Reviewerly/exHarmony huggingface dataset:
Speaking learning based on multimedia

Learning media is one component that determines the success of learning. Learning media that should be used in the present day is a learning medium that facilitates students to be students and graduates are multi-competent, multi-literate, critical thinking, creative, innovative, able to cooperate and collaborate. Learning media that have these characteristics are learning media that combine, collaborate, or integrate several learning media, for example images, graphics, audio, and so on. Because the media uses several media, the media is referred to as multimedia. The multimedia chosen in speaking learning, especially on presentation, should be relevant to the needs of learning materials and learning objectives at each stage of the speaking, which includes (1) pre-speaking stages (e.g. group presentation videos used to analyze concepts and presentation aspects; e-books, journals online, online articles, e-mails used to determine the topics, develop topics, and compose the content of speech, etc.) (b) speaking stage, and (3) post-speaking stage. The use of multimedia in each stage of speaking facilitates students to succeed in learning
ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring

Contracts are a common type of legal document that frequent in several day-to-day business workflows.However, there has been very limited NLP research in processing such documents, and even lesser in generating them.These contracts are made up of clauses, and the unique nature of these clauses calls for specific methods to understand and generate such documents.In this paper, we introduce the task of clause recommendation, as a first step to aid and accelerate the authoring of contract documents.We propose a twostaged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context.We pretrain BERT on an existing library of clauses with two additional tasks and use it for our prediction and recommendation.We experiment with classification methods and similarity-based heuristics for clause relevance prediction, and generation-based methods for clause recommendation, and evaluate the results from various methods on several clause types.We provide analyses on the results, and further outline the advantages and limitations of the various methods for this line of research.
Explainable U-Net model forMedical Image Segmentation

In this nutshell, we propose a simple, efficient, and explainable deep learning-based U-Net algorithm for the MedAI challenge, focusing on precise segmentation of polyp and instrument and transparency on algorithms. We develop a straightforward encoder-decoder-based algorithm for the task above. We make an effort to make a simple network as much as possible. Specially, we focus on input resolution and width of the model to find the best optimal settings for the network. We perform ablation studies to cover this aspect.
Impact and perception of COVID-19 among transgender

Background: Global pandemic of COVID-19 has affected all society sections, including transgender people who face neglect regarding medical care. Here we intended to study the awareness and perception of COVID-19 and its impact among transgender.Methods: A questionnaire-based cross-sectional study was conducted among transgender people and compared with age-matched controls who attended our OPD or consulted telephonically.Results: The study included 40 transgender and 40 controls. Almost half of the transgender group (52.5%) were not graduates, and about 50% worked in a private company. There was no significant difference in the knowledge in terms of information about COVID-19, source of information, awareness of online consultation, treatment of COVID-19, and safe distancing (p=NS). Personal hygiene knowledge was better in controls (p=0.002), and hand washing habit was more in transgender (p=0.014), but transgender people were at par with the control group regarding the type of soap used, protective coverings for face, and availing online consultation (p=NS). However, transgender used hand sanitizers less frequently (p=0.035) and walked barefoot more often (p=0.004). COVID-19 and lockdown had a similar impact in both the groups in terms of healthcare support, source of groceries, and getting stuck away from home (p=NS).Conclusions: Though the education level in transgender was less, the level of awareness, knowledge, practices, and attitude about COVID-19 infections was almost similar to the control group. However, regarding the frequency of handwashing and the use of hand sanitizers, transgender people lagged, which needs to be addressed.
linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser

In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data.
Towards Reliable and Practicable Algorithmic Recourse

As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in developing post hoc techniques which provide recourse to individuals who have been adversely impacted by predicted outcomes. For example, when an individual is denied loan by a predictive model deployed by a bank, they should be informed about reasons for this decision and what can be done to reverse it. While several approaches have been proposed to tackle the problem of generating recourses, these techniques rely heavily on various restrictive assumptions. For instance, these techniques generate recourses under the assumption that the underlying predictive models do not change. In practice, however, models are often updated for a variety of reasons including data distribution shifts. There is little to no research that systematically investigates and addresses these limitations.
Pengembangan LKPD Interaktif Berbasis React Materi Pecahan pada Siswa Kelas 4 SD

Abstract: The purpose of this study is to produce interactive worksheets based on REACT valid fraction material according to material experts, media experts, practitioners and practically according to students. This study uses a research model for developing Dick and Carey. Based on the results of the study, interactive worksheets are very valid according to material experts and practitioners with a percentage of 94.9 percent, very valid according to media experts and practitioners with a percentage of 94.2 percent, and very practical according to users with 88.5 percent. From the percentage obtained, the product can be used without revision.&#x0D; Abstrak: Tujuan dari penelitian ini adalah menghasilkan LKPD interaktif berbasis REACT materi pecahan yang valid menurut ahli materi, ahli media, praktisi dan praktis menurut siswa. Penelitian ini menggunakan model penelitian pengembangan Dick dan Carey. Berdasarkan hasil penelitian LKS interaktif sangat valid menurut ahli dan praktisi materi dengan persentase 94,9 persen, sangat valid menurut ahli dan praktisi media dengan persentase 94,2 persen, dan sangat praktis menurut pengguna dengan 88,5 persen. persentase. Dari prosentase yang diperoleh, produk dapat digunakan tanpa revisi.
A Novel Segmentation Techniques for Red Blood Cells using Clustering Algorithms

RBC called Erythrocytes is one of the important element in blood composition which is main responsible in all living cells for its gaseous exchanges with the environment externally. In general, at the physiological maintained conditions, RBC in view provides circular in the front and also looks bi-concave at side. One of serious disease with reference to blood cells is Cancer where the healthy RBC are affected. This reduces the body's immunity factors. To identify the cancer cell various methods are employed but it does not provide the proper detection of blood cells. In this method, proper identification of the cancer cells from the unaffected RBCs was identified in which are presented in blood samples using various imaging tools and also with the techniques. The proposed novel method called Online Region Based Segmentation (ORBS) method is done which is used to discover the areas of the boundary of the unaffected corpuscles. By using properties of region, a suitable metric is formulated to determine the shape which is abnormal in the blood cells. Overall accuracy of 96.9% is obtained using proposed ORBS methods and deep learning classification (DLC) method is accurate as 97.1% that helps to diagnose cancer cell using the feature extraction process which is done automatically. The computation time was found to be less when related to the other existing method which is 22 seconds. Closeness of Proposed method in relative to True Positive values at ROC curves indicates the performance which is higher than other methods. Experimental results prove proposed systems effectiveness when compared by means of other detection methods.
Unresolved Privacy and Ethics Issues Related to Learning Analytics in Higher Education and Academic Librarianship

Learning analytics involve big data collection, analysis processes, and technology that are used in higher education institutes and academic libraries to support student success and perform organizational assessment. Since these processes require the input of personally identifiable student and patron information to be effective, there are major ethical and legal considerations that must be addressed concerning privacy. This article demonstrates that privacy concerns about learning analytics can be mitigated by requiring informed consent from participants, establishing protocols for the collection and management of personally identifiable information, and advocating privacy rights of patrons. By synthesizing and expanding on viewpoints from the literature, this article offers recommendations pertaining to the collection, analysis, and management of patron data that are gathered for the purpose of learning analytics.
The Importance of Using Hashtags on Raising Awareness about Social Issues

Hashtags are a great way for people to search for topics on social networks that have a common theme. This study tried to explore the importance and power of hashtags that have gained popularity, on raising awareness about social issues. 252 participants (118 males and 134 females) participated in this study. In order to collect data, a questionnaire survey was used. The final questionnaire consisted of 20 items. It was a 4-point Likert scale: (1) Strongly disagree, (2) Disagree, (3) Agree, (4) Strongly agree. Means, standard deviations, a t-test and degrees were scored to analyze the participants’ responses to the items of questionnaire. The results revealed that the overall degree of the expected benefits of using hashtags on raising the awareness about social events was high. Moreover, the results also revealed that there were no statistically significant differences, regarding gender (male and female).
Digital transformation of education and the analysis of possible risks: results of a survey of teachers of general education organizations

