Reviewerly/exHarmony huggingface dataset:
On the issue of Network Predictors of Viability in University Students

Problem and goal. The article actualizes the problem of identifying predictors of viability in University students that influence their successful adaptation and self-development. Viability is considered as a network phenomenon, the elements of which are able to form relationships of various strength and significance with the components of other mental phenomena and phenomena. The purpose of the article is to identify and analyze psychological predictors of viability, to prove that creativity and viability can act as such. Methodology. The empirical research was based on the viability model of E. A. Rylskaya, the creativity model of I. Torrens – S. Mednik, and the semantic system model of D. A. Leontiev. Methods corresponding to these models were used: E. A. Rylskaya’s human viability test, I. Torrens nonverbal creativity test, S. Mednik’s verbal creativity test, and D. A. Leontiev’s life-meaning orientations test. Statistical processing of the results was performed using the program Statistica 10.0, using the Spearman correlation analysis method. When interpreting the results of the empirical research, the author relied on the main provisions of the structural and network approaches in psychology. The research sample consisted of 82 students in the areas of “Linguistics” and “Philology” of the faculty of Humanities of Novosibirsk state technical University. The sample was not divided by gender. In conclusion, it is indicated that the results obtained allow us to present viability as a complex psychological phenomenon with a network organization in which creativity and semantic orientations act as the most important predictors.
The Obstacles Faced by University Students in Learning English Language

This study is entitled (the obstacles faced by university Students in Learning English Language). It addresses the obstacles that are faced by the students in the Faculty of Arts at Soran University in Learning English Language which is one of the live languages that is relied upon as a formal language all over the international and world organizations and offices.&nbsp;The main aim of this study is to determine and analyse the obstacles that are faced by the students in the Faculty of Arts at Soran University. This study adopts a descriptive- statistical method within quantitative approach. This approach is based on close-ended data such as yes, no question or multiple choice questions. This and similar studies make use of data analysis programs such as SPSS. The data was collected from 26 (13 male and 13 female) students. The results reveal that there are a number of obstacles such as lack of learning tools, lack of an appropriate method of teaching, syllabus and the students desire to learn the language. The results also show that the girls face more obstacles in learning the language compared to boys
ATUAÇÃO DOS ACADÊMICOS DE ENFERMAGEM AOS USUÁRIOS ASSISTIDOS PELA ESTRATÉGIA SAÚDE DA FAMÍLIA NO CONTROLE DA DIABETES MELLITUS TIPO 2

Diabetes Mellitus is a syndrome of multiple etiology, resulting from the lack of insulin and / or its inability to exercise its effects properly, resulting in insulin resistance. It is characterized by the presence of chronic hyperglycemia, often accompanied by dyslipidemia, arterial hypertension and endothelial dysfunction. This disease represents a considerable economic burden for the individual and for society, especially when poorly controlled, with most of the direct costs of its treatment related to its complications, which compromise the productivity, quality of life and survival of individuals, and which can often be reduced, delayed or avoided .: This study addresses the experiences lived by Nursing students and teacher during Supervised Internship I in Public Health, developed in the 8th phase of the Nursing Course at the University of the West of Santa Catarina (UNOESC). Such activities took place in the months of August and September 2020. The target audience are users of the Basic Health Unit in monitoring of type 2 diabetes Melittus, of both sexes. The study steps consisted mainly of knowing the profile of the population served by the unit.
ANALYSIS STUDENT’S SPEAKING PERFORMANCE AS AN ACADEMIC SPEAKER’S PRACTICE

Speaking is a productive skill with requires the interactive and performance process. To be able to improve and represent student’s capability in their speaking skill, student also need to drill and practice their speaking as the academic presenter, because if they able to speak academically, their speaking skill will get well improvement automatically. This research is aimed to investigate student’s speaking performance while they have academic speaking practice. The population of this research is 4th semester student of Universitas Muhammadiyah Tangerang, located in Cikokol-Tangerang. The sample is single sample. To find the data, researcher use video record originally from the speaker then doing the analysis with triangulation validity. This research showed that students speaking performance is need to be treating well, especially in their self-confident and vocabulary master, because it will be impossible for them to be a good speaker if they have lots of anxiety.
Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state space models

