检索结果-内蒙古大学图书馆

International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Amin Ul Haq Jian Ping Li Riaz Ullah Khan Cobbinah Bernard Mawuli Bless Lord Y. Agbley Mordecai F. Raj Wang Zhou Jalaluddin Khan Abdul Haq Abdus Saboor Faiza Habib Zafar Khan School of Computer Science and Engineering University of Electronic Science and Tech. of China Chengdu China Yangtze River Delta Research Institute (Huzhou) University of Electronic Science and Tech. of China China School of Computer and Software Engineering Xihua University Chengdu Sichuan China Gomal University D.I. Khan Pakistan Hazzra University Manshera KPK Pakistan Faculty of Engineering and I.T Northern University Nowshera Pakistan

ISBN: (纸本)9781665413657

Accurate diagnostic system is significantly important for timely COVID-19 identification. Diagnosing COVID-19 from chest x-ray images employing the CNN model is recommended for accurate recognition of COVID-19. The existing diagnosis techniques of COVID-19 still lack high accuracy. To handle this problem in this work, we have proposed accurate detection method for COVID-19. In the proposed method, a CNN is incorporated for the diagnosis of COVID-19 using chest x-ray images data. The experimental results illustrate that our technique is good for COVID-19 accurate diagnosis and can be easily implemented in health care systems.

关键词： COVID-19 Deep learning Image recognition Computational modeling Medical services Information processing Media

来源：评论

学校读者我要写书评

暂无评论

A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction

arXiv

引用

arXiv 2021年

作者： Cheng, Jian Zhang, Tian Zhang, Shuang Ren, Huimin Yu, Guo Zhang, Xiliang Gao, Shangce Ma, Lianbo The Research Institute of Mine Artificial Intelligence in Chinese Institute of Coal Science State Key Laboratory of Intelligent Coal Mining and Strata Control Tiandi Science and Technology Co. Ltd. Beijing100013 China The College of Software Northeastern University Shenyang China The Institute of Intelligent Manufacturing Nanjing Tech University Nanjing211816 China The School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai China The Faculty of Engineering University of Toyama Toyama-shi930-8555 Japan Foshan Graduate School of Innovation Northeastern University Foshan China

In knowledge graph construction, a challenging issue is how to extract complex (e.g., overlapping) entities and relationships from a small amount of unstructured historical data. The traditional pipeline methods are to divide the extraction into two separate subtasks, which misses the potential interaction between the two subtasks and may lead to error propagation. In this work, we propose an effective cascade dual-decoder method to extract overlapping relational triples, which includes a text-specific relation decoder and a relation-corresponded entity decoder. Our approach is straightforward and it includes a text-specific relation decoder and a relation-corresponded entity decoder. The text-specific relation decoder detects relations from a sentence at the text level. That is, it does this according to the semantic information of the whole sentence. For each extracted relation, which is with trainable embedding, the relation-corresponded entity decoder detects the corresponding head and tail entities using a span-based tagging scheme. In this way, the overlapping triple problem can be tackled naturally. We conducted experiments on a real-world open-pit mine dataset and two public datasets to verify the method's generalizability. The experimental results demonstrate the effectiveness and competitiveness of our proposed method and achieve better F1 scores under strict evaluation metrics. Our implementation is available at https://***/prastunlp/DualDec. Copyright © 2021, The Authors. All rights reserved.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search

arXiv

引用

arXiv 2021年

作者： Ma, Lianbo Li, Nan Yu, Guo Geng, Xiaoyu Cheng, Shi Wang, Xingwei Huang, Min Jin, Yaochu College of Software Northeastern University Shenyang110819 China The Institute of Intelligent Manufacturing Nanjing Tech University Nanjing211816 China The School of Computer Science Shaanxi Normal University Xi’an710062 China The College of Computer Science Northeastern University Shenyang110819 China The College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Faculty of Technology Bielefeld University Bielefeld33619 Germany The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

