Reviewerly/exHarmony huggingface dataset:
Prática de atividade física na pandemia de Covid-19 : reflexões a partir da revisão de literatura

The Covid-19 pandemic brought several difficulties to people’s daily lives, so a survey thathighlights the notorious relationship between physical activity and health this time is necessary,showing the obstacles and solutions attended in moments of isolation and social distance.Containing the spread of the virus involves measures of hypervigilance, concomitant with this,negative psychological effects and sedentary behavior, which added to everyone’s social andeconomic determinants, constituted an alarming scenario during the pandemic. In order tocircumvent these situations, health promotion is carried out with the objective of promotingquality of life and well-being for people, respecting physic and social issues, through physicalactivity and body practices, configuring ways to maintain themselves active in a sustainableand pleasant way, promoting physical and mental health. The aim of this Course ConclusionWork is to analyze aspects of the practice of physical activity in the middle of the Covid-19pandemic period, in addition to reflecting on possible difficulties, facilitators andrecommendations for this practice in a safe and sustainable way for all. A literature scopereview was carried out, containing content from three databases, BVMS, PubMed and Scielo,using the keywords Physical Activity and Covid-19, in addition to reports from an academicjournal to compose the gray literature of this work. Thus, the guiding question of the researchwas: What were the reflections on the changes in habits to promote health in people's dailylives in relation to the practice of physical activity during the pandemic?. This work includedarticles and reports of all types that were restricted to the Portuguese language, from January2020 to March 2021, in addition to studies that related to the guiding question of this work. Thesearch in the databases, after filtering through the inclusion and exclusion criteria, resulted in(n=18) articles and (n=9) reports from the gray literature. The information obtained wasorganized in tables with the variables title, authors, type of publication and main conclusionsabout physical activity and Covid-19. The main finding of this research is about the need tocontinue the practice of physical activity and body practices, in a safe and sustainable way,taking into account the various determinants of everyone’s daily life, such as social, economic,cultural and behavioral factors. This research enabled the mapping of relationships betweenphysical activities and body practices during the pandemic, based on possible difficulties,facilitators and what were the recommendations for the practices, aiming at improving people’squality of life and well-being. In this sense, the pandemic has shown even more social inequalityin the country, which shows the importance of issuing instructions that are applicable to allsocioeconomic levels in order to promote health through physical activity.
A Novel Proof of Shuffle: Exponentially Secure Cut-and-Choose.

Shuffling is one of the most important techniques for privacy-preserving protocols. Its applications are manifold, including, for example, e-voting, anonymous broadcast, or privacy-preserving machine-learning. For many applications, such as secure e-voting, it is crucial that the correctness of the shuffling operation be (publicly) verifiable. To this end, numerous proofs of shuffle have been proposed in the literature. Several of these proofs are actually employed in the real world.
A Separation Result Between Data-oblivious and Data-aware Poisoning  Attacks

Poisoning attacks have emerged as a significant security threat to machine learning algorithms. It has been demonstrated that adversaries who make small changes to the training set, such as adding specially crafted data points, can hurt the performance of the output model. Some of the stronger poisoning attacks require the full knowledge of the training data. This leaves open the possibility of achieving the same attack results using poisoning attacks that do not have the full knowledge of the clean training set. In this work, we initiate a theoretical study of the problem above. Specifically, for the case of feature selection with LASSO, we show that full-information adversaries (that craft poisoning examples based on the rest of the training data) are provably stronger than the optimal attacker that is oblivious to the training set yet has access to the distribution of the data. Our separation result shows that the two setting of data-aware and data-oblivious are fundamentally different and we cannot hope to always achieve the same attack or defense results in these scenarios.
Applying a total error framework for digital traces to social media research

Researchers across disciplines have begun to discuss the potential errors that can occur when digital traces, such as social media data, are used to learn about humans and social phenomena. This chapter will give concrete application examples of computational social science research with social media data. First, we will introduce our total error framework for digital traces of humans online (TED) that we have proposed previously and that is inspired by existing error frameworks in the area of survey methodology, such as the total survey error framework (TSE). We explain the different error types that we have defined in our error framework and that might be lurking “behind the scenes” of each study. The reader will gain insights into how to diagnose and understand errors that frequently plague computational social science studies to avoid, mitigate, or at least document them. Two illustrative case studies, one about measuring presidential approval as an example of opinion mining and one about using the Google Perspective API to automatically detect toxicity, are used to discuss potential errors that might happen during different steps in the research process.
Ok Boomer- The Approaching DiZruption of Legal Education by Generation Z

