检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,332 篇 会议
32 册 图书
7 篇 期刊文献

馆藏范围

1,371 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

935 篇 工学
- 862 篇 计算机科学与技术...
- 592 篇 软件工程
- 175 篇 信息与通信工程
- 95 篇 电气工程
- 74 篇 控制科学与工程
- 45 篇 生物工程
- 34 篇 化学工程与技术
- 32 篇 生物医学工程（可授...
- 28 篇 机械工程
- 16 篇 电子科学与技术（可...
- 15 篇 仪器科学与技术
- 14 篇 农业工程
304 篇 管理学
- 235 篇 图书情报与档案管...
- 108 篇 管理科学与工程(可...
- 57 篇 工商管理
243 篇 理学
- 111 篇 数学
- 79 篇 物理学
- 50 篇 生物学
- 44 篇 统计学（可授理学、...
- 35 篇 系统科学
- 34 篇 化学
47 篇 教育学
- 47 篇 教育学
41 篇 医学
- 34 篇 基础医学(可授医学...
- 33 篇 临床医学
- 18 篇 公共卫生与预防医...
- 15 篇 药学(可授医学、理...
37 篇 法学
- 33 篇 社会学
34 篇 文学
- 32 篇 外国语言文学
16 篇 经济学
- 16 篇 应用经济学
14 篇 农学
- 14 篇 作物学
1 篇 军事学

主题

155 篇 natural language...
75 篇 natural language...
70 篇 semantics
51 篇 web services
45 篇 information retr...
43 篇 data mining
43 篇 machine learning
40 篇 accuracy
35 篇 artificial intel...
35 篇 ontology
33 篇 feature extracti...
32 篇 semantic web
31 篇 data models
29 篇 training
28 篇 xml
26 篇 ontologies
26 篇 sentiment analys...
24 篇 computational mo...
23 篇 search engines
22 篇 deep learning

机构

6 篇 school of comput...
6 篇 moe-ms key labor...
5 篇 school of inform...
4 篇 korea univ nat l...
4 篇 school of comput...
4 篇 sri lanka instit...
4 篇 mae fah luang un...
3 篇 key laboratory o...
3 篇 national univers...
3 篇 center of excell...
3 篇 wuhan business u...
3 篇 department of co...
3 篇 department of co...
3 篇 university of in...
3 篇 department of in...
3 篇 school of comput...
3 篇 graduate school ...
3 篇 institute of inf...
2 篇 college of basic...
2 篇 department of te...

作者

6 篇 nakagawa seiichi
5 篇 rim hae-chang
4 篇 hirose keikichi
4 篇 nakamura satoshi
4 篇 zheng dequan
4 篇 ren fuji
4 篇 zhang lu
4 篇 shikano kiyohiro
4 篇 fuji ren
4 篇 matsumoto yuji
4 篇 ishizuka mitsuru
4 篇 minematsu nobuak...
3 篇 li sheng
3 篇 li li
3 篇 maceda lany l.
3 篇 uttama surapong
3 篇 stolcke andreas
3 篇 vani v.
3 篇 liu jie
3 篇 kitaoka norihide

语言

1,351 篇 英文
15 篇 其他
6 篇 中文
1 篇 土耳其文

检索条件"任意字段=7th International Conference on Advanced Language Processing and Web Information Technology"

共 1371 条记录，以下是1331-1340 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Deploying a Speech Recognition Model for Under-Resourced languages: A Case Study on Dioula Wake Words 1, 2, 3, and 4 23

Deploying a Speech Recognition Model for Under-Resourced Lan...

引用

Proceedings of the 2023 7th international conference on Natural language processing and information Retrieval

作者： Ismaila Ouedraogo Borlli Michel Jonas Some Zakaria Cheick Oumar Keita Emile Nabaloum Fabrice Bationo Roland Benedikter Gayo Diallo Team AHeaD Inserm 1219 University of Bordeaux France School of informatics University of Nazi Boni Burkina Faso Center for Advanced Studies Eurac Research Italy Team AHeaD Inserm 1219 University of Bordeaux France

