Reviewerly/exHarmony huggingface dataset:
How much pretraining data do language models need to learn syntax

Transformers-based pretrained language models achieve outstanding results in many well-known NLU benchmarks. However, while pretraining methods are very convenient, they are expensive in terms of time and resources. This calls for a study of the impact of pretraining data size on the knowledge of the models. We explore this impact on the syntactic capabilities of RoBERTa, using models trained on incremental sizes of raw text data. First, we use syntactic structural probes to determine whether models pretrained on more data encode a higher amount of syntactic information. Second, we perform a targeted syntactic evaluation to analyze the impact of pretraining data size on the syntactic generalization performance of the models. Third, we compare the performance of the different models on three downstream applications: part-of-speech tagging, dependency parsing and paraphrase identification. We complement our study with an analysis of the cost-benefit trade-off of training such models. Our experiments show that while models pretrained on more data encode more syntactic knowledge and perform better on downstream applications, they do not always offer a better performance across the different syntactic phenomena and come at a higher financial and environmental cost.
Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning

Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, such as node, link or graph classification or node-level unsupervised learning (e.g. node clustering). Despite its wide range of possible applications, graph-level unsupervised learning has not received much attention yet. This might be mainly attributed to the high representation complexity of graphs, which can be represented by n! equivalent adjacency matrices, where n is the number of nodes. In this work we address this issue by proposing a permutation-invariant variational autoencoder for graph structured data. Our proposed model indirectly learns to match the node ordering of input and output graph, without imposing a particular node ordering or performing expensive graph matching. We demonstrate the effectiveness of our proposed model on various graph reconstruction and generation tasks and evaluate the expressive power of extracted representations for downstream graph-level classification and regression.
Analisis Akurasi Modifikasi k-Nearest Neigbour Pada Data Pencemaran Udara

k-Nearest Neighbor (k-NN) is one of the most popular machine learning methods to date. k-NN determines the similarity of data or objects based on the proximity of the distance between data to a class or label or other data group. The distance measurement method on k-NN is considered ineffective because it gives the same weight for each characteristic. The majority vote in determining the data class is also considered problematic because it ignores the closeness between the data and allows multiple majority classes. To solve this problem, the Local Mean Distance Weight k-Nearest Neighbor (LMDWkNN) method emerged. This method is considered capable of providing better accuracy results than k-NN. In this study, we will look at the accuracy results of the LMDWkNN and k-NN methods on the Air Pollution Standard Index (ISPU) data. In this study, 80% of the data was used as training data and the rest was used as testing data. Furthermore, an evaluation will be carried out using the 10-fold cross validation method from the training data so that the best k value will be obtained. The value of k will be used to make predictions on the testing data. The results of this study show that LMDWkNN is able to provide a better accuracy value than k-NN with a more competitive k value. Where the LMDWkNN method found an average accuracy value of 0.9748 while the k-NN was only 0.9371, which means that the LMDWkNN method increased the accuracy value of 0.0377.
AUGMENTED REALITY KATALOG PENJUALAN PERANGKAT KERAS MENGGUNAKAN MAGIC BOOK BERBASIS ANDROID

Abstract At this time almost all circles have used information technology and technology itself is not something that is foreign to society in general. With the development of technology today, it will be easier for users to fulfill their needs and digital product marketing catalogs. The purpose of this research is to design and implement an augmented reality application of hardware sales catalogs using an android-based magic book. The author uses the Multimedia Development Life Cycle method. The purpose of this study is to design and implement an augmented reality application of hardware sales catalogs using an android-based magic book. The use of magic books to display 3D animated models by reading the marker symbols using the camera. Keywords : Android; Augmented Reality; Marker-based Tracking; Multimedia Developmennt Life Cycle; Unity; Vuforia
Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling: An Introduction

The problem of selecting and scheduling a subset of projects is a complex task faced by many organizations. This problem is referred to as the project portfolio selection and scheduling problem (PPSSP). In nearly all cases, a budgetary constraint limits the number of projects that can be selected. Furthermore, complex operational constraints are often present as well. Hence, the selection of the optimal set of projects is a challenging task, which is further complicated by the necessity also to determine the scheduling of their implementation. In many cases, intelligent techniques inspired by evolutionary processes are employed to address such problems. This introductory chapter provides a short overview of the PPSSP and the relevant solution methodologies, providing context for the remainder of this book. A brief description of each contributed chapter is also provided.
Research Title: Identifying the Role of Media Technology Advances in Economics

