Reviewerly/exHarmony huggingface dataset:
Psychological impact of COVID-19 lockdown in children and adolescents from San Carlos de Bariloche, Argentina: Parents' perspective./ Impacto psicológico del aislamiento por COVID-19 en jóvenes de San Carlos de Bariloche, Argentina: la mirada de los padres.

INTRODUCTION: From an infectious perspective, children and adolescents were not highly affected by the COVID-19 pandemic. However, social isolation measures have deeply changed their lifestyle, which is believed to have a psychological impact on them. The objective was to assess the impact of COVID-19 lockdown on the emotional health of children and adolescents attending primary or secondary school. POPULATION AND METHODS: Parents of children and adolescents from San Carlos de Bariloche participated in the study. Adults' perception of the emotional and behavioral impact of lockdown on children and adolescents, changes in sleeping habits, screen use, sports-related activities, eating, and medical consultations, was assessed. RESULTS: A total of 267 parents were included. Of them, 96.3 % noticed emotional and behavioral changes. The most common ones were that their children were more bored (76.8 %), more irritable (59.2 %), more reluctant (56.9 %), and angrier (54.7 %). It was observed that they woke up and went to bed later, and slept 30 minutes more. Moreover, leisure screen use increased by 3 hours on weekdays. Time dedicated to physical activities did not change, but the type of activities did: swimming and team sports were replaced by biking, walking, and skiing. CONCLUSIONS: COVID-19 lockdown affected the emotional health and habits of children and adolescents. Boredom, irritability, and reluctance were more present during lockdown. The possibility of doing outdoor physical activities allowed them to keep practicing sports.
Joint Cramér-Rao Lower Bound for Nonlinear Parametric Systems With Cross-Correlated Noises

Joint state and parameter estimation (JSPE) has a surge of interest due to its dual purposes in many fields, such as sensor registration and signal processing. In this letter, a recursive joint Cramér-Rao lower bound (JCRLB) is developed for JSPE of nonlinear parametric systems with cross-correlated process and measurement noises at the same time. The JCRLBs for two special cases of such systems with additive Gaussian noises are also studied. Illustrative examples show the effectiveness of the JCRLB for the performance evaluation of JSPE of nonlinear parametric systems with cross-correlated noises at the same time.
PECULIARITIES OF COPING STRATEGIES IN DENTAL PATIENTS

Objectives. In the dental office there are a series of reactions, coping mechanisms in the face of stressors. For the attending physician, as well as for the psychologist, the knowledge of the patient's favorite adaptive patterns represents an important advantage, both in the reevaluation of the prognosis through the prism of the behavioral grid, for the correct appreciation of the therapeutic compliance. The investigation of psychic phenomena is done through a series of methods, which are tools for obtaining information about these phenomena them. So, it is there are used: observation, conversation, questionnaire, evaluation scales, etcMaterial and methods. In general, depending on the proposed goal, several methods are used. The study was conducted on a group of 253 subjects selected by sex and environment of origin patients of a private dental office, of which 207 were from urban area and 46 from rural area. In this regard, the “Adjustment Mechanisms Questionnaire” was used – a methodology developed by R. Lazarus and S. Folkman – Ways of coping Questionnaire – WOC, which has 50 items that describe a wide range of thoughts, feelings and actions, at “help” to deal with difficult life situations and overcome their negative consequences.Results. On the Confrontation scale for males, the score is equal to 9.17, and for females – 8.82; on the Distance scale, males obtained the score equal to 8.65, and females – 8.32; on the Self-Control scale, the score of males is equal to 11.06 and of females – 10.79; on the scale Searching for social media support for men is 11.26, and for women it is 12.17; on the scale Acceptance of responsibility for males is indicated the score – 8.63, and for females – 8.56, on the scale Escape-avoidance the score for males is 12.54, and for females – 12.67; in the Planning of solving the problem of the male persons, the score of 13.65 belongs to them, and to the female ones – 13.37; and at the Positive Overestimation the averages are as follows: men – 13.57, women – 13.80.Conclusions. Significant differences between these 2 categories were not noticed, but we can claim that males make more effort to change the situation in terms of confrontational coping just as men are more prone to distance. On the scale of accepting responsibility men have a slightly higher percentage which tells us that they accept the personal role in the problem and look for ways to solve the problem. On the run-avoidance scale women have a higher percentage, which indicates that women avoid dental treatment.
Kessler: a Machine Learning Library for Spacecraft Collision Avoidance

