Symbol–Relation Grammars: A Formalism for Graphical Languages

AbstractA common approach to the formal description of pictorial and visual languages makes use of formal grammars and rewriting mechanisms. The present paper is concerned with the formalism of Symbol–Relation Grammars (SR grammars, for short). Each sentence in an SR language is composed of a set of symbol occurrences representing visual elementary objects, which are related through a set of binary relational items. The main feature of SR grammars is the uniform way they use context-free productions to rewrite symbol occurrences as well as relation items. The clearness and uniformity of the derivation process for SR grammars allow the extension of well-established techniques of syntactic and semantic analysis to the case of SR grammars. The paper provides an accurate analysis of the derivation mechanism and the expressive power of the SR formalism. This is necessary to fully exploit the capabilities of the model. The most meaningful features of SR grammars as well as their generative power are compared with those of well-known graph grammar families. In spite of their structural simplicity, variations of SR grammars have a generative power comparable with that of expressive classes of graph grammars, such as the edNCE and the N-edNCE classes

Third International Conference on Technologies for Music Notation and Representation TENOR 2017

The third International Conference on Technologies for Music Notation and Representation seeks to focus on a set of specific research issues associated with Music Notation that were elaborated at the first two editions of TENOR in Paris and Cambridge. The theme of the conference is vocal music, whereas the pre-conference workshops focus on innovative technological approaches to music notation

A Goal-based Framework for Contextual Requirements Modeling and Analysis

Requirements Engineering (RE) research often ignores, or presumes a uniform nature of the context in which the system operates. This assumption is no longer valid in emerging computing paradigms, such as ambient, pervasive and ubiquitous computing, where it is essential to monitor and adapt to an inherently varying context. Besides influencing the software, context may influence stakeholders' goals and their choices to meet them. In this paper, we propose a goal-oriented RE modeling and reasoning framework for systems operating in varying contexts. We introduce contextual goal models to relate goals and contexts; context analysis to refine contexts and identify ways to verify them; reasoning techniques to derive requirements reflecting the context and users priorities at runtime; and finally, design time reasoning techniques to derive requirements for a system to be developed at minimum cost and valid in all considered contexts. We illustrate and evaluate our approach through a case study about a museum-guide mobile information system

Conceptual analysis knowledge management and conceptual graph theory

There exists an impressive quantity of literature dealing with knowledge Representation that covers highly technical contributions as well as more philosophical ones or again those that have a more or less explicit "cognitive" orientation. So, it is not very astonishing to notice that the definition of what knowledge representation is, is quite vague. It is not our intention to give a historical survey of that notion nor to proceed to a critical enumeration of the several topics that are covered by it. Our objective is, rather, to develop a conceptual framework that should permit us to handle the major descriptive problems in the conception of knowledge based systems. In order to be able to put forth in a systematic way our conception of knowledge representation (KR), we will discuss in the first section some central problems of knowledge description. In the second section, we will introduce the conceptual graph theory developed mainly by Sowa (1984) and try to give a more formal account of KR

Chinese learners and computer assisted language learning: a study of learning styles, learner attitudes and the effectiveness of CALL in Chinese higher education

E-leaming has become a staple diet in many learners’ academic lives in higher education institutions all around the world. This study did not follow the techno- centric standpoint and the comparative research design tradition in this field; instead, it focused on how learners’ learning styles and attitudes interact with the effectiveness of E-leaming implementation in the field of foreign language learning. The research was set in the author’s home institution—a comprehensive university in mainland China, where the first- and second-year undergraduate students who were studying a compulsory English course were surveyed from 2003 to 2004. For this course, the College of Foreign Languages developed an online computer-assisted language learning (CALL) environment—NCE Online which was the basis of this investigation. The author’s former colleagues helped organise the distribution and collection of 4 questionnaires and 9 groups of student interviews over one academic year. A total of 5258 participants completed the first questionnaire in 2003 while the numbers of participants who completed the other questions varied from around 200 to 700. To understand data from the learners in more depth, the language teachers and NCE Online developers were also surveyed with a questionnaire and individual interviews. The results showed that the learners had very positive attitudes towards the use of computer technologies in their study, and that there was an evident tendency to expect an increasing proportion of CALL elements as the students progressed in their English study. Despite these positive attitudes, what was equally clear was that there were still more students who preferred to have traditional classroom learning as their main learning mode, and they did not think of the E- leaming materials available as more effective than the traditional ones. Meanwhile, their teachers’ attitudes and the University’s policies also played an important role in influencing learners’ attitudes and actual behaviour toward the CALL system. In addition, the research revealed that Chinese learners have learning styles distinct from their peers in the west, which suggests that a CALL environment for Chinese learners should not follow blindly the much-advocated constructivist design model in the west. Reconsideration of both the ideals of foreign language teaching methodologies and E-leaming pedagogies, which originated mainly in Europe and Northern America, needs to take place before the design of a CALL system for Chinese learners. The implications of this research were therefore discussed to begin just such a rethinking of CALL implementations in Chinese higher education

Probabilistic Modelling of Morphologically Rich Languages

This thesis investigates how the sub-structure of words can be accounted for in probabilistic models of language. Such models play an important role in natural language processing tasks such as translation or speech recognition, but often rely on the simplistic assumption that words are opaque symbols. This assumption does not fit morphologically complex language well, where words can have rich internal structure and sub-word elements are shared across distinct word forms. Our approach is to encode basic notions of morphology into the assumptions of three different types of language models, with the intention that leveraging shared sub-word structure can improve model performance and help overcome data sparsity that arises from morphological processes. In the context of n-gram language modelling, we formulate a new Bayesian model that relies on the decomposition of compound words to attain better smoothing, and we develop a new distributed language model that learns vector representations of morphemes and leverages them to link together morphologically related words. In both cases, we show that accounting for word sub-structure improves the models' intrinsic performance and provides benefits when applied to other tasks, including machine translation. We then shift the focus beyond the modelling of word sequences and consider models that automatically learn what the sub-word elements of a given language are, given an unannotated list of words. We formulate a novel model that can learn discontiguous morphemes in addition to the more conventional contiguous morphemes that most previous models are limited to. This approach is demonstrated on Semitic languages, and we find that modelling discontiguous sub-word structures leads to improvements in the task of segmenting words into their contiguous morphemes.Comment: DPhil thesis, University of Oxford, submitted and accepted 2014. http://ora.ox.ac.uk/objects/uuid:8df7324f-d3b8-47a1-8b0b-3a6feb5f45c

Automated specification-based testing of graphical user interfaces

Tese de doutoramento. Engenharia Electrónica e de Computadores. 2006. Faculdade de Engenharia. Universidade do Porto, Departamento de Informática, Escola de Engenharia. Universidade do Minh

The Recognition and Representation of 3D Images for a Natural Language Driven Scene Analyzer

Two necessary components of any image understanding system are an object recognizer and a symbolic scene representation. The LandScan system currently being designed is a query driven scene analyzer in which the user\u27s natural language queries will focus the analysis to pertinent regions of the scene. This is different than many image underderstanding systems which present a symbolic description of the entire scene regardless of what portions of that picture are actually of interest. In order to facilitate such a focusing strategy, the high level analysis which includes reasoning and recognition must proceed using a topdown flow of control, and the representation must reflect the current sector of interest. This thesis proposes the design for 3 goal-oriented object recognizer and a dynamic scene representation for Landscan - a system to analyze aerial photographs of urban scenes. The recognizer is an ATN in which the grammar describes sequences of primitives which define objects and the interpreter generates these sets of primitives. The scene model is dynamically built as objects are recognized. The scene model represents both the objects in the image and primitive spatial relations between these objects