Reviewerly/exHarmony huggingface dataset:
MODELING THE EMOTIONAL INTELLIGENCE IMPACT ON DECISION-MAKING IN PROBLEM SITUATIONS

The article is devoted to the research of the problem of subjective factors influence in making individual and group decisions.Subjective factor is considered in terms of the emotional intelligence impact on the decision-making process.In this case, the methods of analysis and influence of emotional intelligence on the decision-making process were applied.Through the implementation of the test for emotional intelligence, a hypothesis was proposed and proved.The hypothesis essence is that the higher the emotional intelligence level, the more objective a person makes decisions.For assessment the influence of emotional intelligence level on the outcome of the decision using neural network, the model of individual decision makers' emotional intelligence classification was proposed and built.The obtained Global Scientific Trends: Economics and Public Administration Vol.2/2021 13 research results made possible to model the subjective factors influence in the decision-making process.The problem of group decision-making by modeling the behavior of decision makers under the influence of advertising, information from other agents and internal factors influencing the decision of decision makers is considered.For modeling the behavior of group decision makers in the market the agent simulation in the AnyLogic environment was used, which made possible to simulate the changes in the decision makers state depending on the impact of the environment and emotional intelligence.The received results showed that with the necessary information about the consumer and knowledge of the behavioral economics mechanisms, it is possible to predict and control his behavior, which is a great competitive advantage in certain segment market.
Autonomous Enterprise as a Model of Hotel Operation in the Aftermath of the COVID-19 Pandemic

The article aims to identify modern technological solutions in the field of automation and robotization of business processes that change the way hotel enterprises operate in the face of the global COVID-19 pandemic. Therefore, the research problem was expressed in the form of a question: what tools favor the dynamic digital transformation of hotel functioning models and the implementation of the paradigm of intelligent and autonomous entities operating in the conditions of the COVID-19 pandemic on the hotel market? During the empirical research, the use and implementation of new solutions in the field of innovative technologies supporting the optimization of processes taking place in hotel enterprises were considered. In addition, technologies and tools were identified that are considered strategic from the perspective of implementing the concept of an autonomous hotel carried out under the influence of the crisis related to the COVID-19 pandemic. Due to the specific purpose of the article, a survey was conducted using the questionnaire method with the Computer Assisted Web Interview technique alongside a self-developed questionnaire. The survey was carried out in June–September 2021 among 462 representatives of hotel companies operating in the three-, four- and five-star standards. The results of the diagnostic survey were statistically analyzed using the Statistica and RStudio software packages. To interpret the obtained data, the descriptive method, principal component analysis, and cluster analysis were used. As a result of the conducted research, it was noticed that the COVID-19 pandemic obligated hotels to automate processes that made it possible to connect devices within digital ecosystems and to optimize processes inside vertical and horizontal value chains. Such activities enabled the creation of a new model of hotel functioning referred to as an autonomous enterprise. Nowadays, one of the most important strategic behaviors of hotel entities is the ability to quickly counteract crises resulting from the COVID-19 pandemic and the related choice of the right course of action, as well as the ability to immediately make the right decisions. The research results may motivate the implementation of new technologies in the area of transforming and developing digital business models by hotels. The issues presented in the article are an attempt to fill the gap by pointing out practical experiences related to the use of individual technological solutions and their effectiveness in process automation and implementation of autonomous models of functioning of entities in the hotel services sector.
An Improved Robust Digital Image Watermarking Algorithm based on two-level DCT

This paper presents a novel image watermarking algorithm based on Discrete Cosine Transform (DCT). To imbed the watermark, we first divide the image into non-overlapping partition. Then, after applying DCT, named as one-level DCT, to the host images blocks, and DC coefficients of each block are extracted. Next, we perform DCT again, named as two-level DCT, on the matrix comprised of the selected DC coefficients of each image blocks. Finally, the watermark is embedded on the part of the coefficients after the two-level DCT. The host signal interference (HIS) is the persistent concern in the spread spectrum principle. This paper overcomes the drawback by substituting spread spectrum watermark (SSW) for the original values. However, in the traditional way, these original values are modified by adding with SSW. Moreover, fixed-number blocks for image segmentation plays an important role in resisting scaling attacks. Experimental results demonstrate that the proposed method is resilient to many kinds of image processing attacks and geometric distortions, such as filtering, noise adding, JPEG compression, scaling, aspect ratio change and some kinds of combined transforms.
Remote Laboratory, Based on Raspberry Pi, to Facilitate Scientific Experimentation for Secondary School Students

