Reviewerly/exHarmony huggingface dataset:
Interactive Pictorial Story as Learning Media for Third Grade Students

This study aims to develop an interactive pictorial story as a learning media on theme 2 Loving Plants and Animals and to determine its feasibility, practicality, and effectiveness. This study uses the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). In this study, the results obtained include: (1) The feasibility of the media obtained from the validation results by 5 validators obtained an average of 0.83 for content quality indicators and objectives, instructional quality indicators obtained an average of 0.84 and technical quality indicators obtained an average of 0.83. The average number of all indicators is 0.83 with the category "very valid". (2) The practicality of the media obtained a percentage of 98% in the "very feasible" category of teacher responses and 96% in the "very feasible" category of student responses. (3) The effectiveness is calculated by using the gain test of 0.56 indicating an increase in student learning outcomes in the "medium" category. The result of this research can be a reference to understand that an interactive pictorial story will be a big help for teachers in conducting an interesting teaching and learning process, especially during this pandemic. The pictorial story was created in an interactive format, so students can learn the material in a fun and interesting way. Besides, in this outbreak, students need to have a technology-based learning resource other than their school books so they can study independently.
Proposta de abordagem para refinamento de pesquisa bibliográfica

Scientific mapping has a long tradition and several approaches have been proposed, such as journal citation analysis, co-citation analysis, bibliographic coupling and words co-occurrence analysis. This paper explores the authors' keywords co-occurrence analysis to refine bibliographic research. An approach is proposed that starts from a more comprehensive bibliographic search and uses the keyword co-occurrence network analysis to identify pairs of keywords that can be used in the construction of a new search expression to refine the initial search. Two examples of how to use the proposed method are presented. A search was carried out on the Covid-19 keyword, selecting the following refinement options: choosing the subject area of Social Sciences and the documents dealing with education. Regarding the types of documents, papers in journals and conferences were included, in addition to the review documents. Then, the two alternatives proposed for the refinement of the initial bibliographic research are exemplified. It is concluded that the proposed approach highlights the main concepts associated with the researched theme and can also be used, in an exploratory way, to identify concepts pertinent to an information need on a thematic domain.
DISTRIBUTION OF ETHEREUM BLOCKCHAIN ADDRESSES

Abstract: In the contemporary world, Ethereum is a very reliable financial saving among cryptocurrencies. It is also well known as a blockchain platform for creating and launching its own cryptocurrency. The applications run on Ethereum executed by a platform-specific cryptographic token, ether. During 2014, Ethereum had launched a pre-sale for ether, which had received an overwhelming response. Ether is used broadly for two purposes: it is traded as a digital currency exchange like other cryptocurrencies, and it is used inside Ethereum to run applications and even to monetize work. It should be noticed that the smart contract has brought a significant share of the success to Ethereum. The smart contract is a computer programme that independently performs assigned tasks between network participants without the participation of a third trusted party. Smart contracts and their intranet transactions have facilitated the rapid expansion of the Ethereum network. Smart contracts are widely represented on the market, either as electronic transaction payments or as applications for the implementation of logistics supplies, gambling, and other sectors. These transactions are irreversible and fully tracked online. Whereas the electronic records are available in a public distributed ledger and include data about user addresses, whereas the real names are hidden. The article explains the usage of decentralized accounts and their electronic transactions in the Ethereum network. The results are presented through different application sectors. Additionally, a new method for extracting blockchain records through node cluster via IPFS path is implemented in the research.
Factors Affecting the Decision of Adoption Cloud Computing Technology: The Case of Jordanian Business Organizations

This research, is a clear case for developing countries, that realize the profits of cloud computing, to enhance their business and develop cutting-edge technology, but first, they need to address the concerns of decision-makers toward cloud computing technology.This research is based on a Technology Acceptance Model, which is modified to fit the context.The factors applied in this research are: Perceived Usefulness, Perceived Ease of Use, Perceived Risk/Security, Cloud Computing Awareness, compatibility, cost and an aim to use where it is a dependent variable in this study.Within this study, the Likert Scale (five-point scale) survey was produced, to get data from IT employees of the Jordanian business organization.Data was collected from 175 IT employees.Linear regression was used to examine the data using SPSS software.The outcomes revealed that perceived ease of use, perceived usefulness, cost, perceived risk were statistically significant, while compatibility and cloud computing awareness were found not to be statistically important.These outcomes explain that cloud computing systems can be quickly deployed in developing countries (avoiding unnecessary innovation); if users accurately understand their utility, it is clear that operating costs might be reduced.The perceived risk of system usage is low.We consider, of particular importance, the research line that technological compatibility is not significant, with the successful introduction of cloud applications.Fast deployment of cloud applications are good opportunities for developing countries, because of the lower cost of new software applications and use of big data.
BreakingBERT@IITK at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables

Recently, there has been an interest in factual verification and prediction over structured data like tables and graphs.To circumvent any false news incident, it is necessary to not only model and predict over structured data efficiently but also to explain those predictions.In this paper, as part of the SemEval-2021 Task 9, we tackle the problem of fact verification and evidence finding over tabular data.There are two subtasks.Given a table and a statement/fact, subtask A determines whether the statement is inferred from the tabular data, and subtask B determines which cells in the table provide evidence for the former subtask.We make a comparison of the baselines and state-of-the-art approaches over the given SemTabFact dataset.We also propose a novel approach CellBERT to solve evidence finding as a form of the Natural Language Inference task.We obtain a 3way F1 score of 0.69 on subtask A and an F1 score of 0.65 on subtask B.
KIT’s IWSLT 2021 Offline Speech Translation System

This paper describes KIT'submission to the IWSLT 2021 Offline Speech Translation Task.We describe a system in both cascaded condition and end-to-end condition.In the cascaded condition, we investigated different endto-end architectures for the speech recognition module.For the text segmentation module, we trained a small transformer-based model on high-quality monolingual data.For the translation module, our last year's neural machine translation model was reused.In the end-toend condition, we improved our Speech Relative Transformer architecture to reach or even surpass the result of the cascade system.
Differentiation of Saccadic Eye Movement Signals

Saccadic electrooculograms are discrete biosignals that contain the instantaneous angular position of the human eyes as a response to saccadic visual stimuli. These signals are essential to monitor and evaluate several neurological diseases, such as Spinocerebellar Ataxia type 2 (SCA2). For this, biomarkers such as peak velocity, latency and duration are computed. To compute these biomarkers, we need to obtain the velocity profile of the signals using numerical differentiation methods. These methods are affected by the noise present in the electrooculograms, specially in subjects that suffer neurological diseases. This noise complicates the comparison of the differentiation methods using real saccadic signals because of the impossibility of establishing exact saccadic onset and offset points. In this work, we evaluate 16 differentiation methods by the design of an experiment that uses synthetic saccadic electrooculograms generated from parametric models of both healthy subjects and subjects suffering from Spinocerebellar Ataxia type 2 (SCA2). For these synthetic electrooculograms the exact velocity profile is known, hence we can use them as a reference for comparison and error computing for the tasks of saccade identification and saccade biomarker computing. Finally, we identify the best fitting method or methods for each evaluated task.
System Description for the CommonGen task with the POINTER model

In a current experiment we were testing Com-monGen dataset for structure-to-text task from GEM living benchmark with the constraint based POINTER model.POINTER represents a hybrid architecture, combining insertionbased and transformer paradigms, predicting the token and the insertion position at the same time.The text is therefore generated gradually in a parallel non-autoregressive manner, given the set of keywords.The pretrained model was fine-tuned on a training split of the Common-Gen dataset and the generation result was compared to the validation and challenge splits. 1 The received metrics outputs, which measure lexical equivalence, semantic similarity and diversity, are discussed in details in a present system description.
ECNUICA at SemEval-2021 Task 11: Rule based Information Extraction Pipeline

This paper presents our endeavor for solving task11, NLPContributionGraph, of SemEval-2021.The purpose of the task is to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph.The task includes three sub-tasks: detecting the contribution sentences in papers, identifying scientific terms and predicate phrases from the contribution sentences; and inferring triples in the form of (subject, predicate, object) as statements for Knowledge Graph building.In this paper, we apply an ensemble of various fine-tuned pretrained language models (PLM) for tasks one and two.In addition, the self-training methods are adopted for tackling the shortage of annotated data.For the third task, rather than using classic neural open information extraction (OIE) architectures, we generate potential triples via manually designed rules and develop a binary classifier to differentiate positive ones from others.The quantitative results show that we obtain the 4 th , 2 nd , and 2 nd rank in three evaluation phases.
Investigation of regulatory role of collective teacher efficacy in the effect of job satisfaction and satisfaction with life on professional burnout

