Extracción automática de modelos UML contenidos en imágenes

Aunque parezca extraño, pese a no poder encontrar sitios web especializados en ofertar diseños de software representados mediante diagramas UML, existe una ingente cantidad de documentación a disposición de cualquiera, y que contiene dichos modelos: como imágenes en documentos textuales. Este universo de información no se encuentra fácilmente accesible para los desarrolladores porque no es posible, con la tecnología actual, buscar de forma precisa información semántica dentro de imágenes. Lo único que pueden hacer los desarrolladores es intentar buscar documentos relevantes, leerlos, y decidir si los diseños le sirven a sus intereses. Para evitar este problema, y conseguir poner a disposición de toda la comunidad de desarrolladores centenas de miles de diseños, este trabajo pretende desarrollar la metodología necesaria para poder extraer la información textual y gráfica de las imágenes que representen diagramas UML, y convertirla en información pura UML (es decir, en modelos UML reales). El poner a disposición de los analistas, desarrolladores de software, o interesados tal cantidad de diagramas y modelos de software permitirá la aplicación de técnicas modernas de reutilización de software basadas en la búsqueda de diagramas UML. La búsqueda de diagramas UML de todo tipo (estáticos, dinámicos, arquitecturales, de Casos de Uso etc.) mediante similitud a uno dado permitirá potenciar los desarrollos de software de calidad, controlados en el coste, y en el tiempo de desarrollo: las tres virtudes de la reutilización de software. La complejidad de esta propuesta radica en muchos aspectos, todos ellos entrelazados: por un lado hay que considerar que la información de partida se encuentra representada con diferentes tipos de calidad, mediante bits de colores o tonos de grises. Por otro lado su semántica viene dada por la combinación de texto en lenguaje natural y estructuras gráficas. Estas estructuras gráficas tienen asociada una información semántica, accesible a la interpretación humana, que depende del tipo de diagrama. Los diagramas que representan diseños de software son documentos en formato visual con alta estructuración y contenido semántico, que se deben distinguir unos de otros. Debido a su formato en forma de imagen requieren un preprocesado mediante técnicas de visión artificial, OCR y técnicas de clusterización o clasificación basadas en aprendizaje automático. Precisamente este será el principal cometido de esta tesis: la extracción de la semántica de los diagramas en forma de imágenes encontrados en la web. La información obtenida de estos diagramas, principalmente UML, debe incluir información textual e información estructural. A la información textual se obtendrá mediante técnicas de OCR mientras que la información estructurada será detectada mediante reconocimiento de formas combinado con Inteligencia Artificial. El resultado de esta propuesta sería una metodología que podría ser aplicada para cargar repositorios de diagramas UML a partir de imágenes existentes en internet, con vistas a su posterior aplicación y puesta a disposición de los usuarios: un GOOGLE de diagramas UML.There are many interesting sites in the web offering reuse of source code, but no one giving the choice to identify, find and reuse design models using UML. However, even if this data seems to be sad, a simple web search can give you astonishing results: Get into GOOGLE images and search for “UML Class diagram”. Thousands of images will suddenly be available for you. The bad news: they are images. You cannot find anything on them, you cannot find them by content. You cannot compare them. You can, simple, download them. Could you be interested in working with those images, finding similar ones, etc.? In order to solve this problem, and reach hundreds of thousands of UML designs, this work intends to develop the necessary methodology to extract the textual and graphical information contented in UML based images, and convert them in, exactly, UML information (real UML Models represented in a UML object model) The possibility to offer such amount of diagrams to software analysts, software developers, or simply interested stakeholders will allow them to apply real, systematic and modern software reuse based on UML diagrams information retrieval. The possibility to find all kinds of diagrams (static, dynamic, architectural, Use Case, etc.) by similar content will strengthen software development based on the best quality, controlled cost and time to market principles: the three real benefits of Software reuse. This proposal has several difficulties in different fronts: to start with, one must consider that all the information is usually stored in low resolution images, where texts are difficult to read and understand and boxes and arrows are not properly drawn. And, on the other side, the semantics comes from the combination of text represented in Natural Language and graphical structures. These structures have associated semantic information, understandable by humans, which depend and change with the diagram types. Due to these problems, Artificial Vision, OCR, classification and automatic learning algorithm must be used in this thesis. This thesis, therefore, will attempt to extract semantic information for images representing UML Diagrams found in the web. The information extracted will be both textual and graphical. OCR technology (existing already) would be used to textual information. In order to extract graphical information a semantic model combined with AI will be used. The result of the proposal will be a methodology that will allow repositories (in the web or private) to offer UML diagrams based on (and pointing to) images found in the web, for further reuse.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Antonio de Amescua Seco.- Secretario: Susana Irene Díaz Rodríguez.- Vocal: Pascual Campoy Cerver

