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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Song, Xiaohu Wang, Ying Cheng, Xiao Liang, Guangtai Wang, Qianxiang Zhu, Zhiliang Northeastern University Shenyang China Huawei Cloud Computing Technologies Co. Ltd. Beijing China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Shenyang China

ISBN: (纸本)9798400702174

Numerous third-party libraries introduced into client projects are not actually required, resulting in modern software being gradually bloated. Software developers may spend much unnecessary effort to manage the bloated dependencies: keeping the library versions up-to-date, making sure that heterogeneous licenses are compatible, and resolving dependency conflict or vulnerability issues. However, the prior debloating techniques can easily produce false alarms of bloated dependencies since they are less effective in analyzing Java reflections. Besides, the solutions given by the existing approaches for removing bloated dependencies may induce new issues that are not conducive to dependency management. To address the above limitations, in this paper, we developed a technique, Slimming, to remove bloated dependencies from software projects reliably. Slimming statically analyzes the Java reflections that are commonly leveraged by popular frameworks (e.g., Spring Boot) and resolves the reflective targets via parsing configuration files (*. xml, *. ym1 and *. properties). By modeling string manipulations, Slimming fully resolves the string arguments of our concerned reflection APIs to identify all the required dependencies. More importantly, it helps developers analyze the debloating solutions by weighing the benefits against the costs of dependency management. Our evaluation results show that the static reflection analysis capability of Slimming outperforms all the other existing techniques with 97.0% of Precision and 98.8% of Recall. Compared with the prior debloating techniques, Slimming can reliably remove the bloated dependencies with a 100% test passing ratio and improve the rationality of debloating solutions. In our large-scale study in the Maven ecosystem, Slimming reported 484 bloated dependencies to 66 open-source projects. 38 reports (57.6%) have been confirmed by developers. © 2024 ACM.

关键词： Java programming language

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Efficiently Trimming the Fat: Streamlining Software Dependencies with Java Reflection and Dependency Analysis

Efficiently Trimming the Fat: Streamlining Software Dependen...

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International Conference on Software Engineering (ICSE)

作者： Xiaohu Song Ying Wang Xiao Cheng Guangtai Liang Qianxiang Wang Zhiliang Zhu Northeastern University Shenyang China Huawei Cloud Computing Technologies Co. Ltd. Beijing China National Frontiers Science Center for Industrial Intelligence and Systems Optimization and Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

关键词： Java Ecosystems XML Licenses Reflection Software Libraries

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ECG Synthesis with Neural ODE and GAN Models

ECG Synthesis with Neural ODE and GAN Models

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Mansura Habiba Eoin Borphy Barak A. Pearlmutter Tomas Ward Cloud Solution Lead IBM in IrelandDublinIreland School of Computing & INFANT Research Centre Dublin City University Dublin Ireland Maynooth University Maynooth Ireland School of Computing & Insight Centre for Data Analytics Dublin City University Dublin Ireland

ISBN: (纸本)9781665442329

This paper uses Neural ODE (NODE) based models to generate continuous medical time series. We also introduce a new technique to design the generative adversarial network (GAN) with Neural ODE (NODE) based Generator and Discriminator. During the Performace evaluation of the proposed model, we find that data generated by a NODE-based generator is more continuous than traditional GAN. Therefore, the proposed GAN model becomes more robust. On the other hand, the traditional GAN model demonstrates unstable training and unsupervised learning, which often make it difficult to determine the quality of the result. For this work, we design NODE based models as a generator and a discriminator to make the GAN model datadriven. The data-driven approach helps us overcome the unstable training of the traditional GAN model and improve the quality of the result. We used different evaluation metrics to quantitatively assess generated synthetic data for real-world applications and data analysis. We also evaluate the existing GAN model and the proposed models to understand the comparative efficiency of medical data synthesis.

关键词： Training Performance evaluation Time series analysis Training data Electrocardiography Generative adversarial networks Generators

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Comparative analysis of SIMILAR to RCD ONE(SRO)family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

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Journal of Cotton Research 2024年第1期7卷 45-57页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture FaisalabadSub-Campus BurewalaVehari 61010Pakistan Department of Chemistry and Biochemistry University of AgricultureFaisalabad 38400Pakistan Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad 38400Pakistan Department of Plant Breeding and Genetics University of AgricultureFaisalabad 38400Pakistan Precision Agriculture and Analytics Lab National Centre in Big Data and Cloud ComputingCentre for Advanced Studies in Agriculture and Food SecurityUniversity of Agriculture FaisalabadFaisalabad 38400Pakistan Department of Plant Pathology University of CaliforniaDavisCA 95616USA State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural SciencesAnyangHenan 455000China Federal Seed Certification and Registration Department Ministry of National Food Security and ResearchIslamabad 44090Pakistan School of Agriculture Sciences Zhengzhou UniversityZhengzhou 450000China

