http://hdl.handle.net/10379/5415 2017-10-29T22:45:32Z http://hdl.handle.net/10379/6920 The path to success: A study of user behaviour and success criteria in online communities Aumayr, Erik; Hayes, Conor Maintaining online communities is vital in order to increase and retain their economic and social value. That is why community managers look to gauge the success of their communities by measuring a variety of user behaviour, such as member activity, turnover and interaction. However, such communities vary widely in their purpose, implementation and user demographics, and although many success indicators have been proposed in the literature, we will show that there is no one- ts-all approach to community success: Different success criteria depend on different user behaviour. To demonstrate this, we put together a set of user behaviour features, including many that have been used in the literature as indicators of success, and then we define and predict community success in three different types of online communities: Questions & Answers (Q&A), Healthcare and Emotional Support (Life & Health), and Encyclopaedic Knowledge Creation. The results show that it is feasible to relate community success to specific user behaviour with an accuracy of 0.67–0.93 F1 score and 0.77–1.0 AUC. 2017-08-23T00:00:00Z http://hdl.handle.net/10379/6896 The role of open data in driving sustainable mobility in nine smart cities Yadav, Piyush; Hasan, Souleiman; Ojo, Adegboyega; Curry, Edward In today’s era of globalization, sustainable mobility is considered as a key factor in the economic growth of any country. With the emergence of open data initiatives, there is tremendous potential to improve mobility. This paper presents findings of a detailed analysis of mobility open data initiatives in nine smart cities – Amsterdam, Barcelona, Chicago, Dublin, Helsinki, London, Manchester, New York and San Francisco. The paper discusses the study of various sustainable indicators in the mobility domain and its convergence with present open datasets. Specifically, it throws light on open data ecosystems in terms of their production and consumption. It gives a comprehensive view of the nature of mobility open data with respect to their formats, interactivity, and availability. The paper details the open datasets in terms of their alignment with different mobility indicators, publishing platforms, applications and API’s available. The paper discusses how these open datasets have shown signs of fostering organic innovation and sustainable growth in smart cities with impact on mobility trends. The results of the work can be used to inform the design of data driven sustainable mobility in smart cities to maximize the utilization of available open data resources. 2017-06-05T00:00:00Z http://hdl.handle.net/10379/6894 Non-partitioning merge-sort: Performance enhancement by elimination of division in divide-and-conquer algorithm Aslam, Asra; Ansari, Mohd. Samar; Varshney, Shikha The importance of a high performance sorting algorithm with low time complexity cannot be over stated. Several benchmark algorithms viz. Bubble Sort, Insertion Sort, Quick Sort, and Merge Sort, etc. have tried to achieve these goals, but with limited success in some scenarios. Newer algorithms like Shell Sort, Bucket Sort, Counting Sort, etc. have their own limitations in terms of category/nature of elements which they can process. The present paper is an attempt to enhance performance of the standard Merge-Sort algorithm by eliminating the partitioning complexity component, thereby resulting in smaller computation times. Both subjective and numerical comparisons are drawn with existing algorithms in terms of time complexity and data sizes, which show the superiority of the proposed algorithm. 2016-03-04T00:00:00Z http://hdl.handle.net/10379/6893 Analysing and improving embedded markup of learning resources on the web Dietze, Stefan; Taibi, Davide; Yu, Ran; Barker, Phil; d'Aquin, Mathieu Web-scale reuse and interoperability of learning resources have been major concerns for the technology-enhanced learning community. While work in this area traditionally focused on learning resource metadata, provided through learning resource repositories, the recent emergence of structured entity markup on the Web through standards such as RDFa and Microdata and initiatives such as schema.org, has provided new forms of entitycentric knowledge, which is so far under-investigated and hardly exploited. The Learning Resource Metadata Initiative (LRMI) provides a vocabulary for annotating learning resources through schema.org terms. Although recent studies have shown markup adoption by approximately 30% of all Web pages, understanding of the scope, distribution and quality of learning resources markup is limited. We provide the first public corpus of LRMI extracted from a representative Web crawl together with an analysis of LRMI adoption on the Web, with the goal to inform data consumers as well as future vocabulary refinements through a thorough understanding of the use as well as misuse of LRMI vocabulary terms. While errors and schema misuse are frequent, we also discuss a set of simple heuristics which significantly improve the accuracy of markup, a prerequisite for reusing learning resource metadata sourced from markup. 2017-04-03T00:00:00Z