A REGION‐BASED MULTI‐ISSUE NEGOTIATION PROTOCOL FOR NONMONOTONIC UTILITY SPACES

Nonmonotonic utility spaces are found in multi‐issue negotiations where the preferences on the issues yield multiple local optima. These negotiations are specially challenging because of the inherent complexity of the search space and the difficulty of learning the opponent’s preferences. Most current solutions successfully address moderately complex preference scenarios, while solutions intended to operate in highly complex spaces are constrained by very specific preference structures. To overcome these problems, we propose the Region‐Based Multi‐issue Negotiation Protocol (RBNP) for bilateral automated negotiation. RBNP is built upon a nonmediated recursive bargaining mechanism which efficiently modulates a region‐based joint exploration of the solution space. We empirically show that RBNP produces outcomes close to the Pareto frontier in reasonable negotiation times, and show that it provides a significantly better performance when compared to a generic Similarity‐Based Multi‐issue Negotiation Protocol (SBNP), which has been successfully used in many negotiation models. We have paid attention to the strategic issues, proposing and evaluating several concession mechanisms, and analyzing the equilibrium conditions. Results suggest that RBNP may be used as a basis to develop negotiation mechanisms in nonmonotonic utility spaces.     

Real-time embedded systems powered by FPGA dynamic partial self-reconfiguration: a case study oriented to biometric recognition applications

This work aims to pave the way for an efficient open system architecture applied to embedded electronic applications to manage the processing of computationally complex algorithms at real-time and low-cost. The target is to define a standard architecture able to enhance the performance-cost trade-off delivered by other alternatives nowadays in the market like general-purpose multi-core processors. Our approach, sustained by hardware/software (HW/SW) co-design and run-time reconfigurable computing, is synthesizable in SRAM-based programmable logic. As proof-of-concept, a run-time partially reconfigurable field-programmable gate array (FPGA) is addressed to carry out a specific application of high-demanding computational power such as an automatic fingerprint authentication system (AFAS). Biometric personal recognition is a good example of compute-intensive algorithm composed of a series of image processing tasks executed in a sequential order. In our pioneer conception, these tasks are partitioned and synthesized first in a series of coprocessors that are then instantiated and executed multiplexed in time on a partially reconfigurable region of the FPGA. The implementation benchmark of the AFAS either as a pure software approach on a PC platform under a dual-core processor (Intel Core 2 Duo T5600 at 1.83 GHz) or as a reconfigurable FPGA co-design (identical algorithm partitioned in HW/SW tasks operating at 50 or 100 MHz on the second smallest device of the Xilinx Virtex-4 LX family) highlights a speed-up of one order of magnitude in favor of the FPGA alternative. These results let point out biometric recognition as a sensible killer application for run-time reconfigurable computing, mainly in terms of efficiently balancing computational power, functional flexibility and cost. Such features, reached through partial reconfiguration, are easily portable today to a broad range of embedded applications with identical system architecture.     

Empirical models for estimating daily surface water vapour pressure,air temperature,and humidity using MODIS and spatiotemporal variables. Applications to peninsular Spain

In this article, we present empirical models for estimating daily surface water vapour pressure (e₀), air temperature (T_a), and relative humidity (RH) over cloud-free land areas in peninsular Spain using Moderate Resolution Imaging Spectroradiometer (MODIS) and spatiotemporal variables. The models were obtained and validated using daily mean, maximum, and minimum e₀, T_a, and RH data (year 2010) from 331 ground-level meteorological stations and the diurnal Terra-MODIS data in peninsular Spain, but the methodology can easily be extrapolated and used to obtain algorithms for other regions around the world. The best e₀ models are based on total precipitable water (W) estimations obtained by MOD05 or IMAPP WVNIR products and the spatiotemporal variables of longitude (λ), distance to the coast (d_coast), and Julian day (JD). Other models based on Sobrino’s W algorithm or on Recondo’s e₀ algorithm for Asturias (in northern Spain) were also tested. The best T_a models are based on land surface temperature (LST) obtained by the MOD11 LST or IMAPP LST products and on other remote-sensing variables, such as W and the normalized difference vegetation index (NDVI), and the spatiotemporal variables λ, JD, and height (h). Models based on Sobrino’s LST algorithms were also tested. RH can be derived directly from e₀ and T_a or from models similar to those used to obtain e₀ and T_a. Models based on the NASA standard products MOD05 and MOD11 LST are slightly better than those based on IMAPP products, but the advantage of IMAPP products for our purposes is that they can be generated in almost real time from the data obtained by the MODIS antenna at the University of Oviedo. IMAPP models obtain the following: R² = 0.83-0.79-0.70 and RSE = 1.62-1.59-1.76 hPa for e_0mean, e_0max, and e_0min; and R² = 0.91-0.91-0.80 and RSE = 1.96-2.25-3.00 K for T_mean, T_max, and T_min. Worse results are obtained for RH: R² = 0.49-0.39 and RSE = 7.21-9.75% for RH_min and RH_mean, with no correlation found for RH_max. Model validations yield R² and RSE values similar to those obtained in the models, with an RMSD = 1.86-1.99-2.21 hPa for e_0mean, e_0max, and e_0min; an RMSD = 2.05-2.40-2.95 K for T_mean, T_max, and T_min; and RMSD = 8-11% for RH_min and RH_mean. The bias is small in all cases: <0.2 hPa for e₀, ≤0.1 K for T_a, and ≤ |1|% for RH. From the results of this article, we propose substituting the traditionally used RH variable with the e₀ variable to be used as meteorological variable in environmental risk models such as, for example, fire risk models.     

