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

2,580 篇 会议
35 篇 期刊文献
12 册 图书

馆藏范围

2,627 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,572 篇 工学
- 1,349 篇 计算机科学与技术...
- 373 篇 软件工程
- 342 篇 电气工程
- 230 篇 信息与通信工程
- 169 篇 控制科学与工程
- 67 篇 交通运输工程
- 33 篇 网络空间安全
- 31 篇 动力工程及工程热...
- 30 篇 仪器科学与技术
- 30 篇 电子科学与技术（可...
- 30 篇 石油与天然气工程
- 27 篇 建筑学
- 27 篇 生物工程
- 24 篇 机械工程
- 24 篇 城乡规划学
- 22 篇 生物医学工程（可授...
- 19 篇 材料科学与工程（可...
- 18 篇 环境科学与工程（可...
- 17 篇 测绘科学与技术
213 篇 管理学
- 162 篇 管理科学与工程(可...
- 55 篇 图书情报与档案管...
- 34 篇 工商管理
145 篇 理学
- 69 篇 数学
- 29 篇 生物学
- 25 篇 物理学
- 19 篇 系统科学
33 篇 经济学
- 32 篇 应用经济学
33 篇 医学
- 28 篇 基础医学(可授医学...
- 17 篇 临床医学
31 篇 法学
- 28 篇 社会学
13 篇 教育学
5 篇 农学
4 篇 艺术学
3 篇 文学

主题

496 篇 big data
202 篇 data models
171 篇 cloud computing
163 篇 data mining
134 篇 smart phones
111 篇 computational mo...
100 篇 smart cities
85 篇 servers
81 篇 internet of thin...
81 篇 mobile communica...
80 篇 sensors
79 篇 monitoring
77 篇 feature extracti...
72 篇 training
71 篇 machine learning
69 篇 conferences
66 篇 deep learning
64 篇 smart grids
59 篇 smart city
59 篇 predictive model...

机构

22 篇 korea adv inst s...
11 篇 school of comput...
11 篇 school of comput...
10 篇 beijing univ pos...
8 篇 chonbuk natl uni...
8 篇 school of comput...
8 篇 sun yat sen univ...
7 篇 univ chinese aca...
7 篇 school of commun...
7 篇 institute of sma...
6 篇 national institu...
6 篇 shanghai jiao to...
6 篇 chinese acad sci...
6 篇 natl inst inform...
6 篇 chinese acad sci...
6 篇 department of co...
6 篇 lanzhou univ sch...
5 篇 xiamen univ fuji...
5 篇 univ tokyo inst ...
5 篇 educ univ hong k...

作者

31 篇 ho-jin choi
14 篇 choi ho-jin
12 篇 wang cheng
12 篇 li jonathan
11 篇 chae-gyun lim
9 篇 young-guk ha
9 篇 ha young-guk
8 篇 young-seob jeong
8 篇 chang jae-woo
8 篇 kim jinho
8 篇 lee suan
7 篇 wookey lee
7 篇 fan xiaoliang
7 篇 hyunjung shin
6 篇 wan wanggen
6 篇 cheng ming
6 篇 suan lee
6 篇 kim jee-in
5 篇 jae-woo chang
5 篇 zaslavsky arkady

语言

2,597 篇 英文
25 篇 中文
5 篇 其他

检索条件"任意字段=2015 International Conference on Big Data and Smart Computing, BIGCOMP 2015"

共 2627 条记录，以下是61-70 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：

Subgraph Matching on Multiple Graph Streams

Subgraph Matching on Multiple Graph Streams

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Duong Thi Thu Van Dolgorsuren, Batjargal Lee, Young-Koo Kyung Hee Univ Dept Comp Sci Suwon South Korea Mongolian Univ Sci & Technol Dept Informat Technol Ulan Bator Mongolia

ISBN: (纸本)9781665421973

Nowadays, graph data can be collected via more than one resource and in parallel. This involves not only changing the information but also immediately updating the outcomes of the data mining task. In this case, it is undesirable to recompute all the tasks once the changes occur to the original data. Therefore, we propose a streaming graph table (SGTable) to capture inserted/removed edges and an incremental graph matching algorithm to retrieve new results on only new data. Besides, we do the multi-streaming environment experiments using Spark that gather graphs from various sources.

