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arXiv 2022年

作者： Dong, Yuxin Gong, Tieliang Yu, Shujian Chen, Hong Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China Machine Learning Group UiT - The Arctic University of Norway Norway College of Science Huazhong Agriculture University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China

The matrix-based Rényi’s entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution. This intriguing property makes it widely applied in statistical inference and machine learning tasks. However, this information theoretical quantity is not robust against noise in the data, and is computationally prohibitive in large-scale applications. To address these issues, we propose a novel measure of information, termed low-rank matrix-based Rényi’s entropy, based on low-rank representations of infinitely divisible kernel matrices. The proposed entropy functional inherits the specialty of of the original definition to directly quantify information from data, but enjoys additional advantages including robustness and effective calculation. Specifically, our low-rank variant is more sensitive to informative perturbations induced by changes in underlying distributions, while being insensitive to uninformative ones caused by noises. Moreover, low-rank Rényi’s entropy can be efficiently approximated by random projection and Lanczos iteration techniques, reducing the overall complexity from O(n3) to O(n2s) or even O(ns2), where n is the number of data samples and s n. We conduct large-scale experiments to evaluate the effectiveness of this new information measure, demonstrating superior results compared to matrix-based Rényi’s entropy in terms of both performance and computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

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Science Bulletin 2019年第20期64卷 1540-1556页

作者： Kun Shi Yunlin Zhang Boqiang Qin Botian Zhou Taihu Laboratory for Lake Ecosystem Research State Key Laboratory of Lake Science and EnvironmentNanjing Institute of Geography and LimnologyChinese Academy ofSciencesNanjing210008China University of Chinese Academy of Sciences Beijing100049China Center for Excellence in Tibetan Plateau Earth Sciences Chinese Academy of SciencesBeijing100101China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing400714China

Timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics. Due to the advantages of simultaneous data acquisition over large geographical areas and high temporal coverage, remote sensing strongly facilitates cyanobacterial bloom monitoring in inland waters. We provide a comprehensive review regarding cyanobacterial bloom remote sensing in inland waters including cyanobacterial optical characteristics, operational remote sensing algorithms of chlorophyll, phycocyanin and cyanobacterial bloom areas, and satellite imaging applications. We conclude that there have many significant progresses in the remote sensing algorithm of cyanobacterial pigments over the past 30 years. The band ratio algorithms in the red and near-infrared(NIR) spectral regions have great potential for the remote estimation of chlorophyll a in eutrophic and hypereutrophic inland waters, and the floating algae index(FAI) is the most widely used spectral index for detecting dense cyanobacterial blooms. Landsat, MODIS(Moderate Resolution Imaging Spectroradiometer) and MERIS(MEdium Resolution Imaging Spectrometer) are the most widely used products for monitoring the spatial and temporal dynamics of cyanobacteria in inland waters due to the appropriate temporal, spatial and spectral resolutions. Future work should primarily focus on the development of universal algorithms, remote retrievals of cyanobacterial blooms in oligotrophic waters, and the algorithm applicability to mapping phycocyanin at a large spatial-temporal scale. The applications of satellite images will greatly improve our understanding of the driving mechanism of cyanobacterial blooms by combining numerical and ecosystem dynamics models.

关键词： Cyanobacterial blooms Inland waters Bio-optical properties Satellite MODIS

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Predicting Large-scale Protein-protein Interactions by Extracting Coevolutionary Patterns with MapReduce Paradigm

Predicting Large-scale Protein-protein Interactions by Extra...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lun Hu Bo-Wei Zhao Shicheng Yang Xin Luo MengChu Zhou Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China School of Computer Science and Technology Wuhan University of Technology Wuhan China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781665442084

Protein-protein interactions are of great significance for us to understand the functional mechanisms of proteins. With the rapid development of high-throughput genomic technology, the amount of protein-protein interaction data has become so big that most of existing prediction algorithms are no longer applicable. To address this problem, we develop a distributed framework by reimplementing one of state-of-the-art algorithms, i.e., CoFex, by using MapReduce. In particular, we adopt a novel tree-based data structure to reduce the heavy memory consumption cased by the huge sequence information of proteins. After that, the procedure of CoFex is modified by following the paradigm of MapReduce such that the prediction task can be completed in a distributed manner, thus fulfilling the demanding requirements of large-scale protein-protein interaction prediction. A series of experiments have been conducted to evaluate the performance of the proposed distributed framework in terms of both efficiency and effectiveness. Experimental results demonstrate that the proposed framework can considerably improve the efficiency of CoFex by achieving more than two-orders-of-magnitude improvement in computational efficiency while retaining a comparable level of accuracy.

