Reviewerly/exHarmony huggingface dataset:
DIGITAL EDUCATION AT THE PRESENT STAGE: DIFFICULTIES AND PROSPECTS

The main goal of the Concept of the State Information Policy of the Russian Federation is the development and improvement of the education system, as well as professional training of personnel that ensures a full life and effective human activity in the modern information society. This goal involves a fundamental change in the organization and essence of the processes of learning and human development. The process of transforming the content, methods and technologies, changing the requirements for learning and its results is the digitalization of education. The purpose of the study is to consider the essence of the concept of "digital education" and identify factors that allow us to present the advantages and disadvantages of digital education. Methods used in the study: literature review, empirical research, matrix analysis, comparative analysis. Empirical survey data were obtained, reflecting the main factors that make it possible to identify the positive and negative aspects of digital education during a pandemic. The article assesses the prospects for the development of digital education. A matrix of assessments of the existing experience of participation in digital education during the pandemic of teachers and students has been compiled. Based on the results of the analysis of the compiled matrix and the diagram that complements it, a conclusion is drawn about the insufficiently effective digital education at the current stage due to the presence of certain problems in the process of organizing online education. The digitalization of education during the crisis period has accelerated significantly, however, in the future, it will require the introduction of certain measures to integrate it with traditional education, while eliminating the shortcomings of both forms of education.
Speech Gender Recognition Using a Multilayer Feature Extraction Method

Human speech contains paralinguistic properties used in automatic speech recognition (ASR) systems. These properties are used in many ASR applications such as gender recognition, which is the main goal of this paper. Gender recognition has been the target of many researchers since recognizing the human gender (female or male) is essential in many applications especially in security applications. Through this work, an ASR has been proposed and implemented. The main goal of any ASR system is to determine the best features that can address the required recognition. The features deployed in this work are smoothness, pitch, the first two formants and spectral centroid variability (SCV). The new approach proposed in this work was using the analysis of variance (ANOVA) as a feature selector to choose the best combination of features that can lead to the best classification accuracy, and then apply the decision tree feature selection algorithm to choose the best group of features. Then use backpropagation neural network (NN), Gaussian mixture models (GMM) and SVM as separate classifiers. The common voice dataset was used as benchmark dataset through all experiments of this work. The best result gained with respect to the three genders was 74.87% using the pitch and the first two formant features and classified by NN. The best result gained with respect to the two genders (female and male) was 97.71% using the pitch, and the first two formant features are classified by NN.
EntSUM: A Data Set for Entity-Centric Extractive Summarization

Controllable summarization aims to provide summaries that take into account userspecified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document.We introduce a human-annotated data set (ENTSUM) for controllable summarization with a focus on named entities as the aspects to control.We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries.We propose extensions to state-of-the-art summarization approaches that achieve substantially better results on our data set.Our analysis and results show the challenging nature of this task and of the proposed data set. 12
Digital Competencies Among Brazilian Pre-service Teachers: An Overview

The development of digital competencies is currently being considered as one of the key factors for everyday life, impacting education, professional careers, as well as the access to a full citizenship. Since these competencies are not innate and they must be developed in some period of life, it is important to investigate how teacher training courses in Brazil are dealing with them, since they are meant to be future teachers of the new generations. For this purpose, a systematic analysis of the literature (peer-reviewed articles) indexed in repositories and scientific databases such as Google Scholar, ERIC (Education Resources Information Center) and SciELO (Scientific Electronic Library Online) was used, and the period established for the search was from 2001 to 2021. It is expected these students to be fully aware of the importance of these competencies and their impacts in current and future lives. However, results have shown that such competencies are not being adequately considered in curricula or in pedagogical projects at teacher training courses. The lack of proper infrastructure in schools and the digital gaps in Brazilian society are additional barriers that must be faced by these pre-service teachers.
AppTek’s Submission to the IWSLT 2022 Isometric Spoken Language Translation Task

To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional encoding.We further increased translation length compliance by sentence-level selection of length-compliant hypotheses from different system variants, as well as rescoring of N-best candidates from a single system.Length-compliant back-translated and forwardtranslated synthetic data, as well as other parallel data variants derived from the original MuST-C training corpus were important for a good quality/desired length trade-off.Our experimental results show that length compliance levels above 90% can be reached while minimizing losses in MT quality as measured in BERT and BLEU scores.
Rolling Bearing Fault Diagnosis Method Based on SSA-ELM

