Edinburgh Napier University

About: Edinburgh Napier University is a education organization based out in Edinburgh, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2665 authors who have published 6859 publications receiving 175272 citations.

Consumer confusion: reduction strategies in higher education

Abstract: This paper highlights the increasingly important topic of consumer confusion. Drawing parallels with experiences in the private sector, the concept of consumer confusion is explored within the higher education sector; what causes the phenomenon, how do consumers react to it and how can it be negated/minimised? The expansion and commercialisation of higher education has seen the wide‐scale adoption of marketing techniques within the sector. Such actions generate increased capacity for consumer confusion, with consumers being overwhelmed with information and potentially making sub‐optimum decisions. Given that the selection of a degree course is normally a life changing event, careful consideration needs to be given, by all parties, to whether marketing helps or hinders this process. While focusing on higher education, the issues considered are equally applicable to any public sector body adopting a more market driven approach.

A lifelong learning hyper-heuristic method for bin packing

Abstract: We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.

Scientific names are ambiguous as identifiers for biological taxa: their context and definition are required for accurate data integration

Abstract: Biologists use scientific names to label the organisms described in their data; however, these names are not unique identifiers for taxonomic entities. Alternative taxonomic classifications may apply the same name, associated with alternative definition or circumscription. Consequently, labelling data with scientific names alone does not unambiguously distinguish between taxon concepts. Accurate integration and comparison of biological data is required on taxon concepts, as defined in alternative taxonomic classifications. We have derived an abstract, inclusive model for the diverse representations of taxonomic concepts used by taxonomists and in taxonomic databases. This model has been implemented as a proposed standard XML schema for the exchange and comparison of taxonomic concepts between data providers and users. The representation and exchange of taxon definitions conformant with this schema will facilitate the development of taxonomic name/concept resolution services, allowing the meaningful integration and comparison of biological datasets, with greater accuracy than on the basis of name alone.