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arXiv 2019年

作者： Wang, Bin Kry, Paul Deng, Yuanmin Ascher, Uri Huang, Hui Chen, Baoquan Advanced Innovation Center for Future Visual Entertainment Beijing Film Academy School of Computer Science McGill University School of Computer Science and Technology Shandong University Department of Computer Science University of British Columbia College of Computer Science and Software Engineering Shenzhen University Advanced Innovation Center for Future Visual Entertainment Beijing Film Academy Peking University

The fidelity of a deformation simulation is highly dependent upon the underlying constitutive material model. Commonly used linear and nonlinear constitutive material models contain many simplifications and only cover a tiny part of possible material behavior. In this work we propose a framework for learning customized models of deformable materials from sparse example surface trajectories. The key idea is to iteratively improve a correction to a nominal model of the elastic and damping properties of the object, which allows new forward simulations with the learned correction to more accurately predict the behavior of a given soft object. The challenge is that such data is sparse as it is typically available only on part of the surface. Sparse reduced space-time optimization identifies gentle control forces with which we extract necessary annotated data for model inference and to finally encapsulate the material correction into a compact parametric form. We demonstrate our method with a set of synthetic examples, as well as with data captured from real world homogeneous elastic objects. Copyright © 2019, The Authors. All rights reserved.

关键词： Iterative methods

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Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems

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Journal on Big Data 2019年第1期1卷 1-7页

作者： Syed Falahuddin Quadri Xiaoyu Li Desheng Zheng Muhammad Umar Aftab Yiming Huang School of Information and Software Engineering University of Electronics Science and Technology of ChinaChengdu610054China School of Computer Science Southwest Petroleum University8 Xindu AvenueChengdu610500China University of Maryland Institute for Advanced Computer Studies Bldg 224 Stadium Dr Room 3100College ParkMaryland20742USA

Given the glut of information on the web,it is crucially important to have a system,which will parse the information appropriately and recommend users with relevant information,this class of systems is known as Recommendation Systems(RS)-it is one of the most extensively used systems on the web ***,Deep Learning(DL)models are being used to generate recommendations,as it has shown state-of-the-art(SoTA)results in the field of Speech Recognition and computer Vision in the last ***,the RS is a much harder problem,as the central variable in the recommendation system’s environment is the chaotic nature of the human’s purchasing/consuming behaviors and their *** user-item interactions cannot be fully represented in the Euclidean-Space,as it will trivialize the interaction and undermine the implicit interactions *** to preserve the implicit as well as explicit interactions of user and items,we propose a new graph based recommendation *** fundamental idea behind this framework is not only to generate the recommendations in the unsupervised fashion but to learn the dynamics of the graph and predict the short and long term interest of the *** this paper,we propose the first step,a heuristic multi-layer high-dimensional graph which preserves the implicit and explicit interactions between users and items using SoTA Deep Learning models such as *** generate recommendation from this generated graph a new class of neural network architecture-Graph Neural Network-can be used.

关键词： Recommendation systems autoencoder knowledge representation representation learning graph-structured data

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Monitoring system of agricultural performance based on multisensor data and cross-validation technique in merging temporal images

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Agronomy Journal 2022年第1期115卷

作者： N. P. G. Bhavani Prameeladevi Chillakuru Ramana Lakshmi Adusumilli Roobaea Alroobaea Saeed Rubaiee Abdulkader S. Hanbazazah Suman Mann Saveetha School of Engineering Chennai Tamil Nadu 602105 India Contribution: Data curation Formal analysis Rajalakshmi Engineering College Chennai Tamil Nadu 602105 India Contribution: Investigation Methodology Dep. of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh 520007 India Contribution: Resources Software Dep. of Computer Science College of Computers and Information Technology Taif Univ. P. O. Box 11099 Taif 21944 Saudi Arabia Contribution: Funding acquisition Supervision Dep. of Industrial and Systems Engineering College of Engineering Univ. of Jeddah Jeddah Saudi Arabia Contribution: Funding acquisition Visualization Contribution: Funding acquisition Writing - original draft Writing - review & editing Maharaja Surajmal Institute of Technology New Delhi 110058 India Contribution: Conceptualization Writing - original draft

