Reactions to imagery generated using computational aesthetic measures

This article examines whether textural generation system imagery evolved with computational aesthetic support can be judged as having aesthetic attributes, both when knowing and not knowing its true origin. Such a generation, depicting a digital landscape, is offered to two groups of participants to appraise. It is hypothesized that there will be no statistically significant difference between the groups on their appraisal of the image. Results from statistical analysis prove to be consistent with this hypothesis. A minority of participants, however, do exhibit significant differences in their perception of the image based on its means of production. This article explores and illustrates these differences

Benford’s law predicted digit distribution of aggregated income taxes: the surprising conformity of Italian cities and regions

The yearly aggregated tax income data of all, more than 8000, Italian municipalities are analyzed for a period of five years, from 2007 to 2011, to search for conformity or not with Benford’s law, a counter-intuitive phenomenon observed in large tabulated data where the occurrence of numbers having smaller initial digits is more favored than those with larger digits. This is done in anticipation that large deviations from Benford’s law will be found in view of tax evasion supposedly being widespread across Italy. Contrary to expectations, we show that the overall tax income data for all these years is in excellent agreement with Benford’s law. Furthermore, we also analyze the data of Calabria, Campania and Sicily, the three Italian regions known for strong presence of mafia, to see if there are any marked deviations from Benford’s law. Again, we find that all yearly data sets for Calabria and Sicily agree with Benford’s law whereas only the 2007 and 2008 yearly data show departures from the law for Campania. These results are again surprising in view of underground and illegal nature of economic activities of mafia which significantly contribute totax evasion. Some hypothesis for the found conformity is presented

Benford's law predicted digit distribution of aggregated income taxes: the surprising conformity of Italian cities and regions

The yearly aggregated tax income data of all, more than 8000, Italian municipalities are analyzed for a period of five years, from 2007 to 2011, to search for conformity or not with Benford's law, a counter-intuitive phenomenon observed in large tabulated data where the occurrence of numbers having smaller initial digits is more favored than those with larger digits. This is done in anticipation that large deviations from Benford's law will be found in view of tax evasion supposedly being widespread across Italy. Contrary to expectations, we show that the overall tax income data for all these years is in excellent agreement with Benford's law. Furthermore, we also analyze the data of Calabria, Campania and Sicily, the three Italian regions known for strong presence of mafia, to see if there are any marked deviations from Benford's law. Again, we find that all yearly data sets for Calabria and Sicily agree with Benford's law whereas only the 2007 and 2008 yearly data show departures from the law for Campania. These results are again surprising in view of underground and illegal nature of economic activities of mafia which significantly contribute to tax evasion. Some hypothesis for the found conformity is presented.Comment: 18 pages, 5 tables, 4 figures, 61 references, To appear in European Physical Journal

An Information Theory Approach to Aesthetic Assessment of Visual Patterns.

The question of beauty has inspired philosophers and scientists for centuries. Today, the study of aesthetics is an active research topic in fields as diverse as computer science, neuroscience, and psychology. Measuring the aesthetic appeal of images is beneficial for many applications. In this paper, we will study the aesthetic assessment of simple visual patterns. The proposed approach suggests that aesthetically appealing patterns are more likely to deliver a higher amount of information over multiple levels in comparison with less aesthetically appealing patterns when the same amount of energy is used. The proposed approach is evaluated using two datasets; the results show that the proposed approach is more accurate in classifying aesthetically appealing patterns compared to some related approaches that use different complexity measures

