Submitted by Haroudo Xavier Filho (haroudo.xavierfo@ufpe.br) on 2016-03-11T14:13:00Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao DANIEL LEITE VIANA.pdf: 4297126 bytes, checksum: fa862ba18fe815a710afe7b3e591cee8 (MD5) / Made available in DSpace on 2016-03-11T14:13:00Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao DANIEL LEITE VIANA.pdf: 4297126 bytes, checksum: fa862ba18fe815a710afe7b3e591cee8 (MD5) Previous issue date: 2015-08-10 / Sistemas baseados em gestos vêm se tornando uma alternativa para o desenvolvimento de aplicações mais intuitivas para os usuários, pois permitem a esses usuários interagirem de forma mais natural. Tais sistemas, em geral, requerem dispositivos de captura junto com alguma técnica de reconhecimento para que os gestos requeridos na interação natural sejam reconhecidos. A ausência de abstrações apropriadas para representação dos gestos dificulta as especificações de novas interações naturais. A representação de um gesto, quase sempre, envolve Aprendizagem de Máquina ou um avançado algoritmo de reconhecimento baseado nos dados da posição tridimensional do corpo humano fornecidos por sensores de profundidade, tal como o Microsoft Kinect. Além disso, as aplicações desenvolvidas tornam-se dependentes das bibliotecas de desenvolvimento dos dispositivos. Dessa forma, se o dispositivo for substituído por outro mais moderno ou de fabricante diferente quase todo o algoritmo de reconhecimento precisa ser reescrito. O principal objetivo desta dissertação é a especificação e implementação da Linguagem para Especificação de Gestos (LEG), uma Domain-Specific Language (DSL) para a especificação e reconhecimento de gestos livres do corpo humano com suporte a diferentes dispositivos de profundidade. A LEG é uma linguagem declarativa, baseada na análise das interfaces gestuais para computador e no estudo das abstrações e representações do movimento humano, a fim de reduzir a complexidade no desenvolvimento de aplicações baseadas em gestos. A implementação da linguagem foi realizada em duas etapas. Primeiro, foi criado um framework (Kinect Gesture) com a lógica para rastrear e identificar gestos descritos na linguagem. Na segunda etapa, foi definida a gramática e o interpretador foi construído. A abordagem adotada foi de DSL externa, sendo sua sintaxe textual e particular. A fim de avaliar a implementação proposta, 15 (quinze) gestos foram especificados em LEG e reconhecidos. Tendo como referência os resultados obtidos, chegou-se a conclusão que a linguagem apresentada neste trabalho diminuiu consideravelmente a complexidade necessária para realizar a especificação e o reconhecimento dos gestos. / Systems Based-gestures are becoming an alternative to the development of more intuitive applications for users, because enable users to interact more naturally. Generally these systems need of capture devices together with some technique for gesture recognition. The lack of appropriate abstractions for the representation of gestures difficult to specifications of new natural interactions. For specify gesture, it is almost always necessary to acquire advanced knowledge in gesture recognition area and skills on chosen device and it is for this reason that the development of gestures is restricted. Often developers are using Machine Learning as support to creating database. Another approach is to create a recognition algorithm based on data from the depth sensor Kinect. Furthermore, due to the nature of the software development kits (SDK) provided by the hardware vendors to build gesture-based applications, the developed applications often become tightly coupled with the SDK. The result is that significant portions of the application need to be rewritten to run it on another device. The main goal of this dissertation is to implement and evaluate GSL (Gesture Specific Language), a Domain-Specific Language for specification and identification of gestures with support to different depth sensors. GSL is a declarative programming language based on the analysis of gestural interfaces for computer and study of abstractions and representations of human movement, in order to reduce the complexity in application development based on gestures. The development was conducted in two phases: the first was implemented a framework (Kinect Gesture) with logic for tracking and identify gestures. In the second phase, we built an grammar and a compiler. We adopted a external DSL approach, with specific and textual syntax. In order to evaluate the proposed implementation, we used GSL for specification and recognition of fifteen gestures. The results obtained show that GSL reduced considerably the complexity on perform the specification and the recognition of gestures.

