检索结果-内蒙古大学图书馆

8th International conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2020

作者： Varela-Rodríguez, Miguel Vicente-Mariño, Miguel Department of Sociology and Social Work Faculty of Commerce University of Valladolid Valladolid Spain Department of Sociology and Social Work Faculty of Social Legal and Communication Sciences University of Valladolid Valladolid Spain

ISBN: (纸本)9781450388504

The use of social media data in social research has grown exponentially since the early 2010s, with many social researchers incorporating some degree of social media analysis. Following the 2018 Cambridge Analytica controversy, most social media providers locked access to their users' data, leaving researchers with limited options to study the information in it. Scholars have taken different stands, advocating data policies that allow for critical research, achieving partnerships with data providers, or - sometimes - violating the Terms of Use of the platforms to access the information. Despite the limitations, great progress has been made using text-based data, while image-based methodologies remain limited, partly because of the lockout. This paper proposes a methodology for automated image extraction from Instagram, using Instalooter. It presents the necessary setup and steps to obtain images based on a series of search parameters before discussing the ethical implications of its potential use. © 2020 ACM.

关键词： visual communication

来源：评论

学校读者我要写书评

暂无评论

An Interactive visualization System for Near-Earth Space exploration data 4

An Interactive Visualization System for Near-Earth Space Exp...

引用

4th International conference on Environmental Science and Material Application (ESMA)

作者： Yu, Haijun Li, Shengyang Zhang, Tao Univ Chinese Acad Sci CAS Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing Peoples R China

Aiming at the actual needs of efficient and convenient visual analysis of near-Earth space exploration data, this paper studies the functional realization mode, data flow and interactive method, analyzes the functional framework and data flow of the interactive visualization system of near-Earth space exploration data in detail, designs a multi-level, loosely coupled and easily extensible system architecture, and focuses on solving the logic model design, the fusion rendering of the multivariable data, the shadow calculation, the data clustering analysis, and other key technologies. The application of the system has realized the interactive and visual analysis of the massive and multi-source near-Earth space exploration data, and provided convenient data analysis service for the vast number of scientific research and application users, which helps to better play the potential value of near-Earth space exploration data.

关键词： Space research

来源：评论

学校读者我要写书评

暂无评论

Urdu Sentiment Corpus (v1.0): Linguistic exploration and visualization of Labeled dataset for Urdu Sentiment analysis 2

Urdu Sentiment Corpus (v1.0): Linguistic Exploration and Vis...

引用

2nd International conference on Information Science and Communication Technology, ICISCT 2020

作者： Khan, Muhammad Yaseen Nizami, Muhammad Suffian Mohammad Ali Jinnah University Center for Language Computing Department of Computer Science Karachi Pakistan

ISBN: (纸本)9781728168999

The significance of the labeled dataset is not obscure from artificial intelligence practitioners. We have seen much phenomenal work, in natural language processing, for many languages (like English, Chinese, and Arabic, etc.), due to the reason for the availability of substantial data. For the Urdu language, despite the third largest spoken language in the world, very little research work is shown;hence, it is adjudged as a 'morphologically rich' but 'resource-poor' language. Further, the researchers working on Urdu natural language processing are in a quandary due to the lack of availability of labeled/annotated datasets. This paper shares the data, 'Urdu Sentiment Corpus' (USC), and insights therein, of Urdu tweets for the sentiment analysis and polarity detection. The dataset is consisting of tweets, such that it casts a political shadow and presents a competitive environment between two separate political parties versus the government of Pakistan. Overall, the dataset is comprising over 17, 185 tokens with 52% records as positive, and 48 % records as negative. This paper shares the visual insights (from document-level to word-level) into the textual similarities, manifold-learning, etc. In addition to it, this paper also presents a Part-of-Speech wise analysis and an unpretentious technique for the extraction of sentiment lexicons from the corpus. © 2020 IEEE.

关键词： visualization

来源：评论

学校读者我要写书评

暂无评论

Large-scale Clustering of People Diagnosed with Parkinson's Disease using Acoustic analysis of Sustained Vowels: Findings in the Parkinson's Voice Initiative Study 13

Large-scale Clustering of People Diagnosed with Parkinson's ...

