Reviewerly/exHarmony huggingface dataset:
Neural Unification for Logic Reasoning over Natural Language

Automated Theorem Proving (ATP) deals with the development of computer programs being able to show that some conjectures (queries) are a logical consequence of a set of axioms (facts and rules). There exists several successful ATPs where conjectures and axioms are formally provided (e.g. formalised as First Order Logic formulas). Recent approaches, such as Clark et al., have proposed transformer-based architectures for deriving conjectures given axioms expressed in natural language (English). The conjecture is verified through a binary text classifier, where the transformers model is trained to predict the truth value of a conjecture given the axioms. The RuleTaker approach of Clark et al. achieves appealing results both in terms of accuracy and in the ability to generalize, showing that when the model is trained with deep enough queries (at least 3 inference steps), the transformers are able to correctly answer the majority of queries (97.6%) that require up to 5 inference steps. In this work we propose a new architecture, namely the Neural Unifier, and a relative training procedure, which achieves state-of-the-art results in term of generalisation, showing that mimicking a well-known inference procedure, the backward chaining, it is possible to answer deep queries even when the model is trained only on shallow ones. The approach is demonstrated in experiments using a diverse set of benchmark data and the source code is released to the research community for reproducibility.
Influence of Learning Motivation, Interest in Learning, Family Environment, and School Environment to the Difficulty of Learning

This study aims to determine whether the effect of learning motivation, learning interest, family environment, and school environment against learning difficulties. This research conducted for two months from October to December 2019. Method: The research used was a survey method with a causality approach. Inside population: This study is a student or millennial students, amounting to 200 respondents. Technique data collection using literature and questionnaire techniques. Data analysis technique using SmartPLS software version 3. PLS (Partial Least Square) with equation analysis structural (SEM). The results show that each variable has the same effect. So it can be said, if Learning Difficulties are increasing then of course motivation learning, learning interest, family environment and school environment will be very influential important in a state of learning difficulties . If on motivation to learn, interest in learning, family environment, and the school environment has a good influence of course resulting in a state of learning difficulties being reduced. The above description shows that there is a joint and significant influence between learning motivation, learning interest, family environment, and school environment.
Analysis of Drainage System Planning in Newtown Park Housing Development Sidoarjo District

Some areas in Indonesia often experience flood problems, therefore it is necessary to have an adequate and good drainage system planning so that community activities in an area are not disturbed. The location of this research is located in the newtown park housing development in Damarsih Village, Buduran District, Sidoarjo Regency. with land area ± 72.548 m². Rain station data used were from Bono, Sruni, Sedati, Banjarkemanten in the period 10 years 2009-2019. The direction of discharge of water from the study site was directed at small afv pepe, then continued to afv pepe. With the analysis of HSS Nakayashu at the 25 year return period, the discharge that occurred was not greater than the carrying capacity of Small afv pepe and afv pepe. The maximum discharge that occurs at the research location with the Rational method is 17,404 m3/s, the maximum discharge in the Weduwen method is 27,917 m3/s. Rational Discharge was chosen because the calculation of the length of rain can be known for sure so as to strengthen the level of certainty of the results obtained. For the capacity requirements in the research location with the rational method, the peak discharge for the 5-year return period is 1,858 m3/s and the volume of capacity required during high rain (27 minutes) is 3009 m3 and can be accommodated with a drainage channel of  5217.43 m3 and bozem. with a capacity of 720 m3 which has an area of ??400 m2 with a depth of 1.8 m.
Pneumonia Recognition Based on Deep Learning

The main manifestations of pneumonia are ground glass shadows and ground glass shadows on the X-rays of the lungs. Therefore, the research on pneumonia recognition is basically focused on the recognition of glass shadows. In this paper, according to the characteristics of pneumonia image in the existing data set, the author also does image registration, enhancement, filtering and denoising, data amplification, binarization, normalization, and other preprocessing on the data set. Based on the VGG16 network, each convolutional layer and a fully connected layer of the supplementary network are used before dating a batch normalization layer. A deep convolutional neural network BNnet with a more complex network level is obtained. And a feature extraction of pneumonia images is associated with it. When training the model, use the migration learning method to date VGG16, combined with the addition of a network layer to train the Kaggle Data Company's data set, and input the extracted features into a classifier composed of a fully connected layer. Finally, these images are divided into two categories: normal images and pneumonia images. The accuracy rate can reach 0.98, which effectively solves the over-fitting problem caused by the imbalance of sample data. At the same time, the mainstream neural network model is dispersed, and its accuracy, sensitivity, and specificity have been improved, and it has better robustness sexuality and generalization.
An enhanced framework for solving cold start problem in movie recommendation systems

Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining principal component analysis with logistic regression to predict the probability of genres of the movies. Finally, combining the most relevant tags based on similarity score with probability and find top N movies with high scores to the user. The proposed model is assessed using the root mean square error (RMSE), the mean absolute error (MAE), recall@N, and precision@N and it is applied to 1M, 10M, and 20M MovieLens datasets, resulting in an accuracy of 0.8806, 0.8791, and 0.8739.
Language use variation of L2 writers in weblog across different gender and genres

Despite the growing interest in investigating learners’ corpora, surprisingly little research has been conducted on the language use of L2 writers and its relation to the gender and genres in writing. Therefore, this study was aimed to find out the variation of language use in different genres or gender in weblogs, one of popular modes of computer-mediated communication (CMC). The study was done by conducting multivariate analysis using R program to weblog entries from a sample balanced of author gender (female or male) and weblog genre (diary or filter). Taking linguistic preferential features by Argamon et al (2003) and Pennebaker (2011) as dependent variables, the effect of genres or gender toward the use of the features was analyzed. The results showed that significant effects of several features can be considered as predictors. Personal pronouns and hedges (I think, and I believe) were found as predictors for diary; while the indefinite articles a/an and numbers were found as predictors for filter. As for the different language use by gender, female predictors were personal pronoun, verbs, negation, certainty words, and hedges. Meanwhile, the indefinite articles a/an, numbers, and preposition were the predictors of male writers.
Deep Learning For Face Spoofing and Liveness Detection

Pandemic situation turns every ones lives in online mode even classes were conducting through virtual meeting sessions. But for most of the meetings duplication or spoofing of faces was traced even for some secured meetings if they got meeting link they used get connected. But our system helps, authenticated users only can login and the rest couldn't make it possible. In this paper, face recognition based identification approach used to provide authentication for the user there is possibility of face spoofing therefore the early stage of the process is to verify the face is real or fake then adjust the database consists of the registered one or not. To handle this problem, face cascaded feature were calculated and using deep learning approach a solution was finalized.
AN INVESTIGATION OF STUDENTS’ WILLINGNESS TO COMMUNICATE IN SPEAKING CLASS IN ONLINE LEARNING

This study aims to investigate students’ willingness to communicate (WTC), the factors influencing students became reluctant to communicate, and teachers’ strategies to encourage students to be willing to communicate. The research was conducted in speaking class during the synchronous online learning. The data were collected from questionnaires and semi-structured interviews, so the mixed methods were employed. The respondents of this study were 25 students of eighth graders at SMPN 69 Jakarta. The results show that the students had low level of willingness to communicate, there were four factors influencing students become reluctant to communicate (self-confidence, anxiety, motivation, students’ personalities, and situational variables), and three strategies that the teachers employed to encourage students to be willing to communicate in speaking class such as playing games, using technology, and providing a variety of interactional activities. Keywords :  Willingness to Communicate, Online Learning, Strategies
Exploiting Domain-Specific Features to Enhance Domain Generalization

Domain Generalization (DG) aims to train a model, from multiple observed source domains, in order to perform well on unseen target domains. To obtain the generalization capability, prior DG approaches have focused on extracting domain-invariant information across sources to generalize on target domains, while useful domain-specific information which strongly correlates with labels in individual domains and the generalization to target domains is usually ignored. In this paper, we propose meta-Domain Specific-Domain Invariant (mDSDI) - a novel theoretically sound framework that extends beyond the invariance view to further capture the usefulness of domain-specific information. Our key insight is to disentangle features in the latent space while jointly learning both domain-invariant and domain-specific features in a unified framework. The domain-specific representation is optimized through the meta-learning framework to adapt from source domains, targeting a robust generalization on unseen domains. We empirically show that mDSDI provides competitive results with state-of-the-art techniques in DG. A further ablation study with our generated dataset, Background-Colored-MNIST, confirms the hypothesis that domain-specific is essential, leading to better results when compared with only using domain-invariant.
Adaptive Machine Unlearning

Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees only for sequences that are chosen independently of the models that are published. If people choose to delete their data as a function of the published models (because they don't like what the models reveal about them, for example), then the update sequence is adaptive. In this paper, we give a general reduction from deletion guarantees against adaptive sequences to deletion guarantees against non-adaptive sequences, using differential privacy and its connection to max information. Combined with ideas from prior work which give guarantees for non-adaptive deletion sequences, this leads to extremely flexible algorithms able to handle arbitrary model classes and training methodologies, giving strong provable deletion guarantees for adaptive deletion sequences. We show in theory how prior work for non-convex models fails against adaptive deletion sequences, and use this intuition to design a practical attack against the SISA algorithm of Bourtoule et al. [2021] on CIFAR-10, MNIST, Fashion-MNIST.
ПІДГОТОВКА МАЙБУТНІХ ФАХІВЦІВ СОЦІАЛЬНО-ПЕДАГОГІЧНИХ СПЕЦІАЛЬНОСТЕЙ У КОНТЕКСТІ НОВІТНІХ ТЕНДЕНЦІЙ ІНКЛЮЗИВНОЇ ОСВІТИ

Introduction. The creation of an inclusive educational environment, which raises the issue of training highly qualified professionals capable of working in accordance with the new guidelines is one of the positive changes in improving the national education system and its integration into the international educational space.Purpose. Substantiation of the model of training specialists of social and pedagogical specialties to work in an inclusive educational environment and experimental verification of its effectiveness.Methods. To solve this goal, a set of methods was used: study and analysis of scientific-methodological and special literature on the issue of research; analysis of educational programs; expert assessments, generalization of independent characteristics, modeling for substantiation and development of model of system of preparation of experts of socio-pedagogical specialties for work in the conditions of inclusive educational environment; pedagogical observation, conversation, questionnaire, interview, testing; pedagogical experiment to substantiate the model; methods of mathematical statistics for quantitative and qualitative analysis of the results of experimental work.Results. Implementation of the model of system of preparation of experts of socio-pedagogical specialties for work in the conditions of inclusive educational environment which consisted in inclusion of an inclusive component in the maintenance of professional training, formation of abilities and skills of interaction with persons with special educational needs, its practical orientation and orientation on self-development improving the efficiency of training highly qualified specialists in the context of the latest trends in inclusive education.Originality. We considered the model of the system of preparation of specialists of socio-pedagogical specialties to work in an inclusive educational environment as a pedagogical process developed over time, based on the ideas of a holistic approach to the development of the future specialist in the field of inclusive education. The implementation of the model took place through the appropriate selection of the content of education and educational technologies, forms and types of practical activities of students.Conclusion. Implementation of proposed model of the system of training of specialists in socio-pedagogical specialties to work in an inclusive educational environment contributed to the development of new approaches based on it, improvement of curricula and practices, creation of additional opportunities for student self-realization.The study does not cover all the issues of inclusive education. Topical issues include political, legal, socio-economic, moral and ethical, pedagogical ways of regulating inclusive education of different age groups. Among the promising areas – determining the main prospects for the development of inclusive education at the state, regional and sectoral levels.
Impact of Information and Communication Technology in Enhancing Food Security in a Rural Area: Alice Community as a Case Study

The contribution of Information and Communication Technology (ICT) has been renowned in a vast number of establishments and organizations to promote the sustainability agenda, a global agenda where the impact of ICT tools has significantly advanced information sharing, thus reducing wastage, increase in productivity, and high level of sustainability in food security. However, due to the incoherence in the productivity and marketing of smallholder farmers in a rural community, this study aimed to investigate the cognizance level and impact of ICT in smallholder farming practice. A quantitative approach was adopted using a self-administered questionnaire to gather information from smallholder farmers across the Alice community in the Eastern Cape province of South Africa. Data analysis involving descriptive statistics and a binary regression model was used to test the relationship between variables, while Spearman rho correlation was used to assess the variability in the response on the contribution of ICT tools. The smallholder farmers are mostly female (56%), with a considerable number of them with diploma qualification (44.2%) and with average years of farming experience between 4 – 9 years (34.6%). The assessment, however, shows an insufficient level of interaction with the extension workers. This has possibly transpired in the insignificant awareness (p = 0.152) and the negative adaptation (p = -.023) of ICT in farming practice. However, few ICT users noted the high impact of ICT on their farm productivity (p = 0.023). In conclusion, smallholder farmers of a rural community in South Africa are highly deprived of information, which significantly impacts their awareness of ICT There is, therefore, the need for improvement in the contact of extension workers with the farmers. In contrast, adequate advancement needs to be made on sensitization with the farmers on ICT roles to better farming productivity and level of sustainability.
Hubungan Interaksi Guru dengan Siswa Terhadap Hasil Belajar Matemtika Siswa SD Kelas V

The problem in this study is the lack of teacher-student interaction so that the students' Mathematics UTS scores are low. This study is a quantitative study with a correlation design that aims to determine the relationship between teacher and student interactions on Mathematics learning outcomes for fifth grade elementary school students in cluster VI. The results showed that there was a significant relationship between teacher-student interaction and students' mathematics learning outcomes with the value of t_count(4.7026) greater (>) than the value of t_(table)(1.67591) so H0 was rejected. Based on the analysis of the data above, it can be concluded that the interaction of teachers with students in the learning process will have an impact on student learning outcomes. So the better the interaction between teachers and students, it will improve student learning outcomes. From these conclusions, the impact resulting from this study is known that good interaction between teachers and students will improve student learning outcomes
Forward to the next mission

Nowadays, the greatest challenge that the educational system faces is the way we learn. This leads to the need for coming up with much better methods in order to develop and form and turn into habit key life skills, also known as ‘21st century skills’, (problem solving, creativity, teamwork, communication, initiative, leadership, adaptability, etc.). A successful way to accomplish this is project-based learning. The missions that are part of the ‘Design thinking’ method are an innovative way for its implementation. Therefore, this method is being used during the school year of 2020/2021 in Bacho Kiro High School, town of Pavlikeni, as part of a project under which turned the school into innovative. ‘Design thinking’ is a process which can be applied to everything that has to be produced, created or solved. This method helps younger pupils organize information and ideas in a better way, understand other people’s point of view and their problems more clearly, manage to lay down challenges accurately, think of more creative ideas and find quick ways to put them into practice. Every realized activity or ‘Design thinking’ project helps pupils understand that they can face unknown challenges as a team and find new solutions. There are seven missions for a single school year. There is one mission in a month, each in five consecutive days for 2 hours a day. Thanks to their creators from the Red Paper Plane, pupils became architects, space-men and discoverers. They await to become ecologists, travelers, meteorologists and automobile designers. All pupils, teachers and parents together have embarked on an incredible journey on the wings of the red paper plane so that the pupils from Bacho Kiro High School, town of Pavlikeni, are happy and successful in the 21st century.
A  Class of Univariate Non-Mesokurtic Distributions Using a Continuous Uniform Symmetrizer and  Chi Generator

In a good number of real life situations, the observations on a random variable of interest tend to concentrate either too closely or too thinly around a central point but symmetrically like the normal distribution. The symmetric structure of the density function appears like that of a normal distribution but the concentration of the observations can be either thicker or thinner around the mean. This paper attempts to generate a family of densities that are symmetric like normal butwith different kurtosis. Drawing inspiration from a recent work on multivariate leptokurtic normal distribution, this paper seeks to consider the univariate case and adopt a different approach to generate a family to be called ’univariate non-mesokurtic normal’ family.The symmetricity of the densities is brought out by a uniform random variable while the kurtosis variation is brought about by a chi generator. Some of the properties of the resulting class of distributions and the pameter estimation are discussed.
Penerapan Pendekatan Whole Language dalam Pengembangan Kemampuan Berbahasa Indonesia Siswa Sekolah Dasar

This study describes the nature of learning Indonesian and to describe the application of the whole language approach in learning Indonesian in elementary schools. The method used in this research is a qualitative research method. This research was conducted at the Elementary School 4 Dharmasaba. The data analysis technique used is Miles and Huberman's qualitative data analysis technique, which comprises reducing data, presenting data and drawing conclusions and verifying. Based on the results of interviews and document analysis conducted in the research process, it can be concluded that: (1) Indonesian language learning at the elementary school level includes listening, speaking, reading and writing skills and has been adapted on the demands of the 2013 curriculum. (2) Application of the whole approach language in Indonesian language learning has improved students' Indonesian learning outcomes, as evidenced by the increase in the average learning outcomes before and after the application of the whole language approach from 76.75 to 80.25.