Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ

Scattertext is an open source tool for visualizing linguistic variation between document categories in a language-independent way. The tool presents a scatterplot, where each axis corresponds to the rank-frequency a term occurs in a category of documents. Through a tie-breaking strategy, the tool is able to display thousands of visible term-representing points and find space to legibly label hundreds of them. Scattertext also lends itself to a query-based visualization of how the use of terms with similar embeddings differs between document categories, as well as a visualization for comparing the importance scores of bag-of-words features to univariate metrics.Comment: ACL 2017 Demos. 6 pages, 5 figures. See the Githup repo https://github.com/JasonKessler/scattertext for source code and documentatio

Future-Viewer: An Efficient Framework for Navigating and Classifying Audio-Visual Documents

In this paper we present an intuitive framework named Future-Viewer, introduced for the effective visualization of spatiotemporal low-level features, in the context of browsing and retrieval of a multimedia document. This tool is used to facilitate the access to the content and to improve the understanding of the semantics associated to the considered multimedia document. The main visualization paradigm employed consists in representing a 2D feature space in which the video document shots are located. The features that characterize the 2D space's axes can be selected by the user. Shots with similar content fall near each other, and the tool offers various functionalities for automatically nding and annotating shot clusters in the feature space. These annotations can also be stored in MPEG7 format. The use of this application to browse the content of few audio-video sequences demonstrate very interesting capabilities

Imaging and visualization at the University of Missouri--Columbia

"The following group of faculty helped prepare this document for MU’s Cyberinfrastructure (CI) Council as part of the 2016 updates to MU’s CI Plan. ... Bimal Balakrishnan, Tommi White, Teresa Lever, David Larsen, Kannappan Palaniappan, Filiz Bunyak, and Chi-Ren ShyuThe document includes a long-term vision for a Show-Me Center for Imaging and Visualization (see page 7). For consistency, with the other parts of the CI Plan, one and three year objectives are provided. This document is designed to help update the CI Plan, and help advance the growing momentum for a imaging and visualization center to support faculty and students from a wide-variety of discipline, and advance a variety of innovative collaborations.Imaging and Visualization and the University of Missouri (MU) -- Return on Proposed Investment -- Imaging and Visualization Needs and Recommendations -- Cross connections with other areas of emphasis in the MU Cyberinfrastructure Plan -- One Year Objectives -- Three to Five Year Objectives -- The Big Picture: the Show-Me Center for Imaging and Visualization. Physical Space ; Where to Start and Why ; Visualization Related ; Infrastructure needed at MU ; Computation, Data Storage and Networking needs ; Management, Staffing, Training and Support ; Needed expertise -- Appendix A: Faculty Perspectives on the Importance of Visualization on Research and Teaching at MU -- Appendix B: Comparable Imaging Facilities -- Appendix C: Visualization Centers at Major Research Universities

2D visualization of terms and documents in Malay language

In the technology era, information is just at our fingertip. Much information nowadays can be searched in digitize way. The output of the data is still in the listed form, and this linear form (one dimension) makes user hardly to find information related to the data requested. In addition, Malay document is still displayed in textual listed form. 676 documents from Jilid 1 of Hadith Al Tarmizi in Malay language are used to visualize the relationship. Method used to develop vector space model for term-document relationship is TF*IDF and Cosine Similarity technique used for document-document relationship. While, Prefuse toolkit is used as the visualization tool. From the 2D graphic, the relationship between Hadis can be found easily. From the questionnaire conducted, 90% participants agree that more relevant documents can be found using the 2D graphics system

Visualization Drivers for Geant4

This document is on Geant4 visualization tools (drivers), evaluating pros and cons of each option, including recommendations on which tools to support at Fermilab for different applications{\cite{Daniel}}. Four visualization drivers are evaluated. They are OpenGL, HepRep, DAWN and VRML. They all have good features, OpenGL provides graphic output with out an intermediate file! HepRep provides menus to assist the user. DAWN provides high quality plots and even for large files produces output quickly. VRML uses the smallest disk space for intermediate files. Large experiments at Fermilab will want to write their own display. They should proceed to make this display graphics independent. Medium experiment will probably want to use HepRep because of it's menu support. Smaller scale experiments will want to use OpenGL in the spirit of having immediate response, good quality output and keeping things simple.Comment: Figures are now distributed througout the pape

Visualising the structure of document search results: A comparison of graph theoretic approaches

This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion