Reviewerly/exHarmony huggingface dataset:
Peculiarities of improvement of the national system of education quality assessment in the conditions of implementation of the curriculum of public secondary education integrated with the curriculum of professional training

This article thoroughly examines such aspects of the topic as determination of new trends and approaches towards ensuring systematicity and comprehensiveness of functionality of the national system of education quality assessment; effective managerial decision-making via development and implementation of the local normative acts under the authority of educational institution with regards to education quality control. In the conditions of establishment of the unified system of education quality assessment in the Russian education and limitations of resources (organizational, personnel, methodological, normative, motivational, etc.), the heads of educational institution are aware of the need and importance of improvement of the national system of education quality assessment . This article examines the key stages in improvement of the national system of education quality assessment in the context of implementation of curriculum of public secondary education integrated with the curriculum of professional training on the disciplines &amp;ldquo;Horticulturist&amp;rdquo;. The novelty of this research consists in the fact that systematic gathering and analysis of information on the content, students&amp;rsquo; performance, and conditions for implementation of the curriculum of public secondary education integrated with the curriculum of professional training on the profession &amp;ldquo;Horticulturist&amp;rdquo;, with subsequent determination of the problems, contributed to the making effective management decisions aimed at elimination of the &amp;ldquo;problem areas&amp;rdquo;. The expected result of improvement of the model of national system of education quality assessment consists in quality improvement of the public general education. The authors&amp;rsquo; special contribution into the research of this topic lies in obtainment of practical results in the course of implementation of the improved model of the national system of education quality assessment on the level of administration, bodies of state administration, pedagogical personnel of the educational institution.
A Two-Pathway Convolutional Neural Network with Temporal Pyramid Network for Action Recognition

In order to solve the problem of imperfect capture of visual rhythm in action recognition, this paper proposes a novel model which is a combination of a two-pathway network and temporal pyramid networks (TPNs). Specifically, our work involves two aspects, on the one hand, we integrate TPNs into the fast pathway and the slow pathway of SlowFast network to capture multi-level features, and then merge the prediction results of the two pathways in the final recognition stage, which boosts performance of our network by enhancing the semantics extraction at input layer and feature layer. On the other hand, we apply a ConvLSTM module to improve the capability of temporal modeling in TPN, which can further strengthen the capture of features in the long-term dimensions, and the advanced TPN promotes the fusion of temporal and spatial features. Experiments on the Kinetics-400 dataset demonstrate the superiority of our novel architecture combining two-pathway network and advanced TPN in action recognition.
Structural Similarity of Boundary Conditions and an Efficient Local Search Algorithm for Goal Conflict Identification

In goal-oriented requirements engineering, goal conflict identification is of fundamental importance for requirements analysis. The task aims to find the feasible situations which make the goals diverge within the domain, called boundary conditions (BCs). However, the existing approaches for goal conflict identification fail to find sufficient BCs and general BCs which cover more combinations of circumstances. From the BCs found by these existing approaches, we have observed an interesting phenomenon that there are some pairs of BCs are similar in formula structure, which occurs frequently in the experimental cases. In other words, once a BC is found, a new BC may be discovered quickly by slightly changing the former. It inspires us to develop a local search algorithm named LOGION to find BCs, in which the structural similarity is captured by the neighborhood relation of formulae. Based on structural similarity, LOGION can find a lot of BCs in a short time. Moreover, due to the large number of BCs identified, it potentially selects more general BCs from them. By taking experiments on a set of cases, we show that LOGION effectively exploits the structural similarity of BCs. We also compare our algorithm against the two state-of-the-art approaches. The experimental results show that LOGION produces one order of magnitude more BCs than the state-of-the-art approaches and confirm that LOGION finds out more general BCs thanks to a large number of BCs.
Speculative Analysis for Quality Assessment of Code Comments

Previous studies have shown that high-quality code comments assist developers in program comprehension and maintenance tasks. However, the semi-structured nature of comments, unclear conventions for writing good comments, and the lack of quality assessment tools for all aspects of comments make their evaluation and maintenance a non-trivial problem. To achieve high-quality comments, we need a deeper understanding of code comment characteristics and the practices developers follow. In this thesis, we approach the problem of assessing comment quality from three different perspectives: what developers ask about commenting practices, what they write in comments, and how researchers support them in assessing comment quality. Our preliminary findings show that developers embed various kinds of information in class comments across programming languages. Still, they face problems in locating relevant guidelines to write consistent and informative comments, verifying the adherence of their comments to the guidelines, and evaluating the overall state of comment quality. To help developers and researchers in building comment quality assessment tools, we provide: (i) an empirically validated taxonomy of comment convention-related questions from various community forums, (ii) an empirically validated taxonomy of comment information types from various programming languages, (iii) a language-independent approach to automatically identify the information types, and (iv) a comment quality taxonomy prepared from a systematic literature review.
Pengaruh Pembelajaran Jarak Jauh Terhadap Semangat Belajar Mahasiswi Semester III Program Studi Pendidikan Agama Islam (PAI) Fakultas Tarbiyah Institut Ilmu Al-Qur’an (IIQ) Jakarta

