Conjunctive Rule Research Articles

New distances between bodies of evidence based on Dempsterian specialization matrices and their consistency with the conjunctive combination rule

Distances in evidence theory are useful tools for belief function approximation or clustering. Efficient approaches are found in the literature, especially full metrics taking focal element interactions into account. In this paper, another aspect is investigated: the ability to detect common evidence pertaining to two different states of belief. This requirement, as well as the previously mentioned ones, are formalized through mathematical properties. To find a belief function distance satisfying the desired properties, matrix norms based distances between Dempsterian specialization matrices are investigated. It is proved that the L1 Dempsterian matrix distance succeeds to fulfill all requirements. Interesting and unprecedented ties between the conjunctive combination rule and this distance are demonstrated. Several basic belief assignment distance experiments are proposed and interpreted thereby allowing to understand advantages and limitations of the newly introduced distances as compared to the existing ones.

Market-System Design Optimization With Consider-Then-Choose Models

Design optimization in market system research commonly relies on Discrete choice analysis (DCA) to forecast sales and revenues for different product variants. Conventional DCA, which represents consumer choice as a compensatory process through maximization of a smooth utility function, has proven to be reasonably accurate at predicting choice and interfaces easily with engineering models. However, the marketing literature has documented significant improvement in modeling choice with the use of models that incorporate both noncompensatory (descriptive) and compensatory (predictive) components. This noncompensatory component can, for example, model a “consider-then-choose” process in which potential customers first narrow their decisions to a small set of products using noncompensatory screening rules and then employ a compensatory evaluation to select from within this consideration set. This article presents solutions to a design optimization challenge that arises when demand is modeled with a consider-then-choose model: the choice probabilities are no longer continuous or continuously differentiable. We examine two different classes of methods to solve optimal design problems–genetic algorithms (GAs) and nonlinear programming (NLP) relaxations based on complementarity constraints–for consider-then-choose models whose screening rules are based on conjunctive (logical “and”) rules.

OS0530 応力比交互変動が平織CFRP擬似等方積層板の疲労寿命に及ぼす影響

Effects of variable fatigue loading with alternate R-ratios on fatigue life of a quasi-isotropic woven fabric CFRP laminate have been studied. Variable R-ratio fatigue tests are carried out in which the stress ratio of fatigue loading alternates between two values that are associated with the same number of cycles to failure. The fatigue life of the composite under variable R-ratio loading is examined not only for different pairs of stress ratios, but also for different constant values of fatigue life. By comparing the fatigue lives under constant and variable R-ratio fatigue loading conditions, the effect of alternation in R-ratio on fatigue life of the CFRP laminate is elucidated. Finally, prediction of fatigue life of the CFRP laminate subjected to the alternate R-ratio loading is attempted using a linear cumulative fatigue damage rule in conjunction with the anisomorphic constant life diagram by which the fatigue lives of component constant amplitude fatigue loadings are evaluated. It is demonstrated that the linear damage accumulation rule predict fatigue lives of the CFRP laminate for the alternate R-ratio fatigue loading with an accuracy of a factor of two.

Key Issues in Vowel Based Splitting of Telugu Bigrams

Splitting of compound Telugu words into its components or root words is one of the important, tedious and yet inaccurate tasks of Natural Language Processing (NLP). Except in few special cases, at least one vowel is necessarily involved in Telugu conjunctions. In the result, vowels are often repeated as they are or are converted into other vowels or consonants. This paper describes issues involved in vowel based splitting of a Telugu bigram into proper root words using Telugu grammar conjunction (‘sandhi’) rules for MT.

SPATIAL ANALYSIS IN PUBLIC HEALTH DOMAIN: AN NLP APPROACH

Remote sensing products are effectively used as a tool for decision making in various fields, especially in medical research and health care analyses. GIS is particularly well suited in this context because of its spatial analysis and display capabilities. The integration of RS techniques in public health has been categorised as continuous and discrete strategies where latter is preferred. We have investigated the integration of these approaches through linguistic interpretation of images. In this paper, we propose a framework for direct natural language interpretation of satellite images using probabilistic grammar rules in conjunction with evolutionary computing techniques. Spectral and spatial information has been dynamically combined using adaptive kernel strategy for effective representation of the contextual knowledge. The developed methodology has been evaluated in different querying contexts and investigations revealed that considerable success has been achieved with the procedure. The methodology has also demonstrated to be effective in intelligent interpolation, automatic interpretation as well as attribute, topology, proximity, and semantic analyses.

