bluuebunny/crossref_metadata_2025 huggingface dataset:
Метод проектів як засіб підвищення професіоналізму у підготовці фахівців зі спортивно-оздоровчого туризму
Authors: Петрова Альона Анатоліївна
Published: May 2022

Проаналізовано доцільність використання проектного методу під час професійної підготовки фахівців туристичної галузі. Акцентовано увагу на позитивних аспектах застосування методу. Виокремлено перелік компетенцій які формуються під час впровадження проектної діяльності у навчально-виховний процес вищого навчального закладу.
Analytical review of augmented reality MOOCs
Authors: Panchenko Liubov F. , Muzyka Ivan O.
Published: May 2022

The aim of the article is to provide an analytical review of the content of massive open online courses about augmented reality and its use in education with the further intent to create a special course for the professional development system for the research and teaching personnel in postgraduate education. The object of research is massive open online courses. The subject of the study is the structure and content of augmented reality MOOCs which are offered by acclaimed providers of the world. The methods of research are: the analysis of publications on the problem; the analysis of MOOCs’ content, including observation; systematization and generalization of research information in order to design a special course about augmented reality for the system of professional training and retraining for educators in postgraduate education. The results of the research are the following: the content and program of specialized course “Augmented Reality as a Storytelling Tool” for the professional development of teachers. The purpose of the specialized course is to consider and discuss the possibilities of augmented reality as a new direction in the development of educational resources, to identify its benefits and constraints, as well as its components and the most appropriate tools for educators, to discuss the problems of teacher and student co-creation on the basis of the use of augmented reality, and to provide students with personal experience in designing their own stories and methodical tools in the form of augmented books and supplementary training aids with the help of modern digital services.
Педагогічні читання з нагоди 90-річчя Криворізького державного педагогічного університету та вшанування пам’яті професорів П. І. Шевченка та В. С. Пікельної
Authors: Кафедра педагогіки та методики ТО
Published: May 2022

У збірнику представлені матеріали Міжнародної науково-практичної Інтернет-конференції «Педагогічні читання з нагоди 90-річчя Криворізького державного педагогічного університету та вшанування пам’яті професорів П.І. Шевченка та В.С. Пікельної». Розглядаються соціальні та психолого-педагогічні проблеми професійної підготовки молоді, управління та моніторинг у професійній освіті, сучасні інноваційні технології в системі підготовки майбутніх фахівців професійної освіти, вимоги Нової Української школи до якісної підготовки майбутнього вчителя трудового навчання та технології, застосування інформаційно-комунікативних технологій у підготовці фахівців з готельно-ресторанної справи. Збірник призначений для науковців, викладачів, аспірантів, здобувачів вищої освіти, вчителів, вихователів дошкільних закладів а також для широкого кола читачів. За достовірність матеріалів та їхню інтерпретацію несуть відповідальність автори та наукові керівники.
Флористична структура рослинного покриву ландшафтно-техногенних систем Криворізького гірничо-металургійного комбінату та космічного виробництва
Authors: Прилипко Вікторія Вікторівна , Перерва Вікторія Вікторівна
Published: May 2022

Досліджувалась таксономічна, екологічна, біоморфічна, еколого-ценотична та географічна структури рослинних угруповань ландшафтно-техногенних систем на прикладі Криворізького державного гірничо-металургійного комбінату та коксохімічного виробництва.
Флористична структура рослинного покриву проммайданчику Інгулецького гірничо-збагачувального комбінату
Authors: Прилипко Вікторія Вікторівна , Перерва Вікторія Вікторівна
Published: May 2022

Ecological, biomorfical, ecology-coenotic and geographical structure of the plant groups of the industrial areas were studied taking the example of Inguletskiy OreDressing Combine. It was found out that changes of participation of ecological groups in relation to environment of life are caused by features edaphical conditions. Infringements of a vegetation and soil cause formation of groups with specific specter of biomorfs. The basic role in a vegetation of the industrial areas has of species of the ruderal coenoelement of the synantropic floroсоеnotyp.
Таксономічна структура та ценотичні зв’язки рослинних угруповань техногенно змінених територій Криворіжжя
Authors: Прилипко Вікторія
Published: May 2022

У рамках дослідження було виявлено структурну організованість рослинних угруповань проммайданчиків основних підприємств Криворіжжя. Встановлено, що еколого-ценотична структура рослинних угруповань характеризується значною участю рудерального флороценотипу, який є закономірним відображенням порушених земель. За коефіцієнтом Жакара щодо флористичної подібності промислові підприємства становлять плеяду з помірним рівнем зв’язку, що свідчить про подібність їхньої флористичної організації.
Макроморфологічні особливості грунтів Північного гірничо-збагачувального комбінату
Authors: Нестор Олена Олександрівна , Сметана Олексій Миколайович , Прилипко Вікторія Вікторівна , Перерва Вікторія Вікторівна
Published: May 2022

