2,441 research outputs found
Knowledge Representation and WordNets
Knowledge itself is a representation of âreal factsâ.
Knowledge is a logical model that presents facts from âthe real worldâ witch can be expressed in a formal language. Representation means the construction of a model of some part of reality.
Knowledge representation is contingent to both cognitive science and artificial intelligence. In cognitive science it expresses the way people store and process the information. In the AI field the goal is to store knowledge in such way that permits intelligent programs to represent information as nearly as possible to human intelligence.
Knowledge Representation is referred to the formal representation of knowledge intended to be processed and stored by computers and to draw conclusions from this knowledge.
Examples of applications are expert systems, machine translation systems, computer-aided maintenance systems and information retrieval systems (including database front-ends).knowledge, representation, ai models, databases, cams
The Application of Metadata Standards to Multimedia in Museums
This paper first describes the application of a multi-level indexing approach, based on Dublin Core extensions and the Resource Description Framework (RDF), to a typical museum video. The advantages and disadvantages of this approach are discussed in the context of the requirements of the proposed MPEG-7 ("Multimedia Content Description Interface") standard. The work on SMIL (Synchronized Multimedia Integration Language) by the W3C SYMM working group is then described. Suggestions for how this work can be applied to video metadata are made. Finally a hybrid approach is proposed based on the combined use of Dublin Core and the currently undefined MPEG-7 standard within the RDF which will provide a solution to the problem of satisfying widely differing user requirements
An analysis of The Oxford Guide to practical lexicography (Atkins and Rundell 2008)
Since at least a decade ago, the lexicographic community at large has been demanding that a modern textbook be designed - one that Would place corpora in the centre of the lexicographic enterprise. Written by two of the most respected practising lexicographers, this book has finally arrived, and delivers on very many levels. This review article presents a critical analysis of its features
Shift Aggregate Extract Networks
We introduce an architecture based on deep hierarchical decompositions to
learn effective representations of large graphs. Our framework extends classic
R-decompositions used in kernel methods, enabling nested "part-of-part"
relations. Unlike recursive neural networks, which unroll a template on input
graphs directly, we unroll a neural network template over the decomposition
hierarchy, allowing us to deal with the high degree variability that typically
characterize social network graphs. Deep hierarchical decompositions are also
amenable to domain compression, a technique that reduces both space and time
complexity by exploiting symmetries. We show empirically that our approach is
competitive with current state-of-the-art graph classification methods,
particularly when dealing with social network datasets
Mapping the Bid Behavior of Conference Referees
The peer-review process, in its present form, has been repeatedly criticized.
Of the many critiques ranging from publication delays to referee bias, this
paper will focus specifically on the issue of how submitted manuscripts are
distributed to qualified referees. Unqualified referees, without the proper
knowledge of a manuscript's domain, may reject a perfectly valid study or
potentially more damaging, unknowingly accept a faulty or fraudulent result. In
this paper, referee competence is analyzed with respect to referee bid data
collected from the 2005 Joint Conference on Digital Libraries (JCDL). The
analysis of the referee bid behavior provides a validation of the intuition
that referees are bidding on conference submissions with regards to the subject
domain of the submission. Unfortunately, this relationship is not strong and
therefore suggests that there exists other factors beyond subject domain that
may be influencing referees to bid for particular submissions
Websites as Facilities Under ADA Title III
Title III of the Americans with Disabilities Act requires public accommodationsâprivate entities that offer goods or services to the publicâto be accessible to individuals with disabilities. There is an ongoing debate about whether Title III applies to websites that offer services to the public, but this debate may be resolved in the coming years by litigation or Department of Justice regulations. Assuming for the sake of argument that Title III will eventually be applied to websites, the next inquiry is what that application should look like. The regulatory definition of âfacilitiesâ should be amended to include nonphysical places of public accommodations. This change would open the door to a multilayered approach to accessible websites, wherein existing websites are subject to relatively lax requirements but new and altered websites are subject to stricter requirements
A note on validity in law and regulatory systems (position paper)
The notion of validity fulfils a crucial role in legal theory. The emerging Web 3.0 opens a new landscape
where Semantic Web languages, legal ontologies, and the construction of Normative Multiagent Systems are built up
to cover new regulatory needs. Conceptual models for complex regulatory systems shape the characteristic features
of rules, norms and principles in different ways. This position paper outlines one of such multilayered governance
models, designed for the CAPER platform
Sparsity and persistence in time-frequency sound representations
13 pagesInternational audienceIt is a well known fact that the time-frequency domain is very well adapted for representing audio signals. The main two features of time-frequency representations of many classes of audio signals are sparsity (signals are generally well approximated using a small number of coefficients) and persistence (significant coefficients are not isolated, and tend to form clusters). This contribution presents signal approximation algorithms that exploit these properties, in the framework of hierarchical probabilistic models. Given a time-frequency frame (i.e. a Gabor frame, or a union of several Gabor frames or time-frequency bases), coefficients are first gathered into groups. A group of coefficients is then modeled as a random vector, whose distribution is governed by a hidden state associated with the group. Algorithms for parameter inference and hidden state estimation from analysis coefficients are described. The role of the chosen dictionary, and more particularly its structure, is also investigated. The proposed approach bears some resemblance with variational approaches previously proposed by the authors (in particular the variational approach exploiting mixed norms based regularization terms). In the framework of audio signal applications, the time-frequency frame under consideration is a union of two MDCT bases or two Gabor frames, in order to generate estimates for tonal and transient layers. Groups corresponding to tonal (resp. transient) coefficients are constant frequency (resp. constant time) time-frequency coefficients of a frequency-selective (resp. time-selective) MDCT basis or Gabor frame
Rethinking affordance
n/a â Critical survey essay retheorising the concept of 'affordance' in digital media context. Lead article in a special issue on the topic, co-edited by the authors for the journal Media Theory
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