Introduction: The purpose of this article is to identify the role of media technology development in the economy. In this study, 30 experts in the field of media were used as a sample. Method: The research method is qualitative. Since in this research, there was a need to address the depth of the subject and a deep and comprehensive understanding of media experts, for this reason, the interview method was used. In order for the interviews to have a specific structure, semi-structured interviews have been used. Data were also analyzed through content analysis using maxqda software. Findings and Results:In this study, 23 effective factors that determine the positive and negative role of media technologies in the economy were identified, including: information production and increasing economic knowledge, more employment, consumer welfare, reducing economic inequality, reducing political risk, increasing Economic security and efficiency, more control and supervision of people over economic activities, expansion of corporate markets, creation of free online market, innovations and innovations resulting from new technologies increase revenues, increase competition between organizations increase the quality of economic activities, improve and Improving the standards of economic development, public participation in economic activities, expressing people's satisfaction or dissatisfaction with economic activities, spending time on social networks.
Multi-Layer Random Perturbation Training for improving Model Generalization Efficiently

We propose a simple yet effective Multi-Layer RAndom Perturbation Training algorithm (RAPT) to enhance model robustness and generalization.The key idea is to apply randomly sampled noise to each input to generate label-preserving artificial input points.To encourage the model to generate more diverse examples, the noise is added to a combination of the model layers.Then, our model regularizes the posterior difference between clean and noisy inputs.We apply RAPT towards robust and efficient BERT training, and conduct comprehensive fine-tuning experiments on GLUE tasks.Our results show that RAPT outperforms the standard fine-tuning approach, and adversarial training method, yet with 22% less training time.
MRU Library Website: Information Design: INFO 2667 - Search Engine Showdown

Find resources to support your study of the design and creation of engaging information resources and services.
Behavior of Modern Pre-trained Language Models Using the Example of Probing Tasks

Moderntransformer-based language models are revolutionizing NLP.However, existing studies into language modelling with BERT have been mostly limited to English-language material and do not pay enough attention to the implicit knowledge of language, such as semantic roles, presupposition and negations, that can be acquired by the model during training.Thus, the aim of this study is to examine behavior of the model BERT in the task of masked language modelling and to provide linguistic interpretation to the unexpected effects and errors produced by the model.For this purpose, we used a new Russianlanguage dataset based on educational texts for learners of Russian and annotated with the help of the National Corpus of the Russian language.In terms of quality metrics (the proportion of words, semantically related to the target word), the multilingual BERT is recognized as the best model.Generally, each model has distinct strengths in relation to a certain linguistic phenomenon.These observations have meaningful implications for research into applied linguistics and pedagogy, contribute to dialogue system development, automatic exercise making, text generation and potentially could improve the quality of existing linguistic technologies.
Word Equations: Inherently Interpretable Sparse Word Embeddings through Sparse Coding

Word embeddings are a powerful natural language processing technique, but they are extremely difficult to interpret. To enable interpretable NLP models, we create vectors where each dimension is inherently interpretable. By inherently interpretable, we mean a system where each dimension is associated with some human-understandable hint that can describe the meaning of that dimension. In order to create more interpretable word embeddings, we transform pretrained dense word embeddings into sparse embeddings. These new embeddings are inherently interpretable: each of their dimensions is created from and represents a natural language word or specific grammatical concept. We construct these embeddings through sparse coding, where each vector in the basis set is itself a word embedding. Therefore, each dimension of our sparse vectors corresponds to a natural language word. We also show that models trained using these sparse embeddings can achieve good performance and are more interpretable in practice, including through human evaluations.
Comparison of University and School Tutorial Teaching

In the Spring term of 2021, I performed two online tutorial-based teaching roles. One was with groups of second year university Statistics students; the other was with groups of Year 11 GCSE Mathematics students. In this essay I aim to compare those experiences and draw out some learnings for PGR teaching practice.
Priority Areas Of Spiritual, Moral And Physical Education Of Youth In The Family

This article is devoted to strengthening the institution of the family in Uzbekistan.From the scientific and pedagogical point of view, the state policy implemented in this area, its priority areas, the nature and content of the institution of the family are revealed.Also, the authors propose pedagogical principles, innovative forms, methods and means of spiritual, moral and physical education of youth in the family, developed conclusions and recommendations on this issue.
Pengelompokan Pembiayaan Nasabah Klaim Asuransi Pengguna Kendaraan Bermotor dengan Metode K-Medoids

