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检索条件"任意字段=21st International Conference on Distributed Computing and Intelligent Technology"

共 1610 条记录，以下是821-830 订阅

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Aggregations of Subjective Probabilities Based on Logical Information Source Model and of Possibilistic CFs Using Dempster's Rule

Aggregations of Subjective Probabilities Based on Logical In...

引用

international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Koichi Yamada Nagaoka University of Technology Nagaoka Niigata JAPAN

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

The paper reviews an aggregation of multiple possibilities for a hypothesis based on a logical information source model proposed recently, and applies the approach to a new aggregation of subjective probabilities. It also recalls an old and controversial uncertainty representation called Certainty Factors (CFs), and reinterprets them as another representation of possibilities. Then, a new aggregation rule of CFs, or equivalently a new combination rule of possibilities is proposed, which employs Dempster's rule of combination as well as uncertainty transformation and approximation between possibility distributions and mass functions.

关键词： Probabilistic logic Uncertainty Mathematical model Possibility theory Risk analysis Bayes methods Analytical models

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Learning Data Conditions for Resolution Improvement Using UAV Data

Learning Data Conditions for Resolution Improvement Using UA...

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international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Kai Matsui Hikaru Shirai Yoichi Kageyama Hiroshi Yokoyama Graduate School of Engineering Science Akita University Akita Japan Center for Information Technology and Management Akita University Akita Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

This study investigates a resolution improvement method using the visible range and its band ratio of remote sensing data. In this paper, we examined the conditions of learning data to improve the accuracy of the resolution improvement method through the band ratio of unmanned aerial vehicle data. Results obtained using the dataset with a large standard deviation had greater accuracy than those using the dataset with a small standard deviation for 72.2 % of the band ratio in the test data.

关键词： standards Water quality Unmanned aerial vehicles Remote sensing Training data Training Lakes

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Hash hardware generator based on mutual perturbed logistic map 21

Hash hardware generator based on mutual perturbed logistic m...

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21st IEEE international conference on High Performance computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Luo, Yuling Han, Shuming Liu, Junxiu Liang, Zhewei Yang, Su School of Electronic Engineering Guangxi Normal University Guilin541004 China School of Computing Engineering and Intelligent Systems Ulster University BT48 7JL United Kingdom

ISBN: (纸本)9781728120584

In this paper, a hash code generator based on logistic map mutual perturbation is implemented on the Field Programmable Gate Array (FPGA) device. The performance of chaotic system under a finite precision is improved by the perturbation method, and a novel hash code generation scheme is constructed by using the improved chaotic system. Due to the good randomness of the perturbation scheme, the generated hash codes have a uniform distribution when implemented on FPGA. The proposed hash generator is sensitive to the input text message, has confusion/diffusion characteristics and excellent anti-collision capabilities. Experimental results show that the proposed hash generator has a lower resource occupation than existing hash hardware systems on FPGAs. It can be used for real applications, especially for these with low resource usage requirements. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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An apache spark-based platform for predicting the performance of undergraduate students 21

An apache spark-based platform for predicting the performanc...

引用

作者： Mai, Thong Le Do, Phat Thanh Chung, Minh Thanh Thoai, Nam Faculty of Computer Science and Engineering Ho Chi Minh University of Technology Viet Nam

ISBN: (纸本)9781728120584

Nowadays, Education Data Mining (EDM) plays a very important role in higher education institutions. Plenty of algorithms have been employed to measure student's GPA in the next semester's courses. The results can be used to early identify dropout students or help students choose the elective courses which are appropriate for them. The most widely used methods are machine learning, however, the problem is the accuracy which can be changed from dataset to dataset. More importantly, the performance of prediction models can be affected by the characteristic of dataset associated with the applied model. In this paper, we build a distributed platform on Spark to predict missing grades of elective courses for undergraduate students. The paper compares several methods that are based on the combination of Collaborative Filtering & Matrix Factorization (namely Alternative Least Square). We evaluate the performance of these algorithms using a dataset provided by Ho Chi Minh University of technology (HCMUT). The dataset consists of information about undergraduate students from 2006 to 2017. Depending on the characteristics of our dataset, the paper highlights that Alternative Least Square with non-negative constraint achieves the better results than others in comparison. © 2019 IEEE.

关键词： Forecasting

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stacking-based ensemble learning on low dimensional features for fake news detection 21

Stacking-based ensemble learning on low dimensional features...

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作者： Li, Songqian Ma, Kun Niu, Xuewei Wang, Yufeng Ji, Ke Yu, Ziqiang Chen, Zhenxiang School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9781728120584

With the age of incoming of self-media, everyone can be the author of the content in the media age of big data. This has caused a mass of fake news appearing in the network. Authors of these fake news will mislead the public by spreading and it will bring economic and social benefits. Existing work focuses on using the various types of features of the article in the hope that a way to accurately identify fake news can be found, but this undermines their universality. In this paper, we propose a pipeline that combines preprocessing, feature extraction and model fusion for a more accurate and automated prediction. Specially we fusion of latent semantic analysis (LSA) and ensemble learning model results using stacking. Experimental analysis of real-world data demonstrates that our pipeline achieves higher accuracy than existing approaches. © 2019 IEEE.

关键词： Pipelines

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Classification of Worries and Consultations with School Refusal students Using Machine Learning

Classification of Worries and Consultations with School Refu...