Досліджувались макроморфологічні особливості грунтів Північного ГЗК Кривого Рогу. У складі грунтового покриву ПівнГЗК відмічені техногенні примітивні карбонатні грунти: несформовані, фрагментарні, багатошарові, гідромофні, з похованими чорноземами звичайними зі вторинним осолонцюваням та субстрати з ознаками грунтоутворення.
Педагогічна культура як важливий чинник професійної компетентності майбутнього вчителя технології
Authors: Пікельна Валерія Семенівна
Published: May 2022

У статті розглянуто проблеми педагогічної культури як важливого чинника професійної компетентності майбутнього вчителя технології. Педагогічна культура, яка притаманна педагогу, дозволяє йому орієнтуватися у різних педагогічних ситуаціях, оцінювати їх, вибирати певні способи спілкування з окремими учнями або з цілим колективом групи.
Про сучасні технології підготовки майбутніх педагогів
Authors: Пікельна Валерія Семенівна
Published: May 2022

Визначено зміст сучасних технологій підготовки майбутніх педагогів.
Мріяти й творити. Організація науково-дослідної роботи в сучасному ліцеї
Authors: Пікельна Валерія Семенівна , Сологуб Анатолій Іванович
Published: May 2022

Висвітлено особливості організації науково-дослідної роботи в сучасному ліцеї.
"Тіло - дім для душі". "Екологія здоров'я" як наукове поняття та складова екологічної культури людини
Authors: Пікельна Валерія Семенівна
Published: May 2022

Розглянуто особливості екології здоров'я як наукового поняття та складової екологічної культури людини
Тайним знанням навчаймося
Authors: Шевченко Павло Іванович , Пікельна Валерія Семенівна
Published: May 2022

У статті розглядаються проблеми народної педагогіки, відродження національної культури.
Педагогічні ідеї Г. С. Сковороди
Authors: Шевченко Павло Іванович
Published: May 2022

Висвітлено педагогічні ідеї Г. С. Сковороди.
Indoor Environmental Sensing Techniques for Occupant Health and Comfort
Authors: Gao Xinghua , More Sidhesh , Agee Philip , Alimoradi Saeid , Chen Jianli , Tang Shu
Published: May 2022

Sensing technologies are essential parts of the smart building paradigm. In recent years, an increasing number of research studies are focusing on using sensing technologies to understand the influences of indoor environmental conditions on the occupant’s health and comfort. Such studies provide a critical perspective on improved human-building interactions and optimized building operations. This research aims to provide a systematic literature review of various sensing techniques and their applications in improving occupant well-being. The authors consulted the guideline put forward by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), ASHRAE Guideline 10-2016, as a framework to categorize different research studies. This research summarizes and discusses both academic and applied studies pertaining to indoor air quality, acoustic comfort, thermal conditions, illumination, odor, and vibrational disturbances. The review results show that, in the built environment, using sensing technologies to mitigate factors disturbing occupant well-being is critical but relevant research is still in its early stage, and most of the current research has focused on indoor air quality and thermal condition. This chapter has identified four main research gaps, 1) cost-effectiveness, 2) sensor range and positioning, 3) data interface and privacy, and 4) occupant expectations and subjective interactions, and provided recommendations for future research.
Optimization of Household Ventilation with Improved Cookstove: An Amicable Approach to Strengthen Indoor Air Quality and Public Health
Authors: Chakraborty Deep , Mukhopadhyay Krishnendu , Mitra Pradip , Mondal Naba Kumar
Published: May 2022

Several research attempts were made but the World Health Organization’s (WHO) intermediate indoor environmental quality guidelines were not achieved targets by replacing traditional biomass chulhas with cleaner cookstoves in many parts of the world. Millions of deaths are still occurring due to uncomfortable indoor air, which is well recognized as one of the foremost causes of death reported in the recent estimates of the global burden of diseases. However, health benefits can be expected through the extensive use of improved cookstoves (ICSs) in households with the support of better ambient air quality. Optimization of indoor ventilation status may come out as a significant addition to ICSs in mitigating indoor air pollution exposure for better public health. It is worthwhile to note that the WHO recommends the reduction of Particulate Matter (PM2.5) and Carbon Monoxide (CO) exposure in the perspective of good public health. Uses of solid biomass cooking fuels have been steadily decreasing in developing countries for the last decade. In spite of that, almost 50 percent of the world population is still being exposed to the indoor air pollution produced by solid fuel burning. The main purpose of this paper is to review the existing scientific evidences concerning improved cookstoves with household ventilation and health improvement. Keeping the above facts in mind, current indoor air quality research should focus on stringent and periodic exposure assessment with modified ventilation and ICSs to improve public health and well-being.
IAQ Assessment for Smart Environments: Conclusion and Future Scope
Authors: Saini Jagriti , Dutta Maitreyee , Marques Gonçalo , Halgamuge Malka N.
Published: May 2022