关键词： subgraph/pattern matching streaming data processing multiple data resources

来源：评论

学校读者我要写书评

暂无评论

Presto Pack: Resource-aware Workload Placement for Higher Performance and Reliability

Presto Pack: Resource-aware Workload Placement for Higher Pe...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Cayiroglu, Cem Zhuang, Zhenyun Chattopadhyay, Biswapesh Pandian, Prithvi Li, Chen Facebook Inc 1 Hacker Way Menlo Pk CA 94025 USA

ISBN: (纸本)9781665421973

Presto is one of the most popular OLAP (Online Analytical Processing) query engines. A Presto cluster consists of multiple worker nodes that execute tasks. As of today, Presto's workload placement is not resource-aware, which leads to wildly unbalanced resource usages across workers, jeopardizing business-important metrics such as reliability, performance and efficiency. We aim to add intelligence to Presto workload placement by making it resource-aware. Referred to as Presto Pack, this feature is mainly implemented on Presto coordinator. It takes into account of two resource types: memory and CPU on worker nodes. Evaluation results show that Presto Pack can achieve much more balanced CPU and memory usage across workers, less reliability issues, and higher throughput. Specifically, Presto Pack can improve overall query throughput by 23% on a per-server basis, leading to higher levels of concurrency and reliability.

关键词： Measurement Concurrent computing conferences Memory management Production big data Throughput

来源：评论

学校读者我要写书评

暂无评论

DNN based Speaker Meta Information Estimation using Privacy-Preserving Speech data

DNN based Speaker Meta Information Estimation using Privacy-...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Baeg, Kwangje Han, Youngsub Jeon, Byoung-Ki LG Uplus Inc AI Tech Unit Seoul South Korea

ISBN: (纸本)9781665421973

There has been concerns about how speech data is collected and shared in the real world since human speech itself has personally identifiable information about the speaker and speech is available to reliably estimate speaker meta information. In this paper, we explore three different types of methods for DNN based speaker meta information estimation and compare the estimation results between the original speech and the anonymized speech. We used McAdam's coefficient-based signal processing technique to make the anonymized speech and privacy-preserving data. Experiments derived using TIMIT dataset show a slight degradation in performance of anonymized speech against the original. Experiments reveal that the model employing both DNN based embedding and voice anonymization can achieve comparable performance to the model using the original speech.

关键词： voice privacy anonymization speaker meta information DNN based embeddings

来源：评论

学校读者我要写书评

暂无评论

Transformer-Based Gene Scoring Model for Extracting Representative Characteristic of Central Dogma Process to Prioritize Pathogenic Genes Applying Breast Cancer Multi-omics data

Transformer-Based Gene Scoring Model for Extracting Represen...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Jhee, Jong Ho Song, Min -Young Kim, Byung Gon Shin, Hyunjung Lee, Soo Youn Ajou Univ Sch Med Ctr KIURI BioArtificial Intelligence Suwon 16499 South Korea Kyung Hee Univ Hosp Dept Internal Med Div Cardiol Seoul 02453 South Korea Ajou Univ Sch Med Ctr KIURI BioArtificial Intelligence Dept Brain Sci & Neurol Suwon 16499 South Korea Ajou Univ Dept Ind Engn Dept Artificial Intelligence Suwon 16499 South Korea

