The Effect of Warming Up with Estafet Ball Game Method on Student's Interest in Learning PJOK

This study aims to determine The Effect of the Warming Up with Estafet Ball Game Method on Student's Interest in Learning PJOK, this type of research is experimental with a quantitative approach, the total sample is 55, namely the experimental sample of 28 students and a sample of 27 students at SDN Batah Barat 2. The instrument is playing an interaction of the ball game and getting a questionnaire. Analyst data uses validity which is said to be valid because the value of r-table > 0,361 and reliability is said to be reliable because it is greater than 0,05. Then the big data analysis technique uses a prerequisite test more than the normality test with a normal distribution because it is greater than 0,05. Namely the experiment with the result of 0,680 and the control class 0,290. The homogeneity test is said be homogeneous because 0,793 > 0,05. And test the hypothesis using an independent t-test. Based on the independent t-test, there is no significant effect because the value obtained is 0,897. This means that H0 is rejected in the warm-up method of the estafet ball game, and the interest in learning PJOK has no significant effect

Uncertainty-Aware Attention for Reliable Interpretation and Prediction

Department of Computer Science and EngineeringAttention mechanism is effective in both focusing the deep learning models on relevant features and interpreting them. However, attentions may be unreliable since the networks that generate them are often trained in a weakly-supervised manner. To overcome this limitation, we introduce the notion of input-dependent uncertainty to the attention mechanism, such that it generates attention for each feature with varying degrees of noise based on the given input, to learn larger variance on instances it is uncertain about. We learn this Uncertainty-aware Attention (UA) mechanism using variational inference, and validate it on various risk prediction tasks from electronic health records on which our model significantly outperforms existing attention models. The analysis of the learned attentions shows that our model generates attentions that comply with clinicians' interpretation, and provide richer interpretation via learned variance. Further evaluation of both the accuracy of the uncertainty calibration and the prediction performance with "I don't know'' decision show that UA yields networks with high reliability as well.ope

Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models

The change detection problem is to determine if the Markov network structures of two Markov random fields differ from one another given two sets of samples drawn from the respective underlying distributions. We study the trade-off between the sample sizes and the reliability of change detection, measured as a minimax risk, for the important cases of the Ising models and the Gaussian Markov random fields restricted to the models which have network structures with $p$ nodes and degree at most $d$, and obtain information-theoretic lower bounds for reliable change detection over these models. We show that for the Ising model, $\Omega\left(\frac{d^2}{(\log d)^2}\log p\right)$ samples are required from each dataset to detect even the sparsest possible changes, and that for the Gaussian, $\Omega\left( \gamma^{-2} \log(p)\right)$ samples are required from each dataset to detect change, where $\gamma$ is the smallest ratio of off-diagonal to diagonal terms in the precision matrices of the distributions. These bounds are compared to the corresponding results in structure learning, and closely match them under mild conditions on the model parameters. Thus, our change detection bounds inherit partial tightness from the structure learning schemes in previous literature, demonstrating that in certain parameter regimes, the naive structure learning based approach to change detection is minimax optimal up to constant factors.Comment: Presented at the 55th Annual Allerton Conference on Communication, Control, and Computing, Oct. 201

Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination

Measurement of operator quality performance has been challenging in the construction fabrication industry. Among various causes, the learning effect is a significant factor, which needs to be incorporated in achieving a reliable operator quality performance analysis. This research aims to enhance a previously developed operator quality performance measurement approach by incorporating the learning effect (i.e., work experience). To achieve this goal, the Plateau learning model is selected to quantitatively represent the relationship between quality performance and work experience through a beta-binomial regression approach. Based on this relationship, an informative prior determination approach, which incorporates operator work experience information, is developed to enhance the previous Bayesian-based operator quality performance measurement. Academically, this research provides a systematic approach to derive Bayesian informative priors through integrating multi-source information. Practically, the proposed approach reliably measures operator quality performance in fabrication quality control processes.Comment: 8 pages, 5 figures, 2 tables, i3CE 201