引用

13th International Joint conference on Biomedical Engineering Systems and Technologies

作者： Tsanas, Athanasios Arora, Siddharth Univ Edinburgh Edinburgh Med Sch Usher Inst Edinburgh Midlothian Scotland Univ Oxford Dept Math Oxford England

ISBN: (纸本)9789897583988

The heterogeneity of symptoms in Parkinson's Disease (PD) has motivated investigating PD subtypes using cluster analysis techniques. Previous studies investigating PD clustering have typically focused on symptoms assessed using standardized clinical evaluations and patient reported outcome measures. Here, we explore PD subtype delineation using speech signals. We used data from the recently concluded Parkinson's Voice Initiative (PVI) study where sustained vowels were solicited and collected under non-controlled acoustic conditions. We acoustically characterized 2097 sustained vowel /a/ recordings from 1138 PD participants using 307 dysphonia measures which had previously been successfully used in applications including differentiating healthy controls from PD participants, and matching speech dysphonia to the standard PD clinical metric quantifying symptom severity. We applied unsupervised feature selection to obtain a concise subset of the originally computed dysphonia measures and explored hierarchical clustering combined with 2D-data projections using t-distributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. We computed four main clusters which provide tentative insights into different dominating speech-associated pathologies. Collectively, these findings provide new insights into the nature of PD towards exploring speech-PD data-driven subtyping.

关键词： Acoustic analysis Clustering Parkinson's Disease Parkinson's Voice Initiative (PVI)

来源：评论

学校读者我要写书评

暂无评论

Shapelet Based visual Assessment of Cluster Tendency in Analyzing Complex Upper Limb Motion

Shapelet Based Visual Assessment of Cluster Tendency in Anal...

引用

IEEE International conference on Acoustics, Speech and Signal Processing

作者： Shreyasi Datta Chandan Karmakar Punit Rathore Marimuthu Palaniswami University of Melbourne Australia School of Information Technology Deakin University Melbourne Australia Senseable City Lab Massachusetts Institute of Technology Cambridge USA

The evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shape-based distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix for VAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring.

关键词： visualization Signal processing algorithms Transforms data models Sensors Motion measurement Speech processing

来源：评论

学校读者我要写书评

暂无评论

visual analysis Based on Research on Mergers and Acquisitions Performance 2

Visual Analysis Based on Research on Mergers and Acquisition...

引用

2nd International conference on Economic Management and Model Engineering, ICEMME 2020

作者： Yan, Jianwen Cai, Xingli Fan, Yu Hefei University of Technology School of Managemeat Hefei Anhui China

ISBN: (纸本)9781728191447

Mergers and acquisitions(M A) is one of the strategic behaviors of corporate growth and evolution, and M A performance is an important index to evaluate whether the function of corporate M A is achieved and the effect of M A. As the M A market becomes more and more active, scholars are doing more research on related aspects, but few studies use bibliometrics and visualization methods to conduct in-depth exploration and show the full picture of the field. This article uses bibliometric research methods to analyze the data samples of 5065 articles in the core collection of Web of Science from 2000 to 2019, and the document type is Article, and the time distribution, spatial distribution, knowledge evolution, and literature of the domain knowledge Co-cited and research hotspots were analyzed in depth and visualized. It reveals the development status of related research on mergers and acquisitions and performance, analyzes and discusses research hotspots and future development trends in this field, and provides important knowledge support for researchers in this field to carry out related research. © 2020 IEEE.

关键词： data visualization

来源：评论

学校读者我要写书评

暂无评论

BrainTrawler: A visual analytics framework for iterative exploration of heterogeneous big brain data

引用

COMPUTERS & GRAPHICS-UK 2019年 82卷 304-320页

作者： Ganglberger, Florian Swoboda, Nicolas Frauenstein, Lisa Kaczanowska, Joanna Haubensak, Wulf Buehler, Katja VRVis Res Ctr Donau City Str 11-13 A-1220 Vienna Austria Vienna Bioctr VBC Res Inst Mol Pathol IMP Campus Vienna Bioctr 1 A-1030 Vienna Austria

In recent years, big brain-initiatives and consortia have created vast resources of publicly available brain data that can be used by neuroscientists for their own research experiments. This includes microscale connectivity data brain-network graphs with billions of edges and vast spatial gene expression resources the representation of tens of thousands genes in brain space. Their joint analysis for higher order relations in structural or functional neuroanatomy would enable the genetic dissection of brain networks on a genome-wide scale. Current experimental workflows involve only time-consuming manual aggregation and extensive graph theoretical analysis of data from different sources, which rarely provide spatial context to operate continuously on different scales. In this paper, we propose BrainTrawler, a task-driven, web-based framework that incorporates visual analytics methods to explore heterogeneous neurobiological data. It facilitates spatial indexing to query large-scale voxel-level connectivity data and gene expression collections in real-time. Relating data to the hierarchical structure of common anatomical atlases enables the retrieval on different anatomical levels. Together with intuitive network visualization, iterative visual queries, and quantitative information this allows the genetic dissection of multimodal networks on local/global scales in a spatial context. We demonstrate the relevance of our approach for neuroscience by exploring social-behavior and memory/learning related functional neuroanatomy in mice. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Big data Networks Neuroscience Gene expression Brain parcellation visual queries

来源：评论

学校读者我要写书评

暂无评论

16th Italian Research conference on Digital Libraries, IRCDL 2020

16th Italian Research Conference on Digital Libraries, IRCDL...

