Reviewerly/exHarmony huggingface dataset:
Facial Emotion Recognition Using Ensemble Learning

Facial emotion recognition (FER) is the task of identifying human emotions from facial expressions. The purpose of this book chapter is to improve accuracy of facial emotion recognition using integrated learning of lightweight networks without increasing the complexity or depth of the network. Compared to single lightweight models, it made a significant improvement. For a solution, the authors proposed an ensemble of mini-Xception models, where each expert is trained for a specific emotion and lets confidence score for the vote. Therefore, the expert model will transform the original multiclass task into binary tasks. The authors target the model to differentiate between a specific emotion and all others, facilitating the learning process. The principal innovation lies in our confidence-based voting mechanism, in which the experts “vote” based on their confidence scores rather than binary decisions.
Human Action Recognition Based on YOLOv7

Human action recognition is a fundamental research problem in computer vision. The accuracy of human action recognition has important applications. In this book chapter, the authors use a YOLOv7-based model for human action recognition. To evaluate the performance of the model, the action recognition results of YOLOv7 were compared with those using CNN+LSTM, YOLOv5, and YOLOv4. Furthermore, a small human action dataset suitable for YOLO model training is designed. This data set is composed of images extracted from KTH, Weizmann, MSR data sets. In this book chapter, the authors make use of this data set to verify the experimental results. The final experimental results show that using the YOLOv7 model for human action recognition is very convenient and effective, compared with the previous YOLO model.
Analysis Model at the Sentence Level for Phishing Detection

Global cyber dangers related to phishing emails have increased dramatically, particularly after the COVID-19 epidemic broke out. Many companies have suffered significant financial losses as a result of this kind of assault. Even though many models have been developed to distinguish between phishing efforts and genuine emails, attackers always come up with new ways to trick their targets into falling for their scams. Many companies have suffered significant financial losses as a result of this kind of assault. Although phishing detection algorithms are being developed, their accuracy and speed in recognizing phishing emails are not up to par right now. Furthermore, the number of phished emails has alarmingly increased lately. To lessen the negative effects of such bogus communications, there is an urgent need for more effective and high-performing phishing detection algorithms. Inside the framework of this study, a thorough examination of an email message's email header and content is carried out. A novel phishing detection model is built using the features of sentences that are extracted. The new dimension of sentence-level analysis is introduced by this model, which makes use of k-nearest neighbor (KNN). Kaggle's well-known datasets were used both to train and evaluate the model. Important performance indicators, including the F1-measure, precision, recall, and accuracy of 0.97, are used to assess the efficacy of this approach.
Decentralized Finance (DeFi) and Blockchain Technology in Healthcare

The chapter examines how block chain technology can revolutionize pharmaceutical supply chain management. Through real-time tracking and traceability, the authenticity of medications can be ensured, thereby combating the persistent issue of counterfeit drugs and enhancing patient safety. Additionally, the clinical trial process stands to benefit from block chain and transparent and secure nature. Smart contracts can streamline patient recruitment, informed consent, and trial result reporting, promoting trust and accountability in the clinical trial ecosystem. Despite the potential benefits, the adoption of DeFi and block chain in healthcare presents several challenges, including regulatory compliance, scalability, and integration with existing healthcare systems. This chapter discusses the current state of block chain integration in healthcare and identifies future research directions to overcome these hurdles.
New online project “Collectors of The Russian Museum of Ethnography”: concept, preliminary results

The article contains a presentation of the concept and preliminary results of the new virtual project "Collectors of the Russian Ethnographic Museum", launched in 2021. Its purpose is to recreate individual portraits of scientists, correspondents and museum staff, personal and creative biographies of those who during the XIX — early XXI c., and were associated with the museum field. The fates of some of them are quite well known, others are little studied due to various circumstances. It's not just the famous specialists (ethnographers, linguists, folklorists, archaeologists, historians, etc.) and correspondents, but also representatives of different social circles, scientific and political views, levels of education, nationalities, confessions, ages, characters; with different motivation for ethnographic research and subjective approaches to their own collecting activities. Currently, according to the concept and well-thought-out scheme, materials about more than 130 people have been published, more than 30 museum employees participated in their creation. URL:https://ethnomuseum.ru/collections/projects/collectors/.
Quantum random number generation using an on-chip nanowire plasmonic waveguide

