53,030 research outputs found
Automated annotation of landmark images using community contributed datasets and web resources
A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image
analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching oers 93.6% classication accuracy; the selection of appropriate tags for use in annotation performs well (F1M of
0.59), and it subsequently automatically identies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases
Bringing Semantic Diversity to the Online Catalog with LibraryThing
While controlled vocabularies, such as the Library of Congress Subject Headings, are an essential component of bibliographic classification, a controlled vocabulary excludes all possibilities of semantic variance by design. Also, a controlled vocabulary tends to lag behind the organic nature of language and does not account for the introduction of new or discipline specific vocabularies. These limitations present unique challenges for our users searching the OP AC. Can importing social tags in the online catalog effectively address the lack of semantic variance?
As part of the Web OPAC redesign project at UNO, LibraryThing tags were added to matching bibliographic records in the online catalog. This presentation will cover the practical aspects of adding LibraryThing tags to most vendor-based OPACs, address the variety of tags employed and offer ideas for effective tagging. In addition, we will explore how a collaborative service learning project with discipline specific university classes encouraged patron participation. We will also examine the overall quality and utility of LibraryTiring\u27s folksonomy. Lastly, additional features to be added in the near future by LibraryThing\u27s developers will be discussed
A Framework for Specifying and Monitoring User Tasks
Knowledge about user task execution can help systems better reason about when to interrupt users. To enable recognition and forecasting of task execution, we develop a novel framework for specifying and monitoring user task sequences. For task specification, our framework provides an XML-based language with tags inspired by regular expressions. For task monitoring, our framework provides an event handler that manages events from any instrumented application and a monitor that observes a user's transitions within and among specified tasks. The monitor supports multiple active tasks and multiple instances of the same task. The use of our framework will enable systems to consider a user's position within a task model when reasoning about when to interrupt
Seeding statistical machine translation with translation memory output through tree-based structural alignment
With the steadily increasing demand for high-quality translation, the localisation industry is constantly searching for technologies that would increase translator
throughput, with the current focus on the use of high-quality Statistical Machine Translation (SMT) as a supplement to the established Translation Memory (TM)
technology. In this paper we present a novel modular approach that utilises state-of-the-art sub-tree alignment to pick out pre-translated segments from a TM match and seed with them an SMT system to produce a final translation. We show that the presented system can outperform pure SMT when a good TM match is found. It can also be used in a Computer-Aided Translation (CAT) environment to present almost perfect translations to the human user with markup highlighting the segments of the translation that need to be checked manually for correctness
Enriching ontological user profiles with tagging history for multi-domain recommendations
Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites
A Grammatical Inference Approach to Language-Based Anomaly Detection in XML
False-positives are a problem in anomaly-based intrusion detection systems.
To counter this issue, we discuss anomaly detection for the eXtensible Markup
Language (XML) in a language-theoretic view. We argue that many XML-based
attacks target the syntactic level, i.e. the tree structure or element content,
and syntax validation of XML documents reduces the attack surface. XML offers
so-called schemas for validation, but in real world, schemas are often
unavailable, ignored or too general. In this work-in-progress paper we describe
a grammatical inference approach to learn an automaton from example XML
documents for detecting documents with anomalous syntax.
We discuss properties and expressiveness of XML to understand limits of
learnability. Our contributions are an XML Schema compatible lexical datatype
system to abstract content in XML and an algorithm to learn visibly pushdown
automata (VPA) directly from a set of examples. The proposed algorithm does not
require the tree representation of XML, so it can process large documents or
streams. The resulting deterministic VPA then allows stream validation of
documents to recognize deviations in the underlying tree structure or
datatypes.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and
Countermeasures ECTCM 201
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