Reviewerly/exHarmony huggingface dataset:
Exam Conduction and Proctoring System Using Face Detection

The Online Examination Portal is a web application for taking an online test productively along with face recognition capabilities to perform live proctoring, and there is no time wasted for checking the paper. This report will incorporate all highlights and procedures which are required to develop this portal. This document incorporates details about the objective of the system, approximately targets of the system, system scope confinement, essential system requirements, group advancement, likely venture risks, schedule of the deployment, and finally observing and reporting mechanisms for the whole system. Online Examination Conducting Portal is exceptionally useful for Instructive Institute's to prepare a complete exam, conduct proctoring to prevent misconduct, secure the time that will take to check the paper, and plan check sheets. Online Examination Portal will help the Institutes to test understudies and develop their abilities. But the impediments for the Online Exam systems, it takes more time when the user prepares the exam at the primary time for utilization. To conduct the exam we require the number of computers with the same number of students. With the successful use of the Examination Portal, the facilitator can utilize this system to create the tests as their requirements and we can get accurate results and save time once deployed.
E-Learning During Covid-19 Pandemic: An Empirical Research

The COVID-19 pandemic changed our way of life, affected many industries, and had a major impact on education. The paper presents observations and analyses of the challenges raised by the short adaptation time of the learning process and the caused technical, academic, and communication difficulties. An overview of the distinctive features of e-learning during crises, with special attention to communication, feedback and pedagogical approaches in an electronic environment, has been made. Empirical research of the opinion of students and teachers about online learning was conducted. The used research methods are survey and direct observation. Special attention is paid to the results of the survey, which are presented and discussed in detail.
DEVELOPMENT OF MATHEMATICAL SNAKES AND LADDERS MEDIA TO IMPROVE ELEMENTARY STUDENTS' LEARNING OUTCOMES ON CIRCLE MATERIALS

The success of the teacher in creating an interesting learning atmosphere and can foster a positive attitude of students in participating in this learning is strongly influenced by several factors, one of which is the use of interesting learning media. The purpose of this research is to produce Snakes and Ladders Game Learning Media on the Circle material for grade VI students of SDN 26 Ampenan which is suitable for use based on the assessment of Material Experts, Media Experts, and Students, as well as improving student learning outcomes. Snakes and Ladders Game Learning Media was developed using research and development or Research and Development (R&amp;D) with the ADDIE development model. At the Development stage, the media developed was assessed for feasibility by 2 Material Experts (lecturers and teachers), 1 Media Expert, 5 Small Group Trial Students, and 25 Large Group Trial Students. Data collection techniques in this development research are through questionnaires and tests. The data obtained from the questionnaire were analyzed descriptively qualitatively and quantitatively. The results of the media assessment based on the Material Experts obtained an average score of 3.6 included in the "Very Eligible" criteria, the Media Experts obtained an average score of 3.1 included in the "Eligible" criteria, and Small Group Trial Students obtained an average score 3.5 is included in the "Very Eligible" criteria, and the Large Group Trial of Students obtained an average score of 3.4 is included in the "Eligible" criteria. Student learning outcomes after using the Snakes and Ladders Game Learning Media that has been developed have increased by 56%. It is concluded that the Snakes and Ladders Game Learning Media is acceptable, feasible and effective to be used as a learning medium.
Out of the BLEU: An Error Analysis of Statistical and Neural Machine Translation of WikiHow Articles from English into Arabic

Most studies that compare the quality of Neural Machine Translation (NMT) to that of Statistical Machine Translation (SMT) rely on automatic evaluation methods, mainly the bilingual evaluation understudy (BLEU), without performing any kind of human assessment.While BLEU is a good indicator of the overall performance of MT systems, it does not offer any detailed linguistic insights into the types of errors generated by those MT models.Such insights are crucial for researchers to identify areas for improvement and for language service providers to understand how upgrading to NMT gives them better results.This paper breaks free from BLEU by conducting an error analysis that compares the performance of Google SMT and NMT engines for English-into-Arabic translation.The corpus consists of six WikiHow articles.The analysis is guided by the DQF-MQM Harmonized Error Typology which classifies translation errors into eight major categories, namely, accuracy, fluency, terminology, style, design, locale convention, verity and other (for any other issues).A fine-grained classification of translation errors as such enables the researcher to explore the error types generated by each MT model, the error types eliminated by NMT, and the new error types introduced by NMT.The paper focuses on the English-Arabic language pair because it is one of the least studied pairs in the comparative literature of SMT and NMT.The results show that NMT generates less grammatical errors and mistranslations than SMT.NMT output is more fluent and robust.However, SMT is more consistent with translating proper nouns and out-of-vocabulary words.
CAN I PROTECT MY FACE IMAGE FROM RECOGNITION?

