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Feature selection for big data based on mapreduce and voting mechanism 19

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Feature selection for big data based on mapreduce and voting...

19th International Conference on machine learning and Cybernetics, ICMLC 2020

作者： Zhang, Su-Fang Zhai, Jun-Hai Tian, Shi Zhou, Xiang Li, Yan China Meteorological Administration Hebei Branch of China Meteorological Administration Training Center Baoding071000 China Hebei University Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Baoding071002 China Beijing Normal University School of Applied Mathematics Zhuhai519087 China

ISBN: (纸本)9780738124261

With the rapid development of computer network technology and wireless sensor technology, as well as the arrival of the era of big data, the dimension and sample number of data are growing rapidly. Accordingly, it is important to investigate the problem of feature selection for big data and to design feature selection algorithm for big data. Based on MapReduce and voting mechanism, a feature selection method for big data is proposed in this paper. The proposed methods include three steps: Firstly, partition big data set into m subsets, and deploy the subsets to m computing nodes of Hadoop. Secondly, on the m computing nodes, we employ a feature selection algorithm based on genetic algorithm to select important features in parallel using local data subset, and obtain m feature subsets. Finally, for each feature, m feature subsets are used to vote on it, and the final feature subset is selected according to the voting results. Experimental results on four big data sets demonstrate that the proposed method is effective and efficient. © 2020 IEEE.

关键词： Genetic algorithms

Research on location and similar comments in point-of-interest recommendation system for users

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Journal of Physics: Conference Series 2021年第1期1774卷

作者： Yu Liu Ning Wei College of Mathematics and Information Science Hebei University Baoding Hebei 071002 Hebei Key Laboratory of Machine Learning and Computational Intelligence

By combining deep learning algorithms and potential probabilistic topic models, this paper proposes a new modeling strategy for individual users and constructs an effective point of interest recommendation system. First, Doc2vec is used to vectorize the user's comments, and the Bow model is used to match the similarity of the points of interest, and then the potential probability model is used to cluster the positions of the points of interest. Finally, relying on the user's historical comment data as a basis, the Top-K ranking recommendation scheme is obtained. The experimental results show that for large-scale location-based social network data sets, the algorithm in this paper has an ideal final effect on users' personalized recommendations. The recommendation model for user location information and similar comment points of interest proposed in this paper is more subjectively and objectively in line with user behavior needs. On the premise of ensuring the real-time performance of recommendation, the model proposed in this paper effectively improves the accuracy of recommendation compared with traditional point-of-interest recommendation algorithms.

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A node deployment model with variable transmission distance for wireless sensor networks

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arXiv 2020年

作者： Tiegang, Fan Junmin, Chen Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University BaodingHebei071002 China College of Mathematics and Information Science Hebei University BaodingHebei071002 China

The deployment of network nodes is essential to ensure the wireless sensor network's regular operation and affects the multiple network performance metrics, such as connectivity, coverage, lifetime, and cost. This paper focuses on the problem of minimizing network costs while meeting network requirements, and proposes a corona-based deployment method by using the variable transmission distance sensor. Based on the analysis of node energy consumption and network cost, an optimization model to minimize Cost Per Unit Area is given. The transmission distances and initial energy of the sensors are obtained by solving the model. The optimization model is improved to ensure the energy consumption balance of nodes in the same corona. Based on these parameters, the process of network node deployment is given. Deploying the network through this method will greatly reduce network costs. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy utilization

HOM 3-LIE-RINEHART ALGEBRAS

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arXiv 2020年

作者： Bai, Ruipu Li, Xiaojuan Wu, Yingli College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province BaodingP.R.071002 China College of Mathematics and Information Science Hebei University Baoding071002 China

After endowing with a 3-Lie-Rinehart structure on Hom 3-Lie algebras, we obtain a class of special Hom 3-Lie algebras, which have close relationships with representations of commutative associative algebras. We provide a special class of Hom 3-Lie-Rinehart algebras, called split regular Hom 3-Lie-Rinehart algebras, and we then characterize their structures by means of root systems and weight systems associated to a splitting Cartan subalgebra. Copyright © 2020, The Authors. All rights reserved.

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DPA-2: a large atomic model as a multi-task learner

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

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Random Occlusion Recovery with Noise Channel for Person Re-i...

16th International Conference on Intelligent Computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

Plant Leaf Recognition Network Based on Feature learning and Metric learning 16th

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Plant Leaf Recognition Network Based on Feature Learning and...

16th International Conference on Intelligent Computing, ICIC 2020

作者： Yang, Hongwei Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhao, Zhongqiu Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuro-Science and Brain-Inspired Intelligence Ministry of Education Shanghai China College of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9783030607982

Plant image recognition is an important thing for protecting plants, protecting the environment, and protecting nature. Recently, most models in the field of plant leaf recognition make classification after extracting global features. In this paper, we propose a plant leaf recognition model based on metric learning. Metric learning calculates the similarity of the extracted feature vectors to obtain the distance between different sample features, so as to determine whether similar pictures belong to the same category, and then achieve the classification effect. In this study, feature triplet are used for metric learning, and the loss function we used is triplet-loss. © 2020, Springer Nature Switzerland AG.

关键词： Image recognition

The infinite dimensional unital 3-lie poisson algebra

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arXiv 2019年

作者： Kang, Chuangchuang Bai, Ruipu Wu, Yingli College of Mathematics and Information Science Hebei University Baoding071002 College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province Baoding071002

From a commutative associative algebra A, the infinite dimensional unital 3-Lie Poisson algebra L is constructed, which is also a canonical Nambu 3-Lie algebra, and the structure of is discussed. It is proved that: (1) there is a minimal set of generators S consisting of six vectors;(2) the quotient algebra L00,0is a simple 3-Lie Poisson algebra;(3) four important infinite dimensional 3-Lie algebras: 3-Virasoro-Witt algebra W3, AI, AIand the 3-Wazalgebra can be embedded in L. Copyright © 2019, The Authors. All rights reserved.

关键词：

3-Lie algebra Aδω-Modules and induced modules

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arXiv 2019年

作者： Bai, Ruipu Ma, Yue Liu, Pei College of Mathematics and Information Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province Baoding071002 College of Mathematics and Information Science Hebei University Baoding071002 China

In this paper, we define the induced modules of Lie algebra ad(B) associated with a 3-Lie algebra B-module, and study the relation between 3-Lie algebra Aδω-modules and induced modules of inner derivation algebra ad(Aδω). We construct two infinite dimensional intermediate series modules of 3-Lie algebra Aδω, and two infinite dimensional modules (V, ψ λμ) and (V, φμ) of the Lie algebra ad(Aδω), and prove that only (V, ψλ0) and (V, ψλ1) are induced modules. Copyright © 2019, The Authors. All rights reserved.

关键词： Algebra