Reviewerly/exHarmony huggingface dataset:
Constructive Universal High-Dimensional Distribution Generation through Deep ReLU Networks.

We present an explicit deep neural network construction that transforms uniformly distributed one-dimensional noise into an arbitrarily close approximation of any two-dimensional Lipschitz-continuous target distribution. The key ingredient of our design is a generalization of the space-filling property of sawtooth functions discovered in (Bailey & Telgarsky, 2018). We elicit the importance of depth - in our neural network construction - in driving the Wasserstein distance between the target distribution and the approximation realized by the network to zero. An extension to output distributions of arbitrary dimension is outlined. Finally, we show that the proposed construction does not incur a cost - in terms of error measured in Wasserstein-distance - relative to generating $d$-dimensional target distributions from $d$ independent random variables.
BUILDING THE DIGITAL COMPETENCE TEACHER PROFILE BY INNOVATIVE TEACHING INSTRUMENTS

This report on the MoPED project contains the results of designing the digital competence of teachers, in particular, developed models of standards of digital competence of university teachers and teachers. The report presents the results of a survey of teachers and students of higher education institutions and teachers of secondary education institutions on their attitudes to digital competence and the composition and content of digital competence of teachers, which was conducted under the project. Models of digital competence standards of a teacher of a higher education institution and a teacher of a general secondary education institution have been built. The levels of digital competence of a university lecturer are determined and certain knowledge and skills are determined for each of them. The standard of digital competence of the teacher which consists of 5 components is offered and requirements to each of them are described in detail.
PERSEPSI MAHASISWA TEKNIK SIPIL TERHADAP PROSES BELAJAR MENGAJAR SECARA ONLINE DI MASA PANDEMI COVID-19

Online lectures have become the main means of academic learning during the COVID-19 pandemic. Without exception,this includes the students of the Civil Engineering study program, Faculty of Civil Engineering &amp; Planning of Trisakti University who use online application facilites, such as google classroom, zoom and other media applications during online lectures. The purpose of this study is to determine the students’ perceptions of the online teaching and learning process during the COVID-19 pandemic. The population of this study are students of Civil Engineering program batch 2016 to 2020 who are registered as active for the odd semester of the 2020/2021 Academic Year. This research is a descriptive quantitative study and was conducted by using a simple random sampling method. The data collection techniques using interviews and questionnaires. The data analysis used to complete this research is the Likert scale survey method with a total sample of 247 student respondents.The results have shown that the majority of students of Civil Engineering as much as 90% attended online lectures using laptops. As many as 90% of the students stated that they used the Zoom application to attend lectures via video conference. Technical issues that are often experienced by students during online lectures are dominated by unstable internet networks, this was stated by 75% of the students. About 66% of the students agree that online lectures can be accessed easily, only 34% of the students are satisfied with the online lecture mode, and on the latest rule of New Normal, 58% of the students agree that learning should be done by blended learning method.
Study on Visualization of Different Teacher Behavior Based on Teacher Experience during Trial Class

A new teacher at an elementary school, junior high school, or a high school has to teach classes from the first day. However, new teachers often find teaching difficult in a real-world teaching environment. In this paper, we analyze the gestures and behaviors of a beginner teacher and an expert teacher with the aim of increasing the teaching quality of beginner teachers. Image processing technology was used to automatically report visualized results of teaching behaviors. We devised the following approach: (1) a new teacher conducts a class in an environment replicating a real class, (2) the class behavior of the new teacher is systematically evaluated, and (3) new teachers objectively look back at their classes and derive insights for their development. The aim of this research is to evaluate and visualize the behavior of teachers during classes, focusing on (2) and (3). Specifically, we took videos of a trial class of expert and beginner teachers using a video camera (third-person view) and egocentric vision. The egocentric vision recognizes objects using YOLO algorithm, while the third-person view classifies the teacher behavior using Spatial-Temporal Graph Convolution Networks (ST-GCN) based on Open Pose. Then, we analyzed the differences between expert teacher behavior and beginner teacher behavior and visualized the results. In the behavioral analysis at STGCN, ‘Writing on the board’ constituted 74.5% of the approach of beginner teachers and ‘Pointing to the board’ was 36.9%, whereas ‘Writing on the board’ was 33.1% of the approach of expert teachers. Further, in the behavioral analysis by the YOLO algorithm, ‘Writing on the board’ was 41.1% and ‘Talking at the front’ 57.8% for beginner teachers, and ‘Writing on the board’ was 25.3% and ‘Talking at the front’ 74.0% for expert teachers. In other words, we confirmed that the experts were conscious of the whole classroom, and that beginners tended to do lessons only by writing on the board.
The ApposCorpus: a new multilingual, multi-domain dataset for factual appositive generation

