Reviewerly/exHarmony huggingface dataset:
Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade

Fully non-autoregressive neural machine translation (NAT) is proposed to simultaneously predict tokens with single forward of neural networks, which significantly reduces the inference latency at the expense of quality drop compared to the Transformer baseline. In this work, we target on closing the performance gap while maintaining the latency advantage. We first inspect the fundamental issues of fully NAT models, and adopt dependency reduction in the learning space of output tokens as the basic guidance. Then, we revisit methods in four different aspects that have been proven effective for improving NAT models, and carefully combine these techniques with necessary modifications. Our extensive experiments on three translation benchmarks show that the proposed system achieves the new state-of-the-art results for fully NAT models, and obtains comparable performance with the autoregressive and iterative NAT systems. For instance, one of the proposed models achieves 27.49 BLEU points on WMT14 En-De with approximately 16.5X speed up at inference time.
Explanation Framework for Intrusion Detection

Abstract Machine learning and deep learning are widely used in various applications to assist or even replace human reasoning. For instance, a machine learning based intrusion detection system (IDS) monitors a network for malicious activity or specific policy violations. We propose that IDSs should attach a sufficiently understandable report to each alert to allow the operator to review them more efficiently. This work aims at complementing an IDS by means of a framework to create explanations. The explanations support the human operator in understanding alerts and reveal potential false positives. The focus lies on counterfactual instances and explanations based on locally faithful decision-boundaries.
Developing Bilingual Learning Multimedia in Integral Application Learning Material For Vocational School

ICT advancement can be utilized to improve the quality of education. In this globalization era, the millennial generation is demanded to be technology literate and fluent in English to compete in the global world considering that English has been established as an international language. Therefore, the integration of English into learning in the form of multimedia bilingual learning is needed. This study aimed to determine the feasibility, attractiveness, and effectiveness of bilingual learning multimedia on the integral application material for vocational schools. This research is research and development. Based on the results of validation and field trials, the developed bilingual mathematics learning media was in the feasible, interesting, and effective categories. The feasibility of the bilingual learning multimedia based on the material expert validation obtained an average of 4.6 and the media expert validation obtained an average of 4.9 which indicated that the developed product was in the feasible category. The attractiveness of bilingual mathematics learning multimedia based on students’ responses was 89% in the very interesting category. The effectiveness of the developed bilingual learning multimedia to improve students' mathematics learning outcomes was known based on the N-Gain value of 0.8 in the high category.
A Protocol for Indefinite Determiners in Italian and Italo-Romance

This paper focuses on weak indefinite nominals in Italian and Italo-Romance varieties. It presents the rich variation found across Italo-Romance in a pan-Romance perspective and claims that Italo-Romance displays a robust use of the definite article with indefinite interpretation unlike many other Romance varieties. The paper provides an overview of different semantic and syntactic contexts in which indefiniteness can be detected and distinguished from reference to kind, which is also expressed by the definite article in all Romance languages. The paper then provides diagnostics to capture the dimensions of variation and optionality among five different indefinite determiners: the so-called “partitive article”, the apparent definite article, the bare preposition di, the zero article of bare nouns, and the grammaticalized adjective “certain”. The diagnostics are structured in a “protocol” fashion, that is, a metatheoretical way to structure research questions, design a questionnaire, present and analyze empirical results, abstracting from theory-internal issues. A pilot running of the questionnaire on informal Italian and dialectal data will be presented and analyzed. The results enable us to give a preliminary answer to two general research questions: What is the distribution of the indefinite determiners in modern Italian dialects in contact with Standard Italian? What is the distribution of indefinite determiners in regional varieties of modern Italian in contact with the local dialects?
Mechatronics Class through Virtual Platforms under COVID-19

Due to the contingency caused by COVID-19, face-to-face courses were unable to be held and have been taught through various online platforms. This represented a particular difficulty for students of the Universidad Panamericana enrolled in the Energy Technologies Engineering degree that were taking their mechatronics classes. In this article, different online platforms were proposed for practices and simulations, alongside document writing and editing; processes that would normally be done physically. The students, coursing their eighth semester at university, were able to successfully complete their mechatronics course using online platforms like Autodesks Tinkercad (for Arduino practices programming and simulation), Factory I/O (for learning and simulating automation processes of a production line), FluidSIM (to gain an understanding of pneumatic actuators and perform practices) and finally, Overleaf, in order to acquire knowledge into writing an investigation article, with an IEEE format. It is important to highlight that there are diverse alternatives for face-to-face classes that can assure and guarantee effective learning, even in courses that can be seen at first glance impossible to be taught in an online modality. The experiences, implementation methodologies, examples and results are presented and described in this paper.
Influence of site-classification approach on geochemical background values

Abstract This study of peri-urban minerogenic topsoil on glacigenic or post-glacial deposits shows the influence of the site-classification approach on the differentiated median background (DMB) values of major elements and the potentially harmful elements (PHEs) Ba, Cr, Cu, Mn, Ni, Pb and Zn. Composite samples from forests and meadows were taken in 25 sites, each of which had five sub-sites. A fraction of &lt;2 mm was used to determine the organic matter by loss on ignition (LOI), grain size by laser diffraction and the elemental contents by X-ray fluorescence. The following five site-classification approaches are compared: geochemical (G), using relative median contents of Al, K, Ti; textural (T), according to mean percentages of clay-sized fraction (CLF) and silt fraction (SIF); lithological (L), based on soil parent material texture from the soil database; soil type (S), presented in the soil database; and parent material (P), generalising the underlying Quaternary deposits. Sites were classified into four level groups in which the DMB values were estimated after eliminating anomalies. The average ranks of three scores according to SIF, CLF, LOI, Al, K, Ti, Fe, Mg, Ca and S in the respective groups revealed the highest value for the G approach. It better eliminates the CLF and SIF influences on the median assessment indices of PHEs in sites.
NEW TENDENCIES IN A SCHOOL DESIGNING IN DIFFERENT COUNTRIES

School is the most important element in modernization and innovative development of a State in XIX century. The face of school is changed significantly. It becomes a center with a rich by intellectual, creative and sport life for school-students and their parents. At the same time, school has meet new social and town-planning requirements. Object of the study is an experience of UK, USA, Finland, Belgium, Japan and China concerning a building designing for regular schools.&#x0D; The research methodology consists of a challenge problem, study and analyzing of experience in different countries, analysis the problem about designing experience in Russia.&#x0D; As a result of the study, it was found that the specifics of the most countries is to create special and unique buildings where school-students can get knowledge in accordance with the school curriculum for a comfortable environment. That leads to communication and personal enhancement. Thus, schools are integrating centers for the development of school-student, their parents, and surround habitants.
A Novel Mixed Wide and PSO-Bi-LSTM-CNN Model for the Effective Web Services Classification

In software technology, over the diversified environment, services can be rendered using an innovative mechanism of a novel paradigm called web services. In a business environment, rapid changes and requirements from various customers can be adapted using this service. For service management and discovery, the classification of Web services having the same functions is an efficient technique. However, there will be short lengthened Web services functional description documents, having less information, and sparse features. This makes difficulties in modelling short text in various topic models and leads to make an effect in the classification of Web services. A Mixed Wide and PSO-Bi-LSTM-CNN model (MW-PSO-Bi-LSTM-CNN) is proposed in this work for solving this issue. In this technique, the Web service category‟s breadth prediction is performed by combining Web services description document‟s discrete features, which exploits the wide learning model. In the next stage, the PSO-Bi-LSTM-CNN model is used for mining Web services description document word‟s context information and word order, for performing the Web service category‟s depth prediction. Here, particle swarm optimization (PSO) is integrated with the Bi-LSTM-CNN network for computing various hyper-parameters in an automatic manner. In third stage, Web service categories, results of depth, and breadth prediction are integrated using a linear regression model as final service classification result. At last, MW-PSO-Bi-LSTM-CNN, Wide&amp;Bi-LSTM, and Wide&amp;Deep web service classification techniques are compared and a better result with respect to web service classification accuracy is obtained using the proposed technique as shown in experimental results.
A Novel Linear Classifier for Class Imbalance Data Arising in Failure-Prone Air Pressure Systems

An Air Pressure System (APS) is one of the crucial components of an automobile. Its failure leads to financial loses, and it may lead to loss of lives. Thus predicting such failure is a critical problem that requires a rigorous solution. Recently, many researchers have presented machine learning techniques to deal with APS failure detection. One of the major challenges in dealing with APS failure data is the presence of high class imbalance. Conventional classification criteria may not be able to efficiently handle such data. In this paper, a new machine learning method for APS failure detection is proposed. It is designed to specifically deal with the class imbalance. The method uses a linear decision boundary by maximizing Area Under the Curve (maxAUC) criterion. The proposed method was experimentally validated on an industrial dataset of APS failure. The results of the proposed method are thoroughly compared with existing linear as well as non-linear classifiers.
Analyzing the Role of Government Efficiency on Financial Development for OECD Countries

Abstract This paper fulfills a gap in the existing literature by analyzing the impact of government efficiency and corruption on the financial development of 31 OECD countries for the period 2002 to 2015 inclusively. To ensure robustness in our estimations, we employed several econometrics techniques, included control variables in our models, used several proxies for the variables under investigation, split the data into subgroups based on the degree of democracy, and repeated the analysis for these groups. Obtained findings provide strong evidence that government efficiency has a significant effect on financial development, and the sign of all the control variables are compatible with the a-priory theoretical expectations. The results of this study propose several policy recommendations to enhance financial development such as enhancing social cohesion through education on the use of tax contributions, revising budget procedures to ensure efficient spending of resources and to improve institutional quality, and reducing corruptive pursuits by targeting the informal economy activities and modifying the rule of law.
SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection

This paper presents a solution for the Span Identification (SI) task in the "Detection of Propaganda Techniques in News Articles" competition at SemEval-2020.The goal of the SI task is to identify specific fragments of each article which contain the use of at least one propaganda technique.This is a binary sequence tagging task.We tested several approaches finally selecting a fine-tuned BERT model as our baseline model.Our main contribution is an investigation of several unsupervised data augmentation techniques based on distributional semantics expanding the original small training dataset as applied to this BERT-based sequence tagger.We explore various expansion strategies and show that they can substantially shift the balance between precision and recall, while maintaining comparable levels of the F1 score.
IRLab_DAIICT at SemEval-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets

The paper describes systems that our team IRLab DAIICT employed for the shared task Sentiment Analysis for Code-Mixed Social Media Text in SemEval 2020.We conducted our experiments on a Hindi-English CodeMixed Tweet dataset which was annotated with sentiment labels.F1-score was the official evaluation metric and our best approach, an ensemble of Logistic Regression, Random Forest and BERT, achieved an F1-score of 0.693.
NLFIIT at SemEval-2020 Task 11: Neural Network Architectures for Detection of Propaganda Techniques in News Articles

Since propaganda became more common technique in news, it is very important to look for possibilities of its automatic detection.In this paper, we present neural model architecture submitted to the SemEval-2020 Task 11 competition: "Detection of Propaganda Techniques in News Articles".We participated in both subtasks, propaganda span identification and propaganda technique classification.Our model utilizes recurrent Bi-LSTM layers with pre-trained word representations and also takes advantage of self-attention mechanism.Our model managed to achieve score 0.405 F1 for subtask 1 and 0.553 F1 for subtask 2 on test set resulting in 17th and 16th place in subtask 1 and subtask 2, respectively.
Mass Media as Manipulator of Audience Opinion: a Model, an Experiment, and Interpretation

The article studies mass media via interdisciplinary methods of analysis and synthesis. For this, the author proposes a concept framework and tools for modelling communication practices. Considering the dialectic methodology of studying the media system and the invariability of control laws, we have to admit that the problem of mass media functioning is a matter of controversy. The author uses the homeostatic approach to modelling such systems. In the field of communication practices, this approach and methodology have not been used to the extent sufficient for solving systemic problems, unlike in studies of complex dynamic systems. The article provides mathematical description of diagrams of mass media models of balance and compensation types, specification of their variables and software support parameters. Special software helped to carry out a series of computer experiments to imitate processes of competing mass media impact on the audience that causes cognitive dissonance. The results are presented in the form of graphs and comments.
A New Receiver Design: Simultaneous Wireless Power Transfer With Modulation Classification

