Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF COOPERATIVE LEARNING MODEL OF SNOWBALL THROWING TYPE ON THE STUDENTS’ SPEAKING ABILITY AT THE EIGHTH GRADE OF SMP NEGERI 5 PAKKAT IN ACADEMIC YEAR 2019/2020

The purpose of this research was to find out whether cooperative learning model of snowball throwing type significantly affects on the students’ speaking ability at the eighth grade of SMP Negeri 5 Pakkat in academic year 2019/2020. The methodology of this research was true experimental research design. The population of this research is the eighth grade of SMP Negeri 5 Pakkat. The total sample was 52 students who was taken from two classes, VIII A and VIII B. the researcher used oral test to measure students’ score from pretest and posttest, the researcher used SPSS 16. Based on the analysis on the data and the testing of hypothesis, the researcher found that the result was tcount  > ttable  = 3.916 > 1.705 and Sig. (2-tailed) =0.00 and α = 0.05 where if Sig. (2-tailed) <α = 0.05 it meant that Ha was accepted. So, there is a significant effect in using cooperative learning model of snowball throwing type toward students’ speaking ability at the eighth grade of SMP Negeri 5 Pakkat in academic year 2019/2020.
Conocimiento y manejo de las TIC por docentes de comercio y marketing: Cambios producidos por la pandemia por la COVID-19 y en plena Era de la Información

espanolActualmente y debido al estado de alarma sanitario producido por la COVID-19, fueron muchos los docentes que tuvieron y tienen que adaptarse a la nueva situacion de aprendizaje teniendo que hacer frente, en poco tiempo, a las demandas de la Educacion 2.0. La falta de ajuste de formacion a las necesidades concretas del profesorado conlleva que, muchas veces, estos mismos profesionales tengan que adquirir conocimientos En la familia de Formacion Profesional de Comercio y Marketing, nos encontramos en un contexto donde los docentes apenas han tenido tiempo para conocer y manejar las Tecnologias de la Informacion y la Comunicacion. EnglishDue to the pandemic of COVID-19 and the state of alarm it brought with it, there were lots of teachers who had to adapt themselves and to a new learning situation, facing the demands of Education 2.0 in a brief period. The lack of training and adjustment to the specific needs of teachers entails that many times, these professionals have to acquire knowledge on their own. In the field of Professional Training in Trade and Marketing, we find ourselves in a context where lots of teachers hardly have the knowledge of ICT.
THE TEACHING OF PRONUNCIATION APPLYING WESTERN MOVIE OF THE EIGHTH GRADE AT MTSN 1 BATAM

ABSTRACTAziz, Haris Hikmah. Student Registered Number. 1723143065. 2019. Analysis of Teaching Pronunciation in MTsN 1 Batam. Thesis. English Education Departement. Faculty of Tarbiyah and Teacher Training. State Islamic Institute (IAIN) of Tulungagung. Advisor: Dr. Susanto, SS., MP.dKeywords: Pronunciation, Western Movie, ImplementationIn EFL context, pronunciation is very important to master for the students. Being able to pronounce English words corretly as can help the students improve their spealing. Practically, in speaking pronunciation should be given special attention. Making mistake in pronunciation makes communication cannot proceed fluently. That why, the teaching of pronunciation should be interesting. Applying Western Movie one of practices to improve students’ pronunciation. This ending was intended to elaborate the practice of teaching pronunciation by using Western Movie.The formulation of research problems were 1) How is the implementation of Western Movie in teaching pronunciation to the eighth grade at MTsN Batam ? 2) How are the contributions of the use of the strategies in teaching pronunciation to the eighth grade at MTsN Batam ?This research belonged to descriptive qualitative research design. The data were the descriptive of the practice of teaching pronunciation using Western Movie and the subject’s responses toward the contributions of the strategies. The subject was the teacher and the informants were the students. The instruments used were projector, laptop and sound speaker. The data were analyzed by using data reduction, data display and conclusion drawing and verification.The results of the study show that 1) In teaching pronunciation the teacher used various and systematic strategies, they were (a) designing lesson plan (b) providing audiovisual media (c) drilling (d) doing repetition (e) guided conversation, 2) The use of Western Movie is contributive for the students in which (a) designing lesson plan (b) providing audiovisual media (c) drilling (d) doing repetition (e) guided conversation.
Error on Elliptical Constructions made by the First Semester to the English Study Program PGRI University of Palembang

