Reviewerly/exHarmony huggingface dataset:
Russian Electronic Resources in Collection Development of the Russian National Public Library for Science and Technology

The Russian National Public Library for Science and Technology has traditionally been and is currently acquiring Russian language paper materials by receiving deposit copies from the Russian Book Chamber. The concept of pervasiveness of a deposit copy dictates the need to use external digital resources – electronic libraries, archives and collections, electronic library systems. Acquiring of all of these sources of information depending on their access features and specifics of library services also meets the definition of «collection development». The article provides a general view of the collection of the Russian National Public Library for Science and Technology and the ways of its development. Specific examples show the status of some library materials available online. Reasons are offered in support of using remote access electronic resources as well as widening the variety of library materials and making subscription models more flexible.
Usage of digital tools and social media for Cooperative and Collaborative Learning

In the context of the COVID-19 pandemic, the process of higher education in many countries has been transformed into online format. Thanks to this, even in traditional universities, digital tools and technologies have become more actively used. A crucial role in this process belongs to social media, which allow more efficient use of various forms of students’ Cooperative and Collaborative Learning.The article is devoted to reviewing the experience of the usage of these educational technologies in Ukraine.The authors summarize the experience of colleagues and their own scientific findings. The main differences between Cooperative Learning and Collaborative Learning and their importance in the development of student competencies are analyzed. The advantages and limitations of the usage of the most popular resources among Ukrainian lecturers (Zoom, Google Meet) are considered. Case studies using these resources in conjunction with the capabilities of social networks (Facebook, Instagram, Twitter) and instant messengers (Telegram, Viber, WhatsApp)) for cooperative and collaborative learning are given. The influence of these forms of training on the development of professional and social competencies of students is analyzed. The authors also pay attention to analyzing the possibilities of using services for knowledge and competencies assessment of students (Google Forms, Kahoot it!) in Cooperative and Collaborative Learning.
LibGuides: ESB SLS Tips and Tricks: Teaching Units

Tips and tricks on a variety of resources found on the Virtual Reference Collection and the Digital Media Library
ANALISIS KOMPARASI ALGORITMA KLASIFIKASI DATA MINING UNTUK PREDIKSI STATUS KELULUSAN MAHASISWA AKADEMI BINA SARANA INFORMATIKA

The role of data mining in solving a large data problem includes estimation, prediction, classification, clustering and association. One of the roles of data mining used in this study is the classification to predict the graduation of Bina Sarana Informatika students in 2017 with the provision of passing on time and passing late. The algorithm used is the Decision Tree, K-Nerest Neighbor, Naïve Bayes, Random tree and Random Forest. The number of students graduating in the 1st period of 2017 was 5870 where there were 4998 students graduating on time and 872 graduating late. Performance testing of several algorithms using validation and different tests T-Test The k-NN algorithm has a high accuracy value of 99.98% which is the highest value and the best AUC is obtained by the Naïve Bayes algorithm with a value of 1,000. While the difference test is t-test significance &lt;0.05, then H0 is rejected and if&gt; 0.05 then H0 is accepted, only Decision Three against Naïve Bayes is accepted, otherwise it is rejected.
FORMATION OF MUSIC PERFORMING EXPERIENCE OF A STUDENT AS A PEDAGOGICAL PROBLEM

The purpose of this one article is analysis the pedagogical aspects of forming a student’s music performing experience in the process of his professional preparation. The article analyzes the category of experience; identifies the types of experience that have become the subject of scientific research in the field of art pedagogy; shows scientific data in philosophy, psychology and music pedagogy as a base for formation of a music performing experience; considers the phenomenon of music performing experience in the context of professional training of the future teacher of music art and substantiates its significance for the future music-pedagogical activity. Priority of instrumental and performing disciplines in forming of music performing experience and their orientation on mastering different types of music performing practice (vocal, instrumental, conducting) in the article are determined. The prerequisites for forming a music performing experience of a future music art teacher in the course of teaching (such as: development of analytical thinking and motivation for music performance activity; systematic replenishment of musical repertoire; disclosure of individual and personal qualities; accumulation of musical impressions and concert practice, etc.) are substantiated. The content of musical performing experience of the future teacher of musical art is formulated, which includes: skills of reading notes from a letter and transposition; ability to play in an ensemble and accompany; formation of performing technique, which allows you to freely own the instrument, voice, conductor’s gesture; confidence, artistic taste, artistry, psychological readiness for public performance of musical works; ability toembody an artistic image, etc. It is emphasized that this problem is of great practical importance, so its solution should be the focus of the teacher. Necessity of purposeful pedagogical management of the process of formation of music-performing experience of the student and the orientation of educational practice on the effectiveness of professional training are declared.
patologIA: online platform for improving image-based diagnostics applied to medical students

This work aims at the development of a platform called pathology. Designed to aid learning, with an emphasis on improving diagnoses based on images, it is aimed at medical students and will be available via the Internet. The platform will make possible the interaction between students, teachers and specialists, extending the experience in the classroom to a dynamic based on the interpretation of images, elaboration of diagnoses and comparison with reports previously provided by specialists. Artificial intelligence (I.A.) resources will enable the analysis of images to help compose the knowledge base of the application, developed using cloud’s APIs.
LibGuides: Library Science: 21st Century Foundations for Success: Introduction

Develop critical thinking skills through understanding the science of libraries and research
Potential field data modelling along Metal Earth's Chibougamau transect using geophysical and geological constraints

The Metal Earth (ME) project aims to understand the underlying geological mechanisms differentiating well endowed and less endowed mineralized zones in Precambrian greenstone belts of the Canadian Shield. ME acquires and collates various geological observations and geophysical data along 13 selected transects within the Superior Craton. The ME objective is to create valid 2D and 3D models of subsurface features, so as to identify components that contribute to mineralization processes. Thus far, the project has acquired gravity readings along 10 transects. The Bouguer reduced data were combined with the existing gravity data provided by the Geological Survey of Canada throughout the areas of interest. In this paper, data along the Chibougamau transect (~†128 line kilometers oriented SW-NE) located in the northeastern part of the Abitibi sub-province is considered. The Chibougamau area is one of the well-endowed transects where the geology consists of volcanic and sedimentary rocks, with tonalitic gneissic cores. Gravity and compiled high-resolution magnetic data along this transect were modelled using an initial petrophysical model constrained by geological observations made at the surface, seismic sections at depth and measured petrophysical properties. Potential field data modelling requires adjusting the shape, densities and magnetic susceptibilities of features at depth and can resolve the geometry of some features which are transparent in seismic sections. For example, the model provides an improvement in estimating the size, shape, and depth of plutonic bodies such as the Barlow and Chibougamau plutons across the Lac DorE Complex. The new modified model is consistent with the known structural geology (the synclinorium and anticlinorium of volcanic and sedimentary rocks). The model also helps us to improve the seismic interpretation and identify dykes and faults. The improved understanding of these subsurface features will potentially help to identify components contributing to mineralising processes in the Chibougamau area.
AUTOMATIC GENERATION OF CONFIGURATION FILES FOR PRODUCT FAMILIES

Over the past years, many software industries have adapted the Software Product Line(SPL) as a paradigm that empowers software reuse by exploiting software similarities and managing variabil- ities ...
Unsupervised Domain Adversarial Training in Angular Space for Facial Expression Recognition

This paper presents unsupervised domain adversarial training in angular space (UDAT-AS), a novel unsupervised domain adversarial training method for facial expression recognition (FER). UDAT is effective as it can adapt existing neural network based classification models to the target domain by utilizing only unlabeled data sets. It is realized by forming a domain adversarial network consisting of a domain classifier and a gradient reversal layer. UDAT reduces the domain dependency of neural network based classification models by making them insensitive to domain labels. However, conventional unsupervised domain adversarial training is not suitable for FER because facial expressions strongly depend on the domain of the training data sets, e.g., race, gender, shooting environment, and pose. In order to learn domain invariance more clearly, our key advance in UDAT-AS is to perform unsupervised domain adversarial training with angular softmax loss. UDAT-AS is an extension of the domain adversarial network; its domain classifier uses angular softmax loss, a commonly utilized metric learning technique. This enables us to efficiently reduce domain bias in FER models and allow the effective use of unlabeled target domain data sets. We evaluate our approach using two different collection methods, and demonstrate that our method outperforms conventional alternatives.
ICT for Development in Rural India: A Longitudinal Study of Women’s Health Outcomes

With a view toward improving the success of information and communication technology (ICT) initiatives in less developed countries in general and India in particular, this work seeks to uncover reasons for success and failure of ICT for development (ICT4D) initiatives. We drew on social networks theory in general and social contagion theory in particular, and examined the impact of advice network constructs on ICT kiosk use and the impact of ICT kiosk use on women’s health outcomes (i.e., seeking modern medical care and maternal mortality). A two-level model (i.e., village and individual) was developed to understand how women in rural India were influenced by other women in their advice networks to use ICT kiosks, and the effects of ICT kiosk use on seeking modern medical care and maternal mortality. At the village level, we proposed lead user network effects. At the individual level, we proposed structural network effects of other women in a focal woman’s network on individual outcomes of ICT kiosk use, seeking modern medical care, and maternal mortality. We focused on network position (i.e., centrality) and network tie strength (i.e., strong ties and weak ties) as explanatory variables. Specifically, we argued that strong tie centrality will have an adverse effect on ICT kiosk use, whereas weak tie centrality will have a favorable effect. We also argued ICT kiosk use will have a positive effect on seeking modern medical care and a negative effect on maternal mortality. Finally, we argued that seeking modern medical care will have a negative effect on maternal mortality. Our model was mostly supported in data collected about 6,710 women in 10 intervention group villages in rural India and 8,344 women in the control group villages over a period of approximately 7 years.
THE STUDENT AS AN ACTIVE RECIPIENT OF LITERARY ART

The theory of reception in the study of literature is based on the knowledge that the reader is an indispensable actor in the realization of a literary work, as well as in the process of its spiritual form. A literary work of art can come to life and realize its true function only if the one receiving it understands and experiences it in the right way, if he manages to interpret and adopt the message that the writer sends him through the work. There is therefore a common opinion that the writer and the reader jointly create and revive the wonderful world of literary art. This paper points out the importance of educating early primary school students for the proper reception, understanding and emotional experience of a literary text. The precondition to achieve success in this endeavor, is that students successfully master the skill of reading and familiarize themselves in a comprehensive way with the different types of reading. Modern schooling also sets an important task for the teacher, which is to motivate students to read and experience literary works; and above all to encourage and help students develop their own internal motivation to read.
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs

The state of the art of language models, previously dominated by pre-trained word embeddings, is now being pushed forward by large pre-trained contextual representations. This success has driven growing interest to understand what these models encode inside their inner workings. Despite this, understanding their semantic skills has been elusive, often leading to unsuccessful, non-conclusive, or contradictory results among different works. In this work, we define a probing classifier that we use to extract the underlying knowledge graph of nine of the currently most influential language models, including word embeddings, context encoders, and text generators. This probe is based on concept relatedness, grounded on WordNet. Our results show that this knowledge is present in all the models, but has several inaccuracies. Furthermore, we show that the different pre-training strategies and architectures lead to different model biases. We conduct a systematic evaluation to discover specific factors that explain why some concepts are challenging for the different families of models. We hope our insights will motivate the future development of models that capture concepts more precisely.
Mitigating Mode Collapse by Sidestepping Catastrophic Forgetting

Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator. Investigative study using a new data generation procedure indicates that the mode collapse of the generator is driven by the discriminator’s inability to maintain classification accuracy on previously seen samples, a phenomenon called Catastrophic Forgetting in continual learning. Motivated by this observation, we introduce a novel training procedure that dynamically spawns additional dis-criminators to remember previous modes of generation. On several datasets, we show that our training scheme can be plugged-in to existing GAN frameworks to mitigate mode collapse and improve standard metrics for GAN evaluation.
Distributed data stream processing for clustering near-the-edge

In this Final Master Project, a Machine Learning algorithm for clustering named CluStream was applied in a data streaming context. Additionally, for the data streamprocessing, a distributed Apache Spark platform for massive processing also was applied. The purpose of this project was to apply the CluStream Algorithm to classifydata and distribute the processing close to the data generation using Spark. This project was divided in two phases. The first phase aimed to take some simulated data, create a DataStream, publish the DataStream in a Kafka streaming bus and let Spark Streaming to subscribe the data and apply a clustering algorithm using Spark MLlib. The simulated data was stored in a database and queried frequently in order to simulate the sending data coming from real in-field sensors. Because the clustering algorithm is a nonsupervised algorithm, the Dataset used was a synthetic Dataset where the group classification is well-known. The second phase of this project aimed to present the clustered data in a graphical representation. Additionally, this second part intended to publish the clustered data again in the Kafka bus under another Topic Name and subscribe an additional Database in order to store that clustered data. Then, a NodeJS application was created in order to listen to any data change in the Database and represent that data graphically. The idea in this second part was to present a friendly onlinerepresentation of that data that is being consumed and processed by the clustering algorithm.
I've been testing out Brave Browser for ...

I've been testing out Brave Browser for a week. It's impressive with its support for IPFS and its Brave Attention Token idea. And it comes
Interaction of the Russian schools with EdTech companies during the mass transition to distance learning

The article analyzes the interaction of comprehensive schools and digital educational technology (EdTech) providers during the transition to distance learning caused by the COVID-19 pandemic. The authors investigate the confidence of school teachers and administrators in state and non-state providers of EdTech, as well as their satisfaction with the resources provided by them. The study is based on data from a survey of teachers and school leaders conducted by the Laboratory for Educational Innovation Research, National Research University Higher School of Economics in March 2020 in 20 regions. The survey involved 8188 respondents from 190 comprehensive schools. The following conclusions were made: - The state is currently the main player in the market of educational technologies. Most teachers and managers of educational organizations trust it and are satisfied with the content that the state provides.-Young teachers, teachers with previous experience in distance learning, and teachers of certain subject areas actively turn to the non-governmental EdTech sector: large businesses, non-profit organizations, and EdTech startups.-The level of satisfaction with the state as a technology provider for teachers with a little teaching experience, as well as for those who had previous experience in distance learning, is lower than for teachers with more than 11 years of general teaching experience or without distance teaching experience.
Transformer-QL: A Step Towards Making Transformer Network Quadratically Large

Transformer networks have shown outstanding performance on many natural language processing tasks. However the context length (the number of previous tokens on which the output states depend) of a Transformer network grows at best linearly with the memory and computational power used. This limitation prevents a transformer network to have very long context in a resource limited application. In this work, we propose a class of transformer networks, namely Transformer-QL (\bf{Q}uadratically \bf{L}arge), in which, the context length can grow at best quadratically with the memory and computational power used. We have empirically evaluated a Transformer-QL model in three long range language modeling datasets. The results show that Transformer-QL can provide significant improvements over other state of the art networks.
Disentangled cyclic reconstruction for domain adaptation

The domain adaptation problem involves learning a unique classification or regression model capable of performing on both a source and a target domain. Although the labels for the source data are available during training, the labels in the target domain are unknown. An effective way to tackle this problem lies in extracting insightful features invariant to the source and target domains. In this work, we propose splitting the information for each domain into a task-related representation and its complimentary context representation. We propose an original method to disentangle these two representations in the single-domain supervised case. We then adapt this method to the unsupervised domain adaptation problem. In particular, our method allows disentanglement in the target domain, despite the absence of training labels. This enables the isolation of task-specific information from both domains and a projection into a common representation. The task-specific representation allows efficient transfer of knowledge acquired from the source domain to the target domain. We validate the proposed method on several classical domain adaptation benchmarks and illustrate the benefits of disentanglement for domain adaptation.
Towards Learning to Remember in Meta Learning of Sequential Domains

Meta-learning has made rapid progress in past years, with recent extensions made to avoid catastrophic forgetting in the learning process, namely continual meta learning. It is desirable to generalize the meta learner’s ability to continuously learn in sequential domains, which is largely unexplored to-date. We found through extensive empirical verification that significant improvement is needed for current continual learning techniques to be applied in the sequential domain meta learning setting. To tackle the problem, we adapt existing dynamic learning rate adaptation techniques to meta learn both model parameters and learning rates. Adaptation on parameters ensures good generalization performance, while adaptation on learning rates is made to avoid catastrophic forgetting of past domains. Extensive experiments on a sequence of commonly used real-domain data demonstrate the effectiveness of our proposed method, outperforming current strong baselines in continual learning. Our code is made publicly available online (anonymous)
Learn how NCAA violations, enforcement process have been evolving in a COVID‐19 world

You could say that when it comes to NCAA violations and enforcement during the COVID‐19 pandemic, not much has changed but somehow everything has changed. While many of the same types of violations continue to occur, and the NCAA continues its enforcement process, it does all look a bit different. To provide an overview and some insight into the violation trends across all divisions, and how the pandemic has impacted the enforcement process, the NCAA's Mark Hicks, Managing Director of Enforcement — Development; Jon Duncan, Vice President of Enforcement; and Derrick Crawford, Managing Director of Enforcement — Investigations and Processing, gave a virtual presentation as part of the annual NCAA Convention.