We propose a novel blocked version of the continuous-time bouncy particle sampler of [Bouchard-Cote et al., 2018] which is applicable to any differentiable probability density. This alternative implementation is motivated by blocked Gibbs sampling for state space models [Singh et al., 2017] and leads to significant improvement in terms of effective sample size per second, and furthermore, allows for significant parallelization of the resulting algorithm. The new algorithms are particularly efficient for latent state inference in high-dimensional state space models, where blocking in both space and time is necessary to avoid degeneracy of MCMC. The efficiency of our blocked bouncy particle sampler, in comparison with both the standard implementation of the bouncy particle sampler and the particle Gibbs algorithm of Andrieu et al. [2010], is illustrated numerically for both simulated data and a challenging real-world financial dataset.
Supplement to 'Estimation in Functional Regression for General Exponential Families'

In this supplemental article, we introduce some useful results in spectral theory and perturbation theory. Some of the results are well-established. We briefly review them for the purpose of easy reference. We derive a novel bound for the perturbation of eigenprojections, which plays a key role in the slope function estimation problem.
Densely Connected Bidirectional LSTM with Max-Pooling of CNN Network for Text Classification

Text classification is a fundamental task in natural language processing (NLP). Context semantics can greatly improve the accuracy of text classification tasks. Although there are some popular methods in obtaining semantics, current context semantic analysis techniques, due to limited accuracy, are still a great bottleneck for text classification. This paper introduces a novel model, the densely connected Bidirectional LSTM with Max-pooling of CNN network (Dense-BiLSTM-MP), which greatly enhances the context of semantic information. In this model, a densely connected bidirectional long short-term memory (BiLSTM) model, as well as multiple max-pooling layers of convolutional network, are applied to obtain an increasingly enhanced assessment of context, and extract the key features, respectively. Experiments were conducted on four public datasets: YELP, 20NewsGroup, THUNews and AG. The experimental results show that the proposed model outperforms state of the art methods on several datasets. Furthermore, discussions on the Dense-BiLSTM-MP model’s performance in short texts and long texts were given, respectively.
Exploring Statistical and Neural Models for Noun Ellipsis Detection and Resolution in English

Computational approaches to noun ellipsis resolution has been sparse, with only a naive rule-based approach that uses syntactic feature constraints for marking noun ellipsis licensors and selecting their antecedents. In this paper, we further the ellipsis research by exploring several statistical and neural models for both the subtasks involved in the ellipsis resolution process and addressing the representation and contribution of manual features proposed in previous research. Using the best performing models, we build an end-to-end supervised Machine Learning (ML) framework for this task that improves the existing F1 score by 16.55% for the detection and 14.97% for the resolution subtask. Our experiments demonstrate robust scores through pretrained BERT (Bidirectional Encoder Representations from Transformers) embeddings for word representation, and more so the importance of manual features– once again highlighting the syntactic and semantic characteristics of the ellipsis phenomenon. For the classification decision, we notice that a simple Multilayar Perceptron (MLP) works well for the detection of ellipsis; however, Recurrent Neural Networks (RNN) are a better choice for the much harder resolution step.
Analysis of Weapon System Unstructured Data Using Text Mining

Purpose: The advent of the fourth industrial revolution has led to increased interest in military defense systems, and demand for new weapon systems involving artificial intelligence techniques has also increased. In particular, data from field operations, which are collected during post- logistics support, can be used for the reliability analysis of weapon systems. The existing reliability analysis method for weapon systems has a limitation in that it cannot reflect the maintenance history during the operation of the weapons systems, practical methods for predicting actual reliability via field operation analysis. Furthermore, typical data from field operations are collected manually, and therefore, they contatin atypical features introduced by operators’ personal decisions.BRMethods: In this research, a text mining approach is proposed to extract meaningful features, and some visualization techniques are presented for enhancing the interpretability of operational behavior. The Doc2Vec algorithm, which can measure the similarity between feature vectors, is used to extract a feature vector, and the t-SNE algorithm is used visualization.BRResults: The proposed algorithm represents the availability of unstructured data through feature extraction and visualization on the basis of post-logistics data. Feature vector based visualization shows that a new classification system for causes of defects can be established through manually wrote data.
Integrating Edublog in the teaching of narrative writing in primary school

The Malaysia Education Blueprint 2013-2025 aims to optimize the use of Information and Communication Technology (ICT) in enhancing teaching and learning (T&L). Edublogs could be a useful platform that can facilitate the T&L of English. This case study explored the effects of the teaching of English narrative writing using Edublogs on pupils’ achievement in narrative writing in terms of content, vocabulary, and grammar. Eight Year 6 pupils were purposely selected as participants. Data was collected using focus group interviews, observation, and tests. Data were analysed using thematic analysis and document analysis. Findings show that learning narrative writing using Edublogs significantly improved the pupils’ ability to generate better content and improved the pupils’ vocabulary development and retention. However, the pupils were found to show no significant improvement in terms of grammar. The findings implicate that Edublogs can help in providing a meaningful T&L of language in the 21st century learning environment. It is suggested that future similar research using other digital applications and tools to be done with other language skills namely, reading, speaking, or listening. In addition, a longer duration of the similar study could also be considered in order to allow the natural course of learning to take place for grammar competence.
Zero-shot cross-lingual identification of direct speech using distant supervision

Prose fiction typically consists of passages alternating between the narrator’s telling of the story and the characters’ direct speech in that story. Detecting direct speech is crucial for the downstream analysis of narrative structure, and may seem easy at first thanks to quotation marks. However, typographical conventions vary across languages, and as a result, almost all approaches to this problem have been monolingual. In contrast, the aim of this paper is to provide a multilingual method for identifying direct speech. To this end, we created a training corpus by using a set of heuristics to automatically find texts where quotation marks appear sufficiently consistently. We then removed the quotation marks and developed a sequence classifier based on multilingual-BERT which classifies each token as belonging to narration or speech. Crucially, by training the classifier with the quotation marks removed, it was forced to learn the linguistic characteristics of direct speech rather than the typography of quotation marks. The results in the zero-shot setting of the proposed model are comparable to the strong supervised baselines, indicating that this is a feasible approach.
Practical Use Cases for Progressive Visual Analytics

Progressive Visual Analytics (PVA) is meant to allow visual ana-lytics application to scale to large amounts of data while remaining interactive and steerable. The visualization community might believe that building progressive systems is difficult since there is no general purpose toolkit yet to build PVA applications, but it turns out that many existing libraries and data structures can be used effectively to help building PVA applications. They are just not well known by the visual analytics community. We report here on some of these techniques and libraries we use to handle larger than RAM data efficiently on three applications: Cartolabe, a system for visualizing large document corpora, ParcoursVis, a system for visualizing large event sequences from the French social security, and PPCA, a progressive PCA visualization system for large amounts of time-sequences. We explain how PVA can benefit from compressed bitset to manage sets internally and perform extremely fast Boolean operations , data sketching to compute approximate results over streaming data, and use Online algorithms to perform analyzes on large data.
An Integrated Attribute Guided Dense Attention Model for Fine-Grained Generalized Zero-Shot Learning.

Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. The global feature exploring EL or FS methods do not explore fine distinction as they ignore local details. And, the local detail exploring EL or FS methods either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, to reduce bias towards the source domain during testing, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets.
A Model for Luggage Re-identification Based on the Network Ensemble

Luggage re-identification based on the image recognition is a highly demanding task for security check. The final result of testing model of using single network and loss function is under our expectation. Therefore, network ensemble which integrate multiple networks and loss ensemble which adopt various loss functions are explored and adopted in this paper. Meantime, most of technology of re-identification are adopted in this paper. In order to improve generalization ability, random erasing, batch feature erasing, random flip are adopted in this paper. Multibranch structure is adopted to extract more features and re-ranking is adopted to improve final accuracy. These methods are integrated into the model of luggage re-identification. Experimental results show that the better accuracy is obtained by our method. The model has strong value of practical application.
Bilingual Transfer Learning for Online Product Classification

