878 resultados para object-oriented language


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This work researches about the authorship marks in academic reviews of graduating students, besides the author discursive points of view. We detect the voices presented in the text, that is, the expressions of the discourse from the I and from the others, trying to explicitate the forms of the author review insertion. It is oriented by the conception that the author is the subject of his/her discourse, creating a position in relation to the discourse of the other(s) with which he/she maintains a dialogue. Our corpus is constituted of ten (10) academic reviews, produced by students from the Course of Letras, from Universidade do Estado do Rio Grande do Norte UERN, do Campus Avançado Prof.ª Maria Elisa de Albuquerque Maia CAMEAM. The data signalize to the perception that the subject constitutes author, when he/she gives voice to the other; showing his/her point of view; pointing to the discourse of the other; and/or getting far from the text. That way, it is observed that the genre academic review can be considered as text that reconfigures the original text, that is, as a text that comments, criticizes, confirms the text that is object of its analyses. The theoretical background that influences the research comes from the dialogical language conception, from discursive genres and from Volochinov/Bakhtin (1997, 2003) authorship. To the discussion and the analyses of the discursive genre academic review , it is mentioned the MOTTA-ROTH and MEURER (2002); POSSENTI (2001, 2002) conceptions.

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This investigation aims at describing, analyzing and interpreting the Commitment in Initial Petitions, which is a genre circumscribed in the judicial domain. For this purpose, we have chosen sections, facts as found and relevant law , sections of the petition, with the understanding that, in this way, respectively, the narration of events, which gives margin to the propositioning to the judicial action, and the exposition of the law that upholds the author s intention. We base our discussion on the field of Linguistics, more precisely, Textual Discourse Analysis (TDA), whose theoretical basis is derived from Textual Linguistics (TL) and Enunciative Linguistics. We foreground, particularly, the way in which the author of texts, objects of analysis, use discursive strategies that evidence ER. The relevance of this study, then, is in the formation of a critique of the judicial text, as it conceives of a dialogical approach to the point of view, raising not only questions about the way in which a linguistic instance conceives an object of discourse, but also considering questions of language inherent to technical writing and, in this aspect, contributing to the work of those operating in Law about the many ways ER is formed in the body of a petition. We selected two categories to analyze that, according to Adam (2011), characterize the degree of ER in the textual material of the propositional enunciations: the different types of representation of speech and the indications of profile of mediators. In this sense, with this task as an objective, we base our study regarding point of view on Rabatel (2003, 2009a, 2010) with relation to the enunciative approach, including the study of PDV in polyphonic and dialogical theoretical framework to study the ER from different types of speech representations that conceive forms of transmission of discourse and the role of the enunciating subject, mainly the responsibility and the prerogative by the propositional contents. In the same way, intending to study the indications of the mediator profiles, we observed the postulations of Guentchéva (1994, 1996), which develop the notion of mediative grammatical categories, of which permit the linguistic marking of distance and engagement of the enunciator with regard to the information expressed. The methodology we adopted was based on qualitative research, of an interpretive and introspective nature, in light of the fact that his study focuses on processes and strategies underlying language use. The corpus of the research is comprised of Initial Petitions, which gave rise to actions originating in the Civil Court of Currais Novos County RN. The data analysis shows that an object of discourse is always perspective oriented and presents the point of view of one or more enunciators. Consequently, the producer of a text, using the PDV of other enunciators, influences and establishes the argumentative orientation of the text. In the same way, it evidences the relevance of the use of mediated constructions in the judicial text, as they function as strategies attenuated to the responsibility of the producer of the text with what is said, and at the same time points to a discourse of authority through the entrance of the sources of law. Moreover, it reveals the documental and international importance of this practice, at the same time that it exposes the compositional and normative difficulties with regard to legal and linguistic aspects

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Models of different degrees of complexity are found in the literature for the estimation of lightning striking distances and attractive radius of objects and structures. However, besides the oversimplifications of the physical nature of the lightning discharge on which most of them are based, till recently the tridimensional structure configuration could not be considered. This is an important limitation, as edges and other details of the object affect the electric field and, consequently, the upward leader initiation. Within this context, the Self-consistent leader initiation and propagation model (SLIM) proposed by Becerra and Cooray is state-of-the-art leader inception and propagation leader model based on the physics of leader discharges which enables the tridimensional geometry of the structure to be taken into account. In this paper, the model is used for estimating the striking distance and attractive radius of power transmission lines. The results are compared with those obtained from the electrogeometric and Eriksson's models. © 2003-2012 IEEE.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Service Oriented Computing is a new programming paradigm for addressing distributed system design issues. Services are autonomous computational entities which can be dynamically discovered and composed in order to form more complex systems able to achieve different kinds of task. E-government, e-business and e-science are some examples of the IT areas where Service Oriented Computing will be exploited in the next years. At present, the most credited Service Oriented Computing technology is that of Web Services, whose specifications are enriched day by day by industrial consortia without following a precise and rigorous approach. This PhD thesis aims, on the one hand, at modelling Service Oriented Computing in a formal way in order to precisely define the main concepts it is based upon and, on the other hand, at defining a new approach, called bipolar approach, for addressing system design issues by synergically exploiting choreography and orchestration languages related by means of a mathematical relation called conformance. Choreography allows us to describe systems of services from a global view point whereas orchestration supplies a means for addressing such an issue from a local perspective. In this work we present SOCK, a process algebra based language inspired by the Web Service orchestration language WS-BPEL which catches the essentials of Service Oriented Computing. From the definition of SOCK we will able to define a general model for dealing with Service Oriented Computing where services and systems of services are related to the design of finite state automata and process algebra concurrent systems, respectively. Furthermore, we introduce a formal language for dealing with choreography. Such a language is equipped with a formal semantics and it forms, together with a subset of the SOCK calculus, the bipolar framework. Finally, we present JOLIE which is a Java implentation of a subset of the SOCK calculus and it is part of the bipolar framework we intend to promote.

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In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

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In this work I address the study of language comprehension in an “embodied” framework. Firstly I show behavioral evidence supporting the idea that language modulates the motor system in a specific way, both at a proximal level (sensibility to the effectors) and at the distal level (sensibility to the goal of the action in which the single motor acts are inserted). I will present two studies in which the method is basically the same: we manipulated the linguistic stimuli (the kind of sentence: hand action vs. foot action vs. mouth action) and the effector by which participants had to respond (hand vs. foot vs. mouth; dominant hand vs. non-dominant hand). Response times analyses showed a specific modulation depending on the kind of sentence: participants were facilitated in the task execution (sentence sensibility judgment) when the effector they had to use to respond was the same to which the sentences referred. Namely, during language comprehension a pre-activation of the motor system seems to take place. This activation is analogous (even if less intense) to the one detectable when we practically execute the action described by the sentence. Beyond this effector specific modulation, we also found an effect of the goal suggested by the sentence. That is, the hand effector was pre-activated not only by hand-action-related sentences, but also by sentences describing mouth actions, consistently with the fact that to execute an action on an object with the mouth we firstly have to bring it to the mouth with the hand. After reviewing the evidence on simulation specificity directly referring to the body (for instance, the kind of the effector activated by the language), I focus on the specific properties of the object to which the words refer, particularly on the weight. In this case the hypothesis to test was if both lifting movement perception and lifting movement execution are modulated by language comprehension. We used behavioral and kinematics methods, and we manipulated the linguistic stimuli (the kind of sentence: the lifting of heavy objects vs. the lifting of light objects). To study the movement perception we measured the correlations between the weight of the objects lifted by an actor (heavy objects vs. light objects) and the esteems provided by the participants. To study the movement execution we measured kinematics parameters variance (velocity, acceleration, time to the first peak of velocity) during the actual lifting of objects (heavy objects vs. light objects). Both kinds of measures revealed that language had a specific effect on the motor system, both at a perceptive and at a motoric level. Finally, I address the issue of the abstract words. Different studies in the “embodied” framework tried to explain the meaning of abstract words The limit of these works is that they account only for subsets of phenomena, so results are difficult to generalize. We tried to circumvent this problem by contrasting transitive verbs (abstract and concrete) and nouns (abstract and concrete) in different combinations. The behavioral study was conducted both with German and Italian participants, as the two languages are syntactically different. We found that response times were faster for both the compatible pairs (concrete verb + concrete noun; abstract verb + abstract noun) than for the mixed ones. Interestingly, for the mixed combinations analyses showed a modulation due to the specific language (German vs. Italian): when the concrete word precedes the abstract one responses were faster, regardless of the word grammatical class. Results are discussed in the framework of current views on abstract words. They highlight the important role of developmental and social aspects of language use, and confirm theories assigning a crucial role to both sensorimotor and linguistic experience for abstract words.

