Approximate Membership Localization within a Web-Based Join Framework

In this paper, we propose a search-based approach to join two tables in the absence of clean join attributes. Non-structured documents from the web are used to express the correlations between a given query and a reference list. To implement this approach, a major challenge we meet is how to efficiently determine the number of times and the locations of each clean reference from the reference list that is approximately mentioned in the retrieved documents. We formalize the Approximate Membership Localization (AML) problem and propose an efficient partial pruning algorithm to solve it. A study using real-word data sets demonstrates the effectiveness of our search-based approach, and the efficiency of our AML algorithm.

PartSS : An efficient partition-based filtering for edit distance constraints

This paper introduces PartSS, a new partition-based fil- tering for tasks performing string comparisons under edit distance constraints. PartSS offers improvements over the state-of-the-art method NGPP with the implementation of a new partitioning scheme and also improves filtering abil- ities by exploiting theoretical results on shifting and scaling ranges, thus accelerating the rate of calculating edit distance between strings. PartSS filtering has been implemented within two major tasks of data integration: similarity join and approximate membership extraction under edit distance constraints. The evaluation on an extensive range of real-world datasets demonstrates major gain in efficiency over NGPP and QGrams approaches.

Fourier spectrum based extraction of an equivalent trap state density in indium gallium zinc oxide transistors

Segregating the dynamics of gate bias induced threshold voltage shift, and in particular, charge trapping in thin film transistors (TFTs) based on time constants provides insight into the different mechanisms underlying TFTs instability. In this Letter we develop a representation of the time constants and model the magnitude of charge trapped in the form of an equivalent density of created trap states. This representation is extracted from the Fourier spectrum of the dynamics of charge trapping. Using amorphous In-Ga-Zn-O TFTs as an example, the charge trapping was modeled within an energy range of Delta E-t approximate to 0.3 eV and with a density of state distribution as D-t(Et-j) = D-t0 exp(-Delta E-t/kT) with D-t0 = 5.02 x 10(11) cm(-2) eV(-1). Such a model is useful for developing simulation tools for circuit design. (C) 2014 AIP Publishing LLC.

Kinetics of ytterbium(III) extraction with Cyanex 272 using a constant interfacial cell with laminar flow

Studies have been made on the kinetics of ytterbium(III) with bis-(2,4,4-trimethylpentyl) phosphinic acid (Cyanex 272, HA) in n-heptane using a constant interfacial cell with laminar flow. The stiochiometry and the equilibrium constant of the extracted complex formation reaction between Yb3+ and Cyanex 272 are determined. The extraction rate is dependent of the stirring rate. This fact together with the Ea value suggests that the mass transfer process is a mixed chemical reaction-diffusion controlled at lower temperature, whereas it is entirely diffusion controlled at higher temperature. The rate equations for the ytterbium extraction with Cyanex 272 have been obtained. The rate-determining step is also made by predictions derived from interfacial reaction models, and through the approximate solutions of the flux equation, diffusion parameters and thickness of the diffusion film have been calculated.

Mass transfer kinetics of yttriium(III) using a constant interfacial cell with laminar flow. Part I. Extraction with Cyanex 302

The interfacial tension is measured for Cyanex 302 in heptane and adsorption parameters are calculated according to Gibbs equation and Szyskowski isotherm. The results indicate that Cyanex 302 has a high interfacial activity, allowing easy extraction reaction to take place at the liquid-liquid interface. The extraction kinetics of yttrium(III) with Cyanex 302 in heptane are investigated by a constant interfacial cell with laminar flow. The effects of stirring rate, temperature and specific interfacial area on the extraction rate are discussed. The results suggest that the extraction kinetics is a mixed regime with film diffusion and an aqueous one-step chemical reaction proposed to be the rate-controlling step. Assuming the mass transfer process can be formally treated as a pseudo-first-order reversible reaction with respect to the metal cation, the rate equation for the extraction reaction of yttrium(III) with Cyanex 302 at pH <5 is obtained as follows:R-f = 10(-7.85)[Y(OH)(2)(+)]((a))[H(2)A(2)]((o))(1.00)[H+]((a))(-1.00)Diffusion parameters and rate constants are calculated through approximate solutions of the flux equation.

