Reviewerly/exHarmony huggingface dataset:
Effects of Online Education on Nursing Students' Learning Motives, Academic Self-Efficacy, and Self-Directed Learning Ability

Considering the characteristics of the COVID-19 RNA virus with many mutations, there is a significant possibility that the online class method will continue for an extended period. This study aims to establish a teaching and learning strategy for online education by identifying its effects on learning motives, academic self-efficacy, and self-directed learning ability of nursing students.  The subjects of the study were 202 nursing students  who had completed more than one semester of online classes. The data collection period was from November 1 to November 20, 2020. The results of this study are as follows: grade in last semester (β=-.171, p=.001), confidence in online classes (β=.113, p=.019), and self-directed learning ability (β=.619, p<.001) were significant predictors of learning motives, explaining 62.0% of them (F=42.826, p<.001). For academic self-efficacy, satisfaction with home life (β=.157, p=.006), preference for online classes (β=-.143, p=.017), and self-directed learning ability (β=.4, p<.001) were significant predictors, and explained 38.6% of learning motives (F=19.028, p<.001). In self-directed learning ability, satisfaction with the nursing major (β=.136, p=.013), learning motives (β=.209, p<.001), and academic self-efficacy (β=.209, p<.001) were significant predictors, and explained 62.4% of learning motives (F=42.641, p<.001). Therefore, it is necessary to develop a teaching and learning program that maps out strategies to improve learning motives, academic self-efficacy, and self-directed learning methods for nursing students in online classes.
Dangers of Bayesian Model Averaging under Covariate Shift

Approximate Bayesian inference for neural networks is considered a robust alternative to standard training, often providing good performance on out-of-distribution data. However, Bayesian neural networks (BNNs) with high-fidelity approximate inference via full-batch Hamiltonian Monte Carlo achieve poor generalization under covariate shift, even underperforming classical estimation. We explain this surprising result, showing how a Bayesian model average can in fact be problematic under covariate shift, particularly in cases where linear dependencies in the input features cause a lack of posterior contraction. We additionally show why the same issue does not affect many approximate inference procedures, or classical maximum a-posteriori (MAP) training. Finally, we propose novel priors that improve the robustness of BNNs to many sources of covariate shift.
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers

We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We begin our investigation with the observation that agnostic algorithms \emph{cannot} be minimax-optimal in the realizable setting. Hence, we design novel computationally efficient algorithms for the realizable setting that match the minimax lower bound up to logarithmic factors and are general-purpose, accommodating a wide variety of function classes including kernel methods, H{\o}lder smooth functions, and convex functions. The sample complexities of our algorithms can be quantified in terms of well-known quantities like the extended teaching dimension and haystack dimension. However, unlike algorithms based directly on those combinatorial quantities, our algorithms are computationally efficient. To achieve computational efficiency, our algorithms sample from the version space using Monte Carlo hit-and-run algorithms instead of maintaining the version space explicitly. Our approach has two key strengths. First, it is simple, consisting of two unifying, greedy algorithms. Second, our algorithms have the capability to seamlessly leverage prior knowledge that is often available and useful in practice. In addition to our new theoretical results, we demonstrate empirically that our algorithms are competitive with Gaussian process UCB methods.
Essays in Information Theoretic Econometrics and High Dimensional Econometrics

Author(s): Mao, Yi | Advisor(s): Ullah, Aman; Lee, Tae-Hwy | Abstract: Since the introduction of the subject of econometrics, parametric functional forms of the relationship between independent and dependent variables are often assumed to be known. Econometric functions built from misspecified parametric assumptions may result in false estimation and invalid implications. For this dissertation I introduce an information theoretic (IT) approach to construct econometric functions based on minimal model assumptions on the variables. I follow the entropy approach initiated by Shannon (1948) to obtain a probability distribution of the variables through maximizing the entropy function. In Chapter 2, I use moment constraints to construct the IT-based maximum entropy density, regression and response functions, the performance of which is investigated through simulation and empirical examples. I demonstrate the maximum entropy econometric functions are purely date-driven, easy to implement and advantageous over parametric and nonparametric kernel methods. Furthermore, in Chapter 3, I extend the use of IT-based maximum entropy estimators to a time series dataset by incorporating a theoretic constraint.Based on the structure of IT-based estimators, the number of moment conditions increase rapidly with the growing dimensionality of variables. Computational burden of maximum entropy estimation with all the observed moments becomes heavier accordingly. Thus, I link the problem of IT-based maximum entropy estimation and moment selection in Chapter 4. We propose a regularized ME approach to select relevant moments. We also use the entropy ratio test to select moments of which the Lagrange multipliers are significant. Simulation examples show that the probability of selecting relevant moments approaches to one as sample size gets larger. Lastly, I explore the regularization of high dimensional matrices in Chapter 5. I deal with the challenge of estimating large precision matrices which are often used in a variety of applications. I propose a dynamic conditional precision (DCP) algorithm by embedding a dynamic structure to conditional precision matrices. I show the consistency of the DCP estimator and apply it to a forecast combination application.
Reinforcement Learning in Reward-Mixing MDPs

