The prior relation extraction approaches were relation-specific and supervised, yielding new instances of relations known a priori. While effective, this model is not applicable in case when the number of relations is high or where the relations are not known a priori. Open Information Extraction (OIE) is a relation-independent extraction paradigm designed to extract relations directly from massive and heterogeneous corpora such as Web. One of the main challenges for an Open IE system is estimating the probability that its extracted relation is correct. A CONFIDENCE measure shows that how an extracted relation is a correct instance of a relation among entities. This paper proposes a new method of CONFIDENCE ESTIMATION for OIE called Relation CONFIDENCE Estimator for Open Information Extraction (RCE-OIE). It investigates the incorporation of some proposed features in assigning CONFIDENCE metric using logistic regression. These features consider diverse lexical, syntactic and semantic knowledge and also some extraction properties such as number of distinct documents from which extractions are drawn, number of relation arguments and their types. We implemented proposed CONFIDENCE measure on the Open IE systems’ extractions and examined how it affects the performance of results. Evaluations show that incorporation of designed features is promising and the accuracy of our method is higher than the base methods while keeping almost the same performance as them. We also demonstrate how semantic information such as coherence measures can be used in feature-based CONFIDENCE ESTIMATION of Open Relation Extraction (ORE) to further improve the performance.

Machine translation has been developed over last years. But this technology is still not able to exactly translate texts. Also post-editing the output may takes longer time than the translation process. So having a quality ESTIMATION of machine translation output can be very useful. Moreover, CONFIDENCE ESTIMATION can be useful for some applications that their goal is to improve machine translation quality such as system combination, regenerating and pruning. But there is not yet any completely satisfactory method for CE task. We propose 5 syntactic and lexico-semantic features that are never used for CONFIDENCE ESTIMATION task. The experimental results show that proposed lexico-semantic feature outperforms the best baseline system (2) by 9.63% in CER, 8.5% in F-measure and 5.1% in negative class F-measure. Moreover the combination of proposed syntactic features outperforms the best baseline system by 4.49% in CER, 4.1% in F-measure and 2% in negative class F-measure.

Background and Objectives: When computing a CONFIDENCE interval for a binomial proportion p, one must choose an exact interval that has a coverage probability of at least 1-α for all values of p. In this study, we compared the CONFIDENCE intervals of Clopper-Pearson, Wald, Wilson, and double ArcSin transformation in terms of maintaining a constant nominal type I error.Methods: Simulations were used to compare four methods of estimating a CONFIDENCE interval, including the Clopper-Pearson, Wald, Wilson, and double ArcSic. The data were generated from the binomial and Poison distribution with parameters p, n and m=np, 1000 were produced. Type I error of each method was calculated per simulation. The above methods were used to estimate CONFIDENCE intervals in a meta-analysis study.Results: The results of the simulation study showed that double ArcSin keep CONFIDENCE interval at [0,1], but for some proportion has high type I error or low coverage probability. The Clopper-Pearson interval guarantees that the coverage probability is always equal to or above the nominal CONFIDENCE level for any fixed p.Conclusion: This study showed that CONFIDENCE interval ESTIMATIONs the Clopper-Pearson than other methods of calculating the type I error fixed and smaller.

In this article, an attempt has been made to estimate the amount of sound transmission loss in a flat oval channel by applying the approach of statistical energy analysis. Correct ESTIMATION of sound transmission loss in an air conditioning channel is of great importance due to the harmful effects of noise pollution in the environment on human health. Simulation with the statistical energy analysis method is a powerful approach to estimate sound and vibration in problems in which we deal with complex and multi-part systems; is considered. In this method, first, a system is divided into several subsystems, and then by writing a matrix equation that includes the energy exchanges between subsystems and energy loss coefficients; It is investigated from the perspective of vibration and sound ESTIMATION.On average, the model presented in this research is able to estimate the sound transmission loss in different dimensions of the air conditioning channels according to the experimental results in the accuracy range of ± 2.5 dB. Considering that it seems that the results obtained from modeling with this method are in good agreement with the experimental data; The results of this research can be used as an efficient approach to estimate noise in oval shaped channels stretched in different lengths.

Background & Aim: The sample standard deviation S is the common point estimator of σ , but S is sensitive to the presence of outliers and may not be an efficient estimator of σ in skewed and leptokurtic distributions. Although S has good efficiency in platykurtic and moderately leptokurtic distributions, its classical inferential methods may perform poorly in non-normal distributions. The classical CONFIDENCE interval for σ relies on the assumption of normality of the distribution. In this paper, a performance comparison of six CONFIDENCE interval estimates of σ is performed under ten distributions that vary in skewness and kurtosis. Methods and Material: A Monte Carlo simulation study is conducted under the following distributions: normal, two contaminated normal, t, Gamma, Uniform, Beta, Laplace, exponential and χ 2 with specific parameters. CONFIDENCE interval estimates obtained using the more powerful ranked set sampling (RSS) are compared with the traditional simple random sampling (SRS) technique. Performance of the CONFIDENCE intervals is assessed based on width and coverage probabilities. A real data example representing birth weight of 189 newborns is used for assessment. Results: It is not surprising that for normal data most of the intervals were close tot he nominal value especially using RSS. Simulation results indicated generally better performance of RSS in terms of coverage probability and smaller interval width as sample size increases, especially for contaminated and heavy-tailed skewed distributions. Conclusion: Simulation results revealed that the use of RSS improved greatly the coverage probability. Also, it was found that the interval labeled (III) due to Bonett (2006) had the best performance in terms of coverage probability over the wide range of distributions investigated in this paper and would be recommended for use by practitioners. There may be a need to develop nonparametric intervals that is robust against outliers and heavy-tailed distributions.

This study aimed to estimate the genetic parameters of body weight traits in Markhoz goats, using B-spline random regression models. The data used in this study included 19549 records collected during 29 years (1992-2021) in Markhoz goat Breeding Research Station, located in Sanandaj, Iran. The model used to analyze data included fixed effects (year of birth, sex, type of birth and age of dam) and random effects including direct additive genetic, maternal additive genetic, permanent environmental and maternal permanent environmental assuming homogeneous and heterogeneous residual variance during the time. Akaike (BIC) and Bayesian (BIC) information criteria were used to compare the models and bspq.4.4.4.4 was selected as the best model. The direct heritability values for birth, 3-month, 6-month, 9-month and 12-month weights were estimated to be 0.14, 0.16, 0.08, 0.28 and 0.26, respectively. Genetic correlation between body weights at birth and 3-month, birth and 6-month, birth and 9-month, birth and 12-month, 3-month and 6-month, 3-month and 9-month, 3-month and 12-month, 6-months and 9-month and 9-month and 12-month were 0.22, 0.38, 0.21, 0.56, -0.26, 0.30, 0.62, 0.86 and 0.77, respectively. The highest phenotypic correlation was between the weight of 9-month and 12-month (0.82) and the lowest correlation was between birth weight and 3-month and 6-month (0.12). The results showed that the 9-month weight is a good criterion for selection in Markhoz goats.

The effective rainfall amounts (ER) as a part of the irrigation requirement were estimated for the premature and serotinous varieties of rice in the Mazandaran Plain, using different methods. Finally the "Dependable Rain" method was selected for the ESTIMATION. Comparison of the maps reveals that the ER amounts are more in the western part of the plain than the eastern part; consequently, the net irrigation requirement is low in the western part. Since knowing the minimum and maximum values of the ER with specific CONFIDENCE, helps the planners in making different decisions, the ER amounts were estimated at 90, 95 and 99 percent CONFIDENCE intervals. The related maps show that the CONFIDENCE for ER amounts is low for both premature and serotinous varieties in the eastern part, while the ER amounts are almost 50 millimeters more for serotinous variety than premature variety at different CONFIDENCE intervals in the whole region. Also, the maps of return periods show that the ER amounts are higher in the western and central parts than the eastern part and that the accessibility of ER varies from lower than 80 to more than 420 millimeters in the growing season in terms of various return periods and different parts of the region.  

It is a common and useful task in a web of trust to evaluate the trust value between two nodes using intermediate nodes. This is widely used when the source node has no experience of direct interaction with the target node or the direct trust is not reliable enough by itself. If trust is used to support decision-making, it is important to have not only an accurate estimate of trust, but also a measure of CONFIDENCE in the intermediate nodes as well as the final estimated value of trust. The present paper thus aims to introduce a novel framework for integrated representation of trust and CONFIDENCE using intervals which provides two operations of trust interval multiplication and summation.The former is used for computing propagated trust and CONFIDENCE, whereas the latter provides a formula for aggregating different trust opinions. The properties of the two operations are investigated in details. This study also proposes a time-variant method that considers freshness, expertise level and two similarity measures in CONFIDENCE ESTIMATION. The results indicate that this method is of more accuracy compared to the existing methods. In this regard, the results of experiments carried out on two well-known trust datasets are reported and analyzed, showing that the proposed method increases the accuracy of trust inference in comparison with the existing methods.