Análisis de los criterios de relevancia documental mediante consultas de información en el entorno web

La búsqueda de información no se entiende sin los motores de búsqueda web. Ante una demanda de información los buscadores web ordenan los resultados de forma que las páginas web más relevantes para la consulta aparezcan en las primeras posiciones. Esto genera un alto grado de competitividad entre las páginas web por obtener mejores asignaciones de relevancia por parte de los buscadores. Por norma general, los usuarios suelen consultar sólo los primeros resultados que devuelve un motor de búsqueda, en consecuencia ocupar estos puestos se traduce en mayor prestigio y visibilidad. Por tanto, la percepción de relevancia documental web por parte de los usuarios está intrínsecamente unida a los motores de búsqueda. En este trabajo se propone y desarrolla una metodología para determinar la relevancia documental web de forma automática, que se puede interpretar como: predicción automática de la posición que otorgaría un motor de búsqueda a un documento web entre los resultados de una consulta. La investigación se completa identificando los factores considerados en el posicionamiento web, a partir del estudio de herramientas empleadas en la optimización y promoción de páginas web. También se analiza el peso de cada uno de estos factores en los algoritmos de ordenación de los buscadores. Finalmente, en relación a las capacidades adquiridas para emular el comportamiento de los motores de búsqueda se propone un método de optimización web que estima previamente la rentabilidad del proceso. De esta forma no se invertirá en una campaña de promoción si los pronósticos de mejora del posicionamiento no se juzgan adecuados

A free mind cannot be digitally transferred

The digital transfer of the mind to a computer system (i.e., mind uploading) requires representing the mind as a finite sequence of bits (1s and 0s). The classic “stored-program computer” paradigm, in turn, implies the equivalence between program and data, so that the sequence of bits themselves can be interpreted as a program, which will be algorithmically executed in the receiving device. Now, according to a previous proof, on which this paper is based, a computational or algorithmic machine, however complex, cannot be free (in the sense of ‘self-determined’). Consequently, a finite sequence of bits cannot adequately represent a free mind and, therefore, a free mind cannot be digitally transferred, quod erat demonstrandum. The impossibility of making this transfer, as demonstrated here, should be a concern especially for those who wish to achieve it. Since we intend this to be a rigorous demonstration, we must give precise definitions and conditions of validity. The most important part of the paper is devoted to explaining the meaning and reasonableness of these definitions and conditions (for example that being truly free means being self-determined). Special attention is paid, also, to the philosophical implications of the demonstration. Finally, this thesis is distinguished from other closely related issues (such as other possible technological difficulties to “discretize” the mind; or, whether it is possible to transfer the mind from one material support to another one in a non-digital way).This research has received funding from the RESTART project “Continuous Reverse Engineering for Software Product Lines/Ingeniería Inversa Continua para Líneas de Productos de Software” (ref. RTI2018-099915-B-I00, Convocatoria Proyectos de I + D Retos Investigación del Programa Estatal de I + D + i Orientada a los Retos de la Sociedad 2018); MOMEBIA project “Monitorización del Mercado Eléctrico Basada en técnicas de Inteligencia Artificial" (ref. RTC2019-007501-7, Convocatoria de Proyectos de I + D + i «Retos-Colaboración» 2019—Ministerio de Ciencia e Innovación—Agencia Estatal de Investigación); it has also been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

Machine Ethics: Do Androids Dream of Being Good People?

