Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)最新文献
Much attention is paid to the process of how new storage services are deployed into production that the challenges therein. Far less is paid to what happens when a storage service is approaching the end of its useful life. The challenges in rationalising and de-scoping a service that, while relatively old, is still critical to production work for both the UK WLCG Tier 1 and local facilities are not to be underestimated. RAL has been running a disk and tape storage service based on CASTOR (Cern Advanced STORage) for over 10 years. CASTOR must cope with both the throughput requirements of supplying data to a large batch farm and the data integrity requirements needed by a long-term tape archive. A new storage service, called ‘Echo’ is now being deployed to replace the disk-only element of CASTOR, but we intend to continue supporting the CASTOR system for tape into the medium term. This, in turn, implies a downsizing and redesign of the CASTOR service in order to improve manageability and cost effectiveness. We will give an outline of both Echo and CASTOR as background. This paper will discuss the project to downsize CASTOR and improve its manageability when running both at a considerably smaller scale (we intend to go from around 140 storage nodes to around 20), and with a considerably lower amount of available staff effort. This transformation must be achieved while, at the same time, running the service in 24/7 production and supporting the transition to the newer storage element. To achieve this goal, we intend to transition to a virtualised infrastructure to underpin the remaining management nodes and improve resilience by allowing management functions to be performed by many different nodes concurrently (‘cattle’ as opposed to ‘pets’), and also intend to streamline the system by condensing the existing 4 CASTOR ‘stagers’ (databases that record the state of the disk pools) into a single one that supports all users.
{"title":"What Goes Up, Must Go Down: A Case Study From RAL on Shrinking an Existing Storage Service","authors":"R. Appleyard, G. Patargias","doi":"10.22323/1.327.0026","DOIUrl":"https://doi.org/10.22323/1.327.0026","url":null,"abstract":"Much attention is paid to the process of how new storage services are deployed into production that the challenges therein. Far less is paid to what happens when a storage service is approaching the end of its useful life. The challenges in rationalising and de-scoping a service that, while relatively old, is still critical to production work for both the UK WLCG Tier 1 and local facilities are not to be underestimated. \u0000 \u0000RAL has been running a disk and tape storage service based on CASTOR (Cern Advanced STORage) for over 10 years. CASTOR must cope with both the throughput requirements of supplying data to a large batch farm and the data integrity requirements needed by a long-term tape archive. A new storage service, called ‘Echo’ is now being deployed to replace the disk-only element of CASTOR, but we intend to continue supporting the CASTOR system for tape into the medium term. This, in turn, implies a downsizing and redesign of the CASTOR service in order to improve manageability and cost effectiveness. We will give an outline of both Echo and CASTOR as background. \u0000 \u0000This paper will discuss the project to downsize CASTOR and improve its manageability when running both at a considerably smaller scale (we intend to go from around 140 storage nodes to around 20), and with a considerably lower amount of available staff effort. This transformation must be achieved while, at the same time, running the service in 24/7 production and supporting the transition to the newer storage element. To achieve this goal, we intend to transition to a virtualised infrastructure to underpin the remaining management nodes and improve resilience by allowing management functions to be performed by many different nodes concurrently (‘cattle’ as opposed to ‘pets’), and also intend to streamline the system by condensing the existing 4 CASTOR ‘stagers’ (databases that record the state of the disk pools) into a single one that supports all users.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121310896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rongqiang Cao, Shasha Lu, Xiaoning Wang, Haili Xiao, Xue-bin Chi
In recent years, High Performance Computing (HPC) has developed rapidly in China. From Chinese Academy of Sciences (CAS) level, Scientific Computing Grid (ScGrid), is a general-purpose computing platform started from 2006 in CAS, which provided a problem solving environment for computing users through grid computing and cloud computing technologies. Then ScGrid becomes Supercomputing Cloud, an important port of Chinese Science Cloud from 2011. From national level, China National Grid (CNGrid) has integrated massive HPC resources from several national supercomputing centers and other large centers distributed geographically, and been providing efficient computing services for users in diverse disciplines and research areas. During more than 10 years, CNGrid and ScGrid has integrated tens of HPC resources distributed geographically across China, comprising 6 National Supercomputer Centers of Tianjin, Jinan, Changsha, and Shenzhen, Guangzhou, Wuxi, and also dozens of teraflops-scale HPC resources belong to universities and institutes. In total, the