Confidence voting method ensemble applied to off-line signature verification

In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed. Also a normalization by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly. The best EER obtained were 6.3 % in the case of skilled forgeries and 2.31 % in the case of random forgeries.     

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.     

Model-based testing of software and systems: recent advances and challenges

Model-based testing is focused on testing techniques which rely on the use of models. The diversity of systems and software to be tested implies the need for research on a variety of models and methods for test automation. We briefly review this research area and introduce several papers selected from the 22nd International Conference on Testing Software and Systems (ICTSS).     

Robust H ∞ static output feedback stabilization of T-S fuzzy systems subject to actuator saturation

This paper proposes a method for designing robust H_?? static output feedback stabilization of Takagi-Sugeno (T-S) fuzzy systems under actuator saturation. In this paper, the input saturation is represented by a polytopic model and the modeling error is assumed a norm-bounded uncertainty. A set invariance condition for robust H_?? static output feedback system under actuator saturation is first established. Then, the estimation of the largest domain of attraction for the system is formulated and solved as a Linear Matrix Inequality (LMI) optimization problem. Two examples are used to demonstrate the effectiveness of the proposed design method.     

Deciding bisimilarity is<Emphasis Type="Italic">P</Emphasis>-complete

In finite labelled transition systems the problems of deciding strong bisimilarity, observation equivalence and observation congruence areP-complete under many—oneNC-reducibility. As a consequence, algorithms for automated analysis of finite state systems based on bisimulation seem to be inherently sequential in the following sense: the design of anNC algorithm to solve any of these problems will require an algorithmic breakthrough, which is exceedingly hard to achieve.     

Effects of cholestanol feeding and cholestyramine treatment on the tissue sterols in the rabbit

Rabbits were fed diets enriched with cholestanol or cholestereol (3.5 g/wk) for 4–12 weeks. During cholestanol feeding, the concentration of cholestanol in blood serum, liver, heart and aorta increased 15–30 times. In serum and liver, the concentration of cholesterol also increased. Cholestanol-fed rabbits developed inflammatory changes in the liver, with proliferation of small bile ducts. Liver tests were only slightly abnormal. Morphological atherosclerosis of the aorta was only occasionally seen in rabbits receiving cholestanol for eight weeks or less. During cholesterol feeding, the amounts of cholesterol in different tissues increased dramatically, most in the aorta. Morphological atherosclerosis in the aorta was found in all rabbits fed cholesterol-enriched diets for more than four weeks. Brain cholestanol was doubled in rabbits fed cholestanol for eight weeks, whereas brain sterols did not change significantly during cholesterol feeding. After an additional regression period with cholestyramine for eight weeks, the increased content of cholestanol in the brain was unchanged in cholestanol-fed rabbits. These observations are discussed in relation to the cholestanolosis of the brain that develops in the rare inherited human disease cerebrotendinous xanthomatosis.     

Gaussian,Lobatto and Radau positive quadrature rules with a prescribed abscissa

For a given $\theta \in (a,b)$ , we investigate the question whether there exists a positive quadrature formula with maximal degree of precision which has the prescribed abscissa $\theta $ plus possibly $a$ and/or $b$ , the endpoints of the interval of integration. This study relies on recent results on the location of roots of quasi-orthogonal polynomials. The above positive quadrature formulae are useful in studying problems in one-sided polynomial $L_1$ approximation.     

(CO2 + 2‐propanol) mixture as a foaming agent for polystyrene: A simple thermodynamic model for the high pressure VLE‐phase diagrams taking into account the foam vitrification

The equation of state model developed by Lacombe and Sanchez (J Phys Chem 1976, 80, 2352) is used in the form proposed later by Sanchez and Stone (Polymer Blends, Vol. 1: Formulation, 2000; Chapter 2) to correlate experimental vapor‐liquid equilibrium (VLE) data for the three binaries and the ternary systems. Experimental data from the binary systems carbon dioxide‐isopropyl alcohol (CO₂‐IPrOH), isopropyl alcohol‐polystyrene (IPrOH‐PS), and carbon dioxide‐polystyrene (CO₂‐PS) are used to calculate VLE properties for the ternary system CO₂‐IPrOH‐PS. Two‐dimensional VLE‐phase diagrams were calculated and used to describe from a thermodynamic point of view the pressure, volume, and temperature values that characterize a thermoplastic foam evolution process, from the extruder to the foaming die. For different initial mixture CO₂ + IPrOH concentrations, pressure reduction produces liquid foaming until the vitrification curve arrests the final foam volume expansion. The dependence of the vitreous transition with the system CO₂ + IPrOH concentration while foaming is represented by the Chow (Macromolecules 1980, 13, 362) equation. The calculation procedure is proposed as a design tool to reduce the amount of experimental data usually needed as a requirement previous to the design stage. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 2663–2671, 2007     

The effect of pre-warming on performance during simulated firefighting exercise

This study examined the effect of active pre-warming on speed and quality of performance during simulated firefighting exercise. Twelve male firefighters performed two trials in counterbalanced order. They were either pre-warmed by 20-min cycling at 1.5 Watt kg⁻¹ body mass (WARM) or remained thermoneutral (CON) prior to a simulated firefighting activity. After the pre-warming, gastrointestinal temperature (P < 0.001), skin temperature (P = 0.002), and heart rate (P < 0.001) were higher in WARM than in CON. During the firefighting activity, rating of perceived exertion, thermal sensation and discomfort were higher for WARM than for CON. Finish time of the firefighting activity was similar, but the last task of the activity was completed slower in WARM than in CON (P = 0.04). In WARM, self-reported performance quality was lower than in CON (P = 0.04). It is concluded that pre-warming reduces the speed during the last part of simulated firefighting activity and reduces self-reported quality of performance.     

Development and implementation of clinical guidelines: An artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.