Reviewerly/exHarmony huggingface dataset:
Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation

Source-free domain adaptation is an emerging line of work in deep learning research since it is closely related to the real-world environment.We study the domain adaption in the sequence labeling problem where the model trained on the source domain data is given.We propose two methods: Self-Adapter and Selective Classifier Training.Self-Adapter is a training method that uses sentence-level pseudolabels filtered by the self-entropy threshold to provide supervision to the whole model.Selective Classifier Training uses token-level pseudo-labels and supervises only the classification layer of the model.The proposed methods are evaluated on data provided by SemEval-2021 task 10 and Self-Adapter achieves 2 nd rank performance.
OPEN LEARNING CONTENT CREATION PLATFORM EMPOWERED BY ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 2504-2513ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.0546Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning

Privacy concerns in client-server machine learning have given rise to private inference (PI), where neural inference occurs directly on encrypted inputs. PI protects clients' personal data and the server's intellectual property. A common practice in PI is to use garbled circuits to compute nonlinear functions privately, namely ReLUs. However, garbled circuits suffer from high storage, bandwidth, and latency costs. To mitigate these issues, PI-friendly polynomial activation functions have been employed to replace ReLU. In this work, we ask: Is it feasible to substitute all ReLUs with low-degree polynomial activation functions for building deep, privacy-friendly neural networks? We explore this question by analyzing the challenges of substituting ReLUs with polynomials, starting with simple drop-and-replace solutions to novel, more involved replace-and-retrain strategies. We examine the limitations of each method and provide commentary on the use of polynomial activation functions for PI. We find all evaluated solutions suffer from the escaping activation problem: forward activation values inevitably begin to expand at an exponential rate away from stable regions of the polynomials, which leads to exploding values (NaNs) or poor approximations.
FlexAE: Flexibly Learning Latent Priors for Wasserstein Auto-Encoders

Auto-Encoder (AE) based neural generative frameworks model the joint-distribution between the data and the latent space using an Encoder-Decoder pair, with regularization imposed in terms of a prior over the latent space. Despite their advantages, such as stability in training, efficient inference, the performance of AE based models has not reached the superior standards of the other generative models such as Generative Adversarial Networks (GANs). Motivated by this, we examine the effect of the latent prior on the generation quality of deterministic AE models in this paper. Specifically, we consider the class of Generative AE models with deterministic Encoder-Decoder pair (such as Wasserstein Auto-Encoder (WAE), Adversarial Auto-Encoder (AAE)), and show that having a fixed prior distribution, \textit{a priori}, oblivious to the dimensionality of the `true' latent space, will lead to the infeasibility of the optimization problem considered. As a remedy to the issue mentioned above, we introduce an additional state space in the form of flexibly learnable latent priors, in the optimization objective of WAE/AAE. Additionally, we employ a latent-space interpolation based smoothing scheme to address the non-smoothness that may arise from highly flexible priors. We show the efficacy of our proposed models, called FlexAE and FlexAE-SR, through several experiments on multiple datasets, and demonstrate that FlexAE-SR is the new state-of-the-art for the AE based generative models in terms of generation quality as measured by several metrics such as Fr\'echet Inception Distance, Precision/Recall score. Code for our paper is available at: \url{https://github.com/dair-iitd/FlexAE}
TELEGRAM CHATBOT AS AN EDUCATIONAL TOOL IN BLENDED LEARNING ENVIRONMENT

1 Moscow State University of Food Production (RUSSIAN FEDERATION)2 Moscow Institute of International Relations (MGIMO University) (RUSSIAN FEDERATION)
Generating Adversarial Examples with Graph Neural Networks

Recent years have witnessed the deployment of adversarial attacks to evaluate the robustness of Neural Networks. Past work in this field has relied on traditional optimization algorithms that ignore the inherent structure of the problem and data, or generative methods that rely purely on learning and often fail to generate adversarial examples where they are hard to find. To alleviate these deficiencies, we propose a novel attack based on a graph neural network (GNN) that takes advantage of the strengths of both approaches; we call it AdvGNN. Our GNN architecture closely resembles the network we wish to attack. During inference, we perform forward-backward passes through the GNN layers to guide an iterative procedure towards adversarial examples. During training, its parameters are estimated via a loss function that encourages the efficient computation of adversarial examples over a time horizon. We show that our method beats state-of-the-art adversarial attacks, including PGD-attack, MI-FGSM, and Carlini and Wagner attack, reducing the time required to generate adversarial examples with small perturbation norms by over 65\%. Moreover, AdvGNN achieves good generalization performance on unseen networks. Finally, we provide a new challenging dataset specifically designed to allow for a more illustrative comparison of adversarial attacks.
Dynamic Employee Engagement and Corporate Creativity: Theoretical and Extant Literature Approach