Consumer Price Indices (CPIs) are one of the major statistics produced by Statistical Offices, and of crucial importance to Central Banks. To calculate CPIs, statistical offices collect a large amount of individual prices of goods and services. Nowadays prices of many consumer goods can be obtained online, enabling a much more detailed measurement of inflation rates. One major challenge is to classify the variety of products, from different shops and languages into the given statistical schema consisting of a complex multi-level classification hierarchy - the European Classification of Individual Consumption according to Purpose (ECOICOP) for European countries, since there is no model, mapping or labelled data available. We focus in our analysis on food, beverage and tobacco which account for 74 of the 258 ECOICOP categories and 19 % of the Euro Area inflation basket. In this paper we build a classifier on web scraped, hand-labeled product data from German retailers and test the transfer to French data using cross lingual word embedding. We compare its performance against a classifier trained on the single languages and a classifier with both languages trained jointly. Furthermore, we propose a pipeline to effectively create a data set with balanced labels using transferred predictions and active learning. In addition we test how much data it takes to build a single language classifier from scratch an if there are benefits from multilingual training. Our proposed system reduces the time to complete the task by about two thirds and is already used to support the analysis of inflation.
제조업의 인적오류 관련 사고분석을 위한 HFACS-K의 개발 및 사례연구

As Korean government and safety-related organizations make continuous efforts to reduce the number of industrial accidents, accident rate has steadily declined since 2010, thereby recording 0.48% in 2017. However, the number of fatalities due to industrial accidents was 1,987 in 2017, which means that more efforts should be made to reduce the number of industrial accidents. As an essential activity for enhancing the system safety, accident analysis can be effectively used for reducing the number of industrial accidents. Accident analysis aims to understand the process of an accident scenario and to identify the plausible causes of the accident. Accident analysis offers useful information for developing measures for preventing the recurrence of an accident or its similar accidents. However, it seems that the current practice of accident analysis in Korean manufacturing companies takes a simplistic accident model, which is based on a linear and deterministic cause-effect relation. Considering the actual complexities underlying accidents, this would be problematic; it could be more significant in the case of human error-related accidents. Accordingly, it is necessary to use a more elaborated accident model for addressing the complexity and nature of human-error related accidents more systematically. Regarding this, HFACS(Human Factors Analysis and Classification System) can be a viable accident analysis method. It is based on the Swiss cheese model and offers a range of causal factors of a human error-related accident, some of which can be judged as the plausible causes of an accident. HFACS has been widely used in several work domains(e.g. aviation and rail industry) and can be effectively used in Korean industries. However, as HFACS was originally developed in aviation industry, the taxonomy of causal factors may not be easily applied to accidents in Korean industries, particularly manufacturing companies. In addition, the typical characteristics of Korean industries need to be reflected as well. With this issue in mind, we developed HFACS-K as a method for analyzing accidents happening in Korean industries. This paper reports the process of developing HFACS-K, the structure and contents of HFACS-K, and a case study for demonstrating its usefulness.
CHATBOT PEMILIHAN PRODUK KOSMETIK BERBASIS AIML

Potential line business now is based online. Technology and the internet have become a part of human life and online business can be run anywhere at any time, it can even further speed up the service or sales process. By seeing this, many business people have turned online. One of them is a cosmetic store Pyung Store which wants to sell online. However, it is adjusted to the habits of buyers who will ask about the best cosmetics, cosmetics that match their skin type, or even surround facial skin health. Therefore a chatbot was created on the cosmetic sales website which can conduct questions and answer interactions about facial skin consultations and cosmetic products. The Chatbot application is built with Artificial Intelligence Markup Language (AIML) through the chatbot platform, Pandorabots.com and the website platform using Wix.com. This application has been evaluated based on a survey of users and got results of 70.5% which stated that the chatbot answered according to the flow that was sent by the user and 80.3% of the cosmetics shop agreed that with the website and chatbot it increased sales.
The Design of a Crowd Density Checker App in Android with People Localization Using the GPS from the Mobile Device

The aim of this work focuses on checking the density of people in a certain place. The idea behind is based on the fact that almost every person has a mobile device with GPS, so getting the coordinated from the mobile device, can get the coordinates of the person owning it. The coordinates can be placed on a map and united them in clusters, to know the number of persons in a specific place. The app can useful to avoid big crowds of not. Sometimes it is needed to avoid big crowds of people when going shopping or in traffic. Other times it is not needed to avoid big crowds, because where many people are, something is happening, big sales, good concerts or good mood in a bar.