News articles, image captions, product reviews and many other texts mention people and organizations whose name recognition could vary for different audiences.In such cases, background information about the named entities could be provided in the form of an appositive noun phrase, either written by a human or generated automatically.We expand on the previous work in appositive generation with a new, more realistic, end-to-end definition of the task, instantiated by a dataset that spans four languages (English, Spanish, German and Polish), two entity types (person and organization) and two domains (Wikipedia and News).We carry out an extensive analysis of the data and the task, pointing to the various modeling challenges it poses.The results we obtain with standard language generation methods show that the task is indeed non-trivial, and leaves plenty of room for improvement.
Subjectivity in Modern Management

The study aims to present the notion of subjectivity in management activity as having multiple meanings, referring to various types of management rationality. Scientific novelty of the research lies in substantiating the influence that moral and value attitudes have on rationality of management subjectivity development, which has allowed the researcher to determine distinct nature of the Russian management. As a result, it was shown that subjectivity can be presented in the form of corporate policies, management position, in the format of file texts. The competence-based approach of institutionalising management activity remains relevant in the Russian realities. It was determined that subjectivity is expressed through management institutions, as well as through a manager’s personality.
Developing Business Skills in High School Students Using Project Activities

The most important requirement for today’s general education is the cultivation of humane and tolerant personality of the information society with well-developed business skills, which will allow him or her to successfully integrate into the complex social environment and act not only as a consumer but also as a producer, executive, leader, who can perform various social roles and determine the effectiveness and quality of his or her activities. The research aims to theoretically justify and experimentally verify the methodology for developing business skills in high school students using project activities. General education schools served as experimental facilities. The analysis of the experiment’s results took into account the number of group participants, as well as sampling representativeness (225 high school students aged between 14 and 17 in each group experimental and control). High school students’ understanding of the need for well-developed business skills is enhanced due to such forms and methods as reading biographies of prominent businessmen from all over the world; role-playing situations; meetings with people who have succeeded in economic, entrepreneurial, pedagogical and other fields; visits to companies, enterprises, cultural and scientific institutions; certain business games (“How can one live within one’s means?”, “Starting a business”, “Business communication”, “Manager’s working week”), conversations on such topics as “Is it difficult to be a businessman?”, “How can one succeed in learning?”, “Tell me about myself”; the “this is my opinion” student platform; participation in the youth forum of student organizations, an insight into business sites. The following diagnostic methods were used to identify the development level of certain business skills: the method called “Determining a self-focus” needed to identify business focus; the method called “Striving for achievements” needed to identify a striving for success; relevant tasks used to identify the level of key competencies; questionnaires about creativity; the communication and organization skills methodology needed to assess respective skills; methods for studying the level of subjective locus of control needed to assess responsibility; methods for identifying risk preparedness; the scale of reactive (situational) and personal anxiety needed to identify the level of self-control; the tolerance index questionnaire. The obtained results show that the number of high school students with a high level of business skills has increased by 12.8% in EG and only by 1.3 in CG. The number of high school students with an average level of business skills has increased by 3.8% in EG and by 0.6 in CG. Therefore, the comparison of EG and CG results proves the effectiveness of the proposed methodology for developing business skills in high school students using project activities.
Efektifitas pembelajaran sejarah kebudayaan Islam melalui google classroom ditinjau dari hasil belajar siswa

ABSTRAKImbas Covid-19 dalam dunia pendidikan memaksa pendidik untuk berinovasi dalam mengembangkan dan mengalihkan proses pembelajaran konvensional ke pembelajaran jarak jauh. Salah satu platform pembelajaran jarak jauh adalah google classroom. Tujuan penelitian ini mendeskripsikan keefektifan pembelajaran daring melalui google classroom yang ditinjau dari hasil belajar siswa. Penelitian ini berjenis quasi experiment yang dirancang memakai one group pretest-postest design. Sampel penelitian ini menggunakan seluruh siswa di kelas 11 yang berjumlah 31 orang pada salah satu sekolah di Kabupaten Purwakarta, kemudian data diolah dengan uji paired sample t test serta N-Gain. Hasil penelitian menggambarkan bahwa pembelajaran jarak jauh menggunakan google classroom apabila ditinjau dari hasil belajar siswa mencapai persentase 58.3% (cukup efektif menurut tafsiran N-Gain). Hal ini berkaitan dengan pelaksanaan pembelajaran dibandingkan sebelum menggunakan google classroom mengalami peningkatan rata-rata hasil belajar sebesar 22.6%. Namun, di sisi lain ditemukan beberapa kelemahan dalam pembelajaran tersebut seperti penguasaan fitur, siswa tidak memiliki paket data, serta jangkauan sinyal yang tidak merata. Kesimpulan penelitian menunjukkan bahwa penggunaan Google classroom cukup efektif dalam pembelajaran sejarah kebudayaan Islam ditinjau dari hasil belajar siswa. Kata kunci: google classroom, hasil belajar, sejarah kebudayaan Islam. ABSTRACTThe impact of current covid-19 has succeeded in changing various educational aspect, thus forcing educators to develop and diverting conventional learning processes into distance learning. One of the distance learning platforms is google classroom. During the Covid-19 pandemic, this application became of its own urgency because of the many features presented to support learning. This research employed a quasi-type experiment design by using a one-group pretest-postest design, which aims to determine the google classroom's effectiveness on Islamic Cultural History learning from the perspective of student learning outcomes. This research employed all students in grade 11 with total of 31 people at one of the Madrasah Aliyah in Purwakarta Regency. The the data were processed by using the paired-sample t-test and N-Gain. The results of the research illustrate that distance learning by using google classroom when viewed from student learning outcomes, reaches a percentage of 58.3% (quite effective according to the interpretation of N-Gain). The result related to the implementation of learning compared to before using Google Classroom, showed an increase in average learning outcomes of 22.6%. However, several weaknesses were found in the learning process, such as mastery of features, students not having data packages, and uneven signal coverage. The conclusion of this research showed that the use of Google classroom is quite effective in learning the history of Islamic culture in terms of student learning outcomes. Keywords: google classroom, history of Islamic culture, learning outcomes
ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for Counterfactual Statement Modeling

ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting counterfactual statements and detecting antecedent and consequence.This paper describes our system which is based on pretrained transformers.For the first subtask, we train several transformer-based classifiers for detecting counterfactual statements.For the second subtask, we formulate antecedent and consequence extraction as a query-based question answering problem.
A Multimodal Dataset of Images and Text to Study Abusive Language

In this we paper present Tint 2.0, an open-source, fast and extendable Natural Language Processing suite for Italian based on Stanford CoreNLP.The new release includes some improvements of the existing NLP modules, and a set of new text processing components for finegrained linguistic analysis that were not available so far, including multi-word expression recognition, affix analysis, readability and classification of complex verb tenses.
Finding Optimal Triangulations Parameterized by Edge Clique Cover

Many graph problems can be formulated as a task of finding an optimal triangulation of a given graph with respect to some notion of optimality. In this paper we give algorithms to such problems parameterized by the size of a minimum edge clique cover (cc) of the graph. The parameter cc is both natural and well-motivated in many problems on this setting. For example, in the perfect phylogeny problem cc is at most the number of taxa, in fractional hypertreewidth cc is at most the number of hyperedges, and in treewidth of Bayesian networks cc is at most the number of non-root nodes of the Bayesian network.Our results are based on the framework of potential maximal cliques. We show that the number of minimal separators of graphs is at most 2^cc and the number of potential maximal cliques is at most 3^cc. Furthermore, these objects can be listed in times O^*(2^cc) and O^*(3^cc), respectively, even when no edge clique cover is given as input; the O^*(⋅) notation omits factors polynomial in the input size. Using these enumeration algorithms we obtain O^*(3^cc) time algorithms for problems in the potential maximal clique framework, including for example treewidth, minimum fill-in, and feedback vertex set. We also obtain an O^*(3^m) time algorithm for fractional hypertreewidth, where m is the number of hyperedges. In the case when an edge clique cover of size cc' is given as an input we further improve the time complexity to O^*(2^cc') for treewidth, minimum fill-in, and chordal sandwich. This implies an O^*(2^n) time algorithm for perfect phylogeny, where n is the number of taxa. We also give polynomial space algorithms with time complexities O^*(9^cc') and O^*(9^(cc + O(log^2 cc))) for problems in this framework.
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation

It is argued that 4-valued paraconsistent truth values (called here "p-bits") can serve as a conceptual, mathematical and practical foundation for highly AI-relevant forms of probabilistic logic and probabilistic programming and concept formation. First it is shown that appropriate averaging-across-situations and renormalization of 4-valued p-bits operating in accordance with Constructible Duality (CD) logic yields PLN (Probabilistic Logic Networks) strength-and-confidence truth values. Then variations on the Curry-Howard correspondence are used to map these paraconsistent and probabilistic logics into probabilistic types suitable for use within dependent type based programming languages. Zach Weber's paraconsistent analysis of the sorites paradox is extended to form a paraconsistent / probabilistic / fuzzy analysis of concept boundaries; and a paraconsistent version of concept formation via Formal Concept Analysis is presented, building on a definition of fuzzy property-value degrees in terms of relative entropy on paraconsistent probability distributions. These general points are fleshed out via reference to the realization of probabilistic reasoning and programming and concept formation in the OpenCog AGI framework which is centered on collaborative multi-algorithm updating of a common knowledge metagraph.
Word Embedding based New Corpus for Low-resourced Language: Sindhi