The "Fawkes" procedure is discussed as a method of protection against unauthorized use andrecognition of facial images from social networks. As an example, the results of an experiment aregiven, confirming the fact of a low result of face image recognition within CNN, when the Fawkesprocedure is applied with the parameter mode = "high". Based on a comparative analysis with theoriginal images of faces, textural changes and graphical features of the structural destruction of imagessubjected to the Fawkes procedure are shown. In addition to this analysis, multilevel parametricestimates of these destructions are given and, on their basis, the reason for the impossibility ofrecognizing images of faces subjected to the Fawkes procedure, as well as their use in deep learningproblems, is explained. The structural similarity index (ISSIM) and phase correlation of images areused as quantitative assessment tools. It is also noted that facial images subjected to the Fawkesprocedure are well recognized outside of deep learning methods. For this purpose, models of twosimple systems for recognizing face images subjected to the Fawkes procedure are proposed, and theresults of the experiments performed are presented. It is argued that the use of simple face imagerecognition systems in a computer complex with CNN will make it possible to train such complexesand destroy the myth about the possibility of protecting face images. In conclusion, the question isposed as to whether it is possible to protect your face from recognition.
Transplante de medula óssea - Prevenção de infecção de cateter central

The aim was to identify in the literature the nursing actions performed to prevent CVC infections in patients undergoing HSCT. This is an integrative literature review, in which quantitative data were used to answer the research problem. As a result, for the construction of the project, articles identified in the BDENF, LILACS, MEDLINE and SciELO literature bases were analyzed. We obtained 15 articles that answer the guiding question. To prevent complications associated with central venous catheters in patients undergoing hematopoietic stem cell transplantation, it is necessary to implement clinical protocols, provide guidance on hand washing technique for health professionals and caregivers, the use of chlorhexidine, and exchange dressing, among others. It is concluded that HSCT is a highly complex alternative treatment that requires specific care with the handling of the central venous catheter to maintain catheter patency and provide safe treatment for patients.
Education Technology Research Trends in Indonesia during the COVID-19 Pandemic

This study aims to determine trends in research on educational technology carried out during the COVID-19 pandemic in Indonesia. The research method used is a descriptive approach using content analysis. The results revealed four prominent trends, namely: (1) The most researched area during the COVID-19 pandemic were online learning; (2) The most used research method is library research; (3) The most widely used learning media are google classroom and WhatsApp; and (4) The trend of combining more than one learning media in online learning. From the trends found, it can be seen that Indonesia has developed in educational technology. It needs research on a larger scale covering the Asia-Pacific region in the future.
Experimental Study of PaaS, Its Implementation Methods and Advantages and Challenges

This paper will explain the concept of PaaS in detail. PaaS is platform as a service. With the help of cloud computing, there are vendors who will provide platform like hardware and software online to be used by the employees of the business or organisation. The business can send their requirements to the vendor who will provide platform as a service to them based on per service charge basis. The paper will explain types of PaaS, Characteristics. Advantages and challenges in implementation of PaaS.
RECRUITMENT APP – A CUSTOM APPLICATION ON SALESFORCE.COM

Cloud Computing is one of the upcoming Internet based technology. It is been considered as the next generation computing model for its advantages. It is the latest computational model after distributed computing, parallel processing and grid computing. To be effective they need to tap all available sources of supply, both internal and external. The system has facilities where prospective candidates can upload their CV’s and other academic achievements. Earlier recruitment was done manually and it was all at a time-consuming work. Now it is all possible in a fraction of second. Better recruitment and selection strategies result in improved organizational outcomes. With reference to this context, the research paper entitled Recruitment and Selection has been prepared to put a light on Recruitment and Selection process.
Face Mask Detection using Mobilenet Technique

