04.2022. Fachada, N. (2022). A computational pipeline for modeling and predicting wildfire behavior. In Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS 2022 (pp. 79-84), Virtual Event. SciTePress/INSTICC. DOI:
Art Modeling Liliana Model Sets
Notably, in general, this framework is in accordance with genetic epistemology (Piaget, 1970/1983) and with a schema-based action-oriented approach to action, music, and language (Arbib, 2003, 2013). Furthermore, Kim proposes developing an approach to experimental research in neurophenomenology that deals with aesthetic experience from a first-person perspective. The conception of aesthetic experience based on shaping and co-shaping is a promising basis for the development of a feasible empirical research strategy as well as ideas for corresponding experimental designs. In total, Kim's proposal entails research on interaction, processes, and phenomenological experience. To this end, a theory of processing and interaction in connection with a theory of consciousness is necessary, which, to my mind, presents three further requirements for empirical aesthetics: 1) Integration of computational cognitive modeling in addition to the development of experimental methods for studying mental processes (Bower & Clapper, 1989); 2) computational models of emotional processes related to music and aesthetics; and 3) a methodology for phenomenology in empirical research and experiments.
Experimental phenomenology requires methodological reconsiderations about experimental research (cf. Kuboy, 1999, p. 347). These reconsiderations might take phenomenological descriptions, such as those of the (philosophical and psychological) phenomenology in the Husserlian tradition, into account (Bischof, 2009; Dreyfus, 1982; Petitot et al., 1999). For example, such descriptions could be used to modify or refine experimental and modeling studies. Considerations in experimental phenomenology can be found in Gestalt or act psychology as developed by Carl Stumpf, Vittorio Benussi, Liliana Albertazzi, Giovanni Vicario, and Paolo Bozzi, to name just a few. In empirical research investigating mental processes, phenomenological reports might also be used to reconstruct the conceptual framework determining the (cultural and social) constraints involved in structuring aesthetic processing. Then, such reports might offer the possibility of discovering or at least giving some hints about relevant general structures, entities, or operations involved in the functional architecture underlying actual mental processing.
A frequent topic in digital humanities concerns the balance between data annotation and machine learning. Manovich rejects annotation for the purposes of Cultural Analytics (the use of visualization to explore large sets of images and video), arguing that the process of assigning keywords to every image thwarts the spontaneous discovery of interesting patterns in an image set, that it is not scalable for massive data sets, and that it cannot help with such data sets because natural languages lack sufficient words to adequately describe the visual characteristics of all human-created images . Notwithstanding researchers' increasing success in using computers for visual concept detection, the higher-order semiotic relationships that frequently constitute film language remain resistant to machine learning. When, then, should one annotate, and for what types of information? Projects and initiatives dedicated to text analysis, which is a more historically developed DH methodology, form an instructive continuum of the many ways in which manual annotation and machine learning techniques can be combined to retrieve information and perform digital corpora analysis. In many cases, digital projects rely solely on manually encoded digital texts to provide their representational and analytical tools. Other models seek to add annotations on higher-level semantic entities such as spatial information , clinical notes , and emotions . A brief survey of the relationship between annotation and machine learning in text analysis provides insight into how this relationship may apply to time-based media and specifically to moving image analysis.
Moving away from Omeka gave us the freedom to take the Kinolab concept back to the data modeling phase and define a database backend specifically for our project. We were able to implement the user interface collaboratively, module by module, with all team members, which helped flush out additional requirements and desirable features in easy-to-regulate advances. The system we ended up building used many of the same tools as Omeka.
Though different in kind, these and other related issues we encountered demonstrated the need to situate individual film language concepts within a broader, machine-readable model of film language such as a thesaurus or ontology. The first case cited above, involving the interchangeability of dutch angle, dutch tilt, or canted angle, is a straightforward problem of synonymy, resolvable through the adoption of a controlled vocabulary for film language spelling out preferred and variant terms and including synonym ring lists to ensure Kinolab's ability to return appropriate clips when queried. The second case cited above, however, demonstrates the need to conceive of film language hierarchically. Both problems reveal how Kinolab could benefit from a data modeling approach capable of explicitly defining the "concepts, properties, relationships, functions, constraints, and axioms" of film language, akin to those proposed by the Getty Research Institute for art, architecture and other cultural works .
This is a modest solution that notably excludes specialized terms and concepts from more technical areas of film language such as sound, color, or computer-generated imagery. Moreover, relying upon authoritative introductory texts like The Film Experience and A History of Narrative Film threatens to reproduce their troubling omissions of aspects of film language like 'blackface', which doesn't appear in the glossary of either book despite being a key element of historical film language and narrative in the United States and beyond. Our flat list is admittedly a makeshift substitute for a more robust form of data modeling that could, for example, deepen our understanding of film language and provide further insight into which aspects of it might be analyzable via artificial intelligence, or enable us to share Kinolab data usefully on the Semantic Web. We have, however, anticipated the need for this and built into Kinolab the possibility of adding hierarchy to our evolving controlled vocabulary. For example, tags like
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