bluuebunny/crossref_metadata_2025 huggingface dataset:
Challenges of Vehicle Classification Using Acoustics
Authors: Evans Raiden , Mullins Stephanie , Noel George
Published: May 2022

Automated acoustic classification of vehicles is a challenging problem with many variables. Vehicles produce complex sounds from many sources, sound signatures vary between similar vehicles, and background noise has a large impact on audio data. Past research studied different vehicle classification techniques but often relied on datasets with little variation in vehicle model, environmental conditions, or microphones. High-accuracy results on these datasets suggest issues of overfitting. This paper highlights the challenges of creating robust datasets for difficult acoustic classification tasks. Challenges include collecting on multiple instances of a target class, recording with varied environmental noise, and collecting across multiple types of microphones. The work presented also evaluates classification performance on combinations of acoustic feature sets and common machine learning algorithms. Classification F-score performance drops 38.67% when the test set has background noise that differs from the classifier’s training set. Performance also significantly drops when the classifier is tested on a vehicle of a model year not included in the training set. Lastly, the vehicle classifier heavily overfits on the signatures of the microphones it is trained on.
Predicting the Severity of COVID-19 Respiratory Illness with Deep Learning
Authors: Shorten Connor , Khoshgoftaar Taghi M. , Hashemi Javad , George Dalmida Safiya , Newman David , Datta Debarshi , Martinez Laurie , Sareli Candice , Eckard Paula
Published: May 2022

Patient care in emergency rooms can utilize urgency labeling to facilitate resource allocation. With COVID-19 care, one of the most important indicators of care urgency is the severity of respiratory illness. We present an early analysis of 5,584 patient records, of whom 5,371 (96.2%) have returned a positive COVID-19 test, to understand how well we can predict the severity of a respiratory illness given other features describing a patient using Deep Learning methods. The goal of our work is to illustrate the connection of our COVID-19 patient dataset with Deep Learning techniques, setting the stage for future work. The features in our dataset include when COVID-19 symptoms began, age, height, weight, demographics, and pre-existing conditions, to give a quick preview. We report train-test performance of a Deep Multi-Layer Perceptron (MLP) to predict the severity of respiratory analysis on a one-hot encoded scale of 5 labels. This 5-level scale is a truncation of our available labels, which we plan to extend and include in future work. We utilize a high-level of Dropout in order to avoid overfitting with our Deep Learning model. Further, we particularly study the impact of class imbalance on this dataset (Johnson and Khoshgoftaar 2019). We find that Random Oversampling (ROS) is an effective solution for decreasing minority class false negatives, as well as increasing overall accuracy. Readers will understand the performance of Deep Learning, with Dropout and ROS, to predict the severity of a COVID-19 patient’s respiratory illness in which patients are described with Tabular Electronic Health Records (EHR).
Knowledge Reformulation and Deception as a Defense Against Automated Cyber Adversaries
Authors: Alford Ron , Chrpa Lukas , Vallati Mauro , Applebaum Andy
Published: May 2022

Leveraging automated planning has been shown to be advantageous for automating network penetration testing, providing a foundation to generate intelligent approaches to attacking a target system. Unfortunately, this same technology has the potential to be abused by actual attackers, presenting a challenge to defenders. In this paper, we investigate how we can leverage ideas from the deception community to reduce the automated planning capacity of an actual attacker. Our extensive experimental analysis sheds some light on the susceptibility of planning-based attackers to knowledge modifications, potentially yielding to new insights on future techniques for cyber defense.
Considering Inter-Case Dependencies During Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning
Authors: Kumar Rahol , Schultheis Alexander , Malburg Lukas , Hoffmann Maximilian , Bergmann Ralph
Published: May 2022

In Case-Based Reasoning (CBR), knowledge gained from previously experienced problem-solving situations is stored as cases that can be used to solve similar upcoming problems. Although these cases act as independent knowledge entities, dependencies between cases are common in real-world scenarios, despite being only rarely considered during case retrieval or other CBR phases. In this paper, we introduce so-called inter-case dependencies, which are considered in the context of Process-Oriented CBR (POCBR). Therefore, we 1) derive requirements that must be satisfied for considering dependencies during the retrieval phase, 2) analyze which knowledge representations are suitable for representing dependencies between cases, and, 3) present our approach for Dependency-Guided Retrieval (DGR) that considers these dependencies between cases during the retrieval phase. In the experimental evaluation, the proposed DGR approach is compared to a regular CBR approach in case retrieval scenarios from the cooking domain. The results demonstrate that the use of the DGR approach leads to significantly reduced times for human problem-solving compared to regular CBR.
Generating Conceptual Explanations for DL based ECG Classification Model
Authors: Sangroya Amit , Jain Suparshva , Vig Lovekesh , C. Anantaram  , Ukil Arijit , Khandelwal Sundeep
Published: May 2022

