Reviewerly/exHarmony huggingface dataset:
Fairness-Aware Node Representation Learning

Node representation learning has demonstrated its effectiveness for various applications on graphs. Particularly, recent developments in contrastive learning have led to promising results in unsupervised node representation learning for a number of tasks. Despite the success of graph contrastive learning and consequent growing interest, fairness is largely under-explored in the field. To this end, this study addresses fairness issues in graph contrastive learning with fairness-aware graph augmentation designs, through adaptive feature masking and edge deletion. In the study, different fairness notions on graphs are introduced, which serve as guidelines for the proposed graph augmentations. Furthermore, theoretical analysis is provided to quantitatively prove that the proposed feature masking approach can reduce intrinsic bias. Experimental results on real social networks are presented to demonstrate that the proposed augmentations can enhance fairness in terms of statistical parity and equal opportunity, while providing comparable classification accuracy to state-of-the-art contrastive methods for node classification.
Integrated Model to Develop Grammar Checker for Afaan Oromo using Morphological Analysis: A Rule-based Approach

This study has implemented a rule-based approach on grammar checkers by integrating a spell-checker with a morphological analyzer to improve the Afaan Oromo grammar checker.A corpus containing about 300,000 words has been prepared to be used for spell-checker.About 300 grammar rules are constructed to detect the grammar error within the Afaan Oromo text and to suggest the possible grammar correction.The developed frameworks have experimented on the document having pairs of 100 correct and incorrect sentences.The experimental result for checking the spelling errors has scored 73% of recall, 76% precision, and 75% of F-measure.The score for suggesting the correct spelling is 78% of recall, 62% precision, and 70% precision F-measure while the evaluation result for detecting the grammar errors has 47% recall, 90% precision and 68% f-measure score.For suggesting the possible correct grammar on the detected error, the system has scored 61% recall, 71% precision and 66% f-measure.The overall performance of the developed system has a good performance.However, there is still a need to conduct further research to improve the Afaan Oromo grammar checker.
Adaptive Neuro Fuzzy Interface System Based Short Circuit Fault Diagnoses in a Three Phase Induction Motor Drive System

Induction motors are commonly used in different industrial, housing and medical applications, such as air conditioners, transportation, elevators and others.These motors are rugged, reliable and economical.Generally, the reliable operation of the motor and its drive system is very important in industry so that any expected fault must be detected immediately by a monitoring system.This paper reviews and simulates a three-phase induction motor model and different types of faults that may happen in the drive system.Due to short circuit severity on the overall drive system, this study focuses only on short circuit faults, e.g., when any Insulated Gate Bipolar Transistor (IGBT) is shorted out and DC link capacitor is shorted out.Also, an Adaptive Neuro Fuzzy Interface System (ANFIS) algorithm is programmed to detect different types of faults and classify them.The ANFIS system takes two parameters as input from the motor: the first one is the load torque whereas the second one is the stator current.Whereas, the previously published works analyze only the motor's stator current.MATLAB is used to train the ANFIS system and also to analyze samples.Additionally, LTSpice is used simulate the drive system.The ANFIS system shows very high accuracy in short circuit fault detection and classification.
Simulating time-varying strong lenses

We present a self-consistent and versatile forward modelling software package that can produce time series and pixel-level simulations of time-varying strongly lensed systems. The time dimension, which needs to take into account different physical mechanisms for variability such as microlensing, has been missing from existing approaches and it is of direct relevance to time delay, and consequently H0, measurements and caustic crossing event predictions. Such experiments are becoming more streamlined, especially with the advent of time domain surveys, and understanding their systematic and statistical uncertainties in a model-aware and physics-driven way can help improve their accuracy and precision. Here we demonstrate the software's capabilities by exploring the effect of measuring time delays from lensed quasars and supernovae in many wavelengths and under different microlensing and intrinsic variability assumptions. In this initial application, we find that the cadence of the observations and combining information from different wavelengths plays an important role in the correct recovery of the time delays. The Mock Lenses in Time (MOLET) software package is available at: \url{this https URL}
Professional Activity of an Elementary School Teacher with Students of the Alpha Generation