As megaconstellations are launched and the space sector grows, space debris pollution is posing an increasing threat to operational spacecraft. Low Earth orbit is a junkyard of dead satellites, rocket bodies, shrapnels, and other debris that travel at very high speed in an uncontrolled manner. Collisions at orbital speeds can generate fragments and potentially trigger a cascade of more collisions endangering the whole population, a scenario known since the late 1970s as the Kessler syndrome. In this work we present Kessler: an open-source Python package for machine learning (ML) applied to collision avoidance. Kessler provides functionalities to import and export conjunction data messages (CDMs) in their standard format and predict the evolution of conjunction events based on explainable ML models. In Kessler we provide Bayesian recurrent neural networks that can be trained with existing collections of CDM data and then deployed in order to predict the contents of future CDMs in a given conjunction event, conditioned on all CDMs received up to now, with associated uncertainty estimates about all predictions. Furthermore Kessler includes a novel generative model of conjunction events and CDM sequences implemented using probabilistic programming, simulating the CDM generation process of the Combined Space Operations Center (CSpOC). The model allows Bayesian inference and also the generation of large datasets of realistic synthetic CDMs that we believe will be pivotal to enable further ML approaches given the sensitive nature and public unavailability of real CDM data.
Digital socialization of schoolchildren and its influence on development of humanitarian orientation of Russian education

Analyzed is the problem of active use of digital technologies in education. Noted is that the theme is presented by many authors as a great benefit, but one should see both their positive and negative sides. The article examines the relationship between digital socialization and educational processes and attempts to find possible ways to reduce the negative impact of new virtual factors and new digital means of influencing the minds of young people.
The using of digital technologies in the design of the developing educational environment of the university

The article raises the problem of introducing digital and distance technologies in the scientific and educational process, as well as issues of developing digital literacy and competence of teachers. The authors made an attempt to highlight various digital education projects implemented in Kazakhstan, provide examples of various scientific seminars and webinars on the training of pedagogical personnel, foreign researches in this area, analyze the impact of online learning on the organization, content and teaching methods. As an illustration of the blended learning strategy, the experience of the annual international competition of scientific research of schoolchildren and students “Zhas zertteushi” in online format is highlighted. The article describes the main provisions of the competition, the algorithm and organization of the competition using distance technologies based on synchronous learning. The authors note the distinctive features of the scientific event and conclude that constant feedback and the readiness of the competition moderators to provide methodological assistance creates a stable motivation for the participants to research activities.
Fixation guided network for salient object detection.

Convolutional neural network (CNN) based salient object detection (SOD) has achieved great development in recent years. However, in some challenging cases, i.e. small-scale salient object, low contrast salient object and cluttered background, existing salient object detect methods are still not satisfying. In order to accurately detect salient objects, SOD networks need to fix the position of most salient part. Fixation prediction (FP) focuses on the most visual attractive regions, so we think it could assist in locating salient objects. As far as we know, there are few methods jointly consider SOD and FP tasks. In this paper, we propose a fixation guided salient object detection network (FGNet) to leverage the correlation between SOD and FP. FGNet consists of two branches to deal with fixation prediction and salient object detection respectively. Further, an effective feature cooperation module (FCM) is proposed to fuse complementary information between the two branches. Extensive experiments on four popular datasets and comparisons with twelve state-of-the-art methods show that the proposed FGNet well captures the main context of images and locates salient objects more accurately.
Noisy Simon Period Finding

Let \(f: \mathbb {F}_2^n \rightarrow \mathbb {F}_2^n\) be a Boolean function with period \(\mathbf {s}\). It is well-known that Simon’s algorithm finds \(\mathbf {s}\) in time polynomial in n on quantum devices that are capable of performing error-correction. However, today’s quantum devices are inherently noisy, too limited for error correction, and Simon’s algorithm is not error-tolerant.We show that even noisy quantum period finding computations may lead to speedups in comparison to purely classical computations. To this end, we implemented Simon’s quantum period finding circuit on the 15-qubit quantum device IBM Q 16 Melbourne. Our experiments show that with a certain probability \(\tau (n)\) we measure erroneous vectors that are not orthogonal to \(\mathbf {s}\). We propose new, simple, but very effective smoothing techniques to classically mitigate physical noise effects such as e.g. IBM Q’s bias towards the 0-qubit.After smoothing, our noisy quantum device provides us a statistical distribution that we can easily transform into an LPN instance with parameters n and \(\tau (n)\). Hence, in the noisy case we may not hope to find periods in time polynomial in n. However, we may still obtain a quantum advantage if the error \(\tau (n)\) does not grow too large. This demonstrates that quantum devices may be useful for period finding, even before achieving the level of full error correction capability.
Estimación del conteo de peatones basada en la dimensión fractal