Deep learning techniques are being used for heart rhythm classification from ECG waveforms. Large networks using end-to-end learning such as convolutional neural networks are not easily interpretable by end-users such as doctors. This is because most of the state of art explainability techniques focus on explanations for data-scientists who have technical knowledge. However, end-users of such systems are normally doctors who are not familiar with the technical details of machine learning. Therefore, to address this gap, we propose a framework that provides explanation of ECG classification model in the language of clinicians. We leverage state of art knowledge-based systems to map domain concepts with outcomes of machine learning model. Our results show that domain concepts based explanations are useful for clinicians and can greatly reduce their cognitive load. This shall lead to larger deployment of these models in real world.
Unsupervised Neural Network for Data-Driven Corrosion Detection of a Mining Pipeline
Authors: Dia Abdou Khadir , Ghazzali Nadia , Axel Gambou Bosca
Published: May 2022

Pipelines failure often caused by corrosion may result in safety, environmental and economic issues. In this study, an unsupervised neural network, Self-Organizing Maps (SOM), is applied to create clusters representing the corrosion impact assessed with ultrasound periodic inspections. Based on this work, it is expected that the new insight into thickness data representation using unsupervised neural network will facilitate planning of corrosion mitigation activities through risk-based inspections of mining slurry pipelines. As a result, SOM led to the reduction of the variables in two-dimensional space nodes. Hierarchical ascending classification (HAC) was then used to classify these nodes regrouping thickness loss measurements. The proposed method by combining both SOM and HAC succeeded in detecting the extent of corrosion in a mining pipeline.
Recommendation System for Open Source Projects for Minimizing Abandonment
Authors: Sayce Sarah , Ghosh Krishnendu
Published: May 2022

The rise in the creation and maintenance of Open Source

Software have facilitated the software developers to contribute

and prevent abandonment. Software developers often

face a daunting task to select the open source projects that remain

active. In the absence of any resource to guide in the selection

of the Open Source Software projects, recommendation

systems is way to provide guidance to open source contributors.

In this work, we describe several approaches in creation

of recommendation systems for Open Source Software

projects. Experiments on a synthetic data set are performed

to evaluate the performance of the recommendation system.
Mitigating the Rashomon Effect in Counterfactual Explanation: A Game-theoretic Approach
Authors: Mehedi Hasan Md Golam Moula , Talbert Douglas
Published: May 2022

Counterfactual examples (CEs) are generally created to interpret the decision of a model. In this case, if a model makes a certain decision for an instance, the CEs of that instance reverse the decision of the model. There are many advantages of using counterfactuals as a way of explaining model decisions; however, there is one issue known as the Rashomon Effect that might dissuade target/intended users from using counterfactuals. If someone is presented with too many options, this might be overwhelming to them, and they might end up choosing an option that is not optimal or ideal for them. In this case, the Rashomon Effect is an impediment to realizing the full potential of counterfactual explanations. To utilize the full power of CEs and make them more helpful, we need to address the Rashomon Effect. In this work, we focus on this issue from game-theoretic perspectives to help target users make informed and feasible decisions by finding highly suitable CEs. In this case, finding good counterfactuals will be a game between two players where each of them tries to find better CEs.
Public Health surveillance from emergency call center data: visualization dashboard and NLP of call reports
Authors: Naprous Alexandre , Avalos-Fernandez Marta , Pradeau Catherine , Lagarde Emmanuel , Gil-Jardine Cedric
Published: May 2022

By focusing on symptoms and not diagnoses, the socalled syndromic surveillance system gains in immediacy what it loses in specificity with respect to other more traditional options for public health surveillance. Reports of calls to emergency medical communication centers (EMCC) supplemented by the data collected by the rescue workers who arrived on the scene constitute a cost-effective and rich source of information. Unfortunately, EMCC data are infrequently used and their utility has not been demonstrated.The aim of this study was to explore the usefulness for public health surveillance of EMCC data when analyzed using text mining and visualization tools. Transformer-based deep learning architectures were used to classify call reports according to the reason for the call. We also extracted indicators that could serve as proxy measures using a keyword-search algorithm. We then developed a dashboard visualization tool to enable dynamic and spatial exploratory analyses. Finally, we explored the potential of this tool for two applications. While the tool proved unable to detect Covid-19 outbreaks, it appeared to be promising for a better understanding of territorial inequalities in healthcare access.
Review of the Validity and Rationality of Artificial Intelligence Regulation
Authors: Lim Eugene , Park Hyesung , Kim Beopyeon , Kim Beopyeon
Published: May 2022

The ‘risk of artificial intelligence technology and services' is a long-standing debate. Such discussion is being raised and sparked whenever incidents that prove the dangers of Artificial Intelligence occur. Recently, the argument that legal control and regulatory measures for artificial intelligence technologies and services is necessary to control risks is gaining strength. In the midst of this, the EU proposed "Proposal for a Regulation laying down harmonized rules on artificial intelligence: Artificial Intelligence Act" in April 2021 and materialized the regulatory discussion on artificial intelligence.