Representing words and phrases into dense vectors of real numbers which encode semantic and syntactic properties is a vital constituent in natural language processing (NLP). The success of neural network (NN) models in NLP largely rely on such dense word representations learned on the large unlabeled corpus. Sindhi is one of the rich morphological language, spoken by large population in Pakistan and India lacks corpora which plays an essential role of a test-bed for generating word embeddings and developing language independent NLP systems. In this paper, a large corpus of more than 61 million words is developed for low-resourced Sindhi language for training neural word embeddings. The corpus is acquired from multiple web-resources using web-scrappy. Due to the unavailability of open source preprocessing tools for Sindhi, the prepossessing of such large corpus becomes a challenging problem specially cleaning of noisy data extracted from web resources. Therefore, a preprocessing pipeline is employed for the filtration of noisy text. Afterwards, the cleaned vocabulary is utilized for training Sindhi word embeddings with state-of-the-art GloVe, Skip-Gram (SG), and Continuous Bag of Words (CBoW) word2vec algorithms. The intrinsic evaluation approach of cosine similarity matrix and WordSim-353 are employed for the evaluation of generated Sindhi word embeddings. Moreover, we compare the proposed word embeddings with recently revealed Sindhi fastText (SdfastText) word representations. Our intrinsic evaluation results demonstrate the high quality of our generated Sindhi word embeddings using SG, CBoW, and GloVe as compare to SdfastText word representations.
Standard Model Particles with Mass Treated as Spheres with Finite Radii

The particle physics Standard Model involves three charge 0 neutrinos, three charge e leptons, three charge (2/3)e quarks, and three charge -(1/3)e quarks, where e is electron charge.However, the Standard Model cannot explain why there are three generations of particles in each charge state and makes no predictions relating to quark and lepton masses.This analysis, treating Standard Model particles as spheres with radii 1/4 the particle Compton wavelength, explains three, and only three fermions are in each charge state and relates first generation quark masses to the electron mass.
Digital education in the coronavirus era

Abstract following work outlines a study of the transformation of the educational process during the coronavirus pandemic. The object of the study is the Moscow region, which in the study is represented by seven universities. The subject of the research is the reports of students of these universities, which contain an opinion about the quality of distance education and the degree of satisfaction with it. Messages were collected and classified using machine learning methods-they were grouped according to various topics, from organizational issues to questions of personal well-being. Based on the results of the research carried out, a general assessment of the digital educational process by students is given.
Universal Approximation Theorem for Equivariant Maps by Group CNNs

Group symmetry is inherent in a wide variety of data distributions. Data processing that preserves symmetry is described as an equivariant map and often effective in achieving high performance. Convolutional neural networks (CNNs) have been known as models with equivariance and shown to approximate equivariant maps for some specific groups. However, universal approximation theorems for CNNs have been separately derived with individual techniques according to each group and setting. This paper provides a unified method to obtain universal approximation theorems for equivariant maps by CNNs in various settings. As its significant advantage, we can handle non-linear equivariant maps between infinite-dimensional spaces for non-compact groups.
The Time Has Come… To Talk About Why Research Data Management Isn’t Easy

For the last decade, academic libraries have talked with each other and with potential partners about their roles in helping to manage research data and their plans to expand or initiate research data services (RDS). Libraries have the capacity to provide these services, but the range and maturity of research data services from libraries vary considerably. In summer 2019, our team surveyed a sample of academic libraries of all sizes who are members of the Association of College and Research Libraries (ACRL) to find out about their current RDS and plans for the future. This study is a follow-up to surveys of this same group in 2012 and 2015. Our findings include the types of RDS currently being offered in academic libraries, the barriers that hinder RDS implementation, and staff capacity for creating RDS.
ICONet: A Lightweight Network with Greater Environmental Adaptivity

With the increasing popularity of artificial intelligence, deep learning has been applied to various fields, especially in computer vision. Since artificial intelligence is migrating from cloud to edge, deep learning nowadays should be edge-oriented and adaptive to complex environments. Aiming at these goals, this paper proposes an ICONet (illumination condition optimized network). Based on OTSU segmentation algorithm and fuzzy c-means clustering algorithm, the illumination condition classification subnet increases the environmental adaptivity of our network. The reduced time complexity and optimized size of our convolutional neural network (CNN) model enables the implementation of ICONet on edge devices. In the field of fatigue driving, we test the performance of ICONet on YawDD and self-collected datasets. Our network achieves a general accuracy of 98.56% and our models are about 590 kilobytes. Compared to other proposed networks, the ICONet shows significant success and superiority. Applying ICONet to fatigue driving detection is helpful to solve the symmetry of the needs of edge-oriented detection under complex illumination condition environments and the scarcity of related approaches.