Educating today’s law students is a difficult and challenging task. Add to this, the arrival of a new generation of students, the first true digital natives, born into the Internet-connected world, taking in information instantaneously and losing interest just as fast—learners accustomed to watching online lessons to learn but still valuing the professor. A generation of students always on social media and consuming mass amounts of content. Enter Generation Z.Since the late seventies, law schools have struggled with educating the Millennial generation. Each generation, particularly Generation Z, comes to law school with varying characteristics and new challenges that distinguish them from their predecessors, challenges requiring changes to our teaching and institutions. In order to recruit, educate, and graduate this new generation of law students, educators must understand the predominant characteristics, perspectives, and learning preferences of Generation Z. With these objectives in mind, this Article examines the characteristics and learning preferences of this new cohort of students. It offers suggestions on ways traditional legal education needs to change and improve to better meet the needs of this new breed of students. This Article begins by briefly describing the generational profiles of those that preceded Generation Z, particularly their bloodline of siblings, parents, and grandparents. The Article then delves into the characteristics and personality traits of this new generation of law students. This Article then examines the learning preferences of this new always-on, tech-savvy, realistic generation. The Article concludes with suggestions on ways to blend Generation Z’s characteristics and learning preferences into a legal education experience, embracing blended learning, multimedia, technology, and curriculum modifications, all allowing both law schools and this new generation to be successful. And maybe, just maybe, with the approach of Generation Z, it may be time to tone down Socrates and tune in YouTube.
Causal KL: Evaluating Causal Discovery.

The two most commonly used criteria for assessing causal model discovery with artificial data are edit-distance and Kullback-Leibler divergence, measured from the true model to the learned model. Both of these metrics maximally reward the true model. However, we argue that they are both insufficiently discriminating in judging the relative merits of false models. Edit distance, for example, fails to distinguish between strong and weak probabilistic dependencies. KL divergence, on the other hand, rewards equally all statistically equivalent models, regardless of their different causal claims. We propose an augmented KL divergence, which we call Causal KL (CKL), which takes into account causal relationships which distinguish between observationally equivalent models. Results are presented for three variants of CKL, showing that Causal KL works well in practice.
Learning with Algorithmic Supervision via Continuous Relaxations

The integration of algorithmic components into neural architectures has gained increased attention recently, as it allows training neural networks with new forms of supervision such as ordering constraints or silhouettes instead of using ground truth labels. Many approaches in the field focus on the continuous relaxation of a specific task and show promising results in this context. But the focus on single tasks also limits the applicability of the proposed concepts to a narrow range of applications. In this work, we build on those ideas to propose an approach that allows to integrate algorithms into end-to-end trainable neural network architectures based on a general approximation of discrete conditions. To this end, we relax these conditions in control structures such as conditional statements, loops, and indexing, so that resulting algorithms are smoothly differentiable. To obtain meaningful gradients, each relevant variable is perturbed via logistic distributions and the expectation value under this perturbation is approximated. We evaluate the proposed continuous relaxation model on four challenging tasks and show that it can keep up with relaxations specifically designed for each individual task.
LibGuides v.2: Finding credible sources: How to evaluate the credibility of sources

This guide will help you determine the credibility of an information source. The CRAP Test can help you determine if your source is credible or ...
Relaxing Local Robustness

Certifiable local robustness, which rigorously precludes small-norm adversarial examples, has received significant attention as a means of addressing security concerns in deep learning. However, for some classification problems, local robustness is not a natural objective, even in the presence of adversaries; for example, if an image contains two classes of subjects, the correct label for the image may be considered arbitrary between the two, and thus enforcing strict separation between them is unnecessary. In this work, we introduce two relaxed safety properties for classifiers that address this observation: (1) relaxed top-k robustness, which serves as the analogue of top-k accuracy; and (2) affinity robustness, which specifies which sets of labels must be separated by a robustness margin, and which can be $\epsilon$-close in $\ell_p$ space. We show how to construct models that can be efficiently certified against each relaxed robustness property, and trained with very little overhead relative to gradient descent. Finally, we demonstrate experimentally that these relaxed variants of robustness are well-suited to several significant classification problems, leading to lower rejection rates and higher certified accuracies than can be obtained when certifying standard local robustness.
Tagging Terms in Text: A Supervised Sequential Labelling Approach to Automatic Term Extraction

