Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Artificial Neural Networks and Naïve Bayes Techniques for the Classification of Radar Targets

The classification of radar targets is one of the most important study topics, especially in the defense and automotive industries. However, in most of the studies in the literature, raw radar signals are used. Raw radar signals may be subject to ambient noise and signal modulation effects. This may make it difficult to classify radar targets. In this study, instead of using raw data, Fourier-based features extracted from Radar Cross-sectional Area have been used. These extracted features are then input to two types of classifiers, ie, Naive Bayes (NB) and Artificial Neural Networks (ANN) for the classification of radar targets. In addition, both classifiers were trained with different algorithms and their performances were compared. In the ANN-based classifiers, the best accuracy was found that 96.69% with using Bayesian regularization and back propagation training function. On the other hand, the best accuracy with the NB classifier was achieved at 93.95% using Epanechnikov Kernel Distribution. The result presented here demonstrates that Fourier transform based feature extraction can be used effectively in radar target classification applications.
Creativity in the Film Director’s Work: An Example of Works by A. Zvyagintsev

This article discusses the main approaches to the definition of creativity, including interdisciplinary ones; in addition, the author highlights a systematic approach to the definition and understanding of creativity at the intersection of psychology, philosophy, and economics.The author reveals the specifics of the analysis of the filmmaker's work through the prism of creativity.The article analyzes the work of the modern Russian film director A. Zvyagintsev in the context of creativity.
Falsification of a Vision-based Automatic Landing System

At smaller airports without an instrument approach or advanced equipment, automatic landing of aircraft is a safety-critical task that requires the use of sensors present on the aircraft. In this paper, we study falsification of an automatic landing system for fixed-wing aircraft using a camera as its main sensor. We first present an architecture for vision-based automatic landing, including a vision-based runway distance and orientation estimator and an associated PID controller. We then outline landing specifications that we validate with actual flight data. Using these specifications, we propose the use of the falsification tool Breach to find counterexamples to the specifications in the automatic landing system.
Application of DPP System Using MPPT of Experts Inference Method for Photovoltaic Power Production Enhancement

This paper improves the power production efficiency by using the MPPT of the experts inference method in the photovoltaic DPP system. experts inference method is used to overcome limitations of existing P&O algorithm. The effectiveness of the experts inference method was verified through simulation. The MPPT was accelerated by the active voltage change and the ripple at the steady state was reduced. As a result, the power production efficiency of the solar panel can be increased. These results suggest that the power production efficiency of solar power plant facilities can be increased.
Towards a Robust and Trustworthy Machine Learning System Development.

Machine Learning (ML) technologies have been widely adopted in many mission critical fields, such as cyber security, autonomous vehicle control, healthcare, etc. to support intelligent decision-making. While ML has demonstrated impressive performance over conventional methods in these applications, concerns arose with respect to system resilience against ML-specific security attacks and privacy breaches as well as the trust that users have in these systems. In this article, firstly we present our recent systematic and comprehensive survey on the state-of-the-art ML robustness and trustworthiness technologies from a security engineering perspective, which covers all aspects of secure ML system development including threat modeling, common offensive and defensive technologies, privacy-preserving machine learning, user trust in the context of machine learning, and empirical evaluation for ML model robustness. Secondly, we then push our studies forward above and beyond a survey by describing a metamodel we created that represents the body of knowledge in a standard and visualized way for ML practitioners. We further illustrate how to leverage the metamodel to guide a systematic threat analysis and security design process in a context of generic ML system development, which extends and scales up the classic process. Thirdly, we propose future research directions motivated by our findings to advance the development of robust and trustworthy ML systems. Our work differs from existing surveys in this area in that, to the best of our knowledge, it is the first of its kind of engineering effort to (i) explore the fundamental principles and best practices to support robust and trustworthy ML system development; and (ii) study the interplay of robustness and user trust in the context of ML systems.
BERT-based similarity learning for product matching

Product matching, i.e., being able to infer the product being sold for a merchant-created offer, is crucial for any e-commerce marketplace, enabling product-based navigation, price comparisons, product reviews, etc. This problem proves a challenging task, mostly due to the extent of product catalog, data heterogeneity, missing product representants, and varying levels of data quality. Moreover, new products are being introduced every day, making it difficult to cast the problem as a classification task. In this work, we apply BERT-based models in a similarity learning setup to solve the product matching problem. We provide a thorough ablation study, showing the impact of architecture and training objective choices. Application of transformer-based architectures and proper sampling techniques significantly boosts performance for a range of e-commerce domains, allowing for production deployment.
THE EFFECT OF AGGREGATE INSTITUTIONAL QUALITY ON FOREIGN DIRECT INVESTMENT IN NIGERIA: EVIDENCE FROM NARDL

The vital role of foreign direct investment has been widely studied and documented in the economic literature; however, the argument remains largely on identifying the main determinants of FDI to developing countries. It is on this note, the quantitative research method was adopted to investigates the asymmetric relationship between aggregate institutional quality and foreign direct investment in Nigeria using the Nonlinear Autoregressive Distributive Lag (NARDL) model on quarterly time-series data from 1999 Q1 – 2019 Q4. The bounds test obtains revealed that long-run co-integrating relationship exist among the variables. The NARDL result shows that both in the short-run and long-run aggregate institutional quality have asymmetric and a statistically significant effect on foreign direct investment. The study recommends that the government should establish or strengthen the quality of institutional indicators and legal framework to assure confidence in the system to motivate Foreign Direct Investment (FDI) inflow.
The Role of Modern Pedagogical Technologies in the Formation of Students' Communicative Competence

The article deals with the issues related to developing communicative competence of future English teachers. It considers the notion that developing communicative competence can benefit not only to the interactive competences of the learner from the educational point of view, but also psycho-emotional characteristics and socio-cultural development of a student as a person.n As we know, communicative competence is the ability that means interacting effectively with others and competence is understood as a combination of language skills an individual has for learning a foreign language. Such potential contributes to his/her attaining high levels of performance. This paper reflects the following: the theory of communicative competence and some of its models; the importance of developing communicative competence of students who are future English teachers and the implications of communicative competence in English language teaching and learning. Furthermore, the work suggests the basic methodic principles of developing communicative competence of future English teachers.
Emotion Detection Using Facial Recognition

Humans express their emotions in various ways like body language, speech, facial expressions, etc. We have used facial expressions to detect emotions which are very crucial and have wide applications in machine learning, especially in medical, communication, education, and entertainment fields. Old age health monitoring, Security systems, Psychology and computer vision, Driver fatigue monitoring are few real-world applications of emotion detection. This system aims to recognize seven emotions namely Anger, Sad, Happiness, Surprise, Neutral, Surprise, and Disgust. Our system proposes the Emotion detection of humans using facial expressions with Convolutional Neural Network (CNN). A literature review is carried out to select the best deep learning model. The dominant algorithm applied is CNN. The data-set used is Fer2013 and JAFFE.
An Adaptive Evolving Fuzzy Technique for Prognosis of Dynamic Systems

The evolving fuzzy technique is a recent development in the soft-computing field that has shown some promising results in applications such as control, classification, and short-term prediction. However, evolving fuzzy techniques still have challenges in terms of high-speed processing of cluster/rule generation, especially in long-term prediction applications due to the broader distribution of the input space. These factors can lead to problems such as overfitting in optimization and high computational costs, which could limit their applications in real-time monitoring. In this article, an adaptive evolving fuzzy (AEF) technique consisting of two novel aspects is developed to tackle these problems. First, an error-assessment method is suggested to monitor the trend of the cumulative training errors and to control the fuzzy cluster evolving process. Second, an adaptive particle filter algorithm is proposed to optimize the fuzzy clusters in order to enhance incremental learning and improve modeling efficiency. The effectiveness of the proposed AEF predictor is verified by simulation tests; it is also implemented for battery remaining useful life forecasting. Test results have shown that the proposed AEF technique can effectively capture the system's dynamic characteristics with fewer rules and can provide more flexibility in fuzzy modeling.
How to construct low-altitude aerial image datasets for deep learning

The combination of Unmanned Aerial Vehicle (UAV) technologies and computer vision makes UAV applications more and more popular. Computer vision tasks based on deep learning usually require a large amount of task-related data to train algorithms for specific tasks. Since the commonly used datasets are not designed for specific scenarios, in order to give UAVs stronger computer vision capabilities, large enough aerial image datasets are needed to be collected to meet the training requirements. In this paper, we take low-altitude aerial image object detection as an example to propose a framework to demonstrate how to construct datasets for specific tasks. Firstly, we introduce the existing low-altitude aerial images datasets and analyze the characteristics of low-altitude aerial images. On this basis, we put forward some suggestions on data collection of low-altitude aerial images. Then, we recommend several commonly used image annotation tools and crowdsourcing platforms for data annotation to generate labeled data for model training. In addition, in order to make up the shortage of data, we introduce data augmentation techniques, including traditional data augmentation and data augmentation based on oversampling and generative adversarial networks.
NLP@VCU: Identifying Adverse Effects in English Tweets for Unbalanced Data

This paper describes our participation in the Social Media Mining for Health Application (SMM4H 2020) Challenge Track 2 for identifying tweets containing Adverse Effects (AEs). Our system uses Convolutional Neural Networks. We explore downsampling, oversampling, and adjusting the class weights to account for the imbalanced nature of the dataset. Our results showed downsampling outperformed oversampling and adjusting the class weights on the test set however all three obtained similar results on the development set.
BERT Implementation for Detecting Adverse Drug Effects Mentions in Russian

This paper describes a system developed for the Social Media Mining for Health 2020 shared task. Our team participated in the second subtask for Russian language creating a system to detect adverse drug reaction presence in a text. For our submission, we exploited an ensemble model architecture, combining BERT’s extension for Russian language, Logistic Regression and domain-specific preprocessing pipeline. Our system was ranked first among others, achieving F-score of 0.51.
Why do we need to Introduce Temporal Behavior in Both Modern Science and Modern Computing, with an Outlook to Researching Modern Effects/Materials and Technologies

Classic science seemed to be completed more than a century ago, facing only a few (but growing number of!) unexplained issues. Introducing time-dependence into classic science explained those issues, and its consequent use led to the birth of a series of modern sciences, including relativistic and quantum physics. Classic computing is based on the paradigm proposed by von Neumann for vacuum tubes only, which seems to be completed in the same sense. Von Neumann warned, however, that implementing computers under more advanced technological conditions, using the paradigm without considering the transfer time (and especially attempting to imitate neural operation), would be unsound. However, classic computing science persists in neglecting the transfer time and is facing a few (but growing number of!) unexplained issues, and its development stalled in most of its fields. Introducing time-dependence into the classic computing science explains those issues and discovers the reasons for its experienced stalling. It can lead to a revolution in computing, resulting in a modern computing science, in the same way, as it resulted in modern science's birth.
Data Modelling To Determine Room Rate with Adaptive Network Based Fuzzy Inference System And Particle Swarm Optimization

Determination of room rate in a hotel isinfluenced by two factors, namely internal andexternal. From an external perspective, PT. PIMhas eight competitor hotels that affect its roomrate. The Hotel Manager analyzes eachcompetitor's room rate changes to staycompetitive. Human analysis has severalshortcomings: subjectivity, fatigue andinconsistency. Then we need a decision supportor decision companion machine to determine theroom rate. ANFIS-PSO is a hybrid algorithmfrom the Adaptive neural network based fuzzyinference system (ANFIS) by utilizing ParticleSwarm Optimization (PSO) optimization.Traditional ANFIS is Gradient Decent (GD) asan algorithm for parameter optimization (model).This often happens to be stuck to get optimallocal results, to overcome this PSO is used as asolution. The results obtained from the ANFIS- PSO training contained a difference of Rp.3173,187 or a percentage of 1.34%. From themodeling obtained applied to the hotel PT.PIM,with the result of an increase in revenue of Rp.17,493,548. The conclusion obtained is thatANFIS-PSO can help managers to determine theroom rate by modeling data obtained from theANFIS-PSO method. Suggestion for thedevelopment of this research is that ANFIS-PSOhas a complex complexity of training algorithmsbecause there is a combination of twoalgorithms, so to make it better a differentalgorithm design is needed.
Gaussian Function On Response Surface Estimation.

We propose a new framework for 2-D interpreting (features and samples) black-box machine learning models via a metamodeling technique, by which we study the output and input relationships of the underlying machine learning model. The metamodel can be estimated from data generated via a trained complex model by running the computer experiment on samples of data in the region of interest. We utilize a Gaussian process as a surrogate to capture the response surface of a complex model, in which we incorporate two parts in the process: interpolated values that are modeled by a stationary Gaussian process Z governed by a prior covariance function, and a mean function mu that captures the known trends in the underlying model. The optimization procedure for the variable importance parameter theta is to maximize the likelihood function. This theta corresponds to the correlation of individual variables with the target response. There is no need for any pre-assumed models since it depends on empirical observations. Experiments demonstrate the potential of the interpretable model through quantitative assessment of the predicted samples.
Transformation of the Educational Services Market in the Russian Federation

