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检索条件"任意字段=5th International Conference on Learning and Teaching in Computing and Engineering, LaTiCE 2017"

共 328 条记录，以下是121-130 订阅

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Accurate Exercise Recommendation Based on Multidimension -al Feature Analysis 5

Accurate Exercise Recommendation Based on Multidimension -al...

引用

2020 5th international conference on Intelligent computing and Signal Processing, ICSP 2020

作者： Zhang, Shu Cai, Jiaqi Zhuge, Bin Dong, Ligang Jiang, Xian School of Information and Electronic Engineering Zhejiang Gongshang University Zhejiang Province Hangzhou310018 China

With the rapid development of the Internet, online learning has developed rapidly, and learners are increasingly demanding the personalization and practicality of exercise. Facing the massive exercises in the online learning platform and the online examination system, how to choose the exercises that can be targeted and can make up for the knowledge loopholes has become a hot topic in the field of personalized recommendation of current teaching resources. In view of the fact that learners have a variety of learning features, and there are a large number of online exercises, various types and varying degrees of difficulty, this paper proposes a precise recommendation method based on Multidimensional features analysis for exercises. It quantifies the potential relationship between the learner and the exercise from three aspects: the heat of the exercise itself, the relevance of the knowledge among the exercises, and the similarity of the learner's style, using the linear combination and the learning to Rank method to build the recommendation model to match learner and exercises exactly. Experiments show that the mean average precision of the ReMFA method reaches 36.8% when recommending five candidate exercises, which can provide learners with personalized exercise recommendation services, thereby improving the learner's learning efficiency. © 2019 Published under licence by IOP Publishing Ltd.

关键词： E-learning

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Unsupervised Pixel-level Crack Detection Based on Generative Adversarial Network 20

Unsupervised Pixel-level Crack Detection Based on Generative...

引用

5th international conference on Multimedia Systems and Signal Processing, ICMSSP 2020

作者： Duan, Lijuan Geng, Huiling Pang, Junbiao Zeng, Jun Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China Natl. Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China

ISBN: (纸本)9781450377485

Automatic crack detection from pavement images is an import research field. Meanwhile, crack detection is a challenge task: (1) manual labels are subjective because of low contrast between crack and the surrounding pavement and heavy workload;(2) the excessive dependence of supervised deep learning training on labels. To address these problems, we present an unsupervised method for learning mapping to translate crack images to binary images based on generative adversarial network. We introduce the cyclic consistent loss to increase accuracy of crack localization. Eight residual blocks connected convolutional neural network for feature extraction is used as generator and a 5-layer fully convolutional network is used as discriminator. We analyze the proposed framework and provide qualitative and quantitative comparison. the experimental results show that the proposed method achieves a better performance than several existing methods. © 2020 ACM.

关键词： Crack detection

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Multi-task learning with deep dual-path network for facial attribute recognition 9

Multi-task learning with deep dual-path network for facial a...

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9th international conference on computing and Pattern Recognition, ICCPR 2020

作者： Lai, Xinyu Chen, Si Wang, Da-Han Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450387835

Facial attribute recognition is a popular and challenging research topic in computer vision. In the traditional deep learning based attribute recognition methods, the mid-level network features and the differences between attribute groups are not fully explored. To solve the above problem, a deep dual-path network is proposed for facial attribute recognition. In the multi-task learning framework, two sub-networks are employed to respectively extract the features of two attribute groups, i.e., local attributes and global ones, and designed with both different scale images and different depth networks. Furthermore, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the class imbalance problem for facial attribute recognition. Experimental results on the challenging CelebA dataset show that the proposed method achieves the better performance than state-of-the-art methods. © 2020 ACM.

关键词： Deep learning

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Review on Evaluation Criteria of Machine learning Based on Big Data 4

Review on Evaluation Criteria of Machine Learning Based on B...

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2019 4th international Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Wu, Hao Jun Meng, Fan Inner Mongolia Normal University Hohhot Inner-Mongolia010020 China

In machine learning in the context of big data, It is indispensable link for the evaluation results and indicators of prediction results. Although it is not as important as the core algorithm, it plays a role in related applications and research fields. the role of horizontal comparison of learning algorithms. the article briefly summarizes and reviews the research results of basic machine learning algorithm evaluation standards. Different evaluation methods are applied to different machine learning problems, and the trends and difficulties of machine learning evaluation methods in the future are analyzed. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Big data

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Mobile Based Augmented Reality to Improve learning of Volcanology for High School Students 5

Mobile Based Augmented Reality to Improve Learning of Volcan...

引用

5th international conference on computing, engineering, and Design (ICCED)

作者： Akhmalludin, Hali Ayu, Media Anugerah Sampoerna Univ Dept Comp Sci Fac Engn & Technol Jakarta 12780 Indonesia

ISBN: (纸本)9781728120942

Augmented Reality (AR) has become popularly used in various fields nowadays. One of the areas that utilized this recent technology is education. this paper presents a study on the use of Augmented Reality to improve students' performance in learning a complicated subject. In this paper, a proposed application was created as a tool for teaching and learning volcanology as part of Geography subject where many students showed weak performance in it. An experimental study and user-based testing were performed to see whether the proposed AR application could really benefit the teaching and learning toward increasing students' understanding of the subject. Pre-test and post-test were conducted and the results are encouraging. the tests' results revealed that the proposed mobile-based AR application is more proficient in improving students' performance than the traditional method.