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating¿ and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of a set of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely "following a moral code". In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society.This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the terms of the Multi-Annual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). This research has received funding also from the RESTART project – “Continuous Reverse Engineering for Software Product Lines / Ingeniería Inversa Continua para Líneas de Productos de Software” (ref. RTI2018-099915-B-I00, Convocatoria Proyectos de I + D Retos Investigación del Programa Estatal de I + D + i Orientada a los Retos de la Sociedad 2018, grant agreement nº: 412122; and from the CritiRed project – “Elaboración de un modelo predictivo para el desarrollo del pensamiento crítico en el uso de las redes sociales”, Convocatoria Retos de Investigación del Ministerio de Ciencia, Innovación y Universidades (2019–2022), ref. RTI2018-095740-B-I00

El Parque Científico UC3M, gestor de los derechos de propiedad industrial e intelectual

Audiovisulaes: Entrevista a Esther Arias, Oficina Española de Patentes y Marcas. Disponible en https://www.youtube.com/watch?v=K38IC0NDctE .La gestión interna de los derechos de propiedad industrial e intelectual procedentes de los resultados de la investigación desarrollada en la Universidad Carlos III de Madrid es competencia de su Parque Científico. Dependiente del vicerrectorado de Investigación, es el responsable de la transferencia de tecnología y conocimiento a las empresas.Un servicio de la UC3M es la plataforma ORIÓN-UC3M, en que la persona investigadora o gestora del grupo de investigación introduce, a medida que se producen, sus ofertas en la plataforma.Contiene: El Parque Científico UC3M, gestor de los derechos de propiedad industrial e intelectual (p. 22) .Los inventores opinan. La experiencia de proteger sus resultados con el Parque Científico UC3M / Valentín Miguel Moreno, Ana García–Armada (p. 23) .-- Más opiniones / Raúl Sánchez–Reíllo, Manuel Armenteros (p. 24)

Automatic classification of web images as UML static diagrams using machine learning techniques

Our purpose in this research is to develop a method to automatically and efficiently classify web images as Unified Modeling Language (UML) static diagrams, and to produce a computer tool that implements this function. The tool receives a bitmap file (in different formats) as an input and communicates whether the image corresponds to a diagram. For pragmatic reasons, we restricted ourselves to the simplest kinds of diagrams that are more useful for automated software reuse: computer-edited 2D representations of static diagrams. The tool does not require that the images are explicitly or implicitly tagged as UML diagrams. The tool extracts graphical characteristics from each image (such as grayscale histogram, color histogram and elementary geometric forms) and uses a combination of rules to classify it. The rules are obtained with machine learning techniques (rule induction) from a sample of 19,000 web images manually classified by experts. In this work, we do not consider the textual contents of the images. Our tool reaches nearly 95% of agreement with manually classified instances, improving the effectiveness of related research works. Moreover, using a training dataset 15 times bigger, the time required to process each image and extract its graphical features (0.680 s) is seven times lower.This research has received funding from the CRYSTAL project – Critical System Engineering Acceleration (European Union’s Seventh Framework Program, FP7/2007-2013, ARTEMIS Joint Undertaking grant agreement n° 332830); and from the AMASS project – Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems (H2020-ECSEL grant agreement nº 692474; Spain’s MINECO ref. PCIN-2015-262)

Application of machine learning techniques to the flexible assessment and improvement of requirements quality

