Phenomena Systematization of Marketing Digitalization: Concept and Implementation

The article is devoted to the research and systematization of the current state and development of marketing digitalization phenomena. The methodological base includes system-wide methods of system analysis, categorization and classification, elements from theories of general marketing and information society. This work clarifies features of the term “digitalization”, formulates a general concept of systematizing marketing digitalization phenomena through the construction of multidimensional morphological box, and defines ontological models of marketing functionality and digital technologies in all main classification cross-sections of the multidimensional morphological box. It also proposes a variant of multidimen- sional morphological box, where structuring by components (analytical, creative-synthetic, communication and organizational-managerial) forms marketing dimension and where clas- sifications and ontologies of international and domestic analytical resources create its “digital” dimension. Based on this variant, current digital phenomena in marketing and related fields are systematized and passed transformation with different impact on separate components of the DIKW model is estimated

Reconnoitering Generative Deep Learning Through Image Generation From Text

A picture is worth a thousand words goes the well-known adage. Generating images from text understandably has many uses. In this chapter, the authors explore a state-of-the-art generative deep learning method to produce synthetic images and a new better way for evaluating the same. The approach focuses on synthesizing high-resolution images with multiple objects present in an image, given the textual description of the images. The existing literature uses object pathway GAN (OP-GAN) to automatically generate images from text. The work described in this chapter attempts to improvise the discriminator network from the original implementation using OP-GAN. This eventually helps the generator network\u27s learning rate adjustment based on the discriminator output. Finally, the trained model is evaluated using semantic object accuracy (SOA), the same metric that is used to evaluate the baseline implementation, which is better than the metrics used previously in the literature

A Review of AI Image Generator: Influences, Challenges, and Future Prospects for Architectural Field

Recently, the topic of text-to-image-based GAI (Generative Artificial Intelligence) or AI Image Generators became so popular because of its sophistication in creating images based on human natural language messages in a short time. On the other hand, the presence of the AI Image Generator is enough to reap various opinions, including in the field of architecture. Therefore, the purpose of this paper is to present a review of the influences, challenges, and prospects of AI Image Generator technology in the architectural design process. The research method used is a systematic literature review by reviewing 12 scientific articles, five books, and five official websites. The results of the study explained that the AI Image Generator could provide one step forward to expanding the design imagination by presenting several design alternatives with high-quality visuals. The challenge lies in the user's proficiency in providing text commands that AI programs can detect. The prospect of this program, if developed in more depth, is to become a rendering tool that can release dependence on devices with high specifications and additional editing applications

Redes adversárias generativas: uma alternativa para modelagem de dados de entrada em projetos de simulação

In general, stochastic simulation consists of input data and logic, the former being the basic source of uncertainty in a simulation model. For this reason, data modeling is an essential step in the development of stochastic simulation projects. Many advances have been observed in recent years in simulation software and in data collection tools. However, the methods for input data modeling have remained largely unchanged for over 30 years. In their daily lives, modelers face difficulties related to the choice of input data models, mainly due to the challenge of modeling non Independent and Identically Distributed Data (IID) data, which requires specific tools not offered by simulation software and their data modeling packages. For this reason, few studies consider elements of complexity such as heterogeneities, dependencies, and autocorrelations, underestimating the uncertainty of the stochastic system. Given the new developments in Artificial Intelligence, it is possible to seek synergies to solve this problem. The present study aims to evaluate the results of the application of Generative Adversarial Networks (GANs) for input data modeling. Such networks constitute one of the most recent architectures of artificial neural networks, being able to learn complex distributions and, therefore, generate synthetic samples with the same behavior as real data. Therefore, this thesis proposes a method for Input Data Modeling based on GANs (MDE-GANs) and implements it through the Python language. Considering a series of theoretical and real study objects, the results are evaluated in terms of representation quality of the input models and comparisons are made with traditional modeling methods. As a main conclusion, it was possible to identify that the application of MDE-GANs allows obtaining input data models with strong accuracy, surpassing the results of traditional methods in cases of non-IID data. Thus, the present thesis contributes by offering a new alternative for input data modeling, capable of overcoming some of the challenges faced by modelers.De forma geral, a simulação estocástica consiste em dados de entrada e lógicas, sendo os primeiros as fontes básicas de incerteza em um modelo de simulação. Por essa razão, a modelagem de dados é uma etapa essencial no desenvolvimento de projetos na área. Muitos avanços foram observados nos últimos anos nos programas de simulação e em ferramentas para coleta. Porém, os métodos para modelagem de dados permanecem praticamente inalterados há mais de 30 anos. Em seu dia a dia, praticantes de simulação enfrentam dificuldades relacionadas à escolha de Modelos de Dados de Entrada (MDEs), principalmente devido ao desafio da modelagem de dados não Independentes e Identicamente Distribuídos (IID), o que requer ferramentas específicas e não oferecidas por programas de simulação e seus pacotes de estatísticos. Por essa razão, poucos estudos consideram elementos de complexidade como heterogeneidades, dependências e autocorrelações, subestimando a incerteza do sistema estocástico. Diante dos novos desenvolvimentos na área de Inteligência Artificial, é possível buscar sinergias para resolução desse problema. O presente estudo tem como objetivo avaliar os resultados da aplicação de Redes Adversárias Generativas, ou Generative Adversarial Networks (GANs) para obtenção de MDEs. Tais redes constituem uma das mais recentes arquiteturas de redes neurais artificiais, sendo capazes de aprender distribuições complexas e, com isso, gerar amostras sintéticas com o mesmo comportamento dos dados reais. Para tanto, esta tese propõe um método para Modelagem de Dados de Entrada baseado em GANs (MDEGANs) e o implementa por meio da linguagem Python. Considerando uma série de objetos de estudo teóricos e reais, são avaliados os resultados em termos de qualidade de representação dos MDEs e realizadas comparações com métodos tradicionais. Como principal conclusão, foi possível identificar que a aplicação de MDE-GANs permite obter MDEs com forte acurácia, superando os resultados dos métodos tradicionais nos casos de dados não IID. Com isso, a presente tese contribui ao oferecer uma nova alternativa para a área, capaz de contornar alguns dos desafios enfrentados por modeladores