关键词： Augmented Reality Volcanology mobile-based AR AR in education Vuforia Unity

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Research on programming courses teaching based on blended learning 2019

Research on programming courses teaching based on blended le...

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5th international conference on Frontiers of Educational Technologies, ICFET 2019, held jointly with its Workshop: 4th international conference on Knowledge engineering and Applications, ICKEA 2019

作者： Shi, Yaqing Huang, Song Zheng, Changyou Army Engineering University of PLA No. 1 Hai Fu Lane Nanjing China

ISBN: (纸本)9781450362931

the teaching of programming courses is an important part of the teaching of computer, which is related to the improvement of students' practical computer application ability. How to improve the teaching effect of programming courses has always been a big problem for educators. Starting from the current situation of the teaching of programming courses, this paper introduces blended learning to realize combine aspects of online and face-to-face instruction, and explains in detail why blended learning should be introduced into programming courses and how to introduce blended learning. teaching practice shows that blended learning meets the teaching requirements of programming courses, can effectively improve the teaching effect, and has broad application prospects. © 2019 Association for computing Machinery.

关键词： teaching

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Research on blended learning of equipment application courses in military academies 2019

Research on blended learning of equipment application course...

引用

5th international conference on Frontiers of Educational Technologies, ICFET 2019, held jointly with its Workshop: 4th international conference on Knowledge engineering and Applications, ICKEA 2019

作者： Wang, Kai Yin, Caihua Song, Chunhui Communication NCO Academy Army Engineering University of PLA No.1 Linyuan Shapingba District Chongqing China

ISBN: (纸本)9781450362931

the traditional teaching mode of vocational and technical education for sergeancy in military academies is difficult to meet the requirements of the serve ability training under the new establishment. In order to optimize the teaching approach and improve the teaching quality, the blended learning mode equipment application courses is proposed in this paper. Combining the blended learning theory with the practical teaching experience, the designing of teaching goals, teaching environment construction, teaching organization and teaching effect evaluation of blended learning is studied respectively. © 2019 Association for computing Machinery.

关键词： E-learning

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A Biomedical Motif for teaching Applied Deep learning 5

A Biomedical Motif for Teaching Applied Deep Learning

引用

5th Experiment at international conference, exp.at 2019

作者： Wolfer, James Indiana University South Bend South BendIN United States

ISBN: (纸本)9781728136370

Deep learning, the revival of artificial neural networks, is having a significant impact in computing, engineering, and medicine among other fields. In an effort to expose students to domain-specific knowledge outside their discipline, and to focus attention on real-world, socially relevant projects, this work in progress describes the background and infrastructure for embedding an Applied Deep learning class in a medical motif or context. © 2019 IEEE.

关键词： Deep learning

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"BDD Assemble!": A Paper-Based Game Proposal for Behavior Driven Development Design learning 1

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1st IFIP TC 14 Joint international conference on 18th IFIP international conference on Entertainment computing (IFIP-ICEC) / 5th international Joint conference on Serious Games (JCSG)

作者： Sarinho, Victor T. Univ Estadual Feira de Santana Lab Entretenimento Digital Aplicado LEnDA Ave Transnordestina S-N Feira De Santana BA Brazil

ISBN: (数字)9783030346447

ISBN: (纸本)9783030346447

Game-based learning represents a promising alternative to teach computing in higher education. this paper presents "BDD Assemble!", a paper-based game proposal for teaching Behavior Driven Development (BDD) competences. For this, the proposed game and the evaluation approach with software engineering students are described. As a result, a simple, interactive and colaborative game was provided, able to teach BDD concepts in a practical, competitive and fun way.

关键词： Behavior Driven Development Educational game Paper-based game

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One Class Support Vector Machine Active learning Method for Unbalanced Data 12

One Class Support Vector Machine Active Learning Method for ...

引用

12th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC 2020

作者： Dai, Jin Liu, Haichuan Zhang, Qirui School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781728184487

Active learning is an important method to efficiently reduce the labeled sample size in large-scale machine learning. As the large amount of data presents the nature of imbalance, the study of active learning methods for corresponding unbalanced datasets highlights important practical significance. there is a problem with the current algorithm. the initialization of active learning becomes difficult with the increase of the unbalanced degree, which causes more labels waste. this paper proposes a one-class support vector machine active learning (OCSVM-AL) method for this problem. Firstly, we obtain the structure information of the sample set by k-means clustering. then, the OCSVM-AL is used on each cluster. For each cluster, OCSVM-AL iteratively marks the sample from the cluster and adds the marked sample to the marked sample pool. A one-class support vector machine model is trained by using majority class samples in the marked sample pool. the trained model is used to predict the unlabeled samples in this cluster, and the labels of unlabeled samples is obtained. By the results of each cluster, all sample labels are obtained. Simulation experiments proved that this method is an effective active learning method and can significantly reduce the labeled sample size. © 2020 IEEE.

关键词： Support vector machines

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