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

933 篇 会议
425 篇 期刊文献

馆藏范围

1,358 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

993 篇 工学
- 777 篇 计算机科学与技术...
- 555 篇 软件工程
- 477 篇 电气工程
- 247 篇 信息与通信工程
- 159 篇 控制科学与工程
- 77 篇 生物工程
- 75 篇 生物医学工程（可授...
- 64 篇 交通运输工程
- 64 篇 网络空间安全
- 63 篇 仪器科学与技术
- 63 篇 安全科学与工程
- 37 篇 机械工程
- 37 篇 电子科学与技术（可...
- 35 篇 测绘科学与技术
- 31 篇 动力工程及工程热...
- 31 篇 土木工程
293 篇 理学
- 127 篇 数学
- 90 篇 生物学
- 66 篇 统计学（可授理学、...
- 57 篇 物理学
- 36 篇 化学
235 篇 管理学
- 148 篇 管理科学与工程(可...
- 122 篇 图书情报与档案管...
- 73 篇 工商管理
120 篇 医学
- 89 篇 临床医学
- 59 篇 基础医学(可授医学...
- 46 篇 公共卫生与预防医...
- 33 篇 药学(可授医学、理...
41 篇 教育学
- 39 篇 教育学
40 篇 法学
- 40 篇 社会学
31 篇 农学
24 篇 经济学
5 篇 文学
1 篇 军事学
1 篇 艺术学

主题

437 篇 visualization
201 篇 data visualizati...
148 篇 feature extracti...
111 篇 data models
105 篇 training
97 篇 task analysis
89 篇 data mining
76 篇 deep learning
64 篇 semantics
48 篇 three-dimensiona...
46 篇 computational mo...
39 篇 transformers
34 篇 predictive model...
31 篇 accuracy
31 篇 adaptation model...
26 篇 machine learning
26 篇 correlation
23 篇 neural networks
22 篇 representation l...
22 篇 big data

机构

9 篇 hong kong univer...
8 篇 state key lab of...
8 篇 peng cheng lab p...
7 篇 university of sc...
7 篇 sun yat sen univ...
6 篇 univ maryland co...
6 篇 zhejiang univ st...
5 篇 hong kong polyte...
5 篇 fuzhou univ coll...
5 篇 school of inform...
5 篇 chitkara univers...
5 篇 state grid info-...
5 篇 school of comput...
5 篇 maharishi school...
5 篇 alibaba-zhejiang...
5 篇 the hong kong un...
4 篇 college of compu...
4 篇 south china univ...
4 篇 nanyang technol ...
4 篇 school of comput...

作者

10 篇 chen wei
8 篇 wu yingcai
7 篇 liu jun
7 篇 chen siming
6 篇 shen han-wei
6 篇 qu huamin
5 篇 wang tao
5 篇 chauhan rahul
4 篇 sharma manish
4 篇 peterka tom
4 篇 chen c. l. phili...
4 篇 blair thornton
4 篇 wan feng
4 篇 elmqvist niklas
4 篇 wang ze
4 篇 cai yi
4 篇 zhe wang
4 篇 xu jiayi
4 篇 li qing
4 篇 wang yu

语言

1,349 篇 英文
8 篇 其他
1 篇 中文

检索条件"任意字段=2023 IEEE Visualization in Data Science, VDS 2023"

共 1358 条记录，以下是1281-1290 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：

The Multimodal Information Based Speech Processing (Misp) 2022 Challenge: Audio-Visual Diarization And Recognition

The Multimodal Information Based Speech Processing (Misp) 20...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhe Wang Shilong Wu Hang Chen Mao-Kui He Jun Du Chin-Hui Lee Jingdong Chen Shinji Watanabe Sabato Siniscalchi Odette Scharenborg Diyuan Liu Baocai Yin Jia Pan Jianqing Gao Cong Liu University of Science and Technology of China China Georgia Institute of Technology USA Northwestern Polytechnical University China Carnegie Mellon University USA Kore University of Enna Italy Delft University of Technology The Netherlands iFlytek China

