Computational media aesthetics: Finding meaning beautiful

Innovative media management, annotation, delivery, and navigation services will enrich online shopping, help-desk services, and anytime-anywhere training over wireless devices. However, the semantic gap between the rich meaning that users want when they query and browse media and the shallowness of the content descriptions that one can actually compute is weakening today's automatic content-annotation systems. To address such problems, an approach that markedly departs from existing methods based on detecting and annotating low-level audio-visual features is advocated.

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

This paper concerns social learning modes and their effects on team performance. Social learning, such as by observing others' actions and their outcomes, allows members of a team to learn what other members know. Knowing what other members know can reduce task communication and co-ordination overhead, which helps the team to perform faster since members can devote their attention to their tasks. This paper describes agent-based simulation studies using a computational model that implements different social learning modes as parameters that can be controlled in the simulations. The results show that social learning from both direct and indirect observations positively contributes to learning about what others know, but the value of social learning is sensitive to prior familiarity such that minimum thresholds of team familiarity are needed to realise the benefits of social learning. This threshold increases with task complexity. These findings clarify the level of influence that sociality has on social learning and sets up a formal framework by which to conduct studies on how social context influences learning and group performance.

Application of computational media aesthetics methodology to extracting color semantics in film

Using film grammar as the underpinning, we study the extraction of structures in video based on color using a wide configuration of clustering methods combined with existing and new similarity measures. We study the visualisation of these structures, which we call Scene-Cluster Temporal Charts and show how it can bring out the interweaving of different themes and settings in a film. We also extract color events that filmmakers use to draw/force a viewer's attention to a shot/scene. This is done by first extracting a set of colors used rarely in film, and then building a probabilistic model for color event detection. We demonstrate with experimental results from ten movies that our algorithms are effective in the extraction of both scene-cluster temporal charts and color events.

New cinema history and the computational turn

This paper outlines how the digitisation of both the film industry and contemporary research practices bear on the work of the new cinema historian. How might the opportunities presented by an unprecedented proliferation of data for example, also challenge the unspoken assumptions and ordinary practices of conventional film studies research? And how might the 'computational turn' present opportunities (and challenges) for a revisionist cinema history at the intersection of qualitative historiographies (focussed on the social experience of the cinema) and quantitative research approaches such as data mining, empirical analysis and digital visualisations?

An introduction to computational intelligence paradigms

This chapter presents an introduction to computational intelligence (CI) paradigms. A number of CI definitions are first presented to provide a general concept of this new, innovative computing field. The main constituents of CI, which include artificial neural networks, fuzzy systems, and evolutionary algorithms, are explained. In addition, different hybrid CI models arisen from synergy of neural, fuzzy, and evolutionary computational paradigms are discussed.

A comparative analysis of decision trees vis-à-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.

Computational simulation of the influence of inert particles on incomplete combustion of methane at a low air factor

It is well known that the gas–solid system plays a significant role in many industrial processes. It is a complex physical and chemical process, generally consisting of heat transfer, mass transfer, species diffusion, and chemical reactions. In this paper, the reaction of methane with air at a low air factor and the gas flow in a fluidized bed with 0.1 mm solid particles are computationally simulated to enable the study of the effect of the inert particles on the species diffusion and the chemical reactions. The reaction of methane and air is modeled by a two-step reaction mechanism that produces a continuous fluid phase composed of six gases (CH₄, CO, O₂, CO₂, H₂O, and N₂) and discrete solid particles in the reactor. The simulation results are compared with experiment and show that the finite rate model and the eddy dissipation model can well describe the reactions of gases in high-density gas–solid systems. The distribution of each gas and the particle behaviors are analyzed for incomplete combustion at different concentrations of loaded solid particles. The inert particles change the reactions by enhancing both the chemical kinetics and the species diffusion dynamics.