Counting the number of pedestrians in urban environments has become an area of interest over the past few years. Its applications include studies to control vehicular traffic lights, urban planning, market studies, and detection of abnormal behaviors. However, these tasks require the use of intelligent algorithms of high computational demand that need to be trained in the environment being studied. This article presents a novel method to estimate pedestrian flow in uncontrolled environments by using the fractal dimension measured through the box-counting algorithm, which does not require the use of image pre-processing and intelligent algorithms. Four scenarios were used to validate the method presented in this article, of which the last scene was a low-light surveillance video, showing experimental results with a mean relative error of 4.92% when counting pedestrians. After comparing the results with other techniques that depend on intelligent algorithms, we can confirm that this method achieves improved performance in the estimation of pedestrian traffic.
Handling Particle Filter Sample Impoverishment for Orbit Determination

The paper aims at discussing techniques for managing one implementation issue that often arises in the application of particle filters: sample impoverishment. Dealing with such problem can significantly improve the performance of particle filters, and can make the difference between success and failure. Sample impoverishment occurs because of the reduction in the number of truly distinct sample values. Eventually, all of the particles will collapse to the same va1ue, and the problem is intensified when modelling errors occur. A simple solution can be to increase the number of particles, which can quickly lead to unreasonable computational demands and often only delays the inevitable sample impoverishment. There are more intelligent ways of dealing with this problem, such as roughening and prior editing, procedures to be discussed herein. The nonlinear particle filter is based on the bootstrap filter for implementing recursive Bayesian filters. The application consists of determining the orbit of an artificial satellite using real data from the GPS receivers. The nonlinear problem of orbit determination consists essentially of estimating values that completely specify the body trajectory in the space, processing a set of observations, like space GPS receivers on-board the satellite. From this set is possible to obtain nonlinear measurements (pseudo-ranges) that can be processed to estimate the orbital state. The standard differential equations describing the orbital motion and the GPS measurements equations are adapted for the nonlinear particle filter, so that the bootstrap algorithm is also used for estimating the orbital state. The evaluation will be done through convergence speed and computational implementation complexity, comparing the bootstrap algorithm results obtained for each technique that deals with sample impoverishment. Based on the analysis of such criteria, the advantages and drawbacks of the implementations will be presented.
Incorporating Background Knowledge into Dialogue Generation Using Multi-task Transformer Learning

Knowledge plays a very important role in the dialogue systems. Inspired by how humans use unstructured background knowledge in the conversations, this paper proposes a dialogue generation model based on multi-task learning. The model divides the conversation generation task into two tasks, a knowledge selection task and a response prediction task, which are regard as a reading comprehension task and a text generation task separately. Specifically, in the task of knowledge selection, a language pre-training model Bidirectional Encoder Representations from Transformers (BERT) is applied to solve the problem of selecting the knowledge from the background knowledge documents in the current context. And in the task of response prediction, a transformer version of pointer-generator network, being composed of an encoder using the shared BERT mentioned in knowledge selection and a decoder using the left-context-only transformer, is applied to copy tokens from the background knowledge via pointing and produce tokens in the vocabulary through a generator. Our experiments on the HOLL-E dataset show that our model achieves better results than the strong baseline models and the related recent work.
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) issues. Specifically, we show both theoretically and via an extensive empirical evaluation that the SNR of the gradient estimates for the latent variable's variational parameters decreases as the number of importance samples increases. As a result, these gradient estimates degrade to pure noise if the number of importance samples is too large. To address this pathology, we show how doubly reparameterized gradient estimators, originally proposed for training variational autoencoders, can be adapted to the DGP setting and that the resultant estimators completely remedy the SNR issue, thereby providing more reliable training. Finally, we demonstrate that our fix can lead to consistent improvements in the predictive performance of DGP models.
The role of governance in quality education in sub-Saharan Africa

This paper examines the governance-“education quality” nexus in a panel of 49 sub-Saharan African countries over the period 2000-2012. Ordinary Least Squares (OLS) and Quantile regression (QR) are employed as estimation strategies. The following findings are established. First, from the OLS, governance variables are negatively correlated with poor education quality. Second, with regards to QR, about half of the governance dynamics are not significantly correlated with poor education quality in the lowest quantile of poor education quality. With the exception of corruption-control, the other governance dynamics are negatively correlated with poor education quality in a non-monotonic pattern.
GANMEX: One-vs-One Attributions using GAN-based Model Explainability