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international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Rikuto Ishikura Masahiro Takeda Shino Iwashita Graduate School of Bionics Computer and Media Sciences Tokyo University of Technology Tokyo Japan School of Computer Science Tokyo University of Technology Tokyo Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

This study proposes a text classification method for the automatic tagging of posts on the an Internet bulletin board system for school refusal students. Four tags are defined on the basis of the reasons for truancy as follows: Friend, Teacher, Family, and study. A classifier is generated for each tag, and multiple tags are assigned to a sentence. The results of classifier verification showed that the accuracy of each classifier was more than 70%, and the highest was more than 85%. However, the precision was low for the classifiers other than Family. Among the solutions, such as reviewing the dataset and changing the vectorizer, we will choose the method that is appropriate for each classifier.

关键词： Training Tagging Machine learning Annotations Predictive models Maintenance engineering Internet

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Dynamic multi-objective optimization model for forest spatial structure with environmental detection mechanism 21

Dynamic multi-objective optimization model for forest spatia...

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作者： Chi, Peng Zhu, Kaiwen Li, Jianjun Ai, Wei Huang, Jing Qing, Dongsheng National Supercomputing Center in Changsha Changsha Hunan410082 China Central South University of Forestry and Technology Changsha Hunan410082 China

ISBN: (纸本)9781728120584

Dynamic multi-objective optimization problem is one of the most current popular research hotspots and faces great difficulties. In this work, based on the multiple objectives of structural optimization of the management space of forest ecosystems, a dynamic multi-objective optimization model of forest spatial structure is constructed by using the variation of forest growth with time. In order to make the model solve the problem more efficiently, an algorithm which focuses on solving multi-objective optimization problems is applied with a new environment detection mechanism. Compared with two multi-objective optimization algorithms PDMIOA and dDMSMOPSO, four test functions i.e., FDA1, FDA3, dMOP1, and dMOP2, are used. The results show that the proposed algorithm is superior to PDMIOA and dDMSMOPSO in convergence performance, distribution performance, and tracking performance. We have also applied the model to the natural secondary forest stand of the Longhushan stateowned Forest Farm in South Dongting Lake, and analyzed the model output. The results show that after optimization, the forest structural indicators such as homogeneity index, mixed degree, competition index, health degree, spatial density index and open ratio, are obviously better than those without optimization. In addition, the forest structure is also greatly improved. The proposed dynamic multi-objective optimization model of forest spatial structure can provide both theoretical basis and technical support for intelligent forest management. © 2019 IEEE.

关键词： structural optimization

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Can technology Make a Difference to the Level of Engagement Within Large Classes in Engineering Education? A Case study in Sri Lanka 21st

Can Technology Make a Difference to the Level of Engagement ...

引用

21st international conference on Interactive Collaborative Learning (ICL) / 47th IGIP international conference on Engineering Pedagogy - Teaching and Learning in a Digital World

作者： Peramunugamage, A. Usoof, H. Dias, P. Univ Colombo Sch Comp Colombo Sri Lanka Univ Moratuwa Moratuwa Sri Lanka

ISBN: (纸本)9783030119355;9783030119348

Engineering education is oriented towards problem based and project-based education. In Sri Lanka, teachers of first-year engineering undergraduate courses have the unique challenge of fostering student engagement especially in large classes. This study concerns a large classroom using the Moodle virtual learning environment. Data was collected qualitatively through focus-group discussions with undergraduates with respect to accessing the Moodle Log and quantitatively via the Moodle records. An important finding in this study is that when students are used to traditional teaching methods, they face difficulties in accepting TEL techniques, unless the latter is made compulsory for some aspects. Hence teachers must use a blended-learning approach and gradually increase the use of TEL techniques.

关键词： Large classes Engineering education Engagement technology aided learning Pedagogy

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Digital Entrepreneurship: MOOCs in Entrepreneurship Education the Case of Graz University of technology 21st

Digital Entrepreneurship: MOOCs in Entrepreneurship Educatio...

引用

21st international conference on Interactive Collaborative Learning (ICL) / 47th IGIP international conference on Engineering Pedagogy - Teaching and Learning in a Digital World

作者： Vorbach, stefan Poandl, Elisabeth Korajman, Ines Graz Univ Technol Inst Gen Management & Org Graz Austria

ISBN: (纸本)9783030119355;9783030119348

Digital Entrepreneurship has gained substantial attention in theory and practice in the last years. In addition, new technological as well as pedagogical approaches have emerged. A special focus is set on massive open online courses (MOOCs) in entrepreneurship education. MOOCs are one of the strongest trends in online education and influence the content and procedure of teaching and learning. The paper contributes to a better understanding of necessary capabilities, chances and risks of using MOOCs as a new way of teaching entrepreneurship for engineers at Graz University of technology. It empirically investigates challenges and drivers for using MOOCs as a novel pedagogical concept. Results show that a lack of self-discipline to finish a MOOC as well as missing interaction with others are main hurdles compared to lectures with compulsory attendance at University. However, findings also reveal that MOOCs are flexible in terms of time and location, thus can add convenience in reaching education, particularly entrepreneurial education.

关键词： Entrepreneurship education Digital entrepreneurship MOOC

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Genetic Algorithms Using Neural Network Regression and Group Lasso for Dynamic Selection of Crossover Operators

Genetic Algorithms Using Neural Network Regression and Group...

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international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Masaya Kato Miho Ohsaki Kei Ohnishi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Graduate School of Science and Engineering Doshisha University Kyotanabe Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

We propose real-coded genetic algorithms that utilize a method for detecting dependency relationships between variables. The method consists of neural network regression and group lasso. The proposed genetic algorithms select an appropriate crossover operator based on dependency information between variables, which are obtained from past solution candidates. Simulation results using the CEC'13 benchmark functions show that the proposed algorithms outperform conventional real-coded genetic algorithms.

关键词： Genetic algorithms statistics Sociology Artificial neural networks Simulation Probabilistic logic Benchmark testing

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