Reviewerly/exHarmony huggingface dataset:
LASSO-Penalized Clusterwise Linear Regression Modeling With Local Least Angle Regression (L-LARS)

In clusterwise regression analysis, the goal is to predict a response variable based on a set of explanatory variables, each with cluster-specific effects. Nowadays, the number of candidates is typically large: whereas some of these variables might be useful, some others might contribute very little to the prediction. A well known method to perform variable selection is the LASSO, with calibration done by minimizing the Bayesian Information Criterion (BIC). However, current LASSO-penalized estimators have several disadvantages: only certain types of penalties are considered; the computations might involve approximate schemes and can be very time consuming, with overly complex tuning of the penalty term. This is usually due to the possibly large number of times the estimator must be evaluated for each plausible value of the tuning parameter(s). To ease such computation, we introduce an Expectation Maximization (EM) algorithm with closed-form updates working with a very general version of the LASSO penalty. Such an EM is based on an iterative scheme where the component specific LASSO regression coefficients are computed according to a coordinate descent update. Least Angle Regression is then used to perform covariate selection by evaluating the estimator only once. The advantages of our proposal, in terms of computation time reduction and accuracy of model estimation and selection, are shown by means of a simulation study and illustrated with a real data application.
Discrimination of quarry blasts from earthquakes using artificial neural networks

&amp;lt;p&amp;gt;Seismic networks record vibrations that are captured by their stations. These vibrations can be coming from various sources, such as tectonic tremors, quarry blasts and anthropogenic sources. Separation of earthquakes from other sources may require human intervention and it can be a labor-intensive work. In case of lack of such a separation, seismic hazard may be miscalculated. Our goal is to discriminate earthquakes from quarry blasts by using artificial neural networks. To do that, we used two different databases for earthquake signals and quarry blasts. Neither of them have data from our study of interest, which is North-East of Italy. We used three channel signals from the stations as an input for the neural networks. Signals are used as both time series and their spectral characteristics and are fed to the neural networks with this information. We then separated earthquakes from quarry blasts in North-East Italy by using our algorithm. We conclude that our algorithm is able to discriminate earthquakes from quarry blasts with high accuracy and the database can be used in different regions with different earthquake and quarry blast sources in a large variety of distances.&amp;lt;/p&amp;gt;
Learning System Dynamics via Deep Recurrent and Conditional Neural Systems

Although there are various mathematical methods for modeling system dynamics, more general solutions can be achieved using deep learning based on data. Alternative deep learning methods are presented in parallel with the improvements in artificial neural networks. In this study, both LSTM-based recurrent deep learning method and CNMP-based conditional deep learning method were used to learn the system dynamics of the selected system using time series data. The effects of the amount of time series data needed for training and the initial input length needed for predictions made using the learned system model on both methods were analyzed.
Pengembangan E-Book Biologi Konstruktivistik Pada Materi Struktur dan Fungsi Sel Untuk Kelas XI SMA

Abstract. Teaching materials in the form of constructivist biology e-books on cell structure and function material for high school grade XI are developed to be valid and practical. The e-book was developed by applying constructivist principles consisting of: (1) personalization learning, (2) reflective thinking, (3) problem solving and investigation, (4) relevance to daily-life, (5) collaborative learning, ( 6) discussion, and (7) teacher scaffolding. The e-book was developed using Microsoft Word and Scribus applications and then converted into e-books with the Flipcreator application. This type of research is Research and Development (R&amp;D) with the ADDIE development model which has five stages, namely analysis, design, development, implementation, and evaluation. The subjects of this study were expert lecturers who tested the validity of the instruments and products developed, biology teachers and students of X MIA 2 SMAN 3 Makassar as subjects to test the practicality of the product. The results showed that the products developed were valid and practical. The validity results are in the valid category with a Va value of 4.33, the practicality test results of the teacher's response are 84.52% with the very practical category and the practicality test results of the students' responses are 95.06% with the very practical category. So, it can be concluded that the constructivist biology e-book on cell structure and function material for high school class XI developed is suitable for use in the learning process.Keywords: e-book, addie, constructivism, and cell: structure and function
SCHEMA-GUIDED PARADIGM FOR ZERO-SHOT DIALOG

Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research. Neural models implicitly memorize task-specific dialog policies from the training data. We posit that this implicit memorization has precluded zero-shot transfer learning. To this end, we leverage the schema-guided paradigm, wherein the task-specific dialog policy is explicitly provided to the model. We introduce the Schema Attention Model (SAM) and improved schema representations for the STAR corpus. SAM obtains significant improvement in zero-shot settings, with a +22 F1 score improvement over prior work. These results validate the feasibility of zero-shot generalizability in dialog. Ablation experiments are also presented to demonstrate the efficacy of SAM.
Implementation of Cloud Computing in Virtual Learning System for Effective Utilization of ICT Based Computing Resources in Academic Environment

Abstract: Now a day's increase in use of digital media influences academic system to adopt ICT based learning tools to improve the accessibility and reliability of learning resources. Information and communication technology (ICT) has significantly affected the process of admission in higher educational institutions. ICT also improves the interaction between the students and the institution as well as academician too. As of late, cloud computing  as another sort of trend setting innovation speeds up the development for the computer industry. Cloud  computing is turning into an appealing innovation because of its dynamic versatility and powerful utilization of the assets; it tends to be used under conditions where the accessibility of assets is restricted. Cloud computing can be used in the field of education, and integration of network and e-learning. In order to give a full play for the advantages of cloud computing with ICT based education in educational institution, we tried to attach cloud computing to e-learning, build an e-learning cloud, and made an active research and exploration for it, so the implementation of ICT based tools used with cloud computing become more familiar.
The Roles of Metacognitive Awareness Reading Strategies and Vocabulary Knowledge on Reading Comprehension of EFL Learners

This paper explores whether metacognitive awareness reading strategies and vocabulary knowledge are strong predictors of reading comprehension. It also addresses the relationship among vocabulary knowledge and metacognitive awareness reading strategies and reading comprehension achievement. To attain this objective, descriptive research using a correlational research design was employed. 100 participants were selected from a population of 250 students using simple random sampling. The participants were asked to respond to a MARSQ developed by Taraban, Kerr and Ryneason (2004) and researcher-made tests of vocabulary knowledge and reading comprehension. Pearson product moment correlation and stepwise multiple regression analysis were used in data analysis. The results of the correlation analysis showed that among the two predictor variables namely, EFL learners' vocabulary knowledge, and metacognitive awareness reading strategies, vocabulary knowledge is highly correlated with reading comprehension; there is strong positive correlation between vocabulary knowledge and the same high correlation holds with reading comprehension, but there were no significant correlation with metacognitive awareness reading strategies. The stepwise multiple regression analysis results also revealed that EFL students' vocabulary knowledge plays a significant role in predicting their reading comprehension achievement compared to metacognitive awareness reading strategies. Finally, some useful implications and recommendations which are of significance to educators, teachers and researchers are proposed based on the research findings. Accordingly, the result contends that instructions on developing reading comprehension should put vocabulary knowledge competencies in priority and then focus on activation of background knowledge and use of reading strategies for the success of reading comprehension. As EFL vocabulary knowledge was found to be the strongest significant predictors of reading comprehension, teachers and parents should also pay special attention to students’ vocabulary knowledge.
Use of Communication Channels for their Information Needs by Farmers of Belagavi District: A Study

This investigation explores how correspondence channels are trading agricultural information were chosen. In particular, it recognizes the correspondence Channels utilized by Farmers in Belagavi District and decides the components impacting the decision of correspondence channels for trading rural information. The significant job that information plays can be estimated from the huge spaces of agrarian exercises where in it discovers applications that include: development of information and intelligence, dynamic and the board, including uses of Information Technology. The outcomes demonstrate that radio, cell phones, TV, individual ranchers, farming expansion specialists and papers were the usually utilized correspondence channels for moving agricultural information
SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection

This paper describes the participation of SINAI team at Task 5: Toxic Spans Detection which consists of identifying spans that make a text toxic.Although several resources and systems have been developed so far in the context of offensive language, both annotation and tasks have mainly focused on classifying whether a text is offensive or not.However, detecting toxic spans is crucial to identify why a text is toxic and can assist human moderators to locate this type of content on social media.In order to accomplish the task, we follow a deep learning-based approach using a Bidirectional variant of a Long Short Term Memory network along with a stacked Conditional Random Field decoding layer (BiLSTM-CRF).Specifically, we test the performance of the combination of different pre-trained word embeddings for recognizing toxic entities in text.The results show that the combination of word embeddings helps in detecting offensive content.Our team ranks 29th out of 91 participants.
Inovasi Kegiatan Literasi FTBM Jawa Barat Dan TBM Jatmika Bandung Dalam Menjaga Energi Literasi Masyarakat Jawa Barat Di Masa Pandemi

This study aims to identify and describe the innovation activities and literacy movements carried out by the West Java Community Library Forum (FTBM) and the Jatmika Bandung Community Library (TBM) in the midst of the Covid-19 pandemic. The method used in this research is descriptive method with a qualitative approach and data collection techniques through interviews, observation, documentation and literature study. This study aims to obtain an empirical picture of creative innovations created by FTBM in the West Java region, especially at TBM Jatmika Bandung, in order to remain productive in providing collection services and activities aimed at improving the culture of reading or community literacy during the pandemic. The results of the study show that each FTBM and TBM in the West Java region collaborate with each other and play an active role in creating innovative literacy activities and movements virtually or online without reducing the spirit of community literacy energy and literacy activists in West Java. FTBM and TBM in West Java created a new strategy, namely by transforming all of their activities into alternative digital media to encourage literacy activities with new faces that are more fun. In addition, this strategy is also useful in providing convenience for the community in meeting literacy needs wherever and whenever. Thus, TBM can continue to play a role as a means to increase reading interest and literacy skills of the people of West Java even in the midst of a pandemic.
ECONOMIC, PSYCHOLOGICAL, PEDAGOGICAL AND METHODOLOGICAL BASIS OF PROFESSIONAL TRAINING OF FUTURE TEACHERS OF PRIMARY CLASSES IN THE CONTEXT OF IDEAS OF A NEW UKRAINIAN SCHOOL

Abstract. The article analyzes the trends in the change of key competencies required by the labor market. It is stated that these changes determine the new competencies that must be formed in the pupils of the school, which in turn changes the requirements for the competencies of the teacher. The directions of economic reforms of the Ukrainian school are studied and it is shown that these reforms will lead to an increase in the requirements for primary school teachers and increase the level of control over his activities from communities, whose funds will be used to finance educational institutions of each community. Therefore, as a priority area of reforming the Ukrainian school, a systematic approach to the training of future teachers was proposed, details of the reassessment of the components of this approach, the content of components and outlined new requirements for the components of primary school teacher competence. It is proved that the psychological and pedagogical principles of primary school teacher training and acquisition of professional competence are a set of interconnected components of the educational process, which form the need for improvement and interest in the profession; stimulate the education of the individual. Not only the importance of knowledge and skills, but also the qualities that will ensure an effective pedagogical process; form a proper self-assessment of the teacher and his objective assessment of students. Negative factors that may reduce the effectiveness of complex psychological and pedagogical principles of future teacher training have also been studied. It is stated that the key prerequisite for these principles should be the formation of a teacher as a person who loves children, enjoys communication and friendship with them, is able to share their joys and sorrows, and does not forget how she was a child. The system of methodical approaches of teacher training is considered and it is specified that it should aim at formation at the future teacher of aspiration of innovative pedagogical work as a basis of its self-realization. The structure of methodical approaches in modern conditions is estimated and its transience is indicated. That is, methodological approaches are only tools in the work of a teacher, and the constant search for pedagogical innovations for the desire to improve will allow him to choose the right tools from the set offered to him. The results of the study and the developed proposals will increase the effectiveness of reforming the New Ukrainian school. Keywords: competencies, system approach, education reform, professional training, future primary school teachers. JEL Classification M53 Formulas: 0; fig.: 2; tabl.: 2; bibl.: 20.
Impact of Language Style on Purchase Intention Mediation Effect of Perceived Credibility: Case from Pakistan