Validação de modelos computacionais: um estudo integrando generative adversarial networks e simulação a eventos discretos

Computer model validation of Discrete Event Simulation (DES) is essential for project success since this stage guarantees that the simulation model corresponds to the real system. Nevertheless, it is not possible to assure that the model represents 100% of the real system. The literature suggests using more than one validation technique, but statistical tests are preferable. However, they have limitations, since the tests usually test the mean or standard deviation individually, and do not consider that the data may be within a pre-established tolerance limit. In this way, Generative Adversarial Networks (GANs) can be used to train, evaluate and discriminate data and validate DES models, because they are two competing neural networks, where one generates data and the other discriminates them. The proposed method is divided into two phases. The first is the "Training Phase" and it aims to train the data. The second, the "Test Phase" aims to discriminate the data. In addition, in the second phase, the Equivalence Test is performed, which statistically analyze if the difference between the judgments is within the tolerance range determined by the modeler. To validate the proposed method and to verify the Power Test, experiments were carried out in continuous, discrete, and conditional distributions and in a DES model. From the tests, the Power Test curves were generated considering a real tolerance of 5.0%, 10.0% and 20.0%. The results showed that it is more efficient to use the dataset that presents larger sample in the “Test Phase” while the set with smaller sample size needs to be used in the “Training Phase”. In addition, the confidence of the Power Test increases with big higher dataset in first phase, presenting smaller confidence intervals. Also, the more metrics are evaluated at once, the greater the amount of data inputted in the GANs' training. The method suggests classifying a validation based on the achieve tolerance: Very Strong, Strong, Satisfying, Marginal, Deficient and Unsatisfying. Finally, the method was applied to three real models, two of them in manufacturing and the last one in the health sector. We conclude that the proposed method was efficient and was able to show the degree of validation of the models that represent the real system.A validação de modelos computacionais de Simulação a Eventos Discretos (SED) é primordial para o sucesso do projeto, pois é a partir dela que se garante que o modelo simulado corresponde ao sistema real. Apesar disso, não é possível assegurar que o modelo represente 100% o sistema real. A literatura sugere várias técnicas de validação, porém é preferível o uso de testes estatísticos pois eles apresentam evidências matemáticas. Entretanto, existem limitações, pois testam média ou desvio padrão de forma individual, sem levar em consideração que os dados podem estar dentro de uma tolerância pré-estabelecida. Pode-se utilizar as Generative Adversarial Networks (GANs) para treinar, avaliar, discriminar dados e validar modelos de SED. As GANs são duas redes neurais que competem entre si, sendo que uma gera dados e a outra os discrimina. Assim, a tese tem como objetivo propor um método de validação de modelos computacionais de SED para avaliar uma ou mais métricas de saída, considerando uma tolerância para a comparação dos dados simulados com os dados reais. O método proposto foi dividido em duas fases, onde a primeira, denominada “Fase de Treinamento”, tem como objetivo o treinamento dos dados e a segunda, “Fase de Teste”, visa discriminar os dados. Na segunda fase, é realizado o Teste de Equivalência, o qual analisa estatisticamente se a diferença entre o julgamento dos dados está dentro da faixa de tolerância determinada pelo modelador. Para validar o método proposto e verificar o Poder do Teste, foram realizados experimentos em distribuições teóricas e em um modelo de SED. Assim, as curvas com o Poder do Teste para a tolerância real de 5.0%, 10.0% e 20.0% foram geradas. Os resultados mostraram que é mais eficiente o uso do conjunto de dados que apresenta uma amostra maior na “Fase de Teste” e é mais adequado o conjunto de tamanho amostral menor na “Fase de Treinamento”. Além disso, a confiança do Poder do Teste aumenta, apresentando intervalos de confiança menores. Ainda, quanto mais métricas são avaliadas de uma só vez, maior deve ser a quantidade de dados inseridos no treinamento das GANs. O método ainda sugere classificar a validação em faixas que mostram o quão válido o modelo é: Muito Forte, Forte, Satisfatória, Marginal, Deficiente e Insatisfatória. Por fim, o método foi aplicado em três modelos reais, sendo dois deles na área de manufatura e um na área da saúde. Concluiu-se que o método proposto foi eficiente e conseguiu mostrar a o grau de validação dos modelos que representam o sistema real