<p>The thesis describes steps taken towards using graphic representation and visual modelling support for the knowledge acquisition process in knowledge-based systems – a process commonly regarded as difficult. The performance of the systems depends on the quality of the embedded knowledge, which makes the knowledge acquisition phase particularly significant. During the acquisition phase, a main obstacle to proper extraction of information is the absence of effective modelling techniques.</p><p>The contributions of the thesis are: introducing a methodology for user-centred knowledge modelling, enhancing transparency to support the modelling of content and of the reasoning strategy, incorporating conceptualisation to simplify the grasp of the contents and to support assimilation of the domain knowledge, and supplying a visual compositional logic programming language for adding and modifying functionality.</p><p>The user-centred knowledge acquisition model, proposed in this thesis, applies a combination of different approaches to knowledge modelling. The aim is to bridge the gap between the users (i.e., knowledge engineers, domain experts and end users) and the system in transferring knowledge, by supporting the users through graphics and visualisation. Visualisation supports the users by providing several different views of the contents of the system.</p><p>The Unified Modelling Language (UML) is employed as a modelling language. A benefit of utilising UML is that the knowledge base can be modified, and the reasoning strategy and the functionality can be changed directly in the model. To make the knowledge base more comprehensible and expressive, we incorporated visual conceptualisation into UML’s diagrams to describe the contents. Visual conceptualisation of the knowledge can also facilitate assimilation in a hypermedia system through visual libraries.</p><p>Visualisation of functionality is applied to a programming paradigm, namely relational programming, often employed in artificial intelligence systems. This approach employs Venn-Euler diagrams as a graphic interface to a compositional operator based relational programming language. </p><p>The concrete result of the research is the development of a graphic representation and visual modelling approach to support the knowledge acquisition process. This approach has been evaluated for two different knowledge bases, one built for hydropower development and river regulation and the other for diagnosing childhood diseases.</p>

The thesis describes steps taken towards using graphic representation and visual modelling support for the knowledge acquisition process in knowledge-based systems – a process commonly regarded as difficult. The performance of the systems depends on the quality of the embedded knowledge, which makes the knowledge acquisition phase particularly significant. During the acquisition phase, a main obstacle to proper extraction of information is the absence of effective modelling techniques. The contributions of the thesis are: introducing a methodology for user-centred knowledge modelling, enhancing transparency to support the modelling of content and of the reasoning strategy, incorporating conceptualisation to simplify the grasp of the contents and to support assimilation of the domain knowledge, and supplying a visual compositional logic programming language for adding and modifying functionality. The user-centred knowledge acquisition model, proposed in this thesis, applies a combination of different approaches to knowledge modelling. The aim is to bridge the gap between the users (i.e., knowledge engineers, domain experts and end users) and the system in transferring knowledge, by supporting the users through graphics and visualisation. Visualisation supports the users by providing several different views of the contents of the system. The Unified Modelling Language (UML) is employed as a modelling language. A benefit of utilising UML is that the knowledge base can be modified, and the reasoning strategy and the functionality can be changed directly in the model. To make the knowledge base more comprehensible and expressive, we incorporated visual conceptualisation into UML’s diagrams to describe the contents. Visual conceptualisation of the knowledge can also facilitate assimilation in a hypermedia system through visual libraries. Visualisation of functionality is applied to a programming paradigm, namely relational programming, often employed in artificial intelligence systems. This approach employs Venn-Euler diagrams as a graphic interface to a compositional operator based relational programming language. The concrete result of the research is the development of a graphic representation and visual modelling approach to support the knowledge acquisition process. This approach has been evaluated for two different knowledge bases, one built for hydropower development and river regulation and the other for diagnosing childhood diseases.

