Composite Deep Belief Network approach for enhanced Antepartum foetal electrocardiogram signal

Antepartum Foetal surveillance is the most vital epoch of investigation during the pregnancy period. This surveillance would provide an opening to plan and manage the Foetus during Intrapartum and Antepartum stages of pregnancy. Moreover, it will help to identify high risk Foetuses during pregnancies which are complicated by maternal health conditions like diabetes mellitus, intrauterine growth restriction, etc. The foetal electrocardiogram (fECG) signal can be detected in the course of pregnancy from the Antepartum stage. Generally, fECG signal analysis is not carried out for Foetal surveillance. Rather, the traditional methodologies like phonocardiogram, etc. are being utilized. The reason is the unavailability of an effective methodology for providing good quality fECG signal. The proposal of a hybrid tactic called Bayesian Deep Belief Network (BDBN) for fECG signal enhancement is presented in this article. The proposed BDBN technique involves Baye’s filtering methodology in amalgamation with Deep Belief Network. The Baye’s filtering was employed to eliminate undesired signal components. Deep learning (DL) technique was utilized with Deep belief network (DBN) to extract high quality fECG signal. The methodology resulted with good quality fECG signal which is indeed valuable for timely Physician analysis.     

EFFECTS OF VIDEO-ASSISTED TRAINING ON EMPLOYMENT-RELATED SOCIAL SKILLS OF ADULTS WITH SEVERE MENTAL RETARDATION

Two studies investigated effects of video-assisted training on employment-related social skills of adults with severe mental retardation. In video-assisted training, participants discriminated a model's behavior on videotape and received feedback from the trainer for responses to questions about video scenes. In the first study, 3 adults in an employment program participated in video-assisted training to request their supervisor's assistance when encountering work problems. Results indicated that participants discriminated the target behavior on video but effects did not generalize to the work setting for 2 participants until they rehearsed the behavior. In the second study, 2 participants were taught to fix and report four work problems using video-assisted procedures. Results indicated that after participants rehearsed how to fix and report one or two work problems, they began to fix and report the remaining problems with video-assisted training alone.     

Incorporating prior theory in covariance structure analysis: A bayesian approach

A Bayesian approach to the testing of competing covariance structures is developed. The method provides approximate posterior probablities for each model under consideration without prior specification of individual parameter distributions. The method is based on ayesian updating using cross-validated pseudo-likelihoods. Given that the observed variables are the samefor all competing models, the approximate posterior probabilities may be obtained easily from the chi square values and other known constants, using only a hand calculator. The approach is illustrated using and example which illustrates how the prior probabilities can alter the results concerning which model specification is preferred.     

Using item-specific instructional information in achievement modeling

The problem of detecting instructional sensitivity (item basis) in test items is considered. An illustration is given which shows that for tests with many biased items, traditional item bias detection schemes give a very poor assessment of bias. A new method is proposed instead. This method extends item response theory (IRT) by including item-specific auxiliary measurement information related to opportunity-to-learn. Item-specific variation in measurement relations across students with varying opportunity-to-learn is allowed for.This paper was presented at the 1987 AERA meeting in Washington, DC. This research was supported by grant OERI-G-86-003 from the Office of Educational Research and Improvement, Department of Education. The author thanks Michael Hollis and Chih-fen Kao for valuable research assistance, and appreciates valuable comments made by an anonymous reviewer.     

TEACHING AUTISTIC CHILDREN CONVERSATIONAL SPEECH USING VIDEO MODELING

We assessed the effects of video modeling on acquisition and generalization of conversational skills among autistic children. Three autistic boys observed videotaped conversations consisting of two people discussing specific toys. When criterion for learning was met, generalization of conversational skills was assessed with untrained topics of conversation; new stimuli (toys); unfamiliar persons, siblings, and autistic peers; and other settings. The results indicated that the children learned through video modeling, generalized their conversational skills, and maintained conversational speech over a 15-month period. Video modeling shows much promise as a rapid and effective procedure for teaching complex verbal skills such as conversational speech.     

Parent and child adjustment dual trajectories at the beginning of the COVID-19 syndemic

Children and their families have been significantly impacted by the unfolding of the COVID-19 syndemic. We sought to identify (1) groups of families with distinct profiles of joint trajectories of parental anxiety and child emotional distress and (2) protective and risk factors associated with these dual-trajectory profiles. A sample of 488 parents (65% White; 77% mothers) with 3- to 8-year-old children (M_Age = 5.04, SD_Age = 1.59) was followed from late March to early July in 2020. Survey data on parent (i.e., anxiety symptoms) and child (i.e., emotional distress) adjustment were collected at three time points. Using multivariate growth mixture modeling, we identified one group with low parental anxiety and child emotional distress (42.7%) and three other distinct groups with varying risk levels among parents and/or children. We also identified protective (e.g., positive parenting) and risk (e.g., child negative affect, negative parenting, perceived stress with racism) factors in predicting parent and child adjustment. It can be concluded that, overall, our sample (mostly middle- and high-socioeconomic status families) demonstrated family resilience amid COVID-19, consistent with prior disaster coping literature. At the same time, our findings also indicated the need to identify at-risk families and modifiable factors for post-disaster public health interventions.     

French validation of the Twenty-Item Value Inventory

IntroductionPersonal values are important for understanding psychological phenomena by serving as predictors of individuals’ attitudes and behavior. The Twenty-Item Value Inventory (TwIVI, Sandy et al., 2017) was developed to assess values with items from the Portrait Values Questionnaire (PVQ, Schwartz et al., 2001).ObjectiveThe goal of this study was to assess the reliability and validity of the French version of the TwIVI.MethodTwo French-Canadian samples of adults (N1 = 825; N2 = 429) were recruited in order to test the reliability and validity of the TwIVI.ResultsResults revealed that the French version of the TwIVI showed satisfying temporal stability, convergence validity with the PVQ, and concurrent validity with personality traits. Some issues were observed for internal consistency and structural validity, assessed with both multidimensional scaling and exploratory structural equation modeling.ConclusionOverall, these findings partially support the psychometric qualities of the French version of the TwIVI. Implications for theory and research on values are discussed.     

How infant-directed actions enhance infants’ attention,learning, and exploration: Evidence from EEG and computational modeling

When teaching infants new actions, parents tend to modify their movements. Infants prefer these infant-directed actions (IDAs) over adult-directed actions and learn well from them. Yet, it remains unclear how parents’ action modulations capture infants’ attention. Typically, making movements larger than usual is thought to draw attention. Recent findings, however, suggest that parents might exploit movement variability to highlight actions. We hypothesized that variability in movement amplitude rather than higher amplitude is capturing infants’ attention during IDAs. Using EEG, we measured 15-month-olds’ brain activity while they were observing action demonstrations with normal, high, or variable amplitude movements. Infants’ theta power (4–5 Hz) in fronto-central channels was compared between conditions. Frontal theta was significantly higher, indicating stronger attentional engagement, in the variable compared to the other conditions. Computational modelling showed that infants’ frontal theta power was predicted best by how surprising each movement was. Thus, surprise induced by variability in movements rather than large movements alone engages infants’ attention during IDAs. Infants with higher theta power for variable movements were more likely to perform actions successfully and to explore objects novel in the context of the given goal. This highlights the brain mechanisms by which IDAs enhance infants’ attention, learning, and exploration.     

The effect of COVID-19 on home advantage in high- and low-stake situations: Evidence from the European national football competitions

The Covid-19 pandemic has significantly altered the way sporting events are observed. With the absence or limited presence of spectators in stadiums, the traditional advantage enjoyed by home teams has diminished considerably. This underscores the notion that the support of home fans can often be considered a key factor of the home advantage (HA) phenomenon, wherein teams perform better in front of their own supporters. However, the impact of reduced attendance on games with higher stakes, as opposed to low-stakes friendly matches, remains uncertain. In this study, we investigate the recently concluded European football championship (EURO 20), wherein several teams had the advantage of playing at home in high-stakes games with only one-third of the stadium capacity filled. Firstly, we demonstrate that the Covid-19 restrictions, leading to reduced fan attendance, resulted in a nearly 50% decrease in HA compared to the HA exhibited by the same teams during the qualification stage preceding EURO 20, even after accounting for team strength. Secondly, we show that while low-stakes friendly matches generally exhibit a smaller overall HA compared to high-stakes games, the absence of fans led to a similar reduction in HA during the low-stakes matches. Utilizing the recently developed Home Advantage Mediated (HAM) model (Bilalić, Gula, & Vaci, 2021, Scientific Reports, 21558), we were able to attribute the reduction in both high- and low-stakes games to poorer team performance, with no significant contribution from referee bias.     

Extending exploratory diagnostic classification models: Inferring the effect of covariates

Diagnostic models provide a statistical framework for designing formative assessments by classifying student knowledge profiles according to a collection of fine-grained attributes. The context and ecosystem in which students learn may play an important role in skill mastery, and it is therefore important to develop methods for incorporating student covariates into diagnostic models. Including covariates may provide researchers and practitioners with the ability to evaluate novel interventions or understand the role of background knowledge in attribute mastery. Existing research is designed to include covariates in confirmatory diagnostic models, which are also known as restricted latent class models. We propose new methods for including covariates in exploratory RLCMs that jointly infer the latent structure and evaluate the role of covariates on performance and skill mastery. We present a novel Bayesian formulation and report a Markov chain Monte Carlo algorithm using a Metropolis-within-Gibbs algorithm for approximating the model parameter posterior distribution. We report Monte Carlo simulation evidence regarding the accuracy of our new methods and present results from an application that examines the role of student background knowledge on the mastery of a probability data set.