Abstract The main problems of this study were “What kinds and causes of error are made by the First semester to the English Study Program PGRI university of Palembang in using elliptical construction? Therefore, the purpose of this study were to find out what kinds of error and the causes are made by eleventh grade students in using elliptical constructions of First semester to the English Study Program PGRI university of Palembang. The total population of this study was 70 students consist of two classes. Choosing the sample for this study, the writer used purposive sampling method with the total 32 students. The method used in this study was the descriptive method. Written and questionnaire had been used for analyzing the data obtained. Based on the study, it is found that the error made by the eleventh grade students was 376. There were 82 (22%) errors in using ‘do’, 66 (18%) in using ‘does’, 58 (15%) in using ‘did’, 39 (10%) in using ‘am’, 78 (21%) in using ‘is’, and 53 (14%) in using ‘are’. In Addition, there were 41 errors of addition errors, 70 errors of omission errors, 211 errors of misformation errors, 46 errors of misordering errors. Keywords: Error Analysis, Elliptical construction.
“Information Isn’t Knowledge”: Denis Rougemont on the Verge of the Information Society

Today it’s not possible to deny the approach of the new epoch – the epoch of the information society. The high technologies have infiltrated the total scope of the everyday life of modern people. In 2020 our civilization confronted the new, but for a long time anticipated, challenge, - mass introduction of distant education in schools and universities. We still will have to comprehend the results of this social experiment in the nearest future. Still one fact arises no doubts: information nowadays is the product that is widely and easily (perhaps, too easily) accessible, but real knowledge remains the lot of the few, and even tend to marginalize. Forty years ago the stated problems became the issue of the studies of the Swiss philosopher Denis de Rougemont. His conclusions turn out to be more and more relevant with the acceleration of the process of culture, communications and education digitalization. His article “Information Isn’t Knowledge” has been published in Russian for the first time. The article deals with the issues of information technologies integration into the human cognitive activity, its influence on the thinking process and cultural, ethic and spiritual values formation. Denis de Rougemont step by step reveals the definition of information technologies, their application in various areas of human existence, their ability to compete with personality and the consequences of their integration in everyday life. These speculations become especially valuable in the era of the triumph for information society and global computerization.
A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness

Two crucial requirements for a successful adoption of deep learning (DL) in the wild are: (1) robustness to distributional shifts, and (2) model compactness for achieving efficiency. Unfortunately, efforts towards simultaneously achieving Out-of-Distribution (OOD) robustness and extreme model compactness without sacrificing accuracy have mostly been unsuccessful. This raises an important question: Is the inability to create compact, accurate, and robust deep neural networks (CARDs) fundamental? To answer this question, we perform a large-scale analysis for a range of popular model compression techniques which uncovers several intriguing patterns. Notably, in contrast to traditional pruning approaches (e.g., fine tuning and gradual magnitude pruning), we find that lottery ticket-style pruning approaches can surprisingly be used to create high performing CARDs. Specifically, we are able to create extremely compact CARDs that are dramatically more robust than their significantly larger and full-precision counterparts while matching (or beating) their test accuracy, simply by pruning and/or quantizing. To better understand these differences, we perform sensitivity analysis in the Fourier domain for CARDs trained using different data augmentation methods. Motivated by our analysis, we develop a simple domain-adaptive test-time ensembling approach (CARD-Deck) that uses a gating module to dynamically select an appropriate CARD from the CARD-Deck based on their spectral-similarity with test samples. By leveraging complementary frequency biases of different compressed models, the proposed approach builds a winning hand of CARDs that establishes a new state-of-the-art on CIFAR-10-C accuracies (i.e., 96.8% clean and 92.75% robust) with dramatically better memory usage than their non-compressed counterparts. We also present some theoretical evidences supporting our empirical findings.
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

Learning sentence embeddings often requires a large amount of labeled data. However, for most tasks and domains, labeled data is seldom available and creating it is expensive. In this work, we present a new state-of-the-art unsupervised method based on pre-trained Transformers and Sequential Denoising Auto-Encoder (TSDAE) which outperforms previous approaches by up to 6.4 points. It can achieve up to 93.1% of the performance of in-domain supervised approaches. Further, we show that TSDAE is a strong domain adaptation and pre-training method for sentence embeddings, significantly outperforming other approaches like Masked Language Model. A crucial shortcoming of previous studies is the narrow evaluation: Most work mainly evaluates on the single task of Semantic Textual Similarity (STS), which does not require any domain knowledge. It is unclear if these proposed methods generalize to other domains and tasks. We fill this gap and evaluate TSDAE and other recent approaches on four different datasets from heterogeneous domains.
Women as Citizen Scientists: Identifying Potential Digital Solutions for Addressing Gender-Based Challenges