Background SRO(Similar to RCD1)genes family is largely recognized for their importance in the growth,develop-ment,and in responding to environmental ***,genome-wide identification and functional characteri-zation of SRO genes from cotton species have not been reported so *** A total of 36 SRO genes were identified from four cotton *** analysis divided these genes into three groups with distinct *** and chromosomal distribution analysis indicated uneven distribu-tion of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,*** duplication analysis revealed the presence of six duplicated gene pairs among GhSRO *** promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and *** miRNAs responsive to plant growth and abiotic stress were predicted to target 12 GhSRO ***-specific expression profiling demonstrated the roles of GhSRO genes in one or more *** addition,specific expression pattern of some GhSRO genes dur-ing ovule development depicted their involvement in these developmental *** The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

关键词： Cotton SRO miRNAs Gene duplications Gene expression Ovule development

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Scientific data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments 5

Scientific Data Lake for High Luminosity LHC project and oth...

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5th International Conference on Particle Physics and Astrophysics, ICPPA 2020

作者： Alekseev, A. Kiryanov, A. Klimentov, A. Korchuganova, T. Mitsyn, V. Oleynik, D. Smirnov, A. Smirnov, S. Zarochentsev, A. Compiler Technology Department Ivannikov Institute for System Programming of the Ras Moscow109004 Russia Cloud Technologies and Big Data Analytics Laboratory Plekhanov Russian University of Economics Moscow117997 Russia Atlas Group Universidad Andres Bello Santiago7550196 Chile Department of Computing Resources and Technologies Nrc "kurchatov Institute"-PNPI Gatchina188300 Russia Physics Department Brookhaven National Laboratory UptonNY11973 United States Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg198504 Russia

The next phase of LHC Operations-High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-The-Art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed. © Published under licence by IOP Publishing Ltd.

关键词： Luminance

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Comparative analysis of SIMILAR to RCD ONE(SRO) family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

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Journal of Cotton Research 2024年第4期 438页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture Faisalabad Sub-Campus Burewala Department of Chemistry and Biochemistry University of AgricultureFaisalabad Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad Department of Plant Breeding and Genetics University of AgricultureFaisalabad Precision Agriculture and Analytics Lab National Centre in Big Data and Cloud Computing Centre for Advanced Studies in Agriculture and Food Security University of Agriculture Faisalabad Department of Plant Pathology University of California State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural Sciences Federal Seed Certification and Registration Department Ministry of National Food Security and Research School of Agriculture Sciences Zhengzhou University

Background SRO(Similar to RCD1) genes family is largely recognized for their importance in the growth,development,and in responding to environmental ***,genome-wide identification and functional characterization of SRO genes from cotton species have not been reported so *** A total of 36 SRO genes were identified from four cotton *** analysis divided these genes into three groups with distinct *** and chromosomal distribution analysis indicated uneven distribution of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,*** duplication analysis revealed the presence of six duplicated gene pairs among GhSRO *** promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and *** miRNAs responsive to plant growth and abiotic stress were predicted to target12 GhSRO ***-specific expression profiling demonstrated the roles of GhSRO genes in one or more *** addition,specific expression pattern of some GhSRO genes during ovule development depicted their involvement in these developmental *** The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

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Decoding the Molecular Universe - Workshop Report

