Reviewerly/exHarmony huggingface dataset:
Hubungan Growth Mindset dengan Penyesuaian Diri pada Mahasiswa Tahun Pertama di Universitas Negeri Padang

Adjustment is something that must be done when entering a new environment. Adjustment is a positive process to achieve a balance between the individual and his environment. One of the factors that influence self-adjustment is the growth mindset. This study uses a correlational research method to describe the relationship between growth mindset and self-adjustment in first-year students at Universitas Negeri Padang. The number of respondents in this study was 153 first-year students. The technique used in the selection of research samples is cluster random sampling. Data analysis in this study used the product-moment correlation test. The results showed that there was a significant positive relationship between growth mindset and self-adjustment in first-year students at Universitas Negeri Padang.
Towards optimally abstaining from prediction with OOD test examples

A common challenge across all areas of machine learning is that training data is not distributed like test data, due to natural shifts, “blind spots,” or adversarial examples; such test examples are referred to as out-of-distribution (OOD) test examples. We consider a model where one may abstain from predicting, at a fixed cost. In particular, our transductive abstention algorithm takes labeled training examples and unlabeled test examples as input, and provides predictions with optimal prediction loss guarantees. The loss bounds match standard generalization bounds when test examples are i.i.d. from the training distribution, but add anadditional term that is the cost of abstaining times the statistical distance between the train and test distribution (or the fraction of adversarial examples). For linear regression, we give a polynomial-time algorithm based on Celis-Dennis-Tapia optimization algorithms. For binary classification, we show how to efficientlyimplement it using a proper agnostic learner (i.e., an Empirical Risk Minimizer) for the class of interest. Our work builds on a recent abstention algorithm of Goldwasser, Kalais, and Montasser for transductive binary classification.
LA PLANIFICACIÓN DOCENTE UTILIZANDO ALGORITMOS GENÉTICOS : PLANIFICACIÓN DOCENTE

The present work is destined to give treatment to the difficulties in the elaboration of the teaching schedule due to the period of time that it takes to carry out it, as well as the difficulties involved in the manual realization of the schedule and the realization of changes in it. This research is aimed at automating the teaching planning process of the Faculty of Natural and Exact Sciences of the University of Oriente of Santiago de Cuba, through genetic algorithms. Which will reduce the time of completion of the teaching schedule and that it is executed with a minimum of effects on teachers who must carry it out. On the other hand, the solution that is proposed must at the same time be able to re-plan the teaching activity from any point of its execution, taking into account the activities carried out until such re-planning.
Siamese Tracking of Cell Behaviour Patterns

Tracking and segmentation of biological cells in video sequences is a challenging problem, especially due to the similarity of the cells and high levels of inherent noise. Most machine learning-based approaches lack robustness and suffer from sensitivity to less prominent events such as mitosis, apoptosis and cell collisions. Due to the large variance in medical image characteristics, most approaches are dataset-specific and do not generalise well on other datasets. In this paper, we propose a simple end-to-end cascade neural architecture that can effectively model the movement behaviour of biological cells and predict collision and mitosis events. Our approach uses U-Net for an initial segmentation which is then improved through processing by a siamese tracker capable of matching each cell along the temporal axis. By facilitating the re-segmentation of collided and mitotic cells, our method demonstrates its capability to handle volatile trajectories and unpredictable cell locations while being invariant to cell morphology. We demonstrate that our tracking approach achieves state-of-the-art results on PhC-C2DL-PSC and Fluo-N2DH-SIM+ datasets and ranks second on the DIC-C2DH-HeLa dataset of the cell tracking challenge benchmarks.
PENERAPAN METODE FIELD TRIP UNTUK MENINGKATKAN HASIL BELAJAR MATERI BAGIAN-BAGIAN TUMBUHAN PADA SISWA KELAS IV SD NEGERI 134417 PEMATANG SIANTAR TAHUN PELAJARAN 2017/2018

This study aims to describe the increase in student learning outcomes and activities in the material of plant parts by applying field trip learning methods in class IV 134417 Public Elementary School Pematang Siantar 2017/2018 school year. This research is a classroom action research carried out in two cycles with two meetings each cycle. The research subjects were grade IV students of 134417 Public Elementary School Pematang Siantar 2017/2018 school year, totaling 32 students. Learning outcomes data were obtained through tests while learning activity data was obtained through observation sheets of student learning activities. The results of the study show that; 1) field trip learning methods can improve student learning outcomes, as evidenced by the results of the students' classical completeness test up from Cycle I by 59% to 88% in Cycle II, so that they successfully provide completeness of classical learning outcomes and achieve established research success criteria; 2) field trip learning methods can improve student learning activities, as evidenced by observers observations in Cycle I writing and reading activities 38%, working on LKS 36%, asking fellow friends 8%, asking teachers 14%, and which is not relevant to KBM 4 %. Whereas in Cycle II the activity of writing and reading 33%, applying 40% LKS, asking fellow friends 18%, asking the teacher 6%, and which is not relevant to KBM 3%.
Psychological Background of Teaching Foreign Languages to Students Majoring in Law