computing capability is more than 200PF and the storage capacity is more than 160PB in CNGrid. As worked in the operation and management center of CNGrid and ScGrid for many years, we notice that users prefer to manage their jobs at different supercomputers and clusters via a global account on different remote clients such as science gateways, desktop applications and even scripts. And they don’ t like to apply for an account to each supercomputer and login into the supercomputer in specific way. Therefore, we described Unified Account Management as a Service (UAMS) to access and use all HPC resources via a global account for each user in this paper. We addressed and solved challenges for mapping a global account to many local accounts, and provided unified account registration, management and authentication for different collaborative web gateways, command toolkits and other desktop applications. UAMS was designed in accordance with the core rules of simplicity, compatibility and reusability. In architecture design, we focused on loosely-coupled style to acquire good scalability and update internal modules transparently. In implementation, we applied widely accepted knowledge for the definitions of the RESTful API and divided them into several isolated microservices according to their usages and scenarios. For security, all sensitive data transferred in wide-network is protected by HTTPS with transport layer security outside of CNGrid and secure communication channels provided by OpenSSH inside of CNGrid. In addition, all parameters submitted to RESTful web services are strictly checked in format and variable type. By providing these frequently important but always challenging capabilities as a service, UAMS allows users to use tens of HPC resources and clients via only an account, and makes it easy for developers to implement clients and services related HPC with advantages of numerous users and s
{"title":"Unified Account Management for High Performance Computing as a Service with Microservice Architecture","authors":"Rongqiang Cao, Shasha Lu, Xiaoning Wang, Haili Xiao, Xue-bin Chi","doi":"10.22323/1.327.0020","DOIUrl":"https://doi.org/10.22323/1.327.0020","url":null,"abstract":"In recent years, High Performance Computing (HPC) has developed rapidly in China. From Chinese Academy of Sciences (CAS) level, Scientific Computing Grid (ScGrid), is a general-purpose computing platform started from 2006 in CAS, which provided a problem solving environment for computing users through grid computing and cloud computing technologies. Then ScGrid becomes Supercomputing Cloud, an important port of Chinese Science Cloud from 2011. From national level, China National Grid (CNGrid) has integrated massive HPC resources from several national supercomputing centers and other large centers distributed geographically, and been providing efficient computing services for users in diverse disciplines and research areas. During more than 10 years, CNGrid and ScGrid has integrated tens of HPC resources distributed geographically across China, comprising 6 National Supercomputer Centers of Tianjin, Jinan, Changsha, and Shenzhen, Guangzhou, Wuxi, and also dozens of teraflops-scale HPC resources belong to universities and institutes. In total, the computing capability is more than 200PF and the storage capacity is more than 160PB in CNGrid. \u0000 As worked in the operation and management center of CNGrid and ScGrid for many years, we notice that users prefer to manage their jobs at different supercomputers and clusters via a global account on different remote clients such as science gateways, desktop applications and even scripts. And they don’ t like to apply for an account to each supercomputer and login into the supercomputer in specific way. \u0000 Therefore, we described Unified Account Management as a Service (UAMS) to access and use all HPC resources via a global account for each user in this paper. We addressed and solved challenges for mapping a global account to many local accounts, and provided unified account registration, management and authentication for different collaborative web gateways, command toolkits and other desktop applications. UAMS was designed in accordance with the core rules of simplicity, compatibility and reusability. In architecture design, we focused on loosely-coupled style to acquire good scalability and update internal modules transparently. In implementation, we applied widely accepted knowledge for the definitions of the RESTful API and divided them into several isolated microservices according to their usages and scenarios. For security, all sensitive data transferred in wide-network is protected by HTTPS with transport layer security outside of CNGrid and secure communication channels provided by OpenSSH inside of CNGrid. In addition, all parameters submitted to RESTful web services are strictly checked in format and variable type. \u0000 By providing these frequently important but always challenging capabilities as a service, UAMS allows users to use tens of HPC resources and clients via only an account, and makes it easy for developers to implement clients and services related HPC with advantages of numerous users and s","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122163747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Available computing resources limit data simulation and processing of LHC experiments. WLCG Tier centers connected