Abstract Quantum random number generators employ the inherent randomness of quantum mechanics to generate truly unpredictable random numbers, which are essential in cryptographic applications. While a great variety of quantum random number generators have been realised using photonics, few exploit the high-field confinement offered by plasmonics, which enables device footprints an order of magnitude smaller in size. Here we integrate an on-chip nanowire plasmonic waveguide into an optical time-of-arrival based quantum random number generation setup. Despite loss, we achieve a random number generation rate of $14.4\,\text{Mbits/s}$ using low light intensity, with the generated bits passing industry standard tests without post-processing. By increasing the light intensity, we were then able to increase the generation rate to $41.4\,\text{Mbits/s}$, with the resulting bits only requiring a shuffle to pass all tests. This is an order of magnitude increase in the generation rate and decrease in the device size compared to previous work. Our experiment demonstrates the successful integration of an on-chip nanoscale plasmonic component into a quantum random number generation setup. This may lead to new opportunities in compact and scalable quantum random number generation.
Gender differences in online abuse: the case of Dutch politicians

Abstract Online abuse and threats towards politicians have become a significant concern in the Netherlands, like in many other countries across the world. This paper analyses gender differences in abuse received by Dutch politicians on X (formerly Twitter), while taking into account the possible additional impact of ethnic minority status. All tweets directed at party leaders throughout the entire year of 2022 were collected. The effect of gender and ethnic minority status were estimated for six different linguistic measures of abuse, namely, toxicity, severe toxicity, identity attacks, profanity, insults, and threats. Contrary to expectations, tweets directed at male politicians scored higher on all forms of abuse. Significant interaction effects between gender and ethnic minority status were found for a number of abuse measures. Tweets directed at ethnic minority female politicians scored higher on severe toxicity, identity attacks, and profanity, compared to those directed at ethnic majority female politicians. Importantly, female ethnic minority politicians received the highest levels of threats compared to all groups. Given that online abuse and threats are reported to have a negative effect on political participation and retention, these results are particularly worrying.
Singer identification model using data augmentation and enhanced feature conversion with hybrid feature vector and machine learning

Abstract Analyzing songs is a problem that is being investigated to aid various operations on music access platforms. At the beginning of these problems is the identification of the person who sings the song. In this study, a singer identification application, which consists of Turkish singers and works for the Turkish language, is proposed in order to find a solution to this problem. Mel-spectrogram and octave-based spectral contrast values are extracted from the songs, and these values are combined into a hybrid feature vector. Thus, problem-specific situations such as determining the differences in the voices of the singers and reducing the effects of the year and album differences on the result are discussed. As a result of the tests and systematic evaluations, it has been shown that a certain level of success has been achieved in the determination of the singer who sings the song, and that the song is in a stable structure against the changes in the singing style and song structure. The results were analyzed in a database of 9 singers and 180 songs. An accuracy value of 89.4% was obtained using the reduction of the feature vector by PCA, the normalization of the data, and the Extra Trees classifier. Precision, recall and f-score values were 89.9%, 89.4% and 89.5%, respectively.
Trends and Transformations of E-Retailing in Jordan Sales Market: Challenges, Growth, and Potential Future Developments

This study aimed to explore the trends and transformations of the e-retail market in Jordan, besides the aspects that have contributed to these changes. It also aimed to examine the behaviour and preferences of consumers about e-shopping. Additionally, it focused on the key players in the e-retail market in Jordan. The current study collected primary data by using a questionnaire where 207 e-shoppers and e-retailers were selected as a typical sample through convenience and obliging sampling techniques. The data was analyzed using SPSS and descriptive statistics were used to sum up the survey answers. The outcomes of the study advisable that e-retail trade had a positive impact on the business sector in Jordan, with its growth probable and trends. The study also set up those changes played a vital role in the growth of e-retail trade in Jordan, due to the rising use of the internet and inconstant consumer preferences. Surveyed companies concentrate on providing personalized shopping experiences, logistics services, and fulfilment services that effectively magnify customer satisfaction and loyalty. To increase online sales, practitioners in the e-retail industry in Jordan can leverage IT and create outreach strategies while investing in IT capabilities. However, the study findings are context-specific, and future research should consider examining the impact of e-retailing on competitive advantage across different industries and countries.
Information technology, gender economic inclusion and environment sustainability in sub-Sahara Africa