Mode Choice Behavior Analysis by Using a Semi-Compensatory Discrete Choice Model

In this study we proposed a semi-compensatory model to analyze the mode choice behavior. The proposed model formulated the conjunctive rule through a straightforward way. The proposed model can take into account the probability distribution of the threshold involved by the conjunctive rule. To estimate the parameters of the proposed model, we derived the posterior distribution of the parameters by using the Bayes theorem and developed a blacked Metropolis-Hastings algorithm to carry out the estimation based on the posterior distribution. We also employed the data augmentation technology to simplify the estimation procedure. The proposed model was validated by using a SP survey dataset. We compared the performance of the proposed model to that of the logit model.

Transgenic: An evolutionary algorithm operator

Traditionally, many evolutionary algorithm operators have biological inspiration. Genetics has contributed to the proposal of a number of different evolutionary operators, such as haploid crossover, mutation, diploid, inversion, gene doubling, deletion, and others. In the present study, we propose a new genetic-inspired evolutionary operator, named Transgenic, which was specially designed for Genetic Algorithms (GA). The proposed operator is inspired by genetically modified organisms (GMOs), where important features are artificially introduced into their genome. Transgenic can be used to artificially insert relevant characteristics in the chromosome of individuals, thus converging to better results faster than traditional GAs. When relevant characteristics are known a prior, then, Transgenic simply forces the presence of such characteristics in part of the population (in an elitism-based approach). Whenever there is no a priori knowledge available, Transgenic automatically identifies relevant features (based on historical information) to perform the elitism approach. The GA, used in this study was designed to allow the discovery of concise, yet accurate, high-level rules (from synthetic and real biological databases) which can be used as a classification system. The empirical results have shown that Transgenic is capable of generating better results than traditional rule classification methods, such as J48, Single Conjunctive Rule Learner, One R and PART, using synthetic datasets.

Conjunctive combination of belief functions from dependent sources using positive and negative weight functions

This paper investigates the conjunctive combination of belief functions from dependent sources based on the cautious conjunctive rule (CCR). Weight functions in the canonical decomposition of a belief function are divided into two parts, namely, positive and negative weight functions, whose characteristics are described. Positive and negative weight functions of two belief functions are used to construct a new partial ordering between the belief functions. The partial ordering determines the committed relationship between two belief functions, which is different from that generated by the weight function based partial ordering in the CCR when one or two belief functions are not unnormalized separable. A new rule is developed using the constructed partial ordering to combine belief functions from dependent sources. The relevant properties are described and demonstrated by examples. A performance assessment problem is investigated to demonstrate the validity and applicability of the proposed rule and compare it with the CCR.

Replacing the Conjunctive Rule and Disjunctive Rule with Tnorms and T-conorms respectively (Tchamova-Smarandache)

Replacing the Conjunctive Rule and Disjunctive Rule with Tnorms and T-conorms respectively (Tchamova-Smarandache)

How to preserve the conflict as an alarm in the combination of belief functions?

In the belief function framework, a unique function is induced from the use of a combination rule so allowing to synthesize all the knowledge of the initial belief functions. When information sources are reliable and independent, the conjunctive rule of combination, proposed by Smets, may be used. This rule is equivalent to the Dempster rule without the normalization process. The conjunctive combination provides interesting properties, as the commutativity and the associativity. However, it is characterized by having the empty set, called also the conflict, as an absorbing element. So, when we apply a significant number of conjunctive combinations, the mass assigned to the conflict tends to 1 which makes impossible returning the distinction between the problem arisen during the fusion and the effect due to the absorption power of the empty set.The objective of this paper is then to define a formalism preserving the initial role of the conflict as an alarm signal announcing that there is a kind of disagreement between sources. More exactly, that allows to preserve some conflict, after the fusion by keeping only the part of conflict reflecting the opposition between the belief functions. This approach is based on dissimilarity measures and on a normalization process between belief functions. Our proposed formalism is tested and compared with the conjunctive rule of combination on synthetic belief functions.

Beyond inhibition: lateral modulation of plasticity of feedforward synapses in a spiking model of V1