As the rise of Corona-virus has affected at least 188 countries in the world as of 2021 period [1], the outbreak has severely affected all kinds of age groups, so wearing a mask becomes utmost responsibility of the person to stop the outbreak from further spreading, but it is impossible to monitor all the people who are wearing mask and who are not.So here comes theNovel Face Mask Detector which is a high accuracy mask detector.It uses Mobile-Net, which is a high accuracy and efficient face detection mask detection.Here Mobile-Net is used for pre-processing of the image, which consists of a feature pyramid network to fuse high-level semantic information with multiple feature maps, and a novel context attention module to focus on detecting face masks.In addition, a novel cross-class object removal algorithm is used to reject predictions with low confidences and the high intersection of union.When the image is processed as array, the array is forwarded to the Mobile-Net after this, the process of max pooling is done then the array is flattenedand through the fully connected layer the output is given.As, Mobile-Net is the light-weighted neural network to be used in the mobile phones.
The New "offline + Online" Teaching Model of College English Based on Cloud Computing

Through literature review, this paper expounds the descriptive parameter framework of College English mixed teaching and the index framework of teachers' effective teaching behavior in College English mixed teaching and its influencing factors. Based on this, this paper studies the new "offline + online" teaching model of College English based on cloud computing. This paper verifies and enriches the framework established based on literature through pilot interviews. For the part of effective teaching behavior, this paper applies Delphi expert survey method to conduct two rounds of questionnaire consultation with 10 information-based foreign language teaching research experts, and forms the final effective teaching behavior questionnaire. The results of this study show that teachers' effective teaching behavior in College English teaching can be summarized into five factors. According to the explanatory power, the order from high to low is: online learning management, teacher support, organizing face-to-face classroom, diversified evaluation and personalized teaching. Therefore, the new "offline + online" teaching model of College English based on cloud computing can improve the efficiency of English teaching.
An approach to partial occlusion using deep metric learning

&lt;span&gt;The human face can be used as an identification and authentication tool in biometric systems. Face recognition in forensics is a challenging task due to the presence of partial occlusion features like wearing a hat, sunglasses, scarf, and beard. In forensics, criminal identification having partial occlusion features is the most difficult task to perform. In this paper, a combination of the histogram of gradients (HOG) with Euclidean distance is proposed. Deep metric learning is the process of measuring the similarity between the samples using optimal distance metrics for learning tasks. In the proposed system, a deep metric learning technique like HOG is used to generate a 128d real feature vector. Euclidean distance is then applied between the feature vectors and a tolerance threshold is set to decide whether it is a match or mismatch. Experiments are carried out on disguised faces in the wild (DFW) dataset collected from IIIT Delhi which consists of 1000 subjects in which 600 subjects were used for testing and the remaining 400 subjects were used for training purposes. The proposed system provides a recognition accuracy of 89.8% and it outperforms compared with other existing methods.&lt;/span&gt;
Garbage Classification Using Ensemble DenseNet169

Garbage is a big problem for the sustainability of the environment, economy, and society, where the demand for waste increases along with the growth of society and its needs. Where in 2019 Indonesia was able to produce 66-67 million tons of waste, which is an increase from the previous year of 2 to 3 million tons of waste. Waste management efforts have been carried out by the government, including by making waste sorting regulations. This sorting is known as 3R (reduce, reuse, recycle), but most people do not sort their waste properly. In this study, a model was developed that can sort out 6 types of waste including: cardboard, glass, metal, paper, plastic, trash. The model was built using the transfer learning method with a pretrained model DenseNet169. Where the optimal results are shown for the classes that have been oversampling previously with an accuracy of 91%, an increase of 1% compared to the model that has an unbalanced data distribution. The next model optimization is done by applying the ensemble method to the four models that have been oversampled on the training dataset with the same architecture. This method shows an increase of 3% to 5% while the final accuracy on the test of dataset is 96%.
SELF-EDUCATION AS A TREND OF THE PARADIGM OF KNOWLEDGE CULTURE STUDENT OF PEDAGOGICAL UNIVERSITY

Self-education is one of the main types of student learning activities of the Pedagogical University. In the process of self-education, the intellectual activity regulator is the culture of knowledge. The knowledge of natural science disciplines occurs from the position of the individual style of self-education “outside the template”, reproduction of values of culture and their transformation in the educational work of the individual. The professional and personal qualities of the student reflect its self-educational competence in the aggregate of the objective, motivational-semantic and regulatory and reflective components. The purpose of our study is to identify the levels of self-education competence in students of pedagogical universities of the Volga and Ural Federal Districts of Russia in the field of natural science disciplines and the factors of its development. Cognitive motive and motive of self-realization are key in manifestation of self-education competence. The level of development of self-education competence was recorded by the statistical program “IBM SPSS Statistics 22” on average indicators and selective standard deviation. In our proposed concept, the phenomenon of self-education competence is first reviewed in the context of its development at the subject of the pedagogical profession. We are proved that the need for the creation of pedagogical constructs increases in a transforming educational environment, which would provide a transition from traditional self-education samples to building its own model of scientific and practical training technologies. The results of the study made it possible to identify differences in students of younger and senior courses in personal interest to continuously engage in self-education activities for their professional personal development.
Paired Reading Strategy and Comprehension Level Among Grade 10 Students

