Global ETD Search

1	A Perception Based Question-Answering Architecture Derived from Computing with Words Torres Parra, Jimena Cecilia 01 December 2009 (has links) Most search applications in use today employ a keyword based search mechanism, which do not have any deductive abilities and are therefore unable to understand human perceptions underlying any given search. This paper proposes a framework for a Fuzzy Expert System for question-answer support while searching within a specific domain. Development of such a framework requires computing theories which can understand and manipulate the knowledge inherent in natural language based documents. To this end, we can now employ the newly introduced theory of Computing with Words (CW). The recent introduction of CW, by Lofti Zadeh, signifies a break from the traditional computing model and promises to enable analysis of natural language based information. In order to provide a bridge between raw natural language text and CW, the use of Probabilistic Context Free Grammar (PCFG) is proposed. Together the two theories form the core of the proposed framework that allows search applications to be constructed with the capabilities of deduction and perception analysis using a natural language interface. Computing With Words Fuzzy Logic Natural Language Processing Probabilistic Context-Free Grammar
2	Consistency of Probabilistic Context-Free Grammars Stüber, Torsten 10 May 2012 (has links) (PDF) We present an algorithm for deciding whether an arbitrary proper probabilistic context-free grammar is consistent, i.e., whether the probability that a derivation terminates is one. Our procedure has time complexity $\\\\mathcal O(n^3)$ in the unit-cost model of computation. Moreover, we develop a novel characterization of consistent probabilistic context-free grammars. A simple corollary of our result is that training methods for probabilistic context-free grammars that are based on maximum-likelihood estimation always yield consistent grammars. Kontextfreie Grammatik Probabilistische Kontextfreie Grammatik Konsistenz Verarbeitung natürlicher Sprachen Context-free Grammar Probabilistic Context-Free Grammar Consistency Natural Language Processing ddc:004 rvk:SS 5514
3	Consistency of Probabilistic Context-Free Grammars Stüber, Torsten 10 May 2012 (has links) We present an algorithm for deciding whether an arbitrary proper probabilistic context-free grammar is consistent, i.e., whether the probability that a derivation terminates is one. Our procedure has time complexity $\\\\mathcal O(n^3)$ in the unit-cost model of computation. Moreover, we develop a novel characterization of consistent probabilistic context-free grammars. A simple corollary of our result is that training methods for probabilistic context-free grammars that are based on maximum-likelihood estimation always yield consistent grammars. info:eu-repo/classification/ddc/004 ddc:004
4	Syntaktický analyzátor pro český jazyk / Syntactic Analyzer for Czech Language Beneš, Vojtěch January 2014 (has links) Master’s thesis describes theoretical basics, solution design, and implementation of constituency (phrasal) parser for Czech language, which is based on a part of speech association into phrases. Created program works with manually built and annotated Czech sample corpus to generate probabilistic context free grammar within runtime machine learning. Parser implementation, based on extended CKY algorithm, then for the input Czech sentence decides if the sentence can be generated by the created grammar and for the positive cases constructs the most probable derivation tree. This result is then compared with the expected parse to evaluate constituency parser success rate.
5	Machine Learning for Speech Forensics and Hypersonic Vehicle Applications Emily R Bartusiak (6630773) 06 December 2022 (has links) <p>Synthesized speech may be used for nefarious purposes, such as fraud, spoofing, and misinformation campaigns. We present several speech forensics methods based on deep learning to protect against such attacks. First, we use a convolutional neural network (CNN) and transformers to detect synthesized speech. Then, we investigate closed set and open set speech synthesizer attribution. We use a transformer to attribute a speech signal to its source (i.e., to identify the speech synthesizer that created it). Additionally, we show that our approach separates different known and unknown speech synthesizers in its latent space, even though it has not seen any of the unknown speech synthesizers during training. Next, we explore machine learning for an objective in the aerospace domain.</p> <p><br></p> <p>Compared to conventional ballistic vehicles and cruise vehicles, hypersonic glide vehicles (HGVs) exhibit unprecedented abilities. They travel faster than Mach 5 and maneuver to evade defense systems and hinder prediction of their final destinations. We investigate machine learning for identifying different HGVs and a conic reentry vehicle (CRV) based on their aerodynamic state estimates. We also propose a HGV flight phase prediction method. Inspired by natural language processing (NLP), we model flight phases as “words” and HGV trajectories as “sentences.” Next, we learn a “grammar” from the HGV trajectories that describes their flight phase transition patterns. Given “words” from the initial part of a HGV trajectory and the “grammar”, we predict future “words” in the “sentence” (i.e., future HGV flight phases in the trajectory). We demonstrate that this approach successfully predicts future flight phases for HGV trajectories, especially in scenarios with limited training data. We also show that it can be used in a transfer learning scenario to predict flight phases of HGV trajectories that exhibit new maneuvers and behaviors never seen before during training.</p> Audio processing Computer vision Digital forensics Deep learning machine learning deep learning speech forensics media forensics convolutional neural network transformer convolutional transformer ensemble spectrogram analysis mel spectrogram analysis synthesized speech detection synthesized speech attribution closed set open set t-stochastic neighbor embedding latent space analysis hypersonics hypersonic glide vehicles vehicle classification flight phase prediction stochastic grammar k-nearest neighbors support vector machine probabilistic context-free grammar automatic distillation of structure generalized earley parser transfer learning

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