Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
325
result(s) for
"Science -- Study and teaching -- Graphic methods"
Sort by:
Atlas of Knowledge
Maps of physical spaces locate us in the world and help us navigate unfamiliar routes. Maps of topical spaces help us visualize the extent and structure of our collective knowledge; they reveal bursts of activity, pathways of ideas, and borders that beg to be crossed. This book, from the author ofAtlas of Science, describes the power of topical maps, providing readers with principles for visualizing knowledge and offering as examples forty large-scale and more than 100 small-scale full-color maps. Today, data literacy is becoming as important as language literacy. Well-designed visualizations can rescue us from a sea of data, helping us to make sense of information, connect ideas, and make better decisions in real time. InAtlas of Knowledge, leading visualization expert Katy Börner makes the case for a systems science approach to science and technology studies and explains different types and levels of analysis. Drawing on fifteen years of teaching and tool development, she introduces a theoretical framework meant to guide readers through user and task analysis; data preparation, analysis, and visualization; visualization deployment; and the interpretation of science maps. To exemplify the framework, the Atlas features striking and enlightening new maps from the popular \"Places & Spaces: Mapping Science\" exhibit that range from \"Key Events in the Development of the Video Tape Recorder\" to \"Mobile Landscapes: Location Data from Cell Phones for Urban Analysis\" to \"Literary Empires: Mapping Temporal and Spatial Settings of Victorian Poetry\" to \"Seeing Standards: A Visualization of the Metadata Universe.\" She also discusses the possible effect of science maps on the practice of science.
eBook
VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R
Background
Visualization of orthogonal (disjoint) or overlapping datasets is a common task in bioinformatics. Few tools exist to automate the generation of extensively-customizable, high-resolution Venn and Euler diagrams in the R statistical environment. To fill this gap we introduce
VennDiagram
, an R package that enables the automated generation of highly-customizable, high-resolution Venn diagrams with up to four sets and Euler diagrams with up to three sets.
Results
The
VennDiagram
package offers the user the ability to customize essentially all aspects of the generated diagrams, including font sizes, label styles and locations, and the overall rotation of the diagram. We have implemented scaled Venn and Euler diagrams, which increase graphical accuracy and visual appeal. Diagrams are generated as high-definition TIFF files, simplifying the process of creating publication-quality figures and easing integration with established analysis pipelines.
Conclusions
The
VennDiagram
package allows the creation of high quality Venn and Euler diagrams in the R statistical environment.
Journal Article
Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification
The employed dictionary plays an important role in sparse representation or sparse coding based image reconstruction and classification, while learning dictionaries from the training data has led to state-of-the-art results in image classification tasks. However, many dictionary learning models exploit only the discriminative information in either the representation coefficients or the representation residual, which limits their performance. In this paper we present a novel dictionary learning method based on the Fisher discrimination criterion. A structured dictionary, whose atoms have correspondences to the subject class labels, is learned, with which not only the representation residual can be used to distinguish different classes, but also the representation coefficients have small within-class scatter and big between-class scatter. The classification scheme associated with the proposed Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.
Journal Article
Robust Visual Tracking via Structured Multi-Task Sparse Learning
In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing
mixed norms
and
we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular
tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259–2272,
2011
) is a special case of our MTT formulation (denoted as the
tracker) when
Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers.
Journal Article
Weakly-Supervised Cross-Domain Dictionary Learning for Visual Recognition
We address the visual categorization problem and present a method that utilizes weakly labeled data from other visual domains as the auxiliary source data for enhancing the original learning system. The proposed method aims to expand the intra-class diversity of original training data through the collaboration with the source data. In order to bring the original target domain data and the auxiliary source domain data into the same feature space, we introduce a weakly-supervised cross-domain dictionary learning method, which learns a reconstructive, discriminative and domain-adaptive dictionary pair and the corresponding classifier parameters without using any prior information. Such a method operates at a high level, and it can be applied to different cross-domain applications. To build up the auxiliary domain data, we manually collect images from Web pages, and select human actions of specific categories from a different dataset. The proposed method is evaluated for human action recognition, image classification and event recognition tasks on the UCF YouTube dataset, the Caltech101/256 datasets and the Kodak dataset, respectively, achieving outstanding results.