In multi-objective evolutionary neural architecture search (NAS), existing predictor-based methods commonly suffer from the rank disorder issue that a candidate high-performance architecture may have a poor ranking compared with the worse architecture in terms of the trained *** alleviate the above issue, we aim to train a Pareto-wise end-to-end ranking classifier to simplify the architecture search process by transforming the complex multi-objective NAS task into a simple classification task. To this end, a classifier-based Pareto evolution approach is proposed, where an online classifier is trained to directly predict the dominance relationship between the candidate and reference architectures. Besides, an adaptive clustering method is designed to select reference architectures for the classifier, and an α-domination assisted approach is developed to address the imbalance issue of positive and negative samples. The proposed approach is compared with a number of state-of-the-art NAS methods on widely-used test datasets, and computation results show that the proposed approach is able to alleviate the rank disorder issue and outperforms other methods. Especially, the proposed method is able to find a set of promising network architectures with different model sizes ranging from 2M to 5M under diverse objectives and constraints. © 2021, CC BY.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Safeml: Safety monitoring of machine learning classifiers through statistical difference measure

arXiv

引用

arXiv 2020年

作者： Aslansefat, Koorosh Sorokos, Ioannis Whiting, Declan Kolagari, Ramin Tavakoli Papadopoulos, Yiannis Department of Computer Science University of Hull Kingston upon HullHU6 7RX United Kingdom Fraunhofer Institute for Experimental Software Engineering IESE Fraunhofer-Gesellschaft Fraunhofer-Platz 1 Kaiserslautern67663 Germany Department of Computer Science University of Hull APD Communications Kingston upon HullHU6 7RX United Kingdom Faculty of Computer Science Nuremberg Tech Keßlerplatz 12 Nürnberg90489 Germany

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems. Especially the interaction between safety and security is a central challenge, as security violations can lead to compromised safety. The contribution of this paper to addressing both safety and security within a single concept of protection applicable during the operation of ML systems is active monitoring of the behavior and the operational context of the data-driven system based on distance measures of the Empirical Cumulative Distribution Function (ECDF). We investigate abstract datasets (XOR, Spiral, Circle) and current security-specific datasets for intrusion detection (CICIDS2017) of simulated network traffic, using statistical distance measures including the Kolmogorov-Smirnov, Kuiper, Anderson-Darling, Wasserstein and mixed Wasserstein-Anderson-Darling measures. Our preliminary findings indicate that there is a meaningful correlation between ML decisions and the ECDF-based distances measures of the input features. Thus, they can provide a confidence level that can be used for a) analyzing the applicability of the ML system in a given field (safety/security) and b) analyzing if the field data was maliciously manipulated1. Copyright © 2020, The Authors. All rights reserved.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Developing an Intelligent Resume Screening Tool With AI-Driven Analysis and Recommendation Features

Applied AI Letters

引用

Applied AI Letters 2025年第2期6卷

作者： K. L. Abhishek M. Niranjanamurthy Shonit Aric Syed Immamul Ansarullah Anurag Sinha G. Tejani Mohd Asif Shah Department of Artificial Intelligence & Machine Learning BMS Institute of Technology Affiliated to Visvesvaraya Technological University Bangalore Karnataka India Software Engineer Fidelity Investments Bangalore India Department of Management Studies University of Kashmir North Campus Srinagar Jammu and Kashmir India Tech School Computer Science Department ICFAI University Ranchi Jharkhand India Department of Computer Science RC University Palamue Bishrampur Jharkhand India Jadara University Research Center Jadara University Amman Jordan Dean of Faculty Department of Economics Kardan University Kabul Afghanistan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Division of Research and Development Lovely Professional University Phagwara Punjab India

