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作者： Li Huang Wei Hu Chong Tan College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology

Aiming at the current situation of poor quality of artificial intelligence teaching and weak students' interest in learning, a case teaching method is proposed. This paper analyzes the teaching status of artificial intelligence firstly, and then systematically describes the case teaching method. Finally, the necessity of introducing case teaching method into artificial intelligence teaching is studied. Practice shows that the case teaching method can effectively enhance students' self-learning ability, improve students' comprehensive innovation ability, and promote the development of artificial intelligence teaching.

关键词： Case teaching method Artificial intelligence Teaching case Course

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Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field

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Journal of Harbin Institute of technology(New Series) 2018年第1期25卷 57-66页

作者： Maozu Guo Shuang Cheng Chunyu Wang Xiaoyan Liu Yang Liu School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044 China Beijing Key Laboratory for Research on Intelligent Processing Method of Building Big Data( Beijing University of Civil Engineering and Architecture) Beijing 100044China Institute of Materials China Academy of Engineering PhysicsMianyang 621700SichuanChina

MicroRNAs( miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However,in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data,the decision fusion method is used to prioritize the results of existing methods. After that,the performance of decision fusion method is validated. Furthermore,in consideration of the long range dependencies of successive expression values,Hidden Conditional Random Field model( HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs.

关键词： expression profiling hidden conditional random field miRNA-disease association network

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Optimal seam line detection in laplacian pyramid domain for image stitching

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Journal of Computers (Taiwan) 2018年第1期29卷 209-219页

作者： Yan, Wu-Xia Liu, Chuan-Cai Hu, Jun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

Parallax handing is a challenging problem for image stitching. Optimal seam line method is employed in this paper to deal with the misalignment on panoramas caused by parallax problem. It's an important method for orthoimage stitching in remote sensing. However, failures occur when it used in common image stitching due to the parallax problem. In order to obtain a better panorama for common image, the proposed approach mainly employs optimal seam line and contains the following three steps. First, the parallax problem can be alleviated by geometric correction. Then, the optimal seam line is detected within the overlapping region by dynamic programming in Laplacian pyramid domain. And images were blended by using blending trick on each corresponding level of the pyramids. Finally, the convincing panorama can be obtained by reconstructing the panoramic pyramid. The experiments demonstrated that the proposed method can obtain the clear and seamless panoramas. © 2018 Computer Society of the Republic of China. All rights reserved.

关键词： Remote sensing

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Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient Flow

arXiv

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arXiv 2020年

作者： Christlieb, Andrew Promislow, Keith Tan, Zengqiang Wang, Sulin Wetton, Brian Wise, Steven M. Department of Mathematics Michigan State University East LansingMI48824 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States School of Science Wuhan University of Technology Wuhan430070 China School of Mathematics Hunan University Changsha410082 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Applied Mathematics Hunan University Changsha410082 China Department of Mathematics The University of British Columbia VancouverBCV6T 1Z2 Canada Department of Mathematics The University of Tennessee KnoxvilleTN37996 United States

Reductions of the self-consistent mean field theory model of amphiphilic molecules in solvent can lead to a singular family of functionalized Cahn-Hilliard (FCH) energies. We modify these energies, mollifying the singularities to stabilize the computation of the gradient flows and develop a series of benchmark problems that emulate the "morphological complexity" observed in experiments. These benchmarks investigate the delicate balance between the rate of absorption of amphiphilic material onto an interface and a least energy mechanism to disperse the arriving mass. The result is a trichotomy of responses in which two-dimensional interfaces either lengthen by a regularized motion against curvature, undergo pearling bifurcations, or split directly into networks of interfaces. We evaluate a number of schemes that use second order backward differentiation formula (BDF2) type time stepping coupled with Fourier pseudo-spectral spatial discretization. The BDF2-type schemes are either based on a fully implicit time discretization with a preconditioned steepest descent (PSD) nonlinear solver or upon linearly implicit time discretization based on the standard implicit-explicit (IMEX) and the scalar auxiliary variable (SAV) approaches. We add an exponential time differencing (ETD) scheme for comparison purposes. All schemes use a fixed local truncation error target with adaptive time-stepping to achieve the error target. Each scheme requires proper "preconditioning" to achieve robust performance that can enhance efficiency by several orders of magnitude. The nonlinear PSD scheme achieves the smallest global discretization error at fixed local truncation error, however the IMEX and SAV schemes are the most computationally efficient as Copyright © 2020, The Authors. All rights reserved.

关键词： Errors

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Design of intelligent Robot Platform based on Multi-sensor Fusion

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Ya Dajin Lei Xiaochun Li Yue Chen Junyan Huang Rongcun Lan Lin School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor Information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system.

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Refining source representations with relation networks for neural machine translation

arXiv

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arXiv 2018年

作者： Zhang, Wen Hu, Jiawei Feng, Yang Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS University of Chinese Academy of Sciences ADAPT Centre School of Computing Dublin City University

Although neural machine translation with the encoder-decoder framework has achieved great success recently, it still suffers drawbacks of forgetting distant information, which is an inherent disadvantage of recurrent neural network structure, and disregarding relationship between source words during encoding step. Whereas in practice, the former information and relationship are often useful in current step. We target on solving these problems and thus introduce relation networks to learn better representations of the source. The relation networks are able to facilitate memorization capability of recurrent neural network via associating source words with each other, this would also help retain their relationships. Then the source representations and all the relations are fed into the attention component together while decoding, with the main encoder-decoder framework unchanged. Experiments on several datasets show that our method can improve the translation performance significantly over the conventional encoder-decoder model and even outperform the approach involving supervised syntactic knowledge. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

arXiv

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Xiong Tianmin Lei Xiaochun Zheng Xingwen Chen Junyan Feng Yizhou School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin 541004 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin 541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields.

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Data Analysis and Planning Analysis Management System Based on Tensorflow Machine Learning Algorithms

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Journal of Physics: Conference Series 2019年第1期1325卷

作者： Shuang Hu Li Huang Feng Bai Jun Zhang School of Computer Science and Technology Wuhan University of Science and Technology Intelligent Information Processing and Real-time Industrial System Hubei Key Laboratory Institute

At present, machine learning is quite popular, and tensorflow framework is also very popular. This system takes the plan management system as the practice platform, realizes the data analysis function through the machine learning understanding and the key point. Most of the current market are planning management system, but there is no self-analysis ability, need to base on data, there are limitations. Firstly, the system collects training data such as weight, body fat rate and so on, then builds a deep learning neural network, and finally runs the model to realize plan analysis, so as to help users analyze the feasibility of the plan and provide suggestions for users, and the analysis results will be displayed through processing.

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