Reviewerly/exHarmony huggingface dataset:
Enhanced Cascaded Genetic Fuzzy System for Counterfeit Banknote Detection

Counterfeit currency is a very serious issue from an economic point of view for any country. In rural communities around the world, where digital currency has not yet become the norm and the usage of banknotes is still dominant, the detection of counterfeit banknotes is very important. Authentication of currency is a classification problem and therefore a Fuzzy Inference System (FIS) is designed with its parameters optimized using a Genetic Algorithm. This FIS takes in features extracted from the images of banknotes and classifies the corresponding banknotes as genuine or fake. A cascaded approach is selected to design the FIS in order to reduce the number of parameters. In this study, an algorithm based on the Fuzzy C-means clustering algorithm is designed to define the best combination of inputs for the cascaded FIS. Also, another cascaded FIS is developed with an attempt at decreasing the number of parameters by introducing symmetry and other constraints. The results are compared with the Machine Learning (ML) approaches, and efforts are also made to get a highly reliable precision similar to that of ML methodologies.
Sustainable Development of a PDO or PGI form the Voluntary Scheme “Traditional Product”

The International Conferences on Economics and Social Sciences (ICESS)organized by Bucharest University of Economic Studies provide an opportunity for all those interested in Economics and Social Sciences to discuss and exchange research ideas. The papers presented at the Conference are available online in the Conference Proceedings series (ISSN 2704-6524): Volume 2019 Collaborative Research for Excellence in Economics and Social Sciences, ISBN 9788366675322 Volume 2020 Innovative Models to Revive the Global Economy, ISBN 9788395815072 Volume 2021 Resilience and Economic Intelligence Through Digitalization and Big Data Analytics, ISBN 9788366675704 During 2020, leaders found themselves at a historic crossroads, taking decisions under remarkable pressures and uncertainties. However, windows of opportunity are being created to shape the economic recovery, restore the health of the environment, develop sustainable business models, strengthen regional development, revitalize global cooperation, harness Industry 4.0, and redesign the social contracts, skills, and jobs. This conference provides an opportunity for all those interested in Economics and Social Sciences to discuss and exchange research ideas especially in the light of recent crisis determined by COVID-19. We welcome both empirical and theoretical work that is broadly consistent with the conference’ general theme. Especially, researchers, PhD
Análise de custos da primeira vaga da pandemia COVID-19 na gestão de recursos humanos num hospital português

Background: The COVID-19 pandemic has brought healthcare systems to the brink of collapse worldwide, imposing relevant economic challenges.Objective: To analyze the costs related to the impact of the first wave of the COVID-19 pandemic on human resource management in a Portuguese hospital.Methodology: Economic, retrospective, and single-center study.This study analyzed the costs of absenteeism, hiring new staff, and overtime work by health professionals (nurses, physicians, operational assistants, and diagnostic and therapeutic technicians) and compared data from March 1 to May 31, 2020 to the same period in 2019 to determine differences in costs.Results: Data from 6,994 health professionals were included.Compared to 2019, an additional 8,817,199.84€were spent on staff in this trimester (absenteeism: €6,842,284.64;hiring new staff: €363,540.03;overtime work: €1,611,375.17).Conclusion: In the first trimester of the COVID-19 pandemic, the overall costs with health professionals almost tripled, representing almost €9 million more than in the same period in 2019.
Towards a new monolingual Hungarian explanatory dictionary

The Lexical Knowledge Representation Research Group at the Department of Lexicology is one of the youngest research groups of the Hungarian Research Centre for Linguistics, founded in February 2020. The group is currently working on a new version of a monolingual explanatory dictionary partly based on The Explanatory Dictionary of the Hungarian Language. The aim is to compile an up-to-date online dictionary of contemporary Hungarian (2001–2020) by corpus-driven methods. The present article describes The Explanatory Dictionary of the Hungarian Language and the Comprehensive Dictionary of Hungarian by presenting their history, the circumstances of their compilation, and the basic editorial guidelines. Then it outlines how the corpus for the planned dictionary is to be set up and how this corpus is to be analysed.
Mapping Constraint Problems onto Quantum Gate and Annealing Devices

