Social Entrepreneurship: Role of Networks in Capturing Opportunities.

Social entrepreneurship, an exercise that amalgamates non-profit operations and business values in quest of social impact, has the ability to empower lots of individuals around the world in social problem-solving. This article investigates how managerial ties assist social entrepreneurs in discovering and capturing opportunities for their course. We also look at the moderating effect of centrality on the relationship between the social entrepreneur, managerial ties and opportunity. Drawing a sample from 300 social entrepreneurs, we recorded that, business ties have a more positive effect on opportunity and both degree and betweenness centrality positively moderates this relationship. However, social entrepreneurs are encouraged to establish ties with both government and business because they have different services to offer. But that notwithstanding, they are encouraged to have more business ties to discover and capture opportunities. Keywords: Social Entrepreneurship, government ties, business ties, opportunity capture. DOI: 10.7176/JRDM/53-06 Publication date:March 31st 201

Identifying influential nodes with centrality indices combinations using symbolic regressions

Numerous strategies for determining the most influential nodes in a connected network have been developed. The use of centrality indices in a network allows the identification of the most important nodes in the network. Specific indices, on the other hand, cannot search for a network's entire meaning because they are only interested in a single attribute. Researchers frequently overlook an index's characteristics in favour of focusing on its application. The purpose of this research is to integrate selected centrality indices classified by their various properties. A symbolic regression approach was used to find meaningful mathematical expressions for this combination of indices. When the efficacy of the combined indices is compared to other methods, the combined indices react similarly and outperform the previous method. Using this adaptive technique, network researchers can now identify the most influential network nodes

Video big data: an agile architecture for systematic exploration and analytics

Video is currently at the forefront of most business and natural environments. In surveillance, it is the most important technology as surveillance systems reveal information and patterns for solving many security problems including crime prevention. This research investigates technologies that currently drive video surveillance systems with a view to optimization and automated decision support. The investigation reveals some features and properties that can be optimised to improve performance and derive further benefits from surveillance systems. These aspects include system-wide architecture, meta-data generation, meta-data persistence, object identification, object tagging, object tracking, search and querying sub-systems. The current less-than-optimum performance is attributable to many factors, which include massive volume, variety, and velocity (the speed at which streaming video transmit to storage) of video data in surveillance systems. Research contributions are 2-fold. First, we propose a system-wide architecture for designing and implementing surveillance systems, based on the authors’ system architecture for generating meta-data. Secondly, we design a simulation model of a multi-view surveillance system from which the researchers generate simulated video streams in large volumes. From each video sequence in the model, the authors extract meta-data and apply a novel algorithm for predicting the location of identifiable objects across a well-connected camera cluster. This research provide evidence that independent surveillance systems (for example, security cameras) can be unified across a geographical location such as a smart city, where each network is administratively owned and managed independently. Our investigation involved 2 experiments - first, the implementation of a web-based solution where we developed a directory service for managing, cataloguing, and persisting metadata generated by the surveillance networks. The second experiment focused on the set up, configuration and the architecture of the surveillance system. These experiments involved the investigation and demonstration of 3 loosely coupled service-oriented APIs – these services provided the capability to generate the query-able metadata. The results of our investigations provided answers to our research questions - the main question being “to what degree of accuracy can we predict the location of an object in a connected surveillance network”. Our experiment also provided evidence in support of our hypothesis – “it is feasible to ‘explore’ unified surveillance data generated from independent surveillance networks”