Steve, A Framework For Augmenting The Visual Identity Design Process With ML

This research is positioned in the field of graphic design and seeks to investigate the working processes in visual identity projects and their augmentation through Machine Learning (ML). It defines identity as the visual elements that, together, create an atmosphere around a client, involving its values and views of the world and society. Through a deep focus on the creative process, this thesis proposes functional approaches to integrate the designer's perspective on the development of new digital tools. My study reveals fruitful ways to augment identity design through ML rather than replace designers through automation. Since its blooming during the Industrial Revolution, visual identity remains the highest-order project in the discipline of graphic design. The parallel evolution of graphic and information technology has undergone numerous phases in which visual identity structures have become more dynamic, and its impact on society has grown along with the designer’s responsibilities. Increasing integration of automation into graphic design in the twenty-first century, as well as potential future developments in ML, represent new challenges for professionals and researchers. Investigation into the intersection of ML and graphic design has been led mainly by computer scientists, leading to misplaced assumptions of creativity. At the same time, research into graphic creative processes is limited. My research addresses these deficiencies, and the gap in the existing literature on the conjunction between graphic design theory and practice, by involving practitioners in the evaluation and proposal of novel design tools. Moreover, it creates a direct link between software development and the actual needs of graphic designers. The novelty of this research lies in the intersection of design methodology, visual identity and ML. Research on design processes is well established in other areas like architecture, industrial design and software development. An understanding of tools and concepts from these fields helps to investigate the possibilities of integrating ML into the design process. Three main questions are addressed in the research: – Is it possible to find coherent working methods in visual identity projects? – What are the most critical phases for the designers in visual identity projects? – How can these be augmented through ML? To answer these questions, I utilize grounded theory methodology, complemented by literature review, to construct a conceptual framework rooted in the expertise of practitioners. By conducting semi-structured interviews with a sample of twenty graphic design studios, I confirmed that they employ consistent and coherent working methods and that ML has the potential to help augment critical phases in the visual identity process. My findings are further explored via non-participant observation that, in conjunction with the interviews, has led to a primary hypothesis subsequently tested through a within-subject design survey. My findings collectively provide a series of propositions that constitute the basis for a concrete ML implementation proposal. The definition of a replicable conceptual framework that incorporates the shared semantic cognition of design teams into an ML recommendation system constitutes the main contribution to the knowledge offered by my thesis.This research is positioned in the field of graphic design and seeks to investigate the working processes in visual identity projects and their augmentation through Machine Learning (ML). It defines identity as the visual elements that, together, create an atmosphere around a client, involving its values and views of the world and society. Through a deep focus on the creative process, this thesis proposes functional approaches to integrate the designer's perspective on the development of new digital tools. My study reveals fruitful ways to augment identity design through ML rather than replace designers through automation. Since its blooming during the Industrial Revolution, visual identity remains the highest-order project in the discipline of graphic design. The parallel evolution of graphic and information technology has undergone numerous phases in which visual identity structures have become more dynamic, and its impact on society has grown along with the designer’s responsibilities. Increasing integration of automation into graphic design in the twenty-first century, as well as potential future developments in ML, represent new challenges for professionals and researchers. Investigation into the intersection of ML and graphic design has been led mainly by computer scientists, leading to misplaced assumptions of creativity. At the same time, research into graphic creative processes is limited. My research addresses these deficiencies, and the gap in the existing literature on the conjunction between graphic design theory and practice, by involving practitioners in the evaluation and proposal of novel design tools. Moreover, it creates a direct link between software development and the actual needs of graphic designers. The novelty of this research lies in the intersection of design methodology, visual identity and ML. Research on design processes is well established in other areas like architecture, industrial design and software development. An understanding of tools and concepts from these fields helps to investigate the possibilities of integrating ML into the design process. Three main questions are addressed in the research: – Is it possible to find coherent working methods in visual identity projects? – What are the most critical phases for the designers in visual identity projects? – How can these be augmented through ML? To answer these questions, I utilize grounded theory methodology, complemented by literature review, to construct a conceptual framework rooted in the expertise of practitioners. By conducting semi-structured interviews with a sample of twenty graphic design studios, I confirmed that they employ consistent and coherent working methods and that ML has the potential to help augment critical phases in the visual identity process. My findings are further explored via non-participant observation that, in conjunction with the interviews, has led to a primary hypothesis subsequently tested through a within-subject design survey. My findings collectively provide a series of propositions that constitute the basis for a concrete ML implementation proposal. The definition of a replicable conceptual framework that incorporates the shared semantic cognition of design teams into an ML recommendation system constitutes the main contribution to the knowledge offered by my thesis