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arXiv 2020年

作者： Tang, Yutian Zhou, Hao Xu, Zhou Luo, Xiapu Cai, Yan Zhang, Tao Department of Computing Hong Kong Polytechnic University Hong Kong School of Big Data and Software Engineering Chongqing University China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Faculty of Information Technology Macau University of Science and Technology China

Software reuse enables developers to reuse architecture, programs and other software artifacts. Realizing a systematical reuse in software brings a large amount of benefits for stakeholders, including lower maintenance efforts, lower development costs, and time to market. Unfortunately, currently implementing a framework for large-scale software reuse in Android apps is still a huge problem, regarding the complexity of the task and lacking of practical technical support from either tools or domain experts. Therefore, proposing a feature location benchmark for apps will help developers either optimize their feature location techniques or reuse the assets created in the benchmark for reusing. In this paper, we release a feature location benchmark, which can be used for those developers, who intend to compose software product lines (SPL) and release reuse in apps. The benchmark not only contributes to the research community for reuse research, but also helps participants in industry for optimizing their architecture and enhancing modularity. In addition, we also develop an Android Studio plugin named caIDE for developers to view and operate on the benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer software reusability

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Study of Star/Galaxy Classification Based on the XGBoost Algorithm

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Chinese Astronomy and Astrophysics 2019年第4期43卷 539-548页

作者： Chao, Li Wen-hui, Zhang Ji-ming, Lin College of Information and Communication Engineering Guilin University of Electronic Technology Guilin 541004 Key Laboratory of Cognitive Radio and Information Processing the Ministry of Education Guilin University of Electronic Technology Guilin 541004 Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin 541004 Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin 541004

Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement. © 2019

关键词： Stars: fundamental parameters—galaxies: fundamental parameters—techniques: photometric—methods: data analysis

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Parallel Geometric Correction for Single Spaceborne SAR Image

Parallel Geometric Correction for Single Spaceborne SAR Imag...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Minghu Fan Xianyu Zuo Junfeng Tian Key Laboratory of Big Data Analysis and Processing of Henan Province College of Computer Science and Information Engineering Henan University Kaifeng China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

The fast processing of SAR images is of great significance for SAR applications, and parallel computing is the most common solution. The paper studied the parallel computing algorithm for the geometric correction of single spaceborne SAR image, which is based on a classical serial geometric correction algorithm. After fully analyzing the parallel features of the algorithm, a CPU-oriented and a GPU-oriented global parallel correction algorithm were designed. Then, two spaceborne SAR images were used to test the speedup performance of the two algorithms. The experimental results show that the two parallel algorithms can greatly improve the speed of the geometric correction of spaceborne SAR image, and the performance of CPU-oriented parallel algorithm is mainly related to the number of CPU/cores, while the performance of GPU-oriented parallel algorithm is mainly related to the data granularity, and the latter has very high speedup ratio and is an efficient method for geometric correction of spaceborne SAR image.

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A unified adaptive recoding framework for Batched network coding

arXiv

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arXiv 2021年

作者： Yin, Hoover H.F. Tang, Bin Ng, Ka Hei Yang, Shenghao Wang, Xishi Zhou, Qiaoqiao The N-hop Technologies Limited Hong Kong The Institute of Network Coding The Chinese University of Hong Kong Hong Kong The School of Computer and Information Hohai University Nanjing China The Department of Physics The Chinese University of Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong

Batched network coding is a variation of random linear network coding which has low computational and storage costs. In order to adapt to random fluctuations in the number of erasures in individual batches, it is not optimal to recode and transmit the same number of packets for all batches. Different distributed optimization models, which are called adaptive recoding schemes, were formulated for this purpose. The key component of these optimization problems is the expected value of the rank distribution of a batch at the next network node, which is also known as the expected rank. In this paper, we put forth a unified adaptive recoding framework with an arbitrary recoding field size. We show that the expected rank functions are concave when the packet loss pattern is a stationary stochastic process, which covers but not limited to independent packet loss and Gilbert-Elliott packet loss model. Under this concavity assumption, we show that there always exists a solution which not only can minimize the randomness on the number of recoded packets but also can tolerate rank distribution errors due to inaccurate measurements or limited precision of the machine. We provide an algorithm to obtain such an optimal optimal solution, and propose tuning schemes that can turn any feasible solution into a desired optimal solution. © 2021, CC BY-NC-ND.

关键词： Packet loss

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Study on the Influence of Rainfall Infiltration on the High rock Slope of Open-pit Mine Stability using a New Nonlinear Strength Reduction Method

Research Square

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Research Square 2021年

作者： Zhou, Tianbai Zhang, Lingfan Cheng, Jian Wang, Jianming Zhang, Xiaoyu Li, Maoyuan Research Institute of Mine Big Data China Coal Research Institute Beijing100013 China State Key Laboratory of Coal Mining and Clean Utilization Beijing100013 China School of Mechanics and Civil Engineering China University of Mining and Technology Beijing100083 China Institute of Geotechnical Engineering RWTH Aachen University Mies-van-der-Rohe-Str. 1 Aachen52074 Germany Ansteel Beijing Research Institute Beijing102209 China Housing and Urban Rural Development Bureau Sichuan Qionglai City611500 China

Due to long-term mining, a series of high and steep rock slopes have been formed in the open-pit mine. For high rock slopes, rainfall infiltration is the main cause of landslide. Therefore, the stability analysis of high rock slope under rainfall has become a key issue in the open-pit mine engineering. In this work, aiming at the high stress condition of high rock slope, the instantaneous internal friction angle and instantaneous cohesion of rock mass under different stress states are deduced, and the a nonlinear strength reduction method for high rock slope is established according to the relationship between normal stress and shear stress of rock mass under the Hoke-Brown criterion. The numerical calculation results show that the factor of safety (FOS) for high rock slope calculated by the proposed method is more reasonable. Taking the southwest slope of Dagushan Iron Mine as the research background, the safety factors of high rock slope under different rainfall conditions are calculated by COMSOL Multiphysics. And the stability analysis of high rock slope in open-pit mine under rainfall are carried out. © 2021, CC BY.

关键词： Rocks

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Computation Offloading and Content Caching with Traffic Flow Prediction for Internet of Vehicles in Edge Computing

Computation Offloading and Content Caching with Traffic Flow...

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IEEE International Conference on Web Services (ICWS)

作者： Zijie Fang Xiaolong Xu Fei Dai Lianyong Qi Xuyun Zhang Wanchun Dou School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China School of Information Science and Engineering Qufu Normal University China Macquarie University Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

The development of the Internet of Vehicles (IoV) enables numerous emerging in-vehicle applications to accommodate users with various contents, thus enhancing their traveling experiences. In IoV, content decoding tasks are typically offloaded to edge servers for implementation, as edge computing is an admirable paradigm to provide low-latency services. However, as different vehicular users may request the same contents, processing these contents repeatedly leads to the waste of storage, computation and bandwidth resources. Therefore, fine-grained computation offloading and content caching are demanded in IoV. In this paper, a joint optimization method for computation offloading and content caching based on traffic flow prediction, named COC, is proposed. Firstly, traffic flow covered by each edge server is predicted by a modified deep spatiotemporal residual network (ST-ResNet). Secondly, the non-dominated sorting genetic algorithm III (NSGA-III) is leveraged to realize the many-objective optimization to shorten the execution time and reduce the energy consumption of computation and transmission in IoV. Finally, evaluated by real-world big data from Nanjing China, COC shows a great reduction in execution time and energy consumption of transmission and computation compared to other methods.

关键词： Energy consumption Intelligent vehicles Web services Optimization methods Bandwidth Servers Edge computing

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Dynamic Task Offloading with Minority Game for Internet of Vehicles in Cloud-Edge Computing

Dynamic Task Offloading with Minority Game for Internet of V...

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IEEE International Conference on Web Services (ICWS)

作者： Bowen Shen Xiaolong Xu Fei Dai Lianyong Qi Xuyun Zhang Wanchun Dou School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China School of Information Science and Engineering Qufu Normal University China Macquaire University Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

With the advent of the Internet of Vehicles (IoV), drivers are now provided with diverse time-sensitive vehicular services that usually require a large scale of computation. As civilian vehicles are generally insufficient in computational resources, their service requests are offloaded to cloud data centers and edge computing devices (ECDs) with ample computational resources to enhance the quality of service (QoS). However, ECDs are often overloaded with excessive service requests. In addition, as the network conditions and service compositions are complicated and dynamic, the centralized control of ECDs is hard to achieve. To tackle these challenges, a dynamic task offloading method with minority game (MG) in cloud-edge computing, named DOM, is proposed in this paper. Technically, MG is an effective tool with a distributed mechanism which can minimize the dependency on centralized control in resource allocation. In the MG, reinforcement learning (RL) is applied to optimize the distributed decision-making of participants. Finally, with a real-world dataset of IoV services, the effectiveness and adaptability of DOM are evaluated.

关键词： Cloud computing Games Quality of service Reinforcement learning Space exploration Task analysis Vehicle dynamics

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Controlled Source Electromagnetic data Denoising Based On CEEMD and Correlation Analysis

Controlled Source Electromagnetic Data Denoising Based On CE...

引用

International Conference on Information Science and Control engineering (ICISCE)

作者： Ding Di Li Guang He Zhu-shi Liu Xiao-qiong Zheng Hao-hao Li Tian-ze Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology East China University of Technology Nanchang Jiangxi China Engineering Research Center for Seismic Disaster Prevention and Engineering Geological Disaster Detection of Jiangxi Province East China University of Technology Nanchang Jiangxi China Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University) Changsha China

Controlled-source electromagnetic method (CSEM) signals are inevitably contaminated by man-made noises. For this reason, a new CSEM data processing method was proposed. First, the 50 Hz powerline interference was removed by FFT method, followed by the CEEMD method to remove baseline drift, and finally the correlation analysis method was used to select the high-quality signal. The method was applied to the processing of measured data in Huidong, Sichuan. As a conclusion, the presented method can effectively remove the strong cultural noise from raw CSEM data and preserve the useful signal completely; the apparent resistivity curves acquired by using the filtered data are improved significantly upon the previous.

关键词： Correlation Power measurement Noise reduction Process control Interference Signal processing data processing

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Bimagnetic Core/Shell Nanoprobes with Tunable Exchange Coupling for High Resolution and Sensitive Magnetic Particle Imaging

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Angewandte Chemie 2024年第6期137卷

作者： Qilong Li Changjian Li Yueqi Wang Miaomiao Li Zeyu Liang Qiyue Wang Bingzhe Wang Canyu Huang Jie Tian Fangyuan Li Prof. Daishun Ling Frontiers Science Center for Transformative Molecules School of Chemistry and Chemical Engineering School of Biomedical Engineering National Center for Translational Medicine Shanghai Jiao Tong University Shanghai 200240 China These authors contributed equally to this work School of Engineering Medicine Beihang University Beijing 100191 China Key Laboratory of Big Data-Based Precision Medicine (Beihang University) Ministry of Industry and Information Technology Beijing 100191 China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing 100190 China Clinical College of Armed Police General Hospital of Anhui Medical University Department of Gastroenterology of The Third Medical Center of Chinese PLA General Hospital Beijing 100039 China Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders (LEAD) Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai 201600 China World Laureates Association (WLA) Laboratories Shanghai 201203 China

Magnetic particle imaging (MPI) has demonstrated versatile applications in biomedicine, including tumor imaging, cell tracking, and image-guided hyperthermia. Despite these advancements, the prevalent use of clinically approved tracers has posed limitations on MPI′s resolution and sensitivity. In this study, we engineered a bimagnetic core/shell nanocrystals (BMCS) tailored for MPI by optimizing the heterostructure and modulating the exchange coupling effect between the two magnetic components. The resulting BMCS exhibited remarkably heightened susceptibility and magnetization while maintaining low coercivity, thereby substantially improved both MPI resolution and sensitivity compared to conventional tracers such as VivoTrax. At an equivalent mass concentration, BMCS demonstrated a notable 5.08-fold increase in signal intensity and achieved an unprecedentedly high resolution down to 1 mm. The excellent MPI performance contributes to high resolution MPI and the sensitive detection of orthotopic colorectal cancer in mice. The design strategy employed in BMCS, centered on the exchange coupling effect, introduces an efficacious approach for the development of high performance MPI tracers.

关键词： bimagnetic core/shell nanocrystals exchange coupling effect high resolution and sensitivity magnetic particle imaging

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Correction to: Machine Learning Accelerates De Novo Design of Antimicrobial Peptides

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Interdisciplinary sciences, computational life sciences 2024年第2期16卷 404页

作者： Kedong Yin Wen Xu Shiming Ren Qingpeng Xu Shaojie Zhang Ruiling Zhang Mengwan Jiang Yuhong Zhang Degang Xu Ruifang Li Key Laboratory of Functional Molecules for Biomedical Research Henan University of Technology Zhengzhou 450001 China. College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China. Key Laboratory of Functional Molecules for Biomedical Research Henan University of Technology Zhengzhou 450001 China. xw@***. Law College Henan University of Technology Zhengzhou 450001 China. xw@***. College of Biological Engineering Henan University of Technology Zhengzhou 450001 China. School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou 450001 China. School of Economics and Trade Henan University of Technology Zhengzhou 450001 China. College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China. xudegang@***. Key Laboratory of Functional Molecules for Biomedical Research Henan University of Technology Zhengzhou 450001 China. lrf@***. College of Biological Engineering Henan University of Technology Zhengzhou 450001 China. lrf@***.

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