The Multi-modal Information based Speech Processing (MISP) challenge aims to extend the application of signal processing technology in specific scenarios by promoting the research into wake-up words, speaker diarization, speech recognition, and other technologies. The MISP2022 challenge has two tracks: 1) audio-visual speaker diarization (AVSD), aiming to solve "who spoken when" using both audio and visual data; 2) a novel audio-visual diarization and recognition (AVDR) task that focuses on addressing "who spoken what when" with audio-visual speaker diarization results. Both tracks focus on the Chinese language, and use far-field audio and video in real home-tv scenarios: 2-6 people communicating each other with TV noise in the background. This paper introduces the dataset, track settings, and baselines of the MISP2022 challenge. Our analyses of experiments and examples indicate the good performance of AVDR baseline system, and the potential difficulties in this challenge due to, e.g., the far-field video quality, the presence of TV noise in the background, and the indistinguishable speakers.

关键词： visualization TV Speech recognition Signal processing Acoustics Quality assessment Speech processing

来源：评论

学校读者我要写书评

暂无评论

Maintaining Image Size by Developing the Cropping Rectangle Method on CT-Scan Images

Maintaining Image Size by Developing the Cropping Rectangle ...

引用

ieee International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Guslendra Yuhandri Sarjon Defit Department of Information System Computer Science Faculty Universitas Putra Indonesia YPTK Padang Indonesia Department of Information Technology Computer Science Faculty Universitas Putra Indonesia YPTK Padang Indonesia

CT-Scan is a computer-based imaging technology, but it presents certain weaknesses, including interpretation challenges, lengthy analysis times, and visualization limitations. The progress in digital image processing science empowers computers to identify objects through recognizing distinctive features. To facilitate further data processing, an image cropping method is essential to narrow down the area of interest, as not all parts of an object require processing. However, using a rectangular cropping method can lead to a reduction in image size, potentially compromising image quality and causing blurring when displayed on the same axes. To uphold image quality and avert blurring, maintaining the cropped image’s size becomes crucial. The developed rectangular cropping method successfully preserves the cropped image’s size by adjusting the intensity values of pixels surrounding the cutting point. Coordinate points within the cropping rectangle delineate the region where pixel intensity values are modified. In this study, input images were sourced from CT-scan images of M. Djamil Hospital, Padang, West Sumatra, in jpg format. The developed algorithm underwent testing using the Matlab program, employing the rectangle method for image cropping to ensure the image size remained constant. The study’s outcomes reveal no disparity in the number of pixels obtained when employing the developed rectangular cropping method. Based on the experiments carried out in this study using 8 input images that were successfully maintained, the size of the image from the cropped image was 8 images and 0 images were unsuccessful, so the accuracy obtained in this study was 100%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Temporal Graph Learning based on Multi-Head Self-Attention Weighted Neighborhood Sequences

Self-Supervised Temporal Graph Learning based on Multi-Head ...

引用

International Conference on Big data and Artificial Intelligence (BDAI)

作者： Yulong Cao School of Data Science University of Science and Technology of China Hefei China

Most current graph representation learning models require labeled samples for training, which may involve high costs in practice. Therefore, self-supervised learning has first been widely applied in static graph representation learning. By adding or removing operations on the adjacency matrix of static graphs to generate supervisory signals, it enables label-free learning of graph nodes. However, the majority of graphs in the real world are temporal graphs. Unlike static graphs, temporal graphs are usually organized through continuous time node interaction sequences, rather than adjacency matrices, making it difficult for self-supervised learning methods on static graphs to be directly migrated to temporal graphs. To address this challenge, we propose a novel self-supervised temporal graph learning approach, named Self-Supervised Temporal Graph Learning based on Neighborhood Sequences (SSHPD). Initially, we perform two types of masking operations on the neighborhood sequences of nodes, then model the neighborhood sequences of nodes using a Hawkes process. Specifically, in order to preserve the maximum neighborhood information of nodes during masking, we introduce a multi-head self-attention mechanism to calculate the attention weights between nodes, selecting those neighbor nodes with relatively small weights for masking. Additionally, we also utilize the multi-head self-attention mechanism to perform a weighted sum on the masked neighborhood sequences, thereby generating virtual nodes for each node and applying it to the Hawkes process, to maximize the extraction of node neighborhood information. We conducted experimental evaluations on three real-world datasets, the results of which demonstrate that, compared to the baseline methods, the embeddings learned by the SSHPD model exhibit superior performance in tasks such as node classification and network visualization.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows

Modeling Uncertainty in Personalized Emotion Prediction with...

引用

ieee International Conference on data Mining Workshops (ICDM Workshops)

作者： Piotr Miłkowski Konrad Karanowski Patryk Wielopolski Jan Kocoń Przemysław Kazienko Maciej Zięba Department of Artificial Intelligence Wrocław University of Science and Technology Poland Tooploox Ul. Wrocław Poland

Designing predictive models for subjective problems in natural language processing (NLP) remains challenging. This is mainly due to its non-deterministic nature and different perceptions of the content by different humans. It may be solved by Personalized Natural Language Processing (PNLP), where the model exploits additional information about the reader to make more accurate predictions. However, current approaches require complete information about the recipients to be straight embedded. Besides, the recent methods focus on deterministic inference or simple frequency-based estimations of the probabilities. In this work, we overcome this limitation by proposing a novel approach to capture the uncertainty of the forecast using conditional Normalizing Flows. This allows us to model complex multimodal distributions and to compare various models using negative log-likelihood (NLL). In addition, the new solution allows for various interpretations of possible reader perception thanks to the available sampling function. We validated our method on three challenging, subjective NLP tasks, including emotion recognition and hate speech. The comparative analysis of generalized and personalized approaches revealed that our personalized solutions significantly outperform the baseline and provide more precise uncertainty estimates. The impact on the text interpretability and uncertainty studies are presented as well. The information brought by the developed methods makes it possible to build hybrid models whose effectiveness surpasses classic solutions. In addition, an analysis and visualization of the probabilities of the given decisions for texts with high entropy of annotations and annotators with mixed views were carried out.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Temporal-Aware Lightweight Visual Tracking Method for Dynamic Traffic Scenes

Temporal-Aware Lightweight Visual Tracking Method for Dynami...

引用

ieee International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xuming Cen Nan Hu Haozhe Wang Shiyi Liu School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Department of Computer Science Faculty of Environment Science and Economy University of Exeter Exeter UK

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In traffic-based tracking scenes, acknowledging category diversity and visual field motion is pivotal. This leads tracking models to prioritize multi-category feature learning, motion analysis, and extended time series modeling rather than singularly focusing on attribute acquisition and background modeling. With this goal in mind, we construct a tracking model by integrating re-identification branches into a lightweight detection model. Simultaneously, we establish an inter-frame spatio-temporal attention mechanism using the Feature Transfer Module. This mechanism aligns the current frame features with the corresponding local features of the previous frame, which can bolster feature representation and downstream task performance. Experimental results on the traffic dataset validate the efficacy of the proposed approach in addressing challenges such as occlusion and deformation within traffic scenes. The proposed method attains a significant 31.5 MOTA and 35.7 AP on the BDD100K dataset with random data selection.

关键词： Representation learning Analytical models visualization Privacy Target tracking Time series analysis Security

来源：评论

学校读者我要写书评

暂无评论

Assessing the Environmental Impact of Plant-Based Diets: A Comprehensive Analysis

Assessing the Environmental Impact of Plant-Based Diets: A C...