In general, insurance is providing risk coverage to the insurer, namely the insurance company for a predetermined period and agreements. Insurance or coverage is an agreement between two or more parties, in which the insurer binds himself to the insured, by receiving an insurance premium, to provide compensation to the insured due to loss, damage or loss. The k-medoids method is one of several clustering methods in data mining which is part of partitional clustering. This method uses objects in a collection of objects to represent a cluster. The k-medoids clustering method can be applied to customer financing data for insurance claims on motor vehicle users, so that the financing grouping can be seen based on these data. From the grouping data, the characteristics can be seen so that it is known that the cluster is low, cluster is medium and cluster is high
Measuring Generalization with Optimal Transport

Understanding the generalization of deep neural networks is one of the most important tasks in deep learning. Although much progress has been made, theoretical error bounds still often behave disparately from empirical observations. In this work, we develop margin-based generalization bounds, where the margins are normalized with optimal transport costs between independent random subsets sampled from the training distribution. In particular, the optimal transport cost can be interpreted as a generalization of variance which captures the structural properties of the learned feature space. Our bounds robustly predict the generalization error, given training data and network parameters, on large scale datasets. Theoretically, we demonstrate that the concentration and separation of features play crucial roles in generalization, supporting empirical results in the literature. The code is available at \url{this https URL}.
Matematičko obrazovanje kroz kompetencije za nivo osnovne škole / Mathematical Education through Competencies for the Primary School Level

In this paper, the notions of mathematical competencies are updated, which pay attention to numerous conceptual issues of studying mathematics. Competencies make communication, presentation, reasoning, devising strategies using symbols and formulas with mathematical vocabulary. They include the ability to analyze and synthesize along with the ability to apply knowledge in practice. It is important to have a basic general knowledge of the profession imbued with basic practical expertise. Oral and written communication with basic computer skills such as: GeoGebre, Mathematic Wolfram, MatLab, etc., are important for the modern concept of mathematical knowledge. Viewed in that linguistic-competence sense, it would imply four generic competences. Primarily it is the ability to understand oral speech and communicate orally with other people. Further, this would be achieved through understanding and creating written works, as well as oral expression which would influence all actors to properly understand and interpret the received messages. Here we clearly recognize language competences with the aim of understanding both oral speech and written content.
FAKTOR PENENTU EFEKTIVITAS PENGELOLAAN KEUANGAN BADAN LAYANAN UMUM UNIVERSITAS BENGKULU

This study aims to examine and obtain empirical evidence about the factors that influence the effectiveness of financial management. The variables to be tested in this study of human resources, the utilization of information technology, systems of internal control, and governance of the effectiveness of financial management.This research was conducted by giving questionnaires to financial managers the University of Bengkulu that have been based on the ease of obtaining the sample.The results of this study are quality human resources and internal control system does not affect the effectiveness of financial management. As for the utilization of research results and governance of information technology or a significant positive impact on the effectiveness of the financial management of public service agencies at the University of Bengkulu
Identifying Vocabulary Learning Strategies Used by Undergraduate Students in Different Proficiency Levels

The aim of this study is to discover diverse vocabulary learning strategies among undergraduate students in three different competence levels (basic, intermediate, and advanced) who are enrolled in the Vocabulary course at Universitas Indraprasta PGRI in semester III. In this study, determination, social, memory, cognitive, and metacognitive methods were used to learn the language. Schmitt's Vocabulary Learning Strategies Questionnaire was used to collect data (VLSQ). Thirty undergraduate students were chosen on the basis of their competence levels. The results of descriptive statistics revealed that most undergraduate students used a medium strategy. The basic level had a mean score of 2.91, the intermediate level of 3.10, and the advanced level of 3.44. It was evident that the more vocabulary acquisition strategies a student used, the greater his or her competency level became. When it came to the most and least frequently utilized strategies by undergraduate students, metacognitive and determination were the most commonly employed techniques across three levels. However, the least frequent strategies of each level were different. Cognitive was slightly used by basic undergraduate students. Memory was the least employed by intermediate undergraduate students, and social was the fewest strategies implemented by advanced undergraduate students. This study revealed that the development of vocabulary learning strategies could increase EFL undergraduate students’ proficiency levels significantly.
Особливості підготовки здобувачів освіти педагогічних спеціальностей закладів вищої освіти до управлінської діяльності