In the pandemic era like now distance learning is one of the efforts to prevent the spread of the virus, even though distance learning looks very easy, it turns out that there are often obstacles in its implementation that sometimes interfere with teaching and learning activities that are or will take place. The purpose of this study was to determine how the influence of distance learning on the learning motivation of Semester III students of the Islamic Religious Education Study Program, Tarbiyah Faculty of the Al-Qur'an Institute of Sciences, Jakarta. As for the sample in this study is Semester III Islamic Education Study Program, Faculty of Tarbiyah using a simple random sampling type sampling technique with 35 respondents through a questionnaire filled out by third semester students of the Institute of Al-Qur'an Science. The results showed that there was a correlation between distance learning and the learning motivation of the third semester female students of the Islamic Religious Education study program, Tarbiyah Faculty, Al-Qur'an Institute of Sciences, Jakarta. This is evidenced by the t value of 2.221. This shows that the t value is 2.221&gt; the t table value is 2.034. This shows that Ho is rejected and Ha is accepted. So it can be concluded that there is a significant influence between distance learning (X) on student learning motivation (Y).
Real-Time AutoML

We present a new zero-shot approach to automated machine learning (AutoML) that predicts a high-quality model for a supervised learning task and dataset in real-time without fitting a single model. In contrast, most AutoML systems require tens or hundreds of model evaluations. Hence our approach accelerates AutoML by orders of magnitude. Our method uses a transformer-based language embedding to represent datasets and algorithms using their free-text descriptions and a meta-feature extractor to represent the data. We train a graph neural network in which each node represents a dataset to predict the best machine learning pipeline for a new test dataset. The graph neural network generalizes to new datasets and new sets of datasets. Our approach leverages the progress of unsupervised representation learning in natural language processing to provide a significant boost to AutoML. Performance is competitive with state-of-the-art AutoML systems while reducing running time from minutes to seconds and prediction time from minutes to milliseconds, providing AutoML in real-time.
Atención al paciente oncológico en tiempos de COVID-19

Introduction: with the emergence of the new coronavirus and the wide worldwide distribution, its effects in people with some comorbidities are a global concern. Cancer is a disease with a high incidence and prevalence in society, included among the main causes of mortality. Objective: to describe the management of cancer patients during COVID-19 Method: a literature review of articles published up to June 2020 was carried out, using the Pubmed / Medline, SCOPUS and SciELO databases. 28 references were selected for the preparation of the present. Development: cancer has variable clinical and prognostic behaviors that generally lead to states of immunosuppression caused by the therapeutics used for its treatment; Therefore, they are more vulnerable to infectious diseases. The proper care of this group of people is the responsibility of the health systems. Some measures are based on social distancing, either in reducing the number of companions of the patient in the consultation or chemotherapy sessions, the prohibition of visits to hospitalized patients and the use of technologies with the use of teleconsultations for routine follow-up, as well as the change from intravenous to oral treatments Conclusions: the study of the behavior of COVID-19 in cancer patients is under development. The measures that the institutions take to achieve quality care for people with cancer are varied and are based mainly on social distancing.
Cross-Probe BERT for Efficient and Effective Cross-Modal Search

Inspired by the great success of BERT in NLP tasks, many text-vision BERT models emerged recently. Benefited from cross-modal attention, text-vision BERT models have achieved excellent performance in many language-vision tasks including text-image retrieval. Nevertheless, cross-modal attentions used in text-vision BERT models require too expensive computation cost when solving text-vision retrieval, which is impractical for large-scale search. In this work, we develop a novel architecture, Cross-Probe BERT. It relies on devised text and vision probes, and cross-modal attentions are conducted on text and vision probes. It takes lightweight computation cost, and meanwhile effectively exploits cross-modal attention. Systematic experiments conducted on two public benchmarks demonstrate state-of-the-art effectiveness and efficiency of the proposed method.
Online learning challenges: students’ vision