Purpose This study examines the relevance of information and communication technologies in the effect of gender economic inclusion on environmental sustainability. Design/methodology/approach The focus is on a panel of 42 sub-Saharan African countries over the period 2005–2020. The empirical evidence is based on generalized method of moments. The environmental sustainability indicator used is CO2 emissions per capita. Three indicators of women’s economic inclusion are considered: female labour force participation, female employment and female unemployment. The chosen ICT indicators are mobile phone penetration, Internet penetration and fixed broadband subscriptions. Findings The results show that: (1) fixed broadband subscriptions represent the most relevant ICT moderator of gender economic inclusion for an effect on CO2 emissions; (2) negative net effects are apparent for the most part with fixed broadband subscriptions (3) both positive ICT thresholds (i.e., critical levels for complementary policies) and negative ICT thresholds (i.e., minimum ICT levels for negative net effects) are provided; (4) ICT synergy effects are apparent for female unemployment, but not for female employment. In general, the joint effect of ICTs or their synergies and economic inclusion should be a concern for policymakers in order to better ensure sustainable development. Moreover, the relevant ICT policy thresholds and mobile phone threshold for complementary policy are essential in promoting a green economy. Originality/value The study complements the extant literature by assessing linkages between information technology, gender economic inclusion and environmental sustainability.
Mineral and S-Isotope Compositions of Cu-Sulfide Deposits in Southern Siberia (Kodar–Udokan Region), Russia

The Kodaro–Udokan region is a huge Cu metallogenic province in Southern Siberia, one of the largest on Earth. It contains world-class copper sandstone-hosted Udokan (Cu reserves of 26.7 Mt) and PGE-Ni-Cu Chineysky deposits related to gabbro–anorthosite pluton (Cu—10 Mt; Fe-Ti-V, 30 Gt of ore). Furthermore, there are many small deposits of sulfide ores in sedimentary and igneous rocks in this region as well. For many decades, their genesis has been hotly debated. We studied the mineral composition and the sulfur isotopes in several deposits located at different levels of the stratigraphic sequence and in gabbro intruded in sandstones of the Udokan complex. The differences in ore compositions were found. The Burpala and Skvoznoy deposits consisting of the chalcocite–bornite association are characterized only by negative δ34S. The δ34S values for the Udokan deposits are mostly &lt;0 (up to −28‰). The positive δ34S data characterize the ores of the Chineysky and Luktursky intrusions. Two Cu sandstone-hosted deposits are characterized by complex ore composition, i.e., the Krasny deposit, comprising chalcopyrite–pyrrhotite ores, is enriched in Co, Ni, Bi, Sb, Mo, Pb, Zn, Se, Te, and U and has a wide range of δ34S = −8.1–+13.5‰, and the Pravoingamakitsky deposit (Basaltovy section), consisting of quartz–chalcopyrite veins, has high PGE contents in ores with δ34S = +2.9–+4.0‰. These deposits are located near the gabbro massifs, and it is supposed that their ore compositions were influenced by magmatic fluids. The general regularities of the localization of the deposits in rift zones, and the proximity of mineral and isotopic composition allow us to conclude that the main source of copper could be rocks of basic composition because only they contain high Cu contents. Fluids from deep zones could penetrate to the surface and form Cu sandstone-hosted deposits.
A Python code for adjusting periodic soil temperature data

This work aims to present a code in Python language for adjusting periodic curves from soil temperature simulation data. We present a methodology to obtain a sinusoidal function using the least squares method that represents the soil temperature of a given locality. The code also uses Newton's method to solve the resulting nonlinear system of equations. Soil temperature simulation data were used in a city in the state of Rio Grande do Sul, Brazil. The results of these curves can help in the study and definition of boundary conditions of new computational models of ground-to-air heat exchangers (TCSA). It is observed that the least squares method is not new , but there is a gap in the literature regarding the presentation of codes for the adjustment of trigonometric functions as presented here.
Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation

Recent advancement in deep-neural network performance led to the development of new state-of-the-art approaches in numerous areas. However, the black-box nature of neural networks often prohibits their use in areas where model explainability and model transparency are crucial. Over the years, researchers proposed many algorithms to aid neural network understanding and provide additional information to the human expert. One of the most popular methods being Layer-Wise Relevance Propagation (LRP). This method assigns local relevance based on the pixel-wise decomposition of nonlinear classifiers. With the rise of attribution method research, there has emerged a pressing need to assess and evaluate their performance. Numerous metrics have been proposed, each assessing an individual property of attribution methods such as faithfulness, robustness or localization. Unfortunately, no single metric is deemed optimal for every case, and researchers often use several metrics to test the quality of the attribution maps. In this work, we address the shortcomings of the current LRP formulations and introduce a novel method for determining the relevance of input neurons through layer-wise relevance propagation. Furthermore, we apply this approach to the recently developed Vision Transformer architecture and evaluate its performance against existing methods on two image classification datasets, namely ImageNet and PascalVOC. Our results clearly demonstrate the advantage of our proposed method. Furthermore, we discuss the insufficiencies of current evaluation metrics for attribution-based explainability and propose a new evaluation metric that combines the notions of faithfulness, robustness and contrastiveness. We utilize this new metric to evaluate the performance of various attribution-based methods. Our code is available at: https://github.com/davor10105/relative-absolute-magnitude-propagation
The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection

Some fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Markov-cardinalized probability hypothesis density (GM-JMNS-CPHD) filter based on a generalized inverse covariance intersection. The state estimation of the JMNS-CPHD filter combines the state evaluation of traditional CPHD filters with the state estimation of jump Markov systems, estimating the target state of multiple motion models without knowing the current motion models. The performances of the generalized covariance intersection (GCI)GCI-GM-JMNS-CPHD and generalized inverse covariance intersection (GICI)GICI-GM-JMNS-CPHD methods are evaluated via simulation results. The simulation results show that, compared with algorithms such as Sensor1, Sensor2, GCI-GM-CPHD, and GICI-GM-CPHD, this algorithm has smaller optimal subpattern assignment (OSPA) errors and a higher fusion accuracy.
Leveraging FinTech Compliance to Mitigate Cryptocurrency Volatility for Secure US Employee Retirement Benefits: Bitcoin ETF Case Study

The integration of cryptocurrencies, particularly Bitcoin, into retirement savings plans has recently garnered significant attention. This interest has been amplified by the U.S. Securities and Exchange Commission's approval of Bitcoin Exchange-Traded Funds (ETFs) in January 2024 and Fidelity Investments' decision to include Bitcoin in their 401(k) plans. These landmark developments represent a paradigm shift in retirement investment strategies, merging traditional financial planning with the dynamic and volatile world of cryptocurrencies. The entry of Bitcoin introduces novel challenges, including increased volatility and regulatory uncertainty, necessitating a comprehensive examination of its impacts on retirement savings. The study sought to explore the role of Financial Technology (FinTech) in managing these risks and assess the adequacy of current regulatory frameworks. Employing a quantitative research approach, the study collected data from 386 participants, including FinTech practitioners and investment portfolio managers through a survey that combining closed-ended and open-ended questions. Multiple regression was used to analyze the relationships between variables such as FinTech integration, regulatory compliance, and the risk associated with cryptocurrency-inclusive retirement portfolios. The study revealed that the inclusion of Bitcoin significantly increases portfolio volatility. It also found that advanced FinTech data management techniques significantly enhance risk mitigation, while current regulatory frameworks are inadequate for governing the inclusion of cryptocurrencies in retirement plans. A comprehensive framework combining FinTech and regulatory compliance was shown to effectively reduce investment risks. The study recommends cautious consideration of cryptocurrencies in retirement portfolios, with an emphasis on assessing the risk appetite of participants. It advocates for dynamic regulatory frameworks and enhanced use of FinTech for real-time risk management. The study suggests that retirement plan providers should adopt an integrated approach, combining technological innovations with regulatory oversight, to navigate the complexities of cryptocurrency investments effectively.
The Model of the Digital Competence Profile of Pedagogical University Students

The article actualizes the assessment of the competencies of students of a pedagogical university through the systematic collection and analysis of the level of competence formation in the process of mastering the main professional educational program. The need to monitor and evaluate the development of students’ competencies makes it possible to identify the absence or insufficient level of competence formation among students and make appropriate managerial and pedagogical decisions to improve the quality of the educational process. One of such means is the digital competence profile of a student of a pedagogical university, by which the authors understand the system of collecting, analyzing and visualizing a set of competencies that are formed and developed by students as a result of mastering the basic educational program and extracurricular activities necessary for the implementation of professional pedagogical activity, presented digitally in the information and educational environment of the university. The article presents a model of the digital competence profile of pedagogical university students, identifies structural elements and functional relationships between them. The purpose of the article is to develop a model of the digital competence profile of pedagogical university students, to identify objects and subjects of the digital profile, to determine the structural elements of the digital profile of a pedagogical university student, to identify functional relationships and relationships between the structural elements of the model. Methodology and methods of research. The study was carried out on the basis of the modeling method and the structural and functional method, which provides identification of the structural elements of the digital profile of a student of a pedagogical university, determination of functional relationships and relationships between structural elements and their functions relative to each other. Conclusion. The model proposed by the authors is aimed at tracking the level of formation of the digital profile of competencies during the transition from formal assessment of knowledge to informal assessment of abilities, forming an individual route of formation in the context of a successful individual career trajectory of a student of a pedagogical university. The model is the basis for the development of an information system of a digital tool and can be used to monitor and evaluate the professional competencies of students of a pedagogical university.