The chapter is based on the analysis of such psychological and methodological problems of current interest as overcoming the language barrier as the means of future specialists’ language competence formation. There were determined the main reasons of psychological barrier occurrence within the process of foreign language learning in non-linguistic higher educational establishments as well as the dynamics and strategies of their overcoming.
The Construction of Curriculum System of “Integration of Courses, Positions, Certificates, and Competitions”for Higher Vocational Software Technology Majorunder the guidance of “Double High Plan”

This article first analyze existing problems of the current curriculum system of software technologyprofessional group in higher vocational colleges, and then explain the connotation of constructing a curriculumsystem of Integration of Courses, Positions, Certificates, and Competitionsfor higher vocational software technology majors, finally discusses on how to build the curriculum system of Integration of Courses, Positions, Certificates, and Competitionsfor Higher Vocational Software Technology Majorsfrom three aspects of job positions, vocational qualification certificates and vocational skills competitionsunder the guidance of Double High Plan.
The #ebooksos campaign in Ireland

Against the backdrop of COVID-19, this article outlines the ebooksos campaign. Theorigin and rationale for the campaign is described. Development of the campaignand possible future developments are considered.
ANALYSIS QOS (QUALITY OF SERVICE) MEASUREMENT OF DELAY, JITTER, PACKET LOSS, THROUGHPUT, BANDWIDTH UTILITY AND RESOURCE OF USING ONLINE VIDEO CONFERENCING SOFTWARE

Video conferencing has become very popular due to the pandemic situation. In addition, working from home becomes the new normal for many employees. Thus, the use of video conferencing is higher and more intensive. There are plenty of video conferencing platforms that many people are using, such as Google Meet, Zoom, Big Blue Button, GoToMeeting, Cisco Webex, and Join.me. In order to overcome a large number of online video conferencing users from big companies to students who want to study, with the comparison of traffic bandwidth, security, and all resources in the video conference application. This journal means creating an efficient effort to reduce how to using video conferences based on QoS Parameter, utility bandwidth, and resource for the online video conference software. The test scenario lasts 5 minutes per session with five conditions measured on the traffic shaper, namely Normal conditions, 100Kbps limit, 50Kbps limit, 20% drop, and 50% drop. The measurement results show that Cisco Webex is more friendly to computers with low specifications, and Google Chrome, according to the TIPHON Standard, gets the highest index than other online video meeting software tested. Keywords: Video Conference, Internet, Connection, Bandwidth
SISTEM INFORMASI RESCHEDULE KERJA KARYAWAN DI MATAHARI DEPARTMENT STORE LIPPO PLAZA JAMBI

Matahari Lippo Plaza Jambi in managing employee data, especially employee schedules are still managed using stationery. Where the data is written in the book which consequently in making employee shift schedules is very less effective and efficient. Based on the results of research conducted by the author of the data or information obtained. To overcome this, a new computerized system is needed to improve work efficiency by creating a Web-Based Shift Work Scheduling Information System in Matahari Lippo Plaza Jambi. This is the basis for conducting research at Matahari Department Store Lippo Plaza Jambi. In conducting research, researchers use several research methodologies, including field research as the main source in obtaining input data such as employee shift schedule data information for processing data on the system. This application was built with the support of PHP, CSS, MySQL, with the final result in the form of employee absence reports.
Unsupervised Multi-document Summarization for News Corpus with Key Synonyms and Contextual Embeddings

Information overload has been one of the challenges regarding information from the Internet. It is not a matter of information access, instead, the focus had shifted towards the quality of the retrieved data. Particularly in the news domain, multiple outlets report on the same news events but may differ in details. This work considers that different news outlets are more likely to differ in their writing styles and the choice of words, and proposes a method to extract sentences based on their key information by focusing on the shared synonyms in each sentence. Our method also attempts to reduce redundancy through hierarchical clustering and arrange selected sentences on the proposed orderBERT. The results show that the proposed unsupervised framework successfully improves the coverage, coherence, and, meanwhile, reduces the redundancy for a generated summary. Moreover, due to the process of obtaining the dataset, we also propose a data refinement method to alleviate the problems of undesirable texts, which result from the process of automatic scraping.
Legal technology: Diversity and inclusion - tech solutions

PwC's global diversity and inclusion survey reports that 87% of global businesses say diversity is an organisational priority.
Influence of Psychological And Pedagogical Features оf Students оn Their Activity in Self-Education