In the motor and its control system fault diagnosis, the rolling bearings’ fault diagnosis is particularly important. In its fault diagnosis, the main method is to classify and recognize faults by the classification of each fault type feature vector. In this paper, the sparrow search algorithm is proposed to optimize the machine learning method of extreme learning machine to classify and diagnose the rolling bearing faults of motor. Firstly, the sparrow search algorithm was used to promote the function of the extreme learning machine, and the SSA-ELM was used to accurately classify and diagnose the rolling bearing faults of the motor. Experimental results show that compared with the original ELM, the SSA-ELM can realize the classification and diagnosis of rolling bearing faults more quickly, which proves the time-based stability of the proposed model.
The Design Of Covid-19 Disaster Mitigation E-Module For Students Of Grades 1 in Primary School

The development of the Covid-19 disaster mitigation e-module for elementary school students is crucial, so it requires a design that takes elementary school children's characteristics into account.The purpose of this research was to design a Covid-19 disaster mitigation e-module for elementary school students in the first grades based on the needs analysis.This research is a qualitative research.Observation sheets, interview sheets, and documentation were utilized to collect data.According to the findings, the developed e-module includes a cover, preface, table of contents, learning objectives, material description, observing activities, discussion activities, and do-you-know activities, summaries, exercises, answer keys, feedback, and follow-up.This research implies and will serve as the foundation for the development of a Covid-19 disaster mitigation e-module for elementary school students particularly to the students in the first grade or students of ages 7 to 8 .
Adversarial Authorship Attribution for Deobfuscation

Recent advances in natural language processing have enabled powerful privacy-invasive authorship attribution.To counter authorship attribution, researchers have proposed a variety of rule-based and learning-based text obfuscation approaches.However, existing authorship obfuscation approaches do not consider the adversarial threat model.Specifically, they are not evaluated against adversarially trained authorship attributors that are aware of potential obfuscation.To fill this gap, we investigate the problem of adversarial authorship attribution for deobfuscation.We show that adversarially trained authorship attributors are able to degrade the effectiveness of existing obfuscators from 20-30% to 5-10%.We also evaluate the effectiveness of adversarial training when the attributor makes incorrect assumptions about whether and which obfuscator was used.While there is a a clear degradation in attribution accuracy, it is noteworthy that this degradation is still at or above the attribution accuracy of the attributor that is not adversarially trained at all.Our results underline the need for stronger obfuscation approaches that are resistant to deobfuscation.
An Evaluation Model of Graduation Thesis Results Based on Triangular Fuzzy Number

The evaluation of thesis by undergraduate has the characteristics of multi-factor, multi-layer and easy to be affected by subjective factors.To reduce the subjectivity, triangular fuzzy number is used as index set to give weight, and on this basis, fuzzy comprehensive evaluation is used to evaluate the quality of graduation thesis.The empirical analysis shows that the combination of triangular fuzzy number and fuzzy comprehensive evaluation has certain practical value in the quality evaluation of graduation thesis.
Dataset Debt in Biomedical Language Modeling

Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP.However, translating these successes to specialized domains such as biomedicine remains challenging, due in part to biomedical NLP's significant dataset debt -the technical costs associated with data that are not consistently documented or easily incorporated into popular machine learning frameworks at scale.To assess this debt, we crowdsourced curation of datasheets for 167 biomedical datasets.We find that only 13% of datasets are available via programmatic access and 30% lack any documentation on licensing and permitted reuse.
Effect of Feature Discretization on Classification Performance of Explainable Scoring-Based Machine Learning Model

We improve the utility of the Risk-calibrated Supersparse Linear Integer Model (RiskSLIM). It is a scoring system that is an interpretable machine learning classification model optimized for performance. Scoring systems are commonly used in healthcare and justice. We implement feature discretization (FD) in the hyperparameter optimization process to improve classification performance and refer to the new approach as FD-RiskSLIM. We test the approach using two medical applications. We compare the results of FD-RiskSLIM, RiskSLIM, and other machine learning (ML) models. We demonstrate that scoring models based on RiskSLIM, in addition to being interpretable, perform at least on par with the state-of-the-art ML models such as Gradient Boosting in terms of classification metrics. We show the superiority of FD-RiskSLIM over RiskSLIM.
PANDAS@TamilNLP-ACL2022: Emotion Analysis in Tamil Text using Language Agnostic Embeddings