It is already common to compute quantitative metrics of requirements to assess their quality. However, the risk is to build assessment methods and tools that are both arbitrary and rigid in the parameterization and combination of metrics. Specifically, we show that a linear combination of metrics is insufficient to adequately compute a global measure of quality. In this work, we propose to develop a flexible method to assess and improve the quality of requirements that can be adapted to different contexts, projects, organizations, and quality standards, with a high degree of automation. The domain experts contribute with an initial set of requirements that they have classified according to their quality, and we extract their quality metrics. We then use machine learning techniques to emulate the implicit expert’s quality function. We provide also a procedure to suggest improvements in bad requirements. We compare the obtained rule-based classifiers with different machine learning algorithms, obtaining measurements of effectiveness around 85%. We show as well the appearance of the generated rules and how to interpret them. The method is tailorable to different contexts, different styles to write requirements, and different demands in quality. The whole process of inferring and applying the quality rules adapted to each organization is highly automatedThis research has received funding from the CRYSTAL project–Critical System Engineering Acceleration (European Union’s Seventh Framework Program FP7/2007-2013, ARTEMIS Joint Undertaking grant agreement no 332830); and from the AMASS project–Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems (H2020-ECSEL grant agreement no 692474; Spain’s MINECO ref. PCIN-2015-262)

Evolution of web positioning factors and adaptation of optimization tools

Las herramientas de optimización web, Search Engine Optimization (SEO), se utilizan para analizar y mejorar los sitios web en relación a distintos factores de posicionamiento. Esta investigación estudia la evolución de las estrate- gias de posicionamiento web y analiza la adaptación de las herramientas de optimización a estos factores. Además, se estudian qué factores de posicionamiento están presentes en las herramientas SEO más populares. En la fase experimen- tal se analiza el grado en que las estrategias de optimización mejoran el posicionamiento, y en qué medida se encuentran esas funcionalidades en las herramientas SEO. Adicionalmente se han analizado foros y blogs oficiales para descubrir nuevas pautas de evolución de los motores de búsqueda y el grado en que las herramientas SEO pueden adaptarse a dichos cambios. Aunque estas herramientas optimizan el posicionamiento, los resultados sugieren la necesidad de intro- ducir importantes mejoras que aumenten su potencialidad futura.Search Engine Optimization (SEO) tools are designed to analyze and optimize resources regarding positioning factors. Web positioning techniques are applied in order to improve the relevancy of web resources. Webmasters usually use SEO tools to analyze a web site according to some positioning factor. They are required to be updated to achieve two basic goals: to increase the user’s satisfaction while searching the web and to decrease web spamming. We have studied the trends that affect positioning algorithms and optimization techniques. Several SEO tools were analysed in order to learn which functionalities have been implemented. Furthermore, an experiment was performed to test how the positioning factors help optimize the results and if these factors are present in the functionalities found in the SEO tools. Finally, a literature review was carried out to detect future trends in search engines’ algorithms. Results show that SEO tools help in the optimization process but to an insufficient degree; therefore the algorithm’s evolution study suggests that there is a need for major updates in the short ter

Automatizing chromatic quality assessment for cultural heritage image digitization

In the context of digitization of photographs and other documents with graphical value, cultural heritage organizations need to give a guarantee that the stored digital image is a faithful representation of the physical image both at the physical level and the perceptual level. On the physical level, image quality can be measured objectively in a simple way by applying certain physical attributes to the image, as well as by measuring how distorting images affects the performance of the attributes. However, on the perceptual level, image quality should correspond to the perception that a human expert would experience when observing the physical image under certain determined and controlled conditions. In this paper we address the problem of image quality assessment (IQA) in the context of cultural heritage digitization by applying machine learning (ML). In particular, we explore the possibility of creating a decision tree that mimics the response of an expert on cultural heritage when observing cultural heritage images

Genetic algorithms: a practical approach to generate textual patterns for requirements authoring

The writing of accurate requirements is a critical factor in assuring the success of a project. Text patterns are knowledge artifacts that are used as templates to guide engineers in the requirements authoring process. However, generating a text pattern set for a particular domain is a time-consuming and costly activity that must be carried out by specialists. This research proposes a method of automatically generating text patterns from an initial corpus of high-quality requirements, using genetic algorithms and a separate-and-conquer strategy to create a complete set of patterns. Our results show this method can generate a valid pattern set suitable for requirements authoring, outperforming existing methods by 233%, with requirements ratio values of 2.87 matched per pattern found; as opposed to 1.23 using alternative methods