ISBN: (纸本)9781665475785

Various deep learning approaches using big multi-omics data of cancer patients are being applied to identify biomarkers of diverse cancer types these days. Because multi-omics data generally have a character with high dimensions compared with relatively few patient samples, this imbalance is a recognized bottleneck to apply integrated characteristics of multi-omics in cancer research. Among the dimensionality reduction techniques, deep learning-based approaches, such as autoencoder, are known to have strength in handling high dimensional data with few samples. However, the black box model makes it difficult to explain which genes are essential. In this study, we develop a transformer-based representative Central tendency Gene score considering Central Dogma process information (CGCD) model to predict optimized potential anti-breast cancer therapeutic target genes. It is based on a unified representation applying the compressed features learned through Transformer using multi-omics data of 105 breast cancer patients from The Cancer Genome Atlas (TCGA). Unlike other autoencoder-based models, CGCD can derive gene scores from the self-attention mechanism in the transformer model. The significant encoding genes were selected by computing the p-value per each gene based on the scores for all the patients. To verify CGCD score ability for predicting target genes, we estimated hazard ratio and p-value per gene by conducting survival analysis using Cox proportional hazard model and calculated area under the curve (AUC) with CGCD score and the p-value per patient, and performed biological functional analysis including Gene Set Enrichment Analysis (GSEA). As the CGCD score became higher, the results showed a pronounced increasing trend in the retention rate of breast cancer marker genes and pathways. From this point of view, the CGCD score that reflects harmony of multi-omics data in a gene is considered suitable as a criterion for predicting cancer diagnostic markers.

关键词： Multi-omics data integration breast cancer gene scoring deep learning

来源：评论

学校读者我要写书评

暂无评论

data Stream Classification by Using Stacked CARU Networks

Data Stream Classification by Using Stacked CARU Networks

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Chan, Ka-Hou Im, Sio-Kei Macao Polytech Inst Sch Appl Sci Macau Peoples R China Macao Polytech Inst Macau Peoples R China

ISBN: (纸本)9781665421973

RNN based networks have been widely used in various applications to obtain impressive performance, and CARU has more advantages in NLP tasks. However, the RNN exerts a great pressure on the CARU unit when a single layer is used. In this work, we propose to implement a multi-layer design, which can gradually extract the main features through multiple CARU units. The advantage of this is that it can consider part of speech and content. It allows each layer to perform its work clearly while alleviating long-term dependencies. Using seven popular data streams, the performance of multi-layer CARU is compared and evaluated with many state-of-the-art technologies. Experiments show that our design can improve the classification performance of various data sets. In addition, the design and implementation can be easily deployed in RNN based systems.

关键词： RNN CARU Classification Multilayer

来源：评论

学校读者我要写书评

暂无评论

Adaptive Autoencoders Exploiting Content Preference for Accurate Recommendation

Adaptive Autoencoders Exploiting Content Preference for Accu...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Chae, Dong-Kyu Shin, Jung Ah Kim, Sang-Wook Hanyang Univ Seoul South Korea Columbia Univ New York NY USA

ISBN: (纸本)9781665421973

Recommender systems have been extensively studied these days. We note that the contents (e.g., genres or tags of movies) of an item are an important factor to determine whether a user prefers the item. Therefore, if available, it would be promising to take advantage of this information to improve the quality of recommendation. Along this line, this paper presents a novel hybrid recommendation framework called Adaptive Autoencoders Exploiting Content Preference. We first propose to use a new notion of content preference, which implies a user's impressions over different contents. We also propose an adaptive learning algorithm for effective training of Autoencoders used in our framework. We verify the superiority of our framework by comparing it with several baselines using real-world data.

关键词： Training Adaptive learning conferences big data Prediction algorithms Motion pictures Encoding

来源：评论

学校读者我要写书评

暂无评论

Optimizing performance of Real-Time big data stateful streaming applications on Cloud

Optimizing performance of Real-Time Big Data stateful stream...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Gupta, Amit Jain, Sushant Hughes Syst Big Data & Cloud Comp Gurgaon Haryana India

ISBN: (纸本)9781665421973

Exponential growth in the volume of data generated over the last decade has triggered massive research and adoption of distributed big data analytics platforms. In real-time streaming analytics, data is received as a continuous event stream. These events are iteratively correlated and analyzed in micro-batches till the processing reaches a logical conclusive stage. For every iteration, data received as part of the new batch is correlated with the results of the previous iteration and cached as interim results. This real-time stateful analysis of streaming data has been made possible by distributed data-intensive computing frameworks like Apache Spark. Though the spark state storage is highly optimized, its access is limited within the context of the Spark job. Since, these interim results may need to be consumed by applications running external to Spark context, saving these results in external memory involves a significant overhead due to the high volume of I/O. Similar challenges are observed when the spark job needs to consume from an external data store. These challenges get compounded in cloud-based deployment. This paper presents the optimized data storage and retrieval pattern from the spark job to/from the independent storage using the standard off the shelf tools. Our focus on standard off the shelf storage is to ensure that the design patterns presented as part of this paper could be adopted for production deployment.

