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61	Skákající jazykové modely / Jumping Language Models Ošmera, Lubomír January 2019 (has links) The main goal of this master thesis is introduction and investigation of extended version of jumping automata and grammars. New versions are primarily focused on bioinformatic applications - DNA computing. This thesis examine their power and other properties of new models and makes comparison with existing computer science models. Then thesis demontrates practical applications, specifically amino acid and protein detections inside DNA sequence and makes comparision with existing tools in DNA computing for example Mark´s probabilistic models.
62	Syntaxí řízený překlad založený na hlubokých zásobníkových automatech / Syntax-Direxted Translation Based on Deep Pushdown Automata Solár, Peter Unknown Date (has links) This thesis introduces syntax-directed translation based on deep pushdown automata. Necessary theoretical models are introduced in the theoretical part. The most important model, introduced in this thesis, is a deep pushdown transducer. The transducer should be used in syntax analysis, significant part of translation. Practical part consists of an implementation of simple-language interpret based on these models.
63	Formální modely distribuovaného výpočtu / Formal Models of Distributed Computation Soukup, Ondřej January 2017 (has links) Tato disertační práce představuje derivační stromy několika různých typů gramatik ve zobecněné Kurodově normální formě; jmenovitě obecné a regulárně řízené gramatiky, gramatiky s rozptýleným kontextem a spolupracující distribuované gramatické systémy. Definuje jednoduché stromové rysy založené na kontextových vlastnostech jednotlivých diskutovaných gramatik a dokazuje, že pokud existuje limitující konstanta k taková, že každá věta generovaného jazyka L odpovídá řetězci listových uzlů derivačního stromu, ve kterém je výskyt definovaných stromových rysů omezen konstantou k, jazyk L je ve skutečnosti bezkontextový. Tato práce dále ukazuje, že dosažený výsledek představuje silný nástroj důkazu bezkontextovosti jazyka. Vše je doplněno příklady praktického využití nástroje.
64	Well-Formed and Scalable Invasive Software Composition / Wohlgeformte und Skalierbare Invasive Softwarekomposition Karol, Sven 26 June 2015 (has links) (PDF) Software components provide essential means to structure and organize software effectively. However, frequently, required component abstractions are not available in a programming language or system, or are not adequately combinable with each other. Invasive software composition (ISC) is a general approach to software composition that unifies component-like abstractions such as templates, aspects and macros. ISC is based on fragment composition, and composes programs and other software artifacts at the level of syntax trees. Therefore, a unifying fragment component model is related to the context-free grammar of a language to identify extension and variation points in syntax trees as well as valid component types. By doing so, fragment components can be composed by transformations at respective extension and variation points so that always valid composition results regarding the underlying context-free grammar are yielded. However, given a language’s context-free grammar, the composition result may still be incorrect. Context-sensitive constraints such as type constraints may be violated so that the program cannot be compiled and/or interpreted correctly. While a compiler can detect such errors after composition, it is difficult to relate them back to the original transformation step in the composition system, especially in the case of complex compositions with several hundreds of such steps. To tackle this problem, this thesis proposes well-formed ISC—an extension to ISC that uses reference attribute grammars (RAGs) to specify fragment component models and fragment contracts to guard compositions with context-sensitive constraints. Additionally, well-formed ISC provides composition strategies as a means to configure composition algorithms and handle interferences between composition steps. Developing ISC systems for complex languages such as programming languages is a complex undertaking. Composition-system developers need to supply or develop adequate language and parser specifications that can be processed by an ISC composition engine. Moreover, the specifications may need to be extended with rules for the intended composition abstractions. Current approaches to ISC require complete grammars to be able to compose fragments in the respective languages. Hence, the specifications need to be developed exhaustively before any component model can be supplied. To tackle this problem, this thesis introduces scalable ISC—a variant of ISC that uses island component models as a means to define component models for partially specified languages while still the whole language is supported. Additionally, a scalable workflow for agile composition-system development is proposed which supports a development of ISC systems