Attribution methods have been shown as promising approaches for identifying key features that led to learned model predictions. While most existing attribution methods rely on a baseline input for performing feature perturbations, limited research has been conducted to address the baseline selection issues. Poor choices of baselines limit the ability of one-vs-one explanations for multi-class classifiers, which means the attribution methods were not able to explain why an input belongs to its original class but not the other specified target class. Achieving one-vs-one explanation is crucial when certain classes are more similar than others, e.g. two bird types among multiple animals, by focusing on key differentiating features rather than shared features across classes. In this paper, we present GANMEX, a novel approach applying Generative Adversarial Networks (GAN) by incorporating the to-be-explained classifier as part of the adversarial networks. Our approach effectively selects the baseline as the closest realistic sample belong to the target class, which allows attribution methods to provide true one-vs-one explanations. We showed that GANMEX baselines improved the saliency maps and led to stronger performance on perturbation-based evaluation metrics over the existing baselines. Existing attribution results are known for being insensitive to model randomization, and we demonstrated that GANMEX baselines led to better outcome under the cascading randomization of the model.
ANALISIS SENTIMEN DEBAT CALON PRESIDEN DAN WAKIL PRESIDEN INDONESIA 2019 MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER

News is an interesting timely report for a large number of people. The information in the news can be a opinions or facts, both positive and negative. Sentiment analysis is a part of text mining research to classify an entity in a text document. This research was conducted by classifying sentiments using a headline dataset and news content about the debates candidates of the presidential and vice presidential for the Republic of Indonesia in 2019. Sentiment analysis in this research is divided into two classes, positive and negative. The data used was taken from the news media Detik, Kompas, Sindonews, Viva, Republika, and CNNIndonesia related to research. Weighting is done using TF-IDF and the algorithm used in this research is the Naive Bayes Classifier algorithm with the Naive Bayes Multinomial model. Based on the calculation that has been done, the comparison value of each debating activity is obtained where 42.3% is positive and 57.7% is negative from 130 datasets for the first debate, 48.0% is positive and 52.0% is negative from 122 datasets for the second debate, 44.0% positive and 56.0% negative from 124 datasets for the third debate, and 44.0% positive and 56.0% negative from 100 datasets for the fourth debate. While the accuracy value obtained for each debate is 76.923% for the first debate, 80% for the second debate, 84% for the third debate, and 95% for the fourth debate.
Gender inequality in it technology: Causes and prospects

The article discusses the impact of the growth of information technology on a sharp increase in the need for IT specialists. More often than not, tech corporations use a policy of attracting women to tech jobs to meet this challenge. In order to bridge the gender gap in technology, to encourage girls and young women to choose professions in the field of information and communication technologies, international organizations are pursuing policies aimed at attracting them to the field of IT technologies.
ANALISIS PENGGUNAAN SINGKATAN MELALUI JEJARING SOSIAL WHATS APPLICATION (WA) PADA GRUP KAZAKHSTAN

The use of good language is very necessary in communicating. In addition to the use of language, the communication process requires active, creative, productive, and appreciative appreciation of language skills. One of them is through the use of abbreviations. Along with the times, the use of abbreviations was often used by Whats App users. Of course, the use of abbreviations must pay attention to grammar in order to be understood, and accepted by the reader. The purpose of this study, namely 1) explain the forms of abbreviations through Whats App social networks, 2) find out the factors that cause the use of abbreviations through Whats App messages. The research method used is descriptive qualitative method to obtain original data. The data collected is a conversation between a Kazakh group who is an alumni of SMK Fithrah Insani. The population in this study was 31 Kazakh group participants. The study sample was 10 Kazakh group participants. The data was obtained through interviews and observations. Based on the results of the study, it can be concluded that the researcher found ten Kazakhstan group participants who used abbreviations by keeping each syllable in their sentences. Factors for using abbreviations, namely 1) saving time in typing messages, 2) following a growing trend, 3) Writing abbreviations is usually only for friends, 4) Users feel that using abbreviations will not feel stiff to fellow friends in communicating.
The Influence of Twitter Media Exposure on the Language Attitude of Students at Brawijaya University

The S-O-R theory by Hovland, et.al (1953) states that organisms produce certain behaviors if there are special stimulus conditions, so that one can expect and estimate the compatibility between messages and communicant reactions (Effendy, 2005: 254). Twitter exposure can affect various aspects of the lives of its users, one of which is in the aspect of language attitudes. Language attitude is defined as the subjective perception of language influenced by behaviors. This research aims to analyze the effect of twitter exposure on language attitude of college students in Brawijaya University. This research is quantitative research. The data were collected using a questionnaire, from the results of the questionnaire were analyzed to determine the effect of each variable calculated using regression analysis. To convert data from questionnaires into numerical data that can be analysed, a Likert scale is used. This study shows the influence of twitter media exposure (X) on language attitude (Y) is significant. In addition, t value is -10.771 which means that the influence is inversely proportional, the more twitter media exposure increase, language attitude will decrease. This means that H0 is rejected, so it can be concluded that language attitude (Y) can be significantly influenced.