4,568 research outputs found
Visualizing Z Notation in HTML Documents
The use of the WWW as a communication medium for software engineers is limited by the lack of tools for writing, sharing, and verifying formal notations. For instance, the Z specification language has a a rich set of mathematical characters, and requires graphic-rich boxes and schemas for its specifications. It is difficult to integrate Z specifications and text on WWW pages written with the current versions of HTML, and traditional tools are not suited for the task.
We present a Java-based tool for rendering Z specifications within HTML documents that can be shown on every WWW browser with Java capabilities. Being a complete rendering engine, text parts and Z specifications can be freely intermixed, and all the standard features of HTML (such as links, etc.) are available outside and inside Z specifications. Furthermore, the extensibility of our engine allows additional notations to be supported and integrated with current ones
Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Context
Mathematical formulae represent complex semantic information in a concise
form. Especially in Science, Technology, Engineering, and Mathematics,
mathematical formulae are crucial to communicate information, e.g., in
scientific papers, and to perform computations using computer algebra systems.
Enabling computers to access the information encoded in mathematical formulae
requires machine-readable formats that can represent both the presentation and
content, i.e., the semantics, of formulae. Exchanging such information between
systems additionally requires conversion methods for mathematical
representation formats. We analyze how the semantic enrichment of formulae
improves the format conversion process and show that considering the textual
context of formulae reduces the error rate of such conversions. Our main
contributions are: (1) providing an openly available benchmark dataset for the
mathematical format conversion task consisting of a newly created test
collection, an extensive, manually curated gold standard and task-specific
evaluation metrics; (2) performing a quantitative evaluation of
state-of-the-art tools for mathematical format conversions; (3) presenting a
new approach that considers the textual context of formulae to reduce the error
rate for mathematical format conversions. Our benchmark dataset facilitates
future research on mathematical format conversions as well as research on many
problems in mathematical information retrieval. Because we annotated and linked
all components of formulae, e.g., identifiers, operators and other entities, to
Wikidata entries, the gold standard can, for instance, be used to train methods
for formula concept discovery and recognition. Such methods can then be applied
to improve mathematical information retrieval systems, e.g., for semantic
formula search, recommendation of mathematical content, or detection of
mathematical plagiarism.Comment: 10 pages, 4 figure
Identification of Design Principles
This report identifies those design principles for a (possibly new) query and transformation
language for the Web supporting inference that are considered essential. Based upon these
design principles an initial strawman is selected. Scenarios for querying the Semantic Web
illustrate the design principles and their reflection in the initial strawman, i.e., a first draft of
the query language to be designed and implemented by the REWERSE working group I4
Web and Semantic Web Query Languages
A number of techniques have been developed to facilitate
powerful data retrieval on the Web and Semantic Web. Three categories
of Web query languages can be distinguished, according to the format
of the data they can retrieve: XML, RDF and Topic Maps. This article
introduces the spectrum of languages falling into these categories
and summarises their salient aspects. The languages are introduced using
common sample data and query types. Key aspects of the query
languages considered are stressed in a conclusion
Interactive web-based visualization and sharing of phylogenetic trees using phylogeny.IO
Traditional static publication formats make visualization, exploration, and sharing of massive phylogenetic trees difficult. A phylogenetic study often involves hundreds of taxa, and the resulting tree has to be split across multiple journal pages, or be shrunk onto one, which jeopardizes legibility. Furthermore, additional data layers, such as species-specific information or time calibrations are often displayed in separate figures, making the entire picture difficult for readers to grasp. Web-based technologies, such as the Data Driven Document (D3) JavaScript library, were created to overcome such challenges by allowing interactive displays of complex data sets. The new phylogeny.IO web server (https://phylogeny.io) overcomes this issue by allowing users to easily import, annotate, and share interactive phylogenetic trees. It allows a range of static (e.g. such as shapes and colors) and dynamic (e.g. pop-up text and images) annotations. Annotated trees can be saved on the server for subsequent modification or they may be shared as IFrame HTML objects, easily embeddable in any web page. The principal goal of phylogeny.IO is not to produce publication-ready figures, but rather to provide a simple and intuitive annotation interface that allows easy and rapid sharing of figures in blogs, lecture notes, press releases, etc
GiViP: A Visual Profiler for Distributed Graph Processing Systems
Analyzing large-scale graphs provides valuable insights in different
application scenarios. While many graph processing systems working on top of
distributed infrastructures have been proposed to deal with big graphs, the
tasks of profiling and debugging their massive computations remain time
consuming and error-prone. This paper presents GiViP, a visual profiler for
distributed graph processing systems based on a Pregel-like computation model.
GiViP captures the huge amount of messages exchanged throughout a computation
and provides an interactive user interface for the visual analysis of the
collected data. We show how to take advantage of GiViP to detect anomalies
related to the computation and to the infrastructure, such as slow computing
units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
The Document Similarity Network: A Novel Technique for Visualizing Relationships in Text Corpora
With the abundance of written information available online, it is useful to be able to automatically synthesize and extract meaningful information from text corpora. We present a unique method for visualizing relationships between documents in a text corpus. By using Latent Dirichlet Allocation to extract topics from the corpus, we create a graph whose nodes represent individual documents and whose edge weights indicate the distance between topic distributions in documents. These edge lengths are then scaled using multidimensional scaling techniques, such that more similar documents are clustered together. Applying this method to several datasets, we demonstrate that these graphs are useful in visually representing high-dimensional document clustering in topic-space
The Weight Function in the Subtree Kernel is Decisive
Tree data are ubiquitous because they model a large variety of situations,
e.g., the architecture of plants, the secondary structure of RNA, or the
hierarchy of XML files. Nevertheless, the analysis of these non-Euclidean data
is difficult per se. In this paper, we focus on the subtree kernel that is a
convolution kernel for tree data introduced by Vishwanathan and Smola in the
early 2000's. More precisely, we investigate the influence of the weight
function from a theoretical perspective and in real data applications. We
establish on a 2-classes stochastic model that the performance of the subtree
kernel is improved when the weight of leaves vanishes, which motivates the
definition of a new weight function, learned from the data and not fixed by the
user as usually done. To this end, we define a unified framework for computing
the subtree kernel from ordered or unordered trees, that is particularly
suitable for tuning parameters. We show through eight real data classification
problems the great efficiency of our approach, in particular for small
datasets, which also states the high importance of the weight function.
Finally, a visualization tool of the significant features is derived.Comment: 36 page
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