City University London

About: City University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 5735 authors who have published 17285 publications receiving 453290 citations.

“Making my own luck”: Serendipity strategies and how to support them in digital information environments

Abstract: Serendipity occurs when unexpected circumstances and an “aha” moment of insight result in a valuable, unanticipated outcome. Designing digital information environments to support serendipity can not only provide users with new knowledge, but also propel them in directions they might not otherwise have traveled in—surprising and delighting them along the way. As serendipity involves unexpected circumstances it cannot be directly controlled, but it can be potentially influenced. However, to the best of our knowledge, no previous work has focused on providing a rich empirical understanding of how it might be influenced. We interviewed 14 creative professionals to identify their self-reported strategies aimed at increasing the likelihood of serendipity. These strategies form a framework for examining ways existing digital environments support serendipity and for considering how future environments can create opportunities for it. This is a new way of thinking about how to design for serendipity; by supporting the strategies found to increase its likelihood rather than attempting to support serendipity as a discrete phenomenon, digital environments not only have the potential to help users experience serendipity but also encourage them to adopt the strategies necessary to experience it more often.

Is the Feldstein-Horioka Puzzle History?

Abstract: A country's intertemporal budget constraint implies current account stationarity or that its saving and investment rates should cointegrate. However, such behaviour may not pertain in finite sample spans where the current account could be subject to persistent shocks. Accordingly, this paper reconsiders the Feldstein-Horioka puzzle for a panel of 12 OECD economies 1980I-2000IV using a mean group regression approach that is robust to persistent innovations and accounts for country heterogeneity and cross-sectional dependence. The mean group estimates are notably smaller than that from the conventional cross-section estimator and are statistically insignificant. Our findings support the view that capital is highly mobile in the long run for OECD economies despite persistence in the current account.

Fast relational learning using bottom clause propositionalization with artificial neural networks

Abstract: Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy.