Soft clustering and interval type-2 fuzzy set based inference strategy for I.T. personnel selection

Impreciseness and uncertainty are the fabrics that make life interesting. For decades, human beings have developed strategies to cope with uncertainties and automate them. In personnel selection for the I.T. field, selectors often find it very difficult to select candidates by going through a set of resumes containing similar kinds of skills. Hence the selection task becomes a fuzzy decision making with the uncertainty involved. A combination of fuzzy clustering and Interval Type-2 fuzzy sets (IT2FS) is proposed in such scenarios. An experiment is conducted over a resume dataset containing fifteen hundred resumes for a particular job description. Firstly, Fuzzy C-means clustering (FCM) is applied for selective clustering, while decision-making under uncertainty is carried through IT2FS. The candidates in the selected cluster are given a score for ranking as per the skillset criteria. The final decision for shortlisting the resumes is carried through IT2FS. The model shows an average accuracy of 88.2% with an F1-score of 0.76 compared to (K-means + IT2FS) model with an F1-score of 0.72. Thus, the proposed model performs better while decision-making under uncertainty.

Typology emerges from simplicity in representations and learning

We derive well-understood and well-studied subregular classes of formal languages purely from the computational perspective of algorithmic learning problems. We parameterise the learning problem along dimensions of representation and inference strategy. Of special interest are those classes of languages whose learning algorithms are necessarily not prohibitively expensive in space and time, since learners are often exposed to adverse conditions and sparse data. Learned natural language patterns are expected to be most like the patterns in these classes, an expectation supported by previous typological and linguistic research in phonology. A second result is that the learning algorithms presented here are completely agnostic to choice of linguistic representation. In the case of the subregular classes, the results fall out from traditional model-theoretic treatments of words and strings. The same learning algorithms, however, can be applied to model-theoretic treatments of other linguistic representations such as syntactic trees or autosegmental graphs, which opens a useful direction for future research.

The Comprehensive Diagnostic Method Combining Rough Sets and Evidence Theory

To solve the difficulties in practice caused by the subjectivity, relativity and evidence combination focus element explosion during the process of solving the uncertain problems of fault diagnosis with evidence theory, this paper proposes a fault diagnosis inference strategy by integrating rough sets with evidence theory along with the theories of information fusion and mete-synthesis. By using rough sets, redundancy of characteristic data is removed and the unrelated essential characteristics are extracted, the objective way of basic probability assignment is proposed, and an evidence synthetic method is put forward to solve high conflict evidence. The method put forward in this paper can improve the accuracy rate of fault diagnosis with the redundant and complementary information of various faults by synthesizing all evidences with the rule of the composition of evidence theory. Besides, this paper proves the feasibility and validity of experiments and the efficiency in improving fault diagnosis.

The Effect of Lexical Inference Strategy Instruction on Saudi EFL learners’ Reading Comprehension

Lexical inference strategy plays an important role in increasing the level of reading comprehension of second or foreign language learners. Lexical inferencing as an efficient strategy to deal with unfamiliar words has attracted much attention in the comprehension literature. However, few studies on lexical inferencing have been conducted in an English as a foreign language (EFL) setting. To fill in the existing gap, the current study aimed at investigating the effect of lexical inferencing strategy instruction on Saudi EFL students’ reading comprehension. Additionally, it sought to identify the lexical inferencing strategies used by Saudi EFL learners while they were inferring unknown words in a text. Last, the current study attempted to find the relationship between lexical inference strategies and reading comprehension among Saudi EFL learners. Sixty students from the English department were selected based on their scores on the Oxford Placement Test, indicating that they were at intermediate levels of English proficiency. The participants were randomly divided into two groups: control and experimental (each consisting of 30 students). The participants in the control group received regular instruction, while the participants in the experimental group were treated using lexical inference strategies. The instruments used for collecting data were Oxford Placement Test, reading comprehension test, and think-aloud protocol. A pre-test and post-test were administered for control and experimental groups. The results of the independent samples t-test revealed that teaching inference skills had a significant effect on reading comprehension performance among EFL learners. The results of the paired t-tests showed that lexical inferencing instruction had a statistically significant effect on EFL learners’ reading comprehension development. The results of the Spearman correlation coefficient indicated that there was a significant relationship between lexical inferencing strategies and reading comprehension. The findings revealed the profound impact of lexical inferencing strategy instruction on the experimental group's performance in understanding reading text. Hence, it was concluded that lexical inferencing strategies were recommended to teach to improve the students’ reading comprehension performance.

Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting

Background Heritability is a central measure in genetics quantifying how much of the variability observed in a trait is attributable to genetic differences. Existing methods for estimating heritability are most often based on random-effect models, typically for computational reasons. The alternative of using a fixed-effect model has received much more limited attention in the literature. Results In this paper, we propose a generic strategy for heritability inference, termed as “boosting heritability”, by combining the advantageous features of different recent methods to produce an estimate of the heritability with a high-dimensional linear model. Boosting heritability uses in particular a multiple sample splitting strategy which leads in general to a stable and accurate estimate. We use both simulated data and real antibiotic resistance data from a major human pathogen, Sptreptococcus pneumoniae, to demonstrate the attractive features of our inference strategy. Conclusions Boosting is shown to offer a reliable and practically useful tool for inference about heritability.

Low-Cost Active Anomaly Detection with Switching Latency

Consider the problem of detecting anomalies among multiple stochastic processes. Each anomaly incurs a cost per unit time until it is identified. Due to the resource constraints, the decision-maker can select one process to probe and obtain a noisy observation. Each observation and switching across processes accompany a certain time delay. Our objective is to find a sequential inference strategy that minimizes the expected cumulative cost incurred by all the anomalies during the entire detection procedure under the error constraints. We develop a deterministic policy to solve the problem within the framework of the active hypothesis testing model. We prove that the proposed algorithm is asymptotic optimal in terms of minimizing the expected cumulative costs when the ratio of the single-switching delay to the single-observation delay is much smaller than the declaration threshold and is order-optimal when the ratio is comparable to the threshold. Not only is the proposed policy optimal in the asymptotic regime, but numerical simulations also demonstrate its excellent performance in the finite regime.

The Effect of Lexical Inference Strategy Instruction on Saudi EFL learners’ Reading Comprehension

Lexical inference strategy plays an important role in increasing the level of reading comprehension of second or foreign language learners. Lexical inferencing as an efficient strategy to deal with unfamiliar words has attracted much attention in the comprehension literature. However, few studies on lexical inferencing have been conducted in an English as a foreign language (EFL) setting. To fill in the existing gap, the current study aimed at investigating the effect of lexical inferencing strategy instruction on Saudi EFL students’ reading comprehension. Additionally, it sought to identify the lexical inferencing strategies used by Saudi EFL learners while they were inferring unknown words in a text. Last, the current study attempted to find the relationship between lexical inference strategies and reading comprehension among Saudi EFL learners. Sixty students from the English department were selected based on their scores on the Oxford Placement Test, indicating that they were at intermediate levels of English proficiency. The participants were randomly divided into two groups: control and experimental (each consisting of 30 students). The participants in the control group received regular instruction, while the participants in the experimental group were treated using lexical inference strategies. The instruments used for collecting data were Oxford Placement Test, reading comprehension test, and think-aloud protocol. A pre-test and post-test were administered for control and experimental groups. The results of the independent samples t-test revealed that teaching inference skills had a significant effect on reading comprehension performance among EFL learners. The results of the paired t-tests showed that lexical inferencing instruction had a statistically significant effect on EFL learners’ reading comprehension development. The results of the Spearman correlation coefficient indicated that there was a significant relationship between lexical inferencing strategies and reading comprehension. The findings revealed the profound impact of lexical inferencing strategy instruction on the experimental group's performance in understanding reading text. Hence, it was concluded that lexical inferencing strategies were recommended to teach to improve the students’ reading comprehension performance.

Universal probabilistic programming offers a powerful approach to statistical phylogenetics

Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here, we show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and efficient estimation of Bayes factors that are used in model testing. We take advantage of this in automatically generating SMC algorithms for several recent diversification models that have been difficult or impossible to tackle previously. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.

The Effect of Lexical Inference Strategy Instruction on Saudi EFL learners’ Reading Comprehension

Lexical inference strategy plays an important role in increasing the level of reading comprehension of second or foreign language learners. Lexical inferencing as an efficient strategy to deal with unfamiliar words has attracted much attention in the comprehension literature. However, few studies on lexical inferencing have been conducted in an English as a foreign language (EFL) setting. To fill in the existing gap, the current study aimed at investigating the effect of lexical inferencing strategy instruction on Saudi EFL students’ reading comprehension. Additionally, it sought to identify the lexical inferencing strategies used by Saudi EFL learners while they were inferring unknown words in a text. Last, the current study attempted to find the relationship between lexical inference strategies and reading comprehension among Saudi EFL learners. Sixty students from the English department were selected based on their scores on the Oxford Placement Test, indicating that they were at intermediate levels of English proficiency. The participants were randomly divided into two groups: control and experimental (each consisting of 30 students). The participants in the control group received regular instruction, while the participants in the experimental group were treated using lexical inference strategies. The instruments used for collecting data were Oxford Placement Test, reading comprehension test, and think-aloud protocol. A pre-test and post-test were administered for control and experimental groups. The results of the independent samples t-test revealed that teaching inference skills had a significant effect on reading comprehension performance among EFL learners. The results of the paired t-tests showed that lexical inferencing instruction had a statistically significant effect on EFL learners’ reading comprehension development. The results of the Spearman correlation coefficient indicated that there was a significant relationship between lexical inferencing strategies and reading comprehension. The findings revealed the profound impact of lexical inferencing strategy instruction on the experimental group's performance in understanding reading text. Hence, it was concluded that lexical inferencing strategies were recommended to teach to improve the students’ reading comprehension performance.