Women in different parts of the world, especially developing countries, experience gender-based challenges including violence against women, limited access to girls’ education, reproductive and sexual health challenges, and traditional harmful practices. Citizen Science projects have shown to have positive outcomes by including members of the public in data collection. The purpose of this paper was to identify studies relating to the use of digital technologies for Citizen Science projects to aid data collection. The overall goal is to inform research in enabling women to participate in addressing gender-based challenges through the use of digital technologies for data collection. The findings revealed that digital technologies such as mobile phone cameras, mobile applications, social media and web applications and videos were used as data collection methods in Citizen Science projects. The study also proposed how these technologies can be used as data collection tools for Citizens Science projects revolving around solving gender-based challenges.
Impact of a CE-marked medical software sensor on SARS-CoV-2 disease pandemic progression estimation

Abstract Background To address the current and any future pandemic, we need a robust, real-time and population-scale collection of data. Rapid and comprehensive knowledge on the trends in reported symptoms in populations provides an earlier window into our progress against the viral spread, and helps predict the need and timing of professional healthcare. We thus used a CE-marked medical online symptom checker service and validated the data against national demand of care to estimate the progression of the pandemic in Finland. Methods Our data comprised real-time ©Omaolo Covid-19 symptom checker responses (414 477 in total) and daily admission counts in nationwide inpatient and outpatient registers provided by the Finnish Institute for Health and Welfare 16th March-15th June, 2020 (the first wave of the pandemic in Finland). The questionnaires responses provide self-triage information input to a medically qualified algorithm that produces a personalized probability of having the Covid-19 and provides graded recommendations for further actions. We trained linear regression and XGBoost models together with F-Score and mutual information feature pre-selectors to predict the admissions 1 week ahead. Results Our models reached a MAPE (Mean Absolute Percentage Error) between 24.2% and 36.4% and MdAPE (Median Average Percentage Error) between 20.1% and 25.1% in predicting national daily patient admissions. The best result was achieved by combining both ©Omaolo and historical patient admission counts. Our best predictor was linear regression with mutual information as the pre-selector with slight advantage over XGBoost. Conclusions Accurate short-term predictions of Covid-19 patient admissions can be made, and both the symptom check questionnaires and the daily admissions data contribute to the accuracy of the predictions. Thus, symptoms checkers can be used to estimate the progression of the pandemic, which can be considered when predicting the health care burden in a future pandemic. Key messages Digital surveillance of symptoms enables rapid and contactless tracing of epidemic progression. Prediction models can help support medical decision making in an epidemic to promote and protect public health and prevent health care system overload.
An Investment Theory Based on Geometric Return

Return is the most fundamental concept in investment. There are two methods to measure returns: Arithmetic return and geometric return. The standard investment theory, Markowitz portfolio theory and CAPM, uses arithmetic return. Geometric return provides more accurate and precise measurement of investment performance. Because of this, geometric return provides many advantages as the foundation of an investment theory.
LibGuides: Landing Your Dream Job: Interviewing

This guide is based on information provided by Jan-Marie Lanuzza, Experiential Education Coordinator and Jean Moats, Director of Library Services.
H&lt;SUB align="right"&gt;∞ model reduction of discrete-time 2D T-S fuzzy systems in finite frequency ranges

This paper addresses the problem of finite frequency (FF) H∞ filter design for nonlinear continuous-time systems described by Takagi-Sugeno (T-S) fuzzy models. The objective is to provide a new des...
Neural Trees for Learning on Graphs

Graph Neural Networks (GNNs) have emerged as a flexible and powerful approach for learning over graphs. Despite this success, existing GNNs are constrained by their local message-passing architecture and are provably limited in their expressive power. In this work, we propose a new GNN architecture -- the Neural Tree. The neural tree architecture does not perform message passing on the input graph but on a tree-structured graph, called the H-tree, that is constructed from the input graph. Nodes in the H-tree correspond to subgraphs in the input graph, and they are reorganized in a hierarchical manner such that a parent-node of a node in the H-tree always corresponds to a larger subgraph in the input graph. We show that the neural tree architecture can approximate any smooth probability distribution function over an undirected graph, as well as emulate the junction tree algorithm. We also prove that the number of parameters needed to achieve an $\epsilon$-approximation of the distribution function is exponential in the treewidth of the input graph, but linear in its size. We apply the neural tree to semi-supervised node classification in 3D scene graphs, and show that these theoretical properties translate into significant gains in prediction accuracy, over the more traditional GNN architectures.
UD-GAN: a dense connected generative adversarial network for pixel-level modal translation of multimodal images

