Reviewerly/exHarmony huggingface dataset:
The Technology of Collecting Initial Data for Constructing Models for Assessing the Functional State of a Person by Pupillary Response to Changes In Illumination

This article solves the problem of collecting initial data for constructing models for assessing the functional state of a person by pupillary response to changes in illumination.We analyzed the drawbacks of the traditional approach to collecting initial data using computer vision and time series smoothing methods.Attention is focused on the importance of the quality of the initial data for the creation of adequate highprecision mathematical models.We actualized the need for manual marking of the iris and pupil circles to improve the accuracy and quality of the initial data.We described the initial data collection stages in the proposed technology.We gave an example of the resulting pupillogram, which has a smooth shape and does not contain outliers, noise, anomalies and missing values.Based on the given technology, we developed a software and hardware complex, which is a collection of specially developed software that has two main modules and hardware implemented on the basis of a Raspberry Pi 4 Model B microcomputer, with peripheral equipment that implements the specified functionality.To evaluate the effectiveness of the given technology for collecting initial data, we used models of a single-layer perspetron and a collective of neural networks, together with the initial data on the functional state of intoxication of a person.The studies have shown that the number of errors of the 1st and 2nd genus in determining the assessment of the functional state of a person is lower, and the classification accuracy is higher when using the initial data generated by manual marking of circles, compared with the initial data collected by computer vision methods.Thus, the given technology for collecting initial data can be effectively used to build models for assessing the functional state of a person by pupillary response to changes in illumination.
Active Learning for Turkish Text Classification

Many natural language processing (NLP) applications are solved in a supervised learning framework, which often requires a sufficiently large amount of labeled data. However, the labeled data is scarce as the process of obtaining class labels is often costly and/or time-consuming and sometimes requires indepth expert knowledge. Thus, obtaining labeled sets is a bottleneck. On the other hand, unlabeled data is abundant and easy to access in a variety of domains. The active learning paradigm addresses this challenge by effectively selecting informative and/or representative instances from the unlabeled pool to be labeled. In this way, active learners aim to reduce the cost of label acquisition without sacrificing model performance. In this work, we apply conventional active learning techniques on various Turkish text classification tasks. Experiments demonstrate that active learning helps to attain good performances while reducing the required labeled data.
A Generalized Circle Agent Based on the Deep Reinforcement Learning for the Game of Geometry Friends

In this paper, we study creating a generalized circle agent based on deep reinforcement learning for the game of Geometry Friends. We use the same setups proposed by another paper studying the game of Geometry Friends. The proposed deep reinforcement learning-based agent is trained with the Rainbow algorithm, which is a combination of solutions to different problems in the field of reinforcement learning. Our trained agent successfully completes all setups and shows a significantly higher performance over the agent trained in the previous study. In addition, performance of our agent is superior compared to human performance in the same setups. The agent demonstrated a performance pattern similar to that of human, i.e., the setups spent longer time are the same for both.
Generalized Graph Convolutional Networks for Action Recognition with Occluded Skeletons

Current methods for skeleton-based action recognition usually work on the assumption that the observed skeletons provided for recognition are complete (i.e., without occlusion). However, it is almost impossible to guarantee the extracting of perfect skeleton samples in practical application processes. In this work, we propose a generalized graph convolutional network for action recognition with occluded skeletons. The key insight of our approach is to look beyond the physical joint connectivity and extract discriminative features, where the richly discovered semantic features will improve the robustness of the model. We have conducted comprehensive experiments with both the normal skeleton dataset and the synthetic occlusion dataset. The experimental results demonstrate that our model can significantly alleviate the performance deterioration.
Russian-English Bidirectional Machine Translation System

This review depicts our submission to the WMT20 shared news translation task. WMT is the conference to assess the level of machine translation capabilities of organizations in the word. We participated in one language pair and two language directions, from Russian to English and from English to Russian. We used official training data, 102 million parallel corpora and 10 million monolingual corpora. Our baseline systems are Transformer models trained with the Sockeye sequence modeling toolkit, supplemented by bi-text data filtering schemes, back-translations, reordering and other related processing methods. The BLEU value of our translation result from Russian to English is 35.7, ranking 5th, while from English to Russian is 39.8, ranking 2th.
Point of Compromise Detection with Unsupervised Learning

With the increase of credit cards usages, credit card frauds have also increased over time. Criminals have developed various methods to steal credit card data from users. In this study, a novel method is proposed to reduce credit card fraud by identifying credit card copying points(point of compromise) where credit card data is stolen by criminals. The proposed method extracts new feature space with an autoencoder. Then, K-means clustering is applied to cluster fraudulent transactions with extracted feature space in order to achieve grouping up similar frauds. Initial results show that, the proposed model has been able to detect 5 points of compromise from 18 points of compromise that have been detected by the banks based on information only on card transactions.
Class-Balanced Text to Image Synthesis With Attentive Generative Adversarial Network

Although the text-to-image synthesis task has shown significant progress, it still remains a challenge in generating high-quality images. In this article, we first propose an attention-driven, cycle-refinement generative adversarial network, AGAN-v1, to bridge the domain gap between visual contents and semantic concepts by constructing spatial configurations of objects. The generation of image contours is the core component, in which an attention mechanism is developed to refine local details of images by focusing on the objects that complement one subregion. Second, an advanced class-balanced generative adversarial network, AGAN-v2, is proposed to address the problem of long-tailed data distribution. Importantly, it is the first method to solve this problem in the text-to-image synthesis task. Our AGAN-v2 introduces a reweighting scheme, which adopts the effective number of samples for each class to rebalance the generative loss. Extensive quantitative and qualitative experiments on CUB and MS-COCO datasets demonstrate that the proposed AGAN-v2 significantly outperforms the state-of-the-art methods.
Evolutionary Search from the Interior of Feasible Space

Constraint handling is a vital component in handling constrained problems using evolutionary algorithms, which is usually performed using an additional step instead of considering it as an integral part of the evolutionary search process. By utilizing the constraint consensus concept, this paper proposes searching from inside the feasible region. This is done by proposing a new differential evolution mutation operator that guides the selected feasible individuals to move towards the current best individual. The new approach algorithm is validated by solving a well-known set of constrained problems, with the results demonstrating the benefits the new concept adds to the algorithm in terms of the quality of solutions and computational time. The algorithm also shows superior performance over many state-of-the-art algorithms. Furthermore, the algorithm is tested on five mechanical engineering problems where the results demonstrate its effectiveness in attaining the best results.
Breast Cancer Identification Using Logistic Regression

Best Cancer is a direct result of uncontrolled chest cell advancement.It happens in females and sometimes in folks.It is the second most compelling motivation for death from harmful development in women around the globe.The likelihood of a woman passing on from chest dangerous development is around 1 of each 38 (around 2.6%).Since 2007, passing rates from chest threatening development have remained steady in women more energetic than 50, anyway have started to diminish in more prepared women.The death rate decreased by 1.3 percent consistently some place in the scope of 2013 and 2017.Dangerous development cells fill either in the lobules or in the chest courses.Lobules are milk-making organs, and lines are channels that pass on the milk to the areola from the organs.In this article, we intend to prescribe an approach to manage the assurance of chest threat subject to a collection of data factors portraying a couple of characteristics of sickness cell.This method uses a model called Logistic Regression for AI.Preliminary revelations show that the backslide model proposed is quantifiably critical and has higher precision.
A GPHH with Surrogate-assisted Knowledge Transfer for Uncertain Capacitated Arc Routing Problem

The Uncertain Capacited Arc Routing Problem is an important and challenging problem that has many real-world applications. Genetic Programming is utilised to evolve routing policies for vehicles to make real-time decisions and handle uncertain environments efficiently. However, when the problem scenario changes (e.g. a new vehicle is bought or an existing vehicle breaks down), the previously trained routing policy becomes ineffective and a new routing policy needs to be retrained. The retraining process is time-consuming. On the other hand, by extraction and transfer of some knowledge learned from the previous similar problems, the efficiency and effectiveness of the retraining process can be improved. Previous studies have found that the lack of diversity in the transferred materials (e.g. sub-trees) could hurt the effectiveness of transfer learning. As a result, instead of using the genetic materials from a source domain directly, in this work, we utilise the knowledge from the source domain to create a surrogate model. This surrogate is used on a large number of randomly generated individuals by GP in the target domain to select the promising initial individuals. This way, the diversity of the initial population can be maintained by randomly generated individuals, but also guided by the transferred surrogate model. Our experiments demonstrate that the proposed surrogate-assisted transfer learning method is superior to existing methods and can improve training efficiency and final performance of GP in the target domain.
Language modeling and bidirectional coders representations: an overview of key technologies

The article is an essay on the development of technologies for natural language processing, which formed the basis of BERT (Bidirectional Encoder Representations from Transformers), a language model from Google, showing high results on the whole class of problems associated with the understanding of natural language. Two key ideas implemented in BERT are knowledge transfer and attention mechanism. The model is designed to solve two problems on a large unlabeled data set and can reuse the identified language patterns for effective learning for a specific text processing problem. Architecture Transformer is based on the attention mechanism, i.e. it involves evaluation of relationships between input data tokens. In addition, the article notes strengths and weaknesses of BERT and the directions for further model improvement.
Effectiveness of the use of synthetic analytical structural methods against the ability to begin writing skills in elementary school students

