The Geo-Doc: Remediating the Documentary Film as an Instrument of Social Change with Locative Theory and Technology

The documentary film has had a long history as an influential communications tool with the ability to effect social change. Its inherent claims to representing the truth provide a foundation of credibility that the filmmaker uses to inform and persuade their audience with a goal of causing them to take action that ideally leads to social change. This goal has been seen to be achieved when the documentary film employs certain methods and technologies. My research questions are these: What methods and technologies are most effective in bolstering the documentary films ability to effect social change and what new and emerging methods and technologies extend that ability? How can the documentary film be remediated to incorporate these attributes and would this new project experience some measure of success in effecting social change when tested in the field? These questions are answered through an investigation of various disciplines of study. The history of the documentary film as an instrument of social change is examined from its origins to the present day. This examination also identifies those methods and technologies that have advanced the documentarys ability to serve as a successful communication tool between filmmaker and changemaker. Focussed investigations into the theory and practice of the documentary film yield specific approaches and techniques that prove to be most successful, such as the Participatory Mode, Ecocinema and Semiotic Storytelling, the Multilinear and the Database Documentary, and the distinct digital affordances provided by Geomedia. Once identified and explained, the most effective theories and practices are combined in an altogether new and remediated documentary form: the geo-doc. The geo-doc is a term I have applied to a structure of the documentary film that is a multilinear, interactive, database documentary film project presented on a platform of a Geographic Information System map. The project was made specifically for an audience of changemakers with the general public in mind as a secondary audience. In collaboration with the changemaker, content and interface suggestions are made to the filmmaker to augment the projects effectiveness as a communications tool

Geometric Expression Invariant 3D Face Recognition using Statistical Discriminant Models

Currently there is no complete face recognition system that is invariant to all facial expressions. Although humans find it easy to identify and recognise faces regardless of changes in illumination, pose and expression, producing a computer system with a similar capability has proved to be particularly di cult. Three dimensional face models are geometric in nature and therefore have the advantage of being invariant to head pose and lighting. However they are still susceptible to facial expressions. This can be seen in the decrease in the recognition results using principal component analysis when expressions are added to a data set. In order to achieve expression-invariant face recognition systems, we have employed a tensor algebra framework to represent 3D face data with facial expressions in a parsimonious space. Face variation factors are organised in particular subject and facial expression modes. We manipulate this using single value decomposition on sub-tensors representing one variation mode. This framework possesses the ability to deal with the shortcomings of PCA in less constrained environments and still preserves the integrity of the 3D data. The results show improved recognition rates for faces and facial expressions, even recognising high intensity expressions that are not in the training datasets. We have determined, experimentally, a set of anatomical landmarks that best describe facial expression e ectively. We found that the best placement of landmarks to distinguish di erent facial expressions are in areas around the prominent features, such as the cheeks and eyebrows. Recognition results using landmark-based face recognition could be improved with better placement. We looked into the possibility of achieving expression-invariant face recognition by reconstructing and manipulating realistic facial expressions. We proposed a tensor-based statistical discriminant analysis method to reconstruct facial expressions and in particular to neutralise facial expressions. The results of the synthesised facial expressions are visually more realistic than facial expressions generated using conventional active shape modelling (ASM). We then used reconstructed neutral faces in the sub-tensor framework for recognition purposes. The recognition results showed slight improvement. Besides biometric recognition, this novel tensor-based synthesis approach could be used in computer games and real-time animation applications

Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

Fuzzy Family Ties: Familial Similarity Between Melodic Contours of Different Cardinalities

All melodies have shape: a pattern of ascents, descents, and plateaus that occur as music moves through time. This shape—or contour—is one of a melody’s defining characteristics. Music theorists such as Michael Friedmann (1985), Robert Morris (1987), Elizabeth Marvin (1987), and Ian Quinn (1997) have developed models for analyzing contour, but only a few compare contours with different numbers of notes (cardinalities), and fewer still compare entire families of contours. Since these models do not account for familial relations between different-sized contours, they apply only to a limited musical repertoire, and therefore it seems unlikely that they reflect how listeners perceive melodic shape. This dissertation introduces a new method for evaluating familial similarities between related contours, even if the contours have different cardinalities. My Familial Contour Membership model extends theories of contour transformation by using fuzzy set theory and probability. I measure a contour’s degree of familial membership by examining the contour’s transformational pathway and calculating the probability that each move in the pathway is shared by other family members. Through the potential of differing alignments along these pathways, I allow for the possibility that pathways may be omitted or inserted within a contour that exhibits familial resemblance, despite its different cardinality. Integrating variable cardinality into contour similarity relations more adequately accounts for familial relationships between contours, opening up new possibilities for analytical application to a wide variety of repertoires. I examine familial relationships between variants of medieval plainchant, and demonstrate how the sensitivity to familial variation illuminated by fuzzy theoretical models can contribute to our understanding of musical ontology. I explain how melodic shape contributes to motivic development and narrative creation in Brahms’s “Regenlied” Op. 59, No. 3, and the related Violin Sonata No. 1, Op. 78. Finally, I explore how melodic shape is perceived within the repetitive context of melodic phasing in Steve Reich’s The Desert Music. Throughout each study, I show that a more flexible attitude toward cardinality can open contour theory to more nuanced judgments of similarity and familial membership, and can provide new and valuable insights into one of music’s most fundamental elements

Bureaucracy vs. efficiency: how does bureaucracy impact the access to european funds - the impact of european funds on R&D investment by firms in Portugal

Due to the dynamic environment in which small and medium-sized businesses (SMEs) operate, they face unique problems. Through various tools and programs, public policies are attempting to boost the competitiveness and creativity of SMEs in this direction. This paper evaluates whether SMEs that received EU funds recorded an improvement in their intangible and tangible fixed assets investments and if those investments impact the perception of bureaucracy in the application process for EU funds. The findings demonstrate the efficiency of EU funds in encouraging investment in tangible assets while having some impact on the perception of bureaucracy