Neural network and crop residue index multiband models for estimating crop residue cover from Landsat TM and ETM+ images

Crop residues on the soil surface provide not only a barrier against water and wind erosion, but they also contribute to improving soil organic matter content, infiltration, evaporation, temperature, and soil structure, among others. In Argentina, soybean (Glycine max (L.) Merill) and corn (Zea mays L.) are the most important crops. The objective of this work was to develop and evaluate two different types of model for estimating soybean and corn residue cover: neural networks (NN) and crop residue index multiband (CRIM) index, from Landsat images. Data of crop residue were acquired throughout the summer growing season in the central plains of Córdoba (Argentina) and used for training and validating the models. The CRIM, a linear mixing model of composite soil and residue, and the NN design, included reflectance and digital numbers from a combination of different TM bands to estimate the fractional residue cover. The results show that both methodologies are appropriate for estimating the residue cover from Landsat data. The best developed NN model yielded R² = 0.95 when estimating soybean and corn residue cover fraction, whereas the best fit using CRIM yielded R² = 0.87; in addition, this index is dependent on the soil and residue lines considered.     

FPGA-based detection of SIFT interest keypoints

The use of local features in images has become very popular due to its promising results. They have shown significant benefits in a variety of applications such as object recognition, image retrieval, robot navigation, panorama stitching, and others. SIFT is one of the local features methods that have shown better results. Among its main disadvantages is its high computational cost. In order to speedup this algorithm, this work proposes the design and implementation of an efficient hardware architecture based on FPGAs for SIFT interest point detection In order to take full advantage of the parallelism in this algorithm and to minimize the device area occupied by its implementation in hardware, part of the algorithm was reformulated. The main contribution of the hardware architecture proposed in this paper and the main difference with the rest of the architectures reported in the literature is that as the number of octaves to be processed is increased, the amount of occupied device area remains almost constant. The evaluations and experiments to the architecture support this contribution, as well as accuracy, repeatability, and distinctiveness of the results. Experiments also showed device area occupation and time constraints of the hardware implementation. The architecture presented in this paper is able to detect interest points in an image of 320 × 240 in 11 ms, which represents a speedup of 250 × with respect to a software implementation.     

Evolutionary algorithms for the multi‐objective test data generation problem

Automatic test data generation is a very popular domain in the field of search‐based software engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, such as the oracle cost, which is the cost of executing the entire test suite and the cost of checking the system behavior. Indeed, in very large software systems, the cost spent to test the system can be an issue, and then it makes sense by considering two conflicting objectives: maximizing the coverage and minimizing the oracle cost. This is what we did in this paper. We mainly compared two approaches to deal with the multi‐objective test data generation problem: a direct multi‐objective approach and a combination of a mono‐objective algorithm together with multi‐objective test case selection optimization. Concretely, in this work, we used four state‐of‐the‐art multi‐objective algorithms and two mono‐objective evolutionary algorithms followed by a multi‐objective test case selection based on Pareto efficiency. The experimental analysis compares these techniques on two different benchmarks. The first one is composed of 800 Java programs created through a program generator. The second benchmark is composed of 13 real programs extracted from the literature. In the direct multi‐objective approach, the results indicate that the oracle cost can be properly optimized; however, the full branch coverage of the system poses a great challenge. Regarding the mono‐objective algorithms, although they need a second phase of test case selection for reducing the oracle cost, they are very effective in maximizing the branch coverage. Copyright © 2011 John Wiley & Sons, Ltd.     

Underlying mechanisms in the relationship between Africentric worldview and depressive symptoms.

This study examines underlying mechanisms in the relationship between an Africentric worldview and depressive symptoms. Participants were 112 African American young adults. An Africentric worldview buffered the association between perceived stress and depressive symptoms. The relationship between an Africentric worldview and depressive symptoms was mediated by perceived stress and emotion-focused coping. These findings highlight the protective function of an Africentric worldview in the context of African Americans’ stress experiences and psychological health and offer promise for enhancing African American mental health service delivery and treatment interventions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Functional network topology learning and sensitivity analysis based on ANOVA decomposition

A new methodology for learning the topology of a functional network from data, based on the ANOVA decomposition technique, is presented. The method determines sensitivity (importance) indices that allow a decision to be made as to which set of interactions among variables is relevant and which is irrelevant to the problem under study. This immediately suggests the network topology to be used in a given problem. Moreover, local sensitivities to small changes in the data can be easily calculated. In this way, the dual optimization problem gives the local sensitivities. The methods are illustrated by their application to artificial and real examples.     

WatchBot: A building maintenance and surveillance system based on autonomous robots

There is a growing interest in the use of intelligent technologies in new buildings. An intelligent system should be designed in a manner that allows minimum human intervention during daily operation. However, large buildings need substantial manpower for maintenance, management as well as surveillance to ensure a quality environment for the occupants. This paper describes a multi-robot system for building maintenance and surveillance applications over the Internet. Each robot can handle autonomously some daily maintenance and surveillance routine tasks, although remote control of the robots via the Internet or intranet is also possible. Apart from the user-started and scheduled tasks, the robots can also execute tasks to handle alarms triggered by the building automation system (BAS). Robots are connected to the central management office via a local area network. This paper shows the advantages of using mobile robots for building maintenance and surveillance tasks by improving efficiency and reducing manpower. The system was developed with the Robotics Integrated Development Environment (RIDE) and was tested intensively in different environments.