Evolutionary Computation for Digital Artefact Design

This thesis presents novel systems for the automatic and semi-automatic design of digital artefacts. Currently, users wanting to create digital models, such as three-dimensional (3D) digital landscapes and website colour schemes, need to possess significant expertise, as the tools involved demand a high level of knowledge and skill. By developing an intuitive algorithmic process, founded on evolutionary computation (EC), this research enables non-specialist human designers to create digital assets more efficiently. This is achieved by replacing design activities that require significant manual input with algorithmic functions, thereby greatly improving the efficiency and accessibility of the practices involved. This research places an initial focus on the generation of 3D landscapes, but the latter aspect concentrates on the identification of text and background colour combinations more amenable to the reading process, particularly for readers with vision impairments. Choosing an ideal combination of colours requires knowledge of the cognitive and psychological procedures involved. Designers need to be aware of colour contrast ratios, brightness, and variations, which would require a series of aesthetic measurements if they are to be manually tested. In an effort to provide a colour design facility, this research offers algorithms that can generate colour schemes, based on the aforementioned principles, which can be used to derive an optimum scheme for a website. This research demonstrates a novel interactive genetic algorithm (IGA), coupled with the use of computational aesthetics, suitable for use in the evolution of terrain generation and digital landscape design. It also provides a tool for automatically creating EC-driven colour palettes for web design via evolutionary searches. Experimental trials use the EC framework developed from this research using both IGA technique and the computational aesthetic measures. Results indicate that the end-users can build any target digital landscape design with less inputs and more comfort, and if required can also automate the whole process to evolve aesthetically pleasing landscape designs. The results obtained for designing colour schemes for website design have proven that end-users can quickly develop a colour scheme, without the need for fine-tuning of colour combinations. Results can compete in quality the colour schemes that are designed by the professional website developers

