Raquel Ureña

Managing incomplete preference relations in decision making

In decision making, situations where all experts are able to efficiently express their preferences over all the available options are the exception rather than the rule. Indeed, the above scenario requires all experts to possess a precise or sufficient level of knowledge of the whole problem to tackle, including the ability to discriminate the degree up to which some options are better than others. These assumptions can be seen unrealistic in many decision making situations, especially those involving a large number of alternatives to choose from and/or conflicting and dynamic sources of information. Some methodologies widely adopted in these situations are to discard or to rate more negatively those experts that provide preferences with missing values. However, incomplete information is not equivalent to low quality information, and consequently these methodologies could lead to biased or even bad solutions since useful information might not being taken properly into account in the decision process. Therefore, alternative approaches to manage incomplete preference relations that estimates the missing information in decision making are desirable and possible. This paper presents and analyses methods and processes developed on this area towards the estimation of missing preferences in decision making, and highlights some areas for future research.

Building consensus in group decision making with an allocation of information granularity

Abstract Consensus is defined as a cooperative process in which a group of decision makers develops and agrees to support a decision in the best interest of the whole. It is a questioning process, more than an affirming process, in which the group members usually modify their choices until a high level of agreement within the group is achieved. Given the importance of forming an accepted decision by the entire group, the consensus problem has attained a great attention as it is a major goal in group decision making. In this study, we propose the concept of the information granularity being regarded as an important and useful asset supporting the goal to reach consensus in group decision making. By using fuzzy preference relations to represent the opinions of the decision makers, we develop a concept of a granular fuzzy preference relation where each pairwise comparison is formed as a certain information granule (say, an interval, fuzzy set, rough set, and alike) instead of a single numeric value. As being more abstract, the granular format of the preference model offers the required flexibility to increase the level of agreement within the group using the fact that we select the most suitable numeric representative of the fuzzy preference relation.

Real-time tone mapping on GPU and FPGA

Low-level computer vision algorithms have high computational requirements. In this study, we present two real-time architectures using resource constrained FPGA and GPU devices for the computation of a new algorithm which performs tone mapping, contrast enhancement, and glare mitigation. Our goal is to implement this operator in a portable and battery-operated device, in order to obtain a low vision aid specially aimed at visually impaired people who struggle to manage themselves in environments where illumination is not uniform or changes rapidly. This aid device processes in real-time, with minimum latency, the input of a camera and shows the enhanced image on a head mounted display (HMD). Therefore, the proposed operator has been implemented on battery-operated platforms, one based on the GPU NVIDIA ION2 and another on the FPGA Spartan III, which perform at rates of 30 and 60 frames per second, respectively, when working with VGA resolution images (640 × 480).

GDM-R

With the incorporation of web 2.0 frameworks the complexity of decision making situations has exponentially increased, involving in many cases a huge number of decision makers, and many different alternatives. In the literature we can find a great variety of methodologies to assist multi-person decision making. However these classical approaches are not suitable to deal with such complexity since there are no tools able to carry out automatically the decision making processes, providing graphical information about its evolution.The main objective of this contribution is to present an open source framework fully developed in R to carry out consensus guided decision making processes using fuzzy preference relations and providing mechanism to deal with missing information. The system includes tools to visualize the evolution of the decision making process and presents various operation modes, including a test operation one which automatically creates a customized decision scenario to validate, test and compare among various decision making approaches.

Reaching Consensus in Digital Libraries: A Linguistic Approach☆

Abstract Libraries are recently changing their classical role of providing stored information into new virtual communities, which involve large number of users sharing real time information. Despite of those good features, there is still a necessity of developing tools to help users to reach decisions with a high level of consensus in those new virtual environments. In this contribution we present a new consensus reaching tool with linguistic preferences designed to minimize the main problems that this kind of organization presents (low and intermittent participation rates, difficulty of establishing trust relations and so on) while incorporating the benefits that a new digital library offers (rich and diverse knowledge due to a large number of users, real-time communication and so on). The tool incorporates some delegation and feedback mechanisms to improve the speed of the process and its convergence towards a consensual solution.