via Grid provide majority of computing and storage capacities, which allow relatively fast and precise analyses of data. Requirements on the number of simulated events must be often reduced to meet installed capacities. Projection of requirements for future LHC runs shows a significant shortage of standard Grid resources if a flat budget is assumed. There are several activities exploring other sources of computing power for LHC projects. The most significant are big HPC centers (supercomputers) and Cloud resources provided both by commercial and academic institutions. The Tier-2 center hosted by the Institute of Physics (FZU) in Prague provides resources for ALICE and ATLAS collaborations on behalf of all involved Czech institutions. Financial resources provided by funding agencies and resources provided by IoP do not allow to buy enough servers to meet demands of experiments. We extend storage resources by two distant sites with additional finance sources. Xrootd servers in the Institute of Nuclear Physics in Rez near Prague store files for the ALICE experiment. CESNET data storage group operates dCache instance with a tape backend for ATLAS (and Pierre Auger Observatory) collaboration. Relatively big computing capacities could be used in the national supercomputing center IT4I in Ostrava. Within the ATLAS collaboration, we explore two different solutions to overcome technical problems arising from different computing environment on the supercomputer. The main difference is that individual worker nodes do not have an external network connection and cannot directly download input and upload output data. One solution is already used for HPC centers in the USA, but until now requires significant adjustments of procedures used for standard ATLAS production. Another solution is based on ARC CE hosted by the Tier-2 center at IoP and resubmission of jobs remotely via ssh.
{"title":"Extending WLCG Tier-2 Resources using HPC and Cloud Solutions","authors":"J. Chudoba, M. Svatos","doi":"10.22323/1.327.0025","DOIUrl":"https://doi.org/10.22323/1.327.0025","url":null,"abstract":"Available computing resources limit data simulation and processing of LHC experiments. WLCG Tier centers connected via Grid provide majority of computing and storage capacities, which allow relatively fast and precise analyses of data. Requirements on the number of simulated events must be often reduced to meet installed capacities. Projection of requirements for future LHC runs shows a significant shortage of standard Grid resources if a flat budget is assumed. There are several activities exploring other sources of computing power for LHC projects. The most significant are big HPC centers (supercomputers) and Cloud resources provided both by commercial and academic institutions. The Tier-2 center hosted by the Institute of Physics (FZU) in Prague provides resources for ALICE and ATLAS collaborations on behalf of all involved Czech institutions. Financial resources provided by funding agencies and resources provided by IoP do not allow to buy enough servers to meet demands of experiments. We extend storage resources by two distant sites with additional finance sources. Xrootd servers in the Institute of Nuclear Physics in Rez near Prague store files for the ALICE experiment. CESNET data storage group operates dCache instance with a tape backend for ATLAS (and Pierre Auger Observatory) collaboration. Relatively big computing capacities could be used in the national supercomputing center IT4I in Ostrava. Within the ATLAS collaboration, we explore two different solutions to overcome technical problems arising from different computing environment on the supercomputer. The main difference is that individual worker nodes do not have an external network connection and cannot directly download input and upload output data. One solution is already used for HPC centers in the USA, but until now requires significant adjustments of procedures used for standard ATLAS production. Another solution is based on ARC CE hosted by the Tier-2 center at IoP and resubmission of jobs remotely via ssh.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130283316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiulan Huang, Gongxing Sun, Qiao Yin, Zhanchen Wei, Qiang Li
This paper explores a way to build a new computing environment based on Hadoop to make the Large High Altitude Air Shower Observatory(LHAASO) jobs run on it transparently. Particularly, we discuss a new mechanism to support LHAASO software to random access data in HDFS. This new feature allows the Map/Reduce tasks to random read/write data on the local file system instead of using Hadoop data streaming interface. This makes HEP jobs run on Hadoop possible. We also develop MapReduce patterns for LHAASO jobs such as Corsika simulation, ARGO detector simulation (Geant4), KM2A simulation and Medea++ reconstruction. And user-friendly interface is provided. In addition, we provide the real-time cluster monitoring in terms of cluster healthy, number of running jobs, finished jobs and killed jobs. Also the accounting system is included. This work has been in production for LHAASO offline data analysis to gain about 20,000 CPU hours per month since September, 2016. The results show the efficiency of IO intensive job can be improved about 46%. Finally, we describe our ongoing work of data migration tool to serve the data move between HDFS and other storage systems.