Public opinion is that legal control over artificial intelligence technology is needed. However, no one has found the answer to what method and content it would be reasonable to approach. From such a perspective, it is unclear how effectively and effectively various regulatory laws, including the EU, will be able to control the risks of artificial intelligence. Since regulation on artificial intelligence can act as an obstacle to the development of AI technology and industry, implementation of regulation in a stage where the possibility of control, the effect of the law, and the subject and scope of the regulation are not clear, should be approached deliberately. Therefore, reviewing whether regulatory measures can achieve the legal effect reasonably motivated by and control the risks of artificial intelligence is necessary. In this context, this paper applies the EU's AI regulatory legislation to cases such as accidents or incidents that emerged as social problems by AI, and analyzes whether the legislative regulatory measures of AI are valid and effective. Furthermore, the prerequisites for ensuring that the legislation of artificial intelligence regulation is reasonable and valid are also presented.
Generative Adversarial learning with Negative Data Augmentation for Semi-supervised Text Classification
Authors: Shayesteh Shahriar , Inkpen Diana
Published: May 2022

In recent years, semi-supervised generative adversarial networks (SS-GANs) models such as GAN-BERT have achieved promising results on the text classification task. One of the techniques used in these models to mitigate the generator from mode collapse is feature matching (FM). Although FM addresses some of the critical issues of SS-GANs, these models still suffer from mode collapse with missing coverage outside the data manifold. Moreover, FM loosely tries to match the distribution between the real data and the fake generated samples. By doing this, the generator can generate fake samples inside high-density regions in the data manifold, where the discriminator learns to misclassify them as out-of-data-manifold regions. In this work, we employ the negative data augmentation (NDA) technique, for the first time in text classification, to alleviate the mentioned problems. NDA is a unique way of producing out-of-distribution fake examples by applying mixup transformation on the fake samples and augmented real data. In our new model (NDA-GAN), we produce NDA samples by combining the generator's output with the contextual representation of the real data. As a result of the mixing, NDA samples are less likely to place in the high-density regions, and due to blending with real data representations, these samples reasonably preserve a close distance to the data manifold. Consequently, the NDA samples increase the discriminator's power to find the optimal decision boundary. Our experimental results demonstrate that the negative augmented samples improve the overall accuracy of our proposed model and make it more confident when detecting out-of-distribution samples.
Vehicle Traffic Estimation Using Deep Learning
Authors: Patel Meetkumar , Silver Daniel
Published: May 2022

For commuters, vehicular traffic is an important planning concern. People have access to the weather forecast and the current traffic situation, but there is no application available to estimate traffic congestion and flow in the near future. Thus, we design and develop a machine learning approach which can predict vehicular traffic density and flowrate up to two days in the future based on the weather, calendar and special events data.First, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks are utilized to predict the number of new vehicles and the total number of vehicles in images captured by a Nova Scotia Webcams (NS Webcams) video camera. The best models provide a Mean Absolute Percentage Error (MAPE) of 20.38% for the number of new vehicles and 18.56% for the total number of vehicles. These values are used to estimate traffic flowrate and density for hourly records over a three-month period.The hourly traffic data is combined with observed and forecasted weather data, and special event data to create a time series data. A Multiple Task Learning (MTL) - LSTM model is trained and tested using these data and a K-fold cross-validation approach. The Mean Absolute Error (MAE) and MAPE are used to evaluate the model performance. The MTL-LSTM model achieves a MAPE of 19.35% and 27.50% for flowrate and density using observed weather data, respectively. In the case of forecasted weather data, the MAPE for flowrate and density increases to 20.51% and 31.10%, respectively.
Enabling AI Adoption through Assurance
Authors: Chandrasekaran Jaganmohan , Batarseh Feras A. , Freeman Laura , Kacker Raghu , Raunak M S , Kuhn Rick
Published: May 2022

With the broader adoption of AI-enabled software systems, it is necessary to provide assurance to the layman user that the AI system will behave as intended. This interactive tutorial will provide an overview of AI assurance, introduce a new set of assurance goals for AI systems, discuss the open challenges in AI assurance, and present recommendations to overcome its drawbacks.
Effects of scale on segmentation of Nissl–stained rat brain tissue images via convolutional neural networks
Authors: Arnal Alexandro , Fuentes Olac
Published: May 2022

There are numerous efforts to automate the delineation of rat brain regions from rat brain histology. A leading approach uses convolutional neural networks which model anatomical variability and determine cytoarchitectonic boundaries. Currently, it is not clear what scale of the input tissue images offers the most information for these models to exploit. In this work, we test a fully convolutional architecture, U–Net, with Nissl–stained rat brain tissue images of different scales. We show that the networks obtain a lower precision and higher recall when trained on large-scale images. Conversely, networks trained with small-scale images produce fewer false-positive predictions and more false-negative predictions. Our work provides valuable insight into the optimal scale needed for convolutional neural networks to segment brain regions from Nissl-based images of the brain.
Pedestrian Traffic Prediction using Deep Learning
Authors: Joshi Riddhi , Silver Daniel
Published: May 2022

Pedestrian traffic information offers useful insights when developing or maintaining a business.This research combines image processing and machine learning methods to predictpedestrian traffic flowrate and density for up to two days into the future, based on weatherdata, calendar data, and special events. To obtain the traffic flowrate and density, we firstdeveloped a neural network model to predict the number of new people and the total numberof people in each sequence of images captured by a Nova Scotia Webcams camera. Thesecounts of people are used to calculate the pedestrian traffic flowrate and density labels forhourly intervals. These labels are then combined with hourly weather data, calendar data,and special event data from the same period to train a recurrent neural network to predictthe traffic flowrate and density for up to two days in advance.We try two different approaches, CNN-LSTM and dual input CNN to predict the numberof new people and the total number of people from the images and compare how well eachapproach performs. The results show that the dual image input CNN models are moreeffective at predicting the number of new people and the total number of people than the CNN- LSTM models. Tested on independent test sets of images using K-fold cross-validation, theMTL CNN model achieved a test accuracy of 72% for the number of new people and 78%accuracy for the total number of people.
HTN Replanning from the Middle
Authors: Bansod Yash , Patra Sunandita , Nau Dana , Roberts Mark
Published: May 2022

When an actor executes a plan, action failures and exogenous events may lead to unexpected states that require replanning from the middle of plan execution. In Hierarchical Task Network (HTN) planning, unless the HTN methods have been carefully written to work well in unexpected states, replanning may either fail or produce plans that perform poorly.

To overcome this problem, we introduce IPyHOP, a reentrant version of GTPyhop (a SHOP-like HTN planner), and Run-Lazy-Refineahead, a modification of the Run-Lazy-Lookahead actor that utilizes IPyHOP's reentrant replanning capability to replan during plan execution. In our experiments, Run-Lazy-Refineahead and IPyHOP expend less search effort (fewer decompositions and fewer iterations), find revised plans with fewer actions and lower total action cost, and finish execution with fewer failures.
A Few-shot Learning Model based on a Triplet Network for the Prediction of Energy Coincident Peak Days
Authors: Liu Jinxiang , Brown Laura
Published: May 2022

In an electricity system, a coincident peak (CP) is defined as the highest daily power demand in a year, which plays an important role in keeping the balance between power supply and its demand. Advanced information about the time of coincident peaks would be helpful for both utility companies and their customers. This work addresses the prediction of the five coincident peak days (5CP) in a year. We present a few-shot learning model to classify a day as a 5CP day or a non-5CP day 24-hours ahead. A triplet network is implemented for the 2-way-5-shot classifications on six different historical datasets. The prediction results have an average (across the six datasets) mean recall of 0.933, mean precision of 0.603, and mean F1 score of 0.733.
Learning to Take Cover on Geo-Specific Terrains via Reinforcement Learning
Authors: Aris Timothy , Ustun Volkan , Kumar Rajay
Published: May 2022

This paper presents a reinforcement learning model designed to learn how to take cover on  geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. We show that increasing the number of novel situations the agent is exposed to increases the performance on the test set. In addition,  the trained models possess some ability to generalize across terrains, and it can also take less time to retrain an agent to a new terrain, if that terrain has a level of complexity less than or equal to the terrain it was previously trained on.
Műszaki kiválóságok: Karl Freiherr von Birago
Authors: Balla Tibor , Padányi József
Published: May 2022

Karl Freiherr von Birago (1792–1845) az egyik legsokoldalúbb műszaki tiszt, aki nem csak a hadihídépítés területén alkotott maradandót. Az 1841-ben rendszeresített Birago-féle hadihídkészlet olyan sikeres alkotás volt, hogy számos ország vette használatba és több mint 100 évig szolgálta a haderőt. Tehetségét mutatja, hogy légzőkészüléket fejlesztett, a hírközlés korszerűsítésén is dolgozott, és belekóstolt az erődítés tudományába is. Szerencsés katona volt, hiszen már életében elismerték tudását, mind katonaként, mind újító elmeként.
The “Evolution” of Improvised Explosive Devices (IED) in the Light of Technical Development
Authors: Domján András
Published: May 2022

In today’s conflicts of war, the various bombings, which are perpetrated against military or civilian targets, have been directed against members of the regular armies considered to be “occupying”, as well as against the locals who collaborated with them or assisted them. In the military, such acts are mentioned as an element of asymmetric warfare, and on various interpretations, they are sometimes classified as a terrorist acts. In case of “peaceful” countries, it can only be interpreted according to the latter concept, which can be aimed at a specific person, political orientation, intimidation, revenge or other criminal activity. The explosive devices used fall into the so-called “home-made” category in terms of their design. The assassin’s primary purpose is to achieve a devastating effect. In this article I deal primarily with improvised explosive devices appearing in so-called “civilian” life.
Erődített körzetek a Szovjetunió nyugati határai mentén
Authors: Rakaczki István
Published: May 2022

Az I. világháborút lezáró békeszerződések után Európa országai az erődépítésben látták biztonságukat. A Szovjetunió nyugati határai mentén erődített körzeteket alakít ki. Az 1939-es német–szovjet paktum eredményeként új nyugati határ létesül, ahol új védelmi erődrendszer kiépítése kezdődik. A német–szovjet háború 1941-es kezdetéig a szovjet erődített körzetek nincsenek harckész állapotban.
A Katolikus Karitász együttműködése az állami szervekkel a katasztrófák elleni védekezésben
Authors: Ujházi Lóránd
Published: May 2022

A II. Vatikáni Zsinat jelentős újdonságot hozott az állam és az egyház kapcsolatának teoretikus megalapozásában. Bár a zsinat előtti szociális enciklikák is eminens helyen tárgyalták a közjót, mint amely az állam és az egyház célkitűzéseinek fő fókuszát jelenti, azonban arról, hogy ennek a közjónak a megvalósításában a két entitás hogyan működjön együtt, kevés szó esett. A II. Vati-káni Zsinat és az azt követő szociális enciklikák már nem hagytak kétséget afelől, hogy a katolikus egyház és annak szervezetei nyitottak az együttműködésre az állammal és az állami szervezetekkel minden emberi ínség csillapításában, legyenek azok akár regionálisak vagy globálisak, emberi tevékenység vagy természeti katasztrófák eredményei, nemzeti vagy éppen nemzetközi együtt-működést igénylők. A II. Vatikáni Zsinat Gaudium et Spes kezdetű lelkipásztori konstitúciója az Egyházról a mai világban már számos helyen (26.; 30.; 42.; 43.; 59. pont) értekezik a közjó szempontjáról. Ami az állam és az egyház közös fellépését illeti, azt jól foglalja össze a konsti-túció 26. pontja: „zsinat nagy tisztelettel tekint mindarra, ami igaz, jó és igazságos abban az oly sokféle intézményben, melyet az emberi nem a maga javára alapított és alapít.” Ezen felül kije-lenti, hogy „az Egyház – amennyiben rajta múlik és küldetésével összefér – segíteni és támogatni akarja az összes ilyen intézményt. Semmit sem kíván jobban, mint mindenki javára szolgálván szabadon fejlődhessen bármilyen kormányzati forma alatt.” (26. pont) Ebben a tanulmányban az együttműködés sajátos aspektusára, konkrétan a katasztrófák elleni védekezésben való közös tevékenységre mutatunk rá.
Students' Motivation under the Study of Foreign Languages in a Pharmaceutical University
Authors: Arkhipova I. V. , Rozhkov G. A. , Pristupa S. V. , Sergopoltseva E. O. , Tumanova A. P.
Published: May 2022

Introduction. The authors of the research aimed to determine the level of students' interest in foreign languages, leading motives, as well as the presence of difficulties arising under the study of a foreign language. The importance of motivation in the process of learning foreign languages in a non-linguistic university is emphasized.

Methodology and sources. The authors of the research describe the questionnaire survey and analysis of the results obtained in its course. This research involved 267 students of 1–2 courses of the Pharmaceutical Faculty of the Saint Petersburg State Chemical and Pharmaceutical University.

Results and discussion. The modern pharmaceutical industry is actively developing, which implies the exchange of technology and knowledge between representatives of different countries. Most pharmaceutical companies have subsidiaries all over the world. A demanded employee needs to have the skill of effective intercultural dialogue in this professional field. In recent decades, the role of foreign languages has increased all over the world, and the necessity to know them is generally recognized.

Conclusion. The paper has shown the level of the development of motivation for learning a foreign language by students in order to further increase the level of development of their motivation in the learning foreign languages. The conclusion was made that student motivation stands out as one of the main factors in achieving success in both educational and professional activities and, therefore, should be a priority for all teachers.
Research on the Structure of Indo-European Dialect Continuum by Comparing Swadesh Lists of the Closest Descendant Languages
Authors: Telezhko G. M.
Published: May 2022

Introduction. This article is an attempt to extract information about the interactions of dialects of the Indo-European dialect continuum with each other using a comparative analysis of the basic vocabularies of some Indo-European (IE) descendant languages.

The search for external borrowings and influence of a common substrate would help to clarify the ethno-linguistic surrounding of the area where the IE proto-dialects developed.

In turn, these data are actual being pro and contra arguments of the well-known hypotheses about the IE ancestral home.

Methodology and sources. The number of mutually understandable basic lexemes taken in relation to the number of lexemes in the compared lists was chosen as a measure of the interaction of IE dialects, indicators of their commonality.

207-word Swadesh lists of 12 languages in their possibly more ancient states were analysed.

For geographical binding of the IE language areal we have selected cross-borrowings from/to neighboring / substrate non-IE languages, the ancient settlement areas of native speakers of which are considered well-known.

