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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7159 条记录，以下是4511-4520 订阅

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Recurrent Graph Neural Networks for Text Classification

Recurrent Graph Neural Networks for Text Classification

引用

IEEE international conference on Software engineering and Service Sciences (ICSESS)

作者： Xinde Wei Hai Huang Longxuan Ma Ze Yang Liutong Xu School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Text classification is an essential and classical problem in natural language processing. Traditional text classifiers often rely on many human-designed features. With the rise of deep learning, Recurrent Neural Networks and Convolutional Neural Networks have widely applied into text classification. Meanwhile, the success of Graph Neural Networks (GNN) on structural data has attracted many researchers to apply GNN to traditional NLP applications. However, when these methods use the GNN, they commonly ignore the word order information of the sentence. In this work, we propose a model that uses a recurrent structure to capture contextual information as far as possible when learning word representations, which keeps word orders information compared to GNN-based networks. then, we use the idea of GNN's message passing to aggregate the contextual information and update the word hidden representation. Like GNN's readout operation, we employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the critical components in texts. We conduct experiments on four widely used datasets, and the experimental results show that our model achieves significant improvements against RNN-based model and GNN-based model.

关键词： Deep learning Recurrent neural networks Message passing Text categorization Natural language processing Context modeling Software engineering

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Drawing Order Recovery based on deep learning 11

Drawing Order Recovery based on deep learning

引用

11th international conference on Advanced Computational Intelligence (ICACI)

作者： Zhang, Rui Chen, Jinlong Yang, Minghao Guilin Univ Elect Technol Guangxi Key Lab Trused Software Guilin Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781538677322

Humans have the ability to recover the order from static handwritten images, after a large amount of data training, the machine may learn some patterns in the training data to imitate or learn a certain skill similar to humans. To overcome the problem of sequence recovery of static image strokes, this paper proposes a stroke recovery method based on deep convolutional neural network model. In the model training phase, by using the two-dimensional static handwritten image, the process of writing a font is convert into three channels includes strokes that have been written, possible positions of next strokes, and the completed font, and state of the input sample are quantified. In the recovery phase, the restored font is preprocessed to obtain the stroke segments of the font, and the trained model is used to evaluate the sequential combination of different stroke segments, so as to obtain the correct stroke order. With no more than one hundred of characters' writing experiences, the proposed method performs robustly and competitively among multiwriter handwriting DOR tasks.

关键词： handwriting time series information deep learning order recovery Convolutional Neural Networks

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swATOP: Automatically Optimizing Deep learning Operators on SW26010 Many-Core Processor 19

swATOP: Automatically Optimizing Deep Learning Operators on ...

引用

48th international conference on Parallel Processing (ICPP)

作者： Gao, Wei Fang, Jiarui Zhao, Wenlai Yang, Jinzhe Wang, Long Gan, Lin Fu, Haohuan Yang, Guangwen Tsinghua Univ Beijing Peoples R China Imperial Coll London London England Syst Dept Baidu Beijing Peoples R China Tsinghua Univ Natl Supercomp Ctr Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781450362955

Achieving an optimized mapping of Deep learning (DL) operators to new hardware architectures is the key to building a scalable DL system. However, handcrafted optimization involves huge engineering efforts, due to the variety of DL operator implementations and complex programming skills. Targeting the innovative many-core processor SW26010 adopted by the 3rd fastest super-computer Sunway TaihuLight, an end-to-end automated framework called swATOP is presented as a more practical solution for DL operator optimization. Arithmetic intensive DL operators are expressed into an auto-tuning-friendly form, which is based on tensorized primitives. By describing the algorithm of a DL operator using our domain specific language (DSL), swATOP is able to derive and produce an optimal implementation by separating hardware-dependent optimization and hardware-agnostic optimization. Hardware-dependent optimization is encapsulated in a set of tensorized primitives with sufficient utilization of the underlying hardware features. the hardware-agnostic optimization contains a scheduler, an intermediate representation (IR) optimizer, an auto-tuner, and a code generator. these modules cooperate to perform an automatic design space exploration, to apply a set of programming techniques, to discover a near-optimal solution, and to generate the executable code. Our experiments show that swATOP is able to bring significant performance improvement on DL operators in over 88% of cases, compared with the best-handcrafted optimization. Compared to a black-box autotuner, the tuning and code generation time can be reduced to minutes from days using swATOP.

关键词： Autotuning Deep learning Operators SW26010

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Development of Sightseeing Spot Recommendation System Considering Users' Visit frequency

Development of Sightseeing Spot Recommendation System Consid...