The study substantiates the relevance of the problem of professional activity of primary school teachers with students of the Alpha generation, presents the chronology of generations in Western Europe, the theoretical analysis of publications of scientists to study the features of the 21st century with the active introduction of digital technologies in society. The purpose of the study is to analyse the specific characteristics of children of the Alpha generation and substantiate the methods and techniques of professional activity of modern primary school teachers with them. In modern world, the field of digital technology is developing rapidly, and children are exposed to their influence from birth. In the course of the study, the authors analysed the scientific literature on philosophy, psychology, and pedagogy, analysed the factors influencing the development of psychological characteristics of students of the Alpha generation, conducted interviews with leading teachers and trainers to optimise the educational process in primary school. The authors recommend methods and techniques for organising the educational process in primary school, which are aimed at achieving the goals and objectives declared in the State Standard of Primary School and the Concept of the New Ukrainian School: nonviolent communication, storytelling, team building, game situations, interactive technologies, and problem-solving methods. Emphasis is placed on finding ways to combine digital technologies with teaching methods that would promote the development of creative and critical thinking. The practical value of scientific research lies in the analysis of the characteristics of modern primary school children born and raised in the digital society, and the presentation of methods to enhance their cognitive activity and the development of subject-subject relations on a partnership basis
Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization

Machine learning is predicated on the concept of generalization: a model achieving low error on a sufficiently large training set should also perform well on novel samples from the same distribution. We show that both data whitening and second order optimization can harm or entirely prevent generalization. In general, model training harnesses information contained in the sample-sample second moment matrix of a dataset. For a general class of models, namely models with a fully connected first layer, we prove that the information contained in this matrix is the only information which can be used to generalize. Models trained using whitened data, or with certain second order optimization schemes, have less access to this information, resulting in reduced or nonexistent generalization ability. We experimentally verify these predictions for several architectures, and further demonstrate that generalization continues to be harmed even when theoretical requirements are relaxed. However, we also show experimentally that regularized second order optimization can provide a practical tradeoff, where training is accelerated but less information is lost, and generalization can in some circumstances even improve.
MTL782_IITD at CMCL 2021 Shared Task: Prediction of Eye-Tracking Features Using BERT Embeddings and Linguistic Features

Reading and comprehension are quintessentially cognitive tasks.Eye movement acts as a surrogate to understand which part of a sentence is critical to the process of comprehension.The aim of the shared task is to predict five eye-tracking features for a given word of the input sentence.We experimented with several models based on LGBM (Light Gradient Boosting Machine) Regression, ANN (Artificial Neural Network) and CNN (Convolutional Neural Network), using BERT embeddings and some combination of linguistic features.Our submission using CNN achieved an average MAE of 4.0639 and ranked 7th in the shared task.The average MAE was further lowered to 3.994 in post task evaluation.
Boosting Randomized Smoothing with Variance Reduced Classifiers.

Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise. In this work we: (i) theoretically motivate why ensembles are a particularly suitable choice as base models for RS, and (ii) empirically confirm this choice, obtaining state of the art results in multiple settings. The key insight of our work is that the reduced variance of ensembles over the perturbations introduced in RS leads to significantly more consistent classifications for a given input, in turn leading to substantially increased certifiable radii for difficult samples. We also introduce key optimizations which enable an up to 50-fold decrease in sample complexity of RS, thus drastically reducing its computational overhead. Experimentally, we show that ensembles of only 3 to 10 classifiers consistently improve on the strongest single model with respect to their average certified radius (ACR) by 5% to 21% on both CIFAR-10 and ImageNet. On the latter, we achieve a state-of-the-art ACR of 1.11. We release all code and models required to reproduce our results upon publication.
Financial Reporting Quality and Operational Efficiency in the Coastal Region of Romania

Working with large databases and selecting indicators for economic analysis purposes raises several challenges and concerns. This paper provides insights on how the choice of indicators in an analysis of reported financial statements may significantly influence the interpretation of the results. The study investigates a sample that includes indicators reported by a large number of companies in the coastal region of Romania. The rigor and compliance of financial reporting ensure the quality and accuracy of information and are key features for assessing sustainable development. The quantitative analysis and case study observation allowed us to capture divergencies in the evolution of financial indicators caused by nonconformities in the financial statements. This study provides an empirical perspective that can raise awareness of potential biases in financial reporting evaluation. The accuracy of financial data directly influences the soundness of managerial decisions and has a major impact on the economic efficiency strategy of companies.
Spaces for All: The Rise of LGBTQ+ Mathematics Conferences