Several approaches to forecasting agricultural production have been used across the country, but they have focused on information widely detected with technology that was not very successful. Unfortunately, because of many difficulties such as climate variables (50% global fog cover) with limited temporal accuracy, the remotely detected information necessary to predict crop production was often insufficient. As a result of these problems, existing methods of estimating agricultural production are ineffective or out of date. Several efforts have been made to overcome these challenges by combining images with high temporal accuracy but poor geographic detail. On the other hand, it is this kind of situation that is most suitable for extremely large and homogeneous agricultural areas. An innovative theoretical framework has developed that explains this absence of high-quality satellite images. This intelligent method was built around this new theoretical framework, which incorporates its use of something like the energy equation to improve the predictions of multiple cultures. Many producers were contacted and data regarding agricultural production were obtained to validate the results of the smart technology. The excellent reliability of this intelligent method has been shown by a comparative contrast between projected crop yields and actual output in various areas.

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Research on helping employment difficulties of students by department of computer science and software engineering,Jincheng college of Sichuan University

Research on helping employment difficulties of students by d...

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2014 International Conference on Information engineering and Education science

作者： Ling Hou Department of Computer Science and Software Engineering Jincheng College of Sichuan University

In developing school,every university should pay full attention to employment-oriented development of each *** outstanding students or stylistic active students,schools should set up the appropriate platform,so that they play a meaningful role,and be demonstrated,especially the employment difficulties faced by the students,they need to be more concerned *** should take effective measures to them,and help needy students to solve the problems pertinently,and then make them back to normal *** article took Sichuan University,Jin Cheng college of computer science and software engineering for example to research helping employment difficulties faced by *** practical work,we explored a classification for students from poor management,and use the bedroom and class structures difficult for students to demonstrate the platform,practical and effective measures to counselor part-time teacher two fold emotional care models.

关键词： help employment difficulties category management class activities part-time teacher

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PRMT: Predicting Risk Factor of Obesity among Middle-Aged People Using Data Mining Techniques

PRMT: Predicting Risk Factor of Obesity among Middle-Aged Pe...

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2018 International Conference on Computational Intelligence and Data science, ICCIDS 2018

作者： Hossain, Rifat Mahmud, S.M. Hasan Hossin, Md Altab Haider Noori, Sheak Rashed Jahan, Hosney Departmentof Software Engineering Daffodil International University Dhaka1207 Bangladesh Departmentof Management Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Departmentof Computer Science and Engineering Daffodil International University Dhaka1207 Bangladesh College of Computer Science Sichuan University Chengdu610064 China

Obesity is an anatomical condition characterized by an extreme growth of body fat. The obesity rate is increasing gradually;from prior research, obesity is the serious health disease in the globe. This study collected 259 data from specified urban and rural areas regarding different risk factor of our daily activities. The purpose of the study is to simulate the risk factor by using statistical tools (SPSS), which helpsto predict the major risk factor of obesity by testing the class level attribute according to cross-sectional study with other attributes. By analyzing the P-value (p © 2018 The Authors. Published by Elsevier Ltd.

关键词： Classification (of information)

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Corrigendum to “Rational Protocols and Attacks in Blockchain System”

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Security and Communication Networks 2021年第1期2021卷

作者： Tao Li Yuling Chen Yanli Wang Yilei Wang Minghao Zhao Haojia Zhu Youliang Tian Xiaomei Yu Yixian Yang State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou 550025 *** College of Computer Science and Technology Guizhou University Guiyang Guizhou 550025 *** School of Information Science and Engineering Qufu Normal University Rizhao 276825 *** School of Software Tsinghua University Beijing 100084 *** School of Information Science and Engineering Shandong Normal University Jinan 250014 ***

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An advanced operating environment for mathematics education resources

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中国科学（信息科学） 2018年第9期61卷 256-258页

作者： Yongsheng RAO Jingzhong ZHANG Yu ZOU Yanchun SUN Xiangping CHEN Songhua XU School of Data and Computer Science Sun Yat-Sen University Guangzhou 510275 China Academy for Intelligent Software Guangzhou University Guangzhou 510006 China Institute of Mathematics and Computer Science Guizhou Normal College Guiyang 550018 China Academy for Intelligent Software Guangzhou University Guangzhou 510006 China Institute of Mathematics and Computer Science Guizhou Normal College Guiyang 550018 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing Beijing 100871 China School of Data and Computer Science Sun Yat-Sen University Guangzhou 510275 China College of Computer Sciences New Jersey Institute of Technology Newark 07102 USA Academy for Intelligent Software Guangzhou University Guangzhou 510006 China

