Decolonising Cinematography Education: Experimenting with Lighting Ratios and Textures for Black and Asian Skin Tones

Cinematographers are trained to control and measure the relative difference in brightness between two parts of a scene, or a face, for expressive purposes. Painting is often referred to for inspiration as practitioners learn to compose and represent light and shadow in an aesthetically considered manner. In this respect, it is noteworthy that the painters generally studied by film-making students are mostly from Renaissance traditions and produced work featuring predominantly White models. This gap of racial representation in cinematographic pedagogy is stark and has long been overlooked. This article mounts an enquiry into the lack of diversity in cinematography education, examining how different aesthetic traditions, such as Asian ink paintings, could pave new ways for decolonising the conventional conceptions of lighting ratios. Drawing on qualitative and quantitative case studies undertaken in university workshops, I discuss how students respond to Black and Asian artwork as visual references when they are tasked with lighting models with non-White skin tones to accommodate the different reflectance of their skins. By comparing the learning outcomes and current industry techniques for optimising screen representation of Black, Asian and minority ethnic groups, the article evaluates how students respond to learning from modern artwork that promotes diverse identities, and argues for the benefits of integrating greater inclusiveness into cinematography

Procedural Group Identification

In this paper we axiomatically characterize two recursive procedures for defining a social group.The first procedure starts with the set of all individuals who define themselves as members of the social group, while the starting point of the second procedure is the set of all individuals who are defined by everyone in the society as group members.Both procedures expand these initial sets by adding individuals who are considered to be appropriate group members by someone in the corresponding initial set, and continue inductively until there is no possibility of expansion any more.identification;social groups;social identity

Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data

Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence-to-sequence Autoencoder (SA). These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with real world applications such as query-by-example Spoken Term Detection (STD). This paper examines the capability of language transfer of Audio Word2Vec. We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language). We found that SA can still catch phonetic structure from the audio segments of the target language if the source and target languages are similar. In query-by-example STD, we obtain the vector representations from the SA learned from a large amount of source language data, and found them surpass the representations from naive encoder and SA directly learned from a small amount of target language data. The result shows that it is possible to learn Audio Word2Vec model from high-resource languages and use it on low-resource languages. This further expands the usability of Audio Word2Vec.Comment: arXiv admin note: text overlap with arXiv:1603.0098