Results and discussion. The results of the comparison of the basic vocabularies of 12 IE languages have been interpreted in the form of a graph demonstrating the relative location of areas of the corresponding IE dialects. Lexemes meaning 'predator (bear, lion, etc.)', 'cattle (bull, ox)' determined the ethno-linguistic surrounding of the IE areal.

Conclusion. The relevant linguistic data permitted to identify in the IE dialect continuum the core of proto-dialects: Baltic, Slavic, Aryan and Italic – and partially superimposed dialect subcontinua:

– Balto-Greco-Aryo-Tocharo-Anatolian subcontinuum in the northern part of the IE areal;

– Tocharo-Celto-Germanic subcontinuum in the eastern part;

– Germano-Celto-Italo-Greco-Armeno-Baltic subcontinuum in the southern part;

– Balto-Slavo-Italo-Aryan subcontinuum in the western part.

The representation of the Proto-IE areal as a dialect continuum solves a number of difficulties inherent in the most common model of a single IE proto-language.
Key Words as Markers of the Communicative Behavior of the Discursive Personality of the Nominee to the USA Presidency (Based on the Genre of Pre-Election Debates)
Authors: Kochetova L. A. , Demkina Ya. Yu.
Published: May 2022

Introduction. This paper presents an analysis of the language indicators of communicative behavior of US presidential candidates during election debates. The scientific novelty consists of determining the specifics of the characteristics of the communicative behavior of the discursive personality, which form the basis of the corresponding social type, which can be identified using the methods of linguistic analysis. The study of the discursive personality is reduced to a description of the types of its performative actions; identification of types of communicative tonality that characterize the manner of behavior of a discursive personality and make it recognizable; identification of axiological characteristics of the studied discursive personality. The relevance of the work lies in the study of the problematic aspects of the discursive personality, explaining the specifics of the models of communicative behavior in situations of political communication.

Methodology and sources. This study used the methods of corpus linguistics and the interpretative method of discourse analysis. The texts of the genre of election debates, which took place from 2000 to 2020, became the empirical basis of this study. The volume of the corpus of these texts amounted to more than 1.5 million words. A corpus method of keywords, which allows to identify unique tokens of pre-election speeches of each of the presidential candidates; a qualitative analysis method that explains typical communicative actions that representatives of the two ruling parties of the United States, were used to establish the characteristics of the communicative behavior of US presidential candidates.

Results and discussion. The results of a comparative study of the discursive personality of the US presidential candidate representatives of the Republican and Democratic parties are presented. The study of keywords showed typical tactics of communicative behavior of candidates for US presidential candidates of the Democratic and Republican parties. It is established that presidential candidates from both parties use prosecution tactics in election debates, Republicans are more likely to resort to direct accusation tactics accusations of violating pragmatic, instrumental values, Democrats prefer indirect accusation tactics, which are based on accusations of violation of moral norms and values by political opponents.

Conclusion. The discursive personality of the US presidential candidate reflects the variable socio-ideological component of political discourse, which is manifested in a specific set of speech strategies and tactics and language structures that implement them.
Three Approaches to Criticisms and Support in Evaluation of Arguments
Authors: Lisanyuk E. N.
Published: May 2022

Introduction. In the article we defend a claim that criticisms and counterarguments play a more important role in modeling and evaluating arguments than defending and supporting the thesis. The novelty of this idea lies in that it shifts the main focus of the study of argumentation from the support and proof, with which it is traditionally associated, to criticism and refutation. Compared to the support and proof, criticism and refutation play more important role in the context of soft skills and communication in the practical activities, including obtaining and verifying information. The relevance of our study is dictated by the fact that argumentation is essential in both of the areas of activities, the importance of which is increasing at the present stage.

Methodology and sources. We identify three ways to understanding criticism with respect to three approaches to understanding of arguments, – inferential, dialogical and abstract. The inferential view relies on verification and aims to justify a conclusion by deriving it from premises. The dialogical view sees in it the grounds offered by its author to its addressee. The author aims at achieving the latter's consent to its conclusion, and the acceptability of the arguments is verified by both justifying their conclusions and rejecting objections to them. The dialogical view relies on the principles of verification and falsification. The abstract view abstracts from the internal structure of arguments and evaluates their acceptability through the falsification, conveying the rejection of arguments in defense of the opposite conclusion.

Results and discussion. The inferential view presupposes one-step criticism based on its analysis and final assessment, the dialogical one presupposes a multi-step revision of the assessment, and the abstract view implies arguments’ ability to reject counterarguments and demonstrates its role in defending the set of arguments to which it belongs protecting many arguments from criticism to which it belongs.

Conclusion. In the dialogical and abstract approaches, objections and criticisms of arguments play a more important role in justifying and defending arguments than the defense and support defined by them, which play a leading role in the inferential approach to arguments.
Professional Activities of Disabled People as a Factor of the Development of Education in an Inclusive Society
Authors: Kudryavtseva M. E. , Lebedeva S. S.
Published: May 2022

Introduction. The article deals with the problem of professional activity and education of persons with disabilities. The implementation of the ideas of inclusive education could be successful if the experience of persons with disabilities who have productively built their professional trajectory and actively participate in public, educational, sociocultural and other initiatives of modern society is taken into account.

