Reviewerly/exHarmony huggingface dataset:
Possibilities of human interaction in the virtual environment

Virtual, augmented and mixed reality (VR, AR and MR) is now enjoying popularity not only in gaming, but as surveys indicate, also in the health care, engineering, live events, entertainment, real estate, retail, military, education, etc. The aim of this article is to show various possibilities of human interaction with emerging technologies in three different areas: playing games, health care and education. There is a lack of software development experts for VR, AR and MR everywhere. Therefore, our intention at the Department of Computer Science, Faculty of Natural Sciences, Matej Bel University in Banská Bystrica, Slovakia, is to focus on the education and enlightenment of these areas. In this paper, we present several fields, which use different human interaction modes.
Analysis and Comparison of Solar Irradiance at Different Locations in Abu Dhabi

This paper presents an analysis of solar radiation measurements and its correlation with atmospheric and cloud based parameters over two different locations in UAE- Madinat Zayed (latitude: 23.561° N & Longitude: 53.709 ° E) and Sweihan (latitude: 24.530° N & Longitude: 55.423 ° E). Direct Normal, Global and Diffuse Horizontal solar irradiances are measured with a high precision solar monitoring station. The data is analyzed for a period of 2 Years from January 2011 to December 2012 and compared with CALIPSO satellite AOD (Aerosol Optical Depth) and COD (Cloud Optical depth) measurements. Moreover, the relation between Direct Normal Irradiance and weather conditions in UAE has been established in this work. The study has been conducted using Matlab based data analysis tools. During the analysis Direct Normal irradiance variation closely relates with the presents of Atmospheric parameters and presence of clouds. During the analysis, DNI is showing very close linear relation with AOD and COD.
Resource-Aware MapReduce Runtime for Multi/Many-core Architectures

Modern multi/many-core processors exhibit high integration densities, e.g. up to several dozens or hundreds of cores. To ease the application development burden for such systems, various programming frameworks have emerged. The MapReduce programming model, after having demonstrated its usability in the area of distributed systems, has been adapted to the needs of shared-memory many-core and multi-processor systems, showing promising results in comparison with conventional multi-threaded libraries, e.g. pthreads. In this paper, we propose a novel resource-aware MapReduce architecture. The proposed runtime decouples map and combine phases in order to enhance the parallelism degree, while it effectively overlaps the memory-intensive combine with the compute-intensive map operation resulting in superior resource utilization and performance improvements. A detailed sensitivity analysis to the framework's tuning knobs is provided. The decoupled MapReduce architecture is evaluated against the state-of-art library into two diverse systems, i.e. a Haswell server and a Xeon Phi co-processor, demonstrating speedups on average up-to 2.2x and 2.9x respectively.
Scheduling Stochastic Real-Time Jobs In Unreliable Workers

We consider a distributed computing network consisting of a master and multiple workers processing tasks of different types. The master is running multiple applications. Each application stochastically generates real-time jobs with a strict job deadline, where each job is a collection of tasks of some types specified by the application. A real-time job is completed only when all its tasks are completed by the corresponding workers within the deadline. Moreover, we consider unreliable workers, whose processing speeds are uncertain. Because of the limited processing abilities of the workers, an algorithm for scheduling the jobs in the workers is needed to maximize the average number of completed jobs for each application. The scheduling problem is not only critical but also practical in distributed computing networks. In this paper, we develop two scheduling algorithms, namely, a feasibility-optimal scheduling algorithm and an approximate scheduling algorithm. The feasibility-optimal scheduling algorithm can fulfill the largest region of applications' requirements for the average number of completed jobs. However, the feasibility-optimal scheduling algorithm suffers from high computational complexity when the number of applications is large. To address the issue, the approximate scheduling algorithm is proposed with a guaranteed approximation ratio in the worst-case scenario. The approximate scheduling algorithm is also validated in the average-case scenario via computer simulations.
Countries Condition of Forestation and Trees Percentage using Machine learning

Most countries are now in a dangerous place for forestation and some are in developed forestation. So forestation and trees percentage prediction are to predict the condition of the countries about their condition of forestation and tress percentage. The paper is about a machine learning model to predict the countries condition. We used logistic regression, SVM AND Naive Bayes to predict the condition also for matrix. we also find the accuracy of logistic regression, SVM, Nave Bayes, Ada boosting classifier, Decision tree, ANN, Linear Discriminant Analysis, Gradient Boosting Classifier, MLP Classifier to find our best accuracy and compare with them with our data. we give details of selected algorithms. We collected some previous data and present data and comparing them to predict the condition of the country. we use some conditions and logic for machine learning. By logistic regression, SVM and Nave Bayes will show us the prediction and condition of those chosen countries.
MODERNIZATION OF THE LABORATORY STAND «INDUSTRIAL LEVEL SENSORS»

