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5th international conference on Information and Education Innovations, ICIEI 2020

ISBN: (纸本)9781450375757

the proceedings contain 24 papers. the topics discussed include: participating in art therapy guidance;study on online teaching of 'rail transit passenger transport organization' course in the context of epidemic prevention and control;a hybrid teaching reform scheme of compiler technology course based on engineering education certification;efficient recommendations for improving quality of teaching: face to face, distance-learning, and learners with disabilities;a unified solution to linear beam bending problems;online teaching platform based on big data recommendation system;using EFL YouTube videos as supplementary resource for teaching vocabulary: a case study in secondary education;blockchain-based trusted achievement record system design;investigation of information literacy of undergraduate;investigating the requirements for building a blockchain- based achievement record system;function design strategies of university campuses in cold regions of china based on analysis of behavior patterns;and global competencies for engineering education: a comparative study of industry and academic perspectives.

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A hybrid teaching reform scheme of compiler technology course based on engineering Education 20

A hybrid teaching reform scheme of compiler technology cours...

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5th international conference on Information and Education Innovations, ICIEI 2020

作者： Wang, Na Li, Liping Shanghai Polytechnic University NO.2360 Jinhai RD Shanghai China

ISBN: (纸本)9781450375757

Combined with our school's orientation as an application-oriented undergraduate college, we have carried out theoretical and experimental teaching reform and exploration on compiler technology course. Considering the professional requirements of engineering education certification, with the help of various forms of online resources, we put forward a hybrid teaching reform scheme of compiling technology course. the test results show that teaching reform on this course has a great advantage in learning. © 2020 ACM.

关键词： engineering education

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Analysis of Online learning Time in Flipped classroom Based on MCMC 2020

Analysis of Online Learning Time in Flipped classroom Based ...

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5th international conference on Distance Education and learning, ICDEL 2020

作者： Yuan, Hongli Sun, Wenjuan Chen, Yan Anhui Xinhua University Hefei Anhui230031 China

ISBN: (纸本)9781450377546

Traditional classrooms focus on knowledge transfer and despise students' ability improvement. the flipped classroom teaching shifts from knowledge-centered to competency-centered. the flipped classroom teaching model has been adopted by more and more college teachers. In the process of flipped classroom teaching, the quality of students' online learning before class is related to the success or failure of the flipped classroom teaching process. Flip class online learning time is a specific time and not a probability. this paper uses the MCMC algorithm to analyze the online learning time of the flipped classroom, and analyzes the student learning time in the form of probability. © 2020 ACM.

关键词： Students

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Deep LSTM Transfer learning for Personalized ECG Anomaly Detection on Wearable Devices 5th

Deep LSTM Transfer Learning for Personalized ECG Anomaly Det...

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5th international conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Deng, Xin Zhou, Jianqiao Xiao, Bin Xiang, Xiaohong Yuan, Yi Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunication Chongqing400065 China Chongqing Key Laboratory of Image Recognition Chongqing University of Posts and Telecommunication Chongqing400065 China

ISBN: (纸本)9789811623356

Cardiovascular diseases are the main cause of global non-communicable deaths, accounting for about one-third of the total deaths in the world. Wearable devices are used for early monitoring and early prevention of cardiovascular diseases. Aimed at the low configuration, low power consumption and personalized characteristics of the wearable ECG equipment, a transfer learned with deep LSTM model is proposed in this work. the model is computational efficient and serves as the patient-specific ECG anomaly detector that can be used on the wearable devices. Based on the MIT-BIH open dataset, the experiment results show that the transfer learning model produces superior weighted F2 scores compared to the general model and the naive model for detecting ECG anomalies, such as the premature ventricular contractions and atrial premature beats. the transfer learning model proposed here provides a sensitive, personalized ECG anomaly detection mechanism that has the added benefit of light, quick and robust learning process to be used on wearable devices, which can meet the requirement of ECG monitoring for patients by wearable devices. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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Improving deep learning performance with missing values via deletion and compensation

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NEURAL computing & APPLICATIONS 2020年第17期32卷 13233-13244页

