Reviewerly/exHarmony huggingface dataset:
An Analysis of Recount Text Comprehension At the Eighth Grade of SMPN 12 Jambi City

In Junior High School, the students should have reading skill. Good achievement in reading is very important for the students. When the researcher did observation at the eighth grade students of SMPN 12 Jambi City, it was found that some of the students have difficulties in comprehending a recount text. The aim of this research is to find out students difficulties in comprehending a recount text at the eighth grade students of SMPN 12 Jambi City Academic Year 2018/2019. The researcher used qualitative method to analyze the data. There were thirty two students of the eighth year of SMPN 12 participated in this research. Observation, questionnaire and interview data were collected, analyzed and collated. The researcher found that some students had difficulty in understanding the content of the text and difficult to interpret the English language, which is almost the same answer choices that confuse students. Students simply read without understanding the text, not thorough in answering questions. The students difficult in comprehending a recount text questions. There are inability to access the content of the text.
The effectiveness of the problem-posing method based on android applications in mathematics learning

This study aims to see the learning outcomes of prospective mathematics teacher students on problem-posing abilities using the Android Package Kit (APK).It is necessary to have innovation in learning so that it has a better problem-posing ability.During a pandemic like this time teaching and learning activities experience many obstacles.To overcome these obstacles, it is necessary to do learning innovations, one of which is online learning.This study used a quasi-experimental design using the first experimental group and the second experimental group known as the pretest-posttest control group design.The subjects of this study were second-semester students majoring in Mathematics Education.Collecting data using the online method or online (Covid-19 pandemic).The sample was determined using a purposive sampling technique to determine the first experimental class as many as 28 students and the second experimental class as many as 29 students.The experimental class uses problem-based learning, while the second experimental class uses direct learning according to the material content.The material in this research is opportunity theory.The results of the observed data were analyzed quantitatively.The learning results show that learning using APK was effective in improving learning outcomes (both in problem-based learning classes and indirect learning).In the evaluation phase, improvements were made to the APK that had been developed after reflection on the results in the field at the implementation stage.The results showed that online learning based on android applications as a very helpful means of maximizing teaching and learning activities.
LibGuides: Sustainable Ecosystems: Plagiarism

Resources to support the Year 9 study of ecosytems and invasive species Strategies for avoiding plagiarism
Solar Photovoltaic System Design and Assessment a Southern Iraq Using Pvsyst Software

This study aims to design and evaluate the grid- connected solar photovoltaic in southern Iraq of city Shatrah. A design and feasibility study of rooftop solar photovoltaic system project is conducted using tools PVsyst and design software by inspecting project area by utilizing the NASA surface meteorology data through geographical coordinator of place. The performance of the system was simulated using PVsyst software and the results were analysed. The analyses of the simulation results show that the project yields energy about 15.05 MWh/year. The Photovoltaic simulation tool is important in predicting the energy production of the solar system.
Early Childhood Development Trainee Teachers’ Perceptions on E-learning Implementation during the COVID-19 era in Harare Metropolitan District, Zimbabwe

Purpose: The study aimed at finding out the extent to which Early Childhood Development (ECD) trainee teachers perceive the feasibility and challenges of online learning platforms and generate strategies for effective implementation. Approach/Methodology/Design: A case study design for which fifteen ECD trainee teachers from two Higher Learning Institutions in Harare were purposively selected in this qualitative study through online structured questionnaires and telephone interviews expressed in English. Data gathered was expanded in thick rich descriptions to form themes. Findings: Innumerable benefits of e-learning were established. E-learning facilitates the continuation of learning, motivates, improves student-to-student interaction, is a convenient way of submitting assignments, reduces commutation and material costs and is an easy way of storing documents. However various setbacks were elucidated. Erratic power cuts and internet failures, exorbitant costs of data bundles and lack of smart devices deter the full embracing of e-learning. Practical Implications: Zimbabwean HLIs have adjusted to the new normal and embraced online learning to ensure the continuation of learning in the midst of the pandemic. Online learning overcomes educational obstacles and students will not only follow courses using the traditional method. Originality/value: The findings suggest trainee teachers require the provision of cheap data bundles, free internet applications and extension of due dates. The use of blended approach/hybrid learning to assist those in remote communities, training workshops and seminars on effective use of e-learning platforms were deemed essential.
INOVASI DAN PRAKTIK TERBAIK PERPUSTAKAAN PERUM LKBN ANTARA:MENDUKUNG TUJUAN STRATEGIS PERUSAHAAN

Perum LKBN Antara’s library once only a small unit that manage news documentation produce by Antara (in the year of 1950), then transform into special library which manage the multimedia databases. Innovation and all the best practices in library must be accordance with corporate strategic policy. Innovation is beneficial to improve product, improve process or effectiveness to create modern values for the stakeholders. The Antara Library is a special library that manages and provides information and documentation services. The purpose of this study is to record Antara’s library innovation and best practices. Method used is qualitative with descriptive study. The best practices are information repackaging and the implementation of knowledge management: 1) packaging the old databases into website antaranews.com/tag/100/antara-doeloe 2) e-book’s kaleidoscope 3) media monitoring 4) research for books and news content 5) activities that support the implementation of knowledge management. Those best practices are measured by key performance indicators and being reviewed adjusted by corporate strategic goal. The innovation closely related with performance, innovation will make radical change or breakthrough to improve performance. Antara’s library innovations are: technology, manufacturing, business, organization behavior, human values and design &amp; interactivity.
A Survey of the Various Steganography Techniques Using Soft Computing Techniques

Steganography is the method of storing information by hiding that information’s existence. It can be used to carry out hidden exchanges and hence can enhance individual privacy. Steganography aims at communicating the secret data in an appropriate multimedia carrier. In this paper, the various techniques used to perform Steganography in a secure way are studied and reviewed. In this the various Artificial Intelligence techniques used for steganography are reviewed and analyzed.
Theories of interactive learning and their implication in the didactic activity

The variety of theoretical perspectives on the learning process demonstrates the complexity of this phenomenon, the multitude of angles from which it can be analyzed and is a proof of the permanence of the development of conceptions regarding the way the learning process takes place. In this article, we make a brief review of the best-known theories of interactive learning, emphasizing on one hand the explanatory mechanism of learning presented in each theory, but also the significant contribution of each to the educational instructional process.
Text pre-processing of multilingual for sentiment analysis based on social network data

Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using Natural Language Processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, brand monitoring, etc. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.
AWARENESS OF BLOCKCHAIN USAGE, STRUCTURE, "&amp;" GENERATION OF PLATFORM?S ENERGY CONSUMPTION: WORKING TOWARDS A GREENER BLOCKCHAIN

Blockchain is a disruptive information technology innovation with energy consumption.As more organizations look to implement or embrace blockchain innovations, research must focus on making the blockchain greener.This research explores the current innovative blockchain usage, structure, generations, and energy consumption.An energy consumption comparison for consensus protocols is provided along with a list of recommendations for implementing green blockchains.This paper provides a significant impact upon previous literature and aids organizations considering implementing a green blockchain.
Changing the Basis of Contextual Representations with Explicit Semantics

The application of transformer-based contextual representations has became a de facto solution for solving complex NLP tasks.Despite their successes, such representations are arguably opaque as their latent dimensions are not directly interpretable.To alleviate this limitation of contextual representations, we devise such an algorithm where the output representation expresses human-interpretable information of each dimension.We achieve this by constructing a transformation matrix based on the semantic content of the embedding space and predefined semantic categories using Hellinger distance.We evaluate our inferred representations on supersense prediction task.Our experiments reveal that the interpretable nature of transformed contextual representations makes it possible to accurately predict the supersense category of a word by simply looking for its transformed coordinate with the largest coefficient.We quantify the effects of our proposed transformation when applied over traditional dense contextual embeddings.We additionally investigate and report consistent improvements for the integration of sparse contextual word representations into our proposed algorithm.
Dropout- A Detailed Survey