The structural scheme of the existing laboratory stand for the study of methods for level for the purpose of its modernization and implementation in the educational process.
m-Naslada: A mobile application for black pepper crop disease information

Abstract This paper presented a mobile application to be used for agricultural advancement. The criteria needed for the development of a mobile application are described. A mobile application known as m-Naslada was also proposed and highlighted. m-Naslada is a mobile application prototype developed for agricultural purposes. m-Naslada is specifically aimed at providing information concerning black pepper crop diseases. m-Naslada is expected to effectively support black pepper crop farmers by meeting their requirements and providing necessary information in relation to diseases that may affect their crops. Feedback received from the evaluation conducted towards m-Naslada was positive. The work projected herein might be used as a baseline to aid in the continuation of various research scopes within the agricultural sector.
Color Image Enhancement Depending on Fuzzy Logic and Nonlinear Transform

Abstract Color image enhancement considered one of the challenging fields in image processing category. The nonlinear function based on a fuzzy logic algorithm is developed within this paper to enhance image color. The suggested algorithm used sigmoid mapping and the membership function which relied on histogram analysis to enhance image contrast. The results of the suggested algorithm compared with fuzzy logic (square method), fuzzy logic (cubic method), Histogram Equalization and Multi-Scale Retinex. Different criteria used to evaluate the performance of the presented algorithm like Mean-S, Mean-H, nature image quality evaluator, Lightness Order Error and entropy. The evaluated criteria show the suggested algorithm is better in color enhancement compared with the other methods.
Research on Natural Language Processing in Financial Risk Detection

With the rise of electronic financial transactions, illegal financial activities have emerged in an endless stream. People have used illegal funds to conceal or cover up illegal sources of funds through small-value transfers, cash orders, traveler’s checks and other gold trading channels to evade supervision by government units. It isn’t easy to trace the information of these golden streams, because the flow of funds includes the source and destination of the wire transfer, the nature of the wire transfer and the relationship between the sender and the bank are very complicated and difficult to check. Natural language processing understands the meaning of people’s commands and inputs in a seamless and simplified way. Further, it can eliminate the mistakes caused by human beings and enable financial risk control personnel to find problematic transactions in a short period of time in a more efficient manner. This study uses BERT’s TensorFlow open source code and the test data set provided by Paysim to conduct risk model construction research.
Organisational Security Dependent on Individual Privacy

Abstract The global proliferation of cloud computing, smart homes, the internet of things and machine learning requires a novel view on the flow of confidential information and its classification. The security of an organisation is affected by the privacy enjoyed by its members. Sufficient data on those members can be leveraged in a so-called abduction attack aiming to extract confidential information from the organisation. To illustrate it we develop a model of actors and data flows and discuss three scenarios in which the confidentiality achievable by an organisation is limited by the privacy of its members. We support the model and the attack scenarios by reviews of the most prolific sources of personal data, its handling and its perceived value to the individuals it pertains to.
Linux Privilege Increase Threat Analysis

Today, Linux is one of the main operating systems (OS) used both on desktop computers and various mobile devices. This OS is also widely applied in state and municipal structures, including law enforcement agencies and automated control systems used in the Armed Forces of the Russian Federation. It's worth noting that the process of replacing the Linux OS with domestic protected OSs that use the Linux kernel has now begun. In this regard, the analysis of threats to information security of the Linux OS is highly relevant. In this article, the authors discuss the security problems of Linux OS associated with unauthorized user privileges increase, as a result of which an attacker can gain full control over the OS. The approaches to differentiating user privileges in Linux are analyzed and their advantages and disadvantages are considered. As an example, the causes of the vulnerability CVE-2018-14665 were identified and measures to eliminate it were proposed.
Granulation-Based Reverse Image Retrieval for Microscopic Rock Images

The paper presents a method of object detection on microscopic images of rocks, which makes it possible to identify images with similar structural features of the rock. These features are understood as the sizes and shapes of its components and the mutual relationships between them. The proposed detection methodology is an adaptive and unsupervised method that analyzes characteristic color clusters in the image. It achieves good detection results for rocks with clear clusters of colored objects. For the analyzed data set, the method finds in the rock image sets with high visual similarity, which translates into the geological classification of rocks at a level of above 78%. Considering the fact that the proposed method is based on segmentation that does not require any input parameters, this result should be considered satisfactory. In the authors’ opinion, this method can be used in issues of rock image search, sorting, or e.g. automatic selection of optimal segmentation techniques.
Changing in Economic Value of Photovoltaic Systems Based on Penetration Level

In Iran, residential Photovoltaic (PV) generation has received continuous support from government in the recent for several years. Government gives an incentive program in the form of feed in tariff (FIT) mechanism. At more penetration, PV output creates major operational problems to the current grid that is operated for unidirectional power flow, which in this case unbalancing between demand and PV output increases. Thus, with enhancing penetration level of PV units, the support polices should be modified. In this context, the present paper aims to concentrate on designing a suitable incentive mechanism with respect to the penetration theory of PV systems in distribution systems. In doing so, first, the economic terms including the value of generated energy and capacity value are estimated using the electricity generation profile of PV systems and electricity grid conditions. By the way, to analyze the PV output, direct insolation simulation code (DISC) is applied for prediction of solar radiation as the key contribution of system advisor model (SAM) software. The findings show that the level of incentives is crucially linking with the RES penetration rate.
The Road Map to FAME: A Framework for Mining and Formal Evaluation of Arguments

Abstract Two different perspectives on argumentation have been pursued in computer science research, namely approaches of argument mining in natural language processing on the one hand, and formal argument evaluation on the other hand. So far these research areas are largely independent and unrelated. This article introduces the agenda of our recently started project “FAME – A framework for argument mining and evaluation”. The main project idea is to link the two perspectives on argumentation and their respective research agendas by employing controlled natural language as a convenient form of intermediate knowledge representation. Our goal is to develop a framework which integrates argument mining and formal argument evaluation to study patterns of empirical argumentation usage. If successful, this combination will allow for new types of queries to be answered by argumentation retrieval systems and large-scale content analysis. Moreover, feeding evaluation results as additional knowledge input to argument mining processes could be utilized to further improve their results.
A grammar for specifying full-body gestures elicited for abstract tasks

A gesture elicitation study consists of a popular method for eliciting a sample of end end users to propose gestures for executing functions in a certain context of use, specified by its users and their functions, the device or the platform used, and the physical environment in which they are working. Gestures proposed in such a study needs to be classified and, perhaps, extended in order to feed a gesture recognizer. To support this process, we conducted a full-body gesture elicitation study for executing functions in a smart home environment by domestic end users in front of a camera. Instead of defining functions opportunistically, we define them based on a taxonomy of abstract tasks. From these elicited gestures, a XML-compliant grammar for specifying resulting gestures is defined, created, and implemented to graphically represent, label, characterize, and formally present such full-body gestures. The formal notation for specifying such gestures is also useful to generate variations of elicited gestures to be applied on-the-fly on gestures in order to allow one-shot learning.
A fast calibration method of the robot’s 3D vision system

Abstract Calibration accuracy and convenience are very important for the rapid production of vision-based robots. Although there are many calibration methods for the 3D vision system, the convenience of calibration cannot meet the needs of industrial sites. In this paper, we propose a fast calibration method based on a 3D calibration block. Its key point is to complete the calibration process through multiple space point constraints, and the space points used are jointly solved by multiple planes obtained by local point cloud fitting. The experimental results show that the proposed method can quickly complete the calibration of the robot’s 3D vision system, and the calibration accuracy can meet low precision occasions.
Methodological aspects of modernization of professional training of future music teachers

espanolEl articulo informa sobre la experiencia de implementar modelos metodologicos innovadores de capacitacion profesional para futuros maestros de musica. Se presentan los metodos y formas basicas de los trabajos pedagogicos experimentales de la Universidad Nacional Kamianets-Podilsky Ivan Ohienko. La confiabilidad de los datos fue confirmada por el procesamiento final de los resultados de acuerdo con los metodos de diagnostico del experimento confirmatorio. 67 estudiantes de titulos de bachiller y especialistas en musica se prepararon de acuerdo con el modelo metodologico de 4 etapas de entrenamiento innovador de ejecucion instrumental. Los resultados pueden servir como modelo para preparar a los futuros maestros de musica para una actividad innovadora de interpretacion instrumental. EnglishThe paper reports the experience of implementing an innovative methodological model of professional training of future music teachers. The basic methods and forms of pedagogical experimental works in Kamianets-Podilsky Ivan Ohienko National University are presented. The reliability of the data was confirmed by the results of final processing according to the diagnostic methods of the confirmatory experiment. 67 students with bachelor and specialist degrees specializing in music were prepared according to the 4-stage methodological model of innovative instrumental-performing training. The results can serve as a model for preparing future music teachers for innovative instrumental-performing activity.
The initial socio-technical solution for phishing attack