Kinetics and mechanism of Yb(III) extraction and separation from Y(III) with mixtures of bis(2,4,4-trimethylpentyl)phosphinic acid and 2-ethylhexyl phosphonic acid mono-2-ethylhexyl ester

The ytterbium(III) extraction kinetics and mechanism with mixtures of bis(2,4,4-trimethylpentyl)phosphinic acid (Cyanex272) and 2-ethylhexyl phosphonic acid mono-2-ethylhexyl ester (P507) dissolved in heptane have been investigated by constant interfacial cell with laminar flow. The effects of the stirring rate, temperature, extractant concentration, and pH on the extraction with mixtures of Cyanex272 and P507 have been studied. The results are compared with those of the system with Cyanex272 or P507 alone. It is concluded that the Yb(III) extraction rate is enhanced with mixtures extractant of Cyanex272 and P507 according to their values of the extraction rate constant, which is due to decreasing the activation energy of the mixtures. At the same time, the mixtures exhibits no synergistic effects for Y(III), which provides better possibilities for Yb(III) and Y(III) separations at a proper conditions than anyone alone. Moreover, thermodynamic extraction separation Yb(III) and Y(III) by the mixtures has been discussed, which agrees with kinetics results. Extraction rate equations have also been obtained, and through the approximate solutions of the flux equation, diffusion parameters and thickness of the diffusion film have been calculated.

Desulfurisation of oils using ionic liquids: selection of cationic and anionic components to enhance extraction efficiency

Extraction of dibenzothiophene from dodecane using ionic liquids as the extracting phase has been investigated for a range of ionic liquids with varying cation classes (imidazolium, pyridinium, and pyrrolidinium) and a range of anion types using liquid-liquid partition studies and QSPR (quantitative structure-activity relationship) analysis. The partition ratio of dibenzothiophene to the ionic liquids showed a clear variation with cation class (dimethylpyridinium > methylpyridinium > pyridinium approximate to imidazolium approximate to pyrrolidinium), with much less significant variation with anion type. Polyaromatic quinolinium-based ionic liquids showed even greater extraction potential, but were compromised by higher melting points. For example, 1-butyl-6-methylquinolinium bis{(trifluoromethyl)sulfonyl} amide (mp 47 degrees C) extracted 90% of the available dibenzothiophene from dodecane at 60 degrees C.

Extraction of polyphenols from processed black currant (Ribes nigrum L.) residues

The total phenol and anthocyanin contents of black currant pomace and black currant press residue (BPR) extracts, extracted with formic acid in methanol or with methanol/water/acetic acid, were studied. Anthocyanins and other phenols were identified by means of reversed phase HPLC, and differences between the two plant materials were monitored. In all BPR extracts, phenol levels, determined by the Folin-Ciocalteu method, were 8-9 times higher than in the pomace extracts. Acid hydrolysis liberated a much higher concentration of phenols from the pomace than from the black currant press residue. HPLC analysis revealed that delphinidin-3-O-glucoside, delphinidin-3-O-rutinoside, cyanidin-3-O-glucoside, and cyanidin-3-O-rutinoside were the major anthocyanins and constituted the main phenol class (approximate to 90%) in both types of black currant tissues tested. However, anthocyanins were present in considerably lower amounts in the pomace than in the BPR. In accordance with the total phenol content, the antioxidant activity determined by scavenging of 2,2'-azinobis(3-ethylbenzothiazoline-6- sulfonic acid) radical cation, the ABTS(center dot+) assay, showed that BPR extracts prepared by solvent extraction exhibited significantly higher (7-10 times) radical scavenging activity than the pomace extracts, and BPR anthocyanins contributed significantly (74 and 77%) to the observed high radical scavenging capacity of the corresponding extracts.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Full surplus extraction with dominant strategies

In this note I consider the fuI! surplus extraction in an auction with private but possibly correlated values. I show that fuI! extraction in the continuum of types case is not possible in general. Neither is approximate fuI! surplus extraction if the sel!er is budget constrained.

Automated extraction of road network from medium-and high-resolution images

This paper presents an automatic methodology for road network extraction from medium-and high-resolution aerial images. It is based on two steps. In the first step, the road seeds (i.e., road segments) are extracted using a set of four road objects and another set of connection rules among road objects. Each road object is a local representation of an approximately straight road fragment and its construction is based on a combination of polygons describing all relevant image edges, according to some rules embodying road knowledge. Each road seed is composed by a sequence of connected road objects in which each sequence of this type can be geometrically structured as a chain of contiguous quadrilaterals. In the second step, two strategies for road completion are applied in order to generate the complete road network. The first strategy is based on two basic perceptual grouping rules, i.e., proximity and collinearity rules, which allow the sequential reconstruction of gaps between every pair of disconnected road segments. This strategy does not allow the reconstruction of road crossings, but it allows the extraction of road centerlines from the contiguous quadrilaterals representing connected road segments. The second strategy for road completion aims at reconstructing road crossings. Firstly, the road centerlines are used to find reference points for road crossings, which are their approximate positions. Then these points are used to extract polygons representing the contours of road crossings. This paper presents the proposed methodology and experimental results. © Pleiades Publishing, Inc. 2006.

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.

Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance. © 2010.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.