This study aims to determine the effectiveness of the Use of the Effect of Synthetic Analytical Structural Methods on the Beginning Writing Skills of Elementary School Students. This study uses a quantitative method with a type of quasi-experimental research. The results of this study found that there was significant use of the influence of synthetic analytical structural methods on the ability to write skills at the beginning of grade 2 elementary school students. This is because the Synthetic Analytical Structural Method is a story approach accompanied by an image that contains synthetic analytic elements. Synthetic Analytical Structural method implementation learning techniques are writing card skills, syllable cards, word cards, and sentence cards, while some students look for letters, syllables and words, the teacher and some students stick to words arranged into meaningful sentences.
Post-Training Weighted Quantization of Neural Networks for Language Models

As a practical model compression technique, parameter quantization is effective especially for language models associated with a large memory footprint. Neural network quantization is usually performed to reduce quantization loss assuming that quantization error of each parameter equally contributes to the overall training loss. The importance of each parameter, however, may highly differ such that for the same number of quantization bits, certain parameters lead to higher training loss than the others after quantization. In this paper, we consider a non-uniform quantization scheme, specifically binary-coding-based quantization, for high compression ratio and efficient computations while avoiding large accuracy degradation by uniform quantization (e.g., INT8). Then, we derive quantization optimization methods to take into account the importance of each parameter. We demonstrate that for post-training quantization, weight magnitude can represent importance and improve model accuracy significantly compared to the previous schemes lacking importance considerations. For various language models including BERT, DistilBERT, AWD-LSTM, and Transformer, we achieve 2-4 bits per weight by our proposed post-training quantization with reasonable accuracy degradation.
A novel methodology to predict regulations using deep learning

This work was funded EUROCONTROL under Ph.D. Research Contract No. 18-220569-C2 and by the Ministry of Economy, Industry, and Competitiveness of Spain under GrantNumber TRA2016-77012-R.
Bibliometric Analysis of Public Literature on Google Drive

Ever since cloud storage has been a trend for data storage technology, Google Drive has been widely used by users and research on the use of Google Drive has initiated starting from 2012. Based on the usefulness and benefits of Google Drive, there are many kinds of research has been conducted on this topic. Thus, this paper aims to analyze and report various types of published works related to Google Drive based on the data obtained from SCOPUS database as of May 2020. This study adopted a bibliometric analysis by using “Google Drive” keyword search and managed to retrieve 356 documents to-be analyzed. Standard bibliometric indicators has been used to present the result in this article; document type, source type, year of publication, languages of document, subject area, most active sources, keyword analysis, geographical distribution of documents, authorship and most productive authors, most influential institution, text and citation analysis. MS Excel, VOS viewer, and Publish and Perish 7 software had been utilize in order to analyze the gathered documents. The result shows there is an increased growth rate of research on Google Drive from 2012 until 2016. However, the number of research is decreasing slowly from 2017 until 2020. In total, there are 9 languages being used, 26 subjects area with one undefined, 73 countries with one undefined, 160 authors and 160 institutions.
A Study on the Impact of Consumer Review on Mobile Commerce Performance

모바일 기술과 네트워크 기술의 발전으로 소비자들은 언제 어디서든 자신이 구매하고자 하는 제품에 대하여 탐색하고 제품을 비교함으로써 최종적으로 구매하는 것이 가능하다. 이러한 유형의 상거래를 모바일 커머스(mobile commerce)라고 정의할 수 있으며 최근에는 스마트폰 이용률이 높아지면서 전통적인 전자상거래(e-commerce)의 성장세를 뛰어넘어 빠르게 성장하고 있다. 하지만 모바일 커머스는 PC 기반 인터넷 중심의 전통적인 전자상거래에 비해 작은 화면과 이동 중에 이뤄진다는 점 때문에 소비자는 자신의 제한적인 정보 획득 공간과 시간에서 정보를 습득하여 의사결정을 해야 한다. 따라서 모바일 커머스의 특성이 잘 갖춰진 환경에서 정보의 특성이 고객 의사결정에 어떠한 영향을 미치는지에 대한 연구가 필요하다. 본 논문은 싱가포르의 모바일 커머스 전문 플랫폼인 Shopee에서 판매 중인 남성용 티셔츠에 대한 계량적 리뷰와 텍스트 리뷰, 가격, 선호도와 판매량의 데이터를 HTMP parsing의 크롤링을 통해 수집하여 텍스트 리뷰는 빈도 분석과 LDA( Latent Dirichlet allocation) 토픽 모델링을 통해 특성과 키워드로 추출하고 Google language API를 통해 감성 분석함으로써 리뷰의 비계량적 특성을 측정하였다. 그 후 선형 회귀분석을 통해 선호도와 구매 의사결정에 리뷰 특성이 어떠한 영향을 미쳤는지 파악하였다. 분석 결과, 비계량적 리뷰 특성에서는 크기를 특성들이 선호도와 판매성과에 유의미한 영향을 미쳤다. 계량적 특성에서는 평균 평점이 선호도에는 유의미한 영향을 미치지 못하였으나 판매성과에 리뷰 총량과 함께 유의미한 영향을 미쳤다. 이를 통해 리뷰의 특성이 선호도와 판매성과에 유의미한 영향을 미치며 의사결정 목적에 따라 다른 의사결정 과정을 거친다는 것을 확인하였다.Since advances in mobile and network technology, consumers can purchase products anytime, anywhere by exploring and comparing products they want to buy. And this type of commerce can be defined as mobile commerce, and more recently, as smartphone usage has grown, surpassing the growth of traditional. However, mobile commerce is used in online virtual space on a smaller screen compared to traditional. Therefore, it is necessary to study how the characteristics of information provided by mobile commerce affect customer decision making. This paper collected data on quantitative reviews and text reviews, prices, favorites and sales volume through the crawling of HTML parsing from men s T-shirts on sale at Shopee, Singapore s mobile commerce specialized platform. From text review, characteristics and keywords were extracted through frequency analysis and LDA (Latent Dirichlet allocation) topic modeling. The sentiment attributes of the product review were analyzed based on keywords through the google language API. Finally, a linear regression analysis performed how the review characteristics affected the favorite and purchasing decision. As a result of the analysis, size characteristics, in the non-measuring review characteristics, don’t had a significant impact on favorites and sales. In quantitative characteristics, the average rating did not have a significant impact on preference, but it had a significant effect on sales performance with the total amount of reviews. This research confirms that the characters of the review have a significant impact on favorite and sales performance and is subject to different decision-making processes depending on the decision-making objective.
Automating and utilising equal-distribution data classification

Data classification, i.e. organising data items in groups (classes), is a general technique widely used in data visualisation and cartography, in particular, for creation of choropleth maps. Conventionally, data are classified by dividing the data range into intervals and assigning the same symbol or colour to all data falling within an interval. For instance, the intervals may be of the same length or may include the same number of data items. We propose a method for defining intervals so that some quantity represented by values of another attribute is equally distributed among the classes. This kind of classification supports exploratory analysis of relationships between the attribute used for the classification and the distribution of the phenomenon whose quantity is represented by the additional attribute. The approach may be especially useful when the distribution of the phenomenon is very unequal, with many data items having zero or low quantities and quite a few items having larger quantities. With such a distribution, standard statistical analysis of the relationships may be problematic. We demonstrate the potential of the approach by analysing data referring to a set of spatially distributed people (patients) in relationship to characteristics of the areas in which the people live.
Adaptation of Unsupervised Term Discovery for Speech to Sign Languages

Unsupervised spoken term discovery (UTD), which is an active research area in speech processing, aims to find repeating units in speech signal using only the signal itself. It is useful for finding terms in low resource languages. Sign languages can also be considered as low resource since annotated corpora are not abundant, thus arises the need for unsupervised methods. In this work, we adapt a well known UTD algorithm to work with sign language videos. Instead of speech features, we used visual features obtained from a pre-trained convolutional neural net. We used an annotated sign dataset in order to evaluate the performance of the algorithm. According to the metrics that are used in UTD for speech, we show that the algorithm for sign language videos achieves similar performance compared to UTD for speech.
Predicting the Spread of the Corona Virus (COVID-19) in Indonesia: Approach Visual Data Analysis and Prophet Forecasting

The development trend of the coronavirus pandemic (COVID-19) in various countries has become a global threat, including in Southeast Asia, such as Indonesia, the Philippines, Brunei, Malaysia, and Singapore. In this paper, we propose an Exploratory Data Analysis (EDA) model approach and a time series forecasting model using the Prophet method to predict the number of confirmed cases and cases of death in Indonesia in the next thirty days. We apply the EDA model to visualize and provide an understanding of this pandemic outbreak in various countries, especially in Indonesia. We present the trends in the spread of epidemics from the countries of China from which the virus originates, then mark the top ten countries and their development and also present the trends in Asian countries. We present an analytical framework comparing the predicted results with the actual data evaluated using the MAPE and MAE models, where the prophet algorithm produces good performance based on the evaluation results, the relative error rate of our estimate (MAPE) is around 6.52%, and the model average false 52.7% (MAE) for confirmed cases, while case mortality was 1.3% for the MAPE and MAE models around 236.6%. The results of the analysis can be used as a reference for the Indonesian government in making decisions to prevent its spread in order to avoid an increase in the number of deaths
Information Technologies in the Training of Historians-Archivists at the Faculty of History of BSU: Achievements and Problems

The article examines the problems of training historians-archivists in the context of the transition to the digital economy. There is a sharp decline in professional educational programs, which does not meet the requirements of employers and modern trends in the economy. The need is declared for specialists in the field of documentation and archival studies to possess knowledge and skills in the field of information technology, as well as the availability of labor functions for the design and operation of information systems in the professional sphere