ISBN: (纸本)9798400709227

Abstract: Speech recognition technology has the potential to provide valuable information and services to the 12.5 million Dioula speakers, especially the illiterates. However, these people, who could benefit the most, often do not have access to this technology because there are few data sets for resource-poor languages. this paper investigates the effectiveness of data augmentation in training wake words such as 1, 2, 3 and 4 in Dioula. the study contains two major contributions: the release of a Dioula language corpus for wake words 1, 2, 3 and 4, comprising 1.4 hours of audio with a labeled dataset, and a training of speech recognition model for 1, 2, 3, and 4 applying the data augmentation technique, which resulted in a significant improvement in accuracy from 51% to 96%. Additionally, the confusion matrices illustrate the model’s enhanced predictive capacity, with an average of 1762 out of 1817 instances of the number "1" being correctly recognized after data augmentation. the study also uncovered an impressive reduction in loss from 205% to 14% after implementing data augmentation. these results underscore the pivotal role of data augmentation in improving the model’s performance and mitigating overfitting issues, underscoring the promise of this technique in addressing data scarcity in underrepresented speech contexts. Training a speech recognition model to detect specific wake words, such as "1," "2," "3," and "4" in Dioula, can be highly valuable in constructing interactive voice response systems, thereby fostering greater inclusivity and accessibility for underserved communities.

关键词： Dioula language under-resourced languages. user interface voice recognition

来源：评论

学校读者我要写书评

暂无评论

Classification Benchmarks for Under-resourced Bengali language based on Multichannel Convolutional-LSTM Network

Classification Benchmarks for Under-resourced Bengali Langua...

引用

international conference on Data Science and advanced Analytics (DSAA)

作者： Md. Rezaul Karim Bharathi Raja Chakravarthi John P. McCrae Michael Cochez Fraunhofer Institute for Applied Information Technology FIT Germany Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Ireland Vrije Universiteit Amsterdam the Netherlands

ISBN: (数字)9781728182063

ISBN: (纸本)9781728182070

Exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behavior like online harassment, cyberbul-lying, and hate speech. Numerous works have been proposed to utilize these data for social and anti-social behavior analysis, document characterization, and sentiment analysis by predicting the contexts mostly for highly resourced languages like English. However, some languages are under-resources, e.g., South Asian languages like Bengali, Tamil, Assamese, Malayalam, that lack of computational resources for natural language processing. In this paper 1 , we provide several classification benchmarks for Bengali, an under-resourced language. We prepared three datasets of expressing hate, commonly used topics, and opinions for hate speech detection, document classification, and sentiment analysis. We built the largest Bengali word embedding models to date based on 250 million articles, which we call BengFastText. We perform three experiments, covering document classification, sentiment analysis, and hate speech detection. We incorporate word embeddings into a Multichannel Convolutional-LSTM (MC-LSTM) network for predicting different types of hate speech, document classification, and sentiment analysis. Experiments demonstrate that BengFastText can capture the semantics of words from respective contexts correctly. Evaluations against several baseline embedding models, e.g., Word2Vec and GloVe yield up to 92.30%, 82.25%, and 90.45% F1-scores in case of document classification, sentiment analysis, and hate speech detection, respectively during 5-fold cross-validation tests.

关键词： Social networking (online) Sentiment analysis Feature extraction Task analysis Voice activity detection Support vector machines Manuals

来源：评论

学校读者我要写书评

暂无评论

Sentiment analysis of hot event comments based on deep learning 23

Sentiment analysis of hot event comments based on deep learn...

引用

Proceedings of the 2023 7th international conference on Electronic information technology and Computer Engineering

作者： Zelong Yin Jinyan Zhou Lianjun Yu Yang Jiao Yang Liu Tian Zhang Guogang Zhao Cybersecurity Academy Changchun University China Changchun Agricultural Information Center China Changchun sixth middle school China Chongqing Public Security Bureau China Jilin provincial transport scientific research instiina China

ISBN: (纸本)9798400708305

Comments on Sina Weibo are one of the main ways for users to interact and give feedback when viewing Weibo content. Some comments may use emotional language to guide public emotions and attitudes, and gather together to form an atmosphere of public opinion. In recent years, deep learning has made great progress and has been widely used in natural language processing to solve problems such as sentiment analysis, question and answer systems, and speech recognition. this article takes the recent ChatGPT popular out-of-circle event as an example. It uses Python to crawl the comment data in related Weibo, and analyzes netizens' emotional attitude towards the event and its positive and negative classification based on the BiLSTM model. Realize the emotional analysis of netizens on the event so as to grasp the public opinion situation of the network in a timely manner.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines

Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Fina...

