2,812 research outputs found
Spoken content retrieval: A survey of techniques and technologies
Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR
Approximation to the theory of affinities to manage the problems of the groupping process
New economic and enterprise needs have increased the interest and utility of the methods of the grouping process based on the theory of uncertainty. A fuzzy grouping (clustering) process is a key phase of knowledge acquisition and reduction complexity regarding different groups of objects. Here, we considered some elements of the theory of affinities and uncertain pretopology that form a significant support tool for a fuzzy clustering process. A Galois lattice is introduced in order to provide a clearer vision of the results. We made an homogeneous grouping process of the economic regions of Russian Federation and Ukraine. The obtained results gave us a large panorama of a regional economic situation of two countries as well as the key guidelines for the decision-making. The mathematical method is very sensible to any changes the regional economy can have. We gave an alternative method of the grouping process under uncertainty
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LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification
Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.
This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of itâs applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems.
We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success.
Adviser: Dr Jitender Deogu
Advertising in Duopoly Market
The paper presents the dynamics of consumer preferences over two competing products acting in duopoly market. The model presented compared the majority and minority rules as well as the modified Snazjd model in the Von Neumann neighborhood. We showed how important advertising in marketing a product is. We show that advertising should also consider the social structure simultaneously with the content of the advertisement and the understanding to the advertised product. Some theoretical explorations are discussed regarding to size of the market, evaluation of effect of the advertising, the types of the advertised products, and the social structure of which the product is marketed. We also draw some illustrative models to be mproved as a further work
Structural Material Property Tailoring Using Deep Neural Networks
Advances in robotics, artificial intelligence, and machine learning are
ushering in a new age of automation, as machines match or outperform human
performance. Machine intelligence can enable businesses to improve performance
by reducing errors, improving sensitivity, quality and speed, and in some cases
achieving outcomes that go beyond current resource capabilities. Relevant
applications include new product architecture design, rapid material
characterization, and life-cycle management tied with a digital strategy that
will enable efficient development of products from cradle to grave. In
addition, there are also challenges to overcome that must be addressed through
a major, sustained research effort that is based solidly on both inferential
and computational principles applied to design tailoring of functionally
optimized structures. Current applications of structural materials in the
aerospace industry demand the highest quality control of material
microstructure, especially for advanced rotational turbomachinery in aircraft
engines in order to have the best tailored material property. In this paper,
deep convolutional neural networks were developed to accurately predict
processing-structure-property relations from materials microstructures images,
surpassing current best practices and modeling efforts. The models
automatically learn critical features, without the need for manual
specification and/or subjective and expensive image analysis. Further, in
combination with generative deep learning models, a framework is proposed to
enable rapid material design space exploration and property identification and
optimization. The implementation must take account of real-time decision cycles
and the trade-offs between speed and accuracy
An Outlook on Correlations in Stock Prices
We present an outlook of the studies on correlations in the price timeseries
of stocks, discussing the construction and applications of "asset tree". The
topic discussed here should illustrate how the complex economic system
(financial market) enrichens the list of existing dynamical systems that
physicists have been studying for long.Comment: 6 pages, RevTeX format. To appear in the Conference Proceedings of
ECONOPHYS-KOLKATA II: International Workshop on Econophysics of Stock Markets
and Minority Games", February 14-17, 2006, SINP, Kolkata, as a book chapter
in Eds. A. Chatterjee and B.K. Chakrabarti, Econophysics of Stock and other
Markets, (Springer-Verlag (Italia), Milan, 2006
LEARNFCA: A FUZZY FCA AND PROBABILITY BASED APPROACH FOR LEARNING AND CLASSIFICATION
Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.
This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of itâs applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems.
We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success.
Adviser: Jitender Deogu
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