Reviewerly/exHarmony huggingface dataset:
The Creative Career of Mikhail V. Shkondin (Analysis of Publication Activity)

Over the past twenty years, Russian scientists have been able to use the resources of the scientific electronic library eLibrary. The scientific and educational experience of the author of the article suggests that not all researchers adequately use the resources of this library to assess their own publication activity, determine their place among the authors of the relevant topics and manage their own publication indicators. It is important in the competitive environment of the scientific world both for the authors themselves and for the scientific and educational organizations affiliated with them. The study analyzed the publication indicators of one of the well-known authors in the field of media research — Mikhail Vasilievich Shkondin in accordance with the possibilities provided by the NEB for such an analysis. The specific reason for the analysis was the anniversary of M.V. Shkondin. The article presents general indicators of the publication activity of M.V. Shkondin (the number of publications, the number of citations, the Hirsch index for eLibrary, for the RSCI and for the core of the RSCI), places in the ranking for the same indicators, a statistical analysis of the dynamics of the number of publications by M.V. Shkondin according to the RSCI and the core of the RSCI, distribution of publications by journals, distribution of citations of M.V. Shkondin's texts by years of citing and cited publications. In addition to quantitative, we also conducted a qualitative analysis of the topics of M.V. Shkondin's publications. The study found that M.V. Shkondin's publication activity indicators are clear evidence of the professor's significant contribution to the development of media and journalism research. The methodology applied in the study is useful for other authors registered in the NEB eLibrary.
The importance of the school principals' role in the digital transformation of the education sector

Purpose This study examines the critical elements that contribute to the effective adoption of educational digital resources (EDRs) in schools, with a focus on school principals and their leadership, from a strategic pedagogical standpoint. Design/methodology/approach Using survey data from 200 school principals, measurement and structure models are tested through structural equation modeling to quantify the impact between constructs. Findings The findings indicate that the most important factor influencing how effectively schools are transforming digitally is how beneficial school principals believe EDRs to be. Other important elements include the environment of the school, the technical assistance and service provided for the EDRs, and the professional and personal background of the principal. Practical implications Principals should be a fundamental component of educational plans for digital transformation, considering things like their age, leadership and teaching experience. Other components include contextual elements like school size, complexity and digital culture. A school principal's ability to promote an open dialogue – that enables educational communities to view the integration of EDRs into pedagogical models as an opportunity to improve outcomes – can assist a digital culture transition, rather than via the principal's authority or bureaucratic influence. Originality/value This research is among the pioneer to study the role of school principals in the UAE towards understanding the direction for digital transformation.
Phonon Mediated Collective Dynamics of Coherently Pumped Two-Level Emitters

"We investigate the collective quantum dynamics of an ensemble of two-level emitters, embedded in a crystal, and coherently pumped by a moderately intense, and externally applied coherent electromagnetic field. The ensemble is damped preponderantly via the surrounding phonon reservoir which mediates the inter-particle collective interactions. We have found that generally phonon transitions among the corresponding dressed states are taking place involving simultaneously many single emitters or pairs of two-level emitters, respectively. In both cases the phonon intensity can be proportional to the squared number of involved two-level emitters."
Computational techniques for virtual reconstruction of fragmented archaeological textiles

Abstract Archaeological artifacts play important role in understanding the past developments of the humanity. However, the artifacts are often highly fragmented and degraded, with many details and parts missing due to centuries’ long degradation. Archaeologists and conservators attempt to reconstruct the original state of the objects either physically or virtually. This process includes characterizing and matching fragments’ features to identify which ones belong together. However, this process currently requires an extensive and tedious manual labor. Recent development in computational techniques gave rise to computer-assisted ways of virtual reconstruction, where the computer suggests solutions to the puzzle of scattered fragments and supplements or fully replaces manual labor. However, the capabilities of computational techniques remain limited in many aspects. This review summarizes the state-of-the-art computational techniques for puzzle and virtual reconstruction problems in cultural heritage applications, in general – with a particular interest in archaeological textiles. We overview existing computational methods, their applications and limitations. Afterward, based on the current knowledge gaps, we discuss where the field should go next.
Data augmentation using virtual word insertion techniques in text classification tasks