Indoor air quality (IAQ) has been a critical matter of concern for public health, environmental, and government agencies over the years due to its negative influence on building occupants. The outcomes of degraded air quality levels have been a challenge not just for developing countries; it puts the potential burden of disease on developed nations. Healthcare cost in the United States is reported to be $150 billion because of air pollution-related consequences. Furthermore, increased air pollutants exposure causes 412,000 premature deaths in Europe each year. Therefore, the research communities are expected to work actively to find potential solutions to IAQ assessment and management. Fortunately, the latest technologies such as the Internet of Things and Artificial Intelligence show great potential in developing smart environments that permit real-time assessment of IAQ levels from building premises. Such systems can be useful to create healthy living conditions in residential and commercial buildings while promising enhanced outcomes for public health management. This chapter provides conclusive thoughts on how IAQ assessment plays an essential role in building smart environments while throwing light on the effective utilization of emerging technologies to create healthy living environments. Along with the summary of potential contributions made by various field experts to address challenges in this problem domain, this chapter also provides insights into future scopes in IAQ assessment for smart environments.
Critical Infrastructure: Design and Behavior of Materials Under Impact and Explosion Loads
Authors: Brara Ahmed
Published: May 2022

Critical infrastructure covering different interdependent sectors (energy, transportation, health, telecommunications, etc.) has the important role of ensuring the daily vital logistics for a comfortable and safe life of a country’s citizens. In recent times, this infrastructure is increasingly subject to various natural and man-made risks. One of the most widespread anthropogenic hazards is the phenomenon of explosions, either due to technological accidents or terrorist attacks, which effects are the most destructive to the infrastructure. Given the extreme importance of its role in the normal functioning of a country, the protection of critical infrastructure is a strategic issue. In order to minimize the vulnerability of infrastructure to these risks, one of the important measures is to take into account its security beginning at the design phase. Indeed, it has been proven that taking this measure into account ab-initio often allows improving the security of the buildings or facilities that make up this infrastructure at a lower cost. However, the optimal design of structures likely to be subjected to blast loads requires an understanding of their responses as well as the dynamic behavior of their constituent materials. This article is a state-of-the-art report on the responses of structures and their constituent materials to blast loads. Experimental techniques for obtaining very high strain rates, of the order of those generated by blast loads, are briefly described. The results of recent tests obtained with these techniques on ordinary and special concretes as well as on reinforced concrete structural elements are presented. Recent constitutive models of these materials and structures subjected to blast loads as well as numerical methods simulating the tests conducted are also reviewed.
Round-Robin Exercise for Compression Testing of Steel Alloy of Pressure Tank at High Strain Rate
Authors: Kruszka Leopold , Sobczyk Kamil
Published: May 2022

The aim of the paper is to show a type of the round-Robin test on the example of experiments at a high strain rate using various split Hopkinson pressure bar test stands located at different locations in Warsaw – a scientific-technical center and a university. The results of the conducted experiments in the form of a circumferential stress and radial stress were shown for each analyzed SHPB test stand, using an identical research specimen. A comparison was made, and the obtained results were compared on the graphs of the stress-strain curve for various SHPB test stands and on the strain rate – strain graphs for the selected SHPB test stand.
Advanced Multi-Layered Protective Systems for Defeating High Energy Projectiles
Authors: Tria Djalel Eddine
Published: May 2022

This study aims to investigate experimentally and numerically the ballistic trauma absorption of different armor ceramics with aramid composite reinforcement. The studied ceramic materials include homogeneous alumina oxide, with different Al2O3 contents, alumina-mullite and reaction-bonded boron carbide (RBBC) ceramics where their composition, structure, and main mechanical properties are examined and analyzed. Likewise, the penetration behavior of a 7.62x39 mm Mild Steel Cored (MSC) bullet from an AK round and 7.62x51 Full Metal Jacket (FMJ) bullets was studied, since the selected armors are often subjected to such particular threats in personal body armor systems. Three dimensional FE models of the bullets and armor plates were developed to study the penetration behavior of the bullets, the fracture pattern of ceramics and to predict the Back Face Deformation (BFD) of the considered armors. It was found that the studied armors have been proved to have very acceptable protection levels due to the measured ballistic trauma, which was in accordance with standard NIJ-0101.06. Also, despite the total erosion of the 7.62x51 mm FMJ bullet during the penetration process, it has the highest recorded ballistic trauma. Blunting and mushrooming of the 7.62x39 mm MSC bullets leads to decreasing of its intrusion in the armor plates. The combination of the constitutive relations and the FE algorithms has successfully matched details of impact tests for the bullets and the armor plates that lead to the explanation of the dispersion of the BFD measures for each impact situation. The propagation of radial and circumferential cracks, fracture conoid formation has been well predicted for alumina ceramics. The highest BFD values were shown in the RBBC ceramics with the absence of residual fracture conoid after the impact.
Convolutional Autoencoder for Image Denoising
Authors: Ghafar Abdul , Sattar Usman
Published: May 2022

