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Proving or Disproving likely Invariants with Constraint Reasoning  [PDF]
Tristan Denmat,Arnaud Gotlieb,Mireille Ducasse
Computer Science , 2005,
Abstract: A program invariant is a property that holds for every execution of the program. Recent work suggest to infer likely-only invariants, via dynamic analysis. A likely invariant is a property that holds for some executions but is not guaranteed to hold for all executions. In this paper, we present work in progress addressing the challenging problem of automatically verifying that likely invariants are actual invariants. We propose a constraint-based reasoning approach that is able, unlike other approaches, to both prove or disprove likely invariants. In the latter case, our approach provides counter-examples. We illustrate the approach on a motivating example where automatically generated likely invariants are verified.
Written Language Exercises: comparing pupil’s achievement in private and public schools  [cached]
Dair Aily Franco de Camargo
Educa??o : Teoria e Prática , 2000,
Abstract: The aim of this descriptive research was to compare pupil’s achievement on written language exercises between public and private school; for this purpose it were taken 5th and 8th grades students of Elementary School.
Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem Proving under Limited Resources  [PDF]
Eric J. Horvitz,Adrian Klein
Computer Science , 2013,
Abstract: In earlier work, we introduced flexible inference and decision-theoretic metareasoning to address the intractability of normative inference. Here, rather than pursuing the task of computing beliefs and actions with decision models composed of distinctions about uncertain events, we examine methods for inferring beliefs about mathematical truth before an automated theorem prover completes a proof. We employ a Bayesian analysis to update belief in truth, given theorem-proving progress, and show how decision-theoretic methods can be used to determine the value of continuing to deliberate versus taking immediate action in time-critical situations.
Reasoning in the OWL 2 Full Ontology Language using First-Order Automated Theorem Proving  [PDF]
Michael Schneider,Geoff Sutcliffe
Computer Science , 2011,
Abstract: OWL 2 has been standardized by the World Wide Web Consortium (W3C) as a family of ontology languages for the Semantic Web. The most expressive of these languages is OWL 2 Full, but to date no reasoner has been implemented for this language. Consistency and entailment checking are known to be undecidable for OWL 2 Full. We have translated a large fragment of the OWL 2 Full semantics into first-order logic, and used automated theorem proving systems to do reasoning based on this theory. The results are promising, and indicate that this approach can be applied in practice for effective OWL reasoning, beyond the capabilities of current Semantic Web reasoners. This is an extended version of a paper with the same title that has been published at CADE 2011, LNAI 6803, pp. 446-460. The extended version provides appendices with additional resources that were used in the reported evaluation.
Mechanical Theorem Proving in Geometry  [cached]
Gao Jun-yu,Zhang Cheng-dong
TELKOMNIKA : Indonesian Journal of Electrical Engineering , 2012, DOI: 10.11591/telkomnika.v10i7.1451
Abstract: Mechanical theorem proving in geometry plays an important role in the research of automated reasoning. In this paper, we introduce three kinds of computerized methods for geometrical theorem proving: the first is Wu’ s method in the international community, the second is Elimination Point Method.; and the third is lower Dimension Method.
Effects of Task Reasoning Demand and Task Condition on Learner Written Output in ESL Classrooms
Lilliati Ismail,Arshad Abd. Samad,Bee Eng Wong,Nooreen Noordin
International Journal of Education , 2012, DOI: 10.5296/ije.v4i4.2249
Abstract: Considering the growing interest in task-based language teaching, classroom-based research that investigates the effects of task complexity on L2 development is needed. Despite the inclusion of task reasoning demand (TRD) as a dimension of task complexity in Robinson’s Cognition Hypothesis (2007), there is insufficient classroom-based research that investigates the language learning outcomes that may occur as a result of engaging in tasks of differing reasoning demands in a variety of task conditions. This study aims to fill in some of the gap by identifying the main and interaction effects of task reasoning demand and individual versus dyadic task conditions (TC) on the grammatical accuracy and syntactic complexity of learner written output. Modified versions of the dictogloss task and the opinion-gap task were used to provide a relatively high reasoning demand task (+TRD) and a relatively low reasoning demand task (-TRD) to the learners respectively. A repeated-measures design was used with 76 participants consisting of 18 year-old learners in a public secondary school randomly assigned into four groups. Data were analysed using descriptive statistics and repeated-measures ANOVAs. Results indicated that both TRD and TC had significant main effects on grammatical accuracy. Also, TRD and TC had significant main and interaction effects on syntactic complexity. The results point to differential effects of using tasks of high and low reasoning demand in dyadic and individual task conditions. The results have pedagogical implications on task design and task selection to elicit higher rates of grammatical accuracy and syntactic complexity in learner written output.
