FearNot! An Anti-Bullying Intervention: Evaluation of an Interactive Virtual Learning Environment

Original paper can be found at: http://www.aisb.org.uk/publications/proceedings.shtm

Smithsonian package for algebra and symbolic mathematics

Symbolic programming system for computer processing of algebraic expression

Hierarchical structuring of Cultural Heritage objects within large aggregations

Huge amounts of cultural content have been digitised and are available through digital libraries and aggregators like Europeana.eu. However, it is not easy for a user to have an overall picture of what is available nor to find related objects. We propose a method for hier- archically structuring cultural objects at different similarity levels. We describe a fast, scalable clustering algorithm with an automated field selection method for finding semantic clusters. We report a qualitative evaluation on the cluster categories based on records from the UK and a quantitative one on the results from the complete Europeana dataset.Comment: The paper has been published in the proceedings of the TPDL conference, see http://tpdl2013.info. For the final version see http://link.springer.com/chapter/10.1007%2F978-3-642-40501-3_2

Coulomb plus power-law potentials in quantum mechanics

We study the discrete spectrum of the Hamiltonian H = -Delta + V(r) for the Coulomb plus power-law potential V(r)=-1/r+ beta sgn(q)r^q, where beta > 0, q > -2 and q \ne 0. We show by envelope theory that the discrete eigenvalues E_{n\ell} of H may be approximated by the semiclassical expression E_{n\ell}(q) \approx min_{r>0}\{1/r^2-1/(mu r)+ sgn(q) beta(nu r)^q}. Values of mu and nu are prescribed which yield upper and lower bounds. Accurate upper bounds are also obtained by use of a trial function of the form, psi(r)= r^{\ell+1}e^{-(xr)^{q}}. We give detailed results for V(r) = -1/r + beta r^q, q = 0.5, 1, 2 for n=1, \ell=0,1,2, along with comparison eigenvalues found by direct numerical methods.Comment: 11 pages, 3 figure

Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability

The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it. We propose a method that uses a machine learning tool for building a predictive model of the optimal planning horizon, and variants of the well-known planner SatPlan and solver MiniSat that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques