Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

Search Efficient Binary Network Embedding

Traditional network embedding primarily focuses on learning a dense vector representation for each node, which encodes network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned dense vector representations are inefficient for large-scale similarity search, which requires to find the nearest neighbor measured by Euclidean distance in a continuous vector space. In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a sparse binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations efficiently through a stochastic gradient descent based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support much quicker network node search compared to Euclidean distance or other distance measures. Our experiments and comparisons show that BinaryNE not only delivers more than 23 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods

Searching for superspreaders of information in real-world social media

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure

From \u27Break Out\u27 to \u27Breakthrough\u27: Successful Market Strategies of Immigrant Entrepreneurs in the UK

This paper explores the strategies that enable ethnic minority immigrant entrepreneurs to \u27break out\u27 of local ethnic markets and \u27break through\u27 into more promising markets with greater opportunities. It analyzes the contextual and personal characteristics of the entrepreneurs that implement those strategies, based on a primary survey of South Asian entrepreneurs in the UK. The analysis suggests that breaking out of co-ethnic customer markets is neither necessary nor sufficient for entrepreneurial expansion. The critical factor is the entrepreneur\u27s ability to break through into customer markets that are larger, by geographical reach or profit margins and value added. Many successful immigrant entrepreneurs leverage market knowledge of their home countries. At the same time, the more successful entrepreneurs break out of ethnic labor markets by hiring non-ethnic employees. The capacity to \u27break out\u27 and \u27break through\u27 into larger, global markets, is strengthened by the entrepreneur\u27s education, experience, access and ability to leverage international business networks, and agility to move into more promising markets

A General Optimization Technique for High Quality Community Detection in Complex Networks

Recent years have witnessed the development of a large body of algorithms for community detection in complex networks. Most of them are based upon the optimization of objective functions, among which modularity is the most common, though a number of alternatives have been suggested in the scientific literature. We present here an effective general search strategy for the optimization of various objective functions for community detection purposes. When applied to modularity, on both real-world and synthetic networks, our search strategy substantially outperforms the best existing algorithms in terms of final scores of the objective function; for description length, its performance is on par with the original Infomap algorithm. The execution time of our algorithm is on par with non-greedy alternatives present in literature, and networks of up to 10,000 nodes can be analyzed in time spans ranging from minutes to a few hours on average workstations, making our approach readily applicable to tasks which require the quality of partitioning to be as high as possible, and are not limited by strict time constraints. Finally, based on the most effective of the available optimization techniques, we compare the performance of modularity and code length as objective functions, in terms of the quality of the partitions one can achieve by optimizing them. To this end, we evaluated the ability of each objective function to reconstruct the underlying structure of a large set of synthetic and real-world networks.Comment: MAIN text: 14 pages, 4 figures, 1 table Supplementary information: 19 pages, 8 figures, 5 table

Dynamics of private social networks

Social networks, have been a significant turning point in ways individuals and companies interact. Various research has also revolved around public social networks, such as Twitter and Facebook. In most cases trying to understand what's happening in the network such predicting trends, and identifying natural phenomenon. Seeing the growth of public social networks several corporations have sought to build their own private networks to enable their staff to share knowledge, and expertise. Little research has been done in regards to the value private networks give to their stake holders. This is primarily due to the fact as their name implies, these networks are private, thus access to internal data is limited to a trusted few. This paper looks at a particular online private social network, and seeks to investigate the research possibilities made available, and how this can bring value to the organisation which runs the network. Notwithstanding the limitations of the network, this paper seeks to explore the connections graph between members of the network, as well as understanding the topics discussed within the network. The findings show that by visualising a social network one can assess the success or failure of their online networks. The Analysis conducted can also identify skill shortages within areas of the network, thus allowing corporations to take action and rectify any potential problems.peer-reviewe

Geographical co-location, social networks and inter-firm marketing co-operation : the case of the salmon industry

This study looks at the factors that influence the development of marketing co-operation among cluster-based firms. It examines data from SMEs operating within the salmon farming industry in two different regions: Scotland and Chile. Analyses indicate that informal social networks help explain the observed relationship between geographical proximity and inter-firm marketing co-operation, especially for firms located in peripheral rural communities. A theoretical model is proposed for further research in the field that, until recently, has been traditionally analysed only by economists. Practical implications are suggested for practitioners and policymaker

Sustainable food? Teikei, co-operatives and food citizenship in Japan and the UK

This paper explores in particular how Teikei groups, as forms of Community Supported Agriculture (CSA), operate in Japan, focussing on one particular group. The paper links the Teikei approach to debates around social capital and consumer-citizenship, arguing that pre-existing consumer/citizen institutions may usefully be engaged in developing food citizenship and CSA operations. The discussion is linked to CSA and various other alternative food networks (AFNs) that have grown up in various forms in Japan, the US, the UK and elsewhere in Europe over the past thirty years or so. CSA in similar fashion to Teikei involves bringing producers and consumers closer together in terms of reconnecting the agricultural producer and consumer to aid food traceability and quality (including organic). CSA also exhibits elements of new assemblies of agricultural governance based on enhanced consumer-citizenship where consumers, to varying degrees, have a say in what and how produce is grown and how the land is managed