Deep Neural Networks are very complex and have large number of parameters. Shortlisting the parameters that influence the model prediction is not possible as each has equal significance. These neural nets have powerful learning skills can model training data well enough. However, in most of these conditions, the models are over-fitting. Combining predictions from large neural nets where neurons are co-dependent alters the performance of the model. Dropout addresses the problem of overfitting and slow convergence in deep neural nets. The core concept of dropout technique is to randomly drop units and their connections from the neural network during training phase. This prevents units from co-adapting and thus improving the performance. The central mechanism behind dropout is to take a large model that overfits easily and repeatedly sample and train smaller sub-models from it. This paper provides an introduction to dropout, the history behind its design and various dropout methods.
Distance learning in out-of-school education institutions

The article considers and outlines the characteristics of the main forms of distance learning, which is an innovation not only for out-of-school education, but also for the entire educational system of the country. For the first time, teachers, educators, teachers, pedagogical workers were forced in a short time to radically change the forms and methods of teaching and move to unadapted in the existing educational process of distance learning. Quarantine forced us educators, including teachers – out-of-school teachers to work with their students in remote access. Although silence has settled for some time, quarantine in classrooms, laboratories and workshops encourages teachers of out-of-school education institutions to creatively search for active forms of communication with students via the Internet, development of their creative abilities and life competencies. The article provides advice on the organization of distance learning in out-of-school education institutions. The practice of teachers’ work includes conducting group and individual online classes, master classes, consultations, minutes of communication, video presentations, online exhibitions, photo reports and more. The author offers distance learning tools for the quarantine period and various distance forms of classes in a circle. Club leaders keep in touch with students and their parents via e-mail, telephone, Facebook, Messenger, Viber, Skype Instagram. The situation in the country has become the biggest and most difficult challenge for the entire educational community and education system of Ukraine as a whole since independence, distance learning has become a forced experiment that should be an impetus for analysis, conclusions, rapid decision-making for the educational process in any -what conditions.
Direction finding in sensors model based automatic modulation classification

In this paper, the RSSI testing as well the Angle of Arrival (AoA) have been examined for position prediction also produce the front specified composition of the possibility distribution of the location of a sensor node."Multiple Signal Classification" (MUSIC) defined as a popular "Eigen" construction approach with large declaration, which broadly utilized for predicting the total of waveforms, as well their corners of arrival.In this research an examination of the ability to development of part of key specifications of the "MUSIC" technique has been presented, which might improve the response of the prediction operation.The outcomes of the simulation of this approach point out that the position of the sensor node may be evaluated in a little time period values as well that the condition of the explanation is competitive beside last techniques.
미용전공 대학생의 공감능력과 회복탄력성의 상관관계

This study sought to determine the effects of empathetic ability on resilience in college students majoring in beauty. To that end, from March 1 to April 30, 2019, a survey of 389 college students majoring in beauty in colleges in the Gyeonggi area was conducted. The students' empathetic ability showed an overall high level in the order of empathetic concern, acceptance of viewpoints, personal pain, and imagination. Meanwhile, their resilience showed a high level in the order of self-control, interpersonal ability, and positive questions. Students generally showed higher levels of empathy ability and resilience in the first year than the second year, and it was also found that the higher their satisfaction with the school and department was, the higher their empathetic ability and resilience were. Regarding the correlation between the sub-factors of empathetic ability and resilience, empathetic concern, acceptance of viewpoints, and imagination had a significant positive correlation, and personal pain had a significant negative correlation. This suggests that the sub-factors of empathetic ability all had effects on resilience, and indicates a need to revise the curriculum aimed at improving this ability since resilience improves according to the improvement of empathy.
ADVANTAGES AND DISADVANTAGES OF TRADITIONAL AND ALTERNATIVE WAYS OF ASSESSMENT

Assessment of student achievement is one of the key components of teaching. Besidetraditional forms of assessment, which are most common in teaching practice, recentlyalternative assessment is becoming more popular and frequent in educational discourse.The following article deals with the importance of assessment, traditional ways ofassessment, alternative or authentic ways of assessment, and their features.
Great Service! Fine-grained Parsing of Implicit Arguments

Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content.More importantly, the scarcity of datasets annotating diverse implicit roles limits empirical studies into their linguistic nuances.For example, in the web review "Great service!", the provider and consumer are implicit arguments of different types.We examine an annotated corpus of fine-grained implicit arguments (Cui and Hershcovich, 2020) by carefully re-annotating it, resolving several inconsistencies.Subsequently, we present the first transition-based neural parser that can handle implicit arguments dynamically, and experiment with two different transition systems on the improved dataset.We find that certain types of implicit arguments are more difficult to parse than others and that the simpler system is more accurate in recovering implicit arguments, despite having a lower overall parsing score, attesting current reasoning limitations of NLP models.This work will facilitate a better understanding of implicit and underspecified language, by incorporating it holistically into meaning representations.
THE IMPLEMENTATION OF PROBLEM SOLVING STRATE-GY TO THE MIDWIFERY STUDENTS IN AKADEMI KE-BIDANAN PANCA BAKTI

ESL students always encounter the issue of solving complicated problems in learning English. Various problem may occur during the learning process including speaking, listening, writing and reading. The most complicated issues are often faced by the students who learn English for the specific purpose for instance English for law, English for tourism, English for business or English for medical purpose. The prob-lems may dealing with types of expression, vocabularies, standard and formal struc-tures that will be used most of all in a specific field. This purpose of this study was to implement the problem solution strategy in the medical field especially for midwifery students in Akademi Kebidanan Panca Bakti Pontianak. The midwifery students were trained to be able to solve the problems regarding with the health issues. This study constitute an attempt to identify the common problems and how to solve by us-ing problem solution strategy. The most common issues found were dealing with terminologies and the risk signs during pregnancy. The result showed that by using problem solution strategy, students were successful in solving the health problems. They were able elaborate the way how to deal with the health sign during pregnancy for instance the risk sign dealing with weighing the mother, the risk sign if a mother has a high fever, the risk sign if a mother has a high blood pressure, the risk sign if a mother has an abnormal pulse rate and the risk sign if a mother grows fast or slowly. By understanding how to solve the problems, the midwifery students will be more professional.
Exploring the Loss Landscape in Neural Architecture Search

Neural architecture search (NAS) has seen a steep rise in interest over the last few years. Many algorithms for NAS consist of searching through a space of architectures by iteratively choosing an architecture, evaluating its performance by training it, and using all prior evaluations to come up with the next choice. The evaluation step is noisy - the final accuracy varies based on the random initialization of the weights. Prior work has focused on devising new search algorithms to handle this noise, rather than quantifying or understanding the level of noise in architecture evaluations. In this work, we show that (1) the simplest hill-climbing algorithm is a powerful baseline for NAS, and (2), when the noise in popular NAS benchmark datasets is reduced to a minimum, hill-climbing to outperforms many popular state-of-the-art algorithms. We further back up this observation by showing that the number of local minima is substantially reduced as the noise decreases, and by giving a theoretical characterization of the performance of local search in NAS. Based on our findings, for NAS research we suggest (1) using local search as a baseline, and (2) denoising the training pipeline when possible.
Scalable Approach for Normalizing E-commerce Text Attributes (SANTA)

In this paper, we present SANTA, a scalable framework to automatically normalize Ecommerce attribute values (e.g."Win 10 Pro") to a fixed set of pre-defined canonical values (e.g."Windows 10").Earlier works on attribute normalization focused on fuzzy string matching (also referred as syntactic matching in this paper).In this work, we first perform an extensive study of nine syntactic matching algorithms and establish that 'cosine' similarity leads to best results, showing 2.7% improvement over commonly used Jaccard index.Next, we argue that string similarity alone is not sufficient for attribute normalization as many surface forms require going beyond syntactic matching (e.g."720p" and "HD" are synonyms).While semantic techniques like unsupervised embeddings (e.g.word2vec/fastText) have shown good results in word similarity tasks, we observed that they perform poorly to distinguish between close canonical forms, as these close forms often occur in similar contexts.We propose to learn token embeddings using a twin network with triplet loss.We propose an embedding learning task leveraging raw attribute values and product titles to learn these embeddings in a self-supervised fashion.We show that providing supervision using our proposed task improves over both syntactic and unsupervised embeddings based techniques for attribute normalization.Experiments on a real-world attribute normalization dataset of 50 attributes show that the embeddings trained using our proposed approach obtain 2.3% improvement over best string matching and 19.3% improvement over best unsupervised embeddings.