Lateral inhibition is typically used to repel neural receptive fields. Here we introduce an additional learning mechanism that modifies the plasticity for feedforward synapses based on lateral interactions. We show a model, based on evidence from biological recordings[1], in which a heterosynaptic learning rule in conjunction with lateral inhibition introduces competition among neurons for input features. We demonstrate an STDP learning rule for feedforward connections to a spiking neuron where plasticity is modulated by activity of neighboring neurons. We apply the learning rule to a spiking model of primary visual cortex. In our model, input to V1 cortical neurons comes from the magnocellular pathway of a simulated spiking retina responding to saccades over a natural image. A group of neurons in V1 respond to a particular input feature and convey information that they have spiked to neighboring neurons by way of specialized lateral connections. The connections convey a direct signal of recent spiking activity. Alternatively, lateral inhibitory synapses and the level of inhibition to a neuron can also be used as this signal. If this recent neighbor activity signal is high, feedforward plasticity to the neuron becomes a simple flat depression as opposed to regular exponential STDP, as shown in Figure ​Figure1A.1A. Thus, late spiking neurons are prevented from developing receptive fields similar to those of earlier spiking neurons for a given input feature. This rule in conjunction with lateral inhibition and slow weight updrift ensures competition between neurons for features over a long timescale. Figure 1 A. Detail of the heterosynaptic rule. Neurons N1 and N2 fire early for the input, and their feedforward synapses undergo normal STDP. They send an activity signal to N3 and N4, whose feedforward synaptic weights are then depressed to prevent learning ... Our V1 model is built on a spatially distributed grid of single-compartment spiking neurons with parameters configured to model regular spiking cortical pyramidal cells, as detailed in [2]. The model has plastic excitatory feedforward connections and local lateral inhibition between spatially proximal neurons. In our model, we observe a better overall coverage of orientation selectivity of V1 Layer 4 neurons with our heterosynaptic rule than without it. We find a larger range of orientations and less redundancy of receptive field features between neighboring neurons, as shown in Figure ​Figure1B.1B. Such a rule could be used in a generic spiking cortical architecture to enforce independence of neural receptive fields.

Incremental Rules Mining Algorithm Based on Incomplete Decision Table

The traditional approach to deal with incomplete information system is to make it completed, when a new object added only need a static attribute reduction algorithm to obtain the rules, wastes a lot of resources. The goal of incremental rules mining is to maintain the consistency of the rules in incomplete decision table. When a new object is added, establish discernibility matrix of the original decision table, get distribution reduction set, then generate conjunctive items export rules set. It introduces incremental learning concept, avoids tedious counting process. It can be effective for large-scale incomplete ocean data reduction and it also provides a strong basis for decision making for the marine environment processing and follow-up processing.

Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products, using Landsat data and C-CAP products

This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.

Evaluation of Rule and Decision Tree Induction Algorithms for Generating Climate Change Scenarios for Temperature and Pan Evaporation on a Lake Basin

Climate change scenarios generated by general circulation models (GCMs) have too coarse a spatial resolution to be useful in planning disaster risk reduction and climate change adaptation strategies at regional to river/lake basin scales. This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely, single conjunctive rule learner, decision table, M5P model tree, decision stump, and REPTree. Downscaling models are developed to obtain projections of mean monthly maximum and minimum temperatures (Tmax and Tmin) as well as pan evaporation to lake-basin scale in an arid region in India using these algorithms. The predictor variables, such as air temperature, zonal wind, meridional wind, and geo-potential height, are extracted from the National Centers for Environmental Prediction (NCEP) reanalysis data set for the period 1948–2000 and from the simulations using third-generation Canadian coupled global climate models for emission scenarios for the period 2001–2100. A simple multiplicative shift was used for correcting predictand values. The performances of various models have been evaluated on several statistical performance parameters such as correlation coefficient, mean absolute error, and root mean square error. The M5P model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. An increasing trend is observed for Tmax and Tmin for emission scenarios, whereas no trend has been observed for pan evaporation in the future.

805 平織CFRP擬似等方積層板の疲労寿命に及ぼす応力比交互変動の影響(OS8 オーガナイズドセッション《複合材料の変形と破壊およびマルチスケール計算技術》)

Effects of variable fatigue loading with alternate R-ratios on fatigue life of a quasi-isotropic woven fabric CFRP laminate have been studied. Variable R-ratio fatigue tests are carried out in which the stress ratio of fatigue loading alternates between two values keeping the number of cycles to failure constant. The fatigue life of the composite under variable R-ratio loading is examined not only for different pairs of stress ratios, but also for different constant values of fatigue life. By comparing the fatigue lives under constant and variable R-ratio fatigue loading conditions, the effect of alternation in R-ratio on fatigue life of the CFRP laminate is lucidated. Finally, prediction of fatigue life of the CFRP laminate subjected to the alternate R-ratio loading is attempted using a linear cumulative fatigue damage rule in conjunction with the anisomorphic constant life diagram by which the fatigue lives of component constant amplitude fatigue loadings are evaluated. It is demonstrated that the linear damage accumulation rule predicts significantly optimistic fatigue lives of the CFRP laminate for the alternate R-ratio fatigue loading.