As with many tasks in natural language processing, automatic term extraction (ATE) is increasingly approached as a machine learning problem. So far, most machine learning approaches to ATE broadly follow the traditional hybrid methodology, by first extracting a list of unique candidate terms, and classifying these candidates based on the predicted probability that they are valid terms. However, with the rise of neural networks and word embeddings, the next development in ATE might be towards sequential approaches, i.e., classifying each occurrence of each token within its original context. To test the validity of such approaches for ATE, two sequential methodologies were developed, evaluated, and compared: one feature-based conditional random fields classifier and one embedding-based recurrent neural network. An additional comparison was added with a machine learning interpretation of the traditional approach. All systems were trained and evaluated on identical data in multiple languages and domains to identify their respective strengths and weaknesses. The sequential methodologies were proven to be valid approaches to ATE, and the neural network even outperformed the more traditional approach. Interestingly, a combination of multiple approaches can outperform all of them separately, showing new ways to push the state-of-the-art in ATE.
Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets

Ensembles of machine learning models yield improved system performance as well as robust and interpretable uncertainty estimates; however, their inference costs may often be prohibitively high. \emph{Ensemble Distribution Distillation} is an approach that allows a single model to efficiently capture both the predictive performance and uncertainty estimates of an ensemble. For classification, this is achieved by training a Dirichlet distribution over the ensemble members' output distributions via the maximum likelihood criterion. Although theoretically principled, this criterion exhibits poor convergence when applied to large-scale tasks where the number of classes is very high. In our work, we analyze this effect and show that the Dirichlet log-likelihood criterion classes with low probability induce larger gradients than high-probability classes. This forces the model to focus on the distribution of the ensemble tail-class probabilities. We propose a new training objective that minimizes the reverse KL-divergence to a \emph{Proxy-Dirichlet} target derived from the ensemble. This loss resolves the gradient issues of Ensemble Distribution Distillation, as we demonstrate both theoretically and empirically on the ImageNet and WMT17 En-De datasets containing 1000 and 40,000 classes, respectively.
ANALYSIS OF THE INTRINSIC MOTIVATION TOWARDS INNOVATIVE BEHAVIOR OF LANGUAGE AMBASSADOR FINALIST DAERAH ISTIMEWA YOGYAKARTA 2018

Penelitian ini bertujuan untuk menganalisis motivasi intrinsik terhadap perilaku inovatif Duta Bahasa di Daerah Istimewa Yogyakarta. Responden adalah finalis Duta Bahasa Daerah Istimewa Yogyakarta, yang berarti penulis menggunakan metode purposive sampling karena ada kriteria tertentu yang digunakan untuk memilih responden sesuai dengan objek penelitian. Metode pengumpulan data menggunakan kuesioner pada sejumlah finalis Duta Bahasa Daerah Istimewa Yogyakarta. Selain itu, penulis juga menerima data tambahan lain dari wawancara oleh anggota Duta Bahasa Daerah Istimewa Yogyakarta dan panitia penyelenggara. Adapun kesimpulan dalam penelitian ini, yaitu (1) responden pria dan wanita yang mencapai skor tertinggi adalah responden lulusan SMA berumur 21 tahun, (2) responden pria yang memiliki skor tertinggi adalah responden lulusan SMA berumur 21 tahun juga, (3) Responden wanita yang mencapai skor terendah adalah responden sarjana berumur 24 tahun. Secara sederhana, korelasi antara motivasi intrinsik dengan perilaku inovatif dalam penelitian yaitu: (1) skor umum lebih tinggi daripada motivasi intrinsik, (2) klasifikasi jenis kelamin, usia, dan latar belakang pendidikan menunjukkan indikasi hasil yang berbeda (3) hasil motivasi intrinsik akhir menunjukkan skor tinggi dalam proses inovasi memiliki dampak positif pada perilaku inovasi.This study aims to analyze the intrinsic motivation towards innovative behavior of Language Ambassador in Daerah Istimewa Yogyakarta. The respondents are the finalists of Language Ambassador of Daerah Istimewa Yogyakarta which means the author used purposive sampling method because there are certain criteria used to select the respondent in accordance with the object of research. Methods of data collection used questionnaires on a number of finalists of Language Ambassador of Daerah Istimewa Yogyakarta. Besides, the author also gets another additional data from the interview both by the member of Language Ambassador of Daerah Istimewa Yogyakarta and also the committee of the competitions. There are three conclusions in this study, they are (1) Male and female respondent who reach the highest score is a 21 years old high school graduate respondent, (2) Male respondent who reach the lowest score is a 21 years old high school respondent as well, (3) Female respondent who reach the lowest score is a 24 years old undergraduate respondent. Simply, the correlation between intrinsic motivation towards innovative behavior can be mentioned: (1) the general score is all high / above average for intrinsic motivation and innovative behavior questionnaires, (2) classification of gender, age, and education background shows different results indications (3) the final intrinsic motivation result indicates high score in the innovation process has positive impact on innovation behavior.
Digital skills: linguistic or technical!