Predición de preferencia de usuario mediante técnicas de Soft Computing

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01Tese por compendio de publicacións[Resumo] O contido desta tese por compendio é a agrupación de tres artigos de investigación publicados en revistas de prestixio, que abordan a necesidade e a forma de mellorar os métodos de predición de preferencia estética de usuario mediante técnicas de soft computing. Realízase un amplo estado da arte do uso de redes de neuronas artificiais e deep learning. Este estudo mostra que existen sistemas baseados en redes de neuronas capaces de realizaren tarefas artísticas con diferente grao de obxectividade. Dende a detección dun obxecto nunha obra pictórica ata a creación de imaxes, pasando pola clasificación segundo o estilo artístico ou autor/a, ou a estimación de calidade e valor estético. Tamén mostra que nos últimos anos se están a realizar máis traballos en tarefas máis complexas como a creación de imaxes, en gran medida grazas ao uso de técnicas de deep learning caso das Convolutional Neural Network (CNN) e as Generative Adversarial Network (GAN). A partir desta base, formúlase o uso de sistemas baseados en redes neuronais para dúas tarefas relevantes no ámbito da predición de preferencia estética. Dunha banda, emprégase un sistema de redes de neuronas artificiais para predición estética, utilizando un dataset explorado polo estado da arte. Non se busca só un erro baixo na predición senón tamén unha rede cuxa topoloxía sexa mínima. Analízanse os resultados extraendo conclusións sobre a información mínima relevante para realizar esta tarefa altamente subxectiva e complexa. Pola outra, analízanse diferentes alternativas para outra tarefa altamente relacionada coa percepción estética: a percepción de complexidade visual. Existen numerosos estudos psicolóxicos que propoñen unha relación directa entre complexidade e valor estético. Proponse buscar un método de machine learning, que obteña mellor predición deste valor. Tamén se realiza unha análise dos outlayers, co fin de comprender mellor os procesos realizados polo mecanismo de predición.[Abstract] The content of this Thesis by Compendium is the grouping of three research articles published in prestigious journals, which shows the need and how to improve the methods of predicting user aesthetic preference using soft computing techniques. An extensive state of the art of the use of artificial neural networks and deep learning is performed. This study shows that there are systems based on neural networks capable of performing artistic tasks with varying degrees of objectivity. From the detection of an object in a pictorial work to the creation of images, the classification according to the artistic style or author, or the estimation of quality and aesthetic value. It also shows that in recent years more work is being done on more complex tasks such as image creation, largely thanks to the use of deep learning techniques such as Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN). From this basis, the use of neural network-based systems is formulated for two relevant tasks in the field of aesthetic preference prediction. On the one hand, a system of artificial neural networks is used for aesthetic prediction, using a dataset explored by the state of the art. Not only a low error in the prediction is sought but also a network whose topology is minimal. The results are analyzed by drawing conclusions about the minimum information relevant to perform this highly subjective and complex task. On the other hand, different alternatives for another task highly related to aesthetic perception are analyzed: the perception of visual complexity. There are numerous psychological studies that propose a direct relationship between complexity and aesthetic value. It is proposed to look for a machine learning method, which obtains better prediction of this value. An analysis of the outlayers is also performed, in order to better understand the processes performed by the prediction mechanism.[Resumen] El contenido de esta Tesis por Compendio es la agrupación de tres artículos de investigación publicados en revistas de prestigio, que abordan la necesidad y la forma de mejorar los métodos de predicción de preferencia estética de usuario mediante técnicas de soft computing. Se realiza un amplio estado del arte del uso de redes de neuronal artificiales y deep learning. Este estudio muestra que existen sistemas basados en redes de neuronas capaces de realizar tareas artísticas con diferente grado de objetividad. Desde la detección de un objeto en una obra pictórica hasta la creación de imágenes, pasando por la clasificación según el estilo artístico o autor, o la estimación de calidad y valor estético. También se muestra que en los últimos años se están realizando más trabajos en tareas más complejas como la creación de imágenes, en gran medida gracias al uso de técnicas de deep learning como las Convolutional Neural Networks (CNN) y las Generative Adversarial Networks (GAN). A partir de esta base, se plantea el uso de sistemas basados en redes neuronales para dos tareas relevantes en el ámbito de la predicción de preferencia estética. Por un lado, se emplea un sistema de redes de neuronas artificiales para predicción estética, utilizando un dataset explorado por el estado del arte. No sólo se busca un error bajo en la predicción, sino también una red cuya topología sea mínima. Se analizan los resultados extrayendo conclusiones sobre la información mínima relevante para realizar esta tarea altamente subjetiva y compleja. Por otro lado, se analizan diferentes alternativas para otra tarea altamente relacionada con percepción estética: la percepción de complejidad visual. Existen numerosos estudios psicológicos que proponen una relación directa entre complejidad y valor estético. Se propone buscar un método de machine learning, que obtenga mejor predicción de este valor. También se realiza un análisis de los outlayers, con el fin de comprender mejor los procesos realizados por el mecanismo de predicción.O Instituto de Saude Carlos III do Plan Nacional Espanol de Investigación e Innovación Cientifica e Técnica 2013-2016 e os Fondos Europeos de Desenvolvemento Rexional (FEDER) “Un xeito de construir Europa” apoian este traballo a traves do “Proxecto colaborativo en integracion de datos xenomicos (CICLOGEN)” PI17/01826. Este traballo conta tamen co apoio da Direccion Xeral de Cultura, Educacion e Ordenacion Universitaria da Xunta de Galicia (Refs. ED431D, ED431G/01 2017/16), a “Rede galega para a investigacion do cancro colorrectal” (Ref. ED431D 2017/23) e Grupos de Referencia Competitivos (Ref. ED431C 2018/49). Este traballo tamen foi apoiado polo CITIC, como Centro de Investigacion do Sistema Universitario de Galicia, financiado pola Conselleria de Educacion, Universidade e Formacion Profesional da Xunta de Galicia a traves do Fondo Europeo de Desenvolvemento Rexional (FEDER) cun 80%, Programa Operativo FEDER Galicia 2014-2020, e o 20% restante pola Secretaria Xeral de Universidades (Ref. ED431G 2019/01). Por outra banda, a instalacion unica BIOCAI (UNLC08-1E-002 UNLC13-13- 3503) foi financiada polo Ministerio de Economia e Competitividade espanol e os Fondos Europeos de Desenvolvemento Rexional (FEDER). Agradecementos tamen ao apoio de NVIDIA Corporation pola doazon da GPU Tesla K40 empregada nesta investigacion.Xunta de Galicia; ED431DXunta de Galicia; ED431G/01 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431G 2019/0

Autonomous Evolutionary Art

Eiben, A.E. [Promotor

Inspiration-triggered search: Za vyššími složitostmi napodobováním tvůrčích procesů