Abstract Labelling multiple t r aining examples for text classification models is usually time‐consuming and complex. Data augmentation can be used to automatically expand the dataset by transforming the original data. However, it may cause semantic changes without modifying the labels, which reduces the effectiveness of the classifiers. In this paper, we propose a data‐augmentation method called the virtual word insertion technique, which generates new sentences by randomly inserting virtual words into existing sentences. Two methods are used to achieve virtual word embedding: unweighted average and weighted average. Furthermore, a new concept of weight is proposed: the class deviation factor, which demonstrates the correlation between words and classes. Based on this new concept, different weights are assigned to words of different classes. Experiments are conducted on five different classification tasks. Ablation experiments are also performed to explore the effects of random operation and number of augmented sentences for classification results. The results of these experiments show that our method improves the classification performance and outperforms two other contrasting data‐augmentation methods in automatically augmenting the dataset. Compared to raw datasets, the average accuracy improvement of our method is 3.5% for a small‐scale dataset and 1% for a large‐scale dataset.
Research on micro parallax adversarial sample generation method based on texture sensitive region

Deep neural networks have been extensively applied in fields such as image classification, object detection, and face recognition. However, research has shown that adversarial samples with subtle perturbations can effectively deceive these networks. Existing methods for generating such adversarial images often lack stealth and robustness. In this study, we present an enhanced attack strategy based on traditional Generative Adversarial Networks (GANs). We integrate image texture into the unsupervised training scheme, guiding the model to focus perturbations in high-texture areas. We also introduce a dynamic equilibrium training strategy that employs Differential Evolution algorithms to adaptively adjust both network weight parameters and the training ratio between the generator and discriminator, achieving a self-balancing training process. Further, we propose an image local optimization algorithm to eliminate perturbations in non-sensitive areas through weighted filtering. The model is validated using benchmark datasets such as MNIST, ImageNet and SVHN. Through extensive experimental evaluations, our approach shows a 4.93% improvement in attack success rate against conventional models and a 10.23% increase against defense models compared to state-of-the-art attack methods.
Revisiting Yoyo Tricks on AES

At Asiacrypt 2017, Rønjom et al. presented key-independent distinguishers for different numbers of rounds of AES, ranging from 3 to 6 rounds, in their work titled “Yoyo Tricks with AES”. The reported data complexities for these distinguishers were 3, 4, 225.8, and 2122.83, respectively. In this work, we revisit those key-independent distinguishers and analyze their success probabilities.We show that the distinguishing algorithms provided for 5 and 6 rounds of AES in the paper of Rønjom et al. are ineffective with the proposed data complexities. Our thorough theoretical analysis has revealed that the success probability of these distinguishers for both 5-round and 6-round AES is approximately 0.5, with the corresponding data complexities mentioned earlier.We investigate the reasons behind this seemingly random behavior of those reported distinguishers. Based on our theoretical findings, we have revised the distinguishing algorithm for 5-round AES. Our revised algorithm demonstrates success probabilities of approximately 0.55 and 0.81 for 5-round AES, with data complexities of 229.95 and 230.65, respectively. We have also conducted experimental tests to validate our theoretical findings, which further support our findings.Additionally, we have theoretically demonstrated that improving the success probability of the distinguisher for 6-round AES from 0.50000 to 0.50004 would require a data complexity of 2129.15. This finding invalidates the reported distinguisher by Rønjom et al. for 6-round AES.
Disparity-Aware Federated Learning for Intrusion Detection Systems in Imbalanced Non-IID Settings

Many variants of Federated Learning have been proposed to settle different challenges that come with numerous practical applications, one of which is dealing with non-IID data sources. As decentralized data sources in real life are bound to be non-IID, this is one of the hardest challenges, and yet the earliest federated algorithms struggle to resolve this issue, resulting in worse non-IID performance. Also, applications that require capturing really intricate insights from data while upholding the latest data privacy standards, such as Intrusion Detection Systems (IDS) have enabled the use of FL in those domains. In this article, we propose a novel Disparity-Aware federated learning approach that tackles non-IID and data imbalance from both global and local learning steps of FL. Our method capitalizes on state-of-the-art loss functions to tackle data imbalance at the client level and a class distribution-dependent clustering algorithm at the server to tackle class distribution skew. The nature of the process renders it applicable even in asynchronous federated learning schemes. Experiments with multiple benchmark intrusion detection datasets reveal improved performance over traditional deep learning approaches as well as earlier federated learning techniques.
Soft-Touch or Hard-Touch: Quantifying Touches through A New System Focusing on A Pool Touchpad for Measuring Timing of Swimmers