Computers are so incredibly easy to deal with, for example compared to a sewing machine, that you really don 't have to be a techie. Preferably not; the government states that insufficient digital skills mainly concern employees in the lowest technical (and care) professions. Working on a computer requires some reading skills, but less than reading a book. However, digital skills are more linguistic than technical.
ANALYSIS OF ENGLISH LANGUAGE NEEDS IN NURSING STUDENTS OF MUHAMMADIYAH PONOROGO UNIVERSITY

English in universities is taught as a general course in non-English majors, as in the case at the Nursing Department, Muhammadiyah Ponorogo University. Ideally, the content presented should be adjusted to the department, which is referred to as ESP (English for Specific Purpose). Perceptions from several parties are needed to find out how important English is for a career as a nurse and the right material according to their needs. This research aimed to analyze the students’ needs on English for Specific Purposes materials, particularly at the Nursing department, University of Muhammadiyah Ponorogo. The researcher used questionnaire as the research instrument. The subject were the sixth semester students of Muhammadiyah Ponorogo Nursing Academy in the academic year 2018/2019 with the total number of 90 students in three classes. The researchers decided to take only 20% of the total students, namely 15 students for all classes. The results of this study indicate that in general, respondents believe that English is very important to be taught in the Nursing Department. English is taught as a general course, but students expect material relevant to their assignments in the workplace. Teacher of ESP English courses or interested individuals can Keywords :  ESP, English Subject, Needs Analysis, Learners’ Needs
Supporting Real-Time ${T}$-Queries on Network Traffic with a Cloud-Based Offloading Model

Traffic measurement provides fundamental statistics for network management functions. To implement the measurement modules on the data plane for real-time query response, modern sketches are designed to work with limited on-die memory allocation from network processors and collect traffic statistics in epochs of a preset length. To handle real-time queries at arbitrary times over traffic in a preceding period <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${T}$</tex> (called <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${T}$</tex> -queries), the prior art sets the epoch length to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${T \over n}$</tex> and keeps the measurement results in a window of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n - 1$</tex> past epochs to support approximate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${T}$</tex> -queries. Such an approach however drastically increases the memory cost or decreases the accuracy in the query results if the memory allocation is fixed. In this paper, motivated by the concept of offloading in today's edge-cloud computing, we propose a collaborative edge-center traffic measurement model, where the traffic measurement modules at all network devices form the edge, which offloads the traffic measurement results to a measurement center possibly hosted in a datacenter. The center synthesizes the measurements from the past epochs and sends the aggregate results back to the measurement modules to support T-queries. We conduct experiments using real traffic traces to evaluate the performance of the proposed edge-center measurement model. The experimental results demonstrate that the proposed designs significantly outperform the prior art.
Understanding the Generalization Benefit of Model Invariance from a Data Perspective

Machine learning models that are developed to be invariant under certain types of data transformations have shown improved generalization in practice. However, a principled understanding of why invariance benefits generalization is limited. Given a dataset, there is often no principled way to select suitable data transformations under which model invariance guarantees better generalization. This paper studies the generalization benefit of model invariance by introducing the sample cover induced by transformations, i.e., a representative subset of a dataset that can approximately recover the whole dataset using transformations. For any data transformations, we provide refined generalization bounds for invariant models based on the sample cover. We also characterize the suitability of a set of data transformations by the sample covering number induced by transformations, i.e., the smallest size of its induced sample covers. We show that we may tighten the generalization bounds for suitable transformations that have a small sample covering number. In addition, our proposed sample covering number can be empirically evaluated and thus provides a guide for selecting transformations to develop model invariance for better generalization. In experiments on multiple datasets, we evaluate sample covering numbers for some commonly used transformations and show that the smaller sample covering number for a set of transformations (e.g., the 3D-view transformation) indicates a smaller gap between the test and training error for invariant models, which verifies our propositions.