In the early morning hours of June 28, 1969, police violently targeted LGBTQ+ people at the Stonewall Inn, and that led to the beginning of what has become known as the Stonewall Uprising.These actions, led by trans women of color, marked a crucial turning point in the LGBTQ+ rights movement in the United States and internationally.In honor and remembrance of these events, as well as the struggles and successes of all LGBTQ+ people, June is commonly know as LGBTQ+ Pride Month in the United States, Canada, and other countries.In celebration of this year's Pride Month, we are happy to discuss a particular aspect of the growing visibility of LGBTQ+ mathematicians.
Towards Bidirectional Protection in Federated Learning

Prior efforts in enhancing federated learning (FL) security fall into two categories. At one end of the spectrum, some work uses secure aggregation techniques to hide the individual client's updates and only reveal the aggregated global update to a malicious server that strives to infer the clients' privacy from their updates. At the other end of the spectrum, some work uses Byzantine-robust FL protocols to suppress the influence of malicious clients' updates. We present a federated learning protocol F2ED-LEARNING, which, for the first time, offers bidirectional defense to simultaneously combat against the malicious centralized server and Byzantine malicious clients. To defend against Byzantine malicious clients, F2ED-LEARNING provides dimension-free estimation error by employing and calibrating a well-studied robust mean estimator FilterL2. F2ED-LEARNING also leverages secure aggregation to protect clients from a malicious server. One key challenge of F2ED-LEARNING is to address the incompatibility between FilterL2 and secure aggregation schemes. Concretely, FilterL2 has to check the individual updates from clients whereas secure aggregation hides those updates from the malicious server. To this end, we propose a practical and highly effective solution to split the clients into shards, where F2ED-LEARNING securely aggregates each shard's update and launches FilterL2 on updates from different shards. The evaluation shows that F2ED-LEARNING consistently achieves optimal or close-to-optimal performance and outperforms five secure FL protocols under five popular attacks.
Provably Secure Generative Linguistic Steganography

Generative linguistic steganography mainly utilized language models and applied steganographic sampling (stegosampling) to generate high-security steganographic text (stegotext). However, previous methods generally lead to statistical differences between the conditional probability distributions of stegotext and natural text, which brings about security risks. In this paper, to further ensure security, we present a novel provably secure generative linguistic steganographic method ADG, which recursively embeds secret information by Adaptive Dynamic Grouping of tokens according to their probability given by an off-the-shelf language model. We not only prove the security of ADG mathematically, but also conduct extensive experiments on three public corpora to further verify its imperceptibility. The experimental results reveal that the proposed method is able to generate stegotext with nearly perfect security.
Library Automation Status in the Universities of the Jabalpur Division Madhya Pradesh

The research article tries to describe the status of Library Automation in the Jabalpur Division of the Madhya Pradesh State with special reference of the Universities.Researcher tries to describe the status of the library automation critically and conclude the study with the suggestion for betterment in future.The research scholar collects the data through questionnaire method for study.Paper also discussed about the term automation and Library -automation.Tries to describe the various issues/challenges in these day's related with library automation, which are directly or indirectly affect the library automation work/process in any Library.The Paper briefly describes some point/remedies for proper implementation of library automation in the universities library housekeeping and service sector.
I-BERT: Integer-only BERT Quantization

Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive efficient inference at the edge, and even at the data center. While quantization can be a viable solution for this, previous work on quantizing Transformer based models use floating-point arithmetic during inference, which cannot efficiently utilize integer-only logical units such as the recent Turing Tensor Cores, or traditional integer-only ARM processors. In this work, we propose I-BERT, a novel quantization scheme for Transformer based models that quantizes the entire inference with integer-only arithmetic. Based on lightweight integer-only approximation methods for nonlinear operations, e.g., GELU, Softmax, and Layer Normalization, I-BERT performs an end-to-end integer-only BERT inference without any floating point calculation. We evaluate our approach on GLUE downstream tasks using RoBERTa-Base/Large. We show that for both cases, I-BERT achieves similar (and slightly higher) accuracy as compared to the full-precision baseline. Furthermore, our preliminary implementation of I-BERT shows a speedup of 2.4-4.0x for INT8 inference on a T4 GPU system as compared to FP32 inference. The framework has been developed in PyTorch and has been open-sourced.
Digital skills vs soft skills: New professional development competences in public service