The pandemic caused by COVID 19 forced education systems to offer their services remotely due to social distancing policies. This article discusses research results on the development of remote laboratory architectures to deliver scientific experimentation in the area of physics for secondary school students using desktop computers or mobile devices.The design of the remote laboratory is based on the Raspberry Pi device, using various sensors and a graphical interface through which access and communication is given.The purpose of the development of this remote laboratory is to provide teachers and students of secondary education access to the development of remote activities for scientific experimentation in physics courses, using low-cost devices and free software.
Identifying the Structure of CSCL Conversations Using String Kernels

Computer-Supported Collaborative Learning tools are exhibiting an increased popularity in education, as they allow multiple participants to easily communicate, share knowledge, solve problems collaboratively, or seek advice. Nevertheless, multi-participant conversation logs are often hard to follow by teachers due to the mixture of multiple and many times concurrent discussion threads, with different interaction patterns between participants. Automated guidance can be provided with the help of Natural Language Processing techniques that target the identification of topic mixtures and of semantic links between utterances in order to adequately observe the debate and continuation of ideas. This paper introduces a method for discovering such semantic links embedded within chat conversations using string kernels, word embeddings, and neural networks. Our approach was validated on two datasets and obtained state-of-the-art results on both. Trained on a relatively small set of conversations, our models relying on string kernels are very effective for detecting such semantic links with a matching accuracy larger than 50% and represent a better alternative to complex deep neural networks, frequently employed in various Natural Language Processing tasks where large datasets are available.
Introduction to Bangladesh Geosciences and Resources Potential

Natural resources management is a prerequisite for a country like Bangladesh in order to maintain the continuous and steady growth of GDP where land is scarce and with a huge population. Bearing this in mind, a systematic description and linkage of the natural resources with the local geology, and current status and future prospects of exploration and development of natural resources in Bangladesh are documented in this chapter. The geological evolution of the Bengal Basin in Bangladesh constrains the occurrences of different mineral resources in the basin, both spatially and temporally. Other than the Recent deposits, the origin, occurrences, and distribution of natural resources of Bangladesh are controlled either structurally, stratigraphically, or in a combination of both. Occurrences of mineral resources in Recent deposits are controlled by surficial processes.
Vibration Analyses of a Gantry Structure by Mobile Phone Digital Image Correlation and Interferometric Radar

The study presents results from applying the Real Aperture Radar interferometry technique and Digital Image Correlation through a mobile phone camera to identify static and dynamic deformations of a gantry during surveying operations on the Michelangelo’s David at the Galleria dell’Accademia di Firenze Museum in Florence. The statue has considerable size and reaches an elevation of more than seven meters on its pedestal. An ad-hoc gantry was designed and deployed, given the cramped operating area around the statue. The scanner had a stability control system that forbid surveying in instrument movements. However, considering the unicity of the survey and its rare occurrence, the previous survey had been carried out in the year 2000; verifying stability and recording deformations is a crucial task, and necessary for validation. As the gantry does not have an on-board stability sensor, and considering the hi-survey accuracy requested, a redundant, contactless, remote monitoring system of the gantry and the statue stability was chosen to guarantee the maximum freedom of movement around the David to avoid any interference during scanning operations. Thanks to the TInRAR technique, the gantry and the statue were monitored with an accuracy of 0.01 mm. At the same time, a Digital Image Correlation analysis was performed on the gantry, which can be considered a Multi-Degree-Of-Freedom (MDOF) system, to accurately calculate the vibration frequency and amplitude. A comparison between TInRAR and DIC results reported substantial accordance in detecting gantry’s oscillating frequencies; a predominant oscillation frequency of 1.33 Hz was identified on the gantry structure by TinSAR and DIC analysis.
Improving the Training of Graph Neural Networks with Consistency  Regularization