引用

ieee International Conference on Big data

作者： Blagica Golubova Fjola Fetaji Jovana Dobreva Milena Trajanoska Ana Todorovska Riste Stojanov Dimitar Trajanov Faculty of Computer Science & Engineering S.Cyril and Methodius University Skopje N. Macedonia

This study examines a pressing issue related to the loss of natural resources and biodiversity driven by the high reliance of food production on ecosystem management services. The well-being of all living species is impacted by this depletion, which represents a huge obstacle in our collaborative effort to improve environmental quality. Our research aims to explain the environmental effects of food production and raise awareness of pollution levels at various phases of this process. This research combines statistical analysis and visualization to show considerable differences in CO2eq emissions among 43 different food products. In particular, it highlights how animal-based diets have much higher emissions than their plant-based equivalents. Subsequently, the products were divided into three distinct groups: plant-based, animal-based, and refined oils and sugars. This demonstrated how well an unsupervised clustering technique separates food products according to their CO2eq emissions. Where, these findings highlight how excellent plant-based products are for the environment. The main goal of this study goes beyond simple observation since it aims to provide an example of how a comprehensive, health-conscious eating habit may live with a stable ecosystem and clean surroundings. Particularly, reductions in cane sugar production yield substantial reductions in CO2 emissions, whereas even marginal decreases in meat production result in a significant reduction in emissions. These results highlight the potential for sustainable eating habits to aid in environmental conservation and deepen our understanding of the complex interactions between dietary decisions and environmental effects.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring fNIRS-Based Brain State Recognition and visualization through the use of Explainable Convolutional Neural Networks

Exploring fNIRS-Based Brain State Recognition and Visualizat...

引用

Annual International Conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Pin-Hua Chen Chun-Shu Wei Chen-Chia Lan Nai-Feng Chen Li-Chun Wang Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Psychiatry Taichung Veterans General Hospital Taichung Taiwan Brain Science and Technology Center National Yang Ming Chiao Tung University Hsinchu Taiwan

Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that has grown vigorously in recent years. With noticeable attention, machine learning methods have also been applied to fNIRS. However, the current approach lacks interpretability of the results. In recent years, the utilization and investigation of fNIRS have experienced significant growth and are now being utilized in clinical research. However, the collection of clinical fNIRS data is limited in sample size. Therefore, our aim is to utilize the collected fNIRS data from all channels and achieve interpretable analysis results with minimal human manipulation, channel selection or feature extraction. We developed an fNIRS-based interpretable model and used class-specific gradient information to visualize the biomarkers captured by the model via locating the important region. The accuracy of our model’s classification was 6% higher than that of the conventional SVM method under within-subject classification. The model focuses on signals from the left brain in the classification of right-hand finger tapping task, while in the task of classifying left-handed movements, the model relies on signals from the right brain. These results were consistent with current understanding of *** Relevance— The machine learning-based fNIRS model has the potential to be used for the diagnosis and prediction of therapeutic efficacy in clinical settings.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification

Class-Aware Patch Embedding Adaptation for Few-Shot Image Cl...

引用

International Conference on Computer Vision (ICCV)

作者： Fusheng Hao Fengxiang He Liu Liu Fuxiang Wu Dacheng Tao Jun Cheng Shenzhen Institute of Advanced Technology Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences China The Chinese University of Hong Kong Hong Kong China AIAI School of Informatics University of Edinburgh United Kingdom School of Computer Science Faculty of Engineering The University of Sydney Australia

"A picture is worth a thousand words", significantly beyond mere a categorization. Accompanied by that, many patches of the image could have completely irrelevant meanings with the categorization if they were independently observed. This could significantly reduce the efficiency of a large family of few-shot learning algorithms, which have limited data and highly rely on the comparison of image patches. To address this issue, we propose a Class-aware Patch Embedding Adaptation (CPEA) method to learn "class-aware embeddings" of the image patches. The key idea of CPEA is to integrate patch embeddings with class-aware embeddings to make them class-relevant. Furthermore, we define a dense score matrix between class-relevant patch embeddings across images, based on which the degree of similarity between paired images is quantified. visualization results show that CPEA concentrates patch embeddings by class, thus making them class-relevant. Extensive experiments on four benchmark datasets, miniImageNet, tieredImageNet, CIFAR-FS, and FC-100, indicate that our CPEA significantly outperforms the existing state-of-the-art methods. The source code is available at https://***/FushengHao/CPEA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal data Fusion Approach Using the Structured and Sparse Principal Component Analysis (MMSSCA)

Multi-Modal Data Fusion Approach Using the Structured and Sp...

