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Brown, Louise P. and Endruweit, Andreas and Long, Andrew C. and Jones, I. Arthur (2018)Characterisation and modeling of complex geometries using TexGen. In: 13th International Conference on Textile Composites (TexComp-13), 17-19 Sept 2018, Milan, Italy.
Egger, Adrian W. and Triantafyllou, Savvas P. and Chatzi, Eleni N. (2016)The scaled boundary finite element method for the efficient modeling of linear elastic fracture. In: 9th International Conference on Fracture Mechanics of Concrete and Concrete Structures (FraMCoS-9), 28 May - 1 June 2016, Berkely, California, USA.
Havens, Timothy C. and Wagner, Christian and Anderson, Derek T. (2017)Efficient modeling and representation of agreement in interval-valued data. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017), 9-12 July 2017, Naples, Italy. (In Press)
Kayacan, Erdal and Coupland, Simon and John, Robert and Khanesar, Mojtaba Ahmadieh (2017)Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 9-12 July 2017, Naples, Italy.
Lee, Youngsil and Vakil, Gaurang and Watson, Alan James and Wheeler, Patrick (2016)An analytical modeling and estimating losses ofpower semiconductors in a three-phase dual activebridge converter for MVDC grids. In: IEEE Power and Energy Conference at Illinois, 23-24 Feb 2017, Illinois, USA.
Soldatos, Dimitris and Triantafyllou, Savvas P. and Chatzi, Eleni N. (2015)On the constitutive modeling of dual-phase steels at finite strains: a generalized plasticity based approach. In: Forming Technology Forum 2015, 29-30 June 2015, Zurich, Switzerland.
Sójka, L. and Furniss, David and Tang, Zhuoqi and Sakr, H. and Barney, Emma R. and Benson, Trevor M. and Seddon, Angela B. and Sujecki, S. and Scholle, K. and Lamrini, S. and Fuhrberg, P. (2016)Numerical modeling of lathanide-ion doped fibre lasers operating within mid-infrared wavelength region. In: 18th International Conference on Transparent Optical Networks, 10-14 July 2016, Trento, Italy.
Wake, Geoffrey and Foster, Colin and Swan, Malcolm (2014)Understanding issues in mathematical problem solving and modeling: lessons from lesson study. In: 38th Conference of the International Group for the Psychology of Mathematics Education and the 36th Conference of the North American Chapter of the Psychology of Mathematics Education, 15 - 20 July 2014, Vancouver, Canada.
Why should cinema scholars pursue DH approaches when, seemingly, they are so fraught with challenges? One answer to the question can be found in the methodology of a groundbreaking analysis in our field that took place before the first wave of DH scholarship in the 1990s and early 2000s and led to the definition of the group style known as classical Hollywood cinema . Associated with narrative films made under the Hollywood studio system between roughly 1916 and 1960 and marked by certain recurrent features of narrative, narration, and visual style, classical Hollywood cinema has come to define our understanding of Golden Age cinema and to serve as a benchmark for scholarly inquiries into film form and style. Remarkably, however, the 100 films that made up the sample for the study comprised just a small percentage (roughly .006%) of the approximately 15,000 films produced by American studios between 1915 and 1960 (10). It is eye-opening to consider that such an axiomatic account of American film style and history excludes over 99% of the films produced in the period under investigation, even if, as Bordwell asserts, Hollywood classical cinema is "excessively obvious", having documented its style in its own technical manuals, memoirs, and publicity handouts (3). Today's film scholars may very well wonder how our understanding of this monolithic group style might evolve if we were to radically increase the sample size using DH approaches that didn't yet exist in the mid 1980s.
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 2ff7e9595c
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