引用

16th Italian Research conference on Digital Libraries, IRCDL 2020

ISBN: (纸本)9783030399047

The proceedings contain 18 papers. The special focus in this conference is on Digital Libraries. The topics include: Towards a Tool for visual Link Retrieval and Knowledge Discovery in Painting datasets;Identifying, Classifying and Searching Graphic Symbols in the NOTAE System;tindArt, an Experiment on User Profiling for Museum Applications;recognition of Concordances for Indexing in Digital Libraries;RepOSGate: Open Science Gateways for Institutional Repositories;Training data Stewards in Italy: Reflection on the FAIR RDM Summer School;Creating Digital Cultural Heritage with Open data: From FAIR to FAIR5 Principles;nanocitation: Complete and Interoperable Citations of Nanopublications;towards a Decision Support Framework for Forensic analysis of Dynamic Signatures;an Information visualization Tool for the Interactive Component-Based Evaluation of Search Engines;3D Average Common Submatrix Measure;lost in Translation: Can We Talk About Big data Fairly?;an Ontology and Knowledge Graph Infrastructure for Digital Library Knowledge Representation;text-to-Image Synthesis Based on Machine Generated Captions;a Streamlined Pipeline to Enable the Semantic exploration of a Bookstore;Re-implementing and Extending Relation Network for R-CBIR.

关键词：

来源：评论

学校读者我要写书评

暂无评论

dg2pix: Pixel-Based visual analysis of Dynamic Graphs

dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs

引用

IEEE conference on visualization in data Science (VDS)

作者： Cakmak, Eren Jackle, Dominik Schreck, Tobias Keim, Daniel Univ Konstanz Constance Germany Graz Univ Technol Graz Austria

ISBN: (纸本)9781728192840

Presenting long sequences of dynamic graphs remains challenging due to the underlying large-scale and high-dimensional data. We propose dg2pix, a novel pixel-based visualization technique, to visually explore temporal and structural properties in long sequences of large-scale graphs. The approach consists of three main steps: (1) the multiscale modeling of the temporal dimension;(2) unsupervised graph embeddings to learn low-dimensional representations of the dynamic graph data;and (3) an interactive pixel-based visualization to simultaneously explore the evolving data at different temporal aggregation scales. dg2pix provides a scalable overview of a dynamic graph, supports the exploration of long sequences of high-dimensional graph data, and enables the identification and comparison of similar temporal states. We show the applicability of the technique to synthetic and real-world datasets, demonstrating that temporal patterns in dynamic graphs can be identified and interpreted over time. dg2pix contributes a suitable intermediate representation between node-link diagrams at the high detail end and matrix representations on the low detail end.

关键词： Human-centered computing visualization visualization techniques Machine learning Learning paradigms Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

新时代社会主义意识形态凝聚力和引领力建设研究综述 ——基于CiteSpace的文献计量与可视化分析

引用

昆明理工大学学报（社会科学版） 2024年第6期24卷 71-80页

作者：王肖东东北师范大学思想政治教育研究中心吉林长春130024

全国宣传思想文化工作会议强调新时代新征程要着力建设具有强大凝聚力和引领力的社会主义意识形态。新时代以来,我国学者围绕“建设具有强大凝聚力和引领力的社会主义意识形态”开展系列研究。以中国学术期刊全文数据库为数据源,依托Cit... 详细信息

全国宣传思想文化工作会议强调新时代新征程要着力建设具有强大凝聚力和引领力的社会主义意识形态。新时代以来,我国学者围绕“建设具有强大凝聚力和引领力的社会主义意识形态”开展系列研究。以中国学术期刊全文数据库为数据源,依托CiteSpace数据处理工具,从整体研究态势、发文机构及作者、研究热点及趋势、研究视角等方面对党的十八大以来关于新时代社会主义意识形态凝聚力和引领力建设的相关文献进行可视化分析及质性分析。研究发现:整体发文量呈上升态势;研究机构和学者形成了一定合作关系;研究热点聚焦学理阐释、话语权建设、习近平总书记关于意识形态建设的重要论述以及互联网和全媒体融合发展带来的机遇和挑战等方面;研究趋势呈现出理论深究向实践探索的延伸、现实性和时代性的彰显;研究视角含括科学内涵阐释、内在逻辑厘析、现实状况考察、实践对策探寻、网络视域探究。未来研究应以多学科视野拓展研究广度和深度,以多元化研究方式丰富研究类型,以现实性为落脚点推进研究微观聚焦。

关键词：社会主义意识形态凝聚力引领力新时代 CiteSpace

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：