The monthly PGRs Research Presentations was held on Wed. 11th November, 2pm, Room MC3108.
This session we had the following presentations:
| Title: “Profiling User Engagement with Promotional Social Media Content“. | Title: “A Bio-inspired Collision Detection Vision System Embedded for Autonomous Micro-robots ” | |
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By: Jamie Mahoney |
By: Cheng Hu |
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| Abstract: Organisations, retailers and brands have a long-established need to gain insight into the characteristics of their customers, users and online followers. Using Twitter as a case study, we describe a method of creating engagement profiles of users based on qualitative analyses of the social media content with which they have publicly engaged. By clustering these engagement profiles, we extend previous work by not only showing how people within a social graph can be clustered in useful ways, but also how these users are likely to engage with specific types of social media content – thus allowing for the creation of targeted social media content strategies. Our findings demonstrate that ‘traditional’ methods of social graph segmentation do not reflect groupings of similar users in terms of engagement behaviour, we also demonstrate that user engagement behaviours do not vary dramatically over time. We provide suggestions for how these findings might be used in the creation of effective strategies for companies, and organisations, wishing to issue promotional material via social media platforms. | Abstract: The vision system takes inspiration from locusts’ response in detecting fast approaching objects. Neurophysiological research suggested that locusts use a wide-field visual neuron called lobula giant movement detector (LGMD) to respond to imminent collisions. In this work, we present the implementation of selected neuron model by a low-cost ARM processor as part of a composite vision module. The developed system is able to perform image acquisition and processing independently. The vision module is placed on top of a micro-robot to initiate obstacle avoidance behaviour. Both simulation and real-world experiments were carried out to test the reliability and robustness of the vision system. The results of the performed experiments with different scenarios demonstrated the amenability of the developed bio-inspired vision system to be used as a low-cost embedded module in autonomous robots with high precision. | |
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