Current resume screening relies on manual review, causing delays and errors in evaluating large volumes of resumes. Lack of automation and data extraction leads to inefficiencies and potential biases. Recruiters face challenges in identifying qualified candidates due to oversight and time constraints. Inconsistent evaluation criteria hinder decision-making. These issues result in prolonged hiring processes, missed opportunities, and potential bias in candidate selection. The goal of this project is to develop an AI-powered Resume Analysis and Recommendation Tool, catering to the trend of recruiters spending less than 2 min on each CV. The tool will rapidly analyze all resume components while providing personalized predictions and recommendations to applicants for improving their CVs. It will present user-friendly data for recruiters, facilitating export to CSV for integration into their recruitment processes. Additionally, the tool will offer insights and analytics on popular roles and skills within the job market. Its user section will enable applicants to continually test and track their resumes, encouraging repeat usage and driving traffic. Colleges can benefit from gaining insights into students' resumes before placements. Overall, this AI-powered tool aims to enhance the resume evaluation process, benefiting both job seekers and employers. The primary aim of this project is to develop a Resume Analyzer using Python, incorporating advanced libraries such as Pyresparser, NLTK (Natural Language Toolkit), and MySQL. This automated system offers an efficient solution for parsing, analyzing, and extracting essential information from resumes. The user-friendly interface, developed using Streamlit, allows for seamless resume uploading, insightful data visualization, and analytics. The Resume Analyzer significantly streamlines the resume screening process, providing recruiters with valuable insights and enhancing their decision-making capabilities.

关键词： natural language processing parsing resume analysis tokenization

来源：评论

学校读者我要写书评

暂无评论

Information security policy compliance behavior based on comprehensive dimensions of information security culture: A conceptual framework 17

Information security policy compliance behavior based on com...

引用

2017 International Conference on Information System and Data Mining, ICISDM 2017

作者： Nasir, Akhyari Arshah, Ruzaini Abdullah Ab Hamid, Mohd Rashid Faculty of Computer Media and Tech. Management TATI University College Teluk Kalong Kemaman Terengganu24000 Malaysia Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Kuantan Pahang26300 Malaysia Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Lebuhraya Tun Razak Kuantan Pahang26300 Malaysia

ISBN: (纸本)9781450348331

The adherence of employees towards Information Security Policy (ISP) established in the organization is crucial in reducing information security risks. Some scholars have suggested that employees' compliance to ISP could be influenced by Information Security Culture (ISC) cultivated in the organization. Several studies on the impact of ISC towards ISP compliance have proposed different dimensions and factors associated to ISC with substantial differences in each finding. This paper is discussing an enhanced conceptual framework of ISP compliance behavior by addressing ISC as a multidimensional concept which consist of seven comprehensive dimensions. These new proposed ISC dimensions developed using all the key factors of ISC in literature and were aligned with the widely accepted concept of organizational culture and ISC. The framework also integrated with the most significant behavioral theory in this domain of study, which is Theory of Planned Behavior to provide more deep understanding and richer findings of the compliance behavior. This framework is expected to give more accurate findings on the relationships between ISC and ISP compliance behavior. © 2017 Association for Computing Machinery.

关键词： Personnel

来源：评论

学校读者我要写书评

暂无评论

Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine

Recent Advances in Systems and Network Medicine: Meeting Rep...