本文探讨了一种基于Hadoop构建新的计算环境的方法，使大型高空风淋观测站(Large High Altitude Air Shower Observatory, LHAASO)的作业能够透明地在Hadoop上运行。特别地，我们讨论了一种支持LHAASO软件随机访问HDFS数据的新机制。这个新特性允许Map/Reduce任务在本地文件系统上随机读取/写入数据，而不是使用Hadoop数据流接口。这使得在Hadoop上运行HEP作业成为可能。我们还开发了用于LHAASO作业的MapReduce模式，如Corsika模拟、ARGO探测器模拟(Geant4)、KM2A模拟和Medea++重建。并提供用户友好的界面。此外，我们还提供实时集群监控，包括集群运行状况、正在运行的作业数量、已完成的作业和已终止的作业。会计系统也包括在内。该工作已投入生产，用于LHAASO离线数据分析，自2016年9月起每月获得约20,000 CPU小时。结果表明，IO密集型作业的效率可提高约46%。最后，我们描述了我们正在进行的数据迁移工具，以服务于HDFS和其他存储系统之间的数据迁移。
{"title":"Explore New Computing Environment for LHAASO Offline Data Analysis","authors":"Qiulan Huang, Gongxing Sun, Qiao Yin, Zhanchen Wei, Qiang Li","doi":"10.22323/1.327.0021","DOIUrl":"https://doi.org/10.22323/1.327.0021","url":null,"abstract":"This paper explores a way to build a new computing environment based on Hadoop to make the Large High Altitude Air Shower Observatory(LHAASO) jobs run on it transparently. Particularly, we discuss a new mechanism to support LHAASO software to random access data in HDFS. This new feature allows the Map/Reduce tasks to random read/write data on the local file system instead of using Hadoop data streaming interface. This makes HEP jobs run on Hadoop possible. We also develop MapReduce patterns for LHAASO jobs such as Corsika simulation, ARGO detector simulation (Geant4), KM2A simulation and Medea++ reconstruction. And user-friendly interface is provided. In addition, we provide the real-time cluster monitoring in terms of cluster healthy, number of running jobs, finished jobs and killed jobs. Also the accounting system is included. This work has been in production for LHAASO offline data analysis to gain about 20,000 CPU hours per month since September, 2016. The results show the efficiency of IO intensive job can be improved about 46%. Finally, we describe our ongoing work of data migration tool to serve the data move between HDFS and other storage systems.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127145039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Fuhrmann, M. Antonacci, G. Donvito, O. Keeble, P. Millar
The core activity within the newly created H2020 project eXtreme DataCloud project will be the policy-driven orchestration of federated data management for data intensive sciences like High Energy Physics, Astronomy, Photon and Life Science. Well-known experts in this field will work on combining already established data management and orchestration tools to provide a highly scalable solution supporting the entire European Scientific Landscape. The work will cover "Data Life Cycle Management" as well as smart data placement on meta data, including storage availability, network bandwidth and data access patterns. Mechanisms will be put in place to trigger computational resources based on data ingestion and data movements. This paper presents the first architecture of this endeavor.