This thesis examines trends in the academic and professional literature around immutable data and its relationship with declarative User Interfaces (UIs). Immutable data types are preferred by academic authors due to their increased safety, and commercial languages are increasing their support for them over time. More recently, declarative UIs are an exploding topic in industry, and these are related, although not as closely as one would expect. Declarative programming tries to focus on highlevel requirements, not low-level details. It is easier to do this if functions have no side effects, and immutable data is a guaranteed way of achieving this. To highlight this property, the declarative UI framework Flutter advertises “stateless widgets”, but their existence puts in highlights the lack of this property in most widgets. Consequently, we ask whether it is feasible to build a Graphical User Interface (GUI) toolkit using purely immutable data structures. To accomplish this objective, a purely immutable GUI toolkit is sketched and partially developed using Elm, a purely functional language in which all data structures are immutable. To understand the requirements of a GUI toolkit, we categorize and put in historical context, different design paradigms for UIs and relate them to core software-design principles. Leading toolkits allow developers to visualize and manage multiple views of their interfaces, including the view hierarchy, layout, interface to business logic, and focus management. By creating a concrete example, the research aims to provide insight into the limitations of utilizing purely immutable data within a GUI framework and suggests future work to mitigate these. / Thesis / Master of Science (MSc)

This paper concerns itself with possibilities of declarative programming in the new version of Java 8 language, specifically using elements adopted from the domain of functional programming languages: function as a value and lazy streams of data. The goal of this paper is to demonstrate possibilities of declarative programming using these elements, analyze its implementation and design own extensions. The contribution lies particularly in showing possibilities of the new elements, implementation analysis and design of a new functionality. The output can be used by a Czech reader, who is at least slightly advanced in the field of information technologies. The paper is divided into a theoretical and practical parts. Theoretical part is covered by chapters 3-8. Theoretical part describes motivation for introduction of the new elements, describes functional programming and its basic principles, then it shows basic principles of the newly introducted elements and ends with the description of the java.util.stream package. Pactical part is covered by chapters 9 and 10. Practical part concerns with stream oper-ations and extension design of existing functionality.

[ES] El proceso de ciencia de datos es esencial para extraer valor de los datos. Sin embargo, la parte más tediosa del proceso, la preparación de los datos, implica una serie de formateos, limpieza e identificación de problemas que principalmente son tareas manuales. La preparación de datos todavía se resiste a la automatización en parte porque el problema depende en gran medida de la información del dominio, que se convierte en un cuello de botella para los sistemas de última generación a medida que aumenta la diversidad de dominios, formatos y estructuras de los datos. En esta tesis nos enfocamos en generar algoritmos que aprovechen el conocimiento del dominio para la automatización de partes del proceso de preparación de datos. Mostramos la forma en que las técnicas generales de inducción de programas, en lugar de los lenguajes específicos del dominio, se pueden aplicar de manera flexible a problemas donde el conocimiento es importante, mediante el uso dinámico de conocimiento específico del dominio. De manera más general, sostenemos que una combinación de enfoques de aprendizaje dinámicos y basados en conocimiento puede conducir a buenas soluciones. Proponemos varias estrategias para seleccionar o construir automáticamente el conocimiento previo apropiado en varios escenarios de preparación de datos. La idea principal se basa en elegir las mejores primitivas especializadas de acuerdo con el contexto del problema particular a resolver. Abordamos dos escenarios. En el primero, manejamos datos personales (nombres, fechas, teléfonos, etc.) que se presentan en formatos de cadena de texto muy diferentes y deben ser transformados a un formato unificado. El problema es cómo construir una transformación compositiva a partir de un gran conjunto de primitivas en el dominio (por ejemplo, manejar meses, años, días de la semana, etc.). Desarrollamos un sistema (BK-ADAPT) que guía la búsqueda a través del conocimiento previo extrayendo varias meta-características de los ejemplos que caracterizan el dominio de la columna. En el segundo escenario, nos enfrentamos a la transformación de matrices de datos en lenguajes de programación genéricos como R, utilizando como ejemplos una matriz de entrada y algunas celdas de la matriz de salida. También desarrollamos un sistema guiado por una búsqueda basada en árboles (AUTOMAT[R]IX) que usa varias restricciones, probabilidades previas para las primitivas y sugerencias textuales, para aprender eficientemente las transformaciones. Con estos sistemas, mostramos que la combinación de programación inductiva, con la selección dinámica de las primitivas apropiadas a partir del conocimiento previo, es capaz de mejorar los resultados de otras