Jeden z hlavních problémů stochastických optimalizačních metod ze strojového učení je uvíznutí v lokálních optimech. Cílem této práce je vytvoření optimalizační metody inspirované uživateli webové služby Picbreeder, ve které mohou společně vyvíjet obrázky pomocí umělé evoluce. Hlavní myšlenkou je, že jejich chování představuje tvůrčí procesy. Představujeme metodu nazvanou inspiration-triggered search, která napodobuje zmíněné procesy a využívá k tomu libovolnou optimalizační techniku. Vyhledávání neobsahuje pevně daný cíl, místo toho je schopno samo si s určitými omezeními definovat vlastní cíle. Cílem optimalizace je vytvoření komplexních výtvorů, které nemohou být nalezeny hladovou a přímou optimalizací. Navržená metoda je otestována v doméně obrázků, kde je cílem nalezení komplexních a esteticky příjemných obrázků pro člověka, a porovnána s přímou optimalizací. Powered by TCPDF (www.tcpdf.org)The trap of local optima is one of the main challenges of stochastic optimization methods from machine learning. The aim of this thesis is to develop an optimization algorithm that is inspired by users interacting with Picbreeder, which is an online service that allows users to collaboratively evolve images via an artificial evolution. The idea is that their behaviours depict creative processes. We propose a general framework on the top of a common optimization technique called inspiration-triggered search, which mimics these processes. Instead of a fixed objective function the search algorithm is free to change the objective within certain constraints. The overall optimization task is to generate complex artefacts that cannot be generated by a greedy and direct optimization approach. The proposed method is tested in the domain of images, that is to find complex and aesthetically pleasant images for humans, and compared with the direct optimization. Powered by TCPDF (www.tcpdf.org)Department of Theoretical Computer Science and Mathematical LogicKatedra teoretické informatiky a matematické logikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Typogenetic design - aesthetic decision support for architectural shape generation

Typogenetic Design is an interactive computational design system combining generative design, evolutionary search and architectural optimisation technology. The active tool for supporting design decisions during architectural shape generation uses an aesthetic system to guide the search process. This aesthetic system directs the search process toward preferences expressed interactively by the designer. An image input as design reference is integrated by means of shape comparison to provide direction to the exploratory search. During the shape generation process, the designer can choose solutions interactively in a graphical user interface. Those choices are then used to support the selection process as part of the fitness function by online classification. Enhancing human decision making capabilities in human-in-the-loop design systems addresses the complexity of architecture in respect to aesthetic requirements. On the strength of machine learning, the integral performance trade-off during multi-criteria optimisation was extended to address aesthetic preferences. The tacit knowledge and subjective understanding of designers can be used in the shape generation process based on interactive mechanisms. As a result, an integrated support system for performance-based design was developed and tested. Closing the loop from design to construction using design optimisation of structural nodes in a set of case studies confirmed the need for intuitive design systems, interfaces and mechanisms to make architectural optimisation more accessible and intuitive to handle. This dissertation investigated Typogenetic Design as a tool for initial morphological search. Novel instruments for human interaction with design systems were developed using mixed-method research. The present investigation consists of an in-depth technological enquiry into the use of interactive generative design for exploratory search as an integrated support system for performance-based design. Associated project-based research on the design potential of Typogenetic Design showcases the application of the design system for architecture. Generative design as an expressive tool to produce architectural geometries was investigated in regard to its ability to drive initial morphological search of complex geometries. The reinterpretation of processes and boosting of productivity by artificial intelligence was instrumental in exploring a holistic approach combining quantitative and qualitative criteria in a human-in-the-loop system. The shift in focus from an objective to a subjective understanding of computational design processes indicates a perspective change from optimisation to learning as a computational paradigm. Integrating learning capabilities in architectural optimisation enhances the capability of architects to explore large design spaces of emergent representations using evolutionary search. The shift from design automation to interactive generative design introduces the possibility for designers to evaluate shape solutions based on their knowledge and expertise to the computational system. At the same time, the aesthetic system is trained in adaptation to the choices made by the designer. Furthermore, an initial image input allows the designer to add a design reference to the Typogenetic Design process. Shape comparison using a similarity measure provides additional guidance to the architectural shape generation using grammar evolution. Finally, a software prototype was built and tested by means of user-experience evaluation. These participant experiments led to the specification of custom software requirements for the software implementation of a parametric Typogenetic tool. I explored semi-automated design in application to different design cases using the software prototype of Typogenetic Design. Interactive mass-customisation is a promising application of Typogenetic Design to interactively specify product structure and component composition. The semi-automated design paradigm is one step on the way to moderating the balance between automation and control of computational design systems