This work presents NchooseK, a unified programming model for constraint satisfaction problems that can be mapped to both quantum circuit and annealing devices through Quadratic Unconstrained Binary Operators (QUBOs). Our mapping provides an approachable and effective way to program both types of quantum computers. We provide examples of NchooseK being used.
The Problem of Developing the Reflexivity of Future Specialists in Social and Humanistic Sciences in the Context of Postmodernism

The article talks about the postmodern approach to studying the problem of reflexive competence of future specialists, requires a comprehensive analysis of the organization and content of the educational process in higher education institutions. The postmodern concept of professional reflection and personal reflexivity of students is highlighted, it determines the ratio of these formations as unique individual phenomena, their influence on the formation and manifestation of professional and professional competence at the creative-professional, cognitive and personal-motivational levels. The concept of reflectivity of the future specialist is specified. The structure of professional reflection of students is specified. The value of pedagogical practice for the development of professional reflection in future professionals from the standpoint of the postmodern approach is emphasized. The model of formation and development of personal-professional reflectivity in future specialists at the competence, cognitive, personal-motivational, emotional and evaluative-behavioral levels is presented.
Knowledge Distillation for U-Net Based Image Denoising

In recent years, algorithms based on convolutional neural networks (CNNs) have shown great advantages in image denoising. However, the existing state-of-the-art (SOTA) algorithms are too computationally complex to be deployed on embedded devices, like mobile devices. Knowledge distillation is an effective model compression method. However, researches on knowledge distillation are mainly on high-level visual tasks, like image classification, and few on low-level visual tasks, such as image denoising. To solve the above problems, we propose a novel knowledge distillation method for the U-Net based on image denoising algorithms. The experimental results show that the performance of the compressed model is comparable with the original model in the case of quadruple compression.
Development of an AI Chatbot-Based Teaching Model for English Picture Book Retelling Activities

The aim of this study was to develop and apply an AI chatbot-based teaching model for English picture book retelling activities in elementary schools. Participants were 18 students of the 5th grade who performed the retelling activity with three English picture books. AI chatbots were built with Google Dialogflow API. The criteria for building them were developed based on previous studies. Results are as follows. First, the model was composed of two lesson periods. In the first period, learners read the story together and checked their comprehension. In the next period, a teacher modelled retelling to students and the AI chatbots mediated learners’ retelling by asking questions. Finally, learners independently retold the story. Second, the criteria consisted of linguistic and affective perspectives, with four elements for each. Finally, students’ productions showed that the design of the chatbots’ prompts and scaffoldings partially affected students’ production. Moreover, the chatbots’ utterances should be sophisticatedly designed according to learners’ levels to gradually elicit their productions. This study suggests that integrating AI chatbots into classrooms can mediate young learners’ story retelling to partially support instructors who teach English reading skills in regular classes.
Leveraging Lexicon-Based and Sentiment Analysis Techniques for Online Reputation Generation

Advanced reputation generation systems analyze user-generated content such as opinions and reviews expressed in natural language in order to produce a reliable and trusted reputation value.According to recent and relevant literature, while many important attributes are exploited such as semantic and sentiment orientation of the reviews, time, and opinions' relevancy, other relevant attributes such as users' credibility, as well as the sentiment intensity of the reviews, are not considered.In this paper, we propose a system that computes a single numerical reputation value between 0 and 10 from Twitter microblogging platform by incorporating the sentiment orientation of the tweets, the sentiment intensity of the positive tweets, as well as the users' and tweets' credibility score.To assess the effectiveness of our system we have compared its computed reputation value with the ground truth one ranging from 0 to 10.This later is the weighted average votes of thousands of users taken from IMDb, Amazon, TripAdvisor, and Yelp concerning respectively four products and services (movie: 7.9, phone product: 7.3, hotel: 9, restaurant: 7.1).The experimental results conducted on four real-world Twitter datasets related to the aforementioned products show that our system provides a reputation value (movie: 7.66, phone product: 7.18, hotel: 8.71, restaurant: 7.08) that is near to the ground truth one.Consequently, it can be applied in practice and used by consumers and businesses to generate a reliable and trusted reputation value from tweets in order to support them during their decision-making process in Ecommerce platforms (buying, renting, booking, etc.).
The impact of using CouchDB on Hyperledger Fabric performance for heterogeneous medical data storage