The potential of information technology (IT) in education modernization has been widely *** the broad efforts for advancing education IT,digital education resources continue to receive a high level of attention,with the goal of making high-quality education resources pervasively accessible anytime and ***,the amount of such high-quality resources is still limited,particularly those suitable for mathematics education [1].General expectations for high-quality digital mathematics education resources (MERs) include interactivity,openness,and transparency,all of which benefit enhancing the sustainability of MERs [2].In this case,openness of an MER refers to the resource being easily edited and extended;transparency means that users have convenient access to the original development process of the *** these features,MERs can be continuously improved and augmented according to users' individual *** development and utilization of such MEPs often rely on the deployment of a specialized 'software or platform',which is commonly referred to as the 'operating environment (OE)'.

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Review of machine-learning methods for RNA secondary structure prediction

arXiv

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arXiv 2020年

作者： Zhao, Qi Zhao, Zheng Fan, Xiaoya Yuan, Zhengwei Mao, Qian Yao, Yudong College of Medicine and Biological Information Engineering Northeastern University ShenyangLiaoning110169 China School of Information Science and Technology Dalian Maritime University Dalian Liaoning116026 China School of Software Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province DalianLiaoning116620 China Key Laboratory of Health Ministry for Congenital Malformation Shengjing Hospital China Medical University Shenyang Liaoning110004 China College of Light Industry Liaoning University Shenyang Liaoning110036 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

Secondary structure plays an important role in determining the function of non-coding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine-learning technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on machine-learning technologies and a tabularized summary of the most important methods in this field. The current pending issues in the field of RNA secondary structure prediction and future trends are also *** Codes I.2.0 General Copyright © 2020, The Authors. All rights reserved.

关键词： RNA

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SEBF: A single-chain based extension model of blockchain for fintech 29

SEBF: A single-chain based extension model of blockchain for...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Ji, Yimu Gu, Weiheng Chen, Fei Xiao, Xiaoying Sun, Jing Liu, Shangdong He, Jing Li, Yunyao Zhang, Kaixiang Mei, Fen Wu, Fei School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Institue of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China Nanjing Center of HPC China Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of Information Technology Nanjing University of Finance and Economics Nanjing China School of Software and Electrical Engineering Swinburne University of Technology Hawthorn Australia WeBank Co. Ltd. Shenzhen China College of Automation Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9780999241165

The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. The research question of how to make blockchain be able to deal with various data types has not been well studied. In this paper, we propose a single-chain based extension model of blockchain for fintech (SEBF). In the financial environment, we design a four-layer architecture for this model. By employing the external trusted oracle group and a financial regulator agency, a variety types of data can be effectively stored in the blockchain, such that the data type extension based on a single-chain is realized. The experimental results indicate that the proposed model can improve the efficiency of simplified payment verification. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Blockchain

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Optimal Wireless Information and Energy Transmissions for UAV-Enabled Cognitive Communication Systems

Optimal Wireless Information and Energy Transmissions for UA...

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International Conference on Communication Systems

作者： Yabin Lai Yue Ling Che Sheng Luo Kaishun Wu College of Computer Science & Software Engineering Shenzhen University

ISBN: (纸本)9781538678657;9781538678640

This paper considers a novel UAV-enabled cognitive communication system, where a battery-powered UAV hovers above a radio frequency (RF) energy harvesting enabled ground terminal (GT), and opportunistically accesses the licensed spectrum of the primary user (PU), to adaptively perform wireless information transfer (WIT) and/or wireless energy transfer (WET) to the GT. We formulate the UAV's optimal WIT and WET design as a constrained Markov decision process (MDP) problem to maximize the sum-throughput in the downlink over a finite time horizon. However, this problem is shown to have high complexity to solve. We then propose an efficient suboptimal policy with low complexity by studying the optimal policy structure. Numerical results show that the proposed suboptimal policy can significantly improve the sum-throughput in the downlink over the benchmark.

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