关键词： big data Cloud AWS Apache Spark Redis MongoDB Distributed computing Real-Time Stateful Streaming

来源：评论

学校读者我要写书评

暂无评论

Collaborative Filtering on Bipartite Graphs using Graph Convolutional Networks

Collaborative Filtering on Bipartite Graphs using Graph Conv...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Kim, Minkyu Kim, Jinho Kangwon Natl Univ Dept Big Data & Med Convergence Chunchon South Korea Kangwon Natl Univ Dept Comp Sci & Engn Chunchon South Korea

ISBN: (纸本)9781665421973

Existing graph neural networks are not suitable to handle bipartite graphs, and existing graph-based collaborative filtering methods cannot model user-item interactions. To overcome these problems, we propose a novel collaborative filtering method named Graph Convolutional Collaborative Filtering (GCCF). Our GCCF aggregates neighbor nodes information with a novel Graph Convolutional Network (GCN) style node aggregation function improved for bipartite graphs. In addition, GCCF models user-item interactions using the user-item crossing layer. We conduct experiments to compare our GCCF with the existing state-of-the-art methods. Experimental results show that GCCF achieved or matched state-of-the-art results over five collaborative filtering datasets without any side information.

关键词： Recommender Systems Collaborative Filtering Graph Neural Networks Graph Convolutional Networks Bipartite Graphs

来源：评论

学校读者我要写书评

暂无评论

A Survey on Deep Learning Advances and Emerging Issues in Pneumonia and COVID19 Prediction

A Survey on Deep Learning Advances and Emerging Issues in Pn...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Makhanov, Nursultan Nguyen Anh Tu Wong, Kok-Seng Nazarbayev Univ Sch Engn & Digital Sci Nur Sultan Kazakhstan VinUniv Coll Engn & Comp Sci Hanoi Vietnam

ISBN: (纸本)9781665421973

As the COVID19 pandemic evolves and coronavirus mutates to different variants, a high workload falls on the shoulders of doctors and radiologists. Identifying COVID19 through X-ray and Computed Tomography (CT) scanning in a short amount of time is vital because it helps doctors start the COVID19 treatment in the early stages. Deep Learning algorithms showed tremendous results in automating COVID19 detection using X-ray and CT scans. As there are not many survey papers on COVID19 detection using deep learning techniques, the goal of this paper is (1) to give a thorough discussion of COVID19 prediction considering Computer Vision problems like COVID19/pneumonia classification, detection, and segmentation, (2) to address new advances in deep learning like Transformers, GANs, and LSTMs, and (3) to cover technical issues like data security and data scarcity of X-ray and CT scans in COVID19.

关键词： COVID-19 diagnosis deep learning federated learning self-supervised learning few-shot learning differential privacy data security data scarcity

来源：评论

学校读者我要写书评

暂无评论

TPC-C Benchmarking for ElasticSearch

TPC-C Benchmarking for ElasticSearch

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Lee, JaeKyu Kwon, Hyuk-Yoon Seoul Natl Univ Sci & Technol Dept Data Sci Seoul South Korea

ISBN: (纸本)9781665421973

ElasticSearch is a distributed search and analysis engine. Even though it is a NoSQL system that supports weak consistency, it has been recently applied to financial services that require strong consistency. In this study, we propose a method to support the TPC-C benchmark for ElasticSearch. To this end, we present the Force Delete Lock (FD Lock) using built-in operations in ElasticSearch to provide the distributed lock for the transaction. Then, we implement the rollback operation in a distributed environment using FD Lock. Finally, we compare the consistency failures and the TPC-C performance of ElasticSearch with FD Lock and that without FD Lock. As a result, we show that FD Lock can completely remove the consistency failures, and the performance degradation of FD Lock decreases as the number of warehouses increases because it increases the consistency failures.

关键词： ElasticSearch Distributed Lock NoSQL Benchmark TPC-C

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263页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：