Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

Sustainable horticultural supply chain: the case of local food networks in the United Kingdom

In the UK there is widespread support from Government, media and consumers for local food networks. These have the potential to provide a more sustainable supply chain and are well suited to the unique production and consumption characteristics of horticultural products. In terms of food marketing, local food is in its relative infancy and is still without any formal definition. This lack of clarity hampers research activities. Although the profile of local food buyers and their expectations has been explored, our knowledge of its social, economic and environmental aspects is minimal. This research contributes by exploring the structure and scope of local food activities in the UK in terms of profiling those specialised retail outlets who provide consumers with the opportunity to purchase locally grown horticultural products.

Informed, uninformed and participative consent in social media research

The use of online data is becoming increasingly essential for the generation of insight in today’s research environment. This reflects the much wider range of data available online and the key role that social media now plays in interpersonal communication. However, the process of gaining permission to use social media data for research purposes creates a number of significant issues when considering compatibility with professional ethics guidelines. This paper critically explores the application of existing informed consent policies to social media research and compares with the form of consent gained by the social networks themselves, which we label ‘uninformed consent’. We argue that, as currently constructed, informed consent carries assumptions about the nature of privacy that are not consistent with the way that consumers behave in an online environment. On the other hand, uninformed consent relies on asymmetric relationships that are unlikely to succeed in an environment based on co-creation of value. The paper highlights the ethical ambiguity created by current approaches for gaining customer consent, and proposes a new conceptual framework based on participative consent that allows for greater alignment between consumer privacy and ethical concerns.

Weighted alpha-rate dominating sets in social networks

We are looking into variants of a domination set problem in social networks. While randomised algorithms for solving the minimum weighted domination set problem and the minimum alpha and alpha-rate domination problem on simple graphs are already present in the literature, we propose here a randomised algorithm for the minimum weighted alpha-rate domination set problem which is, to the best of our knowledge, the first such algorithm. A theoretical approximation bound based on a simple randomised rounding technique is given. The algorithm is implemented in Python and applied to a UK Twitter mentions networks using a measure of individuals’ influence (klout) as weights. We argue that the weights of vertices could be interpreted as the costs of getting those individuals on board for a campaign or a behaviour change intervention. The minimum weighted alpha-rate dominating set problem can therefore be seen as finding a set that minimises the total cost and each individual in a network has at least alpha percentage of its neighbours in the chosen set. We also test our algorithm on generated graphs with several thousand vertices and edges. Our results on this real-life Twitter networks and generated graphs show that the implementation is reasonably efficient and thus can be used for real-life applications when creating social network based interventions, designing social media campaigns and potentially improving users’ social media experience.