Image denoising is a process used to remove noise from the image to create a sharp and clear image. It is mainly used in medical imaging, where due to the malfunctioning of machines or due to the precautions taken to protect patients from radiation, medical imaging machines create a lot of noise in the final image. Several techniques can be used in order to avoid such distortions in the image before their final printing. Autoencoders are the most notable software used to denoise images before their final printing. These software are not intelligent so the resultant image is not of good quality. In this paper, we introduced a modified autoencoder having a deep convolutional neural network. It creates better quality images as compared to traditional autoencoders. After training with a test dataset on the tensor board, the modified autoencoder is tested on a different dataset having various shapes. The results were satisfactory but not desirable due to several reasons. Nevertheless,  our proposed system still performed better than traditional autoencoders.

KEYWORDS: image denoising, deep learning, convolutional neural network, image autoencoder, image convolutional autoencoder
Cloud Forensics: Challenges and Solutions (Blockchain Based Solutions)
Authors: Kaleem Hassan , Ijaz Ahmed
Published: May 2022

Cloud computing is an on-demand service provided for computer resources, data storage and enhancing computing power. Digital forensics is used to help forensic investigators extract evidence against cloud/ cybercriminals and maintain the integrity and security of data stored in a cloud environment. Based on the prior research in this area concerning existing challenges and solutions, this survey focuses on exploring the problems and their proposed solutions on the basis of a detailed literature survey. It critically explores and reviews the prevailing challenges and solutions through an in-depth review of the cloud forensic area. The paper highlights all the current problems in cloud forensics and their solutions previously identified by the researchers to help investigators probe any criminal incident. The three categorization model explores the challenges and solutions of the existing methods and offers directions for future research in this area. Finally, this survey paper can be considered an initiative to carry out research and develop cloud forensic-able services for the cloud environment.

INDEX TERMS: blockchain based solution, cloud forensics, cloud forensics challenges, cloud forensics methodologies, cloud forensics solutions
Morphohistochemical assessment of the effect of a feed mineral supplement on the digestive organs during experimental immunosuppression in animals
Authors: Drozd M. , Usevich Vera
Published: May 2022

Abstract. The conditions of keeping animals and birds are increasingly provoking stressful situations for them. The occurrence of stress helps to reduce the body's resistance and creates conditions for the development of diseases of various etiologies. With a decrease in resistance, the quantity of products and their quality decreases. It is possible to prevent stress in animals and poultry by using adaptogens of various origins. The search for optimal means that meet all the requirements of prevention remains to this day an urgent problem of practical veterinary medicine. The purpose of this study was to give a morphohistochemical assessment of the effect of a feed mineral supplement on the digestive organs during experimental immunosuppression in laboratory animals. In the conditions of experimental immunosuppression, to identify the effect of mineral feed additives on the structure of organ cells and their metabolism. Methods. To confirm the effectiveness of the mineral adaptogen, histological, morphometric and histochemical studies of the digestive organs were performed. As a result of the conducted research on preventive feeding of a feed mineral supplement of domestic production, its immune- and organoprotective role was revealed. The most sensitive organs to the action of adverse factors (lymphoid formations in the intestinal wall) have been identified. Morphometrically and histochemically determined the reliability of morphological changes in them. During prophylactic feeding of a feed mineral supplement of domestic production in the digestive organs, the revealed changes did not have total destruction, approaching the structure and metabolic processes in cells to the structure of these organs in intact animals. Scientific novelty. For the first time, a comparative microstructural, histochemical and morphometric assessment of the digestive organs in the body of laboratory animals under conditions of artificially induced immunosuppression was carried out, confirming the immune- and organoprotective effect of a feed mineral supplement of domestic production, which can be considered as a mineral adaptogen.
Methodological approaches and economic justification of licensing fees for breeding achievements in the flax subcomplex
Authors: Velikanova Irina , Rozhmina Tat'yana , Popov Roman
Published: May 2022

Abstract. The paper presents the results of scientific research in the field of flax growing, based on the study of methods and methods for calculating the value of the royalty rate for a selection achievement on the example of financial and economic relations between the Federal State Budgetary Institution of the LC and economic entities, the factors that can influence the amount of royalties in the flax subcomplex are given. Methodological approaches for calculating the projected amount of royalties at the conclusion of a non-exclusive license agreement with potential licensees are presented. The procedure for calculating the amount of royalties for inventions is set out in a number of regulatory legal acts that have been in force since the USSR. However, there is currently no methodology for calculating rewards for breeding achievements in the field of flax growing. The relevance of the study is to develop a methodology for calculating the amount of mandatory payments for the use of a selection achievement in a linen subcomplex. This technique will speed up the procedure for assessing the cost of breeding achievements – domestic varieties of flax. The application of the methodology will ensure an increase in the efficiency of the management of breeding achievements as a material asset that brings income to a scientific institution. The purpose of this study is to develop a methodology for calculating rewards for the use of a selection achievement as an object of intellectual property, using the example of flax varieties created in the FSBI FNC LC, taking into account the biological characteristics of the culture and the specifics of production in the flax subcomplex. Methods. In the course of the study, methods of comparative, abstract-logical, functional-cost analysis, expert evaluation were used. The result of practical application is the new methodological approaches developed by the authors to the calculation of the license fee for the use of breeding achievements – flax varieties, taking into account factors affecting the amount of royalties. The scientific novelty. It consists in the economic justification of the methodology for calculating license fees for the use of domestic varieties of flax in relation to various subjects of the Russian Federation and the areas of application of flax raw materials (textiles, medicine, etc.), taking into account the biological characteristics of the culture and the specifics of its production.
Automated Assessment of Student Self-explanation During Source Code Comprehension
Authors: Chapagain Jeevan , Tamang Lasang , Banjade Rabin , Oli Priti , Rus Vasile
Published: May 2022

This paper presents a novel method to automatically assess self-explanations generated by students during code comprehension activities. The self-explanations are produced in the context of an online learning environment that asks students to freely explain Java code examples line-by-line. We explored a number of models consisting of textual features in conjunction with machine learning algorithms such as Support Vector Regression (SVR), Decision Trees (DT), and Random Forests (RF). Support Vector Regression (SVR) performed best having a correlation score with human judgments of 0.7088. The best model used a combination of features such as semantic measures obtained using a Sentence BERT pre-trained model and from previously developed semantic algorithms used in a state-of-the-art intelligent tutoring system.
Feature Integration and Feature Augmentation for Predicting GPCR-Drug Interaction
Authors: Bichindaritz Isabelle , Liu Guanghui
Published: May 2022

Accurately predicting the interaction between G-protein-coupled receptors (GPCR) and drugs is of great significance for understanding protein functions and drug discovery and has become a hot spot in current research. To improve the accuracy of GPCR-drug interaction prediction, this paper proposes a new GPCR-Drug interaction prediction method based on multi-feature integration and feature augmentation from deep random forest: First, the sequence features of GPCR from amino acid composition and protein evolution are extracted respectively, and the characteristics of the drug molecule from the molecular fingerprint perspective are formulated; then, the extracted multiple features are combined to obtain the feature representation of the GPCR-Drug pair; finally, based on the proposed GPCR-Drug feature representation method, we use deep random forest to generate augmented features and construct cascaded predictions model. The cross-validation and independent test results on the standard data set verify the effectiveness and greater explainability of the proposed method.
Preliminary Thoughts on Defining f(x) for Ethical Machines
Authors: Peterson Clayton , Hamrouni Naïma
Published: May 2022

There is a growing literature in machine ethics attempting at creating ethical machines through AI and machine learning. Although many concerns with respect to such attempts have been raised, including the difficulties regarding the gathering of relevant contextual information as well as solving ethical dilemmas, it appears that many fundamental ethical notions have been overlooked in the implementation of normative theories to machines. This paper provides a preliminary analysis of important aspects that need to be taken into account in the attempt of defining so called ethical machines.
Best Response Computation in Multiplayer Imperfect-Information Stochastic Games
Authors: Ganzfried Sam
Published: May 2022

Computing a best response is a fundamental task in game theory. One of its uses is to compute the degree of approximation error of an approximation of Nash equilibrium strategies. In many game classes best responses can be computed in polynomial time, but in imperfect-information stochastic games it is equivalent to solving a POMDP and is PSPACE-complete. Prior work has developed an algorithm for computing an approximation of Nash equilibrium strategies in a 4-player imperfect-information naval strategic planning problem which is modeled as a stochastic game. A heuristic approach was developed for computing best responses to determine the approximation error of these strategies, which was shown to have limited scalability. In this paper we present approaches that utilize parallelism to significantly speed up this computation allowing us to compute best responses in significantly larger games.
Study on Title Encoding Methods for e-Commerce Downstream Tasks
Authors: Cardellino Cristian , Carrascosa Rafael
Published: May 2022

In an e-Commerce marketplace there are usually many downstream tasks which have (relatively) less available resources than the few mainstream priority tasks, like recommendation or search. Examples of these tasks are product categorization, counterfeit detection, forbidden products detection, package size estimation, etc. Usually in these tasks the product titles are an appealing feature since they integrate key aspects of the product, are cheaply available and are easy to process. In this setting it makes sense to invest in a few high quality models that extract as much information as possible from the title and are sharedamong many downstream tasks. The present work explores the performance of different models to address different downstream tasks that are present in our marketplace. We also propose an adaptation of a deep network architecture from the Computer Vision field: ``Bootstrap Your Own Latent'' (BYOL), to learn product embeddings based on the title and compare it to several industrial baselines as well as some state-of-the-art supervised models. We found that although in some cases neural network based encoders can be very useful, in many scenarios the baselines given by shallower models are still hard to beat.
Automatic verification of multi-agent systems security properties specified with LTL
Authors: Alghamdi Kholud , Silaghi Marius
Published: May 2022

We propose a way to verify security requirements of critical multi-agent system processes by using logic representationsand automatic reasoning. The typical multi-agent system considered in our work would be an election system with agentsrepresenting their users and aiming to ensure security. Relevant processes are authentication, voting, re-voting, and election verification. The security requirements commonly addressed in such a voting system are: no user can vote unless it got authenticated, no invalid vote should be counted, no vote should be counted twice, and each valid vote should be eventually counted. We show a model of such security requirements by using system liveness properties, and exemplify their verification on a real system that we implement for this purpose.
COM-MABs: From Users' Feedback to Recommendation
Authors: Letard Alexandre , Amghar Tassadit , Camp Olivier , Gutowski Nicolas
Published: May 2022

Recently, the COMbinatorial Multi-Armed Bandits (COM-MAB) problem has arisen as an active research field. In systems interacting with humans, those reinforcement learning approaches use a feedback strategy as their reward function. On the study of those strategies, this paper present three contributions: 1) We model a feedback strategy as a three-step process, where each step influences the performances of an agent ; 2) Based on this model, we propose a novel Reward Computing process, BUSBC, which significantly increases the global accuracy reached by optimistic COM-MAB algorithms -- up to 16.2\% -- ; 3) We conduct an empirical analysis of our approach and several feedback strategies from the literature on three real-world application datasets, confirming our propositions.
Rethinking Inventory Forecasting Problems in E-commerce: Exploring the effect of integrating forecasting and inventory decisions
Authors: Vazquez Hernan Ceferino , Dal Lago Virginia
Published: May 2022

Forecasting for inventory control is the process of calculating the inventory needs to fulfill future consumer demand. In general, this process is divided into two sub-processes. The first sub-process receives the current inventory information and forecasts future information, e.g. forecasts future demand from the demand information in the past. The second sub-process uses the forecast information as input to make inventory decisions, e.g. use a product demand forecast to decide how many units of this product to buy. Recent works highlight the importance of integrating forecasting with final inventory decisions, however, there is very little empirical evidence to support that integrating the decision is the best solution. In this work, we propose to explore the effect of integrating the inventory decision into the forecasting problem and compare it with the state-of-the-art approaches. For this, we evaluated the approaches in different operational tasks belonging to our business. Our preliminary findings show that predicting operative decisions instead of demand information could be better and the benefit can be capitalized even in low data scenarios.
Machine learning approach for studying the neural underpinnings of dyslexia on a phonological awareness task
Authors: Christoforou Christoforos , Papadopoulos Timothy C. , Theodorou Maria
Published: May 2022

Understanding the neural underpinnings of dyslexia is an open and fundamental question in developmental neuroscience. A widely agreed causal risk factor for dyslexia is phonological deficit (PD). However, the causal relationships between PD and dyslexia have been primarily investigated and theorized based on findings derived from behavioral measures. What is missing is evidence of the underlying neurophysiological origins of these relationships. The present study examined whether the performance on a phonological awareness task, namely phoneme elision (PE), differentiated children with dyslexia from their typically developing counterparts at a neural level. We proposed a novel machine-learning-based approach to extract neural activity from EEG to identify neural differences at the group level. Specifically, we formulated an optimization problem to first extract informative EEG components (termed phoneme-related neural-congruency components) by maximizing the congruency in neural activity among typically developing children during phoneme elision. Next, we utilized a machine-learning algorithm to optimally combine the resulting components to differentiate between children with dyslexia and controls. Results showed that the proposed phoneme-related neural-congruency components are predictive about the underlying neuronal differences amongst groups. These results provide empirical evidence towards the neural underpinnings of dyslexia and the potential neural origins of PD as a causal link to dyslexia. Notably, the proposed method could be used to study other behaviorally defined developmental disorders.
Brazilian Portuguese Hate Speech Classification using BERTimbau
Authors: Silva Félix , Freitas Larissa
Published: May 2022