The swimming turn or finishing presents an integral part of competitive swimming, and the time it takes by a swimmer to push off the wall is a significant factor in their ratings. Therefore, it is essential to have a reliable way to measure the timing of touch, i.e., wall-contact, over the finishing or turning points. To do so, swimming pool touchpads are widely used in swimming competitions to overcome human error in measurements by enabling touch-sensing systems at the finishing or turning points. The touch-sensing devices use their sensitive touch sensors to record time with millisecond accuracy, providing athletes with the most precise measurements possible. However, these devices might need a minimal force applied from the swimmers to sense the touch, and thus, to record the timing of the touch. As such, insufficient force applied on the touchpad during swimming could cause skipping the time log. As a result, it is critical to have a trustworthy method for measuring the extent of touch or wall-contact exerted over the touch-sensing systems. This study, for the first time in the literature to the best of our knowledge, proposes such a method for measuring the extent of touch or wall-contact for swimming pool touchpads. In the process of proposing the method, we design and develop a bi-modal sensing module to distinguish the soft-touch and hard-touch exerted over a real pool touchpad used in a swimming pool organizing national and international competitions. Our experiments show that the pool touchpad under experimentation is not always accurate in registering touches. It sometimes fails to register low-force touches while generally registering touches with high force.
Sociala: An Incentivized Decentralized Social Media for writers based on Blockchain using modified Delegated Proof of Stake

Over the past few years, there has been a profound evolution in decentralization methodologies, owing largely to the emergence of novel decentralized technologies like blockchain. In the realm of online social media, a paradigm shift has been suggested with the introduction of blockchain-based online social media (BOSM), heralding a potential future for social media platforms wherein users are duly rewarded for their valuable contributions. Despite the widespread adoption of these platforms by millions of users, it is crucial to acknowledge that their decentralization remains incomplete. One prevailing issue within existing blockchain systems resides in the vulnerability of the Delegated Proof of Stake (DPoS) consensus algorithm, which is susceptible to manipulation by the few selected nodes. Anticipating the demands of the forthcoming generation of social media, the pivotal imperative lies in establishing decentralization as its foundational hallmark. In this paper, we present Sociala, an incentivized blockchain-based framework tailored for writers. To effectively address the pitfalls of centralization and minimize the likelihood of nefarious node selection, we proffer an enhanced multi-step DPoS consensus algorithm, which is referred as modified DPoS (mDPoS). This advanced algorithm embraces both the tenets of randomness and diversity, coupled with the inclusion of input from general users, culminating in fortified system stability.
Random Data Poisoning Attacks on Nonlinear Regression Learning

Nonlinear regression has numerous applications in diverse fields, including biology, economics, engineering, and more, where it is used to model and analyze complex relationships between variables that cannot be adequately represented by linear models. However, these models are susceptible to malicious attacks that manipulate input data to yield false results. This study focuses on unpredictable data poisoning threats based on randomization in nonlinear regression learning and assesses the iTrim defense mechanism’s efficacy. Multiple nonlinear regression datasets and common techniques were used in experiments. Random Data poisoning attack involves regenerating data points with altered labels and inserting them into the training set. The polluted dataset underwent iTrim defense, and model performance on a test set gauged effectiveness. Results show that models suffer significant performance degradation when exposed to random data poisoning attacks. Malicious points cause overfitting and poor test set generalization. This study underscores nonlinear regression models’ vulnerability to random data poisoning and the need for robust security measures, while iTrim offers some protection, further research is vital to develop more potent defense systems against complex attacks.
AN EVALUATION OF PERCEIVED USABILITY: BOZOK LEARNING MANAGEMENT SYSTEM

Technology has been a significant driver of sociological change throughout history. The relationship between technology and society is complex, with technology both shaping and being shaped by social forces. Technological developments, which started with the industrial revolution and gained a new dimension with Industry 4.0, have brought about radical changes in the ways technology is used to offer new products and services (Petrasch and Hentschke 2016). In addition, the 2020 Covid-19 pandemic has accelerated digital transformation causing many services and activities to be transferred to the digital environment and education was no exception. Advances in technology, changes in social structure and unforeseen epidemics and natural disasters have brought learning management systems in the center of our lives as a main part of education services. Although there existed several learning management systems for a while, after the 2020 pandemic we started to see a rapid increase in such systems. However, usability of these systems need to be evaluated preferably before and after their release. In this paper, we examined the usability of Bozok Learning Management System (BOYSIS) developed within Yozgat Bozok University and designed in line with the needs of the university. Since every system performs differently depending on the social and cultural environment it belongs, system usability needs to be evaluated from the point of final users. For this purpose, the System Usability Scale (SUS) was employed and we found that the BOYSIS has acceptable level of usability, yet it is below the expected industry standard.
The quantum internet: an efficient stabilizer states distribution scheme