作者： Sanchez-Morales, Adrian Sancho-Gomez, Jose-Luis Martinez-Garcia, Juan-Antonio Figueiras-Vidal, Anibal R. Univ Politecn Cartagena Dept Tecnol Informac & Comunicac Plaza Hosp1 Edificio Cuartel Antigones Murcia 30202 Spain Univ Carlos III Madrid Dept Teoria Senal & Comunicac Avda Univ 30 Madrid 28911 Spain

Missing values in a dataset is one of the most common difficulties in real applications. Many different techniques based on machine learning have been proposed in the literature to face this problem. In this work, the great representation capability of the stacked denoising auto-encoders is used to obtain a new method of imputating missing values based on two ideas: deletion and compensation. this method improves imputation performance by artificially deleting values in the input features and using them as targets in the training process. Nevertheless, although the deletion of samples is demonstrated to be really efficient, it may cause an imbalance between the distributions of the training and the test sets. In order to solve this issue, a compensation mechanism is proposed based on a slight modification of the error function to be optimized. Experiments over several datasets show that the deletion and compensation not only involve improvements in imputation but also in classification in comparison with other classical techniques.

关键词： Missing values Imputation Classification Deep learning

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Emotion Classification of Text Based on BERT and Broad learning System 5th

Emotion Classification of Text Based on BERT and Broad Learn...

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5th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) international Joint conference on Web and Big Data

作者： Peng, Sancheng Zeng, Rong Liu, Hongzhan Chen, Guanghao Wu, Ruihuan Yang, Aimin Yu, Shui Guangdong Univ Foreign Studies Lab Language Engn & Comp Guangzhou 510006 Peoples R China South China Normal Univ Guangdong Prov Key Lab Nanophoton Funct Mat & Dev Guangzhou 510006 Peoples R China Guangdong Univ Technol Coll Comp Guangzhou 510006 Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China

ISBN: (纸本)9783030858964;9783030858957

Emotion classification is one of the most important tasks of natural language processing (NLP). It focuses on identifying each kind of emotion expressed in text. However, most of the existing models are based on deep learning methods, which often suffer from long training time, difficulties in convergence and theoretical analysis. To solve the above problems, we propose a method for emotion classification of text based on bidirectional encoder representation from transformers (BERT) and broad learning system (BLS) in this paper. the texts are input into BERT pre-trained model to obtain context-related word embeddings and all word vectors are averaged to obtain sentence embedding. the feature nodes and enhancement nodes of BLS are used to extract the linear and nonlinear features of text, and three cascading structures of BLS are designed to transform input data to improve the ability of text feature extraction. the two groups of features are fused and input into the output layer to obtain the probability distribution of each kind of emotion, so as to achieve emotion classification. Extensive experiments are conducted on datasets from SemEval-2019 Task 3 and SMP2020-EWECT, and the experimental results show that our proposed method can better reduce the training time and improve the classification performance than that of the baseline methods.

关键词： Text Emotion classification BERT Broad learning system Cascading structure

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Graph Representation learning: Foundations, Methods, Applications and Systems 21

Graph Representation Learning: Foundations, Methods, Applica...

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27th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD)

作者： Jin, Wei Ma, Yao Wang, Yiqi Liu, Xiaorui Tang, Jiliang Cen, Yukuo Qiu, Jiezhong Tang, Jie Shi, Chuan Ye, Yanfang Zhang, Jiawei Yu, Philip S. Michigan State Univ Comp Sci & Engn E Lansing MI 48824 USA Michigan State Univ Comp Sci & Engn Dept E Lansing MI 48824 USA Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Case Western Reserve Univ Cleveland OH 44106 USA Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Florida State Univ Tallahassee FL 32306 USA Florida State Univ Comp Sci Tallahassee FL 32306 USA Univ Illinois Chicago IL USA Univ Illinois Comp Sci Chicago IL USA

ISBN: (纸本)9781450383325

Graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be facilitated. Instead of designing hand-engineered features, graph representation learning has emerged to learn representations that can encode the abundant information about the graph. It has achieved tremendous success in various tasks such as node classification, link prediction, and graph classification and has attracted increasing attention in recent years. In this tutorial, we systematically review the foundations, techniques, applications and advances in graph representation learning. We first introduce the foundations on graph theory and graph Fourier analysis. We then cover the key achievements of graph representation learning in recent years. Concretely, we discuss the six topics: 1) network embedding theories and systems;2) foundations of graph neural networks (GNNs);3) CogDL toolkit for GNNs;4) scalable GNNs;5) self-supervised learning in GNNs and 6) heterogeneous graphs and heterogeneous GNNs. Finally, we will introduce the applications of graph representation learning with a focus on recommender systems.