Bayesian Predictive Configural Frequency Analysis  [PDF]
Eduardo Gutiérrez-Pe?a
Psychological Test and Assessment Modeling , 2012,
Abstract: Configural Frequency Analysis is a method for cell-wise inspection of cross-classifications. CFA searches for patterns of variable categories that occur either more often or less often than expected from a given base model. In this paper, we propose and discuss an alternative notion of types and antitypes that focuses on the likely values of the cell frequencies in future experiments, as opposed to the average values of such frequencies. The idea is developed from a Bayesian point of view.
A Study of Lateralized Cognitive Processes in Upper-Division Electrical Engineering Students’: Correlating Written Language Functions with Analytical Reasoning in Microelectronics  [PDF]
Robert Melendy
World Journal of Engineering and Technology (WJET) , 2014, DOI: 10.4236/wjet.2014.22008
Abstract: The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who have established entrance into their upper-division programs will have demonstrated cognitive proficiency in math and logical operations, abstract and analytical reasoning and language usage, to include writing. In this study the question was asked: is there a correlation between an upper-division electrical engineering students’ analytical reasoning ability and their descriptive writing ability? Descriptive writing is taken here to mean a students’ ability to identify key physical aspects of a mathematical model and to express—in words—a concise and well-balanced description that demonstrates a deep conceptual understanding of the model. This includes more than a description of the variables or the particular application to an engineering problem; it includes a demonstrated recognition of the basic physics that govern the model, certain limitations (idealizations) inherent in the model, and an understanding of how to make practical experimental measurements to verify the governing physics in the model. A student at this level may demonstrate proficiency in their analytical reasoning skills and hence be capable of correctly solving a given problem. However, this does not guarantee that the same student is skilled in associating equations with their physical meaning on a deep conceptual level or in understanding physical limitations of the equation. Consequently, such a student may demonstrate difficulty in mapping their comprehension of the model into written language that demonstrates a sound conceptual understanding of the governing physics. The findings represent a sample of two independent class sections of Electrical and Computer Engineering junior’s first course in Microe-lectronic Devices and Circuits during fall semesters 2012 and 2013 at a private mid-size university in NW Oregon. A total of three exams were administered to each of the 2012/2013 groups. Correlations between exam scores that students achieved on their descriptive writing of microelectronics phenomena and their analytical problem-solving abilities were examined and found to be quite significant.
The Roles of Featural and Configural Face Processing in Snap Judgments of Sexual Orientation  [PDF]
Joshua A. Tabak, Vivian Zayas
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0036671
Abstract: Research has shown that people are able to judge sexual orientation from faces with above-chance accuracy, but little is known about how these judgments are formed. Here, we investigated the importance of well-established face processing mechanisms in such judgments: featural processing (e.g., an eye) and configural processing (e.g., spatial distance between eyes). Participants judged sexual orientation from faces presented for 50 milliseconds either upright, which recruits both configural and featural processing, or upside-down, when configural processing is strongly impaired and featural processing remains relatively intact. Although participants judged women’s and men’s sexual orientation with above-chance accuracy for upright faces and for upside-down faces, accuracy for upside-down faces was significantly reduced. The reduced judgment accuracy for upside-down faces indicates that configural face processing significantly contributes to accurate snap judgments of sexual orientation.
Nonparametric Conditional Inference for Regression Coefficients with Application to Configural Polysampling  [PDF]
Yvonne Ho,Stephen Lee
Statistics , 2007,
Abstract: We consider inference procedures, conditional on an observed ancillary statistic, for regression coefficients under a linear regression setup where the unknown error distribution is specified nonparametrically. We establish conditional asymptotic normality of the regression coefficient estimators under regularity conditions, and formally justify the approach of plugging in kernel-type density estimators in conditional inference procedures. Simulation results show that the approach yields accurate conditional coverage probabilities when used for constructing confidence intervals. The plug-in approach can be applied in conjunction with configural polysampling to derive robust conditional estimators adaptive to a confrontation of contrasting scenarios. We demonstrate this by investigating the conditional mean squared error of location estimators under various confrontations in a simulation study, which successfully extends configural polysampling to a nonparametric context.
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