Hate speech is a language that attacks or denigrates a specific group based on their characteristics, such as their race, ethnicity, or sexual orientation. Hate speech became widespread and spread through social networks, blogs, videos, and other communication channels. With anonymity and a sense of impunity, people feel encouraged to spread their hatred on the internet. In this work, we used the BERT model for the Portuguese language called BERTimbau to classify hate speech in three datasets in Portuguese, available in the literature: OFFCOMBR-2, OFFCOMBR-3, and Fortuna et. al. (2019) dataset. Still, we performed some preprocessing and an oversampling technique on the datasets. Finally, we compared the results obtained with results obtained by works available in the literature. Experiments with BERTimbau, using preprocessing and oversampling obtained better results than other classification techniques.
Identifying Relation-gaps in Ontologies using TOPSIS
Authors: S Subhashree , P Sreenivasa Kumar
Published: May 2022

Though many ontologies have a huge number of classes, one cannot find a good number of object properties connecting the classes in most of the cases. Adding object properties makes an ontology richer and more applicable for tasks such as Question Answering. In this context, determining the pair of classes that are most likely to have object properties between them becomes very important. We address the above question in this paper. We propose a simple yet powerful framework based on the popular multi-criteria decision making algorithm TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), for identifying relation-gaps. In order to identify such relation-gaps (i.e class-pairs) the proposed system utilizes a mix of criteria derived from the ontology itself and from external sources such as Wikipedia text corpora. Our experimental results show that by means of these carefully chosen criteria and their corresponding weights, the proposed system yields promising results with respect to the precision of the relation-gaps identified.
Automated Computer Network Exploitation with Bayesian Decision Networks
Authors: Roberts Graeme , Peterson Gilbert
Published: May 2022

Penetration Testing (pentesting) is the process of using tactics and techniques to penetrate computer systems and networks to expose any issues in their cybersecurity \cite{rsa}. It is currently a manual process requiring significant experience and time that are in limited supply. One way to supplement the shortage is through automation. This paper presents the Automated Network Discovery and Exploitation System (ANDES) which demonstrates that it is feasible to automate the pentesting process. The uniqueness of ANDES is the use of Bayesian decision networks to represent the pentesting domain and subject matter expert knowledge. ANDES conducts multiple execution cycles, which build upon previous action results. This process simulates the iterative thinking process of human attackers. Cycles begin by modeling the current belief state using Bayesian decision networks. ANDES uses these networks to select and execute an expected best action. Observed results are used to update the systems current belief state before the next cycle begins. ANDES was tested in a live-execution event, taking place within a virtual network environment designed to mimic a small business’s internal network. ANDES successfully performed a series of information gathering and remote exploit actions, across multiple network hosts, to gain access to the objective target.
Toward Multi-Agent Algorithmic Recourse
Authors: O'Brien Andrew , Kim Edward
Published: May 2022

The recent adoption of machine learning as a tool in real-world decision making has spurred interest in potential harm caused by these methods. Providing those negatively impacted by decisions made by   machine learning models with ways to change those decisions is now an important area of machine learning known as algorithmic recourse. Past work has largely focused on the effect algorithmic recourse has on a single agent. In this work, we relax this assumption and examine algorithmic recourse from the multi-agent perspective. We use ideas from game theory to explore challenges from the multi-agent perspective that are unaddressed in the current literature and to propose new criteria to guide future algorithmic recourse research.
Examining Stereotypes in News Articles
Authors: Zhang Damin , Rayz Julia
Published: May 2022

Gender biases or stereotypes have been studied in short text and manually labeled corpora, but little work has been done in real-world unlabeled text corpora like news articles. This work investigated news articles from mainstream U.S. media outlets ranging from 2013 to early 2020. We used structural topic modeling to estimate gender prevalent topics, compared the results with topic modeling embedding, and incorporated qualitative and quantitative analyses to understand the appearance of gender stereotypes in news articles from each gender group. The structural topic modeling results showed that gender prevalent topics align with stereotypical representations of either gender group and media outlets with imbalanced gender distribution are more influential on stereotypical representations. The topic modeling embedding results support prior results and provide additional information supporting the conclusion.
Learning to Rank with BERT for Argument Quality Evaluation
Authors: Favreau Charles-Olivier , Zouaq Amal , Bhatnagar Sameer
Published: May 2022

The task of argument quality ranking, which identifies the quality of free text arguments, remains, to this day, a challenge. While most state-of-the-art initiatives use point-wise ranking methods and predict an absolute quality score for each argument, we instead focus on learning how to order them by their relative convincingness, experimenting with several learning-to-rank methods for argument quality. We leverage BERT's powerful ability in building a representation of an argument, paired with learning-to-rank approaches (point-wise, pairwise, list-wise) to rank arguments according to their measure of convincingness. We also demonstrate how an ensemble of models trained with different ranking losses often improves the performance at identifying the most convincing arguments of a list. Finally, we compare BERT coupled with learning-to-rank methods to state-of-the-art approaches on all major argument quality datasets available for the ranking task, demonstrating how a learning-to-rank approach generally performs better at outlining the topmost convincing arguments.
Adversarial Learning to Reason in an Arbitrary Logic
Authors: Purgał Stanisław , Kaliszyk Cezary
Published: May 2022