引用

Electronic technology, Communication and information (ICETCI), IEEE international conference on

作者： Kangtong Mo Wenyan Liu Xuanzhen Xu Chang Yu Yuelin Zou Fangqing Xia University of Illinois Urbana-Champaign Champaign IL USA Carnegie Mellon University Pittsburgh PA USA Snap Inc. Seattle WA USA Northeastern University Boston MA USA Columbia University New York NY USA Texas A&M University College Station TX USA

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural language processing (NLP) and Large language Models (LLM), we analyze sentiment from the perspective of retail investors. the FinancialPhraseBank dataset, which contains categorized sentiments of financial news headlines, serves as the basis for our analysis. We fine-tuned several models, including distilbert-base-uncased, Llama, and gemma-7b, to evaluate their effectiveness in sentiment classification. Our experiments demonstrate that the fine-tuned gemma7b model outperforms others, achieving the highest precision, recall, and F1-score. Specifically, the gemma-7b model showed significant improvements in accuracy after fine-tuning, indicating its robustness in capturing the nuances of financial sentiment. this model can be instrumental in providing market insights, risk management, and aiding investment decisions by accurately predicting the sentiment of financial news. the results highlight the potential of advanced LLMs in transforming how we analyze and interpret financial information, offering a powerful tool for stakeholders in the financial industry.

关键词： Measurement Analytical models Sentiment analysis Accuracy Predictive models Robustness Physiology

来源：评论

学校读者我要写书评

暂无评论

Empowering Precision in Financial News: A Revolution in Editorial Classification through Cutting-Edge Natural language processing 23

Empowering Precision in Financial News: A Revolution in Edit...

引用

Proceedings of the 2023 7th international conference on Natural language processing and information Retrieval

作者： Shravan Khunti Faizah Mahendi Nawaz Kureshi Rahulkumar Ankola Prikshit Kumar Department of Aerospace Engineering Chandigarh University India School of Computer Science and Engineering VIT Chennai India School of Computer Science and Engineering VIT Vellore India Department of Computer Science JC Bose University of Science and Technology YMCA India

ISBN: (纸本)9798400709227

Amidst the continuous stream of diverse data on the Bloomberg terminal, distinguishing editorial news articles from regular articles is critical to aid its users in tailoring their news experience and further analyzing the impact of news on global financial markets. In this paper, we propose various Artificial Intelligence and Neural Networks models regarding developing an editorial classifier that generalizes well across various news sources. the training set comprises articles published by news sources from the US. We compare the performance of these models using the Aggregate F1-measure and Binary Classification Performance Metric as evaluation metrics to account for the presence of class imbalance in our data. Further, we gauged our models by comparing their performance on a Zero-Shot dataset which comprised 1805 news articles published by Metro Winnipeg, a Canadian news source.

关键词： Bloomberg Terminal Integration Data Stream Analysis Editorial Classification Financial News Analysis Machine Learning in Finance Natural language processing (NLP) Precision in Content Curation Text Classification Algorithms User Experience in News Consumption

来源：评论

学校读者我要写书评

暂无评论

GenCheck: A LoRA-Adapted Multimodal Large language Model for Check Analysis

GenCheck: A LoRA-Adapted Multimodal Large Language Model for...