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This thesis investigated affordances and verbal language to demonstrate the flexibility of embodied simulation processes. Starting from the assumption that both object/action understanding and language comprehension are tied to the context in which they take place, six studies clarified the factors that modulate simulation. The studies in chapter 4 and 5 investigated affordance activation in complex scenes, revealing the strong influence of the visual context, which included either objects and actions, on compatibility effects. The study in chapter 6 compared the simulation triggered by visual objects and objects names, showing differences depending on the kind of materials processed. The study in chapter 7 tested the predictions of the WAT theory, confirming that the different contexts in which words are acquired lead to the difference typically observed in the literature between concrete and abstract words. The study in chapter 8 on the grounding of abstract concepts tested the mapping of temporal contents on the spatial frame of reference of the mental timeline, showing that metaphoric congruency effects are not automatic, but flexibly mediated by the context determined by the goals of different tasks. The study in chapter 9 investigated the role of iconicity in verbal language, showing sound-to-shape correspondences when every-day object figures, result that validated the reality of sound-symbolism in ecological contexts. On the whole, this evidence favors embodied views of cognition, and supports the hypothesis of a high flexibility of simulation processes. The reported conceptual effects confirm that the context plays a crucial role in affordances emergence, metaphoric mappings activation and language grounding. In conclusion, this thesis highlights that in an embodied perspective cognition is necessarily situated and anchored to a specific context, as it is sustained by the existence of a specific body immersed in a specific environment.

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In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

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From the moment of their birth, a person's life is determined by their sex. Goroshko wanted to find out why this difference is so striking, why society is so determined to sustain it, and how it can persist even when certain national or behavioural stereotypes are erased. She believes there are both social and biological differences between men and women, and set out to analyse these distinctions as they are manifested in language. Certain general characteristics can be identified. Males tend to write with less fluency, to refer to events in a verb phrase, to be time-oriented, to involve themselves more in their references to events, to locate events in their personal sphere of activity, and to refer less to others. Goroshko therefore concludes that the male is more active, more ego-involved in what he does and less concerned about others. Women were more fluent, referred to events in a noun-phrase, were less time-oriented, tended to be less involved in their event references, located events within their interactive community, and referred more to others. They spent much more time discussing personal and domestic subjects, relationship problems, family, health and reproductive matters, weight, food and clothing, men, and other women. Computer analysis showed that female speech was substantially more emotional, using hyperbole, metaphor, comparisons, epithets, ways of enumeration, interjections, rhetorical questions and exclamations. The level of literacy was higher in female speech, and women made fewer grammatical and spelling mistakes in written texts. Goroshko believes that her findings have relevance beyond the linguistic field. When working on anonymous texts she has been able to decide on the sex of the author and so believes that her research may even be of benefit to forensic science.

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Behavioral reflection is crucial to support for example functional upgrades, on-the-fly debugging, or monitoring critical applications. However the use of reflective features can lead to severe problems due to infinite metacall recursion even in simple cases. This is especially a problem when reflecting on core language features since there is a high chance that such features are used to implement the reflective behavior itself. In this paper we analyze the problem of infinite meta-object call recursion and solve it by providing a first class representation of meta-level execution: at any point in the execution of a system it can be determined if we are operating on a meta-level or base level so that we can prevent infinite recursion. We present how meta-level execution can be represented by a meta-context and how reflection becomes context-aware. Our solution makes it possible to freely apply behavioral reflection even on system classes: the meta-context brings stability to behavioral reflection. We validate the concept with a robust implementation and we present benchmarks.