Introduction. Digital transformation of the educational space resulted in a new problem area associated with the search for solutions to eliminate the existing gap between the pace of digitalization of society and education and the preparation of teachers to work in the digital environment in the context of digitalization of the educational process. The article is aimed at monitoring and analyzing the possible risks of digital transformation in the general education system. Materials and methods. During the study of the possible risks of digital transformation in the general education system, a survey among teachers of general education organizations of Krasnodar region was conducted (122 people). The professional standard "Teacher (Pedagogical Activity in the Field of Preschool, Primary General, Basic General, Secondary General Education) (Educator, Teacher)" dated October 18, 2013 No. 544n and the Federal State Educational Standard of Higher Education (FSES HE 44.03.01 Pedagogical Education) were analyzed and compared. The results of the study. The study aimed at determining the possible risks of digital transformation in the general education system showed that a significant number of respondents noted the following factors as risk zones: technical working conditions of teachers (21.3% of teachers are not provided with the necessary equipment), their ambiguous attitude to distance education (against 72.1% of teachers) and digital transformation of education (against 29.5% of teachers), incomplete understanding of trends in the development of modern education, insufficient level of mastering information and communication technologies (more than 55% of teachers possess ICT at an average or low level). The identified risks are not fully reflected in the professional standard of the teacher, which gives grounds for its significant revision and updating. Discussion and conclusion. Based on the results of the survey of teachers the following recommendations are offered: a) to provide the subjects of the educational process with the necessary computer and telecommunications equipment with high-speed Internet access; b) to create optimal working conditions for teaching staff contributing to the improvement of professional competencies under digitalization; c) to involve more teachers in the development of academic materials posted on Russian educational online resources; d) to implement educational and enlightenment activities among teachers to form ideas about the directions, opportunities and prospects of digital transformation of general education, to generate a new way of thinking of a modern teacher under digitalization.
EFEKTIVITAS PENGGUNAAN APLIKASI GOOGLE MEET SEBAGAI MEDIA PEMBELAJARAN ONLINE PADA MAHASISWA SAAT PANDEMI COVID-19

Covid-19 is a virus that was first discovered in the city of Wuhan, China at the end of 2019. Its rapid transmission made the WHO declare the Covid-19 outbreak a global pandemic. This prompted the Minister of Education, Culture, Research and Technology of the Republic of Indonesia, Nadiem Anwar Makarim, to issue circular letter number 4 of 2020 concerning the Implementation of Education in the Coronavirus Disease (Covid-19) Emergency Period. Learning must be carried out online. Online learning is learning that is carried out virtually in their respective homes. The application that is often used by lecturers and students in carrying out online learning is Google Meet. The purpose of this study is to find out how effective online learning is using Google Meet. The research method used is descriptive quantitative with a non-test instrument, namely interviews. The results obtained are that online learning using the Google Meet application has been effective in delivering material during the Covid-19 pandemic. Using the Google Meet application as an online learning medium has various advantages, namely a) it is easier to use, b) does not suck up a lot of internet quota, c) does not require a signal that is always stable, sometimes in up-and-down signal conditions can still follow learning, and d) the available features are easy to understand how to use
Students’ Attitudes and Perceptions of Smart Online Learning through Podcast Content Development

Due to the COVID-19 pandemic spreading around the globe, it has affected many fields of human life, including educational services. It has been closed for months to eradicate the transmission of its virus. Corresponding to the situation, which changes dramatically, the educational institutions made some transformations and innovations to keep the class and instructional running safely for all by moving the regular sitting class face-to-face to online learning. Educational institutions, as well as podcasts, have developed several online learning media. This study explored the students' attitudes and preference towards creating content for online learning materials/ content by utilizing podcasts in innovative learning through an online survey of 84 students. The study results showed that most respondents (80%) stated that they were about to be enthusiastic, welcoming a new pattern of the instructional process by applying podcasts to develop subject content. The ﬁndings show the respondents’ attitudes and perceptions toward Podcast Content Development for Smart Learning are very positive with efficiency (80%), accessibility (90%), interactivity (90%), quality (82%). They preferred to access online learning materials by maximizing the smartphone as its effective and efficient reason. They tend to have brief and meaningful content considering the size and broadband connectivity to achieve flexible and convenient online classes for smart learning
Coping with Noisy Training Data Labels in Paraphrase Detection

We present new state-of-the-art benchmarks for paraphrase detection on all six languages in the Opusparcus sentential paraphrase corpus: English, Finnish, French, German, Russian, and Swedish.We reach these baselines by finetuning BERT.The best results are achieved on smaller and cleaner subsets of the training sets than was observed in previous research.Additionally, we study a translation-based approach that is competitive for the languages with more limited and noisier training data.
The Ukrainian Kyrylytsia, Restored: An Automation Project for Adding the Cyrillic Fields to Ukrainian Records in OCLC WorldCat