引用

IEEE conference on Multimedia information processing and Retrieval (MIPR)

作者： Fei Zhao Jiawen Chen Bin Huang Chengcui Zhang Gary Warner Rushi Chen Shaorou Tang Yuanfei Ma Zixi Nan The University of Alabama at Birmingham Birmingham USA Beijing-Dublin International College Beijing University of Technology Beijing China Beijing Hua Yu Xin Zhang Technologies Co. Ltd Beijing China College of Information Science and Technology Shihezi University Shihezi China Computer Science and Technology College University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

Rising incidences of paper check fraud, particularly with checks illicitly sold on platforms such as Telegram, pose significant challenges in financial security. Despite investigators' capability to gain access to these platforms, manually pinpointing checks in images and extracting necessary details to alert banks are inefficient and unscalable. Traditional optical character recognition-based (OCR) systems for extracting textual details from checks specifically struggle with handwritten content and are constrained by their dependency on predefined check layouts, limiting their effectiveness across varied and evolving check designs. To address these challenges, we introduce GenCheck, a generative AI-based framework that automates both the check detection and accurate extraction of check information, ensuring robust performance across various check layouts or styles. GenCheck operates through a two-stage pipeline: the preliminary stage encompasses multiple sub-tasks including check image classification, single check segmentation, image rectification, and check element detection, while the main stage focuses on the key task of check information extraction. Central to our pipeline is the strategic enhancement of a state-of-the-art (SOTA) multimodal large language model (LLaVA-NeXT) using Low-Rank Adaptation (LoRA). this fine-tuning leverages the model's pre-trained knowledge, applying a targeted, parameter-efficient approach that significantly enhances its ability to accurately extract key details such as dates, amounts, and payee information from paper check images. Our framework achieves exceptional accuracy rates in extracting date information with 92.07 % for year, 85.16% for month, and 82.72% for day. It also obtains an accuracy of 80.61 % in extracting monetary amounts and a normalized edit distance of 0.2583 for payee information, demonstrating substantial improvements over pure OCR-based methods. As the first framework of its kind, GenCheck estab-lishes a metho

关键词： Adaptation models Technological innovation Accuracy Large language models Optical character recognition Layout Fraud Data mining Security Standards

来源：评论

学校读者我要写书评

暂无评论

A Network Intrusion Detection Scheme by using Improved Loss Compensated Deep AutoEncoder

A Network Intrusion Detection Scheme by using Improved Loss ...

引用

Machine Learning and Natural language processing (MLNLP), international conference on

作者： Zhanpeng Yang Jianping Shuai Yutong Chen College of Basic Education Liuzhou Railway Vocational Technical College Liuzhou China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

In the current digital era, with the rapid popular-ization of network technology, it brings plenty of conveniences to the public. However, the network is attacked more and more frequently, and the network security problem has become a primary concern of the users. To improve network security, as one of the most important means of active defense in network security field, network intrusion detection has been widely studied, which aims to identify and prevent potential security attacks through monitoring and analyzing the network traffic. However, existing network intrusion detection technologies usually adopts a two-stage detection mode of feature extraction followed by detection. this split detection mode makes it impossible for the detector to alleviate the feature loss arises from feature extraction by the way of optimization and other methods, thereby lowering the detection efficiency and accuracy. For solving the above problem, we present a novel network intrusion detection method. We firstly design a novel deep learning model called Improved Loss Compensated Deep AutoEncoder (ILC-DAE) by improving the traditional Loss Compensated Deep AutoEncoder (LC-DAE). ILC-DAE can perform feature extraction and traffic detecting at the same time. then, we applied ILC-DAE to design a new method for network intrusion detection, which can enhance the intrusion detection accuracy and efficiency. At last, we implement our ILC-DAE network intrusion detection method and deploy a prototype system, in which the effectiveness of our ILC-DAE network intrusion detection method will be verified. Experimental results and analysis prove that compared with the existing intrusion detection methods, our ILC-DAE network intrusion detection method obtains better intrusion detection performance.

关键词： Deep learning Accuracy Autoencoders Network intrusion detection Prototypes Telecommunication traffic Network security Feature extraction Natural language processing Optimization

来源：评论

学校读者我要写书评

暂无评论

PSO-CNN-BiLSTM-MQA for Credit Risk Prediction of Listed Enterprises Based on Hierarchical Feature Modeling

PSO-CNN-BiLSTM-MQA for Credit Risk Prediction of Listed Ente...