A well-working association is the result of its sound, innovative, submitted and persuaded representatives, who can be named as 'drew in representatives.The investigation was set off by thought of how firms flop today.Dynamic as a term has to do with a procedure or framework that is described by steady change, action, or progress.Enterprises are progressively tested to grasp participate imagination and development as key driver to unmistakable and economical capabilities.This enhances the requirement for improvement of reasoning associations and execution of conscious proportions of tapping individuals' inventive and imaginative limits so as to stay important.The endurance of corporate organizations on amplifying benefits from existing abilities, while perceiving and changing in accordance with the way that what may work today may not really work later on (Kortmann, Gelhard, Zimmermann, and Piller, 2014).To make or keep up their organizations' gainfulness, pioneers of organizations must endeavor to connect with workers (Kortmann et al., 2014).Be that as it may, pioneers may once in a while battle to adjust their association because of progress in the event that they limit their concentration to existing items and procedures (Hill and Birkinshaw, 2012).Seeing how to deal with the harmony between representative relations, receiving development, and boosting momentary benefits is basic to business pioneers guaranteeing a suitable future for their companies (Hill and Birkinshaw, 2012).The utilization of trend setting innovations, abilities, best practices, and instruction has assisted with expanding the efficiencies in many significant associations and firms.In any case, withdrew representatives who have brought down profitability since the 2008 budgetary emergency have influenced the money related exhibitions of numerous associations (Purcell, 2014).Conversely, improved worker efficiency positively affected authoritative budgetary execution.Authoritative profitability is controlled by representatives' endeavors and commitment (Musgrove, Ellinger, and Ellinger, 2014).Relational exercises influence profitability; subsequently, authoritative execution.The quickly changing examples in data, ever-expanding rivalry among firms, changes in data bundling and methods of access, the broad effects of innovation have all required a more noteworthy requirement for constant innovativeness and development in all activities today.Be that as it may, innovativeness and development can neither be forced nor driven by separated representatives.A worldwide exploration by Gallup (2005) announced that lone 29% of organization workers are locked in while others are either somewhat connected with or totally separated.Without a doubt, earlier investigations insisted that withdrew workers are a risk to the association and scarcely rock the boat while connected with representatives show enthusiastic
Tracking Semantic Change in Cognate Sets for English and Romance Languages

Semantic divergence in related languages is a key concern of historical linguistics.We crosslinguistically investigate the semantic divergence of cognate pairs in English and Romance languages, by means of word embeddings.To this end, we introduce a new curated dataset of cognates in all pairs of those languages.We describe the types of errors that occurred during the automated cognate identification process and manually correct them.Additionally, we label the English cognates according to their etymology, separating them into two groups: old borrowings and recent borrowings.On this curated dataset, we analyse word properties such as frequency and polysemy, and the distribution of similarity scores between cognate sets in different languages.We automatically identify different clusters of English cognates, setting a new direction of research in cognates, borrowings and possibly false friends analysis in related languages.
Ontology for Informatics Research Artifacts

The IRAO ontology, as a new contribution to the network of ontologies for the scholarly domain, aims to model the most tangible aspect of research in computing disciplines – the research artifacts. It consists of parts focusing on the concepts of researcher, research artifact classification, research artifact meta information, relationships between artifacts, and research artifact quality evaluation benchmarks that are used to express the quality and maturity of each research artifact. We describe the ontology design requirements using competency questions and the evaluation of the ontology by the same questions that helped in defining the concept domain coverage.
Video Steganalysis in the Transform Domain Based on Morphological Structure of the Motion Vector Maps

Steganography is the art of transmitting hidden messages through a cover object without raising any suspicion. In contrast, steganalysis is the science of detecting the presence of hidden information and a significant amount of research has been focused on multimedia steganalysis. In this paper, a video steganalysis method is proposed to detect the presence of hidden data by analysing the structure of the motion vectors in the compressed video data. The proposed method is based on the classification of features extracted from the morphology of the motion vector map. The proposed method has been evaluated on a large dataset of short videos with variable resolution and quality and the results suggest the effectiveness of the proposed modelling scheme.
KGP at SemEval-2021 Task 8: Leveraging Multi-Staged Language Models for Extracting Measurements, their Attributes and Relations