引用

Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), ieee International Conference on

作者： Jyoti Kandpal A Jonitha Janaki Sivakumar Rejon Kumar Ray School of Computing Graphic Era Hill University Dehradun Uttarakhand India Faculty of Data Science & Computing Universiti Malaysia Kelantan Malaysia Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman Department of Business analytics Gannon University Erie PA USA

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

There are many different sorts of data that can be gathered and analyzed, including pictures, videos, texts, speeches, music, and other noises, Video content, for example, generally includes minimum some types of audible sounds or any type of visual image, and often also useful text data that can be of some specific type of titles or subtitles, which may consist of some unique type text language which is different type of speech. “Modality” is basically defines each different type of acquisition strategy. As natural phenomena tend to be quite complex, it is unusual for a single modality to provide exhaustive understanding of the phenomenon of interest. The process of combining and interpreting the useful data between the same modal or in different modal, and also interaction with the human beings with multi-modal data fusion &the accurate and deep interpretation and understanding of data obtained from the combining process from it, are central to the research challenge on multimodal data analysis. We contend that many of these challenges are shared by researchers in different fields. Both the “why” and the “how” of data fusion are addressed in this paper. Various examples from the realms of science and technology serve as inspiration for the first problem, which is then suggested by a various types of mathematical based model framework demonstrating the utility of data fusion. To solve the second problem, we introduce the idea of “diversity” as a central concept and discuss several data-driven solutions based on matrix and tensor decompositions, highlighting how they take into account diversity in the underlying data sets to address the aforementioned issues, this research suggests a novel approach for modeling multi data fusion implemented using sparse technique and structural analysis.

关键词： visualization Tensors Diversity reception Noise data integration Music data models

来源：评论

学校读者我要写书评

暂无评论

A Big data science and Engineering Solution for Transit Performance Analytics

A Big Data Science and Engineering Solution for Transit Perf...

引用

ieee International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Nhu Minh Ngoc Pham Yixi Wu Carson K. Leung Mohammadafaz V. Munshi Vrushil Kiritkumar Patel Connor C. J. Hryhoruk University of Manitoba Winnipeg MB Canada

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Public transit transportation plays a crucial role in the daily lives of many individuals, offering an affordable and convenient means of commuting to work, school, and various destinations. For instance, it serves as a vital mode of transportation to access their workplace, educational institutions or other activities. Any disruptions in bus schedules can lead to significant consequences, including missing meetings and other essential commitments for city residents. Thus, this paper presents a big data science and engineering solution for transit performance analytics in the area of transportation analysis. The insights derived from this data analysis are instrumental in enhancing the performance of public transportation, ultimately leading to an improved commuter experience in the city and contributing to the development of a smart city. To elaborate, our solution employs frequent pattern mining to identify variations in transit performance across different neighborhoods. By uncovering significant patterns, we establish connections that help us pinpoint the factors contributing to bus delays in specific areas. Improving the accuracy of bus arrival and departure times can significantly enhance the overall usability and appeal of public transit for commuters, as people are more likely to rely on buses when they are punctual and ensure timely arrivals at their destinations. Furthermore, our solution equips users with tools to visualize the insights gained from the analysis of bus departure times in various neighborhoods at different times of the day. The practicality of our big data science and engineering solution was demonstrated through an evaluation using real-life public transit data from a Canadian city, underscoring its potential to contribute to the development of a smart city.

关键词： Knowledge engineering Schedules Smart cities data visualization Big data Delays Performance analysis

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136页 << < 125 126 127 128 129 130 131 132 133 134 > >>

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：