Abstract A phishing attack remains popular among security attacks, which has specific characteristics. The attacks have an impact on the economy and involve user behaviour that makes the attack successful. Many solution framework and program has developed but fails for eliminating the attack except minimizing the impact only. Mitigating and cope processes for reducing phishing attacks involve user education and training to raise higher user awareness. The mitigating process itself has two main steps that are prevention and detection. Many algorithms employ machine learning to overthrow the attack intelligently. The remaining problem of this process is detection time is slower than using blacklist filtering. The paper review social and technical aspects to conclude and identify the potential solution that will be proposed for the next stage of the research. The potential solution will include future data analytics such as an immersive and augmented analytic regardless of computing issues. An immersive and augmented analytics should have the ability to learn from the past data of user and attack behaviour to direct the system and user in combating the attacks. The learning result should give preventive suggestions and evaluate user awareness level.
Interval Signs Enlargement Algorithm in the Classification Problem of Biomedical Signals

When solving a number of applied problems, the classifiers that work with samples of two-dimensional graphical dependences are very useful. The paper considers the approach to solving the classification problem of two-dimensional characteristic curves of biomedical signals, which illustrate human emotional state variation. It also describes a localization algorithm of interval consecutively enlarged sings. The intervals of enlarged attributes are determined by analyzing the rules that are generated during classifier training on primary data. The classifier is based on a neural-like hierarchical structure (NLHS) that is adapted to working with fuzzy object descriptions. During the learning process, class models are formed in a hierarchical structure of the classifier. These models are interpreted as fuzzy statements for a fuzzy inference system. Fuzzy statements reflect basic characteristics of all training sample objects and are presented to an expert in an understandable form. The algorithm of automatic generation of interval attributes allows localizing areas with equal values of fuzzy attributes. In fact, this leads to dividing of the investigated characteristic curves into segments with close estimates of their structural properties. The software implementation of the algorithm is tested on EEG and EMG. The paper investigates the effect of the application of successively enlarged attributes on the results of the examined data classification accuracy.
Trustworthiness in Supply Chains : A modular extensible Approach applied to Industrial IoT

Typical transactions in cross-company Industry 4.0 supply chains require a dynamically evaluable form of trustworthiness. Therefore, specific requirements on the parties involved, down to the machine level, for automatically verifiable operations shall facilitate the realization of the economic advantages of future flexible process chains in production. The core of the paper is a modular and extensible model for the assessment of trustworthiness in industrial IoT based on the Industrial Internet Security Framework of the Industrial Internet Consortium, which among other things defines five trustworthiness key characteristics of NIST. This is the starting point for a flexible model, which contains features as discussed in ISO/IEC JTC 1/AG 7 N51 or trustworthiness profiles as used in regulatory requirements.Specific minimum and maximum requirement parameters define the range of trustworthy operation. An automated calculation of trustworthiness in a dynamic environment based on an initial trust metric is presented. The evaluation can be device-based, connection-based, behaviour-based and context-based and thus become part of measurable, trustworthy, monitorable Industry 4.0 scenarios.Finally, the dynamic evaluation of automatable trust models of industrial components is illustrated based on the Multi-Vendor-Industry of the Horizon 2020 project SecureIoT. (grant agreement number 779899)
PEDAGOGICAL COMPETENCE AS A COMPONENT OF PROFESSIONALISM OF PSYCHOLOGY TEACHER

The article studies the relevance of research of pedagogical competence of the future teacher of psychology in higher education in the context of professional component of the psychologist. The scientific approaches to the definition of the term «professionalism of the teacher of higher education» are analyzed. The activity approach is associated with the highest levels of excellence in the work, achieved in the arts and skill in professional activity. Personal approach focuses on the systematic organization of consciousness and human psyche, which includes the properties of the person as a whole; skills, skills, motor skills, cognitive processes, actions, awareness, knowledge, experience and culture of a professional, his emotional world. The third approach to defining the essence of professionalism is manifested in the combination of activities and personal principles, when professionalism is viewed in the dialectical unity of manifestations of activity and personality and, accordingly, is defined by the concepts of «professionalism of activity» and «professionalism of personality». The issue of pedagogical competence of the psychology teacher in the acmeological approach, which is considered at different levels: general, special and individual, has been updated. The essence of teacher’s pedagogical competence as a component of his professionalism is revealed. In the structure of pedagogical competence of the teacher we distinguish general professional (psycho-pedagogical, research and pedagogical management) and key competences (social, general cultural, health-saving, civil, informational). The prospects for further research are to develop and introduce into the educational process the pedagogical conditions for the development of pedagogical competence of a future teacher of higher education psychology.