Introduction: interaction on virtual communication platforms between the authorities and the population brings forward the issue of civil servants professional development. Objectives: to define a conceptual approach to the content of digital professional development programs for civil servants, to detail the strategic knowledge and tactical skills required in modern public administration in the context of the socio-political sphere digitalization. Methods: an expert interview; comparative-comparative method of analyzing digital platforms for advanced training of civil servants; content analysis of the “Leaders of Russia” and “Leaders of Russia. Politics” participants’ comments. Results: the article presents the data on a comparative study of educational platforms for advanced training of civil servants in the field of digital literacy. The framework of the necessary digital knowledge in the public management system has been proposed, aimed at the structural subdivisions of authorities responsible for strategic communications with the population. The results of the expert interviews and content analysis actualize the demand for digital competencies among the civil servants. Conclusions: the system of professional retraining of personnel for the civil service should meet the new requirements, namely to ensure the efficient functioning of e-government services, crowdsourcing platforms, official departmental web resources as well as groups and pages in social networks, and to maintain there a dialogue and interaction with the population. The new functionality of a civil servant arising from digitalization of the public administration system requires the development of new approaches to the competence-based model of his training and retraining. This brings forward the importance of digital competencies and skills complemented by a constant improvement of soft skills. The existing mechanisms for mastering these competencies in Russia make it possible to solve the problems of improving the entire system of public administration, but not with maximum efficiency. It is obvious that the market of advanced training services for civil servants is not sufficiently developed and the resources spent on these purposes aren’t used in the most optimal way.
Dig That Lick (DTL): Analyzing Large-Scale Data for Melodic Patterns in Jazz Performances

Review| April 01 2021 Dig That Lick (DTL): Analyzing Large-Scale Data for Melodic Patterns in Jazz Performances Dig That Lick (DTL): Analyzing Large-Scale Data for Melodic Patterns in Jazz Performances. URL: http://dig-that-lick.eecs.qmul.ac.uk/ Chris Stover Chris Stover CHRIS STOVER is a Senior Lecturer in Music Studies and Research at Queensland Conservatorium, Griffith University, and a Research Fellow at the RITMO Centre for Interdisciplinary Study in Rhythm, Time and Motion at the University of Oslo. He is coeditor of Rancière and Music (Edinburgh University Press, 2020) and is currently finishing two books, one on temporal-relational processes in African and Afrodiasporic musics and one on what he is calling “radical music theory.” Search for other works by this author on: This Site PubMed Google Scholar Journal of the American Musicological Society (2021) 74 (1): 195–214. https://doi.org/10.1525/jams.2021.74.1.195 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Chris Stover; Dig That Lick (DTL): Analyzing Large-Scale Data for Melodic Patterns in Jazz Performances. Journal of the American Musicological Society 1 April 2021; 74 (1): 195–214. doi: https://doi.org/10.1525/jams.2021.74.1.195 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentJournal of the American Musicological Society Search Dig That Lick (DTL) is a multidisciplinary research project that ran from 2017 through 2019 as an awardee of the Trans-Atlantic Platform for the Social Sciences and Humanities Digging into Data Challenge. The project represents a significant international partnership between principal investigator Simon Dixon (Queen Mary University of London, UK), three national project leaders—Hélène-Camille Crayencour (National Center for Scientific Research, France), Martin Pfleiderer (University of Music Franz Liszt, Weimar, Germany), and Gabriel Solis (University of Illinois at Urbana-Champaign, USA)—and an international team of researchers.1 DTL received funding from the Economic and Social Research Council (UK), the French National Research Agency, the Deutsche Forschungsgemeinschaft, and the National Endowment for the Humanities (USA). DTL’s aim, broadly speaking, has been to bring together methods from advanced music information retrieval (MIR), empirical musicology, and jazz studies to develop the following tasks: to “enhance existing infrastructures for the deployment of semantic audio analyses over... You do not currently have access to this content.
NOT ALL OF LMS ARE EQUALLY USEFUL [FOR EDUCATION]

In the context of a massive transition to distance learning, learning management systems have become the primary tool for implementing the pedagogical function of universities. The study aims to identify the possibility of using the above systems as a full-fledged analogue of full-time education to implement pedagogical theories in the context of the transition to the learning paradigm. The work results exposed in concrete conclusions; in the discussion section, some recommendations given on the choice of learning management systems.
Zero-shot Fact Verification by Claim Generation

Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then converts them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zero-shot scenario, QACG improves a RoBERTa model's F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly available.