关键词： heterogeneous graphs graph neural networks graph representation learning

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Computer System Capability Hardware Course Group Construction Based on OBE 2020

Computer System Capability Hardware Course Group Constructio...

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5th international conference on Distance Education and learning, ICDEL 2020

作者： Yuan, Hongli Sun, Wenjuan Chen, Yan Anhui Xinhua University Hefei Anhui230031 China

ISBN: (纸本)9781450377546

the cultivation of system ability has a great impetus to the construction of computer specialty courses. the cultivation of system-oriented capabilities is not only an urgent need for social development, but also a demand for computer courses in colleges and universities. the computer system's ability development enables students to be in an advantageous position in social competition. At present, the computer curriculum system lacks the connection between the courses. the teaching content lacks engineering approaches. the teaching method lacks comprehensive teaching. Based on this problem, this paper proposes a method for building a computer system capability hardware course group based on OBE. this method carries out teaching reform from three aspects: teaching content, teaching method and teaching effect evaluation. © 2020 ACM.

关键词： teaching

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Application of Machine learning on Google Earth Engine to Produce Landslide Susceptibility Mapping (Case Study: Pacitan)

Application of Machine Learning on Google Earth Engine to Pr...

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5th Geomatics international conference 2020, GeoICON 2020

作者： Ilmy, Hafsah Fatihul Darminto, Mohammad Rohmaneo Widodo, Amien Geomatics Department National Cheng Kung University No.1 University Road Tainan City701 Taiwan Department of Geomatics Engineering Sepuluh Nopember Institute of Technology Kampus ITS Keputih Sukolilo Surabaya60111 Indonesia Department of Geophysics Engineering Sepuluh Nopember Institute of Technology Kampus ITS Keputih Sukolilo Surabaya60111 Indonesia

According to the Indonesian Disaster Management Agency (BNPB), Indonesia's losses due to landslides were estimated around hundreds of billion rupiah in 2017. Making landslide as one of the catastrophes with the greatest risk of loss and leaving a couple regions prone to landslides in Indonesia, Pacitan region is one of them. Landslide delineation therefore represents a particularly beneficial application of evolving research trend in disaster reduction, especially for the vulnerable region. In the present times of open-access satellite data, cloud computing and machine-learning algorithms is frequently used for disaster prevention monitoring. By employing Google Earth Engine, this study focuses on the susceptibility of landslide occurrence using a random forest machine-learning framework applied to digital topographic data such as elevation, slope and aspect as the independent variables and landslide inventory data obtained from Ministry of Energy and Mineral Resources Republic of Indonesia as the dependent variable. this study data sets composed from 1000 random points in Pacitan region with 70:30 ratio for training and testing sample points. the model produced good result, with overall accuracy values of 0.94, kappa values of 0.79 and 0.80 for AUC value. this model also showed that elevation is the most important variable in the landslide susceptible area. the results of this study can be used to evaluate the potential future impacts of landslide and help to optimize the management of disaster reduction in the region of Pacitan. © Published under licence by IOP Publishing Ltd.

关键词： Engines

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Research on the Implementation Path of Ideological and Political theory Infiltrating the Science and Technology Course teaching 2020

Research on the Implementation Path of Ideological and Polit...

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5th international conference on Distance Education and learning, ICDEL 2020

作者： Yang, Yi Yu, Dekuang Gao, Yang Biomedical Engineering School Southern Medical University Guangdong Guangzhou510515 China

ISBN: (纸本)9781450377546

the integration of ideological and political theory with science and engineering courses is a difficult point in higher education. After analysis of the characteristics of science and engineering courses, this paper put forward the teaching objectives of multisystem target integration, and designed specific measures from the aspects of curriculum planning and design, teaching content implantation, construction of curriculum model, development of teaching resources, teaching mode process, innovation of teaching methods, emphasizing the integration of craftsman spirit into science and technology courses. this paper provided an effective implementation path plan and practical suggestions for the development and comprehensive promotion of ideological and political education in science and engineering courses. © 2020 ACM.

关键词： Curricula

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