herramientas actuales específicas para la preparación de datos. / [CA] El procés de ciència de dades és essencial per extraure valor de les dades. No obstant això, la part més tediosa del procés, la preparació de les dades, implica una sèrie de transformacions, neteja i identificació de problemes que principalment són tasques manuals. La preparació de dades encara es resisteix a l'automatització en part perquè el problema depén en gran manera de la informació del domini, que es converteix en un coll de botella per als sistemes d'última generació a mesura que augmenta la diversitat de dominis, formats i estructures de les dades. En aquesta tesi ens enfoquem a generar algorismes que aprofiten el coneixement del domini per a l'automatització de parts del procés de preparació de dades. Mostrem la forma en què les tècniques generals d'inducció de programes, en lloc dels llenguatges específics del domini, es poden aplicar de manera flexible a problemes on el coneixement és important, mitjançant l'ús dinàmic de coneixement específic del domini. De manera més general, sostenim que una combinació d'enfocaments d'aprenentatge dinàmics i basats en coneixement pot conduir a les bones solucions. Proposem diverses estratègies per seleccionar o construir automàticament el coneixement previ apropiat en diversos escenaris de preparació de dades. La idea principal es basa a triar les millors primitives especialitzades d'acord amb el context del problema particular a resoldre. Abordem dos escenaris. En el primer, manegem dades personals (noms, dates, telèfons, etc.) que es presenten en formats de cadena de text molt diferents i han de ser transformats a un format unificat. El problema és com construir una transformació compositiva a partir d'un gran conjunt de primitives en el domini (per exemple, manejar mesos, anys, dies de la setmana, etc.). Desenvolupem un sistema (BK-ADAPT) que guia la cerca a través del coneixement previ extraient diverses meta-característiques dels exemples que caracteritzen el domini de la columna. En el segon escenari, ens enfrontem a la transformació de matrius de dades en llenguatges de programació genèrics com a R, utilitzant com a exemples una matriu d'entrada i algunes dades de la matriu d'eixida. També desenvolupem un sistema guiat per una cerca basada en arbres (AUTOMAT[R]IX) que usa diverses restriccions, probabilitats prèvies per a les primitives i suggeriments textuals, per aprendre eficientment les transformacions. Amb aquests sistemes, mostrem que la combinació de programació inductiva amb la selecció dinàmica de les primitives apropiades a partir del coneixement previ, és capaç de millorar els resultats d'altres enfocaments de preparació de dades d'última generació i més específics. / [EN] Data science is essential for the extraction of value from data. However, the most tedious part of the process, data wrangling, implies a range of mostly manual formatting, identification and cleansing manipulations. Data wrangling still resists automation partly because the problem strongly depends on domain information, which becomes a bottleneck for state-of-the-art systems as the diversity of domains, formats and structures of the data increases. In this thesis we focus on generating algorithms that take advantage of the domain knowledge for the automation of parts of the data wrangling process. We illustrate the way in which general program induction techniques, instead of domain-specific languages, can be applied flexibly to problems where knowledge is important, through the dynamic use of domain-specific knowledge. More generally, we argue that a combination of knowledge-based and dynamic learning approaches leads to successful solutions. We propose several strategies to automatically select or construct the appropriate background knowledge for several data wrangling scenarios. The key idea is based on choosing the best specialised background primitives according to the context of the particular problem to solve. We address two scenarios. In the first one, we handle personal data (names, dates, telephone numbers, etc.) that are presented in very different string formats and have to be transformed into a unified format. The problem is how to build a compositional transformation from a large set of primitives in the domain (e.g., handling months, years, days of the week, etc.). We develop a system (BK-ADAPT) that guides the search through the background knowledge by extracting several meta-features from the examples characterising the column domain. In the second scenario, we face the transformation of data matrices in generic programming languages such as R, using an input matrix and some cells of the output matrix as examples. We also develop a system guided by a tree-based search (AUTOMAT[R]IX) that uses several constraints, prior primitive probabilities and textual hints to efficiently learn the transformations. With these systems, we show that the combination of inductive programming with the dynamic selection of the appropriate primitives from the background knowledge is able to improve the results of other state-of-the-art and more specific data wrangling approaches. / This research was supported by the Spanish MECD Grant FPU15/03219;and partially by the Spanish MINECO TIN2015-69175-C4-1-R (Lobass) and RTI2018-094403-B-C32-AR (FreeTech) in Spain; and by the ERC Advanced Grant Synthesising Inductive Data Models (Synth) in Belgium. / Contreras Ochando, L. (2020). Towards Data Wrangling Automation through Dynamically-Selected Background Knowledge [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/160724 / TESIS