A Nonlinear Finite Element Framework for Viscoelastic Beams Based on the High-Order Reddy Beam Theory

A weak form Galerkin finite element model for the nonlinear quasi-static and fully transient analysis of initially straight viscoelastic beams is developed using the kinematic assumptions of the third-order Reddy beam theory. The formulation assumes linear viscoelastic material properties and is applicable to problems involving small strains and moderate rotations. The viscoelastic constitutive equations are efficiently discretized using the trapezoidal rule in conjunction with a two-point recurrence formula. Locking is avoided through the use of standard low-order reduced integration elements as well through the employment of a family of elements constructed using high-polynomial order Lagrange and Hermite interpolation functions.

Thermodynamic modeling of PVTx properties for several water/hydrocarbon systems in near-critical and supercritical conditions

E ) model and the cubic plus association (CPA) equa- tion of state (EoS) are compared in this study with respect to their accuracy in the correlation of PVTx for systems such as water/methanol, water/ethanol, water/benzene, water/toluene, water/methane, water/n-butane, water/n-pentane, water/ n-hexane, water/heptane, and water/octane, in supercritical conditions within temperature and pressure ranges of (573- 698 K) and (7.0-276.0 MPa), respectively. In the proposed EoS/G E model, Peng-Robinson (PR) equation of state, linear combination Vidal-Michelsen (LCVM) and Wong-Sandler (WS) mixing rules in conjunction with UNIQUAC activity coefficient model were used. Correlation of both CPA and EoS/G E models was evaluated by comparing the results with the experimental data. Average absolute relative deviation (AARD) for WS, LCVM, and CPA was found to be 2.99, 11.11 and 5.14%, respectively, indicating better correlation of WS model with the experimental data.

AnRPackage for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies

A common strategy for the analysis of object-attribute associations is to derive a low- dimensional spatial representation of objects and attributes which involves a compensatory model (e.g., principal components analysis) to explain the strength of object-attribute associations. As an alternative, probabilistic latent feature models assume that objects and attributes can be represented as a set of binary latent features and that the strength of object-attribute associations can be explained as a non-compensatory (e.g., disjunctive or conjunctive) mapping of latent features. In this paper, we describe the R package plfm which comprises functions for conducting both classical and Bayesian probabilistic latent feature analysis with disjunctive or a conjunctive mapping rules. Print and summary functions are included to summarize results on parameter estimation, model selection and the goodness of fit of the models. As an example the functions of plfm are used to analyze product-attribute data on the perception of car models, and situation-behavior associations on the situational determinants of anger-related behavior.

Do Reputation Concerns Make Behavioral Biases Disappear? The Conjunction Fallacy on Facebook and Mechanical Turk

This paper reports the results of experiments designed to test whether individuals interacting on Facebook are more likely to succumb to the conjunction fallacy when they post their answers publicly and are exposed to the answers of others. Using the experimental design in Kahneman and Tversky (1983), we find that the proportion of individuals violating the conjunction rule on Facebook is substantially lower than that reported by previous experiments conducted in the lab, regardless of whether responses are public or private. When responses are posted in a public form, however, the participation rate is substantially higher. The violation rate on Facebook is also significantly lower than the rate of violation from the same experiment run on Mechanical Turk, a popular online labor market, with monetary incentives. Adding a bonus for the correct answer reduces the violation rate on Mechanical Turk when answers are private, but not when they are public, suggesting that peer effects may indeed counteract the effect of monetary incentives. Our experiment casts doubts about the robustness of behavioral biases for the understanding of real life decisions in environments in which interaction is not anonymous and people are reputation conscious, and suggests the power of social networks to mitigate their effects.

Online Cleaning of Wireless Sensor Data Resulting in Improved Context Extraction

Sensors enable fine grain monitoring of activities of individual and social interest. Typically these sensors sense & send data continuously directly or through other sensor nodes to a base station. Wireless Sensor Data are inherently noisy and have frequent random spikes due to dynamic nature of the medium. Hence, the decision at the receiving node based on such data is likely to be erroneous. Erroneous data and decisions may affect its transformation to meaningful form like 'context'. It is therefore desirable to clean the data for improved context extraction. Bayesian Belief Networks are used here to quantitatively encode the dependencies among various sensors. These dependencies are then used to estimate missing data and also to detect and recover from errors. Cleaned data is then used for deriving Contextual Information and it results in improved context feature calculation. In this paper five algorithms for Bayesian Belief Network Construction have been evaluated and their performance of classification studied. Conjunctive rules are defined to map the sensors to already defined context. A secondary data obtained from weather sensor boards installed at Intel research lab at Berkeley have been used to demonstrate the approach.