36,136 research outputs found
CBR and MBR techniques: review for an application in the emergencies domain
The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system.
RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to:
a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions
b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location.
In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations.
This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version
Dynamic adaptation of user profiles in recommender systems
In a period of time in which the content available through the Internet
increases exponentially and is more easily accessible every day, techniques
for aiding the selection and extraction of important and personalised
information are of vital importance. Recommender Systems (RS) appear as
a tool to help the user in a decision making process by evaluating a set of
objects or alternatives and aiding the user at choosing which one/s of them
suits better his/her interests or preferences. Those preferences need to be
accurate enough to produce adequate recommendations and should be
updated if the user changes his/her likes or if they are incorrect or
incomplete. In this work an adequate model for managing user preferences
in a multi-attribute (numerical and categorical) environment is presented to
aid at providing recommendations in those kinds of contexts. The
evaluation process of the recommender system designed is supported by a
new aggregation operator (Unbalanced LOWA) that enables the
combination of the information that defines an alternative into a single
value, which then is used to rank the whole set of alternatives. After the
recommendation has been made, learning processes have been designed to
evaluate the user interaction with the system to find out, in a dynamic and
unsupervised way, if the user profile in which the recommendation process
relies on needs to be updated with new preferences. The work detailed in
this document also includes extensive evaluation and testing of all the
elements that take part in the recommendation and learning processes
A Survey of Prediction and Classification Techniques in Multicore Processor Systems
In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems
Using a Machine Learning Approach to Implement and Evaluate Product Line Features
Bike-sharing systems are a means of smart transportation in urban
environments with the benefit of a positive impact on urban mobility. In this
paper we are interested in studying and modeling the behavior of features that
permit the end user to access, with her/his web browser, the status of the
Bike-Sharing system. In particular, we address features able to make a
prediction on the system state. We propose to use a machine learning approach
to analyze usage patterns and learn computational models of such features from
logs of system usage.
On the one hand, machine learning methodologies provide a powerful and
general means to implement a wide choice of predictive features. On the other
hand, trained machine learning models are provided with a measure of predictive
performance that can be used as a metric to assess the cost-performance
trade-off of the feature. This provides a principled way to assess the runtime
behavior of different components before putting them into operation.Comment: In Proceedings WWV 2015, arXiv:1508.0338
CFD modelling of wind turbine airfoil aerodynamics
This paper reports the first findings of an ongoing research programme on wind turbine computational aerodynamics at the
University of Glasgow. Several modeling aspects of wind turbine airfoil aerodynamics based on the solution of the Reynoldsaveraged
Navier-Stokes (RANS) equations are addressed.
One of these is the effect of an a priori method for structured grid adaptation aimed at improving the wake resolution.
Presented results emphasize that the proposed adaptation strategy greatly improves the wake resolution in the far-field,
whereas the wake is completely diffused by the non-adapted grid with the same number and distribution of grid nodes. A grid
refinement analysis carried out with the adapted grid shows that the improvements of flow resolution thus achieved are of a
smaller magnitude with respect to those accomplished by adapting the grid keeping constant the number of nodes. The
proposed adaptation approach can be easily included in the structured generation process of both commercial and in-house
structured mesh generators systems.
The study also aims at quantifying the solution inaccuracy arising from not modeling the laminar-to-turbulent transition. It
is found that the drag forces obtained by considering the flow as transitional or fully turbulent may differ by 50 %.
The impact of various turbulence models on the predicted aerodynamic forces is also analyzed.
All these issues are investigated using a special-purpose hyperbolic grid generator and a multi-block structured finitevolume
RANS code. The numerical experiments consider the flow field past a wind turbine airfoil for which an exhaustive
campaign of steady and unsteady experimental measurements was conducted. The predictive capabilities of the CFD solver
are validated by comparing experimental data and numerical predictions for selected flow regimes. The incompressible
analysis and design code XFOIL is also used to support the findings of the comparative analysis of numerical RANS-based
results and experimental data
Recommended from our members
A monitoring approach for runtime service discovery
Effective runtime service discovery requires identification of services based on different service characteristics such as structural, behavioural, quality, and contextual characteristics. However, current service registries guarantee services described in terms of structural and sometimes quality characteristics and, therefore, it is not always possible to assume that services in them will have all the characteristics required for effective service discovery. In this paper, we describe a monitor-based runtime service discovery framework called MoRSeD. The framework supports service discovery in both push and pull modes of query execution. The push mode of query execution is performed in parallel to the execution of a service-based system, in a proactive way. Both types of queries are specified in a query language called SerDiQueL that allows the representation of structural, behavioral, quality, and contextual conditions of services to be identified. The framework uses a monitor component to verify if behavioral and contextual conditions in the queries can be satisfied by services, based on translations of these conditions into properties represented in event calculus, and verification of the satisfiability of these properties against services. The monitor is also used to support identification that services participating in a service-based system are unavailable, and identification of changes in the behavioral and contextual characteristics of the services. A prototype implementation of the framework has been developed. The framework has been evaluated in terms of comparison of its performance when using and when not using the monitor component
Exploring personality-targeted UI design in online social participation systems
We present a theoretical foundation and empirical findings demonstrating the effectiveness of personality-targeted design. Much like a medical treatment applied to a person based on his specific genetic profile, we argue that theory-driven, personality-targeted UI design can be more effective than design applied to the entire population. The empirical exploration focused on two settings, two populations and two personality traits: Study 1 shows that users' extroversion level moderates the relationship between the UI cue of audience size and users' contribution. Study 2 demonstrates that the effectiveness of social anchors in encouraging online contributions depends on users' level of emotional stability. Taken together, the findings demonstrate the potential and robustness of the interactionist approach to UI design. The findings contribute to the HCI community, and in particular to designers of social systems, by providing guidelines to targeted design that can increase online participation. Copyright © 2013 ACM
- …