The article analyzes circumstances under which most educational institutions in Ukraine have found themselves with the spread of Covid-19 where teachers and students had to quickly adjust to not quite conventional, sometimes stressful learning environment. The results of the analysis of the survey conducted among 150 1st and 2nd year students majoring in sciences at Ivan Franko National University of Lviv have been presented. Having considered the findings of the survey, the advantages and disadvantages of online learning, as well as recommendations both for the teachers and students with the view to make it more effective have been elucidated. There have been outlined the following advantages of online learning: it is less time consuming; it allows flexible time and place management; it improves students’ technical skills; it makes students more self-disciplined; it is less pricy than traditional education; it is more friendly for the students’ health. Although it has been proven that online learning has its disadvantages, among which: lack of vital social contacts and face-to-face communication with peers; preventing students from academic dishonesty; concentrating attention on the subject of studying and ignoring distractions; procrastination; teachers being sometimes unfamiliar with advanced internet technologies; lack of motivating methods and activities. In this respect authentic activities targeted at immersion occured to demonstrate many benefits within online learning environment as they motivate students through real life situations and solving problems. On the basis of literature review of recent research, key characteristics of authentic activities have been defined. The recommendations for both the students as active participants of the learning process and the teachers as organizers and moderators of this process have been outlined. The conclusion was made that teachers in the first place have a chance to change the priorities and make the studying process more exciting, motivating and productive.
The Training Model of College Finance and Accounting Education Talents to Improve Professional Abilities of Students

One of the most important aspects of college education reform is the training model. therefore, to establish and improve the talent training system in the new era and deliver excellent professionals to society have become the core of college education development. The college accounting major has always been a popular major in colleges and universities. Many students are interested in this major, and their parents also have high expectations for their career development. However, at present, there are still some traditional conservative problems in the training model of accounting education personnel in colleges and universities in our country, which is not only not conducive to the future development of students majoring in finance and accounting, but also harmful to meet the society’s demand for professionals in accounting. This article analyzes and discusses the current talent training model of college finance and accounting education, and puts forward a plan for cultivating students’professional ability in line with the current situation of college finance and accounting education, in order with to further improve the learning ability and adaptability of students majoring in finance and accounting with the earnest research and explorations of educators.
Searching for Convolutions and a More Ambitious NAS

An important goal of neural architecture search (NAS) is to automate-away the design of neural networks on new tasks in under-explored domains, thus helping to democratize machine learning. However, current NAS research largely focuses on search spaces consisting of existing operations---such as different types of convolution---that are already known to work well on well-studied problems---often in computer vision. Our work is motivated by the following question: can we enable users to build their own search spaces and discover the right neural operations given data from their specific domain? We make progress towards this broader vision for NAS by introducing a space of operations generalizing the convolution that enables search over a large family of parameterizable linear-time matrix-vector functions. Our flexible construction allows users to design their own search spaces adapted to the nature and shape of their data, to warm-start search methods using convolutions when they are known to perform well, or to discover new operations from scratch when they do not. We evaluate our approach on several novel search spaces over vision and text data, on all of which simple NAS search algorithms can find operations that perform better than baseline layers.
Meta Auxiliary Labels with Constituent-based Transformer for Aspect-based Sentiment Analysis

Aspect based sentiment analysis (ABSA) is a challenging natural language processing task that could benefit from syntactic information. Previous work exploit dependency parses to improve performance on the task, but this requires the existence of good dependency parsers. In this paper, we build a constituent-based transformer for ABSA that can induce constituents without constituent parsers. We also apply meta auxiliary learning to generate labels on edges between tokens, supervised by the objective of the ABSA task. Without input from dependency parsers, our models outperform previous work on three Twitter data sets and match previous work closely on two review data sets.
LibGuides: Tutorial Storage: How Do I...? Pages

This guide stores tutorials so that librarians can easily access them and incorporate them into their research guides.
PABI: A Unified PAC-Bayesian Informativeness Measure for Incidental Supervision Signals

