C-language code generator for SOFA 2

SOFA 2 je komponentový systém založený na hierarchickém komponentovém modelu. K návrhu aplikace slouží jazyk ADL, chování komponent je popsáno behaviorálními protokoly, dále systém umožňuje dynamickou rekonfiguraci komponet a modeluje propojení mezi jednotlivými komponentami pomocí softwarových konektorů. Ty umožňují transparentní rozdistribuování vyvíjené aplikace mezi více počítačů. Implementace konektorů může být automaticky generována, SOFA 2 je primárně vyvíjena pro jazyk Java, proto obsahuje generátor Javovských konektorů. Cílem této magisterské práce je navrhnout generátor kódu pro jazyk C a zaintegrovat tento generátor do stávající struktury generátoru konektorů v systému SOFA 2. Automatické generování konektorů v jazyce C by mělo umožnit transparentní propojení komponent implementovaných v jazyce C. Navržený generátor C kódu je založený na konceptu transformace šablon, kde je vstupní šablona, která obsahuje kombinaci cílového C kódu a speciálně vyvinutého skriptovacího jazyka, převedena na čistý C kód. Pro vyhodnocení šablon je použito strategické přepisování abstraktních syntaktických stromů poskytnuté frameworkem Stratego/XT.SOFA 2 is a component system employing hierarchically composed components. It provides ADL-based design, behavior specification using behavior protocols, dynamic reconfiguration of the components, and modeling of the component communication by software connectors. This allows seamless and transparent distribution of component applications. The connectors can be automatically generated, SOFA 2 contains Java connector generator allowing to connect components with Java interfaces. The aim of this thesis is to implement C code generator and integrate it into the current SOFA 2 connector generator framework, so that C connectors can be automatically generated and thus components written in C language can be transparently connected in distributed environment. The proposed C code generator is based on the concept of template transformation, where templates containing mixture of C code and a scripting Domain Specific Language are transformed to a pure C code. Strategic term rewriting method provided by Stratego/XT framework is used for evaluation of the scripts within the templates.Department of Software EngineeringKatedra softwarového inženýrstvíFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

The affordance-based concept

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 89-95).Natural language use relies on situational context. The meaning of words and utterances depend on the physical environment and the goals and plans of communication partners. These facts should be central to theories of language and automatic language understanding systems. Instead, they are often ignored, leading to partial theories and systems that cannot fully interpret linguistic meaning. I introduce a new computational theory of conceptual structure that has as its core claim that concepts are neither internal nor external to the language user, but instead span the objective-subjective boundary. This theory proposes interaction and prediction as a central theme, rather than solely emphasizing deducing, sensing or acting. To capture the possible interactions between subject and object, the theory relies on the notion of perceived affordances: structured units of interaction that can be used for prediction at certain levels of abstraction. By using perceived affordances as a basis for language understanding, the theory accounts for many aspects of the situated nature of human language use. It provides a unified solution to a number of other demands on a theory of language understanding including conceptual combination, prototypicality effects, and the generative nature of lexical items.(cont.) To support the theory, I describe an implementation that relies on probabilistic hierarchical plan recognition to predict possible interactions. The elements of a recognized plan provide an instance of perceived affordances which are used by a linguistic parser to ground the meaning of words and grammatical constituents. Evaluations performed in a multiuser role playing game environment show that this implementation captures the meaning of free-form spontaneous directive speech acts that cannot be understood without taking into account the intentional and physical situation of speaker and listener.by Peter John Gorniak.Ph.D

Communication and content

Communication and content presents a comprehensive and foundational account of meaning based on new versions of situation theory and game theory. The literal and implied meanings of an utterance are derived from first principles assuming little more than the partial rationality of interacting agents. New analyses of a number of diverse phenomena – a wide notion of ambiguity and content encompassing phonetics, syntax, semantics, pragmatics, and beyond, vagueness, convention and conventional meaning, indeterminacy, universality, the role of truth in communication, semantic change, translation, Frege’s puzzle of informative identities – are developed. Communication, speaker meaning, and reference are defined. Frege’s context and compositional principles are generalized and reconciled in a fixed-point principle, and a detailed critique of Grice, several aspects of Lewis, and some aspects of the Romantic conception of meaning are offered. Connections with other branches of linguistics, especially psycholinguistics, sociolinguistics, historical linguistics, and natural language processing, are explored. The book will be of interest to scholars in philosophy, linguistics, artificial intelligence, and cognitive science. It should also interest readers in related fields like literary and cultural theory and the social sciences

Communication and content

Communication and content presents a comprehensive and foundational account of meaning based on new versions of situation theory and game theory. The literal and implied meanings of an utterance are derived from first principles assuming little more than the partial rationality of interacting agents. New analyses of a number of diverse phenomena – a wide notion of ambiguity and content encompassing phonetics, syntax, semantics, pragmatics, and beyond, vagueness, convention and conventional meaning, indeterminacy, universality, the role of truth in communication, semantic change, translation, Frege’s puzzle of informative identities – are developed. Communication, speaker meaning, and reference are defined. Frege’s context and compositional principles are generalized and reconciled in a fixed-point principle, and a detailed critique of Grice, several aspects of Lewis, and some aspects of the Romantic conception of meaning are offered

Iterative parameter mixing for distributed large-margin training of structured predictors for natural language processing

The development of distributed training strategies for statistical prediction functions is important for applications of machine learning, generally, and the development of distributed structured prediction training strategies is important for natural language processing (NLP), in particular. With ever-growing data sets this is, first, because, it is easier to increase computational capacity by adding more processor nodes than it is to increase the power of individual processor nodes, and, second, because data sets are often collected and stored in different locations. Iterative parameter mixing (IPM) is a distributed training strategy in which each node in a network of processors optimizes a regularized average loss objective on its own subset of the total available training data, making stochastic (per-example) updates to its own estimate of the optimal weight vector, and communicating with the other nodes by periodically averaging estimates of the optimal vector across the network. This algorithm has been contrasted with a close relative, called here the single-mixture optimization algorithm, in which each node stochastically optimizes an average loss objective on its own subset of the training data, operating in isolation until convergence, at which point the average of the independently created estimates is returned. Recent empirical results have suggested that this IPM strategy produces better models than the single-mixture algorithm, and the results of this thesis add to this picture. The contributions of this thesis are as follows. The first contribution is to produce and analyze an algorithm for decentralized stochastic optimization of regularized average loss objective functions. This algorithm, which we call the distributed regularized dual averaging algorithm, improves over prior work on distributed dual averaging by providing a simpler algorithm (used in the rest of the thesis), better convergence bounds for the case of regularized average loss functions, and certain technical results that are used in the sequel. The central contribution of this thesis is to give an optimization-theoretic justification for the IPM algorithm. While past work has focused primarily on its empirical test-time performance, we give a novel perspective on this algorithm by showing that, in the context of the distributed dual averaging algorithm, IPM constitutes a convergent optimization algorithm for arbitrary convex functions, while the single-mixture distribution algorithm is not. Experiments indeed confirm that the superior test-time performance of models trained using IPM, compared to single-mixture, correlates with better optimization of the objective value on the training set, a fact not previously reported. Furthermore, our analysis of general non-smooth functions justifies the use of distributed large-margin (support vector machine [SVM]) training of structured predictors, which we show yields better test performance than the IPM perceptron algorithm, the only version of the IPM to have previously been given a theoretical justification. Our results confirm that IPM training can reach the same level of test performance as a sequentially trained model and can reach better accuracies when one has a fixed budget of training time. Finally, we use the reduction in training time that distributed training allows to experiment with adding higher-order dependency features to a state-of-the-art phrase-structure parsing model. We demonstrate that adding these features improves out-of-domain parsing results of even the strongest phrase-structure parsing models, yielding a new state-of-the-art for the popular train-test pairs considered. In addition, we show that a feature-bagging strategy, in which component models are trained separately and later combined, is sometimes necessary to avoid feature under-training and get the best performance out of large feature sets

Parsing Based on Grammar and Automata Systems

Tato práce se zabývá syntaktickou analýzou s využitím systémů paralelně komunikujících zásobníkových automatů. Zejména se zaměřuje na dopady nedeterminismu v jednotlivých komponentách na celý systém. Také je představen návrh algoritmu pro převod některých paralelně komunikujících gramatických systémů na systémy paralelně komunikujících zásobníkových automatů. Získané poznatky jsou použity při návrhu a implementaci metody syntaktické analýzy.This thesis is concerning with parsing using parallel communicating pushdown automata systems. Focusing especially on impacts of nondeterminism in individual components on the whole system. Also it introduces a proposal of algorithm for converting some parallel communicating grammar systems to parallel communicating pushdown automata systems. The gained knowledge is used to design and implement parsing method.

A SENSORY-MOTOR LINGUISTIC FRAMEWORK FOR HUMAN ACTIVITY UNDERSTANDING

We empirically discovered that the space of human actions has a linguistic structure. This is a sensory-motor space consisting of the evolution of joint angles of the human body in movement. The space of human activity has its own phonemes, morphemes, and sentences. We present a Human Activity Language (HAL) for symbolic non-arbitrary representation of sensory and motor information of human activity. This language was learned from large amounts of motion capture data. Kinetology, the phonology of human movement, finds basic primitives for human motion (segmentation) and associates them with symbols (symbolization). This way, kinetology provides a symbolic representation for human movement that allows synthesis, analysis, and symbolic manipulation. We introduce a kinetological system and propose five basic principles on which such a system should be based: compactness, view-invariance, reproducibility, selectivity, and reconstructivity. We demonstrate the kinetological properties of our sensory-motor primitives. Further evaluation is accomplished with experiments on compression and decompression of motion data. The morphology of a human action relates to the inference of essential parts of movement (morpho-kinetology) and its structure (morpho-syntax). To learn morphemes and their structure, we present a grammatical inference methodology and introduce a parallel learning algorithm to induce a grammar system representing a single action. The algorithm infers components of the grammar system as a subset of essential actuators, a CFG grammar for the language of each component representing the motion pattern performed in a single actuator, and synchronization rules modeling coordination among actuators. The syntax of human activities involves the construction of sentences using action morphemes. A sentence may range from a single action morpheme (nuclear syntax) to a sequence of sets of morphemes. A single morpheme is decomposed into analogs of lexical categories: nouns, adjectives, verbs, and adverbs. The sets of morphemes represent simultaneous actions (parallel syntax) and a sequence of movements is related to the concatenation of activities (sequential syntax). We demonstrate this linguistic framework on real motion capture data from a large scale database containing around 200 different actions corresponding to English verbs associated with voluntary meaningful observable movement

Parallel Parsing of Context-Free Languages on an Array of Processors

Kosaraju [Kosaraju 69] and independently ten years later, Guibas, Kung and Thompson [Guibas 79] devised an algorithm (K-GKT) for solving on an array of processors a class of dynamic programming problems of which general context-free language (CFL) recognition is a member. I introduce an extension to K-GKT which allows parsing as well as recognition. The basic idea of the extension is to add counters to the processors. These act as pointers to other processors. The extended algorithm consists of three phases which I call the recognition phase, the marking phase and the parse output phase. I first consider the case of unambiguous grammars. I show that in that case, the algorithm has O(n2log n) space complexity and a linear time complexity. To obtain these results I rely on a counter implementation that allows the execution in constant time of each of the operations: set to zero, test if zero, increment by 1 and decrement by 1. I provide a proof of correctness of this implementation. I introduce the concept of efficient grammars. One factor in the multiplicative constant hidden behind the O(n2log n) space complexity measure for the algorithm is related to the number of non-terminals in the (unambiguous) grammar used. I say that a grammar is k-efficient if it allows the processors to store not more than k pointer pairs. I call a 1-efficient grammar an efficient grammar. I show that two properties that I call nt-disjunction and rhsdasjunction together with unambiguity are sufficient but not necessary conditions for grammar efficiency. I also show that unambiguity itself is not a necessary condition for efficiency. I then consider the case of ambiguous grammars. I present two methods for outputting multiple parses. Both output each parse in linear time. One method has O(n3log n) space complexity while the other has O(n2log n) space complexity. I then address the issue of problem decomposition. I show how part of my extension can be adapted, using a standard technique, to process inputs that would be too large for an array of some fixed size. I then discuss briefly some issues related to implementation. I report on an actual implementation on the I.C.L. DAP. Finally, I show how another systolic CFL parsing algorithm, by Chang, Ibarra and Palis [Chang 87], can be generalized to output parses in preorder and inorder

Communication and content

Communication and content presents a comprehensive and foundational account of meaning based on new versions of situation theory and game theory. The literal and implied meanings of an utterance are derived from first principles assuming little more than the partial rationality of interacting agents. New analyses of a number of diverse phenomena – a wide notion of ambiguity and content encompassing phonetics, syntax, semantics, pragmatics, and beyond, vagueness, convention and conventional meaning, indeterminacy, universality, the role of truth in communication, semantic change, translation, Frege’s puzzle of informative identities – are developed. Communication, speaker meaning, and reference are defined. Frege’s context and compositional principles are generalized and reconciled in a fixed-point principle, and a detailed critique of Grice, several aspects of Lewis, and some aspects of the Romantic conception of meaning are offered

CLAIRE makes machine translation BLEU no more

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 133-139).We introduce CLAIRE, a mathematically principled model for inferring ranks and scores for arbitrary items based on forced-choice binary comparisons, and show how to apply this technique to statistical models to take advantage of problem-specific assistance from non-experts. We apply this technique to two language processing problems: parsing and machine translation. This leads to an analysis which casts doubts on modern evaluation methods for machine translation systems, and an application of CLAIRE as a new technique for evaluating machine translation systems which is inexpensive, has theoretical guarantees, and correlates strongly in practice with more expensive human judgments of system quality. Our analysis reverses several major tenants of the mainstream machine translation research agenda, suggesting in particular that the use of linguistic models should be reexamined.by Ali Mohammad.Sc.D