the main idea behind batch normalization is to normalize the features of the layers (i.e., transforming them to have a mean equal to zero and a variance equal to one). Such a procedure encourages the optimization landscape of the loss function to be smoother, and improves the learning of the networks for both speed and performance. In this paper, we demonstrate that the performance of the network can be improved, if the distributions of the features of the output in the same layer are similar. As normalizing based on mean and variance does not necessarily make the features to have the same distribution, we propose a new normalization scheme: Batch Normalization with Skewness Reduction (BNSR). Comparing with other normalization approaches, BNSR transforms not just only the mean and variance, but also the skewness of the data. By tackling this property of a distribution, we are able to make the output distributions of the layers to be further similar. the nonlinearity of BNSR may further improve the expressiveness of the underlying network. Comparisons with other normalization schemes are tested on the CIFAR-100 and ImageNet datasets. Experimental results show that the proposed approach can outperform other state-of-the-arts that are not equipped with BNSR.

关键词： Training Neural networks Transforms pattern recognition Optimization

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Deriving patterns coping with the error among evolved controllable/uncontrollable constituent systems within the open System-of-Systems

Deriving patterns coping with the error among evolved contro...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Nobuyuki Kobayashi Hirotaka Fujino Seiko Shirasaka The System Design and Management Research Institute of Graduate School of System Design and Management Keio University Kanagawa Japan Graduate School of System Design and Management Keio University Kanagawa Japan

ISBN: (纸本)9781665424219

the previous study does not clarify how to satisfy the interoperability of open System of Systems (SoS) in runtime assurance. therefore, the purpose of this study is to propose the patterns coping with evolving open SoS, which deal with setting interfaces and functions on the sending side and the receiving side within the open SoS, in order dynamically to set the acceptable interoperability of the open SoS in runtime assurance. Concretely, in order to cope with evolving interfaces and functions on the sending side and the receiving side within the open SoS, we derive those patterns for continuously working open SoS which evolves in runtime which adopts the four axes. the evaluation method shows the results actually applied to the combination information systems which cooperate among Product Lifecycle Management, Enterprise Resource Planning and Manufacturing Execution Systems in one company of the manufacturing industry in Japan, as open SoS.

关键词： Product lifecycle management Manufacturing industries Runtime Open systems Control systems Enterprise resource planning pattern recognition

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Retrieving Large-scale Product Knowledge by Collaborative computing of CPUs and GPUs 2021

Retrieving Large-scale Product Knowledge by Collaborative Co...

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2021 10th international conference on Internet computing for Science and Engineering

作者： Hao Song Chuangxin Fang Zhengguo Huang Yuming Lin Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology China

ISBN: (纸本)9781450384957

the knowledge graph is the essential infrastructure of plenty of intelligent Web applications. there are various types of knowledge graphs designed and deployed to make the applications smarter during the past decade. However, the increasing amount of product data brings new challenges to the query and retrieval of products. In this work, we present a product knowledge representation method that converts text into IDs to reduce the data transmission time. In order to decrease the retrieval time, we transform the join operation in the relational database into a matrix operation. We propose a pipeline query optimization strategy under GPUs to speed up the execution of the query. To evaluate the performance, we compare our method with the state-of-the-art RDF engine RDF-3X and gStore on the large-scale product datasets. the experimental results show that our approach can significantly improve the efficiency of the SPARQL query.

关键词： College students Chinese sex education Correlation analysis for sex issues Sex-related knowledge

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A Comparative Study on CNN Techniques for Cancer Detection in Medical Images

A Comparative Study on CNN Techniques for Cancer Detection i...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Dev Baloni Shashi Kant Verma Vir Madho Singh Bhandari Uttarakhand Technical University Dehradun Uttarakhand India Vir Madho Singh Bhandari UTU Dehradun India

ISBN: (纸本)9781665439718

Cancer is an aggressive disease. Automated techniques based on Convolutional Neural Networks (CNN) are used extensively to predict and diagnose the precise form of cancer, helping to save time and reduce errors. Deep Learning (DL) CNN techniques provide different probabilistic and statistical approaches, allowing intelligent computers to recognize and identify patterns in data sets based on repeated prior experiences. this research paper outlines the evolving CNN technology in cancer diseases by applying deep learning algorithms to cancer data sets. In this paper, we read many techniques about CNN to effectively detect cancer. For this work, we compare main two techniques i.e. Hyper-dense Connected CNN(HDCCNN) and Automatic Tumor segmentation Using Convolutional Neural Networks(ATSCNN).In HDCCCN we work on layers for fast processing and in ATSCNN, it covers a smaller size with little filter size .It gives more profound design. In addition, the research covers a comparative view of past successfully completed researches on cancer disease based on CNN. the resulting outcome shows the best featured CNN model that supports the effective detection of cancer occurrence.

关键词： Deep learning Computers Probabilistic logic Market research Feature extraction pattern recognition Convolutional neural networks

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