arXiv

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arXiv 2023年

作者： Metz, Thomas O. Adkins, Joshua N. Armentrout, Peter B. Chain, Patrick Chu, Fanny Corley, Courtney D. Cort, John R. Denis, Elizabeth Drell, Daniel Duncan, Katherine R. Ewing, Robert G. Fernandez, Facundo M. Fiehn, Oliver Garg, Neha Grimme, Stefan Henry, Christopher Hettich, Robert L. Kind, Tobias Linington, Roger G. Miller, Gary W. Northen, Trent Overdahl, Kirsten Patrinos, Ari Raftery, Daniel Rigor, Paul Smith, Richard D. Sobus, Jon Teeguarden, Justin Vertes, Akos Waters, Katrina Webb-Robertson, Bobbie-Jo Williams, Antony Wishart, David Biological Sciences Division Earth and Biological Sciences Directorate Pacific Northwest National Laboratory RichlandWA United States Department of Chemistry University of Utah Salt Lake CityUT United States Biosciences Division Chemical Earth and Life Sciences Directorate Los Alamos National Laboratory Los AlamosNM United States Nuclear Chemistry and Biology Division National Security Directorate Pacific Northwest National Laboratory RichlandWA United States AI and Data Analytics Division Pacific Northwest National Laboratory RichlandWA United States Office of Biological and Environmental Research U.S. Department of Energy WashingtonDC United States University of Strathclyde Strathclyde Institute of Pharmacy and Biomedical Sciences Glasgow United Kingdom School of Chemistry and Biochemistry Petit Institute for Bioengineering and Bioscience Georgia Institute of Technology AtlantaGA United States University of California Davis West Coast Metabolomics Center DavisCA United States School of Chemistry and Biochemistry Petit Institute for Bioengineering and Bioscience Center for Microbial Dynamics and Infection Georgia Institute of Technology AtlantaGA United States Mulliken Center for Theoretical Chemistry Clausius Institute for Physical and Theoretical Chemistry University of Bonn Germany Data Science and Learning Division Argonne National Laboratory LemontIL United States Biosciences Division Oak Ridge National Laboratory Oak RidgeTN United States Enveda Biosciences BoulderCO United States Department of Chemistry Simon Fraser University BurnabyBC Canada Department of Environmental Health Sciences Mailman School of Public Health Columbia University New YorkNY United States Environmental Genomics and System Biology Division Lawrence Berkeley National Laboratory CA United States Metabolomics Core Facility National Institute of Environmental Health Sciences Research Triangle Park NC United States Novim Group Santa BarbaraCA United States Nor

On August 9-10, 2023, a workshop was convened at the Pacific Northwest National Laboratory (PNNL) in Richland, WA that brought together a group of internationally recognized experts in metabolomics, natural products discovery, chemical ecology, chemical and biological threat assessment, cheminformatics, computational chemistry, cloud computing, artificial intelligence, and novel technology development. These experts were invited to assess the value and feasibility of a grand-scale project to create new technologies that would allow the identification and quantification of all small molecules, or to "decode the molecular universe." The Decoding the Molecular Universe project would extend and complement the success of the Human Genome Project (HGP) by developing new capabilities and technologies to measure small molecules – defined as non-protein, non-polymer molecules less than ~1500 Daltons – of any origin and generated in biological systems or produced abiotically. The workshop was inspired by recognition that these new technologies would drive the next revolution in multiple fields of strategic importance to the nation: biological sciences, medical sciences, national security, chemistry, and the bio-economy. The workshop was supported by PNNL’s investment to advance the field of computational mass spectrometry in support of Department of Energy and Department of Homeland Security missions via a project named the m/q Initiative. The workshop was modeled on similar workshops and related efforts held in the mid-1980s that were the catalyst for the HGP. Workshop participants and observers included representatives from academia, government agencies, industry, and national laboratories whose research involves or where the success of programs and mission areas depends heavily on the study or measurement of such small molecules. Primary motivations for the workshop were a nearly universal need – across scientific disciplines and applied and basic research domains – to ide

关键词： Proteins

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Scientific data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments

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Journal of Physics: Conference Series 2020年第1期1690卷

作者： A Alekseev A Kiryanov A Klimentov T Korchuganova V Mitsyn D Oleynik A Smirnov S Smirnov A Zarochentsev Compiler Technology Department Ivannikov Institute for System Programming of the RAS Moscow 109004 Russia Cloud technologies and Big Data analytics laboratory Plekhanov Russian University of Economics Moscow 117997 Russia ATLAS group Universidad Andres Bello Santiago 7550196 Chile Department of computing resources and technologies NRC 'Kurchatov Institute' - PNPI 188300 Gatchina Russia Physics Department Brookhaven National Laboratory Upton NY 11973 USA Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna 141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow 115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg 198504 Russia

The next phase of LHC Operations – High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-the-art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed.

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Preface

ICMLCA 2021 - 2nd International Conference on Machine Learni...

