Reviewerly/exHarmony huggingface dataset:
Insights Into the Dynamics of Coupled VO<sub>2</sub> Oscillators for ONNs

The collective behavior of many coupled oscillator systems is currently being explored for the implementation of different non-conventional computing paradigms. In particular, VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> based nano-oscillators have been proposed to implement oscillatory neural networks that can serve as associative memories, useful in pattern recognition applications. Although the dynamics of a pair of coupled oscillators have already been extensively analyzed, in this brief, the topic is addressed more practically. Firstly, for the application mentioned above, each oscillator needs to be initialized in a given phase to represent the input pattern. We demonstrate the impact of this initialization mechanism on the final phase relationship of the oscillators. Secondly, such oscillatory networks are based on frequency synchronization, in which the impact of variability is critical. We carried out a comprehensive mathematical analysis of a pair of coupled oscillators taking into account both issues, which is a first step towards the design of the oscillatory neural networks for associative memory applications.
Class-wise Calibration: A Case Study on COVID-19 Hate Speech

Proper calibration of deep-learning models is critical for many high-stakes problems.In this paper, we show that existing calibration metrics fail to pay attention to miscalibration on individual classes, hence overlooking minority classes and causing significant issues on imbalanced classification problems.Using a COVID-19 hate-speech dataset, we first discover that in imbalanced datasets, miscalibration error on an individual class varies greatly, and error on minority classes can be magnitude times worse than what is suggested by the overall calibration performance.To address this issue, we propose a new metric based on expected miscalibration error, named as Contraharmonic Expected Calibration Error (CECE), which punishes severe miscalibration on individual classes.We further devise a novel variant of temperature scaling for imbalanced data to improve class-wise miscalibration, which re-weights the loss function by the inverse class count to tune the scaling parameter to reduce worst-case minority calibration error.Our case study on a benchmarking COVID-19 hate speech task shows the effectiveness of our calibration metric and our temperature scaling strategy.
Syntax-Based Attention Masking for Neural Machine Translation

We present a simple method for extending transformers to source-side trees.We define a number of masks that limit self-attention based on relationships among tree nodes, and we allow each attention head to learn which mask or masks to use.On translation from English to various low-resource languages, and translation in both directions between English and German, our method always improves over simple linearization of the source-side parse tree and almost always improves over a sequence-to-sequence baseline, by up to +2.1% BLEU.
Recognizing Credit Card Fraud Using Machine Learning Methods

The rapid growth of e-commerce and online-based payment possibilities carries along with an empirical universe of economic fraud in which credit card fraud is more preventing for many years; several researchers have developed many data mining base methods to overcome this problem. Recently there has been a major interest in applying machine learning algorithms in place of data mining techniques to discover credit card fraud. Continuous work is done to bring in a conceptual difference between fraud recognition and forecasting probable non genuine opportunities in the digital space of financial transactions. This research shows several algorithms that can be used to solve credit card fraud, however, a number of challenges appear, such as dataset imbalance, variant fraudulent behaviour, etc. In this paper, we are going to address the problem of an imbalanced dataset. SMOTE sampling technique is used to convert the imbalanced dataset to a balanced binary dataset. There are now various machine learning algorithms that are tested by using the European credit card dataset and are compared by using evaluation metrics like accuracy, precession, AUC value, and ROC curve, etc. The results are very encouraging and graphs have been plotted which identify the best suitable algorithm.
Recommending Tasks in Online Judges using Autoencoder Neural Networks

Programming contests such as International Olympiads in Informatics (IOI) and ACM International Collegiate Programming Contest (ICPC) are becoming increasingly popular in recent years. To train for these contests, there are several Online Judges available, in which users can test their skills against a usually large set of programming tasks. In the literature, so far few papers have addressed the problem of recommending tasks in online judges. Most notably, as opposed with traditional Recommender Systems, since the learners improve their skills as they solve more problems, there is an intrinsic dynamic dimension that has to be considered: when recommending movies or books, it is likely that the preferences of the users are more or less stable, whilst in recommending tasks this does not hold true. In order to help the learners, it is crucial to recommend them tasks that are challenging but not unsolvable compared with their current set of skills. In this paper we present a Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN). We also discuss the results of an experimental evaluation of our approach in both the scenarios in which we consider, or not, the intrinsic dynamic dimension of the problem. The ANNs are trained with the dataset of all the submissions in the Italian National Online Judge, used to train students for the Italian Olympiads in Informatics.
Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge

Cant is important for understanding advertising, comedies and dog-whistle politics.However, computational research on cant is hindered by a lack of available datasets.In this paper, we propose a large and diverse Chinese dataset for creating and understanding cant from a computational linguistics perspective.We formulate a task for cant understanding and provide both quantitative and qualitative analysis for tested word embedding similarity and pretrained language models.Experiments suggest that such a task requires deep language understanding, common sense, and world knowledge and thus can be a good testbed for pretrained language models and help models perform better on other tasks.
Multi-Adversarial Learning for Cross-Lingual Word Embeddings

Generative adversarial networks (GANs) have succeeded in inducing cross-lingual word embeddings -maps of matching words across languages-without supervision.Despite these successes, GANs' performance for the difficult case of distant languages is still not satisfactory.These limitations have been explained by GANs' incorrect assumption that source and target embedding spaces are related by a single linear mapping and are approximately isomorphic.We assume instead that, especially across distant languages, the mapping is only piece-wise linear, and propose a multi-adversarial learning method.This novel method induces the seed cross-lingual dictionary through multiple mappings, each induced to fit the mapping for one subspace.Our experiments on unsupervised bilingual lexicon induction and cross-lingual document classification show that this method improves performance over previous single-mapping methods, especially for distant languages.
A Research Data Infrastructure Component for the Automated Metadata and Data Quality Extraction to Foster the Provision of FAIR Data in Earth System Sciences

Abstract. Metadata management is core to support discovery and reuse of data products, and to allow for reproducibility of the research data in Earth System Sciences (ESS). Thus, ensuring acquisition and provision of meaningful and quality assured metadata should become an integral part of data-driven ESS projects.We propose an open-source tool for the automated metadata and data quality extraction to foster the provision of FAIR data (Findable, Accessible, Interoperable Reusable). By enabling researchers to automatically extract and reuse structured and standardized ESS-specific metadata, in particular quality information, in several components of a research data infrastructure, we support researchers along the research data life cycle.
Mobile Open Social Learning for Languages (MOSL4L)

An extensive literature exists on how to help students learn languages. The learning process is particularly challenging since it combines different types of knowledge and skills into a dual process of comprehension and production, using both oral and written modalities. Networked technology has led to the emergence of different types of learning that can be applied to languages. In this article three of these types are highlighted as being particularly useful for language learning, as can be seen by their impact in the literature, namely mobile, open and social learning. After an analysis of each one, a proposal is made to combine them into a single framework called Mobile Open Social Learning for Languages (or MOSL4L). It is subsequently characterized using Activity Theory and some suggestions are made for establishing a rubric that could enable language learning scenarios to be analyzed in terms of the constituent parts that define their nature and enable the causal relations with learning to be highlighted.
PROFESSIONAL SELF-DEVELOPMENT OF LANGUAGE AND LITERATURE TEACHERS AS PERCEIVED BY POSTGRADUATE PEDAGOGICAL EDUCATION COURSES ATTENDEES

The article studies the language and literature teachers’ understanding of the characteristics and stages of professional self-development, their ability to plan their career growth. Attention is paid to the democratic principles and human-centered approach. The novelty of this article lies in the definition of the leading determinants of self-education as a factor of personal growth and professional development, the formation of a culture of personal development, emotional and volitional self-regulation. The methodological basis of the study is identified by the following approaches: acmeological, axiological, anthropocentric, learning-by-doing, competence, culturological, personal, synergetic, systematic. The subject of the study is language and literature teachers’ understanding of their professional development and postgraduate pedagogical education. Thus, this research demonstrates the relevance of the issues raised in this article. Future research needs to clarify the problems that hinder the creativity of teachers to deepen the knowledge about the theoretical and methodological principles of professional self-development, stimulate teachers to improve their professional competencies, focus on the creation of their trajectory of personal development.
Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment

We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective.We demonstrate BDMA by training a model for a pair of languages rather than individual, directed source and target combinations, reducing the number of models by 50%.We show that models trained with BDMA in the "forward" (source to target) direction can successfully map words in the "reverse" (target to source) direction, yielding equivalent (or better) performance to standard unidirectional translation models where the source and target language is flipped.We also show how BDMA reduces the overall size of the model.
Groundwater Level Prediction based on Neural Networks: A case study in Linze, Northwestern China