引用

Machine Learning and Natural language processing (MLNLP), international conference on

作者： Yumiao Xie Le Wei School of Software Engineering Chengdu University of Information Technology Chengdu China Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province School of Software Engineering Chengdu University of Information Technology Sichuan Manufacturing Information Industry Collaborative Innovation Center Chengdu China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

Aiming at the problems of insufficient feature discrimination, difficulty in parameter adjustment, and lack of time dependence capture ability in the existing credit risk prediction models of listed companies, this paper proposes a PSOCNN-BiLSTM-MQA credit risk prediction method PCBM based on hierarchical feature modeling. Based on the financial data of listed companies in Guotai’an database, the method divides the data into four types of first-level characteristics: debt paying ability, operating ability, profitability and development ability. Each class of first-level features is further subdivided into several second-level features. In the feature extraction stage, a twochannel Convolutional Neural Network (CNN) was used to extract the local features of each class of level-1 features. One channel processed the original data, and the other channel processed the features converted to grayscale images. After being processed by different CNNS, the multi-query attention mechanism (MQA) was used to fuse the features from different channels to enhance the attention to features at different levels. At the same time, the bidirectional long short-term memory network is used to capture the long-term dependencies in the time series data, and the fused features are integrated through the MQA mechanism. In order to further improve the performance of the model, the particle swarm optimization algorithm is used to optimize the model parameters. Experimental results show that compared with the existing prediction models, the PCBM model shows significant improvement in accuracy, precision, recall rate and F1-score, which proves the effectiveness of this method in the credit risk prediction of listed companies.

关键词： Transmission line matrix methods Attention mechanisms Profitability Computational modeling Companies Predictive models Gray-scale Feature extraction Data models Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis

Five Years of COVID-19 Discourse on Instagram: A Labeled Ins...

引用

Machine Learning and Natural language processing (MLNLP), international conference on

作者： Nirmalya thakur Department of Electrical Engineering and Computer Science South Dakota School of Mines and Technology Rapid City SD USA

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

the outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse about COVID-19 on Instagram. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. the work presented in this paper aims to address this research gap and makes three scientific contributions to this field. First, it presents a multilingual dataset of 500,153 Instagram posts about COVID19 published between January 2020 and September 2024. this dataset, available at https://***/10.21227/d46p-v480, contains Instagram posts in 161 different languages, and these posts contain 535,021 distinct hashtags. After the development of this dataset, multilingual sentiment analysis was performed, which involved classifying each post as positive, negative, or neutral. the results of sentiment analysis are presented as a separate attribute in this dataset. Second, the paper presents the results of performing sentiment analysis per year from 2020 to 2024. the findings revealed the trends in sentiment related to COVID-19 on Instagram since the beginning of the pandemic. For instance, between 2020 and 2024, the sentiment trends show a notable shift, with positive sentiment decreasing from 38.35% to 28.69%, while neutral sentiment increasing from 44.19% to 58.34%. Finally, the paper also presents findings of language-specific sentiment analysis. this analysis highlighted similar and contrasting trends of sentiment across posts published in different languages on Instagram. For instance, out of all English p

关键词： COVID-19 Sentiment analysis Video on demand Social networking (online) Pandemics Market research Multilingual web sites Multimedia communication Public healthcare

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Vaccines in Indonesia: A Public Opinion Study Based on Twitter Data Multidimensional Sentiment Analysis 22

COVID-19 Vaccines in Indonesia: A Public Opinion Study Based...

引用

Proceedings of the 7th international conference on Sustainable information Engineering and technology

作者： Irvan Wahyu Nurdian Dinda Ockta Nooryawati Ivan Yulfrian Bianca Pingkan Nevista Novanto Yudistira Rizal Setya Perdana University of Brawijaya Indonesia) Brawijaya University Indonesia)

ISBN: (纸本)9781450397117

Vaccination is one of several solutions to control the spread of Coronavirus disease (COVID-19). However, it turns out that vaccination is not well received by the community, resulting in public disputes across the country. this study analyzes the relevance of vaccines to several points of view, such as political, economic, and spiritual, which aims to confirm the effectiveness of mass vaccination in reducing the spread of COVID-19. We used the Indo-BERT (Bahasa Indonesia of Bidirectional Encoder Representations from Transformers) method to see public sentiment towards vaccinations. the result of this research is that people’s sentiments tend to be more political, economic, social, and spiritual.

关键词： natural language processing public opinion sentiment analysis COVID-19 Twitter indoBERT

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共138页 << < 129 130 131 132 133 134 135 136 137 138 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：