Rural Development Group Politics. A hidden cost?

I challenge the popular notion of European rural development group dynamics and argue for a better understanding of the role of micro-politics as a means of enhancing the performance of these groups. The views are research based and have relevance to the broader rural development and regeneration sector. Micro-politics involves knowledge, power, trust, perceptions, understanding, social networks, values and traits that arise as a result of individuals interacting within a group whilst working on a shared goal, such as rural development. The monetary and time costs to a community of failing to address micro-politics and nurture positive group relations are considerable. These include time spent in unproductive meetings and poorly prioritized—and ultimately unsuccessful—funding applications as a result of failure to agree priorities. Successful groups rely on individuals interacting in a way that achieves a greater social good. Mutual trust amongst the actors lies at the heart of effective group activity. Effective management of micro-politics requires steps to nurture a culture of mutual trust to ensure that rural development actors co-operate rather than play destructive games with one another. A case study example of a relatively straightforward approach illustrates how this might be done in practice.

Hidden Conditional Random Fields for Visual Speech Recognition

In this paper we present the application of Hidden Conditional Random Fields (HCRFs) to modelling speech for visual speech recognition. HCRFs may be easily adapted to model long range dependencies across an observation sequence. As a result visual word recognition performance can be improved as the model is able to take more of a contextual approach to generating state sequences. Results are presented from a speaker-dependent, isolated digit, visual speech recognition task using comparisons with a baseline HMM system. We firstly illustrate that word recognition rates on clean video using HCRFs can be improved by increasing the number of past and future observations being taken into account by each state. Secondly we compare model performances using various levels of video compression on the test set. As far as we are aware this is the first attempted use of HCRFs for visual speech recognition.