Groundwater level is an important factor in evaluating groundwater resources. Due to numerous non-linear factors, establishing theoretical models is difficult.. Therefore, this paper proposesthe BP (Back Propagation) neural network and the Radial Basis Function (RBF) neural network. The study area is divided into two zones. The R 2 (coefficient of determination) and RMSE (Root Mean Squared Error) are used to evaluate the performance. The BP neural network is used to predict groundwater level in the two zones with the R 2 of0.57 and 0.54, with the RMSE of 0.0804 meters and 0.1864 meters respectively. The RBF neural network is implemented with R 2 of 0.65 and 0.61, with RMSE of 0.0720 meters and 0.1519 meters, respectively. The results show the RBF neural network performs better than the BP neural network in the accuracy of predicting groundwater level. This study shows the feasibility and superiority of groundwater simulation using neural network.
Google Meet Application as an Online Learning Media for Descriptive Text Material

The Covid-19 pandemic has resulted in all activities being carried out online from home, including teaching and learning activities. So the use of online-based platforms can be a solution to the learning process at this time continues to run smoothly. One application that can be used is Google Meet which is a video communication service. This study aims to explain the use of Google Meet in the learning process of Descriptive Text material in class 10 IPS 4 MAN 4 Jakarta, which includes the advantages and disadvantages that are felt in the implementation of learning this material using Google Meet. This research uses qualitative research with descriptive methods where the research results are obtained through observation and interviews which are then analyzed by describing them. The results of the study found that Google Meet is suitable for delivering Descriptive Text learning material while speaking practice for students in class 10 IPS 4. However, this media cannot be used to carry out written assessments so that other media is needed as an additional medium of taking written scores
Contrastive Learning Inverts the Data Generating Process

Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a large variety of downstream tasks. We here prove that feedforward models trained with objectives belonging to the commonly used InfoNCE family learn to implicitly invert the underlying generative model of the observed data. While the proofs make certain statistical assumptions about the generative model, we observe empirically that our findings hold even if these assumptions are severely violated. Our theory highlights a fundamental connection between contrastive learning, generative modeling, and nonlinear independent component analysis, thereby furthering our understanding of the learned representations as well as providing a theoretical foundation to derive more effective contrastive losses.
THE DEVELOPMENT OF PROFESSIONALLY ORIENTED MEDIA EDUCATION AND ITS FUTURE

The scientific article analyzes the importance and relevance of media education. The author provides teachers with the pedagogical foundations of teaching media education in the field of information literacy in the field of vocational education. The scientific article takes into account the fact that the development of information technology in the world contributes to the latest trends in the development of pedagogical personnel and pedagogical technologies in the development of media education in the educational process, assuming that the younger generation will be crucial for information. In the article, the author draws on scientific conclusions, citing examples of the importance and necessity of media education. Considering that information technologies are developing in society, the ways of teacher education are analyzed in a scientific article. The development of science and technology is associated with the need for widespread use and application of the latest technological methods and equipment in the direction of vocational guidance and the role of media education in this direction.
ONLINE LEARNING MANAGEMENT USING GOOGLE SITES IN COVID-19 PANDEMIC ERA

Covid-19 impacts the world of education, so learning is carried out online due to a policy of maintaining distance. One of the methods used to keep the learning going in the pandemic era is by utilizing e-learning. The use of applications in online learning is an innovation for teachers in preparing material to be used in the online learning management process. The purpose of this study aims to describe learning management using Google Sites in pandemic at SMP Muhammadiyah 4 Singosari. This research uses descriptive qualitative research methods. The data collection methods used in this study were interview, observation, and documentation during the learning process. The subject of this study were students of class VIII SMP Muhammadiyah 4 Singosari. Online learning management during pandemic conditions includes planning, organizing, implementing, and monitoring. Online learning will be carried out with the help of Google Sites as a medium for delivering material. The planning stage includes observation, preparation of lesson plans, collection of material sources, and preparation of learning media using Google Sites. The organizing stage includes determining the communication channels used for the online learning process and focusing resources on the target. The implementation stage is the interaction carried out by teachers and students during learning. The supervision stage includes the evaluation stage in learning. Learning evaluation is carried out through student attendance and student activeness in participating in learning. Student attendance recap shows an increase in each meeting. The use of Google Sites as a learning medium during the pandemic can be further developed to increase student motivation and make it easier for students to learn while carrying out online learning.
Global And Russian Trends Of Modern Education