Modal translation between multimodal images is an effective complementary scheme when images with some certain modal are difficult to obtain. Since pixel level image modal translation method can obtain the images with the highest quality, it has become a research hotspot in recent years. Generative adversarial network (GAN) is a network for image generation, due to the complex structure of GAN and the complexity of the image generation task, the training results of GAN are not stable. In this paper, on the basis of U-nets, the dense block is used to increase the feature information in the subsampling encoding and up-sampling decoding operation, so as to reduce the loss of information and obtain higher quality images. At the same time, the dense long connection is introduced to connect the encoding and decoding operations of the same stage, so that the network can effectively combine the features at low and high level, and improve the performance of the network. Experimental results show that the proposed method is effective in modal translation of multimodal images, and the image quality is better than some state-of-the-art methods.
Agent Modelling under Partial Observability for Deep Reinforcement Learning

Modelling the behaviours of other agents is essential for understanding how agents interact and making effective decisions. Existing methods for agent modelling commonly assume knowledge of the local observations and chosen actions of the modelled agents during execution. To eliminate this assumption, we extract representations from the local information of the controlled agent using encoder-decoder architectures. Using the observations and actions of the modelled agents during training, our models learn to extract representations about the modelled agents conditioned only on the local observations of the controlled agent. The representations are used to augment the controlled agent's decision policy which is trained via deep reinforcement learning; thus, during execution, the policy does not require access to other agents' information. We provide a comprehensive evaluation and ablations studies in cooperative, competitive and mixed multi-agent environments, showing that our method achieves higher returns than baseline methods which do not use the learned representations.
On the Relation between Syntactic Divergence and Zero-Shot Performance.

We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting. While previous work suggests such a relation, it tends to focus on the macro level and not on the level of individual edges—a gap we aim to address. As a test case, we take the transfer of Universal Dependencies (UD) parsing from English to a diverse set of languages and conduct two sets of experiments. In one, we analyze zero-shot performance based on the extent to which English source edges are preserved in translation. In another, we apply three linguistically motivated transformations to UD, creating more cross-lingually stable versions of it, and assess their zero-shot parsability. In order to compare parsing performance across different schemes, we perform extrinsic evaluation on the downstream task of cross-lingual relation extraction (RE) using a subset of a standard English RE benchmark translated to Russian and Korean. In both sets of experiments, our results suggest a strong relation between cross-lingual stability and zero-shot parsing performance.
The Skellam Mechanism for Differentially Private Federated Learning

We introduce the multi-dimensional Skellam mechanism, a discrete differential privacy mechanism based on the difference of two independent Poisson random variables. To quantify its privacy guarantees, we analyze the privacy loss distribution via a numerical evaluation and provide a sharp bound on the R\'enyi divergence between two shifted Skellam distributions. While useful in both centralized and distributed privacy applications, we investigate how it can be applied in the context of federated learning with secure aggregation under communication constraints. Our theoretical findings and extensive experimental evaluations demonstrate that the Skellam mechanism provides the same privacy-accuracy trade-offs as the continuous Gaussian mechanism, even when the precision is low. More importantly, Skellam is closed under summation and sampling from it only requires sampling from a Poisson distribution -- an efficient routine that ships with all machine learning and data analysis software packages. These features, along with its discrete nature and competitive privacy-accuracy trade-offs, make it an attractive practical alternative to the newly introduced discrete Gaussian mechanism.
IQ-Learn: Inverse soft-Q Learning for Imitation

In many sequential decision-making problems (e.g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task. However, imitation learning (IL) from a small amount of expert data can be challenging in high-dimensional environments with complex dynamics. Behavioral cloning is a simple method that is widely used due to its simplicity of implementation and stable convergence but doesn't utilize any information involving the environment's dynamics. Many existing methods that exploit dynamics information are difficult to train in practice due to an adversarial optimization process over reward and policy approximators or biased, high variance gradient estimators. We introduce a method for dynamics-aware IL which avoids adversarial training by learning a single Q-function, implicitly representing both reward and policy. On standard benchmarks, the implicitly learned rewards show a high positive correlation with the ground-truth rewards, illustrating our method can also be used for inverse reinforcement learning (IRL). Our method, Inverse soft-Q learning (IQ-Learn) obtains state-of-the-art results in offline and online imitation learning settings, surpassing existing methods both in the number of required environment interactions and scalability in high-dimensional spaces.