This study examined the effect of job satisfaction and satisfaction with life on perceptions of teachers' professional burnout and the regulatory role of collective teacher efficacy perception by conducting applied research on teachers who work in different educational settings. The measuring tools were "Maslach Burnout Inventory", "Short Form Minnesota Satisfaction Questionnaire", "Satisfaction with Life Scale" and "Collective Teacher Efficacy Scale". The findings of the research showed that: Teachers' perceptions of job satisfaction and satisfaction with life negatively affected their perceptions of professional burnout. Collective teacher efficacy had a regulatory effect on the relationship between satisfaction with life and professional burnout, and on the relationship between job satisfaction and professional burnout.
Cisco at SemEval-2021 Task 5: What’s Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments

Social network platforms are generally used to share positive, constructive, and insightful content.However, in recent times, people often get exposed to objectionable content like threat, identity attacks, hate speech, insults, obscene texts, offensive remarks or bullying.Existing work on toxic speech detection focuses on binary classification or on differentiating toxic speech among a small set of categories.This paper describes the system proposed by team Cisco for SemEval-2021 Task 5: Toxic Spans Detection, the first shared task focusing on detecting the spans in the text that attribute to its toxicity, in English language.We approach this problem primarily in two ways: a sequence tagging approach and a dependency parsing approach.In our sequence tagging approach we tag each token in a sentence under a particular tagging scheme.Our best performing architecture in this approach also proved to be our best performing architecture overall with an F 1 score of 0.6922, thereby placing us 7 th on the final evaluation phase leaderboard.We also explore a dependency parsing approach where we extract spans from the input sentence under the supervision of target span boundaries and rank our spans using a biaffine model.Finally, we also provide a detailed analysis of our results and model performance in our paper.
Classification of forest fire areas using machine learning algorithm

Forest fires that occur will cause various kinds of problems, both in terms of health, such as smoke that can interfere with the respiratory system, in terms of the economy such as the economic wheel cannot run as usual, in terms of the environment can damage the surrounding environment and the environment that is missed by smoke, and other disasters. Forest fires can also have an impact on the costs that will be incurred to resolve the problems that arise due to forest fires, so research is needed to find out and measure the area affected by forest fires that burned in the range of 1980 - 2019 using a dataset of approximately 10,000. The target in this research is to be able to generate the best percentage scenario and find out the model of using the algorithm used to explore the algorithm in the Machine Learning method for the model for estimating the area of forest fires, namely the Siak Kampar Peninsula in Riau Province. In this study, 7 parameters were used to create a forest and land fire hazard map, namely weather temperature, Burned Area Density, hotspot density, wind speed, land cover type, rainfall, and land use. The seven parameters will be searched for accuracy results using the Classification method with Machine Learning algorithms, including Naïve Bayes, SVM, and K-Nearest Neighbor (K-NN). In this study, comparisons were made to obtain the best algorithm for estimating forest fire areas. By generating each algorithm is 71.72% for the Naïve Bayes algorithm, 75.00% for the SVM algorithm, and 64.71% for the K-NN algorithm.
Semantics of the Unwritten: The Effect of End of Paragraph and Sequence Tokens on Text Generation with GPT2

The semantics of a text is manifested not only by what is read, but also by what is not read.In this article, we will study how the implicit "not read" information such as end-of-paragraph (EOP) and end-of-sequence (EOS) affect the quality of text generation.Specifically, we find that the pre-trained language model GPT2 can generate better continuations by learning to generate the EOP in the fine-tuning stage.Experimental results on English story generation show that EOP can lead to higher BLEU score and lower EOS perplexity.We also conduct experiments on a self-collected Chinese essay dataset with Chinese-GPT2, a character level LM without EOP or EOS during pre-training.Experimental results show that the Chinese GPT2 can generate better essay endings with EOP.Our code is available on GitHub.
Scaling bodily fluids for utopian fabulations

This paper explores human bodily fluids for morethan-human collaborative survival.We present four utopian fabulations in which urine, menstrual blood, and human milk are designed with beyond the scale of a singular human body.Each fabulation illustrates queer scales and uses of bodily fluids through extended or improper uses as pathways towards caring multi-species relations within a damaged environment.From these narratives, we reflect on imagining generous collaborations for an openness towards unknowable possibilities and crafting different measures through the tensions of coinciding scales.
Digital Learning Resources In Teaching

