11 research outputs found
An extension of SPARQL for expressing qualitative preferences
In this paper we present SPREFQL, an extension of the SPARQL language that
allows appending a PREFER clause that expresses "soft" preferences over the
query results obtained by the main body of the query. The extension does not
add expressivity and any SPREFQL query can be transformed to an equivalent
standard SPARQL query. However, clearly separating preferences from the "hard"
patterns and filters in the WHERE clause gives queries where the intention of
the client is more cleanly expressed, an advantage for both human readability
and machine optimization. In the paper we formally define the syntax and the
semantics of the extension and we also provide empirical evidence that
optimizations specific to SPREFQL improve run-time efficiency by comparison to
the usually applied optimizations on the equivalent standard SPARQL query.Comment: Accepted to the 2017 International Semantic Web Conference, Vienna,
October 201
Using differential reinforcement of high rates of behavior to improve work productivity : a replication and extension
Background: Due to deficits in adaptive and cognitive functioning, productivity may pose challenges for individuals with intellectual disability in the workplace.Method: Using a changing‐criterion embedded in a multiple baseline across partici‐pants design, we examined the effects of differential reinforcement of high rates of behaviour (DRH) on the rate of data entry (i.e., productivity) in four adults with intel‐lectual disability.Results: Although the DRH procedure increased the rate of correct data entry in all four participants, none of the participants achieved the criterion that we set with novice undergraduate students.Conclusions: Our results indicate that DRH is an effective intervention to increase rate of correct responding in individuals with intellectual disability, but that achiev‐ing the same productivity as workers without disability may not always be possible
A Model of User Preferences for Semantic Services Discovery and Ranking
Current proposals on Semantic Web Services discovery and
ranking are based on user preferences descriptions that often come with
insufficient expressiveness, consequently making more difficult or even
preventing the description of complex user desires. There is a lack of a
general and comprehensive preference model, so discovery and ranking
proposals have to provide ad hoc preference descriptions whose expressiveness
depends on the facilities provided by the corresponding technique,
resulting in user preferences that are tightly coupled with the
underlying formalism being used by each concrete solution. In order to
overcome these problems, in this paper an abstract and sufficiently expressive
model for defining preferences is presented, so that they may be
described in an intuitively and user-friendly manner. The proposed model
is based on a well-known query preference model from database systems,
which provides highly expressive constructors to describe and compose
user preferences semantically. Furthermore, the presented proposal is independent
from the concrete discovery and ranking engines selected, and
may be used to extend current Semantic Web Service frameworks, such
as wsmo, sawsdl, or owl-s. In this paper, the presented model is also
validated against a complex discovery and ranking scenario, and a concrete
implementation of the model in wsmo is outlined.Comisión Interministerial de Ciencia y Tecnología TIN2006-00472Comisión Interministerial de Ciencia y Tecnología TIN2009-07366Junta de Andalucía TIC-253
Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle
Feed for dairy cattle has a major impact on profitability and the environmental impact of farms. Sustainable dairy production relies on continued improvement in feed efficiency as a way to reduce costs and nutrient loss from feed. Advances in breeding, feeding and management have led to the dilution of maintenance energy and thus more efficient dairy cattle. Still, many additional opportunities are available to improve individual animal feed efficiency. Sensing technologies such as wearable sensors, image-based and high-throughput phenotyping technologies (e.g., milk testing) are becoming more available on commercial farm. The application of these technologies as indicator traits for feed intake and efficiency related traits would be advantageous to provide additional information to predict and manage feed efficiency. This review focuses on precision livestock technologies and high-throughput phenotyping in use today as well as those that could be developed in the future as possible indicators of feed intake. Several technologies such as milk spectral data, activity, rumen measures, and image-based phenotypes have been associated with feed intake. Future applications will depend on the ability to repeatably measure and calibrate these data across locations, so that they can be integrated for use in predicting and managing feed intake and efficiency on farm
Neogeography: The Challenge of Channelling Large and Ill-Behaved Data Streams
Neogeography is the combination of user generated data and experiences with mapping technologies. In this paper we propose a research project to extract valuable structured information with a geographic component from unstructured user generated text in wikis, forums, or SMSes. The project intends to help workers communities in developing countries to share their knowledge, providing a simple and cheap way to contribute and get benefit using the available communication technology