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Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation"

IEEE transactions on cybernetics 2024年第11期54卷 7116页

作者： Lei Zhou Liang Feng Kay Chen Tan Jinghui Zhong Zexuan Zhu Kai Liu Chao Chen Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Department of Computer Science City University of Hong Kong kowloon Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Presents corrections to the paper, (Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation").

关键词： Evolutionary computation Knowledge transfer Cybernetics

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Amorphous alloys surpass E/10 strength limit at extreme strain rates

arXiv

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arXiv 2021年

作者： Zhu, Wenqing Li, Zhi Shu, Hua Gao, Huajian Wei, Xiaoding State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China School of Mechanical and Aerospace Engineering College of Engineering Nanyang Technological University 70 Nanyang Drive 637457 Singapore Shanghai Institute of Laser Plasma China Academy of Engineering Physics Shanghai201102 China Institute of High Performance Computing A*STAR 138632 Singapore Beijing Innovation Center for Engineering Science and Advanced Technology Peking University Beijing100871 China Peking University Nanchang Innovation Institute Nanchang330000 China

Theoretical predictions of the ideal strength of materials range from E/30 to E/10 (E is Young’s modulus). However, despite intense interest over the last decade, the value of the ideal strength that can be attained experimentally for metals remains a mystery (1-5). In this study, we demonstrated the unprecedented strength of an amorphous Cu-Zr alloy that surpassed the E/10 limit. Laser-induced shock experiments were conducted on Cu50Zr50 to explore its strength and failure mechanisms at ultrahigh strain rates. The material demonstrated a high spall strength of 9.8 GPa, approximately 1/13 of its P-wave modulus (~ E/6), at strain rates greater than 107 s−1, which sets a new record for the elastic limit of metallic materials. Electron microscopy and large-scale molecular dynamics simulations revealed that void nucleation and growth, not shear-banding, comprised the major failure mechanism for metallic glasses at extremely fast strain rates. A new model for void formation under the control of surface energy explained the rate dependence of the material strength. The results of this study reveal new possible ways to use the amorphous phase in nanostructured metals in future applications under demanding mechanical conditions. Copyright © 2021, The Authors. All rights reserved.

关键词： Copper alloys

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FrepJoin:an efficient partition-based algorithm for edit similarity join

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Frontiers of Information technology & Electronic Engineering 2017年第10期18卷 1499-1510页

作者： Ji-zhou LUO Sheng-fei SHI Hong-zhi WANG Jian-zhong LI School of Computer Science and Technology Harbin Institute of Technology Guangdong Key Laboratory of Popular High Performance Computers Key Laboratory of Service Computing and Application

String similarity join(SSJ) is essential for many applications where near-duplicate objects need to be found. This paper targets SSJ with edit distance constraints. The existing algorithms usually adopt the filter-andrefine framework. They cannot catch the dissimilarity between string subsets, and do not fully exploit the statistics such as the frequencies of characters. We investigate to develop a partition-based algorithm by using such *** frequency vectors are used to partition datasets into data chunks with dissimilarity between them being caught easily. A novel algorithm is designed to accelerate SSJ via the partitioned data. A new filter is proposed to leverage the statistics to avoid computing edit distances for a noticeable proportion of candidate pairs which survive the existing filters. Our algorithm outperforms alternative methods notably on real datasets.

关键词： String similarity join Edit distance Filter and refine Data partition Combined frequency vectors

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A short-term output power prediction model of wind power based on deep learning of grouped time series

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European Journal of Electrical Engineering 2020年第1期22卷 29-38页

作者： Wang, Yongsheng Gao, Jing Xu, Zhiwei Li, Leixiao College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

The output power prediction of wind power plants is an important guarantee to improve the utilization rate of wind energy and reduce wind curtailment. However, due to the strong randomness of wind energy, the ultra-short-term prediction accuracy of wind power output is poor. In view of the problem above, a prediction model based on deep learning of grouped time series (LW-CLSTM) was proposed in this paper. Based on this model, the authors attempted to explore a prediction method of wind power output. For this, first the multivariate data of wind power was fused, cleaned, dimension-reduced, and standardized, and the time period characteristics of the output power itself were extracted. Afterwards, it proposes a time sliding window (TSW) algorithm, and constructs a neural network input data set. Then a deep neural network prediction model combining the Convolutional Neutral Network (CNN) and Long-Short Term Memory (LSTM) was established, and the regression evaluation criteria for output power forecast accuracy in wind power production were designed, to compare the proposed model with four other prediction models. Finally, the experiments on the TensorFlow platform using real data show that this model has better prediction accuracy than the other four models, reaching a prediction accuracy rate of 92.5%, which verified the effectiveness of this prediction method. © 2020 International Information and Engineering technology Association. All rights reserved.

关键词： Wind power

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Semi-supervised classification method of SAR images using spectral clustering in contourlet domain

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Journal of Physics: Conference Series 2020年第4期1486卷

作者： Jiang, Kaiwen Zhang, Degan Xu, Haixia Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education 300384 China Key Laboratory of Computer Vision and System (Tianjin University of Technology) Ministry of Education 300384 China Tianjin Key Lab of Intelligent Computing & Novel software Technology Tianjin University of Technology Tianjin China

A new based on Semi-supervised classification theory for SAR images in contourlet domain is proposed, in this paper. Attempting to get better and faster performance, the PSO algorithm (Particle swarm optimization algorithm) and contourlet domain is proposed to instead of traditional k-means algorithm. PSO is used to find the global optimum by performing a global search in the whole solution space. And then, contourlet is applied in front of construct the similarity matrix to extract more effective eigenvalues. In section five, the proposed algorithm got better classification results than the traditional k-means algorithm which is proved by experimental results show that in terms of running time, classification accuracy and Kappa coefficient.

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Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture:A case study of lettuce production

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Horticulture Research 2019年第1期6卷 906-917页

作者： Alan Bauer Aaron George Bostrom Joshua Ball Christopher Applegate Tao Cheng Stephen Laycock Sergio Moreno Rojas Jacob Kirwan Ji Zhou Earlham Institute Norwich Research ParkNorwich NR47UZUK Plant Phenomics Research Center China-UK Plant Phenomics Research CentreNanjing Agricultural UniversityNanjing 210095 JiangsuChina School of Computing Sciences University of East AngliaNorwich Research ParkNorwich NR47TJUK National Engineering and Technology Center for Information Agriculture MARA Key Laboratory for Crop System Analysis and Decision MakingJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityNanjing 210095 JiangsuChina G’s Growers Limited ElyCambridgeshire CB75TZUK

Aerial imagery is regularly used by crop researchers,growers and farmers to monitor crops during the growing *** extract meaningful information from large-scale aerial images collected from the field,high-throughput phenotypic analysis solutions are required,which not only produce high-quality measures of key crop traits,but also support professionals to make prompt and reliable crop management ***,we report AirSurf,an automated and open-source analytic platform that combines modern computer vision,up-to-date machine learning,and modular software engineering in order to measure yield-related phenotypes from ultra-large aerial *** quantify millions of in-field lettuces acquired by fixed-wing light aircrafts equipped with normalised difference vegetation index(NDVI)sensors,we customised AirSurf by combining computer vision algorithms and a deep-learning classifier trained with over 100,000 labelled lettuce *** tailored platform,AirSurf-Lettuce,is capable of scoring and categorising iceberg lettuces with high accuracy(>98%).Furthermore,novel analysis functions have been developed to map lettuce size distribution across the field,based on which associated global positioning system(GPS)tagged harvest regions have been identified to enable growers and farmers to conduct precision agricultural practises in order to improve the actual yield as well as crop marketability before the harvest.

关键词： computer analysis equipped

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AutoDE: Automated Vulnerability Discovery and Exploitation

AutoDE: Automated Vulnerability Discovery and Exploitation

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yuntao Zhang Tong Liu Zhongru Wang Qiang Ruan Binxing Fang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China Zhejiang Lab Hangzhou China Beijing DigApis Technology Co. Ltd Beijing China

Recently, automated testing for software vulnerability finding and exploitation has attracted more and more attention, due to the high scalability and efficiency. However, it is non-trivial and challenging since the existing methods have limited effectiveness to discover vulnerabilities, that are exploitable to hack the software. To address these challenges, we propose an Automated Vulnerability Discovery and Exploitation framework, AutoDE, which aims to improve the effectiveness of the vulnerability discovery and exploitation. In the vulnerability discovery stage, we propose Anti-Driller to alleviate the “path explosion” problem. It first generates a specific input through the symbolic execution by using the control flow graph (CFG), then leverages a mutation-based fuzzer to mutate this case to find vulnerabilities which can avoid unqualified mutation. In the vulnerability exploitation stage, we analyze the characteristics of vulnerabilities and then propose to generate exploits that produce a shell based on the detected vulnerabilities with several attack techniques. We have conducted extensive experiments on the RHG 2018 challenge dataset and the BCTF-RHG 2019 challenge dataset. The experimental results clearly demonstrate the effectiveness and scalability of the proposed framework.

关键词： Conferences Data science Cyberspace

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Forecasting of Forex Time Series Data Based on Deep Learning

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Procedia Computer Science 2019年 147卷 647-652页

作者： Lina Ni Yujie Li Xiao Wang Jinquan Zhang Jiguo Yu Chengming Qi College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China Beijing Union University Beijing 100101 China

This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method. On the exchange rate data of nine major foreign exchange currencies, the experimental comparison of the forecasting method shows that the C-RNN foreign exchange time series data prediction method constructed in this paper has better applicability and higher accuracy.

关键词： Deep learning Recurrent neural network Convolutional neural network Foreign Exchange Rate Time series analysis

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Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks

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Procedia Computer Science 2019年 147卷 643-646页

作者： Jinquan Zhang Bowen Zhao Guochao Song Lina Ni Jiguo Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China

Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakage of private information nowadays. This paper proposes a privacy preservation scheme of sensitive data publishing in social networks based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to tackle this issue. The scheme is divided into an online process and an offline process. Specifically, we present the Maximum Delay Anonymous Clustering Feature (MDACF) tree data publishing algorithm.

关键词： Privacy protection Data publishing Social networks (α, L)-diversity K-anonymity Clustering feature (CF) tree

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Bilingual Word Embedding with Sentence Combination CNN for 1-to-N Sentence Alignment 20

Bilingual Word Embedding with Sentence Combination CNN for 1...

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Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval

作者： Xinyuan Ren Xiangling Fu Xuesi Zhou Chunsheng Liu Songfeng Gao Lei Peng School of Software Engineering Beijing University of Posts and Telecommunications Beijing China and Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Hai Dian Beijing China and Multimedia Signal and Intelligent Information Processing Lab Tsinghua University Hai Dian Beijing China BUPT and Huarong Joint lab of Smart Finance Beijing P. R. China and HuaRong RongTong (Beijing) Technology Co. Ltd Beijing P. R. China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Convolutional Neural Network

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