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These days, digital art has substantially improved painting art in China's new media period through modern technology and specialized software, which has generated various digital artworks with painting effects, demonstrating their aesthetic, commercial, and cultural worth. As a result, designers must actively investigate intelligently linked and content-diverse techniques to innovate and improve digital media art. This investigation will allow the extraction of pivotal role of digital media communication and drives visual art to new heights in the modern digitized era. Based on this motivation, this paper tries to analyze the application of new media digital technology in painting creation using big data. The major goal is to obtain insights into artists' behavior using behavioral habits and a morphological algorithm in digital painting. The study investigates the design process of digital painting by analyzing the habitual forms displayed by diverse painting user groups and integrating them with user experience design concepts. Two experimental methodologies are used to investigate painting user behavior: the interview method and the observation method. The observation approach collects comprehensive user behavior data to develop a habit form model that includes a both virtual and real activity. This model employs a hierarchical scheme emphasizing various behavior aspects and concepts throughout user interactions and painting system operations. The interview method, on the other hand, captures the psychological activities involved in user behavior decision-making. The study discovers and decomposes the traits specific to these characters by defining painted characters, developing their behavior sets, and making interactive decisions. The learned information and experience are then applied in interactive decision-making processes. Based on these findings, prototype development includes explicit and implicit interaction prototypes, with usability tests determining whether specific roles are included in the design. Throughout the study process, fuzzy mapping associations are built, and behavior mappings are labeled with varying relationship proportions, providing critical data for the habit form model's construction. The paper uses new digital media technology to create paintings and experiments with 50 randomly selected persons. Forty-four participants are well-known authors on major painting websites, with the remaining six being interviewed as professional painters. [ABSTRACT FROM AUTHOR]

In this paper we discuss the role played by abductive reasoning and admirableness in the creative processes in the Big Data era. The conceptions of creativity, in the mechanistic and antimechanistic scope, are introduced by means of examples in the domain of photographic production, as is the case of Maureen Bisilliat (1995) and Nan Goldin (2012) works. As our investigation is essentially theoretical and interdisciplinary, it is mainly based on the literature on the concepts of creativity, abductive reasoning, and admirableness; it is grounded in different areas of knowledge, including Pragmatism, Literature, Visual arts and the Philosophy of Information. We discuss the limitations of mechanistic conceptions of creativity, arguing in defense of the hypothesis that creative action is not strictly related to the created object, but to a long collaborative process between agents that will assist (directly or indirectly) in the production of artistic works. In the conclusion, the role of emotions is highlighted in the dynamics of the creative/abductive process, as proclaimed by Charles Sanders Peirce (1958). Inspired by this author, we emphasize the role of admirableness in the process of creation - understood here as a feeling that encourages us to investigate and get to know the objects that attract our attention, without the desire for controlling them. [ABSTRACT FROM AUTHOR]

We now live in the era of big data, and according to its proponents, big data is poised to change science as we know it. Claims of having no theory and no ideology are made, and there is an assumption that the results of big data are trustworthy because it is considered free from human judgement, which is often considered inextricably linked with human error. These two claims lead to the idea that big data is the source of better scientific knowledge, through more objectivity, more data, and better analysis. In this paper I analyse the philosophy of science behind big data and make the claim that the death of many traditional sciences, and the human scientist, is much exaggerated. The philosophy of science of big data means that there are certain things big data does very well, and some things that it cannot do. I argue that humans will still be needed for mediating and creating theory, and for providing the legitimacy and values science needs as a normative social enterprise. [ABSTRACT FROM AUTHOR]