The modern market for educational services is undergoing major changes. The rapidly evolving digitalization of public life and economic relations, as well as the manifestation of global financial, environmental and, more recently, biological threats, have contributed to the global, large-scale and widespread introduction of remote educational technologies at all levels of the traditional education system. This process has led to the beginning of the transformation of the educational services market in the Russian Federation as well. The emergence of digital remote education at the state level, in fact, changed the classification of the educational services market at the fundamental level, highlighting, besides the market of on-campus and off-campus education, the market of remote educational services. Ideological problems of perception of possible negative results of education, economy and society digitalization by the public arose. The introduction of artificial intelligence-based technologies into the society management system has posed a threat of digital totalitarianism. The prospect of digital design of society in future generations based on mass truncated remote educational services should be a central topic of scientific research and public discussion.
INTERROGATION BENCH MEMBERS? EXAMINATION ON THEIR ADVISEMENT IN CRIMINAL PROCEEDINGS

The provision claiming that all officials involved in judgement rendering collegial body, in returning an illegitimate judgement, that is aimed to harm, are accomplices has been criticized.After all, each bench member, without exception, is obliged to sign the judgement, in case of rendering it.This obligation is incumbent upon the dissenting judge / juror as well.Rendering by a bench member of his/her own (different from the rest) thought has an undeniable advantage: the possibility of avoiding criminal prosecution for rendering a deliberately illegitimate judgement.However, the situation in which the judge / juror, not agreeing with the position of the rest of the bench members, has signed the judgement, without rendering the dissenting opinion in writing for some reasons.At the same time, proving the fact of a deliberately illegitimate judgement rendering in criminal proceedings without the bench member's dissenting opinion cannot automatically signify that the judge or the juror has committed the crime foreseen by Article 375 of the Criminal Code of Ukraine.After all, the bench members can be examined as witnesses to the object of their advisement.Within such examination, the bench members may argue that, although they did not express their dissenting opinion in writing, they did not agree with the position of the others, they even tried to convince the majority of the unreasonableness and unlawfulness of their behavior.In general, having a positive attitude to the introduction of such a rule, the author cannot still ignore the negligence of the legislator in making an analyzed provision and its incorrect construction.That's why a new introduction of Article 65, Part 2 of the CPC of Ukraine is suggested.Key concepts: dissenting opinion, disagreement with a position of a majority.
ERRANT : Assessing and Improving Grammatical Error Type Classification

Grammatical Error Correction (GEC) is the task of correcting different types of errors in written texts. To manage this task, large amounts of annotated data that contain erroneous sentences are required. This data, however, is usually annotated according to each annotator’s standards, making it difficult to manage multiple sets of data at the same time. The recently introduced Error Annotation Toolkit (ERRANT) tackled this problem by presenting a way to automatically annotate data that contain grammatical errors, while also providing a standardisation for annotation. ERRANT extracts the errors and classifies them into error types, in the form of an edit that can be used in the creation of GEC systems, as well as for grammatical error analysis. However, we observe that certain errors are falsely or ambiguously classified. This could obstruct any qualitative or quantitative grammatical error type analysis, as the results would be inaccurate. In this work, we use a sample of the FCE coprus (Yannakoudakis et al., 2011) for secondary error type annotation and we show that up to 39% of the annotations of the most frequent type should be re-classified. Our corrections will be publicly released, so that they can serve as the starting point of a broader, collaborative, ongoing correction process.
Malarial Parasite Identification Using Convolution Neural Network

Malaria -a dreadful and deadly disease caused by a parasite belong to the plasmodium family that commonly infects a female Anopheles mosquito which bite on humans.With the symptoms, the disease can be diagnosed by trained lab technicians who will examine the microscopic blood smear images.Developing an automatic, accurate and efficient model for detecting this disease will reduce the requirement for the trained human resource and it will improve the diagnosis efficiency.Deep learning neural networks can be used to improve the efficiency and the accuracy of the diagnosis.In this paper, we propose a model using Convolutional Neural Network (CNN) for the examination of malaria from the microscopic human red blood smear images.This model will provide a rapid, accurate, low cost outcome.Our model differentiates the infected and uninfected cell images by training the convolutional neural networks.The algorithm involves the methods and architectures of computer vision, image processing operations and deep learning.The proposed CNN model can examine the malarial parasites from microscopic images with an accuracy of 68.38%, in 10000 checkpoint operations.
The Ubiqus English-Inuktitut System for WMT20

This paper describes Ubiqus’ submission to the WMT20 English-Inuktitut shared news translation task. Our main system, and only submission, is based on a multilingual approach, jointly training a Transformer model on several agglutinative languages. The English-Inuktitut translation task is challenging at every step, from data selection, preparation and tokenization to quality evaluation down the line. Difficulties emerge both because of the peculiarities of the Inuktitut language as well as the low-resource context.