Purpose: The current study investigates the new impact of the language style of online reviews (figurative vs. literal) on customers' perceived credibility and purchase intention the use of figurative language to advertise product quality and social presence has been advocated in previous marketing research.&#x0D; Design/Methodology/Approach: The theory of expectation is adopted that is developed by Victor Vroom, is used in this study. It claims that a person would act or behave in a certain way because they are motivated to choose a specific behavior over others because of the desired output of that activity. A Google Forms quiz was created to collect data from persons who have done online purchasing. They were divided into two groups. The sentiment is echoed by a sample of 334 Pakistani students. The results were assessed using structural equation modeling (SPSS).&#x0D; Findings: The findings show that consumers' purchasing intentions are influenced by both the language style of online reviews and their perceived reliability. Literal reviews, as opposed to figurative reviews, can increase users' desire to purchase search products. Consumers' willingness to buy experience products may be improved through figurative reviews.&#x0D; Implications/Originality/Value: In the context of Pakistan customer's trust in online shopping is not good. They think these platforms are not trustworthy. With the help of Literal reviews’, we can build customers' trust in online shopping.
Profile of Digital Literacy Skills of Class IX Students on Online Learning Mathematics

This study aims to determine the profile of digital literacy skills possessed by students in online learning mathematics. This research is a quantitative study. The subjects in this study were students of 9th Grade Imam Syafi'i Junior High School Batam, which may amount to 30 students. The data technique used was a questionnaire, with descriptive data analysis. Based on the research results, it can be seen that the level of digital literacy skills possessed by students is 80% and is in the good category. Where the skill level of each digital literacy indicator, namely internet search skills, was 86% in the very good category, while the hypertext direction guideline skills were 73%, information content evaluation was 82%, and knowledge compilation skills were 82%, the total was in the good category. Furthermore, the results also showed that the most ICT devices used by students in accessing the internet were smartphones 21 students, with 22 students private ownership. The purpose of most students in accessing the internet was to find information 14 students, and the most students accessed in using the internet were academic content 14 students.
An Adaptive, Dynamic and Semantic Approach for Understanding of Sign Language based on Convolution Neural Network

Sign language has been one of the primary sources when it comes to the communication of people with hearing imparity. Just like every country has its own set of languages which people speak over there, similarly, there are different kinds of sign language which differs with regions and nationality. Many research and studies have been going on concerning the recognition of various kinds of sign language. With the help of sign language, people with hearing imparity can communicate with the rest of the society and convey their message, but the communication is not a two-way effective communication that has led to a barrier between us and the people with hearing imparity. Researchers are constantly trying to develop effective Sign Language Recognition (SLR) System. But all these systems that have been devised to date always come with a drawback and i.e., they have been only limited to isolated sign gestures. The main objective that we tend to achieve while devising this Sign Language Recognition System is that the system should assist in converting the input sign into its corresponding text or speech format automatically so that the rest of the society can effectively communicate with the people having hearing imparity and hence the barrier gets removed. Here in this paper, we propose a Sign Language Recognition System which is based on the Convolution Neural Network (CNN). The main purpose of using CNN over TSK is because it not only classifies and recognizes images but also does all the tasks with high accuracy, unlike the other proposed system. This particular technique will extract the important and useful information of the input sign language while ignoring the rest. Then these signed sequences will be modelled by the proposed mechanism and the output will be thrown in form of text or speech. CNN has been recognized as one of the major branches of Neural Networks. It uses multilayer perceptron which gives the system the ability to recognize and classify similarly as the brain does. Moreover, using this technique over all previous techniques will help us to have the least amount of pre-processing.A typical Convolution Neural Network has an input layer, multiple internally connected processing layers, and an output layer. All the images need to be trained through all these layers to achieve all the required results.
A Teaching Assistant for the C Language (Short Paper)

We introduce a C tutor that can help instructors manage classes with many students learning to program in C. Nowadays, it is easy to evaluate code but it is hard to provide good feedback. We introduce a tool to help instructors provide students with feedback concerning their implementation and documentation for honing their programming skills. This tool is implemented in Python and is available at https://github.com/rcm/C_teaching_assistant.