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ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application 2021年 iii页

作者： Ning, Xiansheng Feng, Yongxin Zhang, Wenbo Yue, Mingkai Du, Ke-Lin Huang, Shutao Zhou, Fang Ba, Shuhong Fu, Chong Shui, Penglang Xu, Shuwen Wu, Sheng Wang, Shushan Du, Zhonghua Gao, Zhijun Fan, Chunlong Li, Yufeng Wang, Yupeng Liu, Qingli Yu, Jingang Yin, Zhenyu Zhang, Deyu Gao, Hongwei Wen, Feng Guo, Cean Hao, Yongping Chan, Yung-Kuan Pavlovich, Vinogradov Gennady Kirpichnikova, Irina Qian, Zhihong Ivanov, Dmitriy V. Zhou, Xueguan Nezamabadi-Pour, Hossein Yang, Qing Zhang, Yongtang Liao, Haibin Huang, Ying Srinivasan, Kathiravan Xu, Zhenghua Pachori, Ram Bilas Zhang, Jialu AlShawabkeh, Mahmoud Guo, Chengjun Chaugule, Archana Ajit Lima, Alberto Sampaio Liu, Huayong Vidal, Jorge Maestre Alrshah, Mohamed A. Ghafoor, Kayhan Zrar Kubicek, Jan Chaugule, Archana Ajit Rodríguez-Pérez, Miguel Kumar, Gulshan Nasri, Mehdi Borrego, Carlos Persico, Valerio Wagh, Sanjeev Yan, Wenjun Yang, Li Yang, Dawei Xing, Yongkang Yang, Huadong Shang, Chengguo Chang, Xingong Shi, Hongbo Li, Aijun Feng, Liping Lv, Yali Liang, Minfu Wang, Kaixuan Bai, Zengliang Liang, Anhui Nan, Zhihong Ji, Suqin Chang, Liwei Zhou, Hongbin Yao, Chunli Cao, Guodong Zhang, Juling School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Concordia University Canada School of Automotive and Transportation Shenyang Ligong University China School of Information Science and Engineering Shenyang Ligong University China School of Equipment Engineering Shenyang Ligong University China Northeastern University China Xidian University China Beijing University of Posts and Telecommunications China Beijing Institute of Technology China Nanjing University of Science & Technology China Shenyang Jianzhu University China Shenyang Aerospace University China Dalian University China Shenyang Institute of Computing Technology Chinese Academy of Sciences China Shenyang Ligong University China National Chun Hsing University Taiwan Department of Informatics and Applied Mathematics Russia South Ural State University Russia Jilin University China Samara State University of Transport Russia Naval Universit of Engineering China Shahid Bahonar University of Kermanto Iran Central China Normal University China Guangdong Neusoft Institute China Hubei University of Science and Technology China Liuzhou Railway Vocational Technical College China Vellore Institute of Technology India Hebei University of Technology China Indian Institute of Technology Indore India Xiangnan University China Guangxi Normal University for Nationalities China UESTC China Pimpri-Chinchwad College of Engineering & Research India Federal University of Ceará Brazil Complutense University of Madrid Spain Universiti Putra Malaysia Malaysia Shanghai Jiao Tong University China VŠB - Technical University of Ostrava Cuw Pimpri-Chinchwad College of Engineering & Research Ravet Pune India University of Vigo Spain Division of Research and Development India Islamic Azad University Iran Departament Enginyeria of Information Comunicacions Autonomous University of Barcelona Spain Opportunistic Networks United States Univers

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A conceptual framework for assessing community capabilities to address resource-need alignment 19

A conceptual framework for assessing community capabilities ...

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19th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2015

作者： Micher, Logan Hyman, Harvey Woods, Christopher Mechanical and Industrial Engineering Florida Polytechnic University LakelandFL33805 United States Data Analytics and Cloud Computing Florida Polytechnic University LakelandFL33805 United States

ISBN: (纸本)9781941763254

This paper examines the need for a community capability index as an assessment tool to better align the needs of underdeveloped communities with the resources of aid providers. The development of a community capability index has become more and more important for prioritizing the deployment of increasingly limited resources. In this paper, a survey of current theoretical models is conducted and a proposed framework is developed. A discussion of alternative methods for assessing and measuring the capabilities of a community is presented. A metaanalysis of existing literature in the area is offered, and methods for creating and validating community capability indices are explored. A description of further investigation into resource capabilities is given, and the constructs of these categories are identified. Our findings are: (1) A general model for evaluating community capabilities can be used to support better alignment of resources with needs, (2) A systematic approach for identifying capability constructs can support better insight into prioritizing assistance, and (3) A community capability matrix in the form of a "basic community capability index" (BCCI) can support a relative comparison evaluation along a continuous scaled metric. The framework proposed here is presented as an initial approach for evaluating community distress, prioritizing assistance, and aligning aid resources.

关键词： Community aid Community capability Frameworks Social responsibility Sustainability

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