On incomplete fuzzy and multiplicative preference relations in multi-person decision making

Abstract Rapid changes in the business environment such us the globalization as well as the increasing necessity to make crucial decisions involving a huge range of alternatives in short period of time or even in real time have made that computerized group decision support systems become very useful tools. However in the majority of the cases the panel of experts cannot provide all the information about their preferences due to different reasons such as lack of knowledge, time etc. Therefore different approaches have been presented to deal with the missing preferences in group decision making contexts. In this paper we review and analyse the state-of-the-art research efforts carried out on this topic for incomplete fuzzy preference relations and multiplicative preference relations.

Visual training and emotional state of people with retinitis pigmentosa

The purpose of the study was to improve the visual functioning of people with restriction in contrast sensitivity (CS), such as retinitis pigmentosa (RP), by means of a visual training program. Twenty-six volunteers with RP participated, distributed in two groups: 15 who made up the experimental group (who received the training program) and 11 who participated as a control group (without training). Participants were evaluated before beginning training, on completion, and 3 mo following completion for CS with the Pelli-Robson Contrast Sensitivity (P&R) test, visual functioning with the Visual Function Questionnaire (VFQ), and in emotional state with the Beck Depression Inventory (BDI). The training program is based on software that generates luminous stimuli of varying duration and intensity and registers the stimuli perceived by the subject. The outcomes showed significant differences posttraining in the experimental group in depression (F1,14 = 5.42; p < 0.04), VFQ (Z = -2.27; p < 0.02), and P&R in the right eye (Z = -1.99; p < 0.046) and left eye (Z = -2.30; p < 0.02) but not in binocular (Z = -0.96; p < 0.34). The outcomes showed that the experimental group made significant progress in all variables and these effects remained after 3 mo, which suggests that the program could be a helpful addition to RP rehabilitation and help mitigate the damage.

Real-time bio-inspired contrast enhancement on GPU

This paper describes a new contrast enhancement technique inspired in the way the human retina works to increase the degree of detail, mitigate glares and enhance the visibility of the scene in both bright and dim lighting conditions. The proposed operator and its real-time implementation are specially aimed as an aid system for visually impaired people who struggle to manage themselves in environments where illumination is not uniform or changes rapidly. This aid device processes in real-time, with minimum latency, the input of a camera and shows the enhanced image on a Head Mounted Display (HMD). To meet all these requirements we have implemented the whole model in a light-weight netbook which integrates a GPU NVIDIA ION2 and performs at a maximum rate of 27 frames per second when working with VGA resolution images (640x480RGB images).

Consistency based completion approaches of incomplete preference relations in uncertain decision contexts

Uncertainty, hesitation and vagueness are inherent to human beings when articulating opinions and preferences. Therefore in decision making situations it might well be the case that experts are unable to express their opinions in an accurate way. Under these circumstances, various families of preference relations (PRs) have been proposed (linguistic, intuitionistic and interval fuzzy PRs) to allow the experts to manifest some degree of hesitation when enunciating their opinions. An extreme case of uncertainty happens when an expert is unable to differentiate the degree up to which one preference is preferred to another. Henceforth, incomplete preference relations are possible. It is worth to bear in mind that incomplete information does not mean low quality information, on the contrary, in many occasions experts might prefer no to provide information in other to keep consistency. Consequently mechanism to deal with incomplete information in decision making are necessary. This contribution presents the main consistency based completion approaches to estimate incomplete preference values in linguistic, intuitionistic and interval fuzzy PRs.

On Multi-granular Fuzzy Linguistic Modelling in Decision Making☆

Abstract Nowadays, the human-computer interaction is being a hot topic. In such a way, several methods have been proposed to deal with multi-granularity when people with different knowledge level express their preferences on the same concept using linguistic notation, that is, words instead of numbers. This is a critical problem in group decision making scenarios, but all the existing approaches have their own advantages and drawbacks. Therefore, some work better in certain environments than others. In such a way, choosing the best method in each situation is critical for obtaining good quality results. In this contribution, an analysis on the different fuzzy linguistic multi-granular modelling approaches is presented in order to provide the reader some advice of what method should be chosen depending on the problem and the quality of results that the user expects to obtain.