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In this paper we present XSAMPL3D, a novel language for the high-level representation of actions performed on objects by (virtual) humans. XSAMPL3D was designed to serve as action representation language in an imitation-based approach to character animation: First, a human demonstrates a sequence of object manipulations in an immersive Virtual Reality (VR) environment. From this demonstration, an XSAMPL3D description is automatically derived that represents the actions in terms of high-level action types and involved objects. The XSAMPL3D action description can then be used for the synthesis of animations where virtual humans of different body sizes and proportions reproduce the demonstrated action. Actions are encoded in a compact and human-readable XML-format. Thus, XSAMPL3D describtions are also amenable to manual authoring, e.g. for rapid prototyping of animations when no immersive VR environment is at the animator's disposal. However, when XSAMPL3D descriptions are derived from VR interactions, they can accomodate many details of the demonstrated action, such as motion trajectiories,hand shapes and other hand-object relations during grasping. Such detail would be hard to specify with manual motion authoring techniques only. Through the inclusion of language features that allow the representation of all relevant aspects of demonstrated object manipulations, XSAMPL3D is a suitable action representation language for the imitation-based approach to character animation.

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Brain processing of grammatical word class was studied analyzing event-related potential (ERP) brain fields. Normal subjects observed a randomized sequence of single German nouns and verbs on a computer screen, while 20-channel ERP field map series were recorded separately for both word classes. Spatial microstate analysis was applied, based on the observation that series of ERP maps consist of epochs of quasi-stable map landscapes and based on the rationale that different map landscapes must have been generated by different neural generators and thus suggest different brain functions. Space-oriented segmentation of the mean map series identified nine successive, different functional microstates, i.e., steps of brain information processing characterized by quasi-stable map landscapes. In the microstate from 116 to 172 msec, noun-related maps differed significantly from verb-related maps along the left–right axis. The results indicate that different neural populations represent different grammatical word classes in language processing, in agreement with clinical observations. This word class differentiation as revealed by the spatial–temporal organization of neural activity occurred at a time after word input compatible with speed of reading.

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Objectives: The aim of this content analysis study is to characterize the TV advertisements aired to an at-risk child population along the Texas-Mexico border. Methods: We characterized the early Saturday morning TV advertisements aired by three broadcast network categories (U.S. English language, U.S. Spanish language, and Mexican Spanish language) in Spring 2010. The number, type (food related vs. non-food related), target audience, and persuasion tactics used were recorded. Advertised foods, based on nutrition content, were categorized as meeting or not meeting current dietary guidelines. Results: Most commercials were non-food related (82.7%, 397 of 480). The majority of the prepared foods (e.g., cereals, snacks, and drinks) advertised did not meet the current U.S. Dietary Guidelines. Additionally, nutrition content information was not available for many of the foods advertised on the Mexican Spanish language broadcast network category. Conclusions: For U.S. children at risk for obesity along the Texas-Mexico border exposure to TV food advertisements may result in the continuation of sedentary behavior as well as an increased consumption of foods of poor nutritional quality. An international regulatory effort to monitor and enforce the reduction of child-oriented food advertising is needed. Editors' Note: This article was submitted in response to the first issue of the Journal of Applied Research on Children: Latino Children.

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Software corpora facilitate reproducibility of analyses, however, static analysis for an entire corpus still requires considerable effort, often duplicated unnecessarily by multiple users. Moreover, most corpora are designed for single languages increasing the effort for cross-language analysis. To address these aspects we propose Pangea, an infrastructure allowing fast development of static analyses on multi-language corpora. Pangea uses language-independent meta-models stored as object model snapshots that can be directly loaded into memory and queried without any parsing overhead. To reduce the effort of performing static analyses, Pangea provides out-of-the box support for: creating and refining analyses in a dedicated environment, deploying an analysis on an entire corpus, using a runner that supports parallel execution, and exporting results in various formats. In this tool demonstration we introduce Pangea and provide several usage scenarios that illustrate how it reduces the cost of analysis.