The popularization of Bitcoin and other cryptocurrencies has motivated the interest in using blockchain infrastructure in contexts other than the original. This is due to blockchain allows for a distribution of data with decentralized management and in a secure environment. In this scenario, the goal of this work is to evaluate the impact of database usage when blockchain is employed to manipulate large volumes of heterogeneous data. The methodology used in our evaluation considers the Hyperledger Fabric to set up a network for sharing medical data, which is obtained from a real database. The performance of this network was collected through experimental studies, with the Hyperledger Caliper benchmark, by measuring the throughput and latency of the network with and without the CouchDB database. Our results show the impact of the overhead imposed by the database when it is used in a blockchain network. This work contributes to future developers of blockchain applications as it shows the impact of database usage on such applications.
An Automatic Conflict Detection Framework for Urban Intersections Based on an Improved Time Difference to Collision Indicator

Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately.
A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone

As the world population is ageing rapidly and old age comes with multiple health issues, the need for medical services is likely to increase in a couple of decades beyond the limits of the medical systems of almost any country. In response to this trend, a variety of technologies have been developed with the aim of helping older people live independently as long as possible and reduce the burden on caregivers. In this paper, we propose a solution to encode the information about the activity of the monitored person, captured by a set of binary sensors, in the form of activity maps that reflect not only the intensity, but also the spatial distribution of the activity between a set of behaviorally meaningful places. Then, we propose a method for automatic analysis of the activity maps in order to detect deviations from the previously recorded routine. We have tested the method on two public activity recognition datasets and found that the proposed solution is not only feasible, but also has several important advantages (it is low cost, scalable, adaptable, requires little expert knowledge for setup and protects the privacy of the monitored persons) that make it applicable on a large scale, including for people with low socio-economic status.
Offline and Real-Time Implementation of a Terrain Classification Pipeline for Pushrim-Activated Power-Assisted Wheelchairs

Pushrim-activated power-assisted wheelchairs (PAPAWs) are assistive technologies that provide propulsion assist to wheelchair users and enable access to various indoor and outdoor terrains. Therefore, it is beneficial to use PAPAW controllers that adapt to different terrain conditions. To achieve this objective, terrain classification techniques can be used as an integral part of the control architecture. Previously, the feasibility of using learning-based terrain classification models was investigated for offline applications. In this paper, we examine the effects of three model parameters (i.e., feature characteristics, terrain types, and the length of data segments) on offline and real-time classification accuracy. Our findings revealed that Random Forest classifiers are computationally efficient and can be used effectively for real-time terrain classification. These classifiers have the highest performance accuracy when used with a combination of time- and frequency-domain features. Additionally, we found that increasing the number of data points used for terrain estimation improves the prediction accuracy. Finally, our results revealed that classification accuracy can be improved by considering terrains with similar characteristics under one umbrella group. These findings can contribute to the development of real-time adaptive controllers that enhance PAPAW usability on different terrains.
An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting

With the popularity of Internet of things technology and intelligent devices, the application prospect of accurate step counting has gained more and more attention. To solve the problems that the existing algorithms use threshold to filter noise, and the parameters cannot be updated in time, an intelligent optimization strategy based on deep reinforcement learning is proposed. In this study, the counting problem is transformed into a serialization decision optimization. This study integrates the noise recognition and the user feedback to update parameters. The end-to-end processing is direct, which alleviates the inaccuracy of step counting in the follow-up step counting module caused by the inaccuracy of noise filtering in the two-stage processing and makes the model parameters continuously updated. Finally, the experimental results show that the proposed model achieves superior performance to existing approaches.
Czy bibliotekarze mogą budować sieci społecznego zaangażowania? Wyniki badania postaw personelu bibliotecznego

