Entrelazamiento de los aspectos estático y dinámico en las asociaciones UML

El Lenguaje Unificado de Modelado (Unified Modeling Language - UML) es un lenguaje visual de modelado de propósito general utilizado para especificar, visualizar, construir y documentar los artefactos (piezas de información) de un sistema informático orientado a objetos. Uno de los elementos básicos del lenguaje UML es la "asociación", que se define como "la relación semántica entre dos o más clasificadores que especifica conexiones entre sus instancias". Como ocurre con otros elementos del lenguaje, la definición de asociación y de sus propiedades presenta faltas de precisión, ambigüedades, contradicciones internas y dificultades para su aplicación práctica. En esta Tesis Doctoral se ha desarrollado una investigación acerca del concepto de asociación en UML, centrada en tres grandes aspectos teóricos (la multiplicidad, la navegabilidad y la visibilidad) y buscando siempre las consecuencias de su aplicación práctica (la implementación). La principal conclusión de esta Tesis Doctoral es que la semántica o significado de toda asociación incluye dos aspectos que están íntimamente entrelazados: el aspecto estático y el aspecto dinámico, relacionados respectivamente con la estructura y comportamiento del sistema. También hemos argumentado que, para lograr un mayor desacoplamiento entre los participantes en una asociación, conviene definir una asociación no entre clasificadores, sino entre interfaces

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

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)

An analysis of safety evidence management with the Structured Assurance Case Metamodel

SACM (Structured Assurance Case Metamodel) it a standard for assurance case specification and exchange. It consists of an argumentation metamodel and an evidence metamodel for justifying that a system satisfies certain requirements. For assurance of safety-critical systems, SACM can be used to manage safety evidence and to specify safety cases. The standard is a promising initiative towards harmonizing and improving system assurance practices, but its suitability for safety evidence management needs to be further studied. To this end, this paper studies how SACM 1.1 supports this activity according to requirements from industry and from prior work. We have analysed the notion of evidence in SACM, its evidence lifecycle, the classes and associations of the evidence metamodel, and the link of this metamodel with the argumentation one. As a result, we have identified several improvement opportunities and extension possibilities in SACM

Turing and the Face of the Universe

This article addresses issues and individuals as (seemingly) disparate as Atapuerca, the process of hominization, thought experiments with steel balls, Galileo, Descartes, Blade Runner, Turing, and electronic forms on the Internet, in order to recall something that is forgotten time and time again: God is beyond the knowledge that is provided by and achievable through the experimental scientific method

Turing y el rostro del universo

This comment talks about things and people as (seemingly) disparate as Atapuerca, the process of hominization, thought experiments with steel balls, Galileo, Descartes, Blade Runner, Turing and electronic forms on the Internet, in order to recall something that is forgotten over and over again: God is beyond the knowledge that is provided by the experimental scientific method.Al hilo del comentario se habla de cosas y personas tan (aparentemente) dispares como Atapuerca, el proceso de hominización, experimentos mentales con bolas de acero, Galileo, Descartes, Blade Runner, Turing y formularios electrónicos en Internet, con el fin de recordar algo que se olvida una y otra vez: Dios está más allá del conocimiento que proporciona el método científico-experimental

Máquinas computacionales y conciencia artificial

En este ensayo argumento que la noción de máquina incluye necesariamente el hecho de ser diseñada con un fin. Por tanto, no basta con ser sistema mecánico para ser máquina. Puesto que el método científico-experimental excluye metodológicamente la consideración de la finalidad, resulta que también es insuficiente para entender cabalmente qué son las máquinas. Por el contrario, para entender una máquina es necesario ante todo entender su finalidad (y también su estructura), en claro paralelismo con la causa final (y la formal) de Aristóteles. Obviamente, la finalidad y la estructura no son componentes de la máquina que puedan interaccionar físicamente con otros componentes, y sin embargo son esenciales para comprender su funcionamiento. Esto arroja una interesante luz sobre la relación entre la mente y el cuerpo: de modo análogo a como la finalidad de un artefacto explica su funcionamiento, la conciencia es la explicación del comportamiento específicamente humano. Máquinas y seres humanos tienen en común que, para entenderlos, es necesario acudir al principio de finalidad. Pero mientras en las máquinas es una finalidad dada, los seres humanos nos caracterizamos porque podemos proponernos nuestros propios fines