This work proposes a new simultaneous wireless power transfer and modulation classification (SWPTMC) scheme, appropriate for internet of things (IoT) and military applications. The problem of SWPTMC is investigated for various modulation formats, i.e, quadrature phase-shift-keying (QPSK), 16-pulse amplitude modulation (16-PAM), π/4-QPSK, minimum shift keying (MSK), offset QPSK (OQPSK), and 16-quadrature amplitude modulation (16-QAM). We propose a new receiver architecture that incorporates conventional power splitting under a linear model with a certain level of sensitivity. The blind modulation classification algorithm is based on the higher-order cumulants and cyclic cumulants of the received signal. The cyclic cumulants use the non-zero cycle frequency position, while the higher-order cumulants use threshold values for classifying modulation formats. Monte Carlo simulations are carried out to validate the accuracy of the proposed SWPTMC scheme.
Impact of oily wastewater for public health in rural area: a case study of Kazakhstan

&amp;lt;p&amp;gt;Oil refinery industry historically has been related with groundwater pollution. According to WHO, contaminants may reach groundwater most easily as a result of discharge to the ground surface and subsequent infiltration through the soils, or releases from the ponds. Environmental Performance Review for Kazakhstan from UNECE states that oil refinery factories in Kazakhstan are one of the biggest sources of groundwater contamination despite of existing governmental and industry environmental monitoring systems. Recent studies showed that more than 50% of the people in rural Kazakhstan use groundwater for domestic purposes, consequently, many people potentially drink unsafe water. Despite this, oil refineries in Kazakhstan continue to discharge wastewater with high concentrations of different pollutants and these contaminants reach the groundwater very easily. The aim of this paper is to analyse and interpret a dataset obtained during a 6-year (2014&amp;amp;#8211;2019) monitoring program of the control of groundwater quality surrounding recipient of poorly treated wastewater discharges in one of the Kazakhstani industrial clusters. Multivariate statistical techniques and Heckman selection model were used for assessment of loading of natural and anthropogenic contribution to contamination, including separated evaluation of the impact of toxic substances.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results show that PCAs have high loading of anthropogenic contamination to groundwater from the oil refinery industry coupled with natural geochemical processes. High concentrations of several parameters, such as total petroleum hydrocarbons, phenols, sodium, chlorides, sulphates, total dissolved solids and total hardness were identified. Water containing such exceeding concentrations of substances affects human body significantly and normally is considered to be rejected by consumers. By means of cluster analysis, the examined wells were combined in three groups according to the concentrations of chemicals and their locations. The results enable a prediction of the groundwater flow in the region studied as well as an estimation of which sites would be most severely affected by contamination. According to obtained data from Heckman analysis, focus of further research should be paid to the distribution of toxic contaminants. The industry is continuing to pollute the environment, which means that assessment of existent and future hazards is needed through improved monitoring system. Thus, the next step of the research considers contamination transport modelling for definite identification of groundwater flow and potentially affected rural areas.&amp;lt;/p&amp;gt;
A Navigation Algorithm Using a System of Stationary Pseudolites

A navigation algorithm i , which utilizes a system of stationary pseudolites is derived, analysed, discussed, and simulated in this article.Although this algorithm uses the same Doppler measurements as a conventional least square algorithm (LSA) (which produces user velocity and user clock drift) or Kalman based filter (which obtains user position/velocity etc.), in essence the Doppler measurements are applied differently and therefore this is the reason why this is an innovative algorithm.The solution accuracy obtained using this algorithm is compared with solution accuracy from the conventional pseudorange LSA.The result of this comparison is presented in the end of the article.
Zyy1510 Team at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text with Sub-word Level Representations

This paper reports the zyy1510 team's work in the International Workshop on Semantic Evaluation (SemEval-2020) 1 shared task on Sentiment analysis for Code-Mixed (Hindi-English, English-Spanish) Social Media Text.The purpose of this task is to determine the polarity of the text, dividing it into one of the three labels positive, negative and neutral.To achieve this goal, we propose an ensemble model of word n-grams-based Multinomial Naive Bayes (MNB) and subword level representations in LSTM (Sub-word LSTM) to identify the sentiments of code-mixed data of Hindi-English and English-Spanish.This ensemble model combines the advantage of rich sequential patterns and the intermediate features after convolution from the LSTM model, and the polarity of keywords from the MNB model to obtain the final sentiment score.We have tested our system on Hindi-English and English-Spanish code-mixed social media data sets released for the task.Our model achieves the F1 score of 0.647 in the Hindi-English task and 0.682 in the English-Spanish task, respectively.