{"title":"Smart Policy Driven Data Management and Data Federations","authors":"P. Fuhrmann, M. Antonacci, G. Donvito, O. Keeble, P. Millar","doi":"10.22323/1.327.0001","DOIUrl":"https://doi.org/10.22323/1.327.0001","url":null,"abstract":"The core activity within the newly created H2020 project eXtreme DataCloud project will be the policy-driven orchestration of federated data management for data intensive sciences like High Energy Physics, Astronomy, Photon and Life Science. Well-known experts in this field will work on combining already established data management and orchestration tools to provide a highly scalable solution supporting the entire European Scientific Landscape. The work will cover \"Data Life Cycle Management\" as well as smart data placement on meta data, including storage availability, network bandwidth and data access patterns. Mechanisms will be put in place to trigger computational resources based on data ingestion and data movements. This paper presents the first architecture of this endeavor.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130474818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saqib Haleem, Fawad Saeed, A. Rehman, Muhammad Imran
The National Centre for Physics (NCP) in Pakistan maintains a computing infrastructure for the scientific community. A major portion of the computing and storage resources are reserved for the CMS experiment through the WLCG infrastructure, and a small portion of the computing resources are reserved for other non experimental high-energy physics (EHEP) scientific experiments. For efficient utilization of resources, many scientific organizations have migrated their resources into infrastructure-as-a-service (IaaS) facilities. The NCP has also taken such an initiative last year, and has migrated most of their resources into an IaaS facility. An HT-condor based batch system has been deployed for the local experimental high energy physics community to allow them to perform their analysis task. Recently we deployed an HT-Condor compute element (CE) as a gateway for the CMS jobs. On the network side, our Tier-2 site is completely accessible and operational on IPv6. Moreover, we recently deployed a Perfsonar node to actively monitor the throughput and latency issues between NCP and other WLCG sites. This paper discusses the status of NCP Tier-2 site, its current challenges and future directions.
{"title":"WLCG Tier-2 site at NCP, Status Update and Future Direction","authors":"Saqib Haleem, Fawad Saeed, A. Rehman, Muhammad Imran","doi":"10.22323/1.327.0015","DOIUrl":"https://doi.org/10.22323/1.327.0015","url":null,"abstract":"The National Centre for Physics (NCP) in Pakistan maintains a computing infrastructure for the scientific community. A major portion of the computing and storage resources are reserved for the CMS experiment through the WLCG infrastructure, and a small portion of the computing resources are reserved for other non experimental high-energy physics (EHEP) scientific experiments. For efficient utilization of resources, many scientific organizations have migrated their resources into infrastructure-as-a-service (IaaS) facilities. The NCP has also taken such an initiative last year, and has migrated most of their resources into an IaaS facility. An HT-condor based batch system has been deployed for the local experimental high energy physics community to allow them to perform their analysis task. Recently we deployed an HT-Condor compute element (CE) as a gateway for the CMS jobs. On the network side, our Tier-2 site is completely accessible and operational on IPv6. Moreover, we recently deployed a Perfsonar node to actively monitor the throughput and latency issues between NCP and other WLCG sites. This paper discusses the status of NCP Tier-2 site, its current challenges and future directions.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126501719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Authorship recognition and author names disambiguation are main issues affecting the quality and reliability of bibliographic records retrieved from digital libraries, such as Web of Science, Scopus, Google Scholar and many others. So far, these problems have been faced using methods mainly based on text-pattern-recognition for specific datasets, with high-level degree of errors. In this paper, we propose a different approach using neural networks to learn features automatically for solving authorship recognition and disambiguation of author names. The network learns for each author the set of co-writers, and from this information recovers authorship of papers. In addition, the network can be trained taking into account other features, such as author affiliations, keywords, projects and research areas. The network has been developed using the TensorFlow framework, and run on recent Nvidia GPUs and multi-core Intel CPUs. Test datasets have been selected from records of Scopus digital library, for several groups of authors working in the fields of computer science, environmental science and physics. The proposed methods achieves accuracies above 99% in authorship recognition, and is able to effectively disambiguate homonyms. We have taken into account several network parameters, such as training-set and batch size, number of levels and hidden units, weights initialization, back-propagation algorithms, and analyzed also their impact on accuracy of results. This approach can be easily extended to any dataset and any bibliographic records provider.
