Algorithms for processing the group K nearest-neighbor query on distributed frameworks

Distributed and Parallel Databases - Given two datasets of points (called Query and Training), the Group (K) Nearest-Neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum...     

Does maintainability relate to the energy consumption of software? A case study

Software Quality Journal - Energy consumption of software has been becoming increasingly significant, since it can vary according to how the software has been developed. In recent years, developers...     

The role of visual complexity and prototypicality regarding first impression of websites: Working towards understanding aesthetic judgments

This paper experimentally investigates the role of visual complexity (VC) and prototypicality (PT) as design factors of websites, shaping users' first impressions by means of two studies. In the first study, 119 screenshots of real websites varying in VC (low vs. medium vs. high) and PT (low vs. high) were rated on perceived aesthetics. Screenshot presentation time was varied as a between-subject factor (50 ms vs. 500 ms vs. 1000 ms). Results reveal that VC and PT affect participants' aesthetics ratings within the first 50 ms of exposure. In the second study presentation times were shortened to 17, 33 and 50 ms. Results suggest that VC and PT affect aesthetic perception even within 17 ms, though the effect of PT is less pronounced than the one of VC. With increasing presentation time the effect of PT becomes as influential as the VC effect. This supports the reasoning of the information-processing stage model of aesthetic processing (Leder et al., 2004), where VC is processed at an earlier stage than PT. Overall, websites with low VC and high PT were perceived as highly appealing.     

A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments

This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines.     

Automotive observers based on multibody models and the extended Kalman filter

This work is part of a project aimed to develop automotive real-time observers based on detailed nonlinear multibody models and the extended Kalman filter (EKF). In previous works, a four-bar mechanism was studied to get insight into the problem. Regarding the formulation of the equations of motion, it was concluded that the state-space reduction method known as matrix-R is the most suitable one for this application. Regarding the sensors, it was shown that better stability, accuracy and efficiency are obtained as the sensored magnitude is a lower derivative and when it is a generalized coordinate of the problem. In the present work, the automotive problem has been addressed, through the selection of a Volkswagen Passat as a case-study. A model of the car containing fifteen degrees of freedom has been developed. The observer algorithm that combines the equations of motion and the integrator has been reformulated so that duplication of the problem size is avoided, in order to improve efficiency. A maneuver of acceleration from rest and double lane change has been defined, and tests have been run for the “prototype,” the “model” and the “observer,” all the three computational, with the model having 100 kg more than the prototype. Results have shown that good convergence is obtained for position level sensors, but the computational cost is high, still far from real-time performance.     

Missing data imputation for fuzzy rule-based classification systems

Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation methods conditioned to the type of FRBCSs is required.     

Fuzzy ontologies-based user profiles applied to enhance e-learning activities

Nowadays, the impact of technological developments on improving human activities is becoming more evident. In e-learning, this situation is no different. There are common to use systems that assist the daily activities of students and teachers. Typically, e-learning recommender systems are focused on students; however, teachers can also benefit from these type of tools. A recommender system can propose actions and resources that facilitate teaching activities like structuring learning strategies. In any case, a complete user’s representation is required. This paper shows how a fuzzy ontology can be used to represent user profiles into a recommender engine and enhances the user’s activities into e-learning environments. A fuzzy ontology is an extension of domain ontologies for solving the problems of uncertainty in sharing and reusing knowledge on the Semantic Web. The user profile is built from learning objects published by the user himself into a learning object repository. The initial experiment confirms that the automatically obtained fuzzy ontology is a good representation of the user’s preferences. The experiment results also indicate that the presented approach is useful and warrants further research in recommending and retrieval information.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.     

SIMCAN: A flexible, scalable and expandable simulation platform for modelling and simulating distributed architectures and applications

In this paper we propose a new simulation platform called SIMCAN, for analyzing parallel and distributed systems. This platform is aimed to test parallel and distributed architectures and applications. The main characteristics of SIMCAN are flexibility, accuracy, performance, and scalability. Thence, the proposed platform has a modular design that eases the integration of different basic systems on a single architecture. Its design follows a hierarchical schema that includes simple modules, basic systems (computing, memory managing, I/O, and networking), physical components (nodes, switches, …), and aggregations of components. New modules may also be incorporated as well to include new strategies and components. Also, a graphical configuration tool has been developed to help untrained users with the task of modelling new architectures. Finally, a validation process and some evaluation tests have been performed to evaluate the SIMCAN platform.