Learning a near optimal policy in a partially observable system remains an elusive challenge in contemporary reinforcement learning. In this work, we consider episodic reinforcement learning in a reward-mixing Markov decision process (MDP). There, a reward function is drawn from one of multiple possible reward models at the beginning of every episode, but the identity of the chosen reward model is not revealed to the agent. Hence, the latent state space, for which the dynamics are Markovian, is not given to the agent. We study the problem of learning a near optimal policy for two reward-mixing MDPs. Unlike existing approaches that rely on strong assumptions on the dynamics, we make no assumptions and study the problem in full generality. Indeed, with no further assumptions, even for two switching reward-models, the problem requires several new ideas beyond existing algorithmic and analysis techniques for efficient exploration. We provide the first polynomial-time algorithm that finds an $\epsilon$-optimal policy after exploring $\tilde{O}(poly(H,\epsilon^{-1}) \cdot S^2 A^2)$ episodes, where $H$ is time-horizon and $S, A$ are the number of states and actions respectively. This is the first efficient algorithm that does not require any assumptions in partially observed environments where the observation space is smaller than the latent state space.
〈사랑은 비를 타고〉의 자기반영성을 활용한 교육심리학의 메타인지 개념 교수법 연구

This study is int ended to suggest that analyzing and explaining a film can be used in teaching educational psychology by relating the metacognition concept in learning to the self-reflexivity of Singin’ In The Rain. In other words, while teaching film analysis in film subjects, study the metacognition concept among the concepts of educational psychology, and study the educational method that explains the mechanism of learning by cinematic representation again. The self-reflexivity of the film is emphasized when it is changing its contents reflecting what the films representation indicate. Behind the scenes of representation is the perception of how the essence of reality shown by film images such as the Hollywood star system, the cinematic quality of sound as an independent element of image, the reality of the genre, the convention of romantic comedy, and the scene of the contemporary Hollywood film industry is refracted through the medium, film. This discussion is ultimately aimed at providing an insight into the wide range of film education phenomena by bridging between film studies and educational psychology research traditions.
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information

Efficiently approximating local curvature information of the loss function is a key tool for optimization and compression of deep neural networks. Yet, most existing methods to approximate second-order information have high computational or storage costs, which can limit their practicality. In this work, we investigate matrix-free, linear-time approaches for estimating Inverse-Hessian Vector Products (IHVPs) for the case when the Hessian can be approximated as a sum of rank-one matrices, as in the classic approximation of the Hessian by the empirical Fisher matrix. We propose two new algorithms as part of a framework called M-FAC: the first algorithm is tailored towards network compression and can compute the IHVP for dimension $d$, if the Hessian is given as a sum of $m$ rank-one matrices, using $O(dm^2)$ precomputation, $O(dm)$ cost for computing the IHVP, and query cost $O(m)$ for any single element of the inverse Hessian. The second algorithm targets an optimization setting, where we wish to compute the product between the inverse Hessian, estimated over a sliding window of optimization steps, and a given gradient direction, as required for preconditioned SGD. We give an algorithm with cost $O(dm + m^2)$ for computing the IHVP and $O(dm + m^3)$ for adding or removing any gradient from the sliding window. These two algorithms yield state-of-the-art results for network pruning and optimization with lower computational overhead relative to existing second-order methods. Implementations are available at [9] and [17].
Towards noise robust trigger-word detection with contrastive learning pre-task for fast on-boarding of new trigger-words.

Trigger-word detection plays an important role as the entry point of user's communication with voice assistants. But supporting a particular word as a trigger-word involves huge amount of data collection, augmentation and labelling for that word. This makes supporting new trigger-words a tedious and time consuming process. To combat this, we explore the use of contrastive learning as a pre-training task that helps the detection model to generalize to different words and noise conditions. We explore supervised contrastive techniques and also propose a self-supervised technique using chunked words from long sentence audios. We show that the contrastive pre-training techniques have comparable results to a traditional classification pre-training on new trigger words with less data availability.
Librarians? Building a digital footprint

The rate of digital development and evolution of technologies have brought about tremendous changes in communication and information seeking pattern. As a result of this, the need for librarians to create a digital footprint becomes imperative. The prospects and steps of creating digital footprint were discussed in this paper with the overriding interest of producing librarians that are resilient in terms of being able to cope with innovative and destructive technologies while catering for the needs of library users and non-users alike needing information and allied services.
Medium Design: Knowing How to Work on the World