The main trends in changes in education became a response to external threats caused by globalization processes, new technologies and innovations in the age of new thinking and a new attitude to the changes.The economy and society as a whole need thinking and functionally competent personnel, capable of continuous updating and a reasonable change of their competencies and activities for a successful life in modern society.All this leads to a change in the philosophy of modern education for the formation of the digital economy of Russia.Today we are witnessing large-scale changes affecting a large number of people: globalization of production, communications, cultures, education systems; family structure changes, the population becomes more heterogeneous; economic competitiveness and social cohesion increase.Interest in life-long learning is dictated not so much by the requirements of the labor market as by the awareness of each modern person of the subjectivity of his life path.As a result, in order to satisfy the public request for the development of skills and competencies, the world education system and methodological approaches to training are being transformed.The list of professions of the future and the requirements for the qualifications of specialists are constantly being updated, technologies are rapidly improving and their application is becoming wider, the importance of an interdisciplinary approach and the integration of knowledge for solving socio-economic problems is growing.
Robot Assisting Water Serving to Disabilities by Voice Control

ROS is an open-source robot operating system.In this paper, we use ROS to control Conbe robot arm.By introducing the YOLACT real-time instance segmentation, we trained our own model for Object Detection.Secondly, the Speech-Recognition system is established through Deep speech and Mozilla Text-To-Speech with Tacotron2 DDC model.Deep speech is an end-to-end speech system, where deep learning supersedes these processing stages.Combined with a language model, this approach achieves higher performance than traditional methods on hard speech recognition tasks while also being much simpler.In this way, we create an artificial intelligence, which accomplished a simple conversation with people.And the voice control system is established based on Speech-Recognition system.In the experiment, we successfully control the robot arm move positions and do water serving for disabilities by voice command.With this research, voice control robot arm can be apply in the life support area, it will be more convenient for disabilities in daily life.
Meningkatkan Kemampuan Menulis Pantun Siswa Melalui Model Multiliterasi

This study aims to determine the improvement of students' ability in writing rhymes by applying the multiliterate learning model in Class V SDN 62 Bengkulu City. This research is a Classroom Action Research which is conducted in two cycles, each of which includes: planning, implementing, observing, and reflecting. The subjects in this study were students of SDN 62 Bengkulu City which consisted of 14 female students and 16 male students. The research instruments used were observation sheets and poetry writing test sheets. The data from this study were analyzed using the average value and the percentage of classical learning completeness with the standard conversion formula of 100. The results showed that the average value of the pantun writing test in cycle I was 68.33 with classical learning completeness of 66.66 % ; There was an increase in cycle II with the average score of the rhyme writing test result was 81.66 with classical learning completeness of 83.33 %. Thus, it can be concluded that the application of the multiliterate learning model can improve students' ability to write rhymes in grade V SDN 62 Bengkulu City. It is recommended that Indonesian teachers, especially the material about pantun, apply a multiliterate model in learning to write pantun by paying attention to student involvement in the learning process, and other researchers can also apply it to other subjects.
The Affordances of the Information and Communication Universe, or Who is Behind the Mass Media

This article examines the specifics and perspectives of the information and communication universe theory for the analysis of the mass media. The aim is to explain the application of the concept of affordance in the context of media theory. The term was introduced by psychologist James J. Gibson to describe the specific inviting nature of objects and events, which, through affordances, suggest an algorithm for subsequent actions. From this point of view, the information and communication universe makes it possible to use it for some important purposes for the subjects of communication. However, the quality and options for implementing the possibilities of affordance depend on the goals, interests and skills of the subject who is trying to work with this affordance. To use an analogy, the same axe could invite some people to chop wood for an old lady, and others to use it as a weapon against her in order to seize her pension benefits. The thesis on the functional usefulness of the category information and communication universe for the analysis of processes in the media system is put forward. Traditional ideas about the specifics of the production and consumption of texts are corrected. The author analyzes the affordances of the Internet as a subsystem of the information and communication universe. It helps to explain the radical differences in the assessment of network digital technologies by representatives of different research schools. The article outlines further prospects for the identification and use of hidden affordances.