Graph neural networks (GNNs) have achieved notable success in the semi-supervised learning scenario. The message passing mechanism in graph neural networks helps unlabeled nodes gather supervision signals from their labeled neighbors. In this work, we investigate how consistency regularization, one of widely adopted semi-supervised learning methods, can help improve the performance of graph neural networks. We revisit two methods of consistency regularization for graph neural networks. One is simple consistency regularization (SCR), and the other is mean-teacher consistency regularization (MCR). We combine the consistency regularization methods with two state-of-the-art GNNs and conduct experiments on the ogbn-products dataset. With the consistency regularization, the performance of state-of-the-art GNNs can be improved by 0.3% on the ogbn-products dataset of Open Graph Benchmark (OGB) both with and without external data.
Introducing Sentient Requirements for Information Systems and Digital Technologies

Traditionally requirements for Information Systems are considered as functional and non-functional. However, with current omnipresent Digital Technologies, we believe that new requirements dealing with individuals’ well-being are emerging. We call them sentient requirements using the term from the animal rights protection field. In this paper, we analyze the existing literature to understand better the deep nature of humans’ interactions with digital technologies and we introduce sentient requirements. It is based on a literature review including scientific and science-fiction literature. We apply these requirements to improve user experience in museums through a visiting game as a use case. Our proposal could be used by researchers and practitioners to enforce the design of Information Systems in various application fields to provide a better interaction between humans and digital technologies.
An Introduction to Artificial Neural Networks

This presentation is an introduction to the world of Artificial Neural Networks. Nowadays, Artificial Neural Networks are systems that have been very successful in many different problems, so their study is very useful for researchers in many different environments. Specifically, in the biomedical field, knowledge of Artificial Neural Networks is currently of great importance given the large number of applications they have, especially in the intelligent processing of images and signals. In this presentation we will review from the simplest models such as ADALINE to more complex models such as the multilayer perceptron, leaving the way open to start learning Deep Learning techniques. A historical introduction is given and the main algorithms of operation are reviewed, but special emphasis is placed on practical applications, especially in the biomedical field.
Deep Ensemble Learning with Reconstruction Cancellation for Specific Emitter Identification

Specific emitter identification (SEI) is an effective technique in the target identification field, which can recognize a specific transmitter by extracting the radio frequency (RF) fingerprints from the received signal. In this paper, we propose a novel SEI algorithm based on deep ensemble learning with reconstruction cancellation. Firstly, we demodulate the received signal to obtain the demodulation sequence. And then, we reconstruct an ideal waveform through the demodulation sequence. We design three kinds of characteristic representations both for the received signal and the corresponding ideal waveform. Meanwhile, we construct an ensemble learning model with a deep neural network to extract and fuse multiple features via data-driven strategies. Simulation results demonstrate that the proposed scheme achieves over 20% performance improvement learning than the single feature representation in a low signal-noise ratio.
Conceptualizing Interactions of Augmented Reality Solutions

The rapid evolution of augmented reality has resulted in an ever-increasing number of applications in a wide range of industries and services.Despite this progress, there is still a lack of conceptual understanding of AR interactions and the entire solution space.To bridge this gap, we conceptualize AR solution interactions and provide a comprehensive taxonomy.To represent the state-of-the-art, we build upon an extensive literature review.The resulting taxonomy consists of seven dimensions that encompass 29 characteristics.We contribute to the understanding of AR interactions and, as a result, the applicability of AR solutions in businesses by developing the taxonomy.Likewise, the taxonomy can guide the design of AR solutions as it convincingly describes the solution space.
Performance Analysis of Pre-Trained Residual Neural Network on Micro-Expressions Recognition

This paper presents the performance analysis of pre-trained Residual Neural Network on micro-expression recognitions.Due to recent micro-expression grand challenge, the micro-expressions started to become a hot topic in machine learning domains.The importance of micro-expressions become vitals in searching for the clues that lead to the detection of deceit's behavior.Many researchers usually created the custom deep learning algorithm, but often the existing state-of-the-art algorithms being neglected.Thus, this paper aims to provide an analysis on the performance of the five Convolutional Neural Network's variants, specifically on Residual Neural Network.This is to find the exact point on why the current state-of-the-art models do not behave as expected based on provided datasets.These datasets were prepared in terms of training, validation, and testing dataset, in which three types of frames namely as onset, apex, offset frames were used.The datasets prepared were combined altogether from renowned resources; CASME I, CASME II, CAS(ME) 2 , and SAMM datasets.For the performance results, one of the ResNet variants known as ResNet18 could only achieve up to 17% accurate with the learning rate of 1e-5 on the given datasets.This indicated that the existing models do not performed very well.Another possibility for this result is because the data itself probably hard to distinguish between two or more classes since it is a subtle facial movement.In the future, many existing models will be tested, to find the benchmark of each model on the given datasets.
Toward Evaluating the Reliability of Deep-Neural-Network-Based IoT Devices