Who is a medium designer? If you’re hoping to find out, you’re barking up the wrong book (review). There is no particular medium designer, at least not in the sense of a person enrolled in the work...
PKM PENERAPAN MANAJEMEN REKAM MEDIS SESUAI DENGAN LEGALITAS PADA FISIOTERAPI MANDIRI DI KOTA SEMARANG

ABSTRACT The activity of this society is an effort to increase in knowledge, keterampilah as well as the managing understanding of the Indonesian profession's Physiotherapist at improving the ability to manually recording medical records or electronically appropriate Permenkes numbers 269-year-old 2008 concerning the Medical Records. The special target of PKM's activity is teaching the assistance system on independent physiotherapy that is the IFI branch member of the Semarang to increase skills in creating manual medical records or electronics that suit the legality legality in Indonesia. he method of devotion is carried out by providing assistance from giving extension and discussion, manual medical record-making training, electronic medical record-making training, trials, revisions, and submission medical records from participants. The method of devotion is carried out by providing assistance from giving extension and discussion, manual medical record-making training, electronic medical record-making training, trials, revisions, and submission medical records from participants. The PKM program activities were implemented with the extension and training method over online in the manner) Pre-squad survey, b) Preparation of tools and training materials, c) Preparation pelatiahan's method, d) Training Board and Independent PHYSIOTHERAPIST,) F physiotherapist, G-making and Report. Based on the results of training obtained their significant results so they can create a medical record that they originally ignored the documentation. Said Iinch: Assistance, IFI, the independent physiotherapy, medical record
A Proposed Framework For Improving The Role Of School Leaders In Promoting Digital Citizenship Among Public Education Students In The Kingdom Of Saudi Arabia

The purpose of this study is to propose a framework to improve the role of school leaders in promoting digital citizenship among students at public education schools in Saudi Arabia. The descriptive approach was used to address the purpose of the study. Twenty-six educational leadership scholars have participated in this study to identify their perceptions regarding the role of school leaders in promoting digital citizenship among students. The results of the study emphasized the importance of school leaders’ role in promoting digital citizenship among students at general education schools as the study sample degree of appreciation of their role importance in the three main areas (setting direction, developing people, and developing schools) was high. The study also proposed framework aiming at improving the role of school leaders in promoting digital citizenship among public education students. It consists of three areas: determining the direction of promoting digital citizenship among students, developing school personnel in a way that contributes to enhancing digital citizenship for students, and developing the school in a way that contributes to promoting digital citizenship among students. Each of these areas includes a set of roles that school leaders should play in promoting digital citizenship among students.
Proxy Indicators for the Quality of Open-domain Dialogues.

The automatic evaluation of open-domain dialogues remains a largely unsolved challenge. Despite the abundance of work done in the field, human judges have to evaluate dialogues’ quality. As a consequence, performing such evaluations at scale is usually expensive. This work investigates using a deep-learning model trained on the General Language Understanding Evaluation (GLUE) benchmark to serve as a quality indication of open-domain dialogues. The aim is to use the various GLUE tasks as different perspectives on judging the quality of conversation, thus reducing the need for additional training data or responses that serve as quality references. Due to this nature, the method can infer various quality metrics and can derive a component-based overall score. We achieve statistically significant correlation coefficients of up to 0.7.
NAS-Bench-x11 and the Power of Learning Curves

While early research in neural architecture search (NAS) required extremecomputational resources, the recent releases of tabular and surrogatebenchmarks have greatly increased the speed and reproducibility of NASresearch. However, two of the most popular benchmarks do not provide the fulltraining information for each architecture. As a result, on these benchmarks itis not possible to run many types of multi-fidelity techniques, such aslearning curve extrapolation, that require evaluating architectures atarbitrary epochs. In this work, we present a method using singular valuedecomposition and noise modeling to create surrogate benchmarks, NAS-Bench-111,NAS-Bench-311, and NAS-Bench-NLP11, that output the full training informationfor each architecture, rather than just the final validation accuracy. Wedemonstrate the power of using the full training information by introducing alearning curve extrapolation framework to modify single-fidelity algorithms,showing that it leads to improvements over popular single-fidelity algorithmswhich claimed to be state-of-the-art upon release. Our code and pretrainedmodels are available at https://github.com/automl/nas-bench-x11.
ENHANCING STUDENTS’ WRITING SKILL BY USING INQUIRY BASED LEARNING (IBL) AT TENTH GRADE OF SMA PGRI 20 SIBORONGBORONG IN ACADEMIC YEAR 2018/ 2019