Can Librarians Build Community Involvement Networks? Results of a Survey of Library Staff Attitudes Research on social capital carried out in recent years shows that its development and strengthening of social networks can also be influenced by various types of cultural and scientific institutions working for the benefit of local communities. Although libraries were originally established for a very different purpose, it is noteworthy that they are among the few institutions that enjoy a high level of trust from the public. As such, they can contribute to building local social networks and social capital. In order for these processes to take place, it is necessary to have both the premises and equipment available in the local centres, as well as appropriate personnel with specific attitudes, knowledge, experience, and preparation for these types of activities. To illustrate conditions in library institutions, this paper presents the results of a survey of library staff attitudes, conducted among library staff from twenty countries around the world. The research confirmed the presence of pro-social attitudes in this group.
Hybrid Neural Approach for Face and Associated Emotion Recognition Using Swarm Intelligence-Based Ensemble Network

In this work, a hybrid approach which carries the Radial Basis Function Neural Network and Multilayer Perceptron Neural Network have been applied in a cascaded manner to recognize the face and associated emotions.The variability of individual classifier performances has been reduced by providing the ensemble approach.The formation of ensemble has been developed using the intelligent manner with the help of particle swarm optimization.The applied ensemble approach provided the weighted importance of individual entities according to their performances.The proposed ensemble approach has been proven to be useful over the development of ensemble classifier for XOR classification problem.Each face has been carried with a different form of emotion which has been tested and performance was compared against the individual classifier module.
The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.
A Systematic Review of Blockchain Technology and Its Applications

Blockchain technology could radically transform our business environment in the decades to come. It has the power to redefine our views of business processes and may even impact our overall economy. The works comprehensively document the implementation of blockchain applications in various sectors. Our goal is to determine what state blockchain technology is in and what kinds of applications it has. A large majority of the current blockchain-related research is dedicated to crypto currencies, such as Bitcoin. Only a small percentage of work is dedicated to exploring the wide range of potential blockchain technology applications. Blockchain technology has multiple applications across multiple industries. This study attempts to determine the opportunities and threats presented by Blockchain Technology for current or future applications. The number of published studies that were studied carefully and critically and added to the Block chain’s body of knowledge was large.
Trajectory Tracking Method of Volleyball Player’s Arm Hitting Image Based on D-P Algorithm

Aiming at the problems of poor image tracking effect, low precision, and long time in the process of image tracking of volleyball player’s arm hitting, a volleyball player's arm hitting image tracking method based on D-P algorithm is proposed. This paper analyzes the basic concept, basic principle, and basic equation of D-P algorithm and collects the arm stroke trajectory image of volleyball players under the three-dimensional visual model. Using wavelet multiscale decomposition method, the arm stroke trajectory of volleyball players is filtered, and the edge contour feature points of the arm stroke image of volleyball players are extracted. Using the gray histogram feature extraction method, the gray information of volleyball player's arm hitting trajectory image is enhanced. Combined with pixel adaptive enhancement technology, the key action feature points of volleyball player's arm hitting image trajectory are located. Based on D-P algorithm, the volleyball player's arm hitting image trajectory is adjusted and modified to realize the correct tracking of volleyball player's arm hitting image trajectory. The experimental results show that the trajectory tracking effect of volleyball player's arm hitting image is better, which can effectively improve the tracking accuracy and shorten the tracking time.
KBM Based Variable Size DCT Block Approaches for Video Steganography

In this study, the data hiding operations were performed by a combination of different sizes of Discrete Cosine Transform (DCT), Key Block Matrix (KBM) consisting of different sub-pattern blocks, and additional security algorithms.Mean Square Error (MSE), Structure Similarity Index Measurement (SSIM), and Peak Signal Noise Ratio (PSNR) quality metrics were used to evaluate the results of the proposed approach.4x4, 8x8, and 16x16 pixel DCT blocks were used for testing and evaluation.Each DCT block was used with the proposed KBM that can be created by artificial intelligence techniques and secure data hiding methods.The fact that the KBM structures used in the proposed methods are variable increases data security in the proposed algorithms and fills the gap of security issues in the video steganography.The quality metric values obtained showed that the proposed approach can compete with the results obtained in the literature regarding PSNR, MSE, and SSIM values.