Methodology and sources. The problem of professional and creative realization of people with disabilities is considered in line with the socio-educational approach, which implies the need to integrate people with disabilities into all spheres of modern society, as well as in line with the environmental approach, which involves considering the problem of the quality of life of people with disabilities. The article is based on the articles of such authors as O.S. Andreeva, A.V. Gruzintsev, M.A. Dymochka, A.A. Kirillovykh, S.N. Kashtanova, V.A. Kudryavtsev, S.S. Lebedeva, N.T. Selezneva, A.V. Cherdakova, as well as materials of 25 scientific and practical conferences (1994–2020). The article uses methods of content analysis of an array of 1553 articles based on conference materials, as well as the case study method.

Results and discussion. In the process of evaluating the array of publications, the authors concluded that persons with disabilities actively participating in professional activities and social work reflected many aspects of the problem of inclusive education. Theoretical and practical conclusions and examples given in the case study showed that the experience of people with disabilities working in the socio-cultural and educational fields, who develop the inclusive capabilities of people with disabilities with the help of advanced digital technologies, seems to be especially successful. An analysis of the professional activities of persons with disabilities led to the conclusion that its meaning and essence are associated with the formation of an inclusive society.

Conclusion. The authors conclude that people with disabilities are the initiators and organizers of relevant areas of activity that contribute to the active support of people with disabilities in the process of their lifelong education.
Who Needs Traditional Values? The Problem of Integrating Traditional Cultures into Modern Society
Authors: Marchenko E. E. , Oreshkin V. G. , Plebanek O. V.
Published: May 2022

Introduction. The article analyzes the basic concepts of ethno-cultural discourse, and raises the problem of the uncertainty of the conceptual apparatus, on the basis of which the theory of intercultural interactions is built, which makes it difficult to introduce the results of research into practice. The authors propose to clarify the categories that are relevant specifically for Russia at the present time, in the context of the formation of the foundations of the state's cultural policy and the adoption of relevant legislative acts. The subject of the research is the system of categories underlying the theories of modernization and theories of cultural interactions.

Methodology and sources. The analysis of the categorical apparatus of modern ethno-cultural policy is based on methodological principles that have developed in science in the process of a cultural-centric turn, which was laid down by the works of T. Parsons and P. Sorokin.

Results and discussion. The paradigmatic nature and indefinite use of such categories as “traditional values” and “traditional cultures” creates conditions for speculation and does not allow building a balanced cultural policy. And the unreasonable desire to the conservation of archaic cultural patterns leads to serious contradictions in the goals and results of the implementation of cultural policy. It is concluded that in order to make effective political decisions, it is necessary to take into account the difference in the use of concepts in the social sciences and the humanities and in the political sphere. It is necessary to separate the so-called “traditional values” and archaic societies, understanding by the former the values traditional for a particular cultural system, regardless of the concept of progress. And under the second – archaic cultures (and societies, carriers of these cultures), one should understand cultures that were formed on the basis of primitive technology to ensure the viability of the population.

Conclusion. In such a paradigm of cultural observation of observations of archaic cultures, one should build not only from the study of the intrinsic value of culture, but also from the disclosure of equal accessibility of humanitarian benefits (health protection, choice of occupation, accessibility of cultural achievements and education, etc.).
Semiosis of Artificial Intelligence and Social Development
Authors: Kolianov A. Yu.
Published: May 2022

Introduction. The article analyzes the semiotic aspects of the representation of artificial intelligence in the socio-cultural space and their influence on the nature of social development. The spread and penetration of technologies into social and political processes using artificial intelligence (AI) is accompanied by active discussions in the socio-political environment. Artificial intelligence is considered as one of the most important resources of social and political development, so the desire of states, politicians and public organizations to determine the semantic framework of its public perception is understandable.

Methodology and sources. The theoretical foundations of the work were the classic works on semiotics by Ch. S. Pierce, Ch. W. Morris, as well as theoretical works on modern political semiotics (I.V. Fomin, M.V. Ilyin and others). To work with the empirical base, analyzes of documents (reports and materials from UNESCO, the European Commission, the Royal Society of London, Stanford University), public opinion polls (VTsIOM), discourse analysis, semantic and content analysis of materials from Russian and foreign media were used.

Results and discussion. As a result of the study, we found that the main contradiction that accompanies the understanding of artificial intelligence, its essence and purpose at all levels of socio-political discourse is the polarization of public ideas about AI as a control tool and (or) a development tool. The contextual fields were also outlined, within which further development of the semiosis of artificial intelligence is possible.