作者身份识别和作者姓名消歧是影响Web of Science、Scopus、b谷歌Scholar等数字图书馆检索书目记录质量和可靠性的主要问题。到目前为止，这些问题主要是使用基于特定数据集的文本模式识别方法来解决的，误差程度很高。在本文中，我们提出了一种使用神经网络自动学习特征的方法来解决作者身份识别和作者姓名消歧问题。网络为每个作者学习共同作者的集合，并从这些信息中恢复论文的作者身份。此外，还可以考虑其他特征来训练网络，例如作者隶属关系、关键词、项目和研究领域。该网络是使用TensorFlow框架开发的，并在最新的Nvidia gpu和多核Intel cpu上运行。测试数据集是从Scopus数字图书馆的记录中选择的，适用于计算机科学、环境科学和物理学领域的几组作者。该方法在作者身份识别中准确率达到99%以上，能够有效地消除同音异义。我们考虑了几个网络参数，如训练集和批大小、层次数量和隐藏单元、权重初始化、反向传播算法，并分析了它们对结果准确性的影响。这种方法可以很容易地扩展到任何数据集和任何书目记录提供者。
{"title":"Authorship recognition and disambiguation of scientific papers using a neural networks approach","authors":"S. Schifano, Tommaso Sgarbanti, L. Tomassetti","doi":"10.22323/1.327.0007","DOIUrl":"https://doi.org/10.22323/1.327.0007","url":null,"abstract":"Authorship recognition and author names disambiguation are main issues affecting the quality and reliability of bibliographic records retrieved from digital libraries, such as Web of Science, Scopus, Google Scholar and many others. So far, these problems have been faced using methods mainly based on text-pattern-recognition for specific datasets, with high-level degree of errors. \u0000 \u0000In this paper, we propose a different approach using neural networks to learn features automatically for solving authorship recognition and disambiguation of author names. The network learns for each author the set of co-writers, and from this information recovers authorship of papers. In addition, the network can be trained taking into account other features, such as author affiliations, keywords, projects and research areas. \u0000 \u0000The network has been developed using the TensorFlow framework, and run on recent Nvidia GPUs and multi-core Intel CPUs. Test datasets have been selected from records of Scopus digital library, for several groups of authors working in the fields of computer science, environmental science and physics. The proposed methods achieves accuracies above 99% in authorship recognition, and is able to effectively disambiguate homonyms. \u0000 \u0000We have taken into account several network parameters, such as training-set and batch size, number of levels and hidden units, weights initialization, back-propagation algorithms, and analyzed also their impact on accuracy of results. This approach can be easily extended to any dataset and any bibliographic records provider.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127079288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Sakane, Takeshi Nishimura, K. Aida, Motonori Nakamura
This paper investigates the situation where users must utilize each credential according to the desired services, and clarifies the problems in the situation and the issues addressed by the concept of ``identity federation''. Japan has the GakuNin, which is an academic access management federation, and the HPCI, which is a distributed high performance computing infrastructure. For the provision of the HPCI resources, the HPCI cannot simply behave as a service provider in the GakuNin. Consequently, in performing academic research, especially HPCI users belonging to academic institutions are compelled to manage both the GakuNin and the HPCI credentials. In this paper, based on the situation in Japan mentioned above, we discuss a credential integration model in order to more efficiently utilize a wide variety of services. We first characterize services in an academic federation from the point of view of authorization and investigate the problem that users must utilize each credential issued by different identity providers. Thus, we discuss the issues to integrate user's credentials, and consider a model that solves the issues.
{"title":"A Study of Credential Integration Model in Academic Research Federation Supporting a Wide Variety of Services","authors":"E. Sakane, Takeshi Nishimura, K. Aida, Motonori Nakamura","doi":"10.22323/1.327.0016","DOIUrl":"https://doi.org/10.22323/1.327.0016","url":null,"abstract":"This paper investigates the situation where users must utilize each credential according to the desired services, and clarifies the problems in the situation and the issues addressed by the concept of ``identity federation''. Japan has the GakuNin, which is an academic access management federation, and the HPCI, which is a distributed high performance computing infrastructure. For the provision of the HPCI resources, the HPCI cannot simply behave as a service provider in the GakuNin. Consequently, in performing academic research, especially HPCI users belonging to academic institutions are compelled to manage both the GakuNin and the HPCI credentials. In this paper, based on the situation in Japan mentioned above, we discuss a credential integration model in order to more efficiently utilize a wide variety of services. We first characterize services in an academic federation from the point of view of authorization and investigate the problem that users must utilize each credential issued by different identity providers. Thus, we discuss the issues to integrate user's credentials, and consider a model that solves the issues.","PeriodicalId":135658,"journal":{"name":"Proceedings of International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery — PoS(ISGC 2018 & FCDD)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123484089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：