This report from the field concerns a collaborative project which resulted in successfully adding the Cyrillic fields to about 30,000 Ukrainian bibliographic records in OCLC WorldCat, the world’s largest online catalogue. Historically, the Ukrainian records in English-speaking libraries were only provided in transliteration according to the Library of Congress Romanization Table. However, the current standards also require the original script, such as the Ukrainian Kyrylytsia. While automating the Cyrillicization of Ukrainian legacy records is theoretically straightforward, in practice it faced more than one challenge, from poor quality of transliteration to the historical changes in Ukrainian orthography. The report presents the OCLC Ukrainian Cyrillicization project and discusses the steps in its implementation as an example of a successful collaboration in the areas of bibliographic automation, Ukrainian philology and culture, Slavic cataloguing, and linguistics.
Efficient Multilingual Text Classification for Indian Languages

India is one of the richest language hubs on the earth and is very diverse and multilingual.But apart from a few Indian languages, most of them are still considered to be resource poor.Since most of the NLP techniques either require linguistic knowledge that can only be developed by experts and native speakers of that language or they require a lot of labelled data which is again expensive to generate, the task of text classification becomes challenging for most of the Indian languages.The main objective of this paper is to see how one can benefit from the lexical similarity found in Indian languages in a multilingual scenario.Can a classification model trained on one Indian language be reused for other Indian languages?So, we performed zero-shot text classification via exploiting lexical similarity and we observed that our model performs best in those cases where the vocabulary overlap between the language datasets is maximum.Our experiments also confirm that a single multilingual model trained via exploiting language relatedness outperforms the baselines by significant margins.
The UMD Submission to the Explainable MT Quality Estimation Shared Task: Combining Explanation Models with Sequence Labeling

This paper describes the UMD submission to the Explainable Quality Estimation Shared Task at the Eval4NLP 2021 Workshop on "Evaluation & Comparison of NLP Systems".We participated in the word-level and sentencelevel MT Quality Estimation (QE) constrained tasks for all language pairs: Estonian-English, Romanian-English, German-Chinese, and Russian-German.Our approach combines the predictions of a word-level explainer model on top of a sentence-level QE model and a sequence labeler trained on synthetic data.These models are based on pre-trained multilingual language models and do not require any word-level annotations for training, making them well suited to zero-shot settings.Our best performing system improves over the best baseline across all metrics and language pairs, with an average gain of 0.1 in AUC, Average Precision, and Recall at Top-K score.
WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge

The recent success of neural language models (NLMs) on the Winograd Schema Challenge has called for further investigation of the commonsense reasoning ability of these models.Previous diagnostic datasets rely on crowd-sourcing which fails to provide coherent commonsense crucial for solving WSC problems.To better evaluate NLMs, we propose a logic-based framework that focuses on highquality commonsense knowledge.Specifically, we identify and collect formal knowledge formulas verified by theorem provers and translate such formulas into natural language sentences.Based on these true knowledge sentences, adversarial false ones are generated.We propose a new dataset named WINOLOGIC with these sentences.Given a problem in WINOLOGIC, NLMs need to decide whether the plausible knowledge sentences could correctly solve the corresponding WSC problems in a zero-shot setting.We also ask human annotators to validate WINOLOGIC to ensure it is humanagreeable.Experiments show that NLMs still struggle to comprehend commonsense knowledge as humans do, indicating that their reasoning ability could have been overestimated.
Method of Image Encryption By using Scrambling and Slant Transform

New image encryption methods such as Stage Scrambling and Slant Transform are allowing images to be scaled in new and powerful ways.The process of scaling image points, taking an average of the points' value and then identifying how many points have changed, can increase the capabilities of image encryption.Slant Transform image coding possesses a "discrete saw tooth-like basis vector which efficiently represents linear brightness variations along an image line; A fast computational algorithm has been found for the transformation" [1].Slant Transform is when the first row and column, and last line and column were moved clockwise.The two rows and columns before them were carried out in the same mathematical operations and have been scaled with the rest, while the second line and column, and the line and column before the last were moved counter clockwise.The process was continued in opposite directions to all the lines and columns in the image, where the image is distorted and then applied slant transform in the image.