This study aimed to determine the effectiveness of paired – reading strategy in improving the reading comprehension of Grade ten learners. In this study, the subjects were profiled first about their age, sex, parents’ highest educational attainment, family income and availability of reading materials. Then, the subjects reading comprehension level was also determined before and after the exposure in paired – reading strategy.The subjects of this study are the Grade 10 – Ruby learners which are assigned as control group and Grade 10 – Jade learners which were assigned as experimental group. Both groups are enrolled in Palina East National High School, Urdaneta City, Pangasinan, where the study was conducted, during the school year 2019 – 2020.In this study, the researcher utilized descriptive research design in addressing the problems about the profile of the subjects and their reading comprehension level. Then, a quasi - experimental research design was also used to determine the effectiveness of paired reading strategy in improving the reading comprehension of the learners. And in gathering the data needed in this study, a test which is the Oral Reading Comprehension assessment was utilized.Findings of the study revealed that both groups, the control and the experimental group, are at the literal level of reading comprehension before exposing to paired – reading strategy, then after the experimentation, the control group reached the interpretive level of reading comprehension while the experimental group reached the applied level of reading comprehension. Results were further supported by the findings of test of difference between the post-test of both groups which revealed that the experimental group had statistically better performance compared to the control group. Thus, paired reading strategy is more effective than traditional teaching in facilitating reading comprehension among learners.
Intersectional Identities and Machine Learning: Illuminating Language Biases in Twitter Algorithms

Intersectional analysis of social media data is rare.Social media data is ripe for identity and intersectionality analysis with wide accessibility and easy to parse text data yet provides a host of its own methodological challenges regarding the identification of identities.We aggregate Twitter data that was annotated by crowdsourcing for tags of "abusive," "hateful," or "spam" language.Using natural language prediction models, we predict the tweeter's race and gender and investigate whether these tags for abuse, hate, and spam have a meaningful relationship with the gendered and racialized language predictions.Are certain gender and race groups more likely to be predicted if a tweet is labeled as abusive, hateful, or spam?The findings suggest that certain racial and intersectional groups are more likely to be associated with non-normal language identification.Language consistent with white identity is most likely to be considered within the norm and non-white racial groups are more often linked to hateful, abusive, or spam language.
Studying word order through iterative shuffling

As neural language models approach human performance on NLP benchmark tasks, their advances are widely seen as evidence of an increasingly complex understanding of syntax.This view rests upon a hypothesis that has not yet been empirically tested: that word order encodes meaning essential to performing these tasks.We refute this hypothesis in many cases: in the GLUE suite and in various genres of English text, the words in a sentence or phrase can rarely be permuted to form a phrase carrying substantially different information.Our surprising result relies on inference by iterative shuffling (IBIS), a novel, efficient procedure that finds the ordering of a bag of words having the highest likelihood under a fixed language model.IBIS can use any black-box model without additional training and is superior to existing word ordering algorithms.Coalescing our findings, we discuss how shuffling inference procedures such as IBIS can benefit language modeling and constrained generation.
Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis

Mobile telecommunication companies in Malaysia have been widely used in the recent decade.There is intense competition among them to keep and gain new customers by offering various services.The reviews of the services by the customers are commonly shared on social media such as Twitter.Those reviews are essential for mobile telecommunication companies to improve their services and at the same time to keep their customers from churning to another company.Hence, this study focuses on the public sentiment on Twitter towards mobile telecommunication services in Malaysia.Data on Twitter was scraped using three keywords: Celcom, Digi, and Maxis.The keywords used to refer to Malaysia's top three mobile telecommunication companies.The timeline for the tweets was between December 2020 until January 2021 and was based on the promotion sales commonly used by the organisation to boost their sales which is called Year End Sales.Corpus-based approach and Machine Learning model using RapidMiner were used in this study, namely, Support Vector Machine (SVM), Naïve Bayes, and Deep Learning.The corpus determines the sentiment from the tweets, either positive, negative, or neutral.The models' performances were compared in terms of accuracy, and the outcome shows that Deep Learning classifiers have the highest performance compared to other classifiers.The results of this sentiment analysis are visualised for easy understanding.