引用

作者： Kurnat-Thoma, Emma Baranova, Ancha Baird, Pat Brodsky, Elia Butte, Atul J. Cheema, Amrita K. Cheng, Feixiong Dutta, Shuchismita Grant, Christina Giordano, James Maitland-Van Der Zee, Anke H. Fridsma, Douglas B. Jarrin, Robert Kann, Maricel G. Keeney, Jonathon Loscalzo, Joseph Madhavan, Guru Maron, Bradley A. Mcbride, Dennis K. Mckean, Maeve Mun, Seong K. Palmer, James C. Patel, Bakul Parakh, Kapil Pariser, Anne R. Pristipino, Christian Radstake, Timothy R.D.J. Rajasimha, Harsha K. Rouse, William B. Rozman, Damjana Saleh, Alif Schmidt, Harald H.H.W. Schultz, Nikolaus Sethi, Tavpritesh Silverman, Edwin K. Skopac, Jessica Svab, Igor Trujillo, Sylvia Valentine, James E. Verma, Dinesh West, Bruce J. Vasudevan, Sona BethesdaMD United States Genetics and Genomics Georgetown University School of Nursing and Health Studies WashingtonDC United States School of Systems Biology George Mason University FairfaxVA United States Global Software Standards Philips Pleasant PrairieWI United States Pine Biotech New OrleansLA United States San FranciscoCA United States Department of Biochemistry Molecular and Cellular Biology Georgetown University Medical Center WashingtonDC United States Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Clinic ClevelandOH United States Institute for Quantitative Biomedicine Rutgers University The State University of New Jersey PiscatawayNJ United States Rare Disease Institute Division of Genetics and Metabolism Children's National Medical Center WashingtonDC United States Department of Neurology Neuroethics Studies Program-Pellegrino Center for Clinical Bioethics O'Neill-Pellegrino Program in Brain Sciences and Global Law and Policy Georgetown University Medical Center WashingtonDC United States Department of Biochemistry Neuroethics Studies Program-Pellegrino Center for Clinical Bioethics O'Neill-Pellegrino Program in Brain Sciences and Global Law and Policy Georgetown University Medical Center WashingtonDC United States Department of Respiratory Medicine Amsterdam University Medical Centers University of Amsterdam Amsterdam Netherlands American Medical Informatics Association WashingtonDC United States Department of Biological Sciences University of Maryland Baltimore County BaltimoreMD United States Department of Biochemistry and Molecular Medicine George Washington University WashingtonDC United States Department of Medicine Brigham and Women's Hospital Harvard Medical School BostonMA United States National Academy of Engineering The National Academies of Sciences Engineering and Medicine WashingtonDC United States WashingtonDC United States Georgetown University Global Health

The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice;the role of -omics technologies in Health Care;the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases;and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances. © Emma Kurnat-Thoma et al. 2020 Published by Mary Ann Liebert, Inc. 2020.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Effect of intra-ply voids on the homogenized behavior of a ply in multidirectional laminates

引用

IOP Conference Series: Materials Science and engineering 2018年第1期406卷

作者： A Matveeva D Garoz R D B Sevenois M Zhu L Pyl W Van Paepegem L Farkas Siemens Industry Software NV Interleuvenlaan 68 3001 Leuven Belgium Ghent University Department of Materials Textiles and Chemical Engineering – MaTCh Tech Lane Ghent Science Park – Campus A Technologiepark-Zwijnaarde 903 9052 Zwijnaarde (Gent) Belgium SIM vzw Technologiepark 935 9052 Zwijnaarde (Gent) Belgium KU Leuven Department of Materials Engineering Kasteelpark Arenberg 44 3001 Leuven Belgium Department of Mechanics of Materials and Constructions (MeMC) Faculty of Engineering Sciences Vrije Universiteit Brussel (VUB) Pleinlaan 2 1050 Brussels Belgium

This work focuses on the effect of intra-ply voids on the homogenized nonlinear behavior of a ply in multidirectional composites. Voids were modeled explicitly on the fiber scale and linked to the ply-scale by the recently developed two-scale framework which couples Classical Laminate Theory on the macro-scale with Finite Element analysis on the micro-scale. Laminates [±45]2s and [±67.5]2s were used as validation cases. The computed homogenized behavior of plies with and without voids for each laminate were compared against existing experimental data on manufactured plates. The nonlinearity of the homogenized stress-strain curves of all models is in a good agreement with experiments up to 1% of applied deformation for a laminate [±45]2s and up to 0.4% for a laminate [±67.5]2s. The effect of voids was assessed only virtually and it is shown that 4% of void content decreases the ply strength by 30%, transversal Young's and shear moduli by around 10% and 8% respectively, whereas longitudinal stiffness is only slightly affected by the presence of voids. This work is the first step towards automatization of the virtual identification of the complete set of damage-plasticity parameters for the LMT-Cachan damage model accounting for the presence of intra-ply voids.

关键词：

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

来源：评论

学校读者我要写书评

暂无评论

25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

引用

BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：