122,220 research outputs found
The relationship between IR and multimedia databases
Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud
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Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud
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Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud
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First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud
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Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud
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Third, we add the functionality to process the users' relevance feedback.\ud
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We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud
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We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
The Grammar of Interactive Explanatory Model Analysis
The growing need for in-depth analysis of predictive models leads to a series
of new methods for explaining their local and global properties. Which of these
methods is the best? It turns out that this is an ill-posed question. One
cannot sufficiently explain a black-box machine learning model using a single
method that gives only one perspective. Isolated explanations are prone to
misunderstanding, which inevitably leads to wrong or simplistic reasoning. This
problem is known as the Rashomon effect and refers to diverse, even
contradictory interpretations of the same phenomenon. Surprisingly, the
majority of methods developed for explainable machine learning focus on a
single aspect of the model behavior. In contrast, we showcase the problem of
explainability as an interactive and sequential analysis of a model. This paper
presents how different Explanatory Model Analysis (EMA) methods complement each
other and why it is essential to juxtapose them together. The introduced
process of Interactive EMA (IEMA) derives from the algorithmic side of
explainable machine learning and aims to embrace ideas developed in cognitive
sciences. We formalize the grammar of IEMA to describe potential human-model
dialogues. IEMA is implemented in the human-centered framework that adopts
interactivity, customizability and automation as its main traits. Combined,
these methods enhance the responsible approach to predictive modeling.Comment: 17 pages, 10 figures, 3 table
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
A process-oriented language for describing aspects of reading comprehension
Includes bibliographical references (p. 36-38)The research described herein was supported in part by the National Institute of Education under Contract No. MS-NIE-C-400-76-011
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