关键词： Proteins Memory management Genomics Predictive models Prediction algorithms data structures Computational efficiency

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Automatic Fault Diagnosis of Smart Water Meter Based on BP Neural Network

Automatic Fault Diagnosis of Smart Water Meter Based on BP N...

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2020国际计算机前沿大会

作者： Jing Lin Chunqiao Mi School of Computer Science and Engineering Huaihua University Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province

The smart water meter in water supply network can directly affect water production and usage when faults occur. The traditional method of fault detection is inefficient with time lagging, which is not helpful for modernization of water supply system. The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply. In this paper, an automatic fault diagnosis method for the smart device is proposed based on BP neural network. And it was applied on Google Tensorflow platform. Fault symptom vectors were constructed using water meter status data and were used to train the neural network model. In order to improve the learning convergence speed and fault classification effect of the network, a method of weighted symptom was also employed. Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%.

关键词： Automaticfaultdiagnosis Smartwatermeter BPneuralnetwork Tensorflow

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Associating multi-scale receptive fields for fine-grained recognition

arXiv

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arXiv 2020年

作者： Ye, Zihan Hu, Fuyuan Liu, Yin Xia, Zhenping Lyu, Fan Liu, Pengqing Suzhou University of Science and Technology Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou Key Laboratory for Big Data and Information Service Shanghai Institute of Technology Tianjin University

Extracting and fusing part features have become the key of fined-grained image recognition. Recently, Non-local (NL) module has shown excellent improvement in image recognition. However, it lacks the mechanism to model the interactions between multi-scale part features, which is vital for fine-grained recognition. In this paper, we propose a novel cross-layer non-local (CNL) module to associate multi-scale receptive fields by two operations. First, CNL computes correlations between features of a query layer and all response layers. Second, all response features are weighted according to the correlations and are added to the query features. Due to the interactions of cross-layer features, our model builds spatial dependencies among multi-level layers and learns more discriminative features. In addition, we can reduce the aggregation cost if we set low-dimensional deep layer as query layer. Experiments are conducted to show our model achieves or surpasses state-of-the-art results on three benchmark datasets of fine-grained classification. Our codes can be found at ***/FouriYe/CNL-ICIP2020. Copyright © 2020, The Authors. All rights reserved.

关键词： Image recognition

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The design and application of an web-based online examination system 4th

The design and application of an web-based online examinatio...

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4th International Symposium on Emerging Technologies for Education, SETE 2019, was held in conjunction with the 18th International Conference on Web-based Learning, ICWL 2019

作者： Jiang, Jilu Wu, Baoxian Chang, Liang Liu, Kui Hao, Tianyong Audit Governance and Risk Control Research Center School of Accounting Guangdong University of Foreign Studies Guangzhou China Guangzhou Key Laboratory of Big Data and Intelligent Education School of Computer Science South China Normal University Guangzhou China Network and Modern Educational Technology Center Guangzhou University Guangzhou China

ISBN: (纸本)9783030387778

Online examination has been used more and more widely in the education and other fields due to its advantages of efficiency, convenience, and fairness. This paper proposes a new web-based online examination system, which utilizes PHP, Ajax and other technologies to implement online examination functions, including testing question collection, user management, online testing, real-time score calculation, answer checking, and result analysis. Particularly, an automated test paper generation module was designed and integrated. The system has been applied to a course involving more than 1000 students per semester at Guangzhou University of Foreign Studies. It has been proved to save efforts of teachers and students, demonstrating its effectiveness in assisting teaching and learning. © Springer Nature Switzerland AG 2020.

关键词： E-learning

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A parameter adaptive differential evolution based on depth information

A parameter adaptive differential evolution based on depth i...