Existing approaches to learning to prove theorems focus on particular logics and datasets. In this work, we propose Monte-Carlo simulations guided by reinforcement learning that can work in an arbitrarily specified logic, without any human knowledge or set of problems. Since the algorithm does not need any training dataset, it is able to learn to work with any logical foundation, even when there is no body of proofs or even conjectures available. We practically demonstrate the feasibility of the approach in multiple logical systems. The approach is stronger than training on randomly generated data but weaker than the approaches trained on tailored axiom and conjecture sets. It however allows us to apply machine learning to automated theorem proving for many logics, where no such attempts have been tried to date, such as intuitionistic logic or linear logic.
Drug Repurposing for Rare Orphan Diseases Using Machine Learning Techniques
Authors: Manicavasaga Rajesh , Lamichhane Prabin B , Kandel Prajjwal , Talbert Douglas A.
Published: May 2022

New drug discovery is a time-consuming and costly process. Several drugs have been in clinical trials for a very long period. Finding a new target for existing medications can be an effective strategy to reduce the lengthy and costly drug development cycle. Drug repurposing (or repositioning) is a cost-effective approach or finding drugs that can treat diseases for which thosemedications are not currently prescribed. Drug repurposing to treat both common and rare diseases is becoming an attractive option because it involves using already approved drugs. Through drug repurposing, we can identify promising drugs for further clinical investigations. This paper presents machine learning techniques for drug repurposing to find existing drugs as an alternate medication for other diseases through drug-drug, drug-genes, drug-enzymes, and drug-targets interactions. We develop a model to find similar drugs that can treat similar diseases. We then use the model to predict potential candidate drugs for rare orphan diseases.
Protein-Protein Interaction Extraction using Attention-based Tree-Structured Neural Network Models
Authors: Singha Roy Sudipta , Mercer Robert E.
Published: May 2022

In order to comprehend underlying biological processes, it is necessary to identify interactions between proteins. It is typically quite difficult to extract a protein-protein interaction (PPI) from text data as text data is complex in nature. Unlike sequential models, tree-structured neural network models have the ability to consider syntactic and semantic dependencies between different portions of the text and can provide structural information at the phrase level. This paper investigates tree-structured neural network models for the PPI task and the results show their supremacy over sequential models and their effectiveness for this task.
Normal Forms of Conditional Belief Bases Respecting Inductive Inference
Authors: Beierle Christoph , Haldimann Jonas
Published: May 2022

Normal forms of syntactic entities play an important role in many different areas in computer science. In this paper, weaddress the question of how to obtain normal forms and minimal normal forms of conditional belief bases in order to,e.g., ease reasoning with them or to simplify their comparison. We introduce notions of equivalence of belief basestaking nonmonotonic inductive inference operators into account. Furthermore, we also consider renamings of beliefbases induced by renamings of the underlying signatures. We show how renamings constitute another dimension of normalforms. Based on these different dimensions, we introduce and illustrate various useful normal forms and show their properties, advantages, and interrelationships.
Regularizing Data for Improving Execution Time of NLP Model
Authors: Dang Thang , Sakai Yasufumi , Tabaru Tsuguchika , Kasagi Akihiko
Published: May 2022

Natural language processing (NLP) is a very important part of machine learning that can be applied to different real applications. Several NLP models with huge training datasets are proposed. The primary purpose of these large-scale NLP models is the downstream tasks. However, because of the diversity and rapidly increasing the size of these datasets, they consume a lot of resources and time. In this study, we propose a state-of-the-art method to reduce the training time of NLP models on downstream tasks while maintaining accuracy. Our method focuses on removing unimportant data from the input data set and optimizing the padding of tokens to reduce the processing time for the NLP model. Experiments are conducted on many different GLUE benchmark datasets demonstrated that our method can reduce the most up to 57% in training time compared to other methods.
Ready for Battle?: Legal Considerations for Upcoming AI Hacker and Vulnerability Issues
Authors: Lee Jin-Myeong , Yoon Sangpil
Published: May 2022

While the legal framework for AI is under much discussion, we need to consider the future where AI will be adopted into every aspect of our daily lives. As a software package, AI cannot be perfect and is greatly influenced by the intent of its user or its creator. In this regard, AI can be used to breach or strengthen cybersecurity. As such, there has been much discussions on how to make use of AI technology for cybersecurity while adopting it at the same time. In fact, there are already many cases of using AI to identify vulnerabilities or using AI for hacking. So what are the most reasonable set of regulatory measures to ensure balanced use of AI? This research seeks to identify the legal challenges of dealing with AI hacking. To accomplish this objective, we need to deduce elements of AI hacking from cybersecurity properties of AI technology and find real cases to analyze. Based on analysis of these real cases, this paper will propose the possible legal challenges and tasks ahead.