This article presents research findings of the vocational teachers' experience in reimagining the role of innovative technologies based on the professional development course 'Digital Education for Teachers' which was held in vocational education institutions of Tyumen Region.The main challenge of the course was to help the teachers develop and integrate their digital competence in vocational education, allowing them to create electronic educational resources for action oriented activities and work-based learning by combining digital educational materials and learning opportunities for online interaction with conventional methods.The teachers were introduced to the use of blended learning models, which enable them to create learning content and impart it choosing appropriate media for a specific learning program.Over the course of eight months the teachers created 490 electronic educational resources for specific learning programs and incorporated them into the curriculum content.A total number of 140 teachers have produced digital learning materials for 93 disciplines and tested them in the educational environment whereby enlarging the curriculum scope.Digital education for teachers expands the role of educators and largely contributes to a gradual transition from traditional forms of interaction with students to electronic educational resources (EER) designed for tailoring instruction to individuals' needs and skills for students to become self-directed learners.
Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages

Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models.This stems from the fact that these languages are morphologically very rich as well as being lowresourced.In this paper, we focus on subword segmentation and evaluate Linguistically Motivated Vocabulary Reduction (LMVR) against the more commonly used SentencePiece (SP) for the task of translating from English into four different Dravidian languages.Additionally we investigate the optimal subword vocabulary size for each language.We find that SP is the overall best choice for segmentation, and that larger subword vocabulary sizes lead to higher translation quality.
Multimodal or Text? Retrieval or BERT? Benchmarking Classifiers for the Shared Task on Hateful Memes

The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual information.The challenge consists of three detection tasks: hate, protected category and attack type.The first is a binary classification task, while the other two are multi-label classification tasks.Our participation included a text-based BERT baseline (TxtBERT), the same but adding information from the image (ImgBERT), and neural retrieval approaches.We also experimented with retrieval augmented classification models.We found that an ensemble of TxtBERT and ImgBERT achieves the best performance in terms of ROC AUC score in two out of the three tasks on our development set.
NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformer

This paper presents a technical report of our submission to the 4th task of SemEval-2021, titled: Reading Comprehension of Abstract Meaning.In this task, we want to predict the correct answer based on a question given a context.Usually, contexts are very lengthy and require a large receptive field from the model.Thus, common contextualized language models like BERT miss fine representation and performance due to the limited capacity of the input tokens.To tackle this problem, we used the longformer model to better process the sequences.Furthermore, we utilized the method proposed in the longformer benchmark on wikihop dataset which improved the accuracy on our task data from (23.01% and 22.95%) achieved by the baselines for subtask 1 and 2, respectively, to (70.30% and 64.38%).
NICT-5’s Submission To WAT 2021: MBART Pre-training And In-Domain Fine Tuning For Indic Languages

In this paper we describe our submission to the multilingual Indic language translation task "MultiIndicMT" under the team name "NICT-5".This task involves translation from 10 Indic languages into English and vice-versa.The objective of the task was to explore the utility of multilingual approaches using a variety of in-domain and out-of-domain parallel and monolingual corpora.Given the recent success of multilingual NMT pre-training we decided to explore pre-training an MBART model on a large monolingual corpus collection covering all languages in this task followed by multilingual fine-tuning on small in-domain corpora.Firstly, we observed that a small amount of pretraining followed by fine-tuning on small bilingual corpora can yield large gains over when pre-training is not used.Furthermore, multilingual fine-tuning leads to further gains in translation quality which significantly outperforms a very strong multilingual baseline that does not rely on any pre-training.
Visual Analytics for Information Security: Areas of Application, Tasks, Visualization Models

The purpose of the article: to identify and systematize the areas and problems of information security that are solved using visual analytics methods, as well as analysis of the applied data visualization models and their properties that affect the perception of data by the operator. Research method: a systematic analysis of the application of visual analytics methods for solving information security problems. Analysis of relevant papers in the field of information security and data visualization. The objects of research are: theoretical and practical solutions to information security problems through visual analysis. Visual analytics in the article is considered from several sides: from the point of view of the areas of application of visual analysis methods in information security, from the point of view of the tasks solved by the security analyst, from the point of view of the visualization models used and the data structures used, as well as from the point of view of the properties of data visualization models. The result: classification of visualization models is proposed, which differs from analogs in that it is based on the analysis of areas and tasks of information security and comparison of visualization models to them. The scope of the proposed approach is the creation of visualization models that can be used to increase the efficiency of operator interaction with information security applications. The proposed article will be useful both for specialists who develop information security systems and for students studying in the direction of training “Information Security”.