This research was a classroom action research which is aimed to explain the extent to which Inquiry-Based Learning method can improve the students’ ability in writing a recount text at the Tenth Grade Of SMA PGRI 20 Siborongborong  and to explain the teaching-learning situation. The subject of this research was class XIPA2 which consisted of 20 females and 10 males. It was conducted in two cycles. Each cycle consists of planning, action, observation and reflection. The finding show that the students concerned actively in teaching learning process, especially in conveying their opinions, asking and respond the question, and enthusiasm in doing the test in writing teaching learning process. The students’ writing skill increases from pre-test to post-test .The mean of pre-test in cycle 1 is 54,66 and the mean of post-test in cycle I is 74. Meanwhile, the mean of post-test in cycle II is 90. Based on data the researcher concluded that all the students passed the minimum criterion (75), in which, based on the analysis, the research was succeed. The strength of inquiry-based learning method can be seen that students could memorize about the kinds of texts particularly recount text and the weaknesses of this method can be seen that students get shy to convey their opinions (asking and respond the question) forward their friends when they had to convey their opinion in front of their friends.It meant that Inquiry-Based Learning Method is able to improve the students’ Writing skill
Discovery of Options via Meta-Learned Subgoals

Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster. However, despite prior work on this topic, the problem of discovering options through interaction with an environment remains a challenge. In this paper, we introduce a novel meta-gradient approach for discovering useful options in multi-task RL environments. Our approach is based on a manager-worker decomposition of the RL agent, in which a manager maximises rewards from the environment by learning a task-dependent policy over both a set of task-independent discovered-options and primitive actions. The option-reward and termination functions that define a subgoal for each option are parameterised as neural networks and trained via meta-gradients to maximise their usefulness. Empirical analysis on gridworld and DeepMind Lab tasks show that: (1) our approach can discover meaningful and diverse temporally-extended options in multi-task RL domains, (2) the discovered options are frequently used by the agent while learning to solve the training tasks, and (3) that the discovered options help a randomly initialised manager learn faster in completely new tasks.
Minimal Supervision for Morphological Inflection

Neural models for the various flavours of morphological reinflection tasks have proven to be extremely accurate given ample labeled data, yet labeled data may be slow and costly to obtain. In this work we aim to overcome this annotation bottleneck by bootstrapping labeled data from a seed as small as five labeled inflection tables, accompanied by a large bulk of unlabeled text. Our bootstrapping method exploits the orthographic and semantic regularities in morphological systems in a two-phased setup, where word tagging based on analogies is followed by word pairing based on distances. Our experiments with the Paradigm Cell Filling Problem over eight typologically different languages show that in languages with relatively simple morphology, orthographic regularities on their own allow inflection models to achieve respectable accuracy. Combined orthographic and semantic regularities alleviate difficulties with particularly complex morpho-phonological systems. We further show that our bootstrapping methods substantially outperform hallucination-based methods commonly used for overcoming the annotation bottleneck in morphological reinflection tasks.
Revisiting Deep Learning Models for Tabular Data

The necessity of deep learning for tabular data is still an unanswered question addressed by a large number of research efforts. The recent literature on tabular DL proposes several deep architectures reported to be superior to traditional shallow models like Gradient Boosted Decision Trees. However, since existing works often use different benchmarks and tuning protocols, it is unclear if the proposed models universally outperform GBDT. Moreover, the models are often not compared to each other, therefore, it is challenging to identify the best deep model for practitioners. In this work, we start from a thorough review of the main families of DL models recently developed for tabular data. We carefully tune and evaluate them on a wide range of datasets and reveal two significant findings. First, we show that the choice between GBDT and DL models highly depends on data and there is still no universally superior solution. Second, we demonstrate that a simple ResNet-like architecture is a surprisingly effective baseline, which outperforms most of the sophisticated models from the DL literature. Finally, we design a simple adaptation of the Transformer architecture for tabular data that becomes a new strong DL baseline and reduces the gap between GBDT and DL models on datasets where GBDT dominates.
Teaching Recording and Sampling with Audio Projects

This chapter presents a first set of project plans, which deal mainly with the mechanics of working with recorded sound, with basic editing techniques, and with the concept of the multitrack timeline. As projects progress, they introduce successively more complex techniques. The projects include making a song from pre-existing audio clips, making a radio advertisement, creating a simple remix using acapella tracks, analyzing a professional multitrack recording, creating custom cover songs, and using a variety of editing techniques to create a fully formed soundtrack for a movie clip. The chapter also discusses the specific affordances of Ableton Live for arranging and performing audio clips, compares the traditionally linear Arrangement View with the innovative nonlinear Session View, and explains rationales for using each one depending on musical context.