Journal Article
Storytelling in early childhood education: Time to go digital
Digital storytelling blends the ancient art of storytelling with a range of contemporary tools to weave stories together with the author's narrative voice, including digital images, graphics, music and sound. Digital storytelling, as both a teaching method and a learning resource, has been applied in many innovative ways at all levels of education. Digital storytelling supports student learning and allows teachers to adopt innovative and improved teaching methods. Storytelling is a proven and popular pedagogy, while digital storytelling is relatively recent and still seldom used in the setting of early childhood education. Using a case study of a storytelling–art–science club in Jakarta, Indonesia, the researcher explored how and why digital storytelling is used in early childhood education. This club is one of the few organizations that use digital storytelling for teaching and learning programs in early childhood. Data were collected qualitatively using in-depth interviews with four teachers, document analysis, and twice-observations of storytelling activities in each session with 35 and 37 children. The collected data were analyzed using analytical memoing methods. The results indicate that teachers in this club used digital storytelling for several important reasons. They claimed that simple digital technology made storytelling more entertaining, captivating, engaging, communicative and theatrical. This study suggests that the ability of teachers to use digital technology should be enhanced; schools' information and communication technology (ICT) devices should be equipped; some funding should also be allocated by the government to modernize school equipment; while the curriculum should be tailored to meet technological developments, and provide opportunities for children to learn how to make good use of technology.
Journal Article
Large-Scale Live Active Learning: Training Object Detectors with Crawled Data and Crowds
Active learning and crowdsourcing are promising ways to efficiently build up training sets for object recognition, but thus far techniques are tested in artificially controlled settings. Typically the vision researcher has already determined the dataset’s scope, the labels “actively” obtained are in fact already known, and/or the crowd-sourced collection process is iteratively fine-tuned. We present an approach for
live learning
of object detectors, in which the system autonomously refines its models by actively requesting crowd-sourced annotations on images crawled from the Web. To address the technical issues such a large-scale system entails, we introduce a novel part-based detector amenable to linear classifiers, and show how to identify its most uncertain instances in sub-linear time with a hashing-based solution. We demonstrate the approach with experiments of unprecedented scale and autonomy, and show it successfully improves the state-of-the-art for the most challenging objects in the PASCAL VOC benchmark. In addition, we show our detector competes well with popular nonlinear classifiers that are much more expensive to train.
Journal Article
Kernelized Multiview Projection for Robust Action Recognition
Conventional action recognition algorithms adopt a single type of feature or a simple concatenation of multiple features. In this paper, we propose to better fuse and embed different feature representations for action recognition using a novel spectral coding algorithm called Kernelized Multiview Projection (KMP). Computing the kernel matrices from different features/views via time-sequential distance learning, KMP can encode different features with different weights to achieve a low-dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative. More crucially, KMP is linear for the reproducing kernel Hilbert space, which allows it to be competent for various practical applications. We demonstrate KMP’s performance for action recognition on five popular action datasets and the results are consistently superior to state-of-the-art techniques.
Journal Article
Does a presentation’s medium affect its message? PowerPoint, Prezi, and oral presentations
Despite the prevalence of PowerPoint in professional and educational presentations, surprisingly little is known about how effective such presentations are. All else being equal, are PowerPoint presentations better than purely oral presentations or those that use alternative software tools? To address this question we recreated a real-world business scenario in which individuals presented to a corporate board. Participants (playing the role of the presenter) were randomly assigned to create PowerPoint, Prezi, or oral presentations, and then actually delivered the presentation live to other participants (playing the role of corporate executives). Across two experiments and on a variety of dimensions, participants evaluated PowerPoint presentations comparably to oral presentations, but evaluated Prezi presentations more favorably than both PowerPoint and oral presentations. There was some evidence that participants who viewed different types of presentations came to different conclusions about the business scenario, but no evidence that they remembered or comprehended the scenario differently. We conclude that the observed effects of presentation format are not merely the result of novelty, bias, experimenter-, or software-specific characteristics, but instead reveal a communication preference for using the panning-and-zooming animations that characterize Prezi presentations.
Journal Article