in small increments using modular extensions. All theoretical concepts introduced in this thesis are implemented in the Skeletons and Application Templates framework SkAT. It supports “classic”, well-formed and scalable ISC by leveraging RAGs as its main specification and implementation language. Moreover, several composition systems based on SkAT are discussed, e.g., a well-formed composition system for Java and a C preprocessor-like macro language. In turn, those composition systems are used as composers in several example applications such as a library of parallel algorithmic skeletons. Software Komposition invasiv templates Meta-Programmierung Schnittstellen Code-Generierung generative Softwareentwicklung Java Syntaxbaum Parser Attributgrammariken Referenzattributgrammatiken CPP C-Preprocessor Aspektorientierte Programmierung Wohlgeformtheit abstrakter Syntaxgraph macros Expansion Kompilierer Programme statische Semantik BPMN Programmiersprachen domänenspezifische Sprachen Sprachkomposition C C++ invasive software composition component aspects weaving correct weaving well-formedness attribute grammars island grammars SkAT skeletons generative programming metaprogramming metamodeling Reuseware C C++ Java JastAdd JastAddJ Compilers RAG preprocessor macros decomposition code syntax tree graphs templates grammars context-sensitive context-free COMPOST EMFText JastEMF parsing expression grammars domain-specific languages BPMN embedded languages language composition ddc:004 rvk:ST 233
65	Well-Formed and Scalable Invasive Software Composition Karol, Sven 18 May 2015 (has links) Software components provide essential means to structure and organize software effectively. However, frequently, required component abstractions are not available in a programming language or system, or are not adequately combinable with each other. Invasive software composition (ISC) is a general approach to software composition that unifies component-like abstractions such as templates, aspects and macros. ISC is based on fragment composition, and composes programs and other software artifacts at the level of syntax trees. Therefore, a unifying fragment component model is related to the context-free grammar of a language to identify extension and variation points in syntax trees as well as valid component types. By doing so, fragment components can be composed by transformations at respective extension and variation points so that always valid composition results regarding the underlying context-free grammar are yielded. However, given a language’s context-free grammar, the composition result may still be incorrect. Context-sensitive constraints such as type constraints may be violated so that the program cannot be compiled and/or interpreted correctly. While a compiler can detect such errors after composition, it is difficult to relate them back to the original transformation step in the composition system, especially in the case of complex compositions with several hundreds of such steps. To tackle this problem, this thesis proposes well-formed ISC—an extension to ISC that uses reference attribute grammars (RAGs) to specify fragment component models and fragment contracts to guard compositions with context-sensitive constraints. Additionally, well-formed ISC provides composition strategies as a means to configure composition algorithms and handle interferences between composition steps. Developing ISC systems for complex languages such as programming languages is a complex undertaking. Composition-system developers need to supply or develop adequate language and parser specifications that can be processed by an ISC composition engine. Moreover, the specifications may need to be extended with rules for the intended composition abstractions. Current approaches to ISC require complete grammars to be able to compose fragments in the respective languages. Hence, the specifications need to be developed exhaustively before any component model can be supplied. To tackle this problem, this thesis introduces scalable ISC—a variant of ISC that uses island component models as a means to define component models for partially specified languages while still the whole language is supported. Additionally, a scalable workflow for agile composition-system development is proposed which supports a development of ISC systems in small increments using modular extensions. All theoretical concepts introduced in this thesis are implemented in the Skeletons and Application Templates framework SkAT. It supports “classic”, well-formed and scalable ISC by leveraging RAGs as its main specification and implementation language. Moreover, several composition systems based on SkAT are discussed, e.g., a well-formed composition system for Java and a C preprocessor-like macro language. In turn, those composition systems are used as composers in several example applications such as a library of parallel algorithmic skeletons. info:eu-repo/classification/ddc/004 ddc:004
66	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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