Abstract Quantum networks are a fundamental component of quantum technologies, playing a pivotal role in advancing distributed quantum computing and laying the groundwork for the future quantum internet. They offer a scalable modular architecture for quantum chips and support infrastructure for measurement-based quantum computing. Furthermore, quantum networks serve as the backbone of the quantum internet, ensuring high levels of security. Notably, the advantages of quantum networks in communication are contingent upon entanglement distribution, which faces challenges such as high latency in protocols relying on Bell pair distribution and bipartite entanglement swapping. Additionally, algorithms designed for multipartite entanglement routing encounter intractability issues, rendering them unsolvable within polynomial time. In this paper, we explore a novel approach to distribute graph states in quantum networks, leveraging local quantum coding (LQC) isometries and multipartite states transfer. We also present single-use bounds for stabilizer states distribution. Analogous to network coding, these bounds are attainable when appropriate isometries and stabilizer codes are selected for relay nodes, resulting in reduced latency in entanglement distribution. We further demonstrate the protocol’s advantages across various network performance metrics.
Economic Mechanisms of Entrepreneurship Development and Foreign Experiences for Developing Countries

This study examines the economic mechanisms that foster entrepreneurship development, focusing on lessons from developed countries and their relevance to developing nations. Key factors such as government policies, financial support, educational initiatives, and cultural attitudes are analyzed. The research identifies challenges in developing countries, including limited capital access and bureaucratic barriers. Based on these insights, concise recommendations are proposed to enhance entrepreneurship in developing countries, emphasizing policy reforms, financial accessibility, and educational improvements. The study underscores the need for a multifaceted approach to stimulate economic growth and development through entrepreneurship.
Enhancing Book Classification Efficiency: A Comparative Analysis of Technology-Driven Approaches

As the number of books available in the public domain increases, there is a growing need for inexpensive and efficient classification of these books into categories. While traditional classification systems like the Dewey Decimal Classification and the Library of Congress Classification are still in use today, technology can simplify the process. This paper explores the use of Latent Dirichlet Allocation (LDA), a form of topic modeling, to classify books into categories and compares its performance with other machine learning models. The LDA model achieved an accuracy of 32.29%, while the Naive Bayes, Support Vector Machine, and multinomial Logistic Regression models outperformed it with accuracies of 85.45%, 86.12%, and 85.12%, respectively. This research emphasizes the need for scalable and cost-efficient solutions in categorizing the expanding pool of books within the public domain. Leveraging technology can effectively address this challenge. The comparative analysis of machine learning models offers valuable insights into their potential for enhancing classification accuracy and streamlining the categorization process, thereby opening promising avenues for future development in this field.
Implications of the AI Act in relation to mobility

The ambition of the European Union is to lead the way in digitalization. A cornerstone in fulfilling the ambition is the proposed AI Act. Our ambition with this contribution is to create awareness of how regulating Artificial Intelligence (AI) will affect mobility within the EU. The chosen definition of AI is broad enough to cover most software and the regulated topics will affect areas such as road traffic and public transport, safety components in machines but not vehicles. Such systems deemed to pose a high risk will need to be certified and CE-marked. It will also have an impact on the relationship between provider and user of AI systems, with a clear ambition to open the black box of business to enable compliancy with GDPR and handling of anomalies. At the same time, the AI Act opens up for using personal data for other purposes than originally stated during collection.
The Autonomous Train vision: Embedded AI for pedestrians monitoring

When you develop a perception system for rolling stocks of the railway sector, you are dealing with huge infrastructures and harsh conditions never encountered. From the integration studies to the final software implementation of the computer vision algorithm in the embedded system, our work puts on paper these steps and challenges encountered by focusing on one specific use-case of the environment monitoring of a train: pedestrians tracking in front of the train. Pedestrian monitoring is a key aspect of autonomous train operation as it involves ensuring the safety of people in the vicinity of the tracks. This scenario covers most of the requirements for automatic detection scenarios at the front of a train: close and distant moving object detection, robust object tracking, person state analysis with high confidence rates to ensure safety for human life and for train driving.
Toward new generation railway Traffic Management Systems: the contribution of the OPTIMA project

Legacy Traffic Management Systems (TMS) lack interoperable data structures and standardized communication interfaces. The H2020 Shift2Rail OPTIMA project is developing a demonstrator of a communication platform for a new generation of TMS with advanced functionalities for improving the efficiency, effectiveness, and resilience of railway operations, as well as improved interoperability, modularity, and scalability. In this paper, this platform is described, along with its main components that provide seamless access to persistent and real-time data from heterogeneous data sources. A Common Data Model supports the automated data exchange process by means of a fully standardized structure for representing the data. Furthermore, the testing and validation of the OPTIMA platform and of connected TMS services when it is set up as a testing environment are illustrated.