Conclusion. To understand the nature of the representation of technologies using AI in socio-political discourse and to manage this process, an interdisciplinary approach and an appeal to the scientific potential of the social sciences and the humanities, in particular, semiotics, are appropriate, which makes it possible to study the semiosis of technology at the semantic, syntactic and pragmatic levels. It also seems promising to turn to a comparative analysis of cultural and historical traditions and national-state models of social management that specifically integrate AI into social processes.
Дослідження потребо-мотиваційних пріоритетів у сфері фізичної культури і спорту студентської молоді Китайської Народної Республіки
Authors: Марченко Оксана , Чжімінь Се
Published: May 2022

Анотація. Актуальність проблеми обумовлюється тим фактором, що численні дані наукових розробок китайських учених свідчать про значну кількість студентів, у яких спостерігається низький рівень фізичної підготовленості. У зв’язку з цим вони не в змозі виконати необхідні державні тести з фізичного виховання, які передбачені навчальною програмою, що є вкрай важливими у системі фізичного виховання КНР. Мета. Визначити особливості та відмінності мотиваційних пріоритетів до занять фізичною культурою і спортом студентської молоді КНР з метою усвідомленого підвищення їх інтересу до занять руховою активністю для духовного і фізичного самовдосконалення. Методи. Аналіз, порівняння, узагальнення, систематизація, неформалізовані бесіди, опитування, анкетування, методи математичної статистики. Результати. На основі результатів дослідження визначено, що мотивація студентів КНР до занять фізичною культурою і спортом має певні особливості, які залежать від статі та віку респондентів. Виявлено, що кількість респондентів, залучених до занять спортом, становить близько 90 % загальної кількості тих, хто брав участь у дослідженні. Пріоритетними видами спорту студентської молоді КНР виступають бадмінтон, пінг-понг, заняття фітнесом, плавання та легка атлетика. Внутрішнім фактором, який лімітує формування мотивації до занять фізичною культурою і спортом, як у юнаків, так і у дівчат визначено відсутність бажання займатися спортом. Підтверджено вплив факторів зовнішнього середовища на формування мотивів, інтересів до занять фізичною культурою і спортом. Виявлено, що підвищення інтересу та формування мотивації до рухової активності студентів КНР можливе за умови підвищення теоретичних знань студентів та їх освіченості у практичних питаннях застосування різних засобів і методів підтримання здоров’я, досягнення розуміння, що фізична культура є складовою частиною загальної культури сучасного фахівця.

Ключові слова: студентська молодь, юнаки, дівчата, мотиви, інтерес, фізична культура і спорт.
Фізична активність учнів середньої школи Китайської Народної Республіки
Authors: Трачук Сергій , Ген Янь
Published: May 2022

Анотація. Регулярні заняття фізичною активністю помірної та високої інтенсивності, обмеження малорухливої поведінки вважаються незалежними факторами ризику для здоров’я дітей та молоді у всьому світі. Оцінка фізичної активності та малорухливої поведінки має особливе значення для зміцнення здоров’я китайських дітей та підлітків. Проведені попередні розвідки з цього питання не дозволяють повною мірою визначити огляд через використання різних інструментаріїв оцінювання й інтерпретації їхніх результатів. Використання міжнародного опитувальника з фізичної активності, який є загальноприйнятим і використовується у практиці багатьох країн, дозволить отримати валідні результати і провести їх порівняння. Мета. Дослідити рівень фізичної активності учнів 12–15 років середньої школи КНР. Методи. Теоретичний аналіз і узагальнення спеціальної літератури, контент аналіз; компаративний метод зіставлення та аналізу зарубіжного досвіду; логіко-теоретичний аналіз; міжнародний опитувальник фізичної активності (IPAQ), методи математичної статистики. Результати. У дослідженні представлено результати опитування 200 китайських школярів 12–15 років провінції Шаньдун. Оцінювання фізичної активності здійснювали за допомогою міжнародного опитувальника International Questionnaire on Physical Activity. Встановлено, що у переважній більшості китайські школярі 12–15 років характеризуються помірним рівнем фізичної активності, проте дедалі більше вони схиляються до малорухливого способу життя. Найменшу енергетичну вартість забезпечували фізичні навантаження високої активності через їх епізодичність, а найбільшу щотижневу метаболічну активність у них діагностовано під час ходьби і фізичної активності помірної інтенсивності. Відмічено і гендерні відмінності, так, тривалість фізичної активності високої інтенсивності у хлопців була статистично вища ніж у дівчат на 15 %. Порівнюючи отримані дані з результатами, представленими в літературних джерелах, у китайських школярів 12–15 років констатовано зниження щотижневої метаболічної активності, особливо за рахунок фізичної активності високої інтенсивності і збільшення часу на перебування в сидячому положенні. У період дистанційного навчання, обумовленого карантинними заходами COVID-19, китайські школярі були схильні до малорухливого способу життя, що відображається на невисокому відсотку тих, хто має високий рівень фізичної активності. Гіпотеза, що це пов’язано з існуючою ситуацією і обмеженнями COVID-19, потребує подальшого вивчення та проведення повторних досліджень з оцінювання фізичної активності китайських школярів.

Ключові слова: фізична підготовленість, фізична активність, фізичне виховання, китайські школярі.