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作者： Meng, Zhenyu Yang, Cheng Meng, Fanjia Chen, Yuxin Lin, Fang Institute of Artificial Intelligence Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou China Guanzhuang Central Primary School of Zhangqiu District Jinan China

Differential Evolution (DE) was an easy-coding and efficient stochastic algorithm for global optimization, and the whole optimization process simulates biological evolution. Superior individuals of the population that were suitable for the environment were retained during the evolution, and consequently the tolerable solutions could be obtained in the end. Despite the excellent performance of DE algorithm, there were still some shortcomings. For example, the general performance of DE depended largely on mutation strategy and control parameters, how to design the appropriate control parameters and mutation strategy were difficult tasks. Here a novel DE variant was proposed to overcome these shortcomings. By incorporating the depth information of previous generations of populations, a better diversity of trial vector candidates could be secured during the evolution process. Moreover, the thought that successful parameters should be retained to guide the update of themselves during the evolution was also incorporated into the novel algorithm. The optimization performance of the new proposed DE variant was verified under CEC 2013 test suit containing 28 benchmarks, and the results showed its competitiveness with several state-of-the-art DE variants. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Global optimization

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Adversarial Learning with Cost-Sensitive Classes

arXiv

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arXiv 2021年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach zero while the absolute values of a few parameters are significantly larger becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve an improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs. © 2021, CC BY.

关键词： Deep neural networks

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Global-to-local region-based indicator embedded in edge-based level set model for segmentation

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DIGITAL SIGNAL PROCESSING 2021年 114卷 103061-103061页

作者： Zhou, Zhiheng Dai, Ming Guo, Yongfan Li, Xiangwei South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou 510640 Peoples R China Guangdong Ocean Univ Fac Math & Comp Sci Zhanjiang 524088 Peoples R China Lanzhou Inst Technol Sch Comp & Artificial Intelligence Lanzhou 730050 Peoples R China

Image segmentation is an essential analysis tool in the field of computer vision, and the level set method has been widely used in image segmentation. Specifically, the edge-based level set models can reduce many undesired regions because they mainly rely on the edge information. However, the edge-based level set models are usually sensitive to the initial condition, which limits their application. To overcome this shortcoming, a global-to-local region-based indicator is designed in this paper, which is utilized to embed the region information into the edge-based models. Unlike the edge-based indicator frequently used in the edge-based models, the proposed region-based indicator can allow bidirectional motion of the active contour curve according to the region information. In general, the proposed region-based indicator can intrinsically incorporate the edge information and region information into one single energy function. Experimental results on synthetic images, natural images and medical images validate the effectiveness of the proposed method. Compared with some other level set models, the proposed method generally achieves better performance. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Image segmentation Level set method Edge information Region information Global-to-local

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Industrial Region Planning and Urban Transportation Planning Based on Industry-City Integration

Industrial Region Planning and Urban Transportation Planning...

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International Conference on Transportation Information and Safety (ICTIS)

作者： Shuai Zheng Yugang Liu Jia Zhang Qing Wan Heyang Zhang Jinsong Ye Southwest Jiaotong University School of Transportation and Logistics Chengdu China National United Engineering Laboratory of Integrated Intelligent TransPortation Southwest Jiaotong University Chengdu China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport China Academy of Transportation Sciences Beijing P. R. China

ISBN: (纸本)9781665497145

With urban expansion and economic development, traditional industrial parks with single function cannot adapt to the pace of urban development. As a new type of industrial park, the industry-city integration has gradually become the development direction. There is no tailor-made quantitative planning scheme at this stage. Aiming at filling the gap, this research does the following work to provide quantitative guidance strategy for industrial park planning. This paper first analyzes the Transit-oriented development (TOD) model requirements of new industrial parks, and makes a detailed analysis of the interaction between land use and transportation. Secondly, based on the relationship between land use and transportation, a combined feedback model of the land planning model and the public transportation utility allocation model is established. The process of modeling is to plan the smallest land unit according to the principle of land use function combination and land use type to meet the needs of residents. The other is to plan public transportation with optimal road load according to the principle of public transportation guiding travel and maximizing travel efficiency. Notedly, this paper uses the TOPSIS method to normalize the planning score through the feedback relationship between land use planning and public transportation planning. Based the analysis, the genetic algorithm is used to optimize the planning score and obtain the best plan which provides quantitative guidance strategies for the industrial park planning. Finally, this paper selects the “Blank Land” of Suzhou Industrial Parks for case analysis, and the best standardized score after planning is about 87 points. In order to further analyze the planning effect and the relationship between the planned area and the rest of the land, ArcGIS is used to carry out demand analysis and land use radiation range analysis of the planning scheme. The results show that the travel demand after planning matches with t

关键词： Analytical models Costs Roads Land use planning Transforms Planning Safety

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