Nowadays, the impressive performance of deep neural networks (DNNs) greatly advances the development of Internet of Things (IoT) in diverse scenarios. However, the exceptional vulnerability of DNNs to adversarial attack leads IoT devices to be exposed to potential security issues. Up to now, since adversarial training empirically remains robust against gradient-based adversarial attacks, it is believed to be the most effective defense method. In this article, we find that adversarial examples generated by gradient-based adversarial attacks tend to be less imperceptible induced by the gradient-based optimization methods (adopted in the attacks) being difficult on searching the most effective adversarial examples (i.e., the global extreme points), which may lead to an inaccurate estimation for the effectiveness of the adversarial training. To overcome the inherent defect of gradient-based adversarial attacks, we propose a novel adversarial attack named nongradient attack (NGA), of which search strategy is effective but no longer depends on gradients to enhance the threat of adversarial examples. In detail, NGA first initializes the adversarial examples outside, rather than inside, of decision boundary to make them misclassified by the model and then, under without violation of misclassified condition, adjusts the adversarial examples toward the crafted direction to close the original examples. Extensive experiments show that NGA significantly outperforms the state-of-the-art adversarial attacks on attack success rate (ASR) by 2%–7%. Moreover, we propose a new evaluation metric, i.e., composite criterion (CC) based on both ASR and accuracy, to better measure the effectiveness of adversarial training. In the experiments, CC has shown to be a more comprehensive yet appropriate evaluation metric.
Transformation of communications in the new (modern) digital university in the context of digital globalization

Problem and goal. In the context of digital globalization traditional forms of communication at the university are forced to transform in order to meet educational objectives. However, the established practice of building communications in universities in new formats has revealed a number of problems of ineffective interaction between participants in the educational process. The article aims to identify the main features of the transformation of the forms of communication in distance learning at the university. Particular attention is paid to the conditions for the formation of an effective digital dialogue in the university-student-teacher triad. Materials and research methods. The study of the transformation of communication is considered from an economic, pedagogical, and philological (semiological) point of view. The economic approach includes consideration of resource constraints that prevent the development of various forms of communication in the educational space, and an assessment of the effectiveness of the communication process. The pedagogical approach allows to evaluate the technologies and formats of communication in online learning. The semiological approach is focused on the choice of techniques and schemes that are adequate to digital dialogue. The empirical research was carried out using the questionnaire. The survey involved 2–4-year students at the Ural Federal University named after the first President of Russia B. N. Yeltsin. Research results. In the 2020–2021 academic year, students were able to adapt to rapid inclusion in distance learning and the use of digital technologies, but half of the students prefer to return to the traditional format of education, 39% of students for a mixed (blended learning) format. It was found that if the verbal forms of communication when transferring the course content from the teacher to the students were not transformed taking into account the digital environment and online learning, then this could lead to unnecessary complexity for perception (according to 54.5% of respondents), the appearance of information noise, as a consequence, the appearance of discomfort (for 40% of the respondents) and attempts to avoid communication (for 18%). 52% of students indicated that their opinion about distance improved over time. Discussion and conclusions. The authors concluded that digital skills today mean more than digital literacy. In communication, it is important to develop new technologies, techniques and tools that are adequate to new educational and upbringing tasks. And in modern conditions, burdened by pandemic threats, other ways of digitalizing the university with the construction of effective communications are rather impossible.
A Secure and Efficient Authentication in E-Commerce

E-commerce is the technique to conduct commercial transactions using wireless devices.More precisely, E-commerce is defined as the new emerging applications and services that people can access through internet enabled mobile devices such as smartphones, PDAs, i-pads and laptops anytime & anywhere.Nowadays, E-commerce transactions have exploded around the world due to the advancement of Internet technology.But E-commerce transactions are suffered by many attacks due to the lack of secured and efficient security infrastructure.Therefore, secure and effective security requirements are required to prevent E-commerce transactions from the malicious attacks.However, this paper represents the authentication based secure framework for E-commerce applications & services.This paper is also proposed a secure and efficient authentication technique using ECDSA algorithm to ensure the confirming services of E-Commerce to the end customers.