SemEval-2021 Task 8: MeasEval aims at improving the machine understanding of measurements in scientific texts through a set of entity and semantic relation extraction sub-tasks on identifying quantity spans along with various attributes and relationships.This paper describes our system, consisting of a three-stage pipeline, that leverages pre-trained language models to extract the quantity spans in the text, followed by intelligent templates to identify units and modifiers.Finally, it identifies the quantity attributes and their relations using language models boosted with a feature re-using hierarchical architecture and multi-task learning.Our submission significantly outperforms the baseline, with the best model from the postevaluation phase delivering more than 100% increase on F1 (Overall) from the baseline.
Mineralogy and geochemistry of the Avin kaolin deposit, northeast of Mianeh, East-Azarbaidjan Province, NW Iran

Mineralogy and geochemistry of the Avin kaolin deposit, northeast of Mianeh, East-Azarbaidjan Province, NW Iran
A.I.R.: From Radical Individuality to Connected Subjectivity

Humans are part of an interlinked world crossed by overlapping flows: substances, beings and information. The major global events that have unfolded throughout 2020 have profoundly altered the social system, revealing deep structural weak spots, and pushed its resilience to the limit, nearly causing its suffocation. This context has called into question our anthropocentric mindset and has led us to critically revise how we think about the (eco)systems we are part of, how we act within them, what is our agency to drive meaningful shifts, and with which tools we can do so. For nine months during which life and art became part of a single space, we, three artists and designers in collaboration with a diverse team of researchers, explored the way in which our individual and collective agency is affected by how close – both emotionally and physically – we feel to others, whether human or not. By navigating through art and design approaches, we imagined perspectives to defy our dualist, linear and Cartesian point of view to question how, as our system regains its speed, we can move towards a more connected sense of being. A systemic thinking toolkit, dozens of conversations, a breathing body, a poem and a visual essay have unfolded during this time, giving shape to the project A.I.R. Air[noun, uncountable], the mixture of gases we breathe; air[noun, uncountable], the space that circulates everything; but also A.I.R., an acronym for “artists in residency”, or more accurately, artists in remoteness. Air that we have lacked too often during these nine months. Air that can be the deepest kind of embrace, in these times pierced by radical forms of isolation. We start weaving our ideas around the notions of systems, agency and closeness by asking: how close do you feel?
INFORMATIONAL-COMMUNICATIONAL AND DIGITAL COMPETENCE OF TEACHERS OF RUSSIAN UNIVERSITIES

L. Voronina 1, E. Zaitseva 1, E. Gaisina 2, N. Izmozherova 2 1Ural Federal University (RUSSIAN FEDERATION) 2Ural State Medical University (RUSSIAN FEDERATION)
MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with Pre-trained Language Models for Toxic Spans Detection

This paper describes our system for SemEval-2021 Task 5 on Toxic Spans Detection.We developed ensemble models using BERT-based neural architectures and post-processing to combine tokens into spans.We evaluated several pre-trained language models using various ensemble techniques for toxic span identification and achieved sizable improvements over our baseline fine-tuned BERT models.Finally, our system obtained a F1-score of 67.55% on test data.
Neural Network Branch-and-Bound for Neural Network Verification

Many available formal verification methods have been shown to be instances of a unified Branch-and-Bound (BaB) formulation. We propose a novel machine learning framework that can be used for designing an effective branching strategy as well as for computing better lower bounds. Specifically, we learn two graph neural networks (GNN) that both directly treat the network we want to verify as a graph input and perform forward-backward passes through the GNN layers. We use one GNN to simulate the strong branching heuristic behaviour and another to compute a feasible dual solution of the convex relaxation, thereby providing a valid lower bound. We provide a new verification dataset that is more challenging than those used in the literature, thereby providing an effective alternative for testing algorithmic improvements for verification. Whilst using just one of the GNNs leads to a reduction in verification time, we get optimal performance when combining the two GNN approaches. Our combined framework achieves a 50\% reduction in both the number of branches and the time required for verification on various convolutional networks when compared to several state-of-the-art verification methods. In addition, we show that our GNN models generalize well to harder properties on larger unseen networks.
VALIDATION OF AN INSTRUMENT TO MEASURE SELF-REGULATED LEARNING IN UPPER SECONDARY LEVEL IN MEXICO