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Multi-dimensional Data Visualisation using Mobile Augmented Reality

In the era of “The Internet of Things” (Rodriguez, Kaczmarek, & Depew) devices are becoming smart and each smart device generates data every second. This data is in multi-format, multi-perspective and multi-sectoral. Therefore, the handling of such multi-dimensional data is a significant challenge, as the normal display screen is two dimensional (2D) and data is multi-dimensional. This research proposes and develops a multi-display data visualisation algorithm using mobile augmented reality, as augmented reality (AR) is beyond the two-dimensional screen. Therefore, this algorithm uses AR to provide a multi-display solution for improved data visualisation after processing, summarising and classifying data. The results show that the AR-based multi-display is better than traditional 2D display. This algorithm is mobile based. Therefore, this system can provide rapid and easily accessible information to policy and decision makers.
Neuromorphic Control [About This Issue]

This issue of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Control Systems</i> includes two features and one “Focus on Education” article. The feature “Neuromorphic Control” draws from the Ph.D. dissertation of my coauthor and former student Luka Ribar. Neuromorphic engineering is a rapidly developing field that takes inspiration from the biological organization of neural systems to develop a novel technology for computing, sensing, and actuating. The unique properties of such systems call for new signal processing and control paradigms. The article discusses the mixed-feedback organization of excitable neuronal systems, which consist of interlocked positive and negative feedback loops acting in distinct time scales. The principles of biological neuromodulation suggest a methodology for designing and controlling mixed-feedback systems neuromorphically.
The use of information technology and its influence on Accounting education quality – A study of staff members King Khalid University –: أثر استخدام تكنولوجيا المعلومات على جودة التعليم المحاسبي – دراسة استطلاعية لآراء عينة من أعضاء هيئة التدريس بجامعة الملك خالد –

This study aimed to investigate the relationship between use of information technology dimensions (databases, networks, physical components, and communications) and Accounting education quality at King Khalid University in the Kingdom of Saudi Arabia. The model was built and development of hypotheses and measurements based on the literature review of the previous studies the study used the method of comprehensive inventory to collect data through the questionnaire. 62 questionnaires were analyzed. for the analysis of data and hypotheses testing, a descriptive statistical method was used (frequencies, percentages, deviations,) and analytical statistical methods (Exploratory Factor Analysis, Confirmatory Factor Analysis, Pearson correlation and path analysis) through the statistical package for social sciences (SPSS) program. The results found that a partial relationship between use of information technology dimensions and technical efficiency, existence a partial relationship between use of information technology dimensions and the professional skills quality, the results indicate that The use of information technology partly influence the ethical values quality. The study recommended that the need to enhance use of information technology in accounting education at King Khalid University in order to raise the level of performance of accounting graduates. The need to highlight the importance of information technology in the accounting profession through activating it through conferences, lectures, seminars, scientific research and training courses.
SELF-PRESENTATION AS A REGULATOR OF THE ACTIVITY OF A HIGHER EDUCATION TEACHER

The problem of studying the peculiarities of self-presentation of a high school teacher is relevant and underdeveloped, because the function he performs in society is extremely important. But, unfortunately, it tends to simplify, shift from the actual pedagogical activity to the function of knowledge transfer, passive reproductive activity. The article considers the essence of the phenomenon of self-presentation humanities disciplines teacher. Training of professionally competent teacher is one of the urgent tasks and the subject of scientific discussion and thinking of contemporary and the foretime scholars. Self-presentation during the process of life of the subject acts as an important regulator of its social behavior. It is proved that knowledge of self-presentation contributes itself as a stakeholder; improve itself, the desire to achieve self-esteem and successful interaction with people from the close milieu, which is important for teaching activity. The aim is to identify and theoretically substantiate the relationship between the characteristics of the teacher’s personality and his activities with his self-presentation. Self-presentation is a process by which a person seeks to form other people’s impressions of themselves. The success of self-presentation depends on the ability to present yourself to other people, to attract attention, to actualize people’s interest in their qualities. Self-presentation in the process of the subject’s life is an important regulator of his social behavior. When considering the problems of social determination of personality behavior, special attention is paid to the specifics of knowledge of the world around us, awareness of our attitude to it, knowledge of ourselves as a subject of activity, self-improvement, striving for self-esteem and successful interaction with people close to the teacher and its activities.