Theoretical and empirical research which was provided a study of influence of psychological and pedagogical characteristics of students as a separate socio-age group on their activity in self-education. Theoretical analysis of potential opportunities for activating students self-education due to age and social aspects of their lives are carried out. Empirical study of goals and motives, life plans, awareness of personal and professional „self-concept“ of humanities students showed high interest in self-education in the vast majority of students against the background of partial formation of motivation for personal and professional self-development and generally low awareness of their own characteristics. The positive influences on the self-education of students of humanities faculties of the following psychological and pedagogical factors are revealed: conscious life creation; activity in self-development; ability of self-actualization, self-organization and self-regulation of own development; formation of a humanistic worldview. Тo enhance the self-education of students with purposeful use of this factors can affect the overall quality of future professional trainings.
Reversible Computing: An Introduction

Reversible computing is a paradigm where computing models are defined so that they reflect physical reversibility, one of the fundamental microscopic physical property of Nature. Also, it is one of the basic microscopic physical laws of nature. Reversible computing refers tothe computation that could always be reversed to recover its earlier state. It is based on reversible physics, which implies that we can never truly erase information in a computer. Reversible computing is very difficult and its engineering hurdles are enormous. This paper provides a brief introduction to reversible computing. With these constraints, one can still satisfactorily deal with both functional and structural aspects of computing processes; at the same time, one attains a closer correspondence between the behavior of abstract computing systems and the microscopic physical laws (which are presumed to be strictly reversible) that underlay any implementation of such systems Available online at https://int-scientific-journals.com
A Comparative Study of Image Classification Models using NN and Similarity Distance

The following paper presents a comparative analysis based on two algorithms for Covid-19 image classification. Both of them use wavelet coefficients forming image features vector but they differ in the way the vectors are treated for the purpose of classification. The first algorithm uses the mechanism of similarity distance computation using Euclidean distance. The second model uses neural networks for the process of classification. The comparative analysis on the base of the two models is performed on accuracy, precision and recall performance measures. The obtained results show the advantages and disadvantages of both models for the analyzed parameters.
PRACTICES TO SUPPORT STUDENTS’ SPEAKING SKILL at MODERN ISLAMIC BOARDING SCHOOL RADEN PAKU TRENGGALEK

Nikmah, Eka Agustina A. Student Registered Number (17203163053). Practices to Support Students’ Speaking Skill at Modern Islamic Boarding School Raden Paku Trenggalek. A Thesis of English Education Department. Faculty of Tarbiyah and Teacher Training. State Islamic Institute (IAIN) of Tulungagung 2020. Advisor : Dr. Susanto, SS., M.Pd.Keywords : Speaking Practice Program, Activities Speaking, learning speaking. Speaking is one important and essential skill that must be practices to communicated orally. Speaking is a skill that must be practiced by the students for improving their speaking proficiency by mastering several speaking components such as comprehension, pronunciation, vocabulary and fluency. To support the students speaking skill the teaching process build facilitate the students to have sufficient opportunities to practice their speaking. Modern Islamic Boarding School Raden Paku Trenggalek has targets one of them is to facilitate the students to be skillfull in mastering English, especially speaking. This study is directed to investigate the practices done by the Modern Islamic Boarding School Raden Paku to facilitate the students to improve their speaking proficiency. The formulations of the research problems are : 1) What are the activities set to support students’ speaking skill at Modern Islamic Boarding School Raden Paku Trenggalek? 2) How are the students’ problems in adjusting themselve to the set activities to support their speaking skill at Modern Islamic Boarding School Raden Paku Trenggalek?.The research design in this research was a case study with qualitative approach. The subjects were the teachers who took position as (LAC and LURAH), the Language Division Organization (OPPPM) and three students of Modern Islamic Boarding School Raden Paku Trenggalek,. The researcher use observations, interviews, and documentation as the methods of collecting data of the research. The data analysis in this research were data reduction, data display and the conclusion drawing. To check the trustworthiness of data the study use Data sources triangulation.The result of this study revealed that the Modern Islamic Boarding School Raden Paku Trenggalek set vorious activities to support students’ speaking skill. Those activities were divided into 3 activities, (a) Daily activities consisted of English day, vocabulary building and conversation practice. (b) Weekly activities consisted of drilling vocabulary, grammar practice and speech practice. (c) Annual consisted LCC Language Camp Competition. Meanwhile the students got problems in adjusting themselves to the set activities, they were : (a) Grammar practices activity. (b) Vocabulary building activity. (c) Conversation activity. (d) Speech practice activity.
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning

Self-supervised learning has been shown to be very effective in learning useful representations, and yet much of the success is achieved in data types such as images, audio, and text. The success is mainly enabled by taking advantage of spatial, temporal, or semantic structure in the data through augmentation. However, such structure may not exist in tabular datasets commonly used in fields such as healthcare, making it difficult to design an effective augmentation method, and hindering a similar progress in tabular data setting. In this paper, we introduce a new framework, Subsetting features of Tabular data (SubTab), that turns the task of learning from tabular data into a multi-view representation learning problem by dividing the input features to multiple subsets. We argue that reconstructing the data from the subset of its features rather than its corrupted version in an autoencoder setting can better capture its underlying latent representation. In this framework, the joint representation can be expressed as the aggregate of latent variables of the subsets at test time, which we refer to as collaborative inference. Our experiments show that the SubTab achieves the state of the art (SOTA) performance of 98.31% on MNIST in tabular setting, on par with CNN-based SOTA models, and surpasses existing baselines on three other real-world datasets by a significant margin.
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering

We study k-median clustering under the sequential no-substitution setting. In this setting, a data stream is sequentially observed, and some of the points are selected by the algorithm as cluster centers. However, a point can be selected as a center only immediately after it is observed, before observing the next point. In addition, a selected center cannot be substituted later. We give the first algorithm for this setting that obtains a constant approximation factor on the optimal risk under a random arrival order, an exponential improvement over previous work. This is also the first constant approximation guarantee that holds without any structural assumptions on the input data. Moreover, the number of selected centers is only quasi-linear in k. Our algorithm and analysis are based on a careful risk estimation that avoids outliers, a new concept of a linear bin division, and a multiscale approach to center selection.
Building MT systems in low resourced languages for Public Sector users in Croatia, Iceland, Ireland, and Norway

When developing Machine Translation engines, low resourced language pairs tend to be in a disadvantaged position: less available data means that developing robust MT models can be more challenging.The EU-funded PRINCIPLE project aims at overcoming this challenge for four low resourced European languages: Norwegian, Croatian, Irish and Icelandic. This presentation will give an overview of the project, with a focus on the set of Public Sector users and their use cases for which we have developed MT solutions.We will discuss the range of language resources that have been gathered through contributions from public sector collaborators, and present the extensive evaluations that have been undertaken, including significant user evaluation of MT systems across all of the public sector participants in each of the four countries involved.
Does Knowledge Distillation Really Work

Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks. We show that while knowledge distillation can improve student generalization, it does not typically work as it is commonly understood: there often remains a surprisingly large discrepancy between the predictive distributions of the teacher and the student, even in cases when the student has the capacity to perfectly match the teacher. We identify difficulties in optimization as a key reason for why the student is unable to match the teacher. We also show how the details of the dataset used for distillation play a role in how closely the student matches the teacher -- and that more closely matching the teacher paradoxically does not always lead to better student generalization.
The Spherical Glass Concentrator On Photovoltaic Module With The Installation Of Dual-Axis Solar Tracking System

This research aims at creating a new model for solar panels that is more effective than the current solar panel. The purpose of the spherical glass to concentrate the light from the sun to the solar module as a lens. The spherical glass allows maximizing light absorption by the solar module. The average solar energy harvested by the conventional solar panels during the course day is not always maximized which is due to the static placement of the panel which limits their area of exposure. From the experimental point of view, there is a clear difference in the efficiency of the power generated between solar and non-lens solar panels with an average ratio of 1:2 which is 50% more efficient. It was obvious that the research through the attachment of the spherical glass and the installation of the solar tracking system for improving optimum sun energy are very much recommended as it will save cost, space and produce more power generation. The model is presented with the attachment of spherical glass lens that focuses the solar rays on a small area of solar module with the support of dual-axis solar tracking system to keep the solar rays maximum absorption in check From the experiments conducted, the average power generation over seven consecutive days showed that the Sun's radiation temperature focused on the solar panel was not directly proportional to the solar panel’s power. However, the density of the concentrated Sun rays is directly proportional to the solar power output.
Unsupervised Speech Recognition

Despite rapid progress in the recent past, current speech recognition systems still require labeled training data which limits this technology to a small fraction of the languages spoken around the globe. This paper describes wav2vec-U, short for wav2vec Unsupervised, a method to train speech recognition models without any labeled data. We leverage self-supervised speech representations to segment unlabeled audio and learn a mapping from these representations to phonemes via adversarial training. The right representations are key to the success of our method. Compared to the best previous unsupervised work, wav2vec-U reduces the phoneme error rate on the TIMIT benchmark from 26.1 to 11.3. On the larger English Librispeech benchmark, wav2vec-U achieves a word error rate of 5.9 on test-other, rivaling some of the best published systems trained on 960 hours of labeled data from only two years ago. We also experiment on nine other languages, including low-resource languages such as Kyrgyz, Swahili and Tatar.