As the world around us continues to become increasingly digital, it has been acknowledged that there is a growing need for emotion analysis of social media content.The task of identifying the emotion in a given text has many practical applications ranging from screening public health to business and management.In this paper, we propose a language agnostic model that focuses on emotion analysis in Tamil text.Our experiments yielded an F1-score of 0.010.
Two person interaction recognition based on a dual-coded modified metacognitive (DCMMC) extreme learning machine

Human action recognition has been an active research area for over three decades.However, state-of-the-art proposed algorithms are still far from developing error-free and fully-generalized systems to perform accurate interaction recognition.This work proposes a new method for two-person interaction recognition from videos, based on well-known cognitive theories.The main idea is to perform classification based on a theory of cognition known as dual coding theory.The theory states that human brain processes and represents two types of information to learn/classify data named analogue and symbolic codes, i.e. (verbal as analogue and visual as symbolic).To implement such a theory in a two-person interaction classification system, we exploit dense trajectories as analogue codes and a bag of words as symbolic codes which are two code types hypothesized in the theory.In addition to dual coding theory, we propose to implement a metacognitive classifier model which adds a metalevel with its own rules to perform more accurate training process.We also propose a modification in a metacognitive component to prevent cognitive interference well known as the Stroop effect.Evaluations on both datasets revealed that the method offers comparable recognition accuracy (95.6% for the SBU interaction dataset and 91.1% for the UT-interaction dataset).
Vision-Language Pretraining: Current Trends and the Future

In the last few years, there has been an increased interest in building multimodal (vision-language) models that are pretrained on larger but noisier datasets where the two modalities (e.g., image and text) loosely correspond to each other (e.g., Lu et al., 2019; Radford et al., 2021). Given a task (such as visual question answering), these models are then often fine-tuned on task-specific supervised datasets. (e.g., Lu et al., 2019; Chen et al.,2020; Tan and Bansal, 2019; Li et al., 2020a,b). In addition to the larger pretraining datasets, the transformer architecture (Vaswani et al., 2017) and in particular self-attention applied to two modalities are responsible for the impressive performance of the recent pretrained models on downstream tasks (Hendricks et al., 2021). In this tutorial, we focus on recent vision-language pretraining paradigms. Our goal is to first provide the background on image–language datasets, benchmarks, and modeling innovations before the multimodal pretraining area. Next we discuss the different family of models used for vision-language pretraining, highlighting their strengths and shortcomings. Finally, we discuss the limits of vision-language pretraining through statistical learning, and the need for alternative approaches such as causal representation learning.
A New Stratified Block Model to Process Large-Scale Data for a Small Cluster

Recently, big data analytics has been a hot topic in different fields for many researchers. Several big clusters based on Spark and Hadoop are developed to handle big data files. In this paper, we study big data analytics through the problem of classification. We propose a new stratified block (SB) model for a small cluster that can execute a big data file. In this model, a stratified sampling method is used to split a big file into a specific number k of small files (k data blocks). For each block, we build a classifier model (decision tree) to predict a result of a testing file. To save the memory of the cluster, we only keep the predicted result of each block in the memory; then, the next block is loaded to generate a new tree model. Hence, k blocks yield k predicted results of the testing file. Finally, we aggregate the k results into the final predicted result of the testing file. Three big data files are used to evaluate the performance of SB in terms of computing time and accuracy metrics.
F-CHILS maps - a novel encryption scheme for secured medical data transmission using FoG-ban environment

With internet of things (IoT), remote health monitoring plays an effective role in human disease diagnosis. The gathered patient health information from sensors is transferred to the medical physician, care givers and medical centre in remote mode. Even though different solutions have been proposed, to prevent security issues, malicious user's involvement, timely delivery and also to ensure data integrity, high-end security algorithms are needed. Many traditional algorithms were incorporated in the IoT network with low power and memory requirements. But these algorithms can be easily broken and affected by several attacks due to the poor mathematical operations. Hence new hybrid 3D-chaotic maps FoG-based chaotic Henon integrated logistic-tents schemes used for key generation for crypto graphical operations are proposed. These 3D hybrid chaotic maps are hard to break and have been evaluated by extensive experimentations. Proposed 3D-chaotic maps have significantly increased the security of the clinical data when compared with conventional algorithms.
Real-Time Machine Learning Methods for Two-Way End-to-End Wireless Communication Systems