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 8374-8380ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1695Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Leadership Skills Development Of Students Of Psychological And Pedagogicaldirection

The article presents the possibilities of the Youth Workshop of Psychological Сounselling in leadership skills development of students of psychological and pedagogical direction.Many students are not able to demonstrate social activity, take responsibility and initiative, conduct psychological training courses, that is, they do not show leadership skills.The question that arises is what are the psychological conditions that ensure the effective development of leadership skills of students of psychological and pedagogical direction.Thirty-three students participating in the YWPC took part in the study.The conditions for the implementation of this process are identified and substantiated.New targets of higher education require changes in the content and structure of psychologists' training.Training of students in higher education institutions should not only prepare them for research activity but also help to foster psychological readiness for professional activity in the education system.The modernization of the training system of psycho-pedagogical personnel requires the integration of science, education, and production.This article contains a comparative analysis of changes in the level of leadership skills development of students, provides data on the ascertaining and control stages of the experiment and describes the results of the experimental study.The results of the development and approbation of the training program aimed at developing leadership skills of students of psychological and pedagogical direction convinced of the program effectiveness and the positive dynamics of change in leadership skills and professional competence of future specialists.
A model‐driven robust deep learning wireless transceiver

Recently, deep learning (DL) has been successfully applied in computer vision and natural language processing. The communication physical layer based on deep learning has received widespread attention. Introducing domain-knowledge into neural networks (NNs), autoencoder based end-to-end communication system, incorporating radio transformer networks (RTNs) (RTNs-AE) has achieved desirable performance with a channel model in the middle layer. The advent of RTNs underscores the power of expertise at DL. However, a tap-length in the design of RTNs network must be assumed, which requires some channel information. To address this issue, a new deep learning wireless transceiver named pilot-aided autoencoder (PA-AE) is proposed. It can decode on a multipath fading channel without knowing the channel information and the equalization module design. The proposed scheme introduces a well-designed auxiliary pilot, which carries the learned channel information into decoding with the transmitted signal. The decoding part recovers the sent information from the collected signal without specially designed modules for parameter estimation and equalization.
Clock Gating-Assisted Malware (CGAM): Leveraging Clock Gating On ARM Cortex M For Attacking Subsystems Availability

Due to the ever-increasing demand for developing Internet of Things (IoT) technologies that support low power consumption and high-performance mobile computing, several power optimization techniques have been implemented and deployed at the processors level, especially the ARM Cortex-M family chipsets. Such techniques include clock gating, power gating, dynamic voltage, and frequency scaling. Although such mechanisms help in minimizing dynamic power consumption by shutting off current to blocks or chip-operational units that are not in use, power gating and clock-gating techniques can be exploited by an attacker to disable blocks within the chipset targeting the subsystem availabilities. Attacks include disabling serial communication blocks, wireless/ethernet communication modules, Random Number Generator (RNG) block, interrupt module, etc. This paper proposed the development of a live clock-gating assisted malware attack for IoT systems based on the ARM Cortex-M processor. Four variants of the proposed malware were codded and deployed on a real IoT testbed integrated with a multisensor data fusion algorithm. Behavioral patterns based on power spectral density estimations and current measurements were recorded for each strain of the proposed malware. In this research, collected behavioral patterns were utilized to identify an infected device and its injected malware strain.
LRG at SemEval-2021 Task 4: Improving Reading Comprehension with Abstract Words using Augmentation, Linguistic Features and Voting

In this article, we present our methodologies for SemEval-2021 Task-4: Reading Comprehension of Abstract Meaning.Given a fill-inthe-blank-type question and a corresponding context, the task is to predict the most suitable word from a list of 5 options.There are three sub-tasks within this task: Imperceptibility (subtask-I), Non-Specificity (subtask-II), and Intersection (subtask-III).We use encoders of transformers-based models pre-trained on the masked language modelling (MLM) task to build our Fill-in-the-blank (FitB) models.Moreover, to model imperceptibility, we define certain linguistic features, and to model non-specificity, we leverage information from hypernyms and hyponyms provided by a lexical database.Specifically, for non-specificity, we try out augmentation techniques, and other statistical techniques.We also propose variants, namely Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc.Additionally, we perform a thorough ablation study, and use Integrated Gradients to explain our predictions on a few samples.Our best submissions achieve accuracies of 75.31% and 77.84%, on the test sets for subtask-I and subtask-II, respectively.For subtask-III, we achieve accuracies of 65.64% and 62.27%.The code is available here.