Real-world applications often require making use of {\em a range of incidental supervision signals}. However, we currently lack a principled way to measure the benefit an incidental training dataset can bring, and the common practice of using indirect, weaker signals is through exhaustive experiments with various models and hyper-parameters. This paper studies whether we can, {\em in a single framework, quantify the benefit of various types of incidental signals for one's target task without going through combinatorial experiments}. We propose PABI, a unified informativeness measure motivated by PAC-Bayesian theory, characterizing the reduction in uncertainty that indirect, weak signals provide. We demonstrate PABI's use in quantifying various types of incidental signals including partial labels, noisy labels, constraints, cross-domain signals, and combinations of these. Experiments with various setups on two natural language processing (NLP) tasks, named entity recognition (NER) and question answering (QA), show that PABI correlates well with learning performance, providing a promising way to determine, ahead of learning, which supervision signals would be beneficial.
Learning a unified label space

How do we build a general and broad object detection system? We use all labels of all concepts ever annotated. These labels span many diverse datasets with potentially inconsistent semantic labels. In this paper, we show how to integrate these datasets and their semantic taxonomies in a completely automated fashion. Once integrated, we train an off-the-shelf object detector on the union of the datasets. This unified recognition system performs as well as dataset-specific models on each training domain, but generalizes much better to new unseen domains. Entries based on the presented methodology ranked first in the object detection and instance segmentation tracks of the ECCV 2020 Robust Vision Challenge.
Sistem E-Voting Pemilihan Ketua OSIS SMA N 2 Pati Berbasis Website

Conventional voting has been considered ineffective because it has many shortcomings such as a waste of costs and time. One of the votes in the surrounding environment is the election of the student council president. The election of the student council president at school often uses the manual method like the general election so that it can affect students' interest in using their voting rights. Therefore the authors conducted this study aimed to build an e-voting system in SMA N 2 Pati based on websites. The system was developed with the PHP programming language using the Laravel framework and the MySQL database. The research that was developed produced an e-voting system that could help the voting process more efficiently and easily be used by students. Based on the black box testing that has been done, the e-voting system is functioning properly functionally and is as expected without any errors.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI PENGGAJIAN KARYAWAN BERBASIS WEB PADA PT. BATAM BINTAN TELEKOMUNIKASI LAGOI

The payroll system is an important thing in a company. Salary is one of the important things that encourages employee performance so that the company continues to run well. PT. Batam Bintan Telecommunications Lagoi Service Operations in the calculation of salaries still manually only use MS.Excel and pay slips are still manually in the form of a salary slip to be submitted to employees. This makes work in the finance department slow, and employees cannot get information about overtime income. The purpose of this study is to design an information system to facilitate the performance of salary management. With a SWOT analysis, it was found that the factors that cause problems in the current payroll system at PT. Batam Bintan Telecommunications Lagoi Service Operations, and solutions were found to solve or minimize problems with the current payroll system. The method in this research is divided into two stages, namely the data interview method with observation and interviews and the software development method using the Waterfall model. Website-based payroll information system is created using the Laravel Framework using the PHP programming language, and MySQL as its database. With a web-based payroll system can help calculate salaries automatically and systematically, as well as quickly and accurately. Thus, employee performance is better and the company gets the benefits too.
Basic principles of developing a progressive web application

The subject of this article is a technology known as progressive web application, which is gaining momentum and is likely to play a significant role in the field of web technologies in the near future.This article explains what a progressive web application is and how and with what technologies it can be developed.This type of web application is also compared with other types of applications, such as native, hybrid, and mobile applications, and explains what each of these types of applications is.
From Words to Numbers: Getting Started with Text Analysis for Applied Social Scientists

Objectives: With texts as unstructured data everywhere, text analysis or natural language processing (NLP) is a rapidly growing academic field that has great potential for novel research among many applied social scientists and practitioners.This paper presents a practical introduction to NLP using Python as a useful tool for text analysis.Methods: Starting with installation of Python and an external library for NLP, this paper describes a step-by-step process of data preparation, transformation, and summarization for text data using examples.The example texts were obtained from a transcribed business meeting record of a multinational company based in Helsinki.Results: From the initial unstructured text data having numerous irrelevant elements, the data preparation procedures of tokenization, removing stop words, stemming, and lemmatization result in a set of words useful for main analyses.The next step of transforming the words to numbers was conducted using a bag-of-words method by assigning a unique value to each word in a matrix.As the last step, the matrix is computed for frequency summarization using TF-IDF (Term Frequency and Inverse Document Frequency).Conclusions: Unlike structured data, many unstructured text data are not generated for the purpose of data analysis.With numeric data reproduced by the process presented in this paper, communication researchers can perform various statistical methods or use machine learning algorithms.Beyond the scope of this paper, it is strongly recommended to study statistics and computational linguistics as well as have a working knowledge on R and/or Python for advanced text analysis.