In this article, we study a data-driven real-time machine learning method for end-to-end wireless systems for the Internet of Things (IoT). For a two-way communication link between two IoT devices, we propose an efficient learning algorithm that can train the autoencoder-based transmitter and receiver in each device without needing to know the channel between two devices. To this end, we adopt the conditional generative adversarial network (cGAN) that can learn an output distribution of the channel for a given conditioning signal. Our proposed training method consists of the link update stage and the self-update stage. In the link update stage, two devices transmit the training data and update their own receiver and the cGAN simultaneously. Subsequently, in the self-update stage, the two devices train their transmitters at the same time for the given receivers and cGANs. Our proposed real-time training method is updated without the knowledge of the channel models nor information feedback for training. Finally, we demonstrate that the proposed training method achieves significant performance gains over conventional schemes in various practical communication scenarios.
Image Analysis

One of the biggest impacts on the advancement of information technologies is the easy acquisition and processing of information.
Integration of Politeness Principle at <i>Mata Najwa’s</i> Talk Show “<i>Coba-coba Tatap Muka</i>” in Discussion Text Learning

Politeness is an important aspect of communication.Based on the application of the politeness principle, it can be integrated into Indonesian language learning as the learning resource.This study aims to examine in-depth realization of the politeness principles contained in the Mata Najwa's Trans 7 talk show in Coba-coba Tatap Muka series and its use in learning discussion texts in Indonesian subjects.This research uses a qualitative approach by applying a descriptive method.The source of the data in this study is Najwa Shihab's YouTube account uploaded on September 1, 2021, regarding to Coba-coba Tatap Muka series, which consists of part 1-7.The data in this study are utterances which are produced by the presenter and informants in the Mata Najwa's Talk show "Coba-coba Tatap Muka" which shows the use of politeness principles in it.The data in this study were collected using listening and note-taking techniques.Data analysis of this study uses the extra lingual equivalent technique.The used advanced analysis technique is the differential comparison technique (DCT).The results of this study indicate that the realization of the politeness principle contained in Mata Najwa's talk show "Coba-coba Tatap Muka" has 46 utterances, they are 24 maxims of compatibility; 7 maxims of approbation; 7 maxims of sympathy; 3 maxims of wisdom; 3 maxims of humility; and 2 maxims of generosity.In addition, regarding the use of the politeness principle in the learning of discussion texts, integrated into Indonesian language learning for grade 9 th junior high school, on basic competence 3.9 identifies discussion text information in the form of opinions on the pros and cons of actual problems that are read and heard and 4.9 concludes the content of ideas, opinions, arguments for and against as well as solutions to actual problems in the discussion texts that are heard and read.Based on these findings, it can be concluded that the realization of the politeness principle in the Mata Najwa's talk show "Coba-coba Tatap Muka" can be integrated as a teaching material into the learning of discussion texts for grade 9 th of junior high school.
Blockchain framework for smart contract and distributive ledger for entity marketplace

In the present era of digitalisation, maintaining tamper-resistant records at centralised nodes is becoming more and more difficult. Blockchain technology, which is considered as the backbone of cryptocurrency, is gaining immense popularity due to its decentralised approach to store and verify the data. Blockchain technology gives the freedom to develop a user-specific validation protocol, which helps to maintain tamper-resistant logs of all the activities, known as transactions, performed on a private/public network of blockchain. Because of blockchain components such as data privacy and security, not only the private sector is shifting its interest towards the technology, the public sector and government authorities are also looking forward to this technology. This manuscript targets to propose the use case model of blockchain technology by developing the smart contract and distributive ledger for entity market place. This framework contains all required processes, functions, and services for making a reliable platform for marketing new and old entities.
Optimize_Prime@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil

This paper tries to address the problem of abusive comment detection in low-resource indic languages.Abusive comments are statements that are offensive to a person or a group of people.These comments are targeted toward individuals belonging to specific ethnicities, genders, caste, race, sexuality, etc. Abusive Comment Detection is a significant problem, especially with the recent rise in social media users.This paper presents the approach used by our team -Optimize_Prime, in the ACL 2022 shared task "Abusive Comment Detection in Tamil."This task detects and classifies YouTube comments in Tamil and Tamil-English Codemixed format into multiple categories.We have used three methods to optimize our results: Ensemble models, Recurrent Neural Networks, and Transformers.In the Tamil data, MuRIL and XLM-RoBERTA were our best performing models with a macro-averaged f1 score of 0.43.Furthermore, for the Codemixed data, MuRIL and M-BERT provided sublime results, with a macro-averaged f1 score of 0.45.