引用

international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Yudai Kato Kayoko Yamamoto Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

the present study aimed to design, develop, operate and evaluate a sightseeing spot recommendation system that can efficiently support tourists while considering their visit frequencies. this system was developed by integrating social networking service (SNS), Web-geographic information systems (GIS) and recommendation system. the system recommends sightseeing spots to users with different visit frequencies, adopting two recommendation methods (knowledge-based recommendation and collaborative recommendation methods). Additionally, the system was operated for 6 weeks in Kamakura City, Kanagawa Prefecture, Japan, and the total number of users was 61. Based on the results of the web questionnaire survey, the usefulness of the system when users travel or plan tours was high, and the recommendation function of sightseeing spots, which is an original function, has received mainly good ratings. From the results of access analyses of users' log data, the total number of sessions in this system was 329, 77% used mobile devices, and smartphones were used most frequently. therefore, it is evident that the system has been used by different types of devices just as it was designed for, and that the system has been used according to the purpose of the present study, which is to support the sightseeing activities of users.

关键词： Collaboration Knowledge based systems Mathematical model History Urban areas Time-frequency analysis Geographic information systems

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Rain-Density Squeeze-and-Excitation Residual Network for Single Image Rain-removal 11

Rain-Density Squeeze-and-Excitation Residual Network for Sin...

引用

11th international conference on Advanced Computational Intelligence (ICACI)

作者： Hu, Xipu Wang, Wenhao Pang, Cheng Lan, Rushi Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Intelligent Proc Comp Image & Gra Guilin Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Guilin Univ Elect Technol Natl & Local Joint Engn Res Ctr Satellite Nav & L Guilin Peoples R China

ISBN: (纸本)9781538677322

the removal of rain streaks in a single image is an extremely challenging task due to the uneven rainfall density in the image. Methods based on deep learning have boosted the performance of rain removal significantly in recent years. However, most of these methods have a certain demand for different density of rain marks in the training data, which prevent them to further improve the performance in some outdoor scenarios. In this paper, we present a novel Rain-Density Squeeze-and-Exdtation residual network (RDSER-NET), which adopts the squeeze-and-excitation blocks into the ResNet framework. the proposed network remove rain streaks based on single density of rain marks in the training data, reducing the limitation of multi-density proposals and achieving better results. Extensive experiments on synthetic and real datasets demonstrate that the proposed network outperform the recent state-of-the-art methods greatly.

关键词： rain-removal single image computer vision systems deep network framework

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Motion Tracking Detection and Tracking Technology Based on Aerial Video

Motion Tracking Detection and Tracking Technology Based on A...

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international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Yiming Xu Haifeng Gu Quixia Dai Guan Lu Juping Gu Liang Hua School of Electrical Engineering Nantong University Nantong Jiangsu China Nantong University Nantong China

Aiming at the problems of complex outdoor background lighting conditions, unsatisfactory moving target features, and high degree of background fusion of wind turbines with large target background, the proposed wind turbine detection and tracking method based on deep learning convolutional neural network is proposed. this paper uses migration learning technology to use a pre-trained SSD model trained with COCO data sets and makes a dedicated localized wind turbine dataset . through data enhancement, the neural network model can effectively improve the accuracy of wind turbine identification and the average accuracy remains above 96%. the fast recognition speed enables detection and tracking of the fan target in aerial video quickly and accurately.

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Mesoscale Ocean Eddy Detection Using High-Resolution Network

Mesoscale Ocean Eddy Detection Using High-Resolution Network

引用

international conference on Awareness Science and Technology (iCAST)

作者： Xirong Lu Shaoxiang Guo Meng Zhang Junyu Dong Xueen Chen Xin Sun College of Information Science and Engineering Ocean University of China QingDao China

ISBN: (数字)9781728191195

ISBN: (纸本)9781728191201

Ocean eddies are common phenomena of ocean water movement. they have a significant impact on the physical properties of marine hydrology, marine chemistry and marine biological environment. Current study of ocean eddy detection has already become one of the most active research areas in physical oceanography. Recent trend in eddy detection attempts to employ deep learning methods, but it is still in the early stage. Accordingly, this work takes the advantage of the rapid development of deep learning to improve the current result on ocean eddy detection. We apply the improved and reliable high-resolution representation network to eddy detection and classification from Sea Surface Height (SSH) maps based on semantic segmentation. this high-resolution network can aggregate representations from all the parallel convolutions and repeat the operation of feature fusion. It can therefore maintain and eventually produce high-resolution representations throughout the whole feature extraction process. We then effectively combine the segmentation result with a CascadePSP module and obtain more accurate results than those produced by existing approaches. Our work shows a good performance based on the sea surface height data, which also verifies the application value of deep learning technology in the field of ocean monitoring and data mining.

关键词： Oceans Image segmentation Semantics Sea surface Deep learning Training Sun

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Technology-Needs Assessment data Analysis using PCA for the Design of a WLAN-based Hybrid Classroom Response and learning Management System Android Application 11

Technology-Needs Assessment Data Analysis using PCA for the ...

引用

11th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019

作者： Ibarra, Joseph Bryan Vincent Caya, Meo Paglinawan, Arnold Turingan, Jerry Bentir, Sarah Alma Joseph Monta, Jim Mocon, Jumarie Yumang, Analyn Sabino, Lilibeth Teogangco, Mariciel Mapúa University School of Electrical Electronics and Computer Engineering Manila1002 Philippines Mapua University Senior High School Department Manila1002 Philippines University of Perpetual Help DALTA College of Engineering Molino Cavite4102 Philippines

ISBN: (纸本)9781728130446

the influence of technology in the evolution of modern-day education grows significantly as more educational institutions attempt to improve the quality of education. this study aims to develop a hybrid of a classroom response system and learning management system web and android applications without the use of the internet. A technology-needs assessment (TNA) was administered on 213 high school students. the TNA questionnaire was comprised by 16 closed-ended questions, 15 of which required Likert scale-based responses and one required a yes/no response. the TNA data was analyzed with the help of principal component analysis (PCA). the results of the PCA became the basis for the configuration of a wireless local area network of devices connected to a central server through a wireless router dedicated for the implementation of the web and android applications designed for the needs identified by the study. the web and android applications were developed using a test-driven development (TDD) approach. the system configuration was based on the system capacity requirements to enable simultaneous wireless connection of up to 100 mobile devices. Two groups of high school students were identified by the school to be part of the field testing of the web/android app since these students were of significantly similar academic standing in their respective classes. the study group (n=7) used the web/android app in their statistics class and a control group (n=10) were subjected to the traditional classroom set-up. the field testing lasted for two weeks covering various teaching and learning activities including exams. the exam results were subjected to Analysis of Variance using two-sample F-test and t-test. the results show that at 5% significance level, a p-value of 0.0026 was obtained, which indicates that the means between the summative test results data sets are significantly different. the performance of the students in the study group is significantly different from the

关键词： Students

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Modelling of Taxi Dispatch Problem Using Heuristic Algorithms 11th

Modelling of Taxi Dispatch Problem Using Heuristic Algorithm...

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11th international conference on Multimedia and Network Information Systems (MISSI)

作者： Adamczyk, Mateusz Krol, Dariusz Wroclaw Univ Sci & Technol Fac Comp Sci & Management Wroclaw Poland Wroclaw Univ Sci & Technol Dept Informat Syst Fac Comp Sci & Management Wroclaw Poland

ISBN: (纸本)9783319986784;9783319986777

Taxi routing is a complex task that involves the pickup and delivery by a fleet of vehicles in a specified time, taking into account many parameters and criteria. this paper describes issues related to this problem. It proposes a formal description of the problem and a goal function using a wide variety of criteria. three heuristic algorithms: Ant Colony Optimization, Artificial Bee Colony and Genetic Algorithm are selected for testing. As a result three variants of these algorithms are implemented: Ant Colony System (ACS), Predict and Select - Artificial Bee Colony (PS-ABC) and Genetic Algorithm (GA) to conduct a survey. Operators used in the algorithms are adapted to a taxi dispatch problem. the analysis is performed in order to find the best parameters for the algorithms according to the data input. Finally, the efficiency of the algorithms are compared in order to determine the best algorithm. In several experiments the Ant Colony System outperforms any other algorithm presented here with respect to taxi working time currently in service.

关键词： Evolutionary computation Real world applications Vehicle routing Collective decision Reinforcement learning Fleet management

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Cross-Domain Fault Diagnosis Method for Rotating Machinery Based on Multi-Representation Adaptation Neural Network

Cross-Domain Fault Diagnosis Method for Rotating Machinery B...

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Prognostics and System Health Management (PHM-Jinan), international conference on

作者： Kaifeng Zhang Guannan Cao Kaibo Zhou Jie Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Hydropower and Information Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728151816

ISBN: (纸本)9781728151823

the success of the fault diagnosis method based on deep learning depends on two conditions: 1) the training and test data sets must come from the same data distribution. 2) A large amount of labeled data with fault information is required. However, the working environment of rotating machinery is usually harsh and complex, the speed and load will change constantly, and it is very difficult to obtain plenty of labeled data with fault information. To solve these problems, a cross-domain rotating machinery fault diagnosis method is proposed. Firstly, the vibration signal of time series is transformed into a time-frequency image by continuous wavelet transform. then the multi-representation adaptation network model is used for cross-domain fault diagnosis. Finally, the effectiveness of the proposed method is verified by